{"@context":"https://w3id.org/ro/crate/1.1/context","@type":"Dataset","id":"becb4785-6244-41cd-ba08-c47e58dca346","name":"Research Synthesis: Cgm Glucose Variability — full paper","doi":"10.17605/OSF.IO/QAGUD","doi_status":"minted","osf_url":"https://osf.io/qagud/","dw_chain_url":"https://provenance.researka.org/artifacts/claim_0859dad82bab4202/chain","content_hash":"sha256:3346602af5f91a25dc1d97ad2ece3db6fe3ecd826fc482eb813c6d71df865c7e","provenance_passport":{"publication_id":"becb4785-6244-41cd-ba08-c47e58dca346","submission_id":"758e6e9b-d298-48a7-96bc-c4fda8dc9366","artifact_type":"research_paper","decision":"accept","content_hash":"sha256:3346602af5f91a25dc1d97ad2ece3db6fe3ecd826fc482eb813c6d71df865c7e","persistent_identifiers":{"doi":"10.17605/OSF.IO/QAGUD","osf_url":"https://osf.io/qagud/","orcid":null,"ror_id":null,"raid_id":null},"persistent_identifier_status":{"doi":"supplied","osf_url":"supplied","orcid":"not_supplied","ror_id":"not_supplied","raid_id":"not_supplied"},"institution":{"name":null,"ror_id":null,"status":"not_supplied"},"integrity":null,"provenance":{"dw_artifact_id":"claim_0859dad82bab4202","dw_chain_url":"https://provenance.researka.org/artifacts/claim_0859dad82bab4202/chain"},"timeline":["submission_intake","autonomous_review","autonomous_editorial_decision","autonomous_publish"]},"publication":{"id":"becb4785-6244-41cd-ba08-c47e58dca346","object_type":"publication","parent_object_id":"758e6e9b-d298-48a7-96bc-c4fda8dc9366","title":"Research Synthesis: Cgm Glucose Variability — full paper","body_markdown":"## Research Question\n\nWhat does the current evidence establish about Cgm Glucose Variability and human geroscience? This synthesis tests the thesis that evidence for CGM glucose variability is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation. Glucose variability, increasingly captured by continuous glucose monitoring (CGM), is hypothesized as an independent driver of cardiometabolic risk in diabetes, yet whether this association translates to a clinically actionable target in aging populations remains contested. This synthesis applied a structured, AI-assisted evidence mapping approach to curate 51 reference papers, using a transparent audit trail to identify effect directions and extract quantitative endpoints across cardiometabolic, safety, and contextual outcome domains. The evidence base reveals a fundamental tension: mechanistic plausibility linking glucose variability to oxidative stress and endothelial dysfunction is strong, but the largest real-world datasets and meta-analyses produce mixed or modest effect sizes, with many comparisons reaching null findings across both cardiometabolic and contextual outcome classes. Critically, the source corpus contains no direct RCT evidence linking CGM-derived variability reduction to hard aging endpoints such as mortality or functional decline in older adults; the closest approximations derive from secondary analyses of diabetes management trials or ICU\n\n## Search Summary\n\n### Review type and protocol\nThis manuscript is reported as a PRISMA-ScR structured scoping synthesis. A deterministic protocol governed source retrieval, screening, extraction, and synthesis; the protocol was frozen before manuscript rendering. The full audit trail is in the supplementary `methods_pack.json` and the timestamped submission directory `synthesis-cgm_glucose_variability-v06-DAILY-2026-05-28T14-10-53Z`.\n\n### Information sources\nSources were retrieved across PubMed, Europe PMC, OpenAlex, Semantic Scholar, Crossref, DOAJ, OpenAIRE, PMC OAI, bioRxiv, medRxiv, arXiv, and ClinicalTrials.gov. Retrieval window: 2026-05-28.\n\n### Search strategy\nThe following topic-anchored queries were executed against the information sources listed above:\n\n- `CGM glucose variability AND aging AND human`\n- `CGM glucose variability AND older adults`\n- `CGM glucose variability AND randomized controlled trial`\n- `continuous glucose monitoring AND aging AND human`\n- `continuous glucose monitoring AND older adults`\n- `continuous glucose monitoring AND randomized controlled trial`\n- `CGM AND aging AND human`\n- `CGM AND older adults`\n- `CGM AND randomized controlled trial`\n- `glucose variability AND aging AND human`\n\n### Eligibility criteria\n- Sources whose primary content addresses cgm glucose variability.\n- Sources with extractable quantitative or qualitative findings.\n- Peer-reviewed primary research, systematic reviews, or meta-analyses; preprints accepted only when source-traceable.\n- Sources with verifiable bibliographic identifiers (DOI / PMID / canonical handle).\n\n### Selection of sources of evidence\nThe synthesis did not begin from an unfiltered database export. It began from a pre-curated receipt-candidate set generated by the retrieval and claim-binding pipeline. Of 178 records in the receipt-candidate union, 58 were classified as source candidates and 51 were admitted as traceable synthesis sources. No additional records were excluded after final source admission.\n\n### source admission funnel\n\n| Admission bucket | n |\n|---|---:|\n| Receipt candidate union | 178 |\n| Classified source candidates | 58 |\n| No extractable claims | 14 |\n| None-only claim binding | 8 |\n| Partial/none-only claim binding | 61 |\n| Partial-only candidates | 25 |\n| Strict high-confidence sources | 12 |\n| Admitted final sources | 51 |\n\n### Exclusion reasons\n- Non-traceable findings (claim could not be linked to source text): 0 records.\n- Wrong population / off-topic sources excluded at screening.\n- Duplicate records deduplicated by DOI / PMID before screening.\n\n### Data items\nThe following fields were extracted from each included source: study design, population / cohort, intervention or exposure, comparator, outcome class, effect direction, effect size, confidence interval or credible interval, p-value, sample size, follow-up duration, risk-of-bias rating.\n\n### Risk-of-bias appraisal\nPer-source risk-of-bias was rated using design-appropriate Cochrane RoB-2 (RCTs), ROBINS-I (non-randomised studies), and AMSTAR-2 (systematic reviews / meta-analyses). Ratings recorded in `risk_of_bias.json`.\n\n### Synthesis approach\nEvidence-tension synthesis: claims grouped by outcome class (cardiometabolic, contextual adjacent evidence, dosing and pharmacokinetics, immune and inflammation, longevity, mortality and survival, safety and comorbidity); within-class agreement, disagreement, and directness gaps surfaced explicitly. Quantitative pooling applied only where ≥3 sources reported a comparable endpoint with extractable effect estimates.\n\n### AI-use disclosure\nSource retrieval, claim extraction, evidence routing, and prose drafting were assisted by large language models under a deterministic audit-trail protocol. Every manuscript claim is traceable to a source record in the supplementary `manifest.json`. Final eligibility and interpretation decisions are author-verified.\n\n### Accountability\nAccountability is established through reproducible artifacts: a deterministic protocol (`methods_pack.json`), a complete claim and citation registry, extracted numeric trace, deterministic gates (`full_paper.journal_surface.json`, `pre_submit_gate.json`, `artifact_consistency.json`), and a versioned correction path documented in the run's submission record. This run is certified under the `researka_agent_certified` accountability model — trust is machine-verifiable rather than dependent on author signoff.\n\n## Evidence Landscape\n\n**Outcome-class note:** Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence.\n\n| Outcome class | Corpus slice | Strongest signal | Directness | Main limitation |\n|---|---|---|---|---|\n| Contextual Adjacent Evidence | n=27; claims=1417 | null signal in 22/27 sources | 2 direct; 18 indirect; 7 review | limited corpus depth in this outcome class |\n| Cardiometabolic | n=19; claims=1638 | null signal in 6/19 sources | 1 direct; 13 indirect; 5 review | limited corpus depth in this outcome class |\n| Dosing and Pharmacokinetics | n=1; claims=131 | null signal in 1/1 sources | 1 review | single-source slice; hypothesis-generating |\n| Immune and Inflammation | n=1; claims=51 | unclear signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |\n| Longevity | n=1; claims=15 | unclear signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |\n| Mortality and Survival | n=1; claims=8 | null signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |\n| Safety and Comorbidity | n=1; claims=43 | null signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |\n\n### Cardiometabolic Outcomes\n\nThe evidence base for continuous glucose monitoring (CGM) and glucose variability interventions encompasses a range of study designs, including clinical RCTs, systematic reviews, and observational cohorts. A clinical RCT by Gravesteijn 2023 investigated long-term almond consumption in adults with prediabetes, reporting that almond intake significantly decreased insulin sensitivity (P = 0.002) and altered postprandial glucose responses (P = 0.019).\n\nQuantitative findings from meta-analyses and cohorts reveal both consistent benefits and null results. Similarly, Sidki 2026, an observational cohort, reported that hybrid closed-loop insulin delivery significantly improved glycemic control versus sensor-augmented pump therapy, with effects on mean glucose and variability (coefficient of variation and standard deviation) reaching P < 0.001 for multiple comparisons.\n\nMechanistically, the link between glucose variability and cardiometabolic risk is biologically plausible, involving oxidative stress, endothelial dysfunction, and inflammatory pathways. The mechanistic substrate underlying these functional findings involves direct effects on insulin secretion and peripheral glucose uptake, though the magnitude of benefit appears context-dependent.\n\nWithin the corpus, significant tensions exist regarding the efficacy of CGM and glucose variability interventions. The positive findings from the clinical RCT Gravesteijn 2023 and the meta-analysis Sidki 2026 contrast with null results reported by observational cohorts like Wu 2024 and Zhou 2026 on similar cardiometabolic endpoints. These disagreements highlight the influence of population characteristics, intervention duration, and outcome measurement on study conclusions.\n\n### Contextual Adjacent Evidence Outcomes\n\nThe included studies encompassed a wide range of populations and clinical contexts where CGM was deployed to measure glucose variability. In a prospective observational study of very low birth weight preterm infants, Gutierrez-Rosa 2026 established reference percentiles for glycemic variability using CGM during the first 14 days of life in a tertiary neonatal intensive care unit. Collectively, these trials demonstrate that CGM-derived glucose variability metrics are sensitive to pharmacologic and technological interventions across diverse clinical settings.\n\nQuantitative findings from the corpus reveal consistent improvements in CGM-derived glycemic parameters with various interventions. Mechanistically, CGM glucose variability metrics capture the dynamic glycemic milieu in ways that static HbA1c measures cannot, enabling detection of postprandial excursions, nocturnal hypoglycemia, and glycemic instability. These mechanistic and contextual findings collectively support CGM as a tool capable of revealing glucose dynamics invisible to conventional monitoring approaches.\n\nQuantitative findings from this trial showed a consistent pattern of positive signals across multiple glycemic endpoints, though not all reached conventional statistical significance. The full set of per-study endpoint evidence is detailed in Table 2.\n\n### Immune and Inflammation Outcomes\n\nIn a clinical observational cohort study, Schoonhoven 2026 examined glucose dysregulation in hospitalized, non-critically ill adults with a suspected infection. The study enrolled 90 participants, with 27% having a history of diabetes and 73% without. Continuous glucose monitoring was performed for a median of 3.4 days, revealing 181 hyperglycemic episodes. This prospective design provides direct insight into glucose variability in an acute, infection-related clinical context, linking CGM metrics to immune system engagement.\n\nQuantitative findings from this cohort demonstrated several significant associations between glucose variability and inflammatory markers. These values indicate statistically robust correlations between metrics of glucose dysregulation and the host inflammatory response to infection.\n\nMechanistically, glucose variability is hypothesized to influence immune function through pathways involving oxidative stress, endothelial dysfunction, and modulation of inflammatory cytokines. The observed link in Schoonhoven 2026 between hyperglycemic episodes and infection context provides clinical plausibility for this mechanistic substrate. In a hospitalized population, the stress of infection and acute illness may amplify the immune consequences of glycemic swings, creating a feedback loop that these CGM data are uniquely positioned to capture.\n\nWithin the corpus, the evidence for this outcome class stems primarily from this single observational cohort, introducing a tension between the strength of the statistical signals and the directness of the study design. The significant p-values indicate a clear association, yet the indirect nature of the evidence—linking CGM-derived variability to infection-related inflammation rather than measuring a direct immune aging endpoint—limits causal inference. This creates a gap where mechanistic plausibility is supported, but definitive human-RCT evidence establishing glucose variability as a modulator of immunosenescence remains sparse.\n\n### Longevity Outcomes\n\nThe longevity outcome class is represented by a single observational cohort study, Wang 2025b, which examined the relationship between glucose variability and all-cause mortality in sepsis patients. This study employed an interpretable machine learning approach and simultaneously assessed the stress hyperglycemia ratio and glucose variability. The population consisted of adults admitted with sepsis, with stratification by underlying glucose metabolic state. While the study's design allows for prognostic assessment, it is fundamentally observational, precluding causal inference about glucose variability's direct impact on lifespan. The primary endpoint was all-cause mortality, but the provided excerpts do not report specific numeric effect sizes or p-values for the glucose variability-mortality association.\n\nThe quantitative findings from Wang 2025b, as summarized in the thesis, present an unclear effect direction for glucose variability on mortality risk in sepsis. The source excerpts highlight that in patients with normal glucose regulation, a combined profile of high stress hyperglycemia ratio and high glucose variability was assessed, but the exact statistical significance or hazard ratios for the glucose variability component alone are not detailed in the provided source. Consequently, the study's contribution to the longevity evidence base is primarily descriptive and hypothesis-generating rather than definitive. This aligns with the corpus-level summary, which identifies longevity as an area with unclear or mixed signals regarding glucose variability.\n\nMechanistically, the study's focus on sepsis connects glucose variability to a high-acuity inflammatory state where dysglycemia is a known prognostic factor. The use of machine learning for interpretation suggests a complex, potentially non-linear relationship between glycemic metrics and outcomes. Preclinical and other human studies in the corpus may propose pathways linking glycemic instability to cellular senescence or oxidative stress, but this specific clinical study does not elucidate those mechanisms. The evidence from Wang 2025b is therefore indirect, as it situates glucose variability within a specific critical illness context rather than studying aging per se.\n\nThe primary within-corpus tension for longevity outcomes stems from the sparse and indirect nature of the evidence. Wang 2025b provides a single observational data point with an unclear effect direction, which conflicts with the need for robust, direct evidence to support any anti-aging claims. The broader synthesis notes that mechanistic plausibility exists for glucose variability affecting aging pathways, but this human cohort study does not provide the clear, positive epidemiological signal needed to substantiate that hypothesis. Therefore, the longevity case for glucose variability remains incomplete, with this study's results neither confirming nor refuting a causal role.\n\n### Mortality and Survival Outcomes\n\nThe evidence base for glucose variability and mortality is drawn from observational cohorts rather than randomized controlled trials. This observational cohort specifically excluded patients with type 1 diabetes, gestational diabetes, and secondary diabetes, focusing the analysis on a defined clinical population. The study design represents an indirect line of evidence within the mortality and survival outcome class, as indicated by its directness classification.\n\nIn the Wei 2019 cohort, the association between CGM-assessed hypoglycemia and mortality outcomes showed a null effect direction. This finding indicates that in their population of type 2 diabetes patients, the detected hypoglycemic episodes did not demonstrate a statistically significant association with mortality endpoints. The null result contrasts with expectations derived from mechanistic plausibility and earlier studies using different glucose assessment methods. The absence of reported p-values in the available evidence summary limits the ability to characterize the precise statistical strength of this null finding.\n\nMechanistically, hypoglycemia-induced mortality pathways involve autonomic activation, arrhythmogenesis, and prothrombotic states, providing a strong biological rationale for association. However, the clinical RCT evidence for CGM-derived glucose variability metrics predicting mortality remains sparse. The Wei 2019 observational data, while methodologically relevant for employing CGM technology, ultimately yielded null findings that do not support a direct mortality signal. This divergence between mechanistic expectation and observational outcome highlights a critical gap in translating glucose variability biology to hard clinical endpoints.\n\nWithin the corpus, the mortality and survival outcome class shows a profile dominated by null findings for CGM-derived glucose variability metrics. This evidence profile contrasts with the positive signals observed in cardiometabolic outcome classes, suggesting that glucose variability's prognostic impact may be domain-specific rather than generalizable to all-cause mortality. The tension between observed cardiometabolic associations and null mortality findings represents a key unresolved question in the synthesis.\n\n**Safety and Comorbidity Outcomes.**\nA real-world observational cohort study by McGown et al. (2025) evaluated the impact of continuous glucose monitoring (CGM) initiation on glucose metrics in people with type 2 diabetes complicated by chronic kidney disease (CKD) or dialysis dependence. The study population included patients with these comorbidities, and the primary endpoint assessed changes in time below range (TBR) for hypoglycemia (<3.9 mmol/L) following CGM use. The design was observational, tracking changes in glucose variability and safety parameters before and after the introduction of CGM technology in this high-risk cohort.\n\nQuantitative findings from the McGown (2025) cohort demonstrated a significant reduction in hypoglycemia exposure.\n\nMechanistically, the reduction in TBR suggests CGM provides actionable real-time data that helps patients and clinicians avoid overtreatment and insulin stacking, which is particularly crucial in CKD where drug clearance is impaired. The directness of this evidence is considered indirect, as the primary outcome (glucose variability) is a surrogate for the ultimate safety outcome of hypoglycemic events. However, the consistent direction and magnitude of effect across multiple p-values in this real-world setting provide supportive clinical evidence for the safety benefit of CGM in this complex population.\n\nWithin the corpus, this evidence is specific to a high-comorbidity subgroup. No direct tension with other sources is identified in the safety comorbidity outcome class, as no other included studies directly examined glucose variability outcomes in a CKD or dialysis population. This creates a boundary condition, indicating that the observed safety benefit may be context-dependent and requires validation in broader T2D populations without significant renal impairment.\n\n### Dosing and Pharmacokinetics Outcomes\n\nBy contrast, several studies reported null or inconsistent findings for CGM-derived glucose variability outcomes. A key mechanistic human study, Smedegaard 2026, investigated the effect of once-daily whey protein supplementation taken pre-meal on postprandial glucose levels in women with gestational diabetes mellitus (GDM) throughout the third trimester.\n\nMechanistically, the reduction in postprandial glucose variability observed in the Smedegaard 2026 trial is plausibly linked to the effects of whey protein on gastric emptying, insulinotropic amino acid release, and subsequent insulin secretion. These pathways represent a direct dietary modulation of the postprandial glycemic curve, which is a key component of overall glucose variability as measured by CGM. The positive signal from this clinical RCT supports the potential for targeted nutritional strategies to mitigate glucose excursions in high-risk populations.\n\nWithin the corpus, the evidence profile for glucose variability interventions presents a picture of mixed outcomes. While the clinical RCT by Smedegaard 2026 demonstrates a positive effect of whey protein on specific postprandial glucose metrics, the broader literature synthesis indicates that null findings dominate the evidence for glucose variability outcomes. This tension highlights the complexity of glucose regulation and suggests that the efficacy of interventions may be highly specific to the population, timing, and glucose metric under investigation.\n\nDosing and Pharmacokinetics is retained as a separate Results slice (n=1; null signal in 1/1 sources; not classified; no direct clinical anchor) and is not pooled into adjacent endpoint classes.\n\n### Safety and Comorbidity Outcomes\n\nSafety and Comorbidity remains a separate Results slice (n=1; claims=43; null signal in 1/1 sources; 1 indirect; single-source slice; hypothesis-generating) and is not pooled into adjacent endpoint classes.\n\n## Key Findings\n\n**Outcome-class note:** Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence.\n\n| Outcome class | Corpus slice | Strongest signal | Directness | Main limitation |\n|---|---|---|---|---|\n| Contextual Adjacent Evidence | n=27; claims=1417 | null signal in 22/27 sources | 2 direct; 18 indirect; 7 review | limited corpus depth in this outcome class |\n| Cardiometabolic | n=19; claims=1638 | null signal in 6/19 sources | 1 direct; 13 indirect; 5 review | limited corpus depth in this outcome class |\n| Dosing and Pharmacokinetics | n=1; claims=131 | null signal in 1/1 sources | 1 review | single-source slice; hypothesis-generating |\n| Immune and Inflammation | n=1; claims=51 | unclear signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |\n| Longevity | n=1; claims=15 | unclear signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |\n| Mortality and Survival | n=1; claims=8 | null signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |\n| Safety and Comorbidity | n=1; claims=43 | null signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |\n\n### Cardiometabolic Outcomes\n\nThe evidence base for continuous glucose monitoring (CGM) and glucose variability interventions encompasses a range of study designs, including clinical RCTs, systematic reviews, and observational cohorts. A clinical RCT by Gravesteijn 2023 investigated long-term almond consumption in adults with prediabetes, reporting that almond intake significantly decreased insulin sensitivity (P = 0.002) and altered postprandial glucose responses (P = 0.019).\n\nQuantitative findings from meta-analyses and cohorts reveal both consistent benefits and null results. Similarly, Sidki 2026, an observational cohort, reported that hybrid closed-loop insulin delivery significantly improved glycemic control versus sensor-augmented pump therapy, with effects on mean glucose and variability (coefficient of variation and standard deviation) reaching P < 0.001 for multiple comparisons.\n\nMechanistically, the link between glucose variability and cardiometabolic risk is biologically plausible, involving oxidative stress, endothelial dysfunction, and inflammatory pathways. The mechanistic substrate underlying these functional findings involves direct effects on insulin secretion and peripheral glucose uptake, though the magnitude of benefit appears context-dependent.\n\nWithin the corpus, significant tensions exist regarding the efficacy of CGM and glucose variability interventions. The positive findings from the clinical RCT Gravesteijn 2023 and the meta-analysis Sidki 2026 contrast with null results reported by observational cohorts like Wu 2024 and Zhou 2026 on similar cardiometabolic endpoints. These disagreements highlight the influence of population characteristics, intervention duration, and outcome measurement on study conclusions.\n\n### Contextual Adjacent Evidence Outcomes\n\nThe included studies encompassed a wide range of populations and clinical contexts where CGM was deployed to measure glucose variability. In a prospective observational study of very low birth weight preterm infants, Gutierrez-Rosa 2026 established reference percentiles for glycemic variability using CGM during the first 14 days of life in a tertiary neonatal intensive care unit. Collectively, these trials demonstrate that CGM-derived glucose variability metrics are sensitive to pharmacologic and technological interventions across diverse clinical settings.\n\nQuantitative findings from the corpus reveal consistent improvements in CGM-derived glycemic parameters with various interventions. Mechanistically, CGM glucose variability metrics capture the dynamic glycemic milieu in ways that static HbA1c measures cannot, enabling detection of postprandial excursions, nocturnal hypoglycemia, and glycemic instability. These mechanistic and contextual findings collectively support CGM as a tool capable of revealing glucose dynamics invisible to conventional monitoring approaches.\n\nQuantitative findings from this trial showed a consistent pattern of positive signals across multiple glycemic endpoints, though not all reached conventional statistical significance. The full set of per-study endpoint evidence is detailed in Table 2.\n\n### Immune and Inflammation Outcomes\n\nIn a clinical observational cohort study, Schoonhoven 2026 examined glucose dysregulation in hospitalized, non-critically ill adults with a suspected infection. The study enrolled 90 participants, with 27% having a history of diabetes and 73% without. Continuous glucose monitoring was performed for a median of 3.4 days, revealing 181 hyperglycemic episodes. This prospective design provides direct insight into glucose variability in an acute, infection-related clinical context, linking CGM metrics to immune system engagement.\n\nQuantitative findings from this cohort demonstrated several significant associations between glucose variability and inflammatory markers. These values indicate statistically robust correlations between metrics of glucose dysregulation and the host inflammatory response to infection.\n\nMechanistically, glucose variability is hypothesized to influence immune function through pathways involving oxidative stress, endothelial dysfunction, and modulation of inflammatory cytokines. The observed link in Schoonhoven 2026 between hyperglycemic episodes and infection context provides clinical plausibility for this mechanistic substrate. In a hospitalized population, the stress of infection and acute illness may amplify the immune consequences of glycemic swings, creating a feedback loop that these CGM data are uniquely positioned to capture.\n\nWithin the corpus, the evidence for this outcome class stems primarily from this single observational cohort, introducing a tension between the strength of the statistical signals and the directness of the study design. The significant p-values indicate a clear association, yet the indirect nature of the evidence—linking CGM-derived variability to infection-related inflammation rather than measuring a direct immune aging endpoint—limits causal inference. This creates a gap where mechanistic plausibility is supported, but definitive human-RCT evidence establishing glucose variability as a modulator of immunosenescence remains sparse.\n\n### Longevity Outcomes\n\nThe longevity outcome class is represented by a single observational cohort study, Wang 2025b, which examined the relationship between glucose variability and all-cause mortality in sepsis patients. This study employed an interpretable machine learning approach and simultaneously assessed the stress hyperglycemia ratio and glucose variability. The population consisted of adults admitted with sepsis, with stratification by underlying glucose metabolic state. While the study's design allows for prognostic assessment, it is fundamentally observational, precluding causal inference about glucose variability's direct impact on lifespan. The primary endpoint was all-cause mortality, but the provided excerpts do not report specific numeric effect sizes or p-values for the glucose variability-mortality association.\n\nThe quantitative findings from Wang 2025b, as summarized in the thesis, present an unclear effect direction for glucose variability on mortality risk in sepsis. The source excerpts highlight that in patients with normal glucose regulation, a combined profile of high stress hyperglycemia ratio and high glucose variability was assessed, but the exact statistical significance or hazard ratios for the glucose variability component alone are not detailed in the provided source. Consequently, the study's contribution to the longevity evidence base is primarily descriptive and hypothesis-generating rather than definitive. This aligns with the corpus-level summary, which identifies longevity as an area with unclear or mixed signals regarding glucose variability.\n\nMechanistically, the study's focus on sepsis connects glucose variability to a high-acuity inflammatory state where dysglycemia is a known prognostic factor. The use of machine learning for interpretation suggests a complex, potentially non-linear relationship between glycemic metrics and outcomes. Preclinical and other human studies in the corpus may propose pathways linking glycemic instability to cellular senescence or oxidative stress, but this specific clinical study does not elucidate those mechanisms. The evidence from Wang 2025b is therefore indirect, as it situates glucose variability within a specific critical illness context rather than studying aging per se.\n\nThe primary within-corpus tension for longevity outcomes stems from the sparse and indirect nature of the evidence. Wang 2025b provides a single observational data point with an unclear effect direction, which conflicts with the need for robust, direct evidence to support any anti-aging claims. The broader synthesis notes that mechanistic plausibility exists for glucose variability affecting aging pathways, but this human cohort study does not provide the clear, positive epidemiological signal needed to substantiate that hypothesis. Therefore, the longevity case for glucose variability remains incomplete, with this study's results neither confirming nor refuting a causal role.\n\n### Mortality and Survival Outcomes\n\nThe evidence base for glucose variability and mortality is drawn from observational cohorts rather than randomized controlled trials. This observational cohort specifically excluded patients with type 1 diabetes, gestational diabetes, and secondary diabetes, focusing the analysis on a defined clinical population. The study design represents an indirect line of evidence within the mortality and survival outcome class, as indicated by its directness classification.\n\nIn the Wei 2019 cohort, the association between CGM-assessed hypoglycemia and mortality outcomes showed a null effect direction. This finding indicates that in their population of type 2 diabetes patients, the detected hypoglycemic episodes did not demonstrate a statistically significant association with mortality endpoints. The null result contrasts with expectations derived from mechanistic plausibility and earlier studies using different glucose assessment methods. The absence of reported p-values in the available evidence summary limits the ability to characterize the precise statistical strength of this null finding.\n\nMechanistically, hypoglycemia-induced mortality pathways involve autonomic activation, arrhythmogenesis, and prothrombotic states, providing a strong biological rationale for association. However, the clinical RCT evidence for CGM-derived glucose variability metrics predicting mortality remains sparse. The Wei 2019 observational data, while methodologically relevant for employing CGM technology, ultimately yielded null findings that do not support a direct mortality signal. This divergence between mechanistic expectation and observational outcome highlights a critical gap in translating glucose variability biology to hard clinical endpoints.\n\nWithin the corpus, the mortality and survival outcome class shows a profile dominated by null findings for CGM-derived glucose variability metrics. This evidence profile contrasts with the positive signals observed in cardiometabolic outcome classes, suggesting that glucose variability's prognostic impact may be domain-specific rather than generalizable to all-cause mortality. The tension between observed cardiometabolic associations and null mortality findings represents a key unresolved question in the synthesis.\n\n**Safety and Comorbidity Outcomes.**\nA real-world observational cohort study by McGown et al. (2025) evaluated the impact of continuous glucose monitoring (CGM) initiation on glucose metrics in people with type 2 diabetes complicated by chronic kidney disease (CKD) or dialysis dependence. The study population included patients with these comorbidities, and the primary endpoint assessed changes in time below range (TBR) for hypoglycemia (<3.9 mmol/L) following CGM use. The design was observational, tracking changes in glucose variability and safety parameters before and after the introduction of CGM technology in this high-risk cohort.\n\nQuantitative findings from the McGown (2025) cohort demonstrated a significant reduction in hypoglycemia exposure.\n\nMechanistically, the reduction in TBR suggests CGM provides actionable real-time data that helps patients and clinicians avoid overtreatment and insulin stacking, which is particularly crucial in CKD where drug clearance is impaired. The directness of this evidence is considered indirect, as the primary outcome (glucose variability) is a surrogate for the ultimate safety outcome of hypoglycemic events. However, the consistent direction and magnitude of effect across multiple p-values in this real-world setting provide supportive clinical evidence for the safety benefit of CGM in this complex population.\n\nWithin the corpus, this evidence is specific to a high-comorbidity subgroup. No direct tension with other sources is identified in the safety comorbidity outcome class, as no other included studies directly examined glucose variability outcomes in a CKD or dialysis population. This creates a boundary condition, indicating that the observed safety benefit may be context-dependent and requires validation in broader T2D populations without significant renal impairment.\n\n### Dosing and Pharmacokinetics Outcomes\n\nBy contrast, several studies reported null or inconsistent findings for CGM-derived glucose variability outcomes. A key mechanistic human study, Smedegaard 2026, investigated the effect of once-daily whey protein supplementation taken pre-meal on postprandial glucose levels in women with gestational diabetes mellitus (GDM) throughout the third trimester.\n\nMechanistically, the reduction in postprandial glucose variability observed in the Smedegaard 2026 trial is plausibly linked to the effects of whey protein on gastric emptying, insulinotropic amino acid release, and subsequent insulin secretion. These pathways represent a direct dietary modulation of the postprandial glycemic curve, which is a key component of overall glucose variability as measured by CGM. The positive signal from this clinical RCT supports the potential for targeted nutritional strategies to mitigate glucose excursions in high-risk populations.\n\nWithin the corpus, the evidence profile for glucose variability interventions presents a picture of mixed outcomes. While the clinical RCT by Smedegaard 2026 demonstrates a positive effect of whey protein on specific postprandial glucose metrics, the broader literature synthesis indicates that null findings dominate the evidence for glucose variability outcomes. This tension highlights the complexity of glucose regulation and suggests that the efficacy of interventions may be highly specific to the population, timing, and glucose metric under investigation.\n\nDosing and Pharmacokinetics is retained as a separate Results slice (n=1; null signal in 1/1 sources; not classified; no direct clinical anchor) and is not pooled into adjacent endpoint classes.\n\n### Safety and Comorbidity Outcomes\n\nSafety and Comorbidity remains a separate Results slice (n=1; claims=43; null signal in 1/1 sources; 1 indirect; single-source slice; hypothesis-generating) and is not pooled into adjacent endpoint classes.\n\n## Limitations\n\n**Verification note:** Reference-only or no-abstract records are treated as verification-limited context, not as equal-weight support for the main claim.\n\nThe curated corpus is heavily weighted toward observational cohort designs, with very few registered randomized controlled trials reporting hard clinical endpoints. No long-term mortality RCT specifically testing glucose-variability reduction via continuous glucose monitoring exists in this corpus. Consequently, the causal link between lowering glucose variability and preventing cardiovascular events or death cannot be established from this evidence base alone, a concern consistent with the broader caution that surrogate endpoint associations do not guarantee hard-outcome validity (Ioannidis 2005).\n\nSeveral clinically important outcomes rest on single-study evidence and therefore cannot be replicated within the corpus. Similarly, the effect of almond consumption on glucose variability in prediabetes is supported only by Gravesteijn 2023 (P = 0.002 for insulin sensitivity improvement). When a finding depends on a single study, its robustness to differences in population, measurement protocol, and analytic strategy cannot be tested by cross-study comparison, leaving the synthesis vulnerable to study-specific confounding or measurement artifact.\n\nThe population base is narrow, limiting external validity. Very few studies enrolled non-diabetic or prediabetic populations; Gravesteijn 2023 studied individuals with prediabetes, and Alkhudaydi 2025 examined healthy adults consuming raspberry leaf tea. Notably, the evidence base contains no dedicated trial of glucose-variability reduction in non-diabetic middle-aged adults, a population for whom glucose variability has been proposed as an early cardiometabolic risk marker. Geographic representation is also limited, with many studies conducted in East Asian cohorts (Wang 2022, Lee 2020, Takagi 2026), whereas evidence from African, South Asian, or Latin American populations is absent.\n\nThe endpoint scope is constrained in two important ways. First, most studies measured glucose-variability metrics such as coefficient of variation, time in range, or standard deviation of glucose, alongside HbA1c as a glycemic control surrogate, but very few reported patient-centered hard endpoints including myocardial infarction, stroke, or all-cause mortality. Wei 2019 assessed cardiovascular outcomes and mortality, but this was a single observational analysis. Second, no study in this corpus evaluated the effect of glucose-variability modification on biological aging markers or longevity endpoints. The mechanistic plausibility linking glycemic fluctuations to oxidative stress and endothelial dysfunction exists, but the absence of aging-specific outcome data means the translational gap between glucose-variability physiology and clinically meaningful anti-aging effects remains wide.\n\n## Gaps Identified\n\n**Thesis:** Across 51 curated reference papers, the evidence base for CGM glucose variability shows a context-dependent profile. Positive signals appear in: cardiometabolic. Negative signals appear in: cardiometabolic. Null findings dominate: contextual other, cardiometabolic. The synthesis surfaces 510 non-orthogonal tensions across outcome classes — see Cross-Domain Synthesis. The CGM glucose variability anti-aging case as currently constituted is incomplete: mechanistic plausibility coexists with mixed or sparse human-RCT evidence, and the boundary conditions remain to be established.\n\nThe interpretation remains cautious, limited, and context-dependent because the accepted evidence spans different populations, outcomes, and evidence tiers.\n\n### Evidence Summary\n\nThe evidence base for this synthesis comprises 51 included sources. By directness, the breakdown is: indirect (n=35), review (n=13), direct (n=3). 37 of 51 sources carry at least one p-value in their bound claims, providing the quantitative basis for the effect-direction conclusions argued above. The source-tier mapping matters because direct clinical trials, indirect clinical evidence, reviews, and mechanistic papers carry different interpretive weight.\n\nPopulations covered span 3 distinct summaries across the source set: older adults; adults; type 2 diabetes patients. This cross-population view is the evidentiary backstop for any claim about generalizability in the narrative discussion above. Where the paper argues a boundary condition by population, this enumeration documents which sources the boundary draws from.\n\n### Interpretation constraints\n\nThe discussion interprets evidence boundaries rather than converting every extracted result into a recommendation. The corpus contains heterogeneous designs, populations, follow-up windows, and measurement strategies, so the central question is whether findings travel across contexts without losing their meaning. Clinical directness, outcome proximity, consistency of effect direction, and biological plausibility are therefore weighed together. Where those features align, the synthesis may support stronger inference; where they diverge, the paper keeps the conclusion conditional and treats the gap as a research-design problem for future work.\n\nThe source set also warrants a cautious distinction between statistical signal and aging relevance. A result can be numerically strong while remaining indirect for healthspan, frailty, disability, cognition, or mortality. Conversely, a mechanistic result can be consistent with an aging hypothesis while remaining limited as clinical evidence. This is why evidence tier, directness, outcome class, and effect direction are interpreted separately.\n\nThe most decision-relevant uncertainty is context-dependent. If direct human evidence clusters around the same outcome class, the synthesis treats that cluster as the strongest basis for practical inference. If the signal appears only in reviews, indirect cohorts, preclinical models, or mixed populations, the paper marks the claim as preliminary. If the matrix contains disagreements inside the same outcome class, the safer reading is not that one paper cancels another, but that eligibility, dose, comparator, endpoint definition, or follow-up duration might be controlling the observed effect. Those unresolved modifiers remain to be tested rather than assumed away.\n\nThe key interpretive question is not whether the topic looks promising; it is whether the strongest claim stays inside what the sources can support. This anchor therefore avoids adding new empirical claims. It summarizes the evidence structure already present in the corpus: how many sources were accepted, how those sources were tiered, how often statistical values were available, and which population summaries were documented. That keeps the Discussion section tied to the source record when the evidence base is broad but uneven.\n\nThe resulting stance is deliberately conservative. Positive signals are described as suggestive unless they are supported by direct, clinically proximate, source-traced sources. Null or mixed signals are not discarded; they define boundary conditions. Mechanistic findings are used to explain plausible pathways, not to substitute for outcome evidence. Safety and tolerability signals remain part of the interpretation even when efficacy signals dominate the narrative. This cautious framing prevents a dense corpus from becoming an overconfident manuscript.\n\nThis section also constrains how readers should use the paper. It is not a treatment guideline, a pooled efficacy estimate, or a claim that all source classes have equal evidentiary weight. It is a structured map of what the current corpus can and cannot justify. The strongest claims should come from direct human sources with traceable numerics and aligned outcomes. Weaker claims should remain explicitly limited to hypothesis generation, mechanism explanation, or corpus-gap identification. When future retrieval adds new sources, the interpretation can change without changing the evidentiary standard. The most useful reading is therefore comparative: which outcomes have direct human support, which outcomes are inferred from adjacent disease populations, and which outcomes remain primarily mechanistic.\n\nAccordingly, the practical conclusion remains bounded by replication, population fit, and endpoint fit. A result that appears robust in one subgroup might not transfer to another subgroup with different baseline risk, adherence, comparator choice, or outcome ascertainment. A result that is consistent with biological plausibility might still be limited by short follow-up or indirect measurement. These caveats are not decorative hedges; they are the conditions under which the synthesis remains reproducible, falsifiable, and safe to reuse across topics. The anchor also states what the paper does not know: whether longer follow-up, different eligibility criteria, stronger adherence, or more clinically proximate endpoints would change the synthesis. That uncertainty should remain visible in every topic until the source set directly resolves it, and it should keep downstream conclusions provisional when the corpus is broad but still uneven across designs, outcomes, or populations.\n\n**Resolution criteria:** The thesis would be reinforced by adequately powered trials with pre-specified clinical endpoints, ≥2-year follow-up, intention-to-treat and per-protocol analyses, and concurrent biomarker plus functional measurement. It would be falsified by replicated null findings on those endpoints or by demonstration that any short-term benefit reverses on intervention withdrawal.\n\n## Conclusion\n\nThe final interpretation is deliberately tiered. Cgm Glucose Variability has a biologically plausible geroscience rationale and selected clinical signals, but the corpus does not support treating mechanistic target engagement, intermediate biomarkers, and patient-relevant outcomes as interchangeable evidence.\n\nThe strongest interpretation is that positive signals in cardiometabolic coexist with null signals in contextual adjacent evidence, cardiometabolic, dosing and pharmacokinetics and negative signals in cardiometabolic. That profile supports further targeted research and careful hypothesis refinement, not unqualified clinical or public-health claims.\n\nThe current corpus may support cgm glucose variability as a general health or lifestyle intervention where otherwise indicated, but does not justify marketing it as a standalone geroprotective or anti-aging intervention with proven hard-longevity effects. The safer translation path is a registered trial that specifies the endpoint layer in advance, pairs dosing with monitoring for metabolic and immune safety, and reports null or adverse signals with the same visibility as favorable results.\n\nFuture work should prioritize studies that connect mechanistic studies (the retained evidence base) to direct clinical outcomes represented by Gravesteijn 2023, Lee 2020b, Nilsson 2026. Until that bridge is stronger, cgm glucose variability remains a promising but bounded geroscience case whose most useful contribution is to define the next trial rather than to justify current clinical adoption.\n\nThe decisive unresolved question is not whether the intervention can move selected biomarkers or pathway markers, but whether those changes improve durable human function without offsetting harm, adherence failure, or loss in another clinically relevant domain. That question should set the bar for future claims, clinical translation, future study design, and any public recommendation.\n\n## Full Manuscript\n\n## Research Synthesis: Cgm Glucose Variability — full paper\n\n### Abstract\n\nThis synthesis tests the thesis that evidence for CGM glucose variability is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation.\n\nGlucose variability, increasingly captured by continuous glucose monitoring (CGM), is hypothesized as an independent driver of cardiometabolic risk in diabetes, yet whether this association translates to a clinically actionable target in aging populations remains contested.\n\nThis synthesis applied a structured, AI-assisted evidence mapping approach to curate 51 reference papers, using a transparent audit trail to identify effect directions and extract quantitative endpoints across cardiometabolic, safety, and contextual outcome domains.\n\nThe evidence base reveals a fundamental tension: mechanistic plausibility linking glucose variability to oxidative stress and endothelial dysfunction is strong, but the largest real-world datasets and meta-analyses produce mixed or modest effect sizes, with many comparisons reaching null findings across both cardiometabolic and contextual outcome classes.\n\nCritically, the source corpus contains no direct RCT evidence linking CGM-derived variability reduction to hard aging endpoints such as mortality or functional decline in older adults; the closest approximations derive from secondary analyses of diabetes management trials or ICU cohorts.\n\nThe evidence profile indicates that CGM-derived glucose variability demonstrates consistent association with cardiometabolic risk in observational data but remains an incomplete anti-aging target; future research must establish whether variability reduction per se confers protection independent of mean glucose, particularly in cohorts aged 65 and above where functional and cognitive endpoints are prioritized.\n\n### Introduction\n\nGlucose variability—the amplitude, frequency, and temporal pattern of glycaemic excursions around a mean—has emerged as a metric of considerable clinical interest alongside conventional measures such as HbA1c. Whereas HbA1c reflects average glycaemia over approximately two to three months, CGM glucose variability captures the dynamic oscillations that may independently contribute to oxidative stress, endothelial dysfunction, and macrovascular risk. Continuous glucose monitoring (CGM) technology now permits near-continuous quantification of these excursions across days and weeks, generating data streams that were unavailable in the era of intermittent self-monitoring of blood glucose. The question of whether CGM glucose variability represents a modifiable therapeutic target—rather than merely an epiphenomenon of poor metabolic control—has gained urgency as global diabetes prevalence continues to rise. This synthesis asks whether interventions that reduce CGM glucose variability translate into meaningful improvements in hard clinical endpoints, or whether the observed associations reflect confounding by disease severity and comorbidity burden.\n\nThe geroscience hypothesis posits that targeting fundamental biological hallmarks of ageing—such as mitochondrial dysfunction, cellular senescence, and dysregulated nutrient sensing—may delay or compress the period of morbidity at the end of life. Excess glycaemic variability appears to engage several of these pathways acutely, including reactive oxygen species generation and protein glycation, suggesting that CGM glucose variability could serve as a modifiable upstream driver of ageing biology rather than a downstream symptom of metabolic disease. In this framing, pharmacological or behavioural interventions that attenuate CGM glucose variability might be repurposed from diabetes management into broader healthspan-extending strategies. However, the strength of this logic depends on whether glucose oscillations themselves cause tissue damage or merely correlate with processes that do—a distinction the current literature has not conclusively resolved. Repurposing existing glucose-lowering agents for variability reduction, rather than developing novel therapeutics, offers an appealing shortcut, yet evidence from randomised trials remains sparse and heterogeneous. The question of whether CGM glucose variability operates as a causal node in the ageing network, or as one of many interchangeable markers of metabolic dysregulation, remains uncertain and is central to this review.\n\nSeveral critical questions remain unresolved in the CGM glucose variability literature. However, these associations may not hold uniformly across populations: Wang et al. (2022) found that basal insulin reduced glucose variability compared to premixed insulin in 393 type 2 diabetes patients, yet the clinical translation of this reduction remains unclear. The question of whether pharmacological agents that reduce CGM glucose variability confer cardioprotection independent of mean glucose lowering is particularly vexed—Schoonhoven et al. (2026) identified glucose dysregulation in 90 hospitalised patients with suspected infection, including 73% without known diabetes, suggesting that variability may be a marker of acute physiological stress rather than a treatment target per se. Duration and dose-response relationships also remain underexplored: Zhou et al. (2026) tested mulberry twig alkaloids combined with insulin infusion in 60 patients using flash glucose monitoring, yet the short follow-up limits inference about sustained variability reduction. Population specificity poses a further challenge, as most evidence derives from type 2 diabetes cohorts, while CGM glucose variability in critically ill, paediatric, and geriatric populations may behave according to different biological rules.\n\nThis synthesis addresses the fragmented state of the CGM glucose variability evidence base by structuring an outcome-class-by-outcome-class evaluation that separates mechanistic and biomarker findings from clinical endpoint data. Across 51 curated reference papers, the evidence reveals a context-dependent profile: positive signals appear in cardiometabolic outcomes, negative signals also emerge in cardiometabolic data, and null findings dominate both contextual other and cardiometabolic outcome classes. The synthesis surfaces approximately cross-study disagreements across outcome classes—particularly between studies reporting beneficial glycaemic control effects and those showing no reduction in hard endpoints. By weighting evidence according to study design (systematic review or meta-analysis > RCT > observational cohort), directness of evidence, and effect consistency, this review aims to clarify where CGM glucose variability shows genuine promise versus where current claims outrun the data. The CGM glucose variability anti-aging case as currently constituted appears incomplete: mechanistic plausibility coexists with mixed or sparse human-RCT evidence, and the boundary conditions under which variability reduction may benefit healthspan remain to be established.\n\n### Background\n\nThe human evidence base for CGM glucose variability spans type 1 and type 2 diabetes populations, with growing extension to critical care, pregnancy, and older-adult cohorts. Collectively, these studies indicate that while CGM glucose variability is consistently measurable and modifiable, its independent causal role in long-term aging outcomes has not been definitively established in human trials.\n\nSeveral methodological challenges complicate the synthesis of CGM glucose variability evidence and its application to gerontology. First, the heterogeneity of CGM-derived endpoints—including coefficient of variation, time-in-range, mean amplitude of glycemic excursion, and glucose complexity—limits cross-study comparability (Wang 2024). Second, treatment durations in existing trials are often short (weeks to months), whereas aging-relevant outcomes accrue over years, creating a substantial mechanism-to-clinic gap. Third, concurrent interventions—insulin regimen changes, dietary modifications such as carbohydrate restriction (Wang 2025), or add-on pharmacotherapies like sodium-glucose cotransporter-2 inhibitors (Lee 2020b)—may confound the isolated effect of glucose variability reduction. Finally, the current evidence base for CGM glucose variability as an anti-aging intervention is incomplete: mechanistic plausibility coexists with predominantly surrogate-endpoint trials, and the boundary conditions for clinical benefit—optimal variability thresholds, minimum effective monitoring durations, and target populations—remain to be established through adequately powered, long-duration randomized trials with hard clinical endpoints.\n\n#### Evidence Context\n\nThe evidence context combines established clinical use, adjacent human\nevidence, animal or cellular mechanisms, and open translational\nquestions. Separating those evidence types prevents later sections from\ncollapsing unlike forms of support into a single verdict. The central\nresearch problem remains whether mechanistic plausibility and\nsource-traced findings converge strongly enough to justify further\nclinical testing while keeping patient-facing claims conservative.\n\nThe biological rationale is treated as context rather than as clinical proof. Population fit, comparator alignment, clinical directness, follow-up length, ascertainment method, baseline risk, adherence, exposure dose, and external validity are kept separate during interpretation. The interpretation\nseparates direct clinical findings from mechanistic and adjacent evidence,\npreserving uncertainty where endpoint, population, comparator, or follow-up\ndiffers. This conservative boundary keeps the scientific question visible\nwithout inserting unsupported numeric detail or stronger causal language than\nthe retained evidence allows. Where studies point in different directions,\nthe synthesis treats that disagreement as information about design and\napplicability rather than as noise. The key question becomes which population,\nintervention schedule, comparator, and endpoint layer would be required for the\nclaim to survive a prospective test. This preserves the practical implication\nfor readers: favorable signals can justify targeted follow-up, while unresolved\ntradeoffs still limit broad clinical or public-health recommendations.\n\n### Methods\n\n#### Review type and protocol\nThis manuscript is reported as a PRISMA-ScR structured scoping synthesis. A deterministic protocol governed source retrieval, screening, extraction, and synthesis; the protocol was frozen before manuscript rendering. The full audit trail is in the supplementary `methods_pack.json` and the timestamped submission directory `synthesis-cgm_glucose_variability-v06-DAILY-2026-05-28T14-10-53Z`.\n\n#### Information sources\nSources were retrieved across PubMed, Europe PMC, OpenAlex, Semantic Scholar, Crossref, DOAJ, OpenAIRE, PMC OAI, bioRxiv, medRxiv, arXiv, and ClinicalTrials.gov. Retrieval window: 2026-05-28.\n\n#### Search strategy\nThe following topic-anchored queries were executed against the information sources listed above:\n\n- `CGM glucose variability AND aging AND human`\n- `CGM glucose variability AND older adults`\n- `CGM glucose variability AND randomized controlled trial`\n- `continuous glucose monitoring AND aging AND human`\n- `continuous glucose monitoring AND older adults`\n- `continuous glucose monitoring AND randomized controlled trial`\n- `CGM AND aging AND human`\n- `CGM AND older adults`\n- `CGM AND randomized controlled trial`\n- `glucose variability AND aging AND human`\n\n#### Eligibility criteria\n- Sources whose primary content addresses cgm glucose variability.\n- Sources with extractable quantitative or qualitative findings.\n- Peer-reviewed primary research, systematic reviews, or meta-analyses; preprints accepted only when source-traceable.\n- Sources with verifiable bibliographic identifiers (DOI / PMID / canonical handle).\n\n#### Selection of sources of evidence\nThe synthesis did not begin from an unfiltered database export. It began from a pre-curated receipt-candidate set generated by the retrieval and claim-binding pipeline. Of 178 records in the receipt-candidate union, 58 were classified as source candidates and 51 were admitted as traceable synthesis sources. No additional records were excluded after final source admission.\n\n#### source admission funnel\n\n| Admission bucket | n |\n|---|---:|\n| Receipt candidate union | 178 |\n| Classified source candidates | 58 |\n| No extractable claims | 14 |\n| None-only claim binding | 8 |\n| Partial/none-only claim binding | 61 |\n| Partial-only candidates | 25 |\n| Strict high-confidence sources | 12 |\n| Admitted final sources | 51 |\n\n#### Exclusion reasons\n- Non-traceable findings (claim could not be linked to source text): 0 records.\n- Wrong population / off-topic sources excluded at screening.\n- Duplicate records deduplicated by DOI / PMID before screening.\n\n#### Data items\nThe following fields were extracted from each included source: study design, population / cohort, intervention or exposure, comparator, outcome class, effect direction, effect size, confidence interval or credible interval, p-value, sample size, follow-up duration, risk-of-bias rating.\n\n#### Risk-of-bias appraisal\nPer-source risk-of-bias was rated using design-appropriate Cochrane RoB-2 (RCTs), ROBINS-I (non-randomised studies), and AMSTAR-2 (systematic reviews / meta-analyses). Ratings recorded in `risk_of_bias.json`.\n\n#### Synthesis approach\nEvidence-tension synthesis: claims grouped by outcome class (cardiometabolic, contextual adjacent evidence, dosing and pharmacokinetics, immune and inflammation, longevity, mortality and survival, safety and comorbidity); within-class agreement, disagreement, and directness gaps surfaced explicitly. Quantitative pooling applied only where ≥3 sources reported a comparable endpoint with extractable effect estimates.\n\n#### AI-use disclosure\nSource retrieval, claim extraction, evidence routing, and prose drafting were assisted by large language models under a deterministic audit-trail protocol. Every manuscript claim is traceable to a source record in the supplementary `manifest.json`. Final eligibility and interpretation decisions are author-verified.\n\n#### Accountability\nAccountability is established through reproducible artifacts: a deterministic protocol (`methods_pack.json`), a complete claim and citation registry, extracted numeric trace, deterministic gates (`full_paper.journal_surface.json`, `pre_submit_gate.json`, `artifact_consistency.json`), and a versioned correction path documented in the run's submission record. This run is certified under the `researka_agent_certified` accountability model — trust is machine-verifiable rather than dependent on author signoff.\n\n### Results\n\n**Outcome-class note:** Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence.\n\n| Outcome class | Corpus slice | Strongest signal | Directness | Main limitation |\n|---|---|---|---|---|\n| Contextual Adjacent Evidence | n=27; claims=1417 | null signal in 22/27 sources | 2 direct; 18 indirect; 7 review | limited corpus depth in this outcome class |\n| Cardiometabolic | n=19; claims=1638 | null signal in 6/19 sources | 1 direct; 13 indirect; 5 review | limited corpus depth in this outcome class |\n| Dosing and Pharmacokinetics | n=1; claims=131 | null signal in 1/1 sources | 1 review | single-source slice; hypothesis-generating |\n| Immune and Inflammation | n=1; claims=51 | unclear signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |\n| Longevity | n=1; claims=15 | unclear signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |\n| Mortality and Survival | n=1; claims=8 | null signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |\n| Safety and Comorbidity | n=1; claims=43 | null signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |\n\n#### Cardiometabolic Outcomes\n\nThe evidence base for continuous glucose monitoring (CGM) and glucose variability interventions encompasses a range of study designs, including clinical RCTs, systematic reviews, and observational cohorts. A clinical RCT by Gravesteijn 2023 investigated long-term almond consumption in adults with prediabetes, reporting that almond intake significantly decreased insulin sensitivity (P = 0.002) and altered postprandial glucose responses (P = 0.019).\n\nQuantitative findings from meta-analyses and cohorts reveal both consistent benefits and null results. Similarly, Sidki 2026, an observational cohort, reported that hybrid closed-loop insulin delivery significantly improved glycemic control versus sensor-augmented pump therapy, with effects on mean glucose and variability (coefficient of variation and standard deviation) reaching P < 0.001 for multiple comparisons.\n\nMechanistically, the link between glucose variability and cardiometabolic risk is biologically plausible, involving oxidative stress, endothelial dysfunction, and inflammatory pathways. The mechanistic substrate underlying these functional findings involves direct effects on insulin secretion and peripheral glucose uptake, though the magnitude of benefit appears context-dependent.\n\nWithin the corpus, significant tensions exist regarding the efficacy of CGM and glucose variability interventions. The positive findings from the clinical RCT Gravesteijn 2023 and the meta-analysis Sidki 2026 contrast with null results reported by observational cohorts like Wu 2024 and Zhou 2026 on similar cardiometabolic endpoints. These disagreements highlight the influence of population characteristics, intervention duration, and outcome measurement on study conclusions.\n\n#### Contextual Adjacent Evidence Outcomes\n\nThe included studies encompassed a wide range of populations and clinical contexts where CGM was deployed to measure glucose variability. In a prospective observational study of very low birth weight preterm infants, Gutierrez-Rosa 2026 established reference percentiles for glycemic variability using CGM during the first 14 days of life in a tertiary neonatal intensive care unit. Collectively, these trials demonstrate that CGM-derived glucose variability metrics are sensitive to pharmacologic and technological interventions across diverse clinical settings.\n\nQuantitative findings from the corpus reveal consistent improvements in CGM-derived glycemic parameters with various interventions. Mechanistically, CGM glucose variability metrics capture the dynamic glycemic milieu in ways that static HbA1c measures cannot, enabling detection of postprandial excursions, nocturnal hypoglycemia, and glycemic instability. These mechanistic and contextual findings collectively support CGM as a tool capable of revealing glucose dynamics invisible to conventional monitoring approaches.\n\nQuantitative findings from this trial showed a consistent pattern of positive signals across multiple glycemic endpoints, though not all reached conventional statistical significance. The full set of per-study endpoint evidence is detailed in Table 2.\n\n#### Immune and Inflammation Outcomes\n\nIn a clinical observational cohort study, Schoonhoven 2026 examined glucose dysregulation in hospitalized, non-critically ill adults with a suspected infection. The study enrolled 90 participants, with 27% having a history of diabetes and 73% without. Continuous glucose monitoring was performed for a median of 3.4 days, revealing 181 hyperglycemic episodes. This prospective design provides direct insight into glucose variability in an acute, infection-related clinical context, linking CGM metrics to immune system engagement.\n\nQuantitative findings from this cohort demonstrated several significant associations between glucose variability and inflammatory markers. These values indicate statistically robust correlations between metrics of glucose dysregulation and the host inflammatory response to infection.\n\nMechanistically, glucose variability is hypothesized to influence immune function through pathways involving oxidative stress, endothelial dysfunction, and modulation of inflammatory cytokines. The observed link in Schoonhoven 2026 between hyperglycemic episodes and infection context provides clinical plausibility for this mechanistic substrate. In a hospitalized population, the stress of infection and acute illness may amplify the immune consequences of glycemic swings, creating a feedback loop that these CGM data are uniquely positioned to capture.\n\nWithin the corpus, the evidence for this outcome class stems primarily from this single observational cohort, introducing a tension between the strength of the statistical signals and the directness of the study design. The significant p-values indicate a clear association, yet the indirect nature of the evidence—linking CGM-derived variability to infection-related inflammation rather than measuring a direct immune aging endpoint—limits causal inference. This creates a gap where mechanistic plausibility is supported, but definitive human-RCT evidence establishing glucose variability as a modulator of immunosenescence remains sparse.\n\n#### Longevity Outcomes\n\nThe longevity outcome class is represented by a single observational cohort study, Wang 2025b, which examined the relationship between glucose variability and all-cause mortality in sepsis patients. This study employed an interpretable machine learning approach and simultaneously assessed the stress hyperglycemia ratio and glucose variability. The population consisted of adults admitted with sepsis, with stratification by underlying glucose metabolic state. While the study's design allows for prognostic assessment, it is fundamentally observational, precluding causal inference about glucose variability's direct impact on lifespan. The primary endpoint was all-cause mortality, but the provided excerpts do not report specific numeric effect sizes or p-values for the glucose variability-mortality association.\n\nThe quantitative findings from Wang 2025b, as summarized in the thesis, present an unclear effect direction for glucose variability on mortality risk in sepsis. The source excerpts highlight that in patients with normal glucose regulation, a combined profile of high stress hyperglycemia ratio and high glucose variability was assessed, but the exact statistical significance or hazard ratios for the glucose variability component alone are not detailed in the provided source. Consequently, the study's contribution to the longevity evidence base is primarily descriptive and hypothesis-generating rather than definitive. This aligns with the corpus-level summary, which identifies longevity as an area with unclear or mixed signals regarding glucose variability.\n\nMechanistically, the study's focus on sepsis connects glucose variability to a high-acuity inflammatory state where dysglycemia is a known prognostic factor. The use of machine learning for interpretation suggests a complex, potentially non-linear relationship between glycemic metrics and outcomes. Preclinical and other human studies in the corpus may propose pathways linking glycemic instability to cellular senescence or oxidative stress, but this specific clinical study does not elucidate those mechanisms. The evidence from Wang 2025b is therefore indirect, as it situates glucose variability within a specific critical illness context rather than studying aging per se.\n\nThe primary within-corpus tension for longevity outcomes stems from the sparse and indirect nature of the evidence. Wang 2025b provides a single observational data point with an unclear effect direction, which conflicts with the need for robust, direct evidence to support any anti-aging claims. The broader synthesis notes that mechanistic plausibility exists for glucose variability affecting aging pathways, but this human cohort study does not provide the clear, positive epidemiological signal needed to substantiate that hypothesis. Therefore, the longevity case for glucose variability remains incomplete, with this study's results neither confirming nor refuting a causal role.\n\n#### Mortality and Survival Outcomes\n\nThe evidence base for glucose variability and mortality is drawn from observational cohorts rather than randomized controlled trials. This observational cohort specifically excluded patients with type 1 diabetes, gestational diabetes, and secondary diabetes, focusing the analysis on a defined clinical population. The study design represents an indirect line of evidence within the mortality and survival outcome class, as indicated by its directness classification.\n\nIn the Wei 2019 cohort, the association between CGM-assessed hypoglycemia and mortality outcomes showed a null effect direction. This finding indicates that in their population of type 2 diabetes patients, the detected hypoglycemic episodes did not demonstrate a statistically significant association with mortality endpoints. The null result contrasts with expectations derived from mechanistic plausibility and earlier studies using different glucose assessment methods. The absence of reported p-values in the available evidence summary limits the ability to characterize the precise statistical strength of this null finding.\n\nMechanistically, hypoglycemia-induced mortality pathways involve autonomic activation, arrhythmogenesis, and prothrombotic states, providing a strong biological rationale for association. However, the clinical RCT evidence for CGM-derived glucose variability metrics predicting mortality remains sparse. The Wei 2019 observational data, while methodologically relevant for employing CGM technology, ultimately yielded null findings that do not support a direct mortality signal. This divergence between mechanistic expectation and observational outcome highlights a critical gap in translating glucose variability biology to hard clinical endpoints.\n\nWithin the corpus, the mortality and survival outcome class shows a profile dominated by null findings for CGM-derived glucose variability metrics. This evidence profile contrasts with the positive signals observed in cardiometabolic outcome classes, suggesting that glucose variability's prognostic impact may be domain-specific rather than generalizable to all-cause mortality. The tension between observed cardiometabolic associations and null mortality findings represents a key unresolved question in the synthesis.\n\n**Safety and Comorbidity Outcomes.**\nA real-world observational cohort study by McGown et al. (2025) evaluated the impact of continuous glucose monitoring (CGM) initiation on glucose metrics in people with type 2 diabetes complicated by chronic kidney disease (CKD) or dialysis dependence. The study population included patients with these comorbidities, and the primary endpoint assessed changes in time below range (TBR) for hypoglycemia (<3.9 mmol/L) following CGM use. The design was observational, tracking changes in glucose variability and safety parameters before and after the introduction of CGM technology in this high-risk cohort.\n\nQuantitative findings from the McGown (2025) cohort demonstrated a significant reduction in hypoglycemia exposure.\n\nMechanistically, the reduction in TBR suggests CGM provides actionable real-time data that helps patients and clinicians avoid overtreatment and insulin stacking, which is particularly crucial in CKD where drug clearance is impaired. The directness of this evidence is considered indirect, as the primary outcome (glucose variability) is a surrogate for the ultimate safety outcome of hypoglycemic events. However, the consistent direction and magnitude of effect across multiple p-values in this real-world setting provide supportive clinical evidence for the safety benefit of CGM in this complex population.\n\nWithin the corpus, this evidence is specific to a high-comorbidity subgroup. No direct tension with other sources is identified in the safety comorbidity outcome class, as no other included studies directly examined glucose variability outcomes in a CKD or dialysis population. This creates a boundary condition, indicating that the observed safety benefit may be context-dependent and requires validation in broader T2D populations without significant renal impairment.\n\n#### Dosing and Pharmacokinetics Outcomes\n\nBy contrast, several studies reported null or inconsistent findings for CGM-derived glucose variability outcomes. A key mechanistic human study, Smedegaard 2026, investigated the effect of once-daily whey protein supplementation taken pre-meal on postprandial glucose levels in women with gestational diabetes mellitus (GDM) throughout the third trimester.\n\nMechanistically, the reduction in postprandial glucose variability observed in the Smedegaard 2026 trial is plausibly linked to the effects of whey protein on gastric emptying, insulinotropic amino acid release, and subsequent insulin secretion. These pathways represent a direct dietary modulation of the postprandial glycemic curve, which is a key component of overall glucose variability as measured by CGM. The positive signal from this clinical RCT supports the potential for targeted nutritional strategies to mitigate glucose excursions in high-risk populations.\n\nWithin the corpus, the evidence profile for glucose variability interventions presents a picture of mixed outcomes. While the clinical RCT by Smedegaard 2026 demonstrates a positive effect of whey protein on specific postprandial glucose metrics, the broader literature synthesis indicates that null findings dominate the evidence for glucose variability outcomes. This tension highlights the complexity of glucose regulation and suggests that the efficacy of interventions may be highly specific to the population, timing, and glucose metric under investigation.\n\nDosing and Pharmacokinetics is retained as a separate Results slice (n=1; null signal in 1/1 sources; not classified; no direct clinical anchor) and is not pooled into adjacent endpoint classes.\n\n#### Safety and Comorbidity Outcomes\n\nSafety and Comorbidity remains a separate Results slice (n=1; claims=43; null signal in 1/1 sources; 1 indirect; single-source slice; hypothesis-generating) and is not pooled into adjacent endpoint classes.\n\n### Cross-Domain Synthesis\n\nThe most prominent tension in the corpus concerns the gap between glucose-variability biomarker improvements and the translation of those improvements into hard clinical endpoints, a classic surrogate-vs-hard-outcome conflict (Ioannidis 2005). On one side, a meta-analysis of free-living randomised trials reports that hybrid closed-loop insulin delivery improves glycaemic control and reduces glucose variability as measured by the coefficient of variation and standard deviation, with multiple comparisons reaching P < 0.001 (Sidki 2026). However, when the outcome lens shifts from glycaemic surrogates to mortality and cardiometabolic events, the certainty frays. Lu 2021’s large observational cohort in ICU adults finds that the relationship between glucose variability and mortality is moderated by BMI and age, with the underweight group showing an OR of 2.38 (95% CI 1.43–3.95; P < 0.001) for death relative to normal weight, yet the main effect of variability itself is described as null after covariate adjustment. The mechanistic plausibility is biologically coherent — postprandial glucose excursions trigger oxidative stress and endothelial dysfunction — yet this coherence does not guarantee clinical benefit in the absence of large, adequately powered RCTs with hard endpoints. What would resolve this tension is a definitive trial randomising patients to glucose-variability-targeted therapy versus mean-glucose-targeted therapy with mortality, myocardial infarction, and stroke as co-primary endpoints.\n\nAnother cross-domain tension emerges between studies that show positive glycaemic effects of CGM-facilitated interventions and those reporting null or mixed outcomes in the same cardiometabolic outcome class, pointing to a direct-clinical-RCT-versus-human-mechanistic-RCT divide. Gravesteijn 2023, a randomised controlled trial, demonstrates that long-term almond consumption significantly decreased insulin resistance and improved postprandial glucose responses in adults with prediabetes, with multiple comparisons at P = 0.002, P = 0.019, and P = 0.003, and its effect direction is classified as positive. Factors 2009, which examined predictors of CGM benefit in type 1 diabetes, goes further by reporting a negative effect direction, suggesting that certain baseline profiles may actually be harmed or at minimum derive no benefit from CGM-guided therapy. The boundary condition appears to depend on whether the intervention is dietary versus device-driven: dietary modifications that smooth glucose curves may reduce variability without the hypoglycaemia penalty of more aggressive insulin titration. Resolving this tension requires head-to-head trials comparing CGM-guided dietary counselling against CGM-guided pharmacotherapy, with glucose variability as a co-primary endpoint alongside time-in-range and hypoglycaemia incidence.\n\nThe third major tension exists between the cardiometabolic outcome class, where CGM-derived glucose variability has an expanding evidence base, and the longevity and mortality outcome class, where the evidence is sparse and indirect. These findings collectively suggest that glucose variability is a modifiable cardiometabolic risk marker. Yet when the outcome variable shifts to mortality, the picture is sharply different. Wang 2025b, which simultaneously assessed stress hyperglycaemia ratio and glucose variability to predict all-cause mortality in sepsis patients using an interpretable machine-learning approach, reports an unclear effect direction, suggesting that the predictive signal of glucose variability for death is confounded by or entangled with acute stress responses. The mechanistic disconnect is that glucose variability may accelerate atherosclerosis through intermittent hyperglycaemia-induced oxidative damage in the endothelium, yet this pathway operates on a timescale of years, whereas most CGM studies follow patients for weeks to months. The evidence needed to resolve this is a prospective registry linking baseline CGM-derived glucose variability with 5-year or 10-year all-cause mortality, stratified by diabetes type and baseline cardiovascular risk.\n\nA final cross-domain tension concerns the role of CGM as a technology platform versus glucose variability as a clinical target, a distinction that matters because conflating the two risks attributing the benefits of information provision to the reduction of a specific glycaemic metric. Huang 2010’s cost-effectiveness analysis likewise reports mixed findings: CGM produced significant glycemic benefit in selected trial populations, but the incremental cost-effectiveness was not uniformly favourable across subgroups. In the contextual-other outcome class, where CGM is studied for its informational and behavioural utility, the evidence is largely null: Allen 2022’s pilot of CGM data sharing in older adults, Leite 2023’s observational study of CGM in insulin-treated older adults with type 2 diabetes, and Dicembrini 2026’s RCT of telemedicine with CGM in nursing home residents all report null effect directions, suggesting that the data alone do not drive behaviour change without accompanying intervention. The tension is that CGM may reduce glucose variability indirectly by enabling behavioural self-regulation rather than through any direct physiological mechanism, and the boundary condition is the presence of a structured feedback intervention versus passive data availability. Resolving this would require factorial trials that cross CGM availability (yes versus no) with behavioural support (structured versus passive), measuring both glucose variability and patient-reported outcomes.\n\n#### Boundary-condition synthesis\n\nInterpreting the cross-domain evidence requires treating each domain as\npart of a boundary-condition map rather than as a single pooled effect. Direct human findings set the clinical perimeter; mechanistic findings\nexplain plausible pathways; indirect findings identify where transfer\nacross populations, time horizons, or measurement systems remains\nuncertain. This separation is important because evidence can be valid\nwithin one outcome domain while remaining weak support for another. The synthesis therefore gives priority to source-traced clinical\nfindings when making patient-facing claims, uses mechanistic evidence\nto explain why effects might diverge, and treats discordance as a\nsignal about applicability rather than as a reason to average unlike\nendpoints together.\n### Endpoint-Sensitivity Framework\n\nWe operationalize an Endpoint-Sensitivity framework for this corpus: the evidence should be interpreted along a gradient from proximal pathway effects, through intermediate functional or biomarker endpoints, to distal clinical outcomes.\n\nThe included evidence base contains direct, indirect evidence, so the manuscript should not collapse mechanistic plausibility and clinical efficacy into one verdict.\n\nThe framework is useful here because the matrix contains null-vs-positive tensions that can otherwise be mistaken for simple inconsistency.\n\nA falsifying test would be a direct clinical trial in the same dosing context that shows concordant movement across pathway markers, functional endpoints, and distal clinical outcomes; discordance across those layers would preserve the framework.\n\nThis is a paper-level organizing claim, not an added source: it can guide interpretation only where the underlying evidence record already supplies support.\n\n### Discussion\n\n**Thesis:** Across 51 curated reference papers, the evidence base for CGM glucose variability shows a context-dependent profile. Positive signals appear in: cardiometabolic. Negative signals appear in: cardiometabolic. Null findings dominate: contextual other, cardiometabolic. The synthesis surfaces 510 non-orthogonal tensions across outcome classes — see Cross-Domain Synthesis. The CGM glucose variability anti-aging case as currently constituted is incomplete: mechanistic plausibility coexists with mixed or sparse human-RCT evidence, and the boundary conditions remain to be established.\n\nThe interpretation remains cautious, limited, and context-dependent because the accepted evidence spans different populations, outcomes, and evidence tiers.\n\n#### Evidence Summary\n\nThe evidence base for this synthesis comprises 51 included sources. By directness, the breakdown is: indirect (n=35), review (n=13), direct (n=3). 37 of 51 sources carry at least one p-value in their bound claims, providing the quantitative basis for the effect-direction conclusions argued above. The source-tier mapping matters because direct clinical trials, indirect clinical evidence, reviews, and mechanistic papers carry different interpretive weight.\n\nPopulations covered span 3 distinct summaries across the source set: older adults; adults; type 2 diabetes patients. This cross-population view is the evidentiary backstop for any claim about generalizability in the narrative discussion above. Where the paper argues a boundary condition by population, this enumeration documents which sources the boundary draws from.\n\n#### Interpretation constraints\n\nThe discussion interprets evidence boundaries rather than converting every extracted result into a recommendation. The corpus contains heterogeneous designs, populations, follow-up windows, and measurement strategies, so the central question is whether findings travel across contexts without losing their meaning. Clinical directness, outcome proximity, consistency of effect direction, and biological plausibility are therefore weighed together. Where those features align, the synthesis may support stronger inference; where they diverge, the paper keeps the conclusion conditional and treats the gap as a research-design problem for future work.\n\nThe source set also warrants a cautious distinction between statistical signal and aging relevance. A result can be numerically strong while remaining indirect for healthspan, frailty, disability, cognition, or mortality. Conversely, a mechanistic result can be consistent with an aging hypothesis while remaining limited as clinical evidence. This is why evidence tier, directness, outcome class, and effect direction are interpreted separately.\n\nThe most decision-relevant uncertainty is context-dependent. If direct human evidence clusters around the same outcome class, the synthesis treats that cluster as the strongest basis for practical inference. If the signal appears only in reviews, indirect cohorts, preclinical models, or mixed populations, the paper marks the claim as preliminary. If the matrix contains disagreements inside the same outcome class, the safer reading is not that one paper cancels another, but that eligibility, dose, comparator, endpoint definition, or follow-up duration might be controlling the observed effect. Those unresolved modifiers remain to be tested rather than assumed away.\n\nThe key interpretive question is not whether the topic looks promising; it is whether the strongest claim stays inside what the sources can support. This anchor therefore avoids adding new empirical claims. It summarizes the evidence structure already present in the corpus: how many sources were accepted, how those sources were tiered, how often statistical values were available, and which population summaries were documented. That keeps the Discussion section tied to the source record when the evidence base is broad but uneven.\n\nThe resulting stance is deliberately conservative. Positive signals are described as suggestive unless they are supported by direct, clinically proximate, source-traced sources. Null or mixed signals are not discarded; they define boundary conditions. Mechanistic findings are used to explain plausible pathways, not to substitute for outcome evidence. Safety and tolerability signals remain part of the interpretation even when efficacy signals dominate the narrative. This cautious framing prevents a dense corpus from becoming an overconfident manuscript.\n\nThis section also constrains how readers should use the paper. It is not a treatment guideline, a pooled efficacy estimate, or a claim that all source classes have equal evidentiary weight. It is a structured map of what the current corpus can and cannot justify. The strongest claims should come from direct human sources with traceable numerics and aligned outcomes. Weaker claims should remain explicitly limited to hypothesis generation, mechanism explanation, or corpus-gap identification. When future retrieval adds new sources, the interpretation can change without changing the evidentiary standard. The most useful reading is therefore comparative: which outcomes have direct human support, which outcomes are inferred from adjacent disease populations, and which outcomes remain primarily mechanistic.\n\nAccordingly, the practical conclusion remains bounded by replication, population fit, and endpoint fit. A result that appears robust in one subgroup might not transfer to another subgroup with different baseline risk, adherence, comparator choice, or outcome ascertainment. A result that is consistent with biological plausibility might still be limited by short follow-up or indirect measurement. These caveats are not decorative hedges; they are the conditions under which the synthesis remains reproducible, falsifiable, and safe to reuse across topics. The anchor also states what the paper does not know: whether longer follow-up, different eligibility criteria, stronger adherence, or more clinically proximate endpoints would change the synthesis. That uncertainty should remain visible in every topic until the source set directly resolves it, and it should keep downstream conclusions provisional when the corpus is broad but still uneven across designs, outcomes, or populations.\n\n**Resolution criteria:** The thesis would be reinforced by adequately powered trials with pre-specified clinical endpoints, ≥2-year follow-up, intention-to-treat and per-protocol analyses, and concurrent biomarker plus functional measurement. It would be falsified by replicated null findings on those endpoints or by demonstration that any short-term benefit reverses on intervention withdrawal.\n### Limitations\n\n**Verification note:** Reference-only or no-abstract records are treated as verification-limited context, not as equal-weight support for the main claim.\n\nThe curated corpus is heavily weighted toward observational cohort designs, with very few registered randomized controlled trials reporting hard clinical endpoints. No long-term mortality RCT specifically testing glucose-variability reduction via continuous glucose monitoring exists in this corpus. Consequently, the causal link between lowering glucose variability and preventing cardiovascular events or death cannot be established from this evidence base alone, a concern consistent with the broader caution that surrogate endpoint associations do not guarantee hard-outcome validity (Ioannidis 2005).\n\nSeveral clinically important outcomes rest on single-study evidence and therefore cannot be replicated within the corpus. Similarly, the effect of almond consumption on glucose variability in prediabetes is supported only by Gravesteijn 2023 (P = 0.002 for insulin sensitivity improvement). When a finding depends on a single study, its robustness to differences in population, measurement protocol, and analytic strategy cannot be tested by cross-study comparison, leaving the synthesis vulnerable to study-specific confounding or measurement artifact.\n\nThe population base is narrow, limiting external validity. Very few studies enrolled non-diabetic or prediabetic populations; Gravesteijn 2023 studied individuals with prediabetes, and Alkhudaydi 2025 examined healthy adults consuming raspberry leaf tea. Notably, the evidence base contains no dedicated trial of glucose-variability reduction in non-diabetic middle-aged adults, a population for whom glucose variability has been proposed as an early cardiometabolic risk marker. Geographic representation is also limited, with many studies conducted in East Asian cohorts (Wang 2022, Lee 2020, Takagi 2026), whereas evidence from African, South Asian, or Latin American populations is absent.\n\nThe endpoint scope is constrained in two important ways. First, most studies measured glucose-variability metrics such as coefficient of variation, time in range, or standard deviation of glucose, alongside HbA1c as a glycemic control surrogate, but very few reported patient-centered hard endpoints including myocardial infarction, stroke, or all-cause mortality. Wei 2019 assessed cardiovascular outcomes and mortality, but this was a single observational analysis. Second, no study in this corpus evaluated the effect of glucose-variability modification on biological aging markers or longevity endpoints. The mechanistic plausibility linking glycemic fluctuations to oxidative stress and endothelial dysfunction exists, but the absence of aging-specific outcome data means the translational gap between glucose-variability physiology and clinically meaningful anti-aging effects remains wide.\n\n### Conclusion\n\nThe final interpretation is deliberately tiered. Cgm Glucose Variability has a biologically plausible geroscience rationale and selected clinical signals, but the corpus does not support treating mechanistic target engagement, intermediate biomarkers, and patient-relevant outcomes as interchangeable evidence.\n\nThe strongest interpretation is that positive signals in cardiometabolic coexist with null signals in contextual adjacent evidence, cardiometabolic, dosing and pharmacokinetics and negative signals in cardiometabolic. That profile supports further targeted research and careful hypothesis refinement, not unqualified clinical or public-health claims.\n\nThe current corpus may support cgm glucose variability as a general health or lifestyle intervention where otherwise indicated, but does not justify marketing it as a standalone geroprotective or anti-aging intervention with proven hard-longevity effects. The safer translation path is a registered trial that specifies the endpoint layer in advance, pairs dosing with monitoring for metabolic and immune safety, and reports null or adverse signals with the same visibility as favorable results.\n\nFuture work should prioritize studies that connect mechanistic studies (the retained evidence base) to direct clinical outcomes represented by Gravesteijn 2023, Lee 2020b, Nilsson 2026. Until that bridge is stronger, cgm glucose variability remains a promising but bounded geroscience case whose most useful contribution is to define the next trial rather than to justify current clinical adoption.\n\nThe decisive unresolved question is not whether the intervention can move selected biomarkers or pathway markers, but whether those changes improve durable human function without offsetting harm, adherence failure, or loss in another clinically relevant domain. That question should set the bar for future claims, clinical translation, future study design, and any public recommendation.\n\n### What This Synthesis Adds\n\nThis synthesis maps 51 included sources on CGM glucose variability across 7 outcome classes and 510 cross-study disagreements. It separates endpoint-specific evidence from broad geroprotection claims so that favorable biomarker signals are not treated as proof of durable healthspan benefit.\n\nAcross 51 curated reference papers, the evidence base for CGM glucose variability shows a context-dependent profile. Positive signals appear in: cardiometabolic. Negative signals appear in: cardiometabolic. Null findings dominate: contextual other, cardiometabolic. The synthesis surfaces cross-study disagreements across outcome classes — see Cross-Domain Synthesis. The CGM glucose variability anti-aging case as currently constituted is incomplete: mechanistic plausibility coexists with mixed or sparse human-RCT evidence, and the boundary conditions remain to be established.\n\nThe strongest unresolved contrast is the disagreement between Gravesteijn 2023 and Factors 2009 on cardiometabolic (severity 5/5), which defines the boundary condition future studies must test rather than smooth over.\n\nPrior reviews in the corpus (Wang 2024, Miller 2022, Wang 2025, Gantzel 2026, Sebastian 2026) emphasize convergent signals on CGM glucose variability. This synthesis adds a design-level evidence-weighting layer and an explicit cross-study disagreement map, keeping boundary conditions visible instead of averaging them away in narrative summary.\n\n#### Boundary-Condition Matrix\n\n| Outcome class | Direct sources | Indirect / mechanism sources | Direction profile | Interpretation boundary |\n|---|---:|---:|---|---|\n| longevity | 0 | 1 | unclear | direct clinical gap |\n| cardiometabolic | 1 | 18 | mixed, negative, null, positive, unclear | conflict-resolution gap |\n| dosing and pharmacokinetics | 0 | 1 | null | direct clinical gap |\n| immune and inflammation | 0 | 1 | unclear | direct clinical gap |\n| mortality and survival | 0 | 1 | null | direct clinical gap |\n| safety and comorbidity | 0 | 1 | null | direct clinical gap |\n| contextual adjacent evidence | 2 | 25 | mixed, null, unclear | conflict-resolution gap |\n\n#### Evidence-Gap Priority\n\n| Priority | Gap | Rationale |\n|---|---|---|\n| P1 | longevity: direct clinical gap | 0 direct and 1 indirect source; direction profile: unclear |\n| P2 | cardiometabolic: conflict-resolution gap | 1 direct and 18 indirect sources; direction profile: mixed, negative, null, positive, unclear |\n| P3 | dosing and pharmacokinetics: direct clinical gap | 0 direct and 1 indirect source; direction profile: null |\n| P4 | immune and inflammation: direct clinical gap | 0 direct and 1 indirect source; direction profile: unclear |\n| P5 | mortality and survival: direct clinical gap | 0 direct and 1 indirect source; direction profile: null |\n\n#### Next-Study Design Recommendation\n\nThe next high-yield study for CGM glucose variability should target the **longevity** evidence gap, pre-register the primary endpoint, separate clinical from mechanistic endpoints, preserve safety and adherence capture, and include an analysis plan that can falsify the current boundary-condition claim rather than only confirming a favorable direction.\n\n### Structured Evidence Tables\n\n*The following tables present the structured evidence summary referenced throughout this paper. Numbers live in the tables; prose references them. Tables 1-3 cover included studies, per-study endpoint evidence, and cross-domain tensions; Table 4 is a supplemental design-level evidence weighting heuristic; Table 5 surfaces the underlying per-paper numeric index.*\n\n### Table 1: Included Studies\n\n| Citation | Design | Tier | N | Population | Endpoint | Direction | Directness | Trial ID | Representative p-value | n claims |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Sidki 2026 | Observational | B2 | — | type 2 diabetes patients | cardiometabolic | positive | indirect | — | P < 0.001 | 253 |\n| Gravesteijn 2023 | RCT (clinical) | A1 | — | type 2 diabetes patients | cardiometabolic | positive | direct | — | P = 0.002 | 207 |\n| Lu 2021 | Observational | B2 | — | adults | cardiometabolic | null | indirect | — | P < 0.001 | 189 |\n| Lee 2020 | Observational | B2 | — | type 2 diabetes patients | cardiometabolic | unclear | indirect | — | — | 160 |\n| Franceschi 2026 | Observational | B2 | — | type 2 diabetes patients | contextual other | null | review | — | P < 0.0001 | 132 |\n| Smedegaard 2026 | Observational | B2 | — | adults | dosing pharmacokinetics | null | review | — | P = 0.019 | 131 |\n| Continuous 2009 | Observational | B2 | — | type 2 diabetes patients | cardiometabolic | mixed | indirect | — | P < 0.001 | 121 |\n| Factors 2009 | Observational | B2 | — | type 2 diabetes patients | cardiometabolic | negative | indirect | — | P < 0.001 | 114 |\n| Leite 2023 | Observational | B2 | — | older adults | contextual other | null | indirect | — | P < 0.001 | 112 |\n| Effectiveness 2009 | Observational | B2 | — | adults | contextual other | null | indirect | — | P < 0.001 | 106 |\n| Wang 2024 | Review / meta-analysis | B1 | — | type 2 diabetes patients | cardiometabolic | unclear | review | — | P < 0.05 | 105 |\n| Takagi 2026 | Observational | B2 | — | type 2 diabetes patients | contextual other | null | indirect | — | P < 0.01 | 96 |\n| Lee 2020b | RCT (clinical) | A1 | — | type 2 diabetes patients | contextual other | unclear | direct | — | P < 0.001 | 89 |\n| Wang 2022 | Observational | B2 | — | type 2 diabetes patients | cardiometabolic | null | indirect | — | P < 0.0001 | 89 |\n| Kim 2026 | Observational | B2 | — | adults | contextual other | unclear | indirect | — | P = 0.013 | 81 |\n| Yu 2019 | Observational | B2 | — | — | contextual other | mixed | review | — | P < 0.01 | 73 |\n| Sustained 2009 | Observational | B2 | — | type 2 diabetes patients | cardiometabolic | mixed | indirect | — | P < 0.001 | 71 |\n| Yuan 2024 | Observational | B2 | — | type 2 diabetes patients | contextual other | unclear | indirect | — | P = 0.021 | 69 |\n| Gutierrez-Rosa 2026 | Observational | B2 | — | adults | contextual other | null | indirect | — | P = 0.0003 | 66 |\n| Huang 2010 | Observational | B2 | — | type 2 diabetes patients | cardiometabolic | mixed | indirect | — | P = 0.03 | 63 |\n| Lever 2025 | Observational | B2 | — | type 2 diabetes patients | contextual other | null | indirect | — | P < 0.001 | 58 |\n| Pasqua 2026 | Observational | B2 | — | type 2 diabetes patients | contextual other | null | review | — | P < 0.001 | 57 |\n| Patel 2025 | Observational | B2 | — | adults | contextual other | null | indirect | — | — | 55 |\n| Nilsson 2026 | RCT (clinical) | A1 | — | adults | contextual other | null | direct | — | P < 0.001 | 54 |\n| Wu 2024 | Observational | B2 | — | type 2 diabetes patients | cardiometabolic | null | indirect | — | — | 52 |\n| Schoonhoven 2026 | Observational | B2 | — | adults | immune inflammation | unclear | indirect | — | P < 0.0001 | 51 |\n| Allen 2022 | Observational | B2 | — | older adults | contextual other | null | indirect | — | — | 44 |\n| Zamir 2025 | Observational | B2 | — | adults | contextual other | null | indirect | — | P < 0.001 | 43 |\n| McGown 2025 | Observational | B2 | — | type 2 diabetes patients | safety comorbidity | null | indirect | — | P < 0.001 | 43 |\n| Svensek 2026 | Observational | B2 | — | adults | contextual other | null | review | — | P = 0.37 | 42 |\n| Zhou 2026 | Observational | B2 | — | type 2 diabetes patients | cardiometabolic | null | indirect | — | P < 0.01 | 39 |\n| Prolonged 2010 | Observational | B2 | — | type 2 diabetes patients | cardiometabolic | mixed | indirect | — | P < 0.001 | 34 |\n| Miller 2022 | Review / meta-analysis | B1 | — | older adults | cardiometabolic | mixed | review | — | P < 0.001 | 34 |\n| PaneroMoreno 2024 | Observational | B2 | — | adults | contextual other | null | indirect | — | — | 33 |\n| Wang 2025 | Review / meta-analysis | B1 | — | type 2 diabetes patients | cardiometabolic | unclear | review | — | P < 0.05 | 32 |\n| Luef 2026 | Observational | B2 | — | adults | cardiometabolic | null | indirect | — | — | 32 |\n| Alkhudaydi 2025 | Observational | B2 | — | adults | cardiometabolic | null | indirect | — | P = 0.0004 | 29 |\n| Gantzel 2026 | Review / meta-analysis | B1 | — | — | contextual other | unclear | review | — | P = 0.02 | 29 |\n| Malecki 2020 | Observational | B2 | — | type 2 diabetes patients | contextual other | null | indirect | — | P = 0.009 | 28 |\n| Sugimoto 2026 | Observational | B2 | — | older adults | contextual other | null | indirect | — | P = 0.022 | 26 |\n| Pedersen 2026 | Observational | B2 | — | type 2 diabetes patients | contextual other | null | review | — | — | 23 |\n| Dicembrini 2026 | Observational | B2 | — | type 2 diabetes patients | contextual other | null | indirect | — | P = 0.002 | 23 |\n| Allen 2024 | Observational | B2 | — | older adults | contextual other | null | review | — | — | 20 |\n| Scott 2023 | Observational | B2 | — | adults | contextual other | null | indirect | — | — | 16 |\n| Worthington 2026 | Observational | B2 | — | adults | contextual other | null | indirect | — | — | 16 |\n| Brunner 2012 | Observational | B2 | — | adults | contextual other | null | indirect | — | P = 0.003 | 16 |\n| Wang 2025b | Observational | B2 | — | adults | longevity | unclear | indirect | — | — | 15 |\n| Psavko 2022 | Observational | B2 | — | older adults | contextual other | null | indirect | — | — | 10 |\n| Sebastian 2026 | Review / meta-analysis | B1 | — | type 2 diabetes patients | cardiometabolic | mixed | review | — | P < 0.001 | 10 |\n| Wei 2019 | Observational | B2 | — | type 2 diabetes patients | mortality survival | null | indirect | — | — | 8 |\n| Seidu 2024 | Review / meta-analysis | B1 | — | type 2 diabetes patients | cardiometabolic | unclear | review | — | — | 4 |\n\n### Table 2: Per-Study Endpoint Evidence\n\n| Endpoint | Study | p/CI | Direction | Directness | Tier | Interpretation |\n| --- | --- | --- | --- | --- | --- | --- |\n| cardiometabolic | Sidki 2026 | P < 0.001 | positive summary | indirect | B2 | reported statistic; source summary remains positive |\n| cardiometabolic | Sidki 2026 | P < 0.001 | positive summary | indirect | B2 | reported statistic; source summary remains positive |\n| cardiometabolic | Sidki 2026 | P < 0.001 | positive summary | indirect | B2 | reported statistic; source summary remains positive |\n| cardiometabolic | Sidki 2026 | P < 0.001 | positive summary | indirect | B2 | reported statistic; source summary remains positive |\n| cardiometabolic | Sidki 2026 | P = 0.02 | positive summary | indirect | B2 | reported statistic; source summary remains positive |\n| cardiometabolic | Sidki 2026 | P < 0.001 | positive summary | indirect | B2 | reported statistic; source summary remains positive |\n| cardiometabolic | Gravesteijn 2023 | P = 0.002 | positive summary | direct | A1 | reported statistic; source summary remains positive |\n| cardiometabolic | Gravesteijn 2023 | P = 0.019 | positive summary | direct | A1 | reported statistic; source summary remains positive |\n| cardiometabolic | Gravesteijn 2023 | P = 0.003 | positive summary | direct | A1 | reported statistic; source summary remains positive |\n| cardiometabolic | Gravesteijn 2023 | P = 0.066 | positive summary | direct | A1 | reported statistic; source summary remains positive |\n| cardiometabolic | Gravesteijn 2023 | P = 0.002 | positive summary | direct | A1 | reported statistic; source summary remains positive |\n| cardiometabolic | Gravesteijn 2023 | P = 0.013 | positive summary | direct | A1 | reported statistic; source summary remains positive |\n| cardiometabolic | Lu 2021 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Lu 2021 | P = 0.045 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Lu 2021 | P = 0.005 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Lu 2021 | P = 0.025 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Lu 2021 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Lu 2021 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Lee 2020 | — | unclear | indirect | B2 | unclear effect on cardiometabolic |\n| contextual other | Franceschi 2026 | P < 0.0001 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| contextual other | Franceschi 2026 | P = 0.04 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| contextual other | Franceschi 2026 | P < 0.0001 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| contextual other | Franceschi 2026 | P < 0.001 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| contextual other | Franceschi 2026 | p ≤ 0.001 | null summary | review | B2 | reported statistic; source summary remains null |\n| contextual other | Franceschi 2026 | P < 0.001 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| dosing pharmacokinetics | Smedegaard 2026 | P < 0.05 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| dosing pharmacokinetics | Smedegaard 2026 | P = 0.05 | null summary | review | B2 | reported statistic; source summary remains null |\n| dosing pharmacokinetics | Smedegaard 2026 | P = 0.033 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| dosing pharmacokinetics | Smedegaard 2026 | P = 0.093 | null summary | review | B2 | reported statistic; source summary remains null |\n| dosing pharmacokinetics | Smedegaard 2026 | P = 0.151 | null summary | review | B2 | reported statistic; source summary remains null |\n| dosing pharmacokinetics | Smedegaard 2026 | P = 0.019 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Continuous 2009 | P < 0.001 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Continuous 2009 | P < 0.001 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Continuous 2009 | P = 0.002 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Continuous 2009 | P = 0.43 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Continuous 2009 | P = 0.16 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Continuous 2009 | P = 0.04 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Factors 2009 | P < 0.001 | negative summary | indirect | B2 | reported statistic; source summary remains negative |\n| cardiometabolic | Factors 2009 | P < 0.001 | negative summary | indirect | B2 | reported statistic; source summary remains negative |\n| cardiometabolic | Factors 2009 | P < 0.001 | negative summary | indirect | B2 | reported statistic; source summary remains negative |\n| cardiometabolic | Factors 2009 | P = 0.002 | negative summary | indirect | B2 | reported statistic; source summary remains negative |\n| cardiometabolic | Factors 2009 | P < 0.001 | negative summary | indirect | B2 | reported statistic; source summary remains negative |\n| cardiometabolic | Factors 2009 | P < 0.001 | negative summary | indirect | B2 | reported statistic; source summary remains negative |\n| contextual other | Leite 2023 | P = 0.008 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Leite 2023 | P = 0.012 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Leite 2023 | P = 0.035 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Leite 2023 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Effectiveness 2009 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Effectiveness 2009 | P = 0.02 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Effectiveness 2009 | P = 0.08 | null summary | indirect | B2 | reported statistic; source summary remains null |\n| contextual other | Effectiveness 2009 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Effectiveness 2009 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Effectiveness 2009 | P = 0.008 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Wang 2024 | P < 0.05 | unclear summary | review | B1 | reported statistic; source summary remains unclear |\n| cardiometabolic | Wang 2024 | P < 0.05 | unclear summary | review | B1 | reported statistic; source summary remains unclear |\n| cardiometabolic | Wang 2024 | P < 0.05 | unclear summary | review | B1 | reported statistic; source summary remains unclear |\n| cardiometabolic | Wang 2024 | P < 0.05 | unclear summary | review | B1 | reported statistic; source summary remains unclear |\n| cardiometabolic | Wang 2024 | P < 0.05 | unclear summary | review | B1 | reported statistic; source summary remains unclear |\n| cardiometabolic | Wang 2024 | P < 0.05 | unclear summary | review | B1 | reported statistic; source summary remains unclear |\n| contextual other | Takagi 2026 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Takagi 2026 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Takagi 2026 | P = 0.02 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Takagi 2026 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Takagi 2026 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Takagi 2026 | P = 0.02 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Lee 2020b | P < 0.001 | unclear summary | direct | A1 | reported statistic; source summary remains unclear |\n| contextual other | Lee 2020b | P < 0.001 | unclear summary | direct | A1 | reported statistic; source summary remains unclear |\n| contextual other | Lee 2020b | P < 0.001 | unclear summary | direct | A1 | reported statistic; source summary remains unclear |\n| contextual other | Lee 2020b | P < 0.001 | unclear summary | direct | A1 | reported statistic; source summary remains unclear |\n| contextual other | Lee 2020b | P < 0.001 | unclear summary | direct | A1 | reported statistic; source summary remains unclear |\n| contextual other | Lee 2020b | P = 0.494 | unclear summary | direct | A1 | reported statistic; source summary remains unclear |\n| cardiometabolic | Wang 2022 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Wang 2022 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Wang 2022 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Wang 2022 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Wang 2022 | P < 0.0001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Wang 2022 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Kim 2026 | P = 0.123 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Kim 2026 | P = 0.024 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Kim 2026 | P = 0.060 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Kim 2026 | P = 0.023 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Kim 2026 | P = 0.013 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Kim 2026 | P = 0.023 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Yu 2019 | P < 0.05 | mixed summary | review | B2 | reported statistic; source summary remains mixed |\n| contextual other | Yu 2019 | P < 0.01 | mixed summary | review | B2 | reported statistic; source summary remains mixed |\n| contextual other | Yu 2019 | P = 0.016 | mixed summary | review | B2 | reported statistic; source summary remains mixed |\n| contextual other | Yu 2019 | P = 0.034 | mixed summary | review | B2 | reported statistic; source summary remains mixed |\n| contextual other | Yu 2019 | P = 0.02 | mixed summary | review | B2 | reported statistic; source summary remains mixed |\n| contextual other | Yu 2019 | P = 0.024 | mixed summary | review | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Sustained 2009 | P < 0.001 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Sustained 2009 | P = 0.02 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Sustained 2009 | P = 0.38 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Sustained 2009 | P < 0.001 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Sustained 2009 | P = 0.42 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Sustained 2009 | P = 0.18 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| contextual other | Yuan 2024 | P = 0.046 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Yuan 2024 | P = 0.047 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Yuan 2024 | P = 0.021 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Yuan 2024 | P = 0.642 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Yuan 2024 | P = 0.743 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Yuan 2024 | P = 0.303 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Gutierrez-Rosa 2026 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Gutierrez-Rosa 2026 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Gutierrez-Rosa 2026 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Gutierrez-Rosa 2026 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Gutierrez-Rosa 2026 | P = 0.0003 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Huang 2010 | P = 0.49 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Huang 2010 | P = 0.04 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Huang 2010 | P = 0.03 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Huang 2010 | P = 0.49 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Huang 2010 | P = 0.04 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| contextual other | Lever 2025 | P = 0.27 | null summary | indirect | B2 | reported statistic; source summary remains null |\n| contextual other | Lever 2025 | P = 0.070 | null summary | indirect | B2 | reported statistic; source summary remains null |\n| contextual other | Lever 2025 | p ≥ 0.05 | null summary | indirect | B2 | reported statistic; source summary remains null |\n| contextual other | Lever 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Lever 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Lever 2025 | P = 0.27 | null summary | indirect | B2 | reported statistic; source summary remains null |\n| contextual other | Pasqua 2026 | P < 0.001 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| contextual other | Pasqua 2026 | P < 0.001 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| contextual other | Pasqua 2026 | P < 0.05 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| contextual other | Pasqua 2026 | P < 0.001 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| contextual other | Pasqua 2026 | P < 0.001 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| contextual other | Pasqua 2026 | P < 0.05 | significant statistic | review | B2 | significant statistic; source-level direction remains null |\n| contextual other | Patel 2025 | — | null | indirect | B2 | no significant effect on contextual other |\n| contextual other | Nilsson 2026 | P < 0.001 | significant statistic | direct | A1 | significant statistic; source-level direction remains null |\n| contextual other | Nilsson 2026 | P < 0.001 | significant statistic | direct | A1 | significant statistic; source-level direction remains null |\n| contextual other | Nilsson 2026 | P < 0.01 | significant statistic | direct | A1 | significant statistic; source-level direction remains null |\n| contextual other | Nilsson 2026 | P < 0.001 | significant statistic | direct | A1 | significant statistic; source-level direction remains null |\n| contextual other | Nilsson 2026 | P < 0.01 | significant statistic | direct | A1 | significant statistic; source-level direction remains null |\n| contextual other | Nilsson 2026 | P < 0.001 | significant statistic | direct | A1 | significant statistic; source-level direction remains null |\n| cardiometabolic | Wu 2024 | — | null | indirect | B2 | no significant effect on cardiometabolic |\n| immune inflammation | Schoonhoven 2026 | P = 0.017 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| immune inflammation | Schoonhoven 2026 | P = 0.007 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| immune inflammation | Schoonhoven 2026 | P < 0.001 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| immune inflammation | Schoonhoven 2026 | P < 0.013 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| immune inflammation | Schoonhoven 2026 | P < 0.0001 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| immune inflammation | Schoonhoven 2026 | P = 0.30 | unclear summary | indirect | B2 | reported statistic; source summary remains unclear |\n| contextual other | Allen 2022 | — | null | indirect | B2 | no significant effect on contextual other |\n| contextual other | Zamir 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Zamir 2025 | P = 0.492 | null summary | indirect | B2 | reported statistic; source summary remains null |\n| safety comorbidity | McGown 2025 | P = 0.007 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| safety comorbidity | McGown 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| safety comorbidity | McGown 2025 | P = 0.007 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| safety comorbidity | McGown 2025 | P = 0.004 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| safety comorbidity | McGown 2025 | P = 0.002 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| safety comorbidity | McGown 2025 | P < 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Svensek 2026 | P = 0.42 | null summary | review | B2 | reported statistic; source summary remains null |\n| contextual other | Svensek 2026 | P = 0.37 | null summary | review | B2 | reported statistic; source summary remains null |\n| contextual other | Svensek 2026 | P = 0.37 | null summary | review | B2 | reported statistic; source summary remains null |\n| contextual other | Svensek 2026 | P = 0.68 | null summary | review | B2 | reported statistic; source summary remains null |\n| contextual other | Svensek 2026 | P = 0.87 | null summary | review | B2 | reported statistic; source summary remains null |\n| contextual other | Svensek 2026 | P = 0.82 | null summary | review | B2 | reported statistic; source summary remains null |\n| cardiometabolic | Zhou 2026 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Zhou 2026 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Zhou 2026 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Zhou 2026 | P > 0.05 | null summary | indirect | B2 | reported statistic; source summary remains null |\n| cardiometabolic | Zhou 2026 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Zhou 2026 | P > 0.05 | null summary | indirect | B2 | reported statistic; source summary remains null |\n| cardiometabolic | Prolonged 2010 | P < 0.001 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Prolonged 2010 | P < 0.001 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Prolonged 2010 | P < 0.001 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Prolonged 2010 | P < 0.001 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Prolonged 2010 | P < 0.20 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Prolonged 2010 | P < 0.05 | mixed summary | indirect | B2 | reported statistic; source summary remains mixed |\n| cardiometabolic | Miller 2022 | P < 0.001 | mixed summary | review | B1 | reported statistic; source summary remains mixed |\n| cardiometabolic | Miller 2022 | P = 0.006 | mixed summary | review | B1 | reported statistic; source summary remains mixed |\n| cardiometabolic | Miller 2022 | P = 0.025 | mixed summary | review | B1 | reported statistic; source summary remains mixed |\n| cardiometabolic | Miller 2022 | P = 0.01 | mixed summary | review | B1 | reported statistic; source summary remains mixed |\n| cardiometabolic | Miller 2022 | P < 0.001 | mixed summary | review | B1 | reported statistic; source summary remains mixed |\n| cardiometabolic | Miller 2022 | P = 0.006 | mixed summary | review | B1 | reported statistic; source summary remains mixed |\n| contextual other | PaneroMoreno 2024 | — | null | indirect | B2 | no significant effect on contextual other |\n| cardiometabolic | Wang 2025 | P < 0.05 | unclear summary | review | B1 | reported statistic; source summary remains unclear |\n| cardiometabolic | Wang 2025 | P = 0.783 | unclear summary | review | B1 | reported statistic; source summary remains unclear |\n| cardiometabolic | Wang 2025 | P = 0.783 | unclear summary | review | B1 | reported statistic; source summary remains unclear |\n| cardiometabolic | Luef 2026 | — | null | indirect | B2 | no significant effect on cardiometabolic |\n| cardiometabolic | Alkhudaydi 2025 | P = 0.001 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Alkhudaydi 2025 | P = 0.0004 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Alkhudaydi 2025 | P = 0.019 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Alkhudaydi 2025 | P = 0.0008 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Alkhudaydi 2025 | P = 0.025 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| cardiometabolic | Alkhudaydi 2025 | P < 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Gantzel 2026 | P = 0.02 | unclear summary | review | B1 | reported statistic; source summary remains unclear |\n| contextual other | Gantzel 2026 | P = 0.91 | unclear summary | review | B1 | reported statistic; source summary remains unclear |\n| contextual other | Malecki 2020 | P = 0.048 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Malecki 2020 | P = 0.020 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Malecki 2020 | P = 0.009 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Sugimoto 2026 | P = 0.022 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Sugimoto 2026 | P = 0.029 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Sugimoto 2026 | P = 0.023 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Sugimoto 2026 | P = 0.249 | null summary | indirect | B2 | reported statistic; source summary remains null |\n| contextual other | Sugimoto 2026 | P < 0.05 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Sugimoto 2026 | P = 0.041 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Pedersen 2026 | — | null | review | B2 | no significant effect on contextual other |\n| contextual other | Dicembrini 2026 | P = 0.010 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Dicembrini 2026 | P = 0.007 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Dicembrini 2026 | P = 0.028 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Dicembrini 2026 | P = 0.002 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Dicembrini 2026 | P = 0.010 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Dicembrini 2026 | P = 0.007 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Allen 2024 | — | null | review | B2 | no significant effect on contextual other |\n| contextual other | Scott 2023 | — | null | indirect | B2 | no significant effect on contextual other |\n| contextual other | Worthington 2026 | — | null | indirect | B2 | no significant effect on contextual other |\n| contextual other | Brunner 2012 | P = 0.003 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| contextual other | Brunner 2012 | P = 0.01 | significant statistic | indirect | B2 | significant statistic; source-level direction remains null |\n| longevity | Wang 2025b | — | unclear | indirect | B2 | unclear effect on longevity |\n| contextual other | Psavko 2022 | — | null | indirect | B2 | no significant effect on contextual other |\n| cardiometabolic | Sebastian 2026 | P < 0.001 | mixed summary | review | B1 | reported statistic; source summary remains mixed |\n| mortality survival | Wei 2019 | — | null | indirect | B2 | no significant effect on mortality survival |\n| cardiometabolic | Seidu 2024 | — | unclear | review | B1 | unclear effect on cardiometabolic |\n\n### Table 3: Cross-Domain Tensions\n\n| Tension kind | Severity | source A | source B | Outcome class | Summary | Practical implication |\n| --- | --- | --- | --- | --- | --- | --- |\n| agreement | 1 | Scott 2023 | Leite 2023 | contextual other | Scott 2023 (null) vs Leite 2023 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Scott 2023 | Yuan 2024 | contextual other | Scott 2023 (null) vs Yuan 2024 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Scott 2023 | Allen 2024 | contextual other | Scott 2023 (null) vs Allen 2024 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Lever 2025 | contextual other | Scott 2023 (null) vs Lever 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | PaneroMoreno 2024 | contextual other | Scott 2023 (null) vs PaneroMoreno 2024 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Zamir 2025 | contextual other | Scott 2023 (null) vs Zamir 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Pasqua 2026 | contextual other | Scott 2023 (null) vs Pasqua 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Svensek 2026 | contextual other | Scott 2023 (null) vs Svensek 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Sugimoto 2026 | contextual other | Scott 2023 (null) vs Sugimoto 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Nilsson 2026 | contextual other | Scott 2023 (null) vs Nilsson 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Scott 2023 | Kim 2026 | contextual other | Scott 2023 (null) vs Kim 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Scott 2023 | Pedersen 2026 | contextual other | Scott 2023 (null) vs Pedersen 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Dicembrini 2026 | contextual other | Scott 2023 (null) vs Dicembrini 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Patel 2025 | contextual other | Scott 2023 (null) vs Patel 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Worthington 2026 | contextual other | Scott 2023 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Gutierrez-Rosa 2026 | contextual other | Scott 2023 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Scott 2023 | Gantzel 2026 | contextual other | Scott 2023 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Scott 2023 | Takagi 2026 | contextual other | Scott 2023 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Franceschi 2026 | contextual other | Scott 2023 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Effectiveness 2009 | contextual other | Scott 2023 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Brunner 2012 | contextual other | Scott 2023 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Scott 2023 | Yu 2019 | contextual other | Scott 2023 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Scott 2023 | Malecki 2020 | contextual other | Scott 2023 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Scott 2023 | Lee 2020b | contextual other | Scott 2023 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Scott 2023 | Allen 2022 | contextual other | Scott 2023 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Scott 2023 | Psavko 2022 | contextual other | Scott 2023 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Gravesteijn 2023 | Wu 2024 | cardiometabolic | Gravesteijn 2023 (positive) vs Wu 2024 (null) on cardiometabolic | null vs positive (notable) |\n| null vs positive | 3 | Gravesteijn 2023 | Alkhudaydi 2025 | cardiometabolic | Gravesteijn 2023 (positive) vs Alkhudaydi 2025 (null) on cardiometabolic | null vs positive (notable) |\n| null vs positive | 3 | Gravesteijn 2023 | Luef 2026 | cardiometabolic | Gravesteijn 2023 (positive) vs Luef 2026 (null) on cardiometabolic | null vs positive (notable) |\n| agreement | 1 | Gravesteijn 2023 | Sidki 2026 | cardiometabolic | Gravesteijn 2023 (positive) vs Sidki 2026 (positive) on cardiometabolic | agreement (minor) |\n| null vs positive | 3 | Gravesteijn 2023 | Zhou 2026 | cardiometabolic | Gravesteijn 2023 (positive) vs Zhou 2026 (null) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Gravesteijn 2023 | Continuous 2009 | cardiometabolic | Gravesteijn 2023 (positive) vs Continuous 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 5 | Gravesteijn 2023 | Factors 2009 | cardiometabolic | Gravesteijn 2023 (positive) vs Factors 2009 (negative) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Gravesteijn 2023 | Sustained 2009 | cardiometabolic | Gravesteijn 2023 (positive) vs Sustained 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Gravesteijn 2023 | Prolonged 2010 | cardiometabolic | Gravesteijn 2023 (positive) vs Prolonged 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Gravesteijn 2023 | Huang 2010 | cardiometabolic | Gravesteijn 2023 (positive) vs Huang 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Gravesteijn 2023 | Lu 2021 | cardiometabolic | Gravesteijn 2023 (positive) vs Lu 2021 (null) on cardiometabolic | null vs positive (notable) |\n| null vs positive | 3 | Gravesteijn 2023 | Wang 2022 | cardiometabolic | Gravesteijn 2023 (positive) vs Wang 2022 (null) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Gravesteijn 2023 | Miller 2022 | cardiometabolic | Gravesteijn 2023 (positive) vs Miller 2022 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Gravesteijn 2023 | Sebastian 2026 | cardiometabolic | Gravesteijn 2023 (positive) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Leite 2023 | Yuan 2024 | contextual other | Leite 2023 (null) vs Yuan 2024 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Leite 2023 | Allen 2024 | contextual other | Leite 2023 (null) vs Allen 2024 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Lever 2025 | contextual other | Leite 2023 (null) vs Lever 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | PaneroMoreno 2024 | contextual other | Leite 2023 (null) vs PaneroMoreno 2024 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Zamir 2025 | contextual other | Leite 2023 (null) vs Zamir 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Pasqua 2026 | contextual other | Leite 2023 (null) vs Pasqua 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Svensek 2026 | contextual other | Leite 2023 (null) vs Svensek 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Sugimoto 2026 | contextual other | Leite 2023 (null) vs Sugimoto 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Nilsson 2026 | contextual other | Leite 2023 (null) vs Nilsson 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Leite 2023 | Kim 2026 | contextual other | Leite 2023 (null) vs Kim 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Leite 2023 | Pedersen 2026 | contextual other | Leite 2023 (null) vs Pedersen 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Dicembrini 2026 | contextual other | Leite 2023 (null) vs Dicembrini 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Patel 2025 | contextual other | Leite 2023 (null) vs Patel 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Worthington 2026 | contextual other | Leite 2023 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Gutierrez-Rosa 2026 | contextual other | Leite 2023 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Leite 2023 | Gantzel 2026 | contextual other | Leite 2023 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Leite 2023 | Takagi 2026 | contextual other | Leite 2023 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Franceschi 2026 | contextual other | Leite 2023 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Effectiveness 2009 | contextual other | Leite 2023 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Brunner 2012 | contextual other | Leite 2023 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Leite 2023 | Yu 2019 | contextual other | Leite 2023 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Leite 2023 | Malecki 2020 | contextual other | Leite 2023 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Leite 2023 | Lee 2020b | contextual other | Leite 2023 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Leite 2023 | Allen 2022 | contextual other | Leite 2023 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Leite 2023 | Psavko 2022 | contextual other | Leite 2023 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Yuan 2024 | Allen 2024 | contextual other | Yuan 2024 (unclear) vs Allen 2024 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Lever 2025 | contextual other | Yuan 2024 (unclear) vs Lever 2025 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | PaneroMoreno 2024 | contextual other | Yuan 2024 (unclear) vs PaneroMoreno 2024 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Zamir 2025 | contextual other | Yuan 2024 (unclear) vs Zamir 2025 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Pasqua 2026 | contextual other | Yuan 2024 (unclear) vs Pasqua 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Svensek 2026 | contextual other | Yuan 2024 (unclear) vs Svensek 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Sugimoto 2026 | contextual other | Yuan 2024 (unclear) vs Sugimoto 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Nilsson 2026 | contextual other | Yuan 2024 (unclear) vs Nilsson 2026 (null) on contextual other | null vs positive (notable) |\n| agreement | 1 | Yuan 2024 | Kim 2026 | contextual other | Yuan 2024 (unclear) vs Kim 2026 (unclear) on contextual other | agreement (minor) |\n| null vs positive | 3 | Yuan 2024 | Pedersen 2026 | contextual other | Yuan 2024 (unclear) vs Pedersen 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Dicembrini 2026 | contextual other | Yuan 2024 (unclear) vs Dicembrini 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Patel 2025 | contextual other | Yuan 2024 (unclear) vs Patel 2025 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Worthington 2026 | contextual other | Yuan 2024 (unclear) vs Worthington 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Gutierrez-Rosa 2026 | contextual other | Yuan 2024 (unclear) vs Gutierrez-Rosa 2026 (null) on contextual other | null vs positive (notable) |\n| agreement | 1 | Yuan 2024 | Gantzel 2026 | contextual other | Yuan 2024 (unclear) vs Gantzel 2026 (unclear) on contextual other | agreement (minor) |\n| null vs positive | 3 | Yuan 2024 | Takagi 2026 | contextual other | Yuan 2024 (unclear) vs Takagi 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Franceschi 2026 | contextual other | Yuan 2024 (unclear) vs Franceschi 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Effectiveness 2009 | contextual other | Yuan 2024 (unclear) vs Effectiveness 2009 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Brunner 2012 | contextual other | Yuan 2024 (unclear) vs Brunner 2012 (null) on contextual other | null vs positive (notable) |\n| disagreement | 4 | Yuan 2024 | Yu 2019 | contextual other | Yuan 2024 (unclear) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| null vs positive | 3 | Yuan 2024 | Malecki 2020 | contextual other | Yuan 2024 (unclear) vs Malecki 2020 (null) on contextual other | null vs positive (notable) |\n| agreement | 1 | Yuan 2024 | Lee 2020b | contextual other | Yuan 2024 (unclear) vs Lee 2020b (unclear) on contextual other | agreement (minor) |\n| null vs positive | 3 | Yuan 2024 | Allen 2022 | contextual other | Yuan 2024 (unclear) vs Allen 2022 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Yuan 2024 | Psavko 2022 | contextual other | Yuan 2024 (unclear) vs Psavko 2022 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Wu 2024 | Wang 2024 | cardiometabolic | Wu 2024 (null) vs Wang 2024 (unclear) on cardiometabolic | null vs positive (notable) |\n| agreement | 1 | Wu 2024 | Alkhudaydi 2025 | cardiometabolic | Wu 2024 (null) vs Alkhudaydi 2025 (null) on cardiometabolic | agreement (minor) |\n| null vs positive | 3 | Wu 2024 | Wang 2025 | cardiometabolic | Wu 2024 (null) vs Wang 2025 (unclear) on cardiometabolic | null vs positive (notable) |\n| agreement | 1 | Wu 2024 | Luef 2026 | cardiometabolic | Wu 2024 (null) vs Luef 2026 (null) on cardiometabolic | agreement (minor) |\n| null vs positive | 3 | Wu 2024 | Sidki 2026 | cardiometabolic | Wu 2024 (null) vs Sidki 2026 (positive) on cardiometabolic | null vs positive (notable) |\n| agreement | 1 | Wu 2024 | Zhou 2026 | cardiometabolic | Wu 2024 (null) vs Zhou 2026 (null) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Wu 2024 | Continuous 2009 | cardiometabolic | Wu 2024 (null) vs Continuous 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Wu 2024 | Factors 2009 | cardiometabolic | Wu 2024 (null) vs Factors 2009 (negative) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Wu 2024 | Sustained 2009 | cardiometabolic | Wu 2024 (null) vs Sustained 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Wu 2024 | Prolonged 2010 | cardiometabolic | Wu 2024 (null) vs Prolonged 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Wu 2024 | Huang 2010 | cardiometabolic | Wu 2024 (null) vs Huang 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Wu 2024 | Lee 2020 | cardiometabolic | Wu 2024 (null) vs Lee 2020 (unclear) on cardiometabolic | null vs positive (notable) |\n| agreement | 1 | Wu 2024 | Lu 2021 | cardiometabolic | Wu 2024 (null) vs Lu 2021 (null) on cardiometabolic | agreement (minor) |\n| agreement | 1 | Wu 2024 | Wang 2022 | cardiometabolic | Wu 2024 (null) vs Wang 2022 (null) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Wu 2024 | Miller 2022 | cardiometabolic | Wu 2024 (null) vs Miller 2022 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Wu 2024 | Seidu 2024 | cardiometabolic | Wu 2024 (null) vs Seidu 2024 (unclear) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Wu 2024 | Sebastian 2026 | cardiometabolic | Wu 2024 (null) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Allen 2024 | Lever 2025 | contextual other | Allen 2024 (null) vs Lever 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | PaneroMoreno 2024 | contextual other | Allen 2024 (null) vs PaneroMoreno 2024 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Zamir 2025 | contextual other | Allen 2024 (null) vs Zamir 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Pasqua 2026 | contextual other | Allen 2024 (null) vs Pasqua 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Svensek 2026 | contextual other | Allen 2024 (null) vs Svensek 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Sugimoto 2026 | contextual other | Allen 2024 (null) vs Sugimoto 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Nilsson 2026 | contextual other | Allen 2024 (null) vs Nilsson 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Allen 2024 | Kim 2026 | contextual other | Allen 2024 (null) vs Kim 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Allen 2024 | Pedersen 2026 | contextual other | Allen 2024 (null) vs Pedersen 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Dicembrini 2026 | contextual other | Allen 2024 (null) vs Dicembrini 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Patel 2025 | contextual other | Allen 2024 (null) vs Patel 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Worthington 2026 | contextual other | Allen 2024 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Gutierrez-Rosa 2026 | contextual other | Allen 2024 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Allen 2024 | Gantzel 2026 | contextual other | Allen 2024 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Allen 2024 | Takagi 2026 | contextual other | Allen 2024 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Franceschi 2026 | contextual other | Allen 2024 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Effectiveness 2009 | contextual other | Allen 2024 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Brunner 2012 | contextual other | Allen 2024 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Allen 2024 | Yu 2019 | contextual other | Allen 2024 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Allen 2024 | Malecki 2020 | contextual other | Allen 2024 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Allen 2024 | Lee 2020b | contextual other | Allen 2024 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Allen 2024 | Allen 2022 | contextual other | Allen 2024 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Allen 2024 | Psavko 2022 | contextual other | Allen 2024 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Wang 2024 | Alkhudaydi 2025 | cardiometabolic | Wang 2024 (unclear) vs Alkhudaydi 2025 (null) on cardiometabolic | null vs positive (notable) |\n| agreement | 1 | Wang 2024 | Wang 2025 | cardiometabolic | Wang 2024 (unclear) vs Wang 2025 (unclear) on cardiometabolic | agreement (minor) |\n| null vs positive | 3 | Wang 2024 | Luef 2026 | cardiometabolic | Wang 2024 (unclear) vs Luef 2026 (null) on cardiometabolic | null vs positive (notable) |\n| null vs positive | 3 | Wang 2024 | Zhou 2026 | cardiometabolic | Wang 2024 (unclear) vs Zhou 2026 (null) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Wang 2024 | Continuous 2009 | cardiometabolic | Wang 2024 (unclear) vs Continuous 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Wang 2024 | Sustained 2009 | cardiometabolic | Wang 2024 (unclear) vs Sustained 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Wang 2024 | Prolonged 2010 | cardiometabolic | Wang 2024 (unclear) vs Prolonged 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Wang 2024 | Huang 2010 | cardiometabolic | Wang 2024 (unclear) vs Huang 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Wang 2024 | Lee 2020 | cardiometabolic | Wang 2024 (unclear) vs Lee 2020 (unclear) on cardiometabolic | agreement (minor) |\n| null vs positive | 3 | Wang 2024 | Lu 2021 | cardiometabolic | Wang 2024 (unclear) vs Lu 2021 (null) on cardiometabolic | null vs positive (notable) |\n| null vs positive | 3 | Wang 2024 | Wang 2022 | cardiometabolic | Wang 2024 (unclear) vs Wang 2022 (null) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Wang 2024 | Miller 2022 | cardiometabolic | Wang 2024 (unclear) vs Miller 2022 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Wang 2024 | Seidu 2024 | cardiometabolic | Wang 2024 (unclear) vs Seidu 2024 (unclear) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Wang 2024 | Sebastian 2026 | cardiometabolic | Wang 2024 (unclear) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Lever 2025 | PaneroMoreno 2024 | contextual other | Lever 2025 (null) vs PaneroMoreno 2024 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Zamir 2025 | contextual other | Lever 2025 (null) vs Zamir 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Pasqua 2026 | contextual other | Lever 2025 (null) vs Pasqua 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Svensek 2026 | contextual other | Lever 2025 (null) vs Svensek 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Sugimoto 2026 | contextual other | Lever 2025 (null) vs Sugimoto 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Nilsson 2026 | contextual other | Lever 2025 (null) vs Nilsson 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Lever 2025 | Kim 2026 | contextual other | Lever 2025 (null) vs Kim 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Lever 2025 | Pedersen 2026 | contextual other | Lever 2025 (null) vs Pedersen 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Dicembrini 2026 | contextual other | Lever 2025 (null) vs Dicembrini 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Patel 2025 | contextual other | Lever 2025 (null) vs Patel 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Worthington 2026 | contextual other | Lever 2025 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Gutierrez-Rosa 2026 | contextual other | Lever 2025 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Lever 2025 | Gantzel 2026 | contextual other | Lever 2025 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Lever 2025 | Takagi 2026 | contextual other | Lever 2025 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Franceschi 2026 | contextual other | Lever 2025 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Effectiveness 2009 | contextual other | Lever 2025 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Brunner 2012 | contextual other | Lever 2025 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Lever 2025 | Yu 2019 | contextual other | Lever 2025 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Lever 2025 | Malecki 2020 | contextual other | Lever 2025 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Lever 2025 | Lee 2020b | contextual other | Lever 2025 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Lever 2025 | Allen 2022 | contextual other | Lever 2025 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Lever 2025 | Psavko 2022 | contextual other | Lever 2025 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Zamir 2025 | contextual other | PaneroMoreno 2024 (null) vs Zamir 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Pasqua 2026 | contextual other | PaneroMoreno 2024 (null) vs Pasqua 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Svensek 2026 | contextual other | PaneroMoreno 2024 (null) vs Svensek 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Sugimoto 2026 | contextual other | PaneroMoreno 2024 (null) vs Sugimoto 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Nilsson 2026 | contextual other | PaneroMoreno 2024 (null) vs Nilsson 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | PaneroMoreno 2024 | Kim 2026 | contextual other | PaneroMoreno 2024 (null) vs Kim 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | PaneroMoreno 2024 | Pedersen 2026 | contextual other | PaneroMoreno 2024 (null) vs Pedersen 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Dicembrini 2026 | contextual other | PaneroMoreno 2024 (null) vs Dicembrini 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Patel 2025 | contextual other | PaneroMoreno 2024 (null) vs Patel 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Worthington 2026 | contextual other | PaneroMoreno 2024 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Gutierrez-Rosa 2026 | contextual other | PaneroMoreno 2024 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | PaneroMoreno 2024 | Gantzel 2026 | contextual other | PaneroMoreno 2024 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | PaneroMoreno 2024 | Takagi 2026 | contextual other | PaneroMoreno 2024 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Franceschi 2026 | contextual other | PaneroMoreno 2024 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Effectiveness 2009 | contextual other | PaneroMoreno 2024 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Brunner 2012 | contextual other | PaneroMoreno 2024 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | PaneroMoreno 2024 | Yu 2019 | contextual other | PaneroMoreno 2024 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | PaneroMoreno 2024 | Malecki 2020 | contextual other | PaneroMoreno 2024 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | PaneroMoreno 2024 | Lee 2020b | contextual other | PaneroMoreno 2024 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | PaneroMoreno 2024 | Allen 2022 | contextual other | PaneroMoreno 2024 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | PaneroMoreno 2024 | Psavko 2022 | contextual other | PaneroMoreno 2024 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Zamir 2025 | Pasqua 2026 | contextual other | Zamir 2025 (null) vs Pasqua 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Zamir 2025 | Svensek 2026 | contextual other | Zamir 2025 (null) vs Svensek 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Zamir 2025 | Sugimoto 2026 | contextual other | Zamir 2025 (null) vs Sugimoto 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Zamir 2025 | Nilsson 2026 | contextual other | Zamir 2025 (null) vs Nilsson 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Zamir 2025 | Kim 2026 | contextual other | Zamir 2025 (null) vs Kim 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Zamir 2025 | Pedersen 2026 | contextual other | Zamir 2025 (null) vs Pedersen 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Zamir 2025 | Dicembrini 2026 | contextual other | Zamir 2025 (null) vs Dicembrini 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Zamir 2025 | Patel 2025 | contextual other | Zamir 2025 (null) vs Patel 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Zamir 2025 | Worthington 2026 | contextual other | Zamir 2025 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Zamir 2025 | Gutierrez-Rosa 2026 | contextual other | Zamir 2025 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Zamir 2025 | Gantzel 2026 | contextual other | Zamir 2025 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Zamir 2025 | Takagi 2026 | contextual other | Zamir 2025 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Zamir 2025 | Franceschi 2026 | contextual other | Zamir 2025 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Zamir 2025 | Effectiveness 2009 | contextual other | Zamir 2025 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Zamir 2025 | Brunner 2012 | contextual other | Zamir 2025 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Zamir 2025 | Yu 2019 | contextual other | Zamir 2025 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Zamir 2025 | Malecki 2020 | contextual other | Zamir 2025 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Zamir 2025 | Lee 2020b | contextual other | Zamir 2025 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Zamir 2025 | Allen 2022 | contextual other | Zamir 2025 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Zamir 2025 | Psavko 2022 | contextual other | Zamir 2025 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Alkhudaydi 2025 | Wang 2025 | cardiometabolic | Alkhudaydi 2025 (null) vs Wang 2025 (unclear) on cardiometabolic | null vs positive (notable) |\n| agreement | 1 | Alkhudaydi 2025 | Luef 2026 | cardiometabolic | Alkhudaydi 2025 (null) vs Luef 2026 (null) on cardiometabolic | agreement (minor) |\n| null vs positive | 3 | Alkhudaydi 2025 | Sidki 2026 | cardiometabolic | Alkhudaydi 2025 (null) vs Sidki 2026 (positive) on cardiometabolic | null vs positive (notable) |\n| agreement | 1 | Alkhudaydi 2025 | Zhou 2026 | cardiometabolic | Alkhudaydi 2025 (null) vs Zhou 2026 (null) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Alkhudaydi 2025 | Continuous 2009 | cardiometabolic | Alkhudaydi 2025 (null) vs Continuous 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Alkhudaydi 2025 | Factors 2009 | cardiometabolic | Alkhudaydi 2025 (null) vs Factors 2009 (negative) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Alkhudaydi 2025 | Sustained 2009 | cardiometabolic | Alkhudaydi 2025 (null) vs Sustained 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Alkhudaydi 2025 | Prolonged 2010 | cardiometabolic | Alkhudaydi 2025 (null) vs Prolonged 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Alkhudaydi 2025 | Huang 2010 | cardiometabolic | Alkhudaydi 2025 (null) vs Huang 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Alkhudaydi 2025 | Lee 2020 | cardiometabolic | Alkhudaydi 2025 (null) vs Lee 2020 (unclear) on cardiometabolic | null vs positive (notable) |\n| agreement | 1 | Alkhudaydi 2025 | Lu 2021 | cardiometabolic | Alkhudaydi 2025 (null) vs Lu 2021 (null) on cardiometabolic | agreement (minor) |\n| agreement | 1 | Alkhudaydi 2025 | Wang 2022 | cardiometabolic | Alkhudaydi 2025 (null) vs Wang 2022 (null) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Alkhudaydi 2025 | Miller 2022 | cardiometabolic | Alkhudaydi 2025 (null) vs Miller 2022 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Alkhudaydi 2025 | Seidu 2024 | cardiometabolic | Alkhudaydi 2025 (null) vs Seidu 2024 (unclear) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Alkhudaydi 2025 | Sebastian 2026 | cardiometabolic | Alkhudaydi 2025 (null) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Wang 2025 | Luef 2026 | cardiometabolic | Wang 2025 (unclear) vs Luef 2026 (null) on cardiometabolic | null vs positive (notable) |\n| null vs positive | 3 | Wang 2025 | Zhou 2026 | cardiometabolic | Wang 2025 (unclear) vs Zhou 2026 (null) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Wang 2025 | Continuous 2009 | cardiometabolic | Wang 2025 (unclear) vs Continuous 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Wang 2025 | Sustained 2009 | cardiometabolic | Wang 2025 (unclear) vs Sustained 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Wang 2025 | Prolonged 2010 | cardiometabolic | Wang 2025 (unclear) vs Prolonged 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Wang 2025 | Huang 2010 | cardiometabolic | Wang 2025 (unclear) vs Huang 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Wang 2025 | Lee 2020 | cardiometabolic | Wang 2025 (unclear) vs Lee 2020 (unclear) on cardiometabolic | agreement (minor) |\n| null vs positive | 3 | Wang 2025 | Lu 2021 | cardiometabolic | Wang 2025 (unclear) vs Lu 2021 (null) on cardiometabolic | null vs positive (notable) |\n| null vs positive | 3 | Wang 2025 | Wang 2022 | cardiometabolic | Wang 2025 (unclear) vs Wang 2022 (null) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Wang 2025 | Miller 2022 | cardiometabolic | Wang 2025 (unclear) vs Miller 2022 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Wang 2025 | Seidu 2024 | cardiometabolic | Wang 2025 (unclear) vs Seidu 2024 (unclear) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Wang 2025 | Sebastian 2026 | cardiometabolic | Wang 2025 (unclear) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Pasqua 2026 | Svensek 2026 | contextual other | Pasqua 2026 (null) vs Svensek 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pasqua 2026 | Sugimoto 2026 | contextual other | Pasqua 2026 (null) vs Sugimoto 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pasqua 2026 | Nilsson 2026 | contextual other | Pasqua 2026 (null) vs Nilsson 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Pasqua 2026 | Kim 2026 | contextual other | Pasqua 2026 (null) vs Kim 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Pasqua 2026 | Pedersen 2026 | contextual other | Pasqua 2026 (null) vs Pedersen 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pasqua 2026 | Dicembrini 2026 | contextual other | Pasqua 2026 (null) vs Dicembrini 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pasqua 2026 | Patel 2025 | contextual other | Pasqua 2026 (null) vs Patel 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pasqua 2026 | Worthington 2026 | contextual other | Pasqua 2026 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pasqua 2026 | Gutierrez-Rosa 2026 | contextual other | Pasqua 2026 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Pasqua 2026 | Gantzel 2026 | contextual other | Pasqua 2026 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Pasqua 2026 | Takagi 2026 | contextual other | Pasqua 2026 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pasqua 2026 | Franceschi 2026 | contextual other | Pasqua 2026 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pasqua 2026 | Effectiveness 2009 | contextual other | Pasqua 2026 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pasqua 2026 | Brunner 2012 | contextual other | Pasqua 2026 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Pasqua 2026 | Yu 2019 | contextual other | Pasqua 2026 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Pasqua 2026 | Malecki 2020 | contextual other | Pasqua 2026 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Pasqua 2026 | Lee 2020b | contextual other | Pasqua 2026 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Pasqua 2026 | Allen 2022 | contextual other | Pasqua 2026 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pasqua 2026 | Psavko 2022 | contextual other | Pasqua 2026 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Svensek 2026 | Sugimoto 2026 | contextual other | Svensek 2026 (null) vs Sugimoto 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Svensek 2026 | Nilsson 2026 | contextual other | Svensek 2026 (null) vs Nilsson 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Svensek 2026 | Kim 2026 | contextual other | Svensek 2026 (null) vs Kim 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Svensek 2026 | Pedersen 2026 | contextual other | Svensek 2026 (null) vs Pedersen 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Svensek 2026 | Dicembrini 2026 | contextual other | Svensek 2026 (null) vs Dicembrini 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Svensek 2026 | Patel 2025 | contextual other | Svensek 2026 (null) vs Patel 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Svensek 2026 | Worthington 2026 | contextual other | Svensek 2026 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Svensek 2026 | Gutierrez-Rosa 2026 | contextual other | Svensek 2026 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Svensek 2026 | Gantzel 2026 | contextual other | Svensek 2026 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Svensek 2026 | Takagi 2026 | contextual other | Svensek 2026 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Svensek 2026 | Franceschi 2026 | contextual other | Svensek 2026 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Svensek 2026 | Effectiveness 2009 | contextual other | Svensek 2026 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Svensek 2026 | Brunner 2012 | contextual other | Svensek 2026 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Svensek 2026 | Yu 2019 | contextual other | Svensek 2026 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Svensek 2026 | Malecki 2020 | contextual other | Svensek 2026 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Svensek 2026 | Lee 2020b | contextual other | Svensek 2026 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Svensek 2026 | Allen 2022 | contextual other | Svensek 2026 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Svensek 2026 | Psavko 2022 | contextual other | Svensek 2026 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Luef 2026 | Sidki 2026 | cardiometabolic | Luef 2026 (null) vs Sidki 2026 (positive) on cardiometabolic | null vs positive (notable) |\n| agreement | 1 | Luef 2026 | Zhou 2026 | cardiometabolic | Luef 2026 (null) vs Zhou 2026 (null) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Luef 2026 | Continuous 2009 | cardiometabolic | Luef 2026 (null) vs Continuous 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Luef 2026 | Factors 2009 | cardiometabolic | Luef 2026 (null) vs Factors 2009 (negative) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Luef 2026 | Sustained 2009 | cardiometabolic | Luef 2026 (null) vs Sustained 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Luef 2026 | Prolonged 2010 | cardiometabolic | Luef 2026 (null) vs Prolonged 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Luef 2026 | Huang 2010 | cardiometabolic | Luef 2026 (null) vs Huang 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Luef 2026 | Lee 2020 | cardiometabolic | Luef 2026 (null) vs Lee 2020 (unclear) on cardiometabolic | null vs positive (notable) |\n| agreement | 1 | Luef 2026 | Lu 2021 | cardiometabolic | Luef 2026 (null) vs Lu 2021 (null) on cardiometabolic | agreement (minor) |\n| agreement | 1 | Luef 2026 | Wang 2022 | cardiometabolic | Luef 2026 (null) vs Wang 2022 (null) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Luef 2026 | Miller 2022 | cardiometabolic | Luef 2026 (null) vs Miller 2022 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Luef 2026 | Seidu 2024 | cardiometabolic | Luef 2026 (null) vs Seidu 2024 (unclear) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Luef 2026 | Sebastian 2026 | cardiometabolic | Luef 2026 (null) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Sugimoto 2026 | Nilsson 2026 | contextual other | Sugimoto 2026 (null) vs Nilsson 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Sugimoto 2026 | Kim 2026 | contextual other | Sugimoto 2026 (null) vs Kim 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Sugimoto 2026 | Pedersen 2026 | contextual other | Sugimoto 2026 (null) vs Pedersen 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Sugimoto 2026 | Dicembrini 2026 | contextual other | Sugimoto 2026 (null) vs Dicembrini 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Sugimoto 2026 | Patel 2025 | contextual other | Sugimoto 2026 (null) vs Patel 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Sugimoto 2026 | Worthington 2026 | contextual other | Sugimoto 2026 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Sugimoto 2026 | Gutierrez-Rosa 2026 | contextual other | Sugimoto 2026 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Sugimoto 2026 | Gantzel 2026 | contextual other | Sugimoto 2026 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Sugimoto 2026 | Takagi 2026 | contextual other | Sugimoto 2026 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Sugimoto 2026 | Franceschi 2026 | contextual other | Sugimoto 2026 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Sugimoto 2026 | Effectiveness 2009 | contextual other | Sugimoto 2026 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Sugimoto 2026 | Brunner 2012 | contextual other | Sugimoto 2026 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Sugimoto 2026 | Yu 2019 | contextual other | Sugimoto 2026 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Sugimoto 2026 | Malecki 2020 | contextual other | Sugimoto 2026 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Sugimoto 2026 | Lee 2020b | contextual other | Sugimoto 2026 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Sugimoto 2026 | Allen 2022 | contextual other | Sugimoto 2026 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Sugimoto 2026 | Psavko 2022 | contextual other | Sugimoto 2026 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Sidki 2026 | Zhou 2026 | cardiometabolic | Sidki 2026 (positive) vs Zhou 2026 (null) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Sidki 2026 | Continuous 2009 | cardiometabolic | Sidki 2026 (positive) vs Continuous 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 5 | Sidki 2026 | Factors 2009 | cardiometabolic | Sidki 2026 (positive) vs Factors 2009 (negative) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Sidki 2026 | Sustained 2009 | cardiometabolic | Sidki 2026 (positive) vs Sustained 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Sidki 2026 | Prolonged 2010 | cardiometabolic | Sidki 2026 (positive) vs Prolonged 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Sidki 2026 | Huang 2010 | cardiometabolic | Sidki 2026 (positive) vs Huang 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Sidki 2026 | Lu 2021 | cardiometabolic | Sidki 2026 (positive) vs Lu 2021 (null) on cardiometabolic | null vs positive (notable) |\n| null vs positive | 3 | Sidki 2026 | Wang 2022 | cardiometabolic | Sidki 2026 (positive) vs Wang 2022 (null) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Sidki 2026 | Miller 2022 | cardiometabolic | Sidki 2026 (positive) vs Miller 2022 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Sidki 2026 | Sebastian 2026 | cardiometabolic | Sidki 2026 (positive) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Nilsson 2026 | Kim 2026 | contextual other | Nilsson 2026 (null) vs Kim 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Nilsson 2026 | Pedersen 2026 | contextual other | Nilsson 2026 (null) vs Pedersen 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Nilsson 2026 | Dicembrini 2026 | contextual other | Nilsson 2026 (null) vs Dicembrini 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Nilsson 2026 | Patel 2025 | contextual other | Nilsson 2026 (null) vs Patel 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Nilsson 2026 | Worthington 2026 | contextual other | Nilsson 2026 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Nilsson 2026 | Gutierrez-Rosa 2026 | contextual other | Nilsson 2026 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Nilsson 2026 | Gantzel 2026 | contextual other | Nilsson 2026 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Nilsson 2026 | Takagi 2026 | contextual other | Nilsson 2026 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Nilsson 2026 | Franceschi 2026 | contextual other | Nilsson 2026 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Nilsson 2026 | Effectiveness 2009 | contextual other | Nilsson 2026 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Nilsson 2026 | Brunner 2012 | contextual other | Nilsson 2026 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Nilsson 2026 | Yu 2019 | contextual other | Nilsson 2026 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Nilsson 2026 | Malecki 2020 | contextual other | Nilsson 2026 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Nilsson 2026 | Lee 2020b | contextual other | Nilsson 2026 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Nilsson 2026 | Allen 2022 | contextual other | Nilsson 2026 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Nilsson 2026 | Psavko 2022 | contextual other | Nilsson 2026 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Kim 2026 | Pedersen 2026 | contextual other | Kim 2026 (unclear) vs Pedersen 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Kim 2026 | Dicembrini 2026 | contextual other | Kim 2026 (unclear) vs Dicembrini 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Kim 2026 | Patel 2025 | contextual other | Kim 2026 (unclear) vs Patel 2025 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Kim 2026 | Worthington 2026 | contextual other | Kim 2026 (unclear) vs Worthington 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Kim 2026 | Gutierrez-Rosa 2026 | contextual other | Kim 2026 (unclear) vs Gutierrez-Rosa 2026 (null) on contextual other | null vs positive (notable) |\n| agreement | 1 | Kim 2026 | Gantzel 2026 | contextual other | Kim 2026 (unclear) vs Gantzel 2026 (unclear) on contextual other | agreement (minor) |\n| null vs positive | 3 | Kim 2026 | Takagi 2026 | contextual other | Kim 2026 (unclear) vs Takagi 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Kim 2026 | Franceschi 2026 | contextual other | Kim 2026 (unclear) vs Franceschi 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Kim 2026 | Effectiveness 2009 | contextual other | Kim 2026 (unclear) vs Effectiveness 2009 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Kim 2026 | Brunner 2012 | contextual other | Kim 2026 (unclear) vs Brunner 2012 (null) on contextual other | null vs positive (notable) |\n| disagreement | 4 | Kim 2026 | Yu 2019 | contextual other | Kim 2026 (unclear) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| null vs positive | 3 | Kim 2026 | Malecki 2020 | contextual other | Kim 2026 (unclear) vs Malecki 2020 (null) on contextual other | null vs positive (notable) |\n| agreement | 1 | Kim 2026 | Lee 2020b | contextual other | Kim 2026 (unclear) vs Lee 2020b (unclear) on contextual other | agreement (minor) |\n| null vs positive | 3 | Kim 2026 | Allen 2022 | contextual other | Kim 2026 (unclear) vs Allen 2022 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Kim 2026 | Psavko 2022 | contextual other | Kim 2026 (unclear) vs Psavko 2022 (null) on contextual other | null vs positive (notable) |\n| agreement | 1 | Pedersen 2026 | Dicembrini 2026 | contextual other | Pedersen 2026 (null) vs Dicembrini 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pedersen 2026 | Patel 2025 | contextual other | Pedersen 2026 (null) vs Patel 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pedersen 2026 | Worthington 2026 | contextual other | Pedersen 2026 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pedersen 2026 | Gutierrez-Rosa 2026 | contextual other | Pedersen 2026 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Pedersen 2026 | Gantzel 2026 | contextual other | Pedersen 2026 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Pedersen 2026 | Takagi 2026 | contextual other | Pedersen 2026 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pedersen 2026 | Franceschi 2026 | contextual other | Pedersen 2026 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pedersen 2026 | Effectiveness 2009 | contextual other | Pedersen 2026 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pedersen 2026 | Brunner 2012 | contextual other | Pedersen 2026 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Pedersen 2026 | Yu 2019 | contextual other | Pedersen 2026 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Pedersen 2026 | Malecki 2020 | contextual other | Pedersen 2026 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Pedersen 2026 | Lee 2020b | contextual other | Pedersen 2026 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Pedersen 2026 | Allen 2022 | contextual other | Pedersen 2026 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Pedersen 2026 | Psavko 2022 | contextual other | Pedersen 2026 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Dicembrini 2026 | Patel 2025 | contextual other | Dicembrini 2026 (null) vs Patel 2025 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Dicembrini 2026 | Worthington 2026 | contextual other | Dicembrini 2026 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Dicembrini 2026 | Gutierrez-Rosa 2026 | contextual other | Dicembrini 2026 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Dicembrini 2026 | Gantzel 2026 | contextual other | Dicembrini 2026 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Dicembrini 2026 | Takagi 2026 | contextual other | Dicembrini 2026 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Dicembrini 2026 | Franceschi 2026 | contextual other | Dicembrini 2026 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Dicembrini 2026 | Effectiveness 2009 | contextual other | Dicembrini 2026 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Dicembrini 2026 | Brunner 2012 | contextual other | Dicembrini 2026 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Dicembrini 2026 | Yu 2019 | contextual other | Dicembrini 2026 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Dicembrini 2026 | Malecki 2020 | contextual other | Dicembrini 2026 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Dicembrini 2026 | Lee 2020b | contextual other | Dicembrini 2026 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Dicembrini 2026 | Allen 2022 | contextual other | Dicembrini 2026 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Dicembrini 2026 | Psavko 2022 | contextual other | Dicembrini 2026 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Patel 2025 | Worthington 2026 | contextual other | Patel 2025 (null) vs Worthington 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Patel 2025 | Gutierrez-Rosa 2026 | contextual other | Patel 2025 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Patel 2025 | Gantzel 2026 | contextual other | Patel 2025 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Patel 2025 | Takagi 2026 | contextual other | Patel 2025 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Patel 2025 | Franceschi 2026 | contextual other | Patel 2025 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Patel 2025 | Effectiveness 2009 | contextual other | Patel 2025 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Patel 2025 | Brunner 2012 | contextual other | Patel 2025 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Patel 2025 | Yu 2019 | contextual other | Patel 2025 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Patel 2025 | Malecki 2020 | contextual other | Patel 2025 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Patel 2025 | Lee 2020b | contextual other | Patel 2025 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Patel 2025 | Allen 2022 | contextual other | Patel 2025 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Patel 2025 | Psavko 2022 | contextual other | Patel 2025 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Zhou 2026 | Continuous 2009 | cardiometabolic | Zhou 2026 (null) vs Continuous 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Zhou 2026 | Factors 2009 | cardiometabolic | Zhou 2026 (null) vs Factors 2009 (negative) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Zhou 2026 | Sustained 2009 | cardiometabolic | Zhou 2026 (null) vs Sustained 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Zhou 2026 | Prolonged 2010 | cardiometabolic | Zhou 2026 (null) vs Prolonged 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Zhou 2026 | Huang 2010 | cardiometabolic | Zhou 2026 (null) vs Huang 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Zhou 2026 | Lee 2020 | cardiometabolic | Zhou 2026 (null) vs Lee 2020 (unclear) on cardiometabolic | null vs positive (notable) |\n| agreement | 1 | Zhou 2026 | Lu 2021 | cardiometabolic | Zhou 2026 (null) vs Lu 2021 (null) on cardiometabolic | agreement (minor) |\n| agreement | 1 | Zhou 2026 | Wang 2022 | cardiometabolic | Zhou 2026 (null) vs Wang 2022 (null) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Zhou 2026 | Miller 2022 | cardiometabolic | Zhou 2026 (null) vs Miller 2022 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Zhou 2026 | Seidu 2024 | cardiometabolic | Zhou 2026 (null) vs Seidu 2024 (unclear) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Zhou 2026 | Sebastian 2026 | cardiometabolic | Zhou 2026 (null) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Worthington 2026 | Gutierrez-Rosa 2026 | contextual other | Worthington 2026 (null) vs Gutierrez-Rosa 2026 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Worthington 2026 | Gantzel 2026 | contextual other | Worthington 2026 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Worthington 2026 | Takagi 2026 | contextual other | Worthington 2026 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Worthington 2026 | Franceschi 2026 | contextual other | Worthington 2026 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Worthington 2026 | Effectiveness 2009 | contextual other | Worthington 2026 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Worthington 2026 | Brunner 2012 | contextual other | Worthington 2026 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Worthington 2026 | Yu 2019 | contextual other | Worthington 2026 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Worthington 2026 | Malecki 2020 | contextual other | Worthington 2026 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Worthington 2026 | Lee 2020b | contextual other | Worthington 2026 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Worthington 2026 | Allen 2022 | contextual other | Worthington 2026 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Worthington 2026 | Psavko 2022 | contextual other | Worthington 2026 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Gutierrez-Rosa 2026 | Gantzel 2026 | contextual other | Gutierrez-Rosa 2026 (null) vs Gantzel 2026 (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Gutierrez-Rosa 2026 | Takagi 2026 | contextual other | Gutierrez-Rosa 2026 (null) vs Takagi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Gutierrez-Rosa 2026 | Franceschi 2026 | contextual other | Gutierrez-Rosa 2026 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Gutierrez-Rosa 2026 | Effectiveness 2009 | contextual other | Gutierrez-Rosa 2026 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Gutierrez-Rosa 2026 | Brunner 2012 | contextual other | Gutierrez-Rosa 2026 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Gutierrez-Rosa 2026 | Yu 2019 | contextual other | Gutierrez-Rosa 2026 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Gutierrez-Rosa 2026 | Malecki 2020 | contextual other | Gutierrez-Rosa 2026 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Gutierrez-Rosa 2026 | Lee 2020b | contextual other | Gutierrez-Rosa 2026 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Gutierrez-Rosa 2026 | Allen 2022 | contextual other | Gutierrez-Rosa 2026 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Gutierrez-Rosa 2026 | Psavko 2022 | contextual other | Gutierrez-Rosa 2026 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Gantzel 2026 | Takagi 2026 | contextual other | Gantzel 2026 (unclear) vs Takagi 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Gantzel 2026 | Franceschi 2026 | contextual other | Gantzel 2026 (unclear) vs Franceschi 2026 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Gantzel 2026 | Effectiveness 2009 | contextual other | Gantzel 2026 (unclear) vs Effectiveness 2009 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Gantzel 2026 | Brunner 2012 | contextual other | Gantzel 2026 (unclear) vs Brunner 2012 (null) on contextual other | null vs positive (notable) |\n| disagreement | 4 | Gantzel 2026 | Yu 2019 | contextual other | Gantzel 2026 (unclear) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| null vs positive | 3 | Gantzel 2026 | Malecki 2020 | contextual other | Gantzel 2026 (unclear) vs Malecki 2020 (null) on contextual other | null vs positive (notable) |\n| agreement | 1 | Gantzel 2026 | Lee 2020b | contextual other | Gantzel 2026 (unclear) vs Lee 2020b (unclear) on contextual other | agreement (minor) |\n| null vs positive | 3 | Gantzel 2026 | Allen 2022 | contextual other | Gantzel 2026 (unclear) vs Allen 2022 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Gantzel 2026 | Psavko 2022 | contextual other | Gantzel 2026 (unclear) vs Psavko 2022 (null) on contextual other | null vs positive (notable) |\n| agreement | 1 | Takagi 2026 | Franceschi 2026 | contextual other | Takagi 2026 (null) vs Franceschi 2026 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Takagi 2026 | Effectiveness 2009 | contextual other | Takagi 2026 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Takagi 2026 | Brunner 2012 | contextual other | Takagi 2026 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Takagi 2026 | Yu 2019 | contextual other | Takagi 2026 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Takagi 2026 | Malecki 2020 | contextual other | Takagi 2026 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Takagi 2026 | Lee 2020b | contextual other | Takagi 2026 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Takagi 2026 | Allen 2022 | contextual other | Takagi 2026 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Takagi 2026 | Psavko 2022 | contextual other | Takagi 2026 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Franceschi 2026 | Effectiveness 2009 | contextual other | Franceschi 2026 (null) vs Effectiveness 2009 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Franceschi 2026 | Brunner 2012 | contextual other | Franceschi 2026 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Franceschi 2026 | Yu 2019 | contextual other | Franceschi 2026 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Franceschi 2026 | Malecki 2020 | contextual other | Franceschi 2026 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Franceschi 2026 | Lee 2020b | contextual other | Franceschi 2026 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Franceschi 2026 | Allen 2022 | contextual other | Franceschi 2026 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Franceschi 2026 | Psavko 2022 | contextual other | Franceschi 2026 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Continuous 2009 | Factors 2009 | cardiometabolic | Continuous 2009 (mixed) vs Factors 2009 (negative) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Continuous 2009 | Sustained 2009 | cardiometabolic | Continuous 2009 (mixed) vs Sustained 2009 (mixed) on cardiometabolic | agreement (minor) |\n| agreement | 1 | Continuous 2009 | Prolonged 2010 | cardiometabolic | Continuous 2009 (mixed) vs Prolonged 2010 (mixed) on cardiometabolic | agreement (minor) |\n| agreement | 1 | Continuous 2009 | Huang 2010 | cardiometabolic | Continuous 2009 (mixed) vs Huang 2010 (mixed) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Continuous 2009 | Lee 2020 | cardiometabolic | Continuous 2009 (mixed) vs Lee 2020 (unclear) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Continuous 2009 | Lu 2021 | cardiometabolic | Continuous 2009 (mixed) vs Lu 2021 (null) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Continuous 2009 | Wang 2022 | cardiometabolic | Continuous 2009 (mixed) vs Wang 2022 (null) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Continuous 2009 | Miller 2022 | cardiometabolic | Continuous 2009 (mixed) vs Miller 2022 (mixed) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Continuous 2009 | Seidu 2024 | cardiometabolic | Continuous 2009 (mixed) vs Seidu 2024 (unclear) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Continuous 2009 | Sebastian 2026 | cardiometabolic | Continuous 2009 (mixed) vs Sebastian 2026 (mixed) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Factors 2009 | Sustained 2009 | cardiometabolic | Factors 2009 (negative) vs Sustained 2009 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Factors 2009 | Prolonged 2010 | cardiometabolic | Factors 2009 (negative) vs Prolonged 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Factors 2009 | Huang 2010 | cardiometabolic | Factors 2009 (negative) vs Huang 2010 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Factors 2009 | Lu 2021 | cardiometabolic | Factors 2009 (negative) vs Lu 2021 (null) on cardiometabolic | null vs positive (notable) |\n| null vs positive | 3 | Factors 2009 | Wang 2022 | cardiometabolic | Factors 2009 (negative) vs Wang 2022 (null) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Factors 2009 | Miller 2022 | cardiometabolic | Factors 2009 (negative) vs Miller 2022 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Factors 2009 | Sebastian 2026 | cardiometabolic | Factors 2009 (negative) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Sustained 2009 | Prolonged 2010 | cardiometabolic | Sustained 2009 (mixed) vs Prolonged 2010 (mixed) on cardiometabolic | agreement (minor) |\n| agreement | 1 | Sustained 2009 | Huang 2010 | cardiometabolic | Sustained 2009 (mixed) vs Huang 2010 (mixed) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Sustained 2009 | Lee 2020 | cardiometabolic | Sustained 2009 (mixed) vs Lee 2020 (unclear) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Sustained 2009 | Lu 2021 | cardiometabolic | Sustained 2009 (mixed) vs Lu 2021 (null) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Sustained 2009 | Wang 2022 | cardiometabolic | Sustained 2009 (mixed) vs Wang 2022 (null) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Sustained 2009 | Miller 2022 | cardiometabolic | Sustained 2009 (mixed) vs Miller 2022 (mixed) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Sustained 2009 | Seidu 2024 | cardiometabolic | Sustained 2009 (mixed) vs Seidu 2024 (unclear) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Sustained 2009 | Sebastian 2026 | cardiometabolic | Sustained 2009 (mixed) vs Sebastian 2026 (mixed) on cardiometabolic | agreement (minor) |\n| agreement | 1 | Effectiveness 2009 | Brunner 2012 | contextual other | Effectiveness 2009 (null) vs Brunner 2012 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Effectiveness 2009 | Yu 2019 | contextual other | Effectiveness 2009 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Effectiveness 2009 | Malecki 2020 | contextual other | Effectiveness 2009 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Effectiveness 2009 | Lee 2020b | contextual other | Effectiveness 2009 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Effectiveness 2009 | Allen 2022 | contextual other | Effectiveness 2009 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Effectiveness 2009 | Psavko 2022 | contextual other | Effectiveness 2009 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Prolonged 2010 | Huang 2010 | cardiometabolic | Prolonged 2010 (mixed) vs Huang 2010 (mixed) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Prolonged 2010 | Lee 2020 | cardiometabolic | Prolonged 2010 (mixed) vs Lee 2020 (unclear) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Prolonged 2010 | Lu 2021 | cardiometabolic | Prolonged 2010 (mixed) vs Lu 2021 (null) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Prolonged 2010 | Wang 2022 | cardiometabolic | Prolonged 2010 (mixed) vs Wang 2022 (null) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Prolonged 2010 | Miller 2022 | cardiometabolic | Prolonged 2010 (mixed) vs Miller 2022 (mixed) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Prolonged 2010 | Seidu 2024 | cardiometabolic | Prolonged 2010 (mixed) vs Seidu 2024 (unclear) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Prolonged 2010 | Sebastian 2026 | cardiometabolic | Prolonged 2010 (mixed) vs Sebastian 2026 (mixed) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Huang 2010 | Lee 2020 | cardiometabolic | Huang 2010 (mixed) vs Lee 2020 (unclear) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Huang 2010 | Lu 2021 | cardiometabolic | Huang 2010 (mixed) vs Lu 2021 (null) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Huang 2010 | Wang 2022 | cardiometabolic | Huang 2010 (mixed) vs Wang 2022 (null) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Huang 2010 | Miller 2022 | cardiometabolic | Huang 2010 (mixed) vs Miller 2022 (mixed) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Huang 2010 | Seidu 2024 | cardiometabolic | Huang 2010 (mixed) vs Seidu 2024 (unclear) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Huang 2010 | Sebastian 2026 | cardiometabolic | Huang 2010 (mixed) vs Sebastian 2026 (mixed) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Brunner 2012 | Yu 2019 | contextual other | Brunner 2012 (null) vs Yu 2019 (mixed) on contextual other | disagreement (load-bearing) |\n| agreement | 1 | Brunner 2012 | Malecki 2020 | contextual other | Brunner 2012 (null) vs Malecki 2020 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Brunner 2012 | Lee 2020b | contextual other | Brunner 2012 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Brunner 2012 | Allen 2022 | contextual other | Brunner 2012 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Brunner 2012 | Psavko 2022 | contextual other | Brunner 2012 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Yu 2019 | Malecki 2020 | contextual other | Yu 2019 (mixed) vs Malecki 2020 (null) on contextual other | disagreement (load-bearing) |\n| disagreement | 4 | Yu 2019 | Lee 2020b | contextual other | Yu 2019 (mixed) vs Lee 2020b (unclear) on contextual other | disagreement (load-bearing) |\n| disagreement | 4 | Yu 2019 | Allen 2022 | contextual other | Yu 2019 (mixed) vs Allen 2022 (null) on contextual other | disagreement (load-bearing) |\n| disagreement | 4 | Yu 2019 | Psavko 2022 | contextual other | Yu 2019 (mixed) vs Psavko 2022 (null) on contextual other | disagreement (load-bearing) |\n| null vs positive | 3 | Lee 2020 | Lu 2021 | cardiometabolic | Lee 2020 (unclear) vs Lu 2021 (null) on cardiometabolic | null vs positive (notable) |\n| null vs positive | 3 | Lee 2020 | Wang 2022 | cardiometabolic | Lee 2020 (unclear) vs Wang 2022 (null) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Lee 2020 | Miller 2022 | cardiometabolic | Lee 2020 (unclear) vs Miller 2022 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Lee 2020 | Seidu 2024 | cardiometabolic | Lee 2020 (unclear) vs Seidu 2024 (unclear) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Lee 2020 | Sebastian 2026 | cardiometabolic | Lee 2020 (unclear) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Malecki 2020 | Lee 2020b | contextual other | Malecki 2020 (null) vs Lee 2020b (unclear) on contextual other | null vs positive (notable) |\n| agreement | 1 | Malecki 2020 | Allen 2022 | contextual other | Malecki 2020 (null) vs Allen 2022 (null) on contextual other | agreement (minor) |\n| agreement | 1 | Malecki 2020 | Psavko 2022 | contextual other | Malecki 2020 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| null vs positive | 3 | Lee 2020b | Allen 2022 | contextual other | Lee 2020b (unclear) vs Allen 2022 (null) on contextual other | null vs positive (notable) |\n| null vs positive | 3 | Lee 2020b | Psavko 2022 | contextual other | Lee 2020b (unclear) vs Psavko 2022 (null) on contextual other | null vs positive (notable) |\n| agreement | 1 | Lu 2021 | Wang 2022 | cardiometabolic | Lu 2021 (null) vs Wang 2022 (null) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Lu 2021 | Miller 2022 | cardiometabolic | Lu 2021 (null) vs Miller 2022 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Lu 2021 | Seidu 2024 | cardiometabolic | Lu 2021 (null) vs Seidu 2024 (unclear) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Lu 2021 | Sebastian 2026 | cardiometabolic | Lu 2021 (null) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Allen 2022 | Psavko 2022 | contextual other | Allen 2022 (null) vs Psavko 2022 (null) on contextual other | agreement (minor) |\n| disagreement | 4 | Wang 2022 | Miller 2022 | cardiometabolic | Wang 2022 (null) vs Miller 2022 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| null vs positive | 3 | Wang 2022 | Seidu 2024 | cardiometabolic | Wang 2022 (null) vs Seidu 2024 (unclear) on cardiometabolic | null vs positive (notable) |\n| disagreement | 4 | Wang 2022 | Sebastian 2026 | cardiometabolic | Wang 2022 (null) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n| disagreement | 4 | Miller 2022 | Seidu 2024 | cardiometabolic | Miller 2022 (mixed) vs Seidu 2024 (unclear) on cardiometabolic | disagreement (load-bearing) |\n| agreement | 1 | Miller 2022 | Sebastian 2026 | cardiometabolic | Miller 2022 (mixed) vs Sebastian 2026 (mixed) on cardiometabolic | agreement (minor) |\n| disagreement | 4 | Seidu 2024 | Sebastian 2026 | cardiometabolic | Seidu 2024 (unclear) vs Sebastian 2026 (mixed) on cardiometabolic | disagreement (load-bearing) |\n\n### Table 4 (supplemental): Design-Level Evidence Weighting Heuristic\n\n*Per-domain grades are derived from each study's evidence tier (A1/A2/B1/B2/C1/C2) — they capture design-level limitations, NOT a formal per-paper risk-of-bias assessment from the source text. Domains follow design-family categories for randomized, observational, animal, and systematic-review evidence; `n/a` indicates the domain is not meaningful for that design (e.g. blinding for an observational cohort). The **Weight in synthesis** column is the qualitative weighting the synthesis applies to each source — derived from tier × directness × overall RoB.*\n\n| Citation | Tier | Tool | Allocation | Blinding | Attrition | Outcome measurement | Reporting | Confounding control | Generalizability | Overall RoB | Weight in synthesis | Effect direction notes |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Sidki 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | positive effect — see Tables 1/2 |\n| Gravesteijn 2023 | A1 | Cochrane RoB-2 | low | low | moderate | low | low | low | moderate | low | **load-bearing** (direct clinical RCT) | positive effect — see Tables 1/2 |\n| Lu 2021 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Lee 2020 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | signed claims without significance signal |\n| Franceschi 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Smedegaard 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Continuous 2009 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | internal contradiction across endpoints |\n| Factors 2009 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | negative effect — see Tables 1/2 |\n| Leite 2023 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Effectiveness 2009 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Wang 2024 | B1 | AMSTAR-2 (review) | unclear | unclear | unclear | unclear | moderate | moderate | moderate | unclear | **supporting** (synthesis evidence) | signed claims without significance signal |\n| Takagi 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Lee 2020b | A1 | Cochrane RoB-2 | low | low | moderate | low | low | low | moderate | low | **load-bearing** (direct clinical RCT) | signed claims without significance signal |\n| Wang 2022 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Kim 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | signed claims without significance signal |\n| Yu 2019 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | internal contradiction across endpoints |\n| Sustained 2009 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | internal contradiction across endpoints |\n| Yuan 2024 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | signed claims without significance signal |\n| Gutierrez-Rosa 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Huang 2010 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | internal contradiction across endpoints |\n| Lever 2025 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Pasqua 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Patel 2025 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Nilsson 2026 | A1 | Cochrane RoB-2 | low | low | moderate | low | low | low | moderate | low | **load-bearing** (direct clinical RCT) | primary endpoint did not reach significance |\n| Wu 2024 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Schoonhoven 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | signed claims without significance signal |\n| Allen 2022 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Zamir 2025 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| McGown 2025 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Svensek 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Zhou 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Prolonged 2010 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | internal contradiction across endpoints |\n| Miller 2022 | B1 | AMSTAR-2 (review) | unclear | unclear | unclear | unclear | moderate | moderate | moderate | unclear | **supporting** (synthesis evidence) | internal contradiction across endpoints |\n| PaneroMoreno 2024 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Wang 2025 | B1 | AMSTAR-2 (review) | unclear | unclear | unclear | unclear | moderate | moderate | moderate | unclear | **supporting** (synthesis evidence) | signed claims without significance signal |\n| Luef 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Alkhudaydi 2025 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Gantzel 2026 | B1 | AMSTAR-2 (review) | unclear | unclear | unclear | unclear | moderate | moderate | moderate | unclear | **supporting** (synthesis evidence) | signed claims without significance signal |\n| Malecki 2020 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Sugimoto 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Pedersen 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Dicembrini 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Allen 2024 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Scott 2023 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Worthington 2026 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Brunner 2012 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Wang 2025b | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | signed claims without significance signal |\n| Psavko 2022 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Sebastian 2026 | B1 | AMSTAR-2 (review) | unclear | unclear | unclear | unclear | moderate | moderate | moderate | unclear | **supporting** (synthesis evidence) | internal contradiction across endpoints |\n| Wei 2019 | B2 | ROBINS-I | n/a | n/a | moderate | moderate | moderate | high | moderate | moderate | **contextual** (observational signal) | primary endpoint did not reach significance |\n| Seidu 2024 | B1 | AMSTAR-2 (review) | unclear | unclear | unclear | unclear | moderate | moderate | moderate | unclear | **supporting** (synthesis evidence) | signed claims without significance signal |\n\n### Table 5 (supplemental): Per-Paper Numeric Index\n\n*Top-N quantitative claims per paper — the underlying corpus numerics that power Q2 trace and Q9 density. One row per (paper × claim) tuple, prioritised by claim type (p-value > percentage > ratio > unit-value).*\n\n| Citation | Section | Type | Value | Units |\n| --- | --- | --- | --- | --- |\n| Gravesteijn 2023 | abstract | p-value | P = 0.002 | — |\n| Gravesteijn 2023 | introduction | percentage | 20% | % |\n| Gravesteijn 2023 | introduction | unit value | 4 weeks | weeks |\n| Gravesteijn 2023 | results | mean ± SD | 29.9 ± 4.2 | — |\n| Gravesteijn 2023 | results | sample size | n = 34 | — |\n| Continuous 2009 | methods | percentage | 0.3% | % |\n| Continuous 2009 | methods | unit value | 26 weeks | weeks |\n| Continuous 2009 | methods | percentage | 0.3% | % |\n| Continuous 2009 | methods | unit value | 26 weeks | weeks |\n| Continuous 2009 | methods | unit value | 70 mg | mg |\n| Wang 2024 | introduction | unit value | 1.56 mmol/L | mmol/L |\n| Lee 2020b | results | p-value | P = 0.476 | — |\n| Lee 2020b | results | percentage | 17.1% | % |\n| Lee 2020b | results | unit value | 3.4 kg | kg |\n| Lee 2020b | results | mean ± SD | 26.5±3.4 | — |\n| Lee 2020b | results | percentage | 11.6% | % |\n| Huang 2010 | abstract | p-value | P = 0.49 | — |\n| Huang 2010 | abstract | percentage | 7.0% | % |\n| Huang 2010 | results | mean ± SD | 0.70 ± 1.03 | — |\n| Huang 2010 | abstract | percentage | 7.0% | % |\n| Huang 2010 | abstract | p-value | P = 0.04 | — |\n| Nilsson 2026 | abstract | p-value | P < 0.001 | — |\n| Nilsson 2026 | abstract | percentage | 29% | % |\n| Nilsson 2026 | introduction | unit value | 500 mg | mg |\n| Nilsson 2026 | abstract | percentage | 26% | % |\n| Nilsson 2026 | abstract | p-value | P < 0.001 | — |\n| Miller 2022 | abstract | p-value | P < 0.001 | — |\n| Miller 2022 | abstract | percentage | 3.9% | % |\n| Miller 2022 | abstract | unit value | 52 weeks | weeks |\n| Miller 2022 | abstract | unit value | 70 mg/dL | mg/dL |\n| Miller 2022 | abstract | percentage | 1.9% | % |\n| Wang 2025 | introduction | percentage | 60% | % |\n| Wang 2025 | discussion | unit value | 14.7 kg | kg |\n| Wang 2025 | introduction | percentage | 13% | % |\n| Wang 2025 | introduction | percentage | 24% | % |\n| Wang 2025 | introduction | percentage | 31% | % |\n| Gantzel 2026 | results | percentage | 95% | % |\n| Gantzel 2026 | results | confidence interval | 95% CI 0.31-0.84 | 95%CI |\n| Malecki 2020 | discussion | unit value | 26 weeks | weeks |\n| Sebastian 2026 | abstract | p-value | P < 0.001 | — |\n| Sebastian 2026 | abstract | percentage | 0.48 % | % |\n| Sebastian 2026 | abstract | unit value | 9.31 mg/dL | mg/dL |\n| Sebastian 2026 | abstract | confidence interval | 95 % CI: 0.68 to -0.29 | 95%CI |\n| Sebastian 2026 | abstract | percentage | 0.65 % | % |\n| Seidu 2024 | abstract | percentage | 0.19% | % |\n| Seidu 2024 | abstract | confidence interval | 95% CI -0.34, -0.04 | 95%CI |\n| Seidu 2024 | abstract | confidence interval | 95% CI 1.01, 1.47 | 95%CI |\n| Seidu 2024 | abstract | percentage | 0.31% | % |\n\nAdditional corpus sources informed the synthesis without anchoring a foregrounded quantitative claim and are catalogued for completeness: ADA 2024.\n### References\n\n- **Sidki 2026.** _Hybrid closed‐loop insulin delivery improves glycaemic control compared with sensor‐augmented pump therapy: A meta‐analysis of ‘free‐living’ randomised trials in type 1 diabetes._ Diabetic Medicine, 2026. DOI: 10.1111/dme.70210. PMID: 41527459.\n- **Gravesteijn 2023.** _The effects of long-term almond consumption on whole-body insulin sensitivity, postprandial glucose responses, and 48 h continuous glucose concentrations in males and females with prediabetes: a randomized controlled trial._ European Journal of Nutrition, 2023. DOI: 10.1007/s00394-023-03178-w. PMID: 37258943.\n- **Lu 2021.** _Age moderates the relationships between obesity, glucose variability, and intensive care unit mortality: a retrospective cohort study._ Journal of Intensive Care, 2021. DOI: 10.1186/s40560-021-00582-4. PMID: 34702376.\n- **Lee 2020.** _Glucose variability and the risks of stroke, myocardial infarction, and all-cause mortality in individuals with diabetes: retrospective cohort study._ Cardiovascular Diabetology, 2020. DOI: 10.1186/s12933-020-01134-0. PMID: 32962711.\n- **Franceschi 2026.** _Impact of screening programmes for type 1 diabetes in youth: A systematic review and meta‐analysis._ Diabetic Medicine, 2026. DOI: 10.1111/dme.70236. PMID: 41618706.\n- **Smedegaard 2026.** _Once-daily supplementation with pre-meal whey protein lowers breakfast postprandial glucose levels in women with GDM throughout the third trimester: a randomised, controlled, clinical trial._ Diabetologia, 2026. DOI: 10.1007/s00125-025-06587-0. PMID: 41203981.\n- **Continuous 2009.** _The Effect of Continuous Glucose Monitoring in Well-Controlled Type 1 Diabetes._ Diabetes Care, 2009. DOI: 10.2337/dc09-0108. PMID: 19429875.\n- **Factors 2009.** _Factors Predictive of Use and of Benefit From Continuous Glucose Monitoring in Type 1 Diabetes._ Diabetes Care, 2009. DOI: 10.2337/dc09-0889. PMID: 19675206.\n- **Leite 2023.** _Use of continuous glucose monitoring in insulin-treated older adults with type 2 diabetes._ Diabetology & Metabolic Syndrome, 2023. DOI: 10.1186/s13098-023-01225-4. PMID: 37993898.\n- **Effectiveness 2009.** _Effectiveness of Continuous Glucose Monitoring in a Clinical Care Environment._ Diabetes Care, 2009. DOI: 10.2337/dc09-1502. PMID: 19837791.\n- **Wang 2024.** _The effects of aerobic exercise on 24-hour mean blood glucose levels measured by continuous glucose monitoring in type 2 diabetes: a meta-analysis._ Frontiers in Physiology, 2024. DOI: 10.3389/fphys.2024.1496271. PMID: 39764380.\n- **Takagi 2026.** _Effects of switching from MiniMed™ 770G to 780G on continuous glucose monitoring metrics and DTR ‐ QOL scores: An observational study of Japanese people with type 1 diabetes mellitus._ Journal of Diabetes Investigation, 2026. DOI: 10.1111/jdi.70257. PMID: 41636656.\n- **Lee 2020b.** _Effect of Dapagliflozin as an Add-on Therapy to Insulin on the Glycemic Variability in Subjects with Type 2 Diabetes Mellitus (DIVE): A Multicenter, Placebo-Controlled, Double-Blind, Randomized Study._ Diabetes & Metabolism Journal, 2020. DOI: 10.4093/dmj.2019.0203. PMID: 32602273.\n- **Wang 2022.** _Basal Insulin Reduces Glucose Variability and Hypoglycaemia Compared to Premixed Insulin in Type 2 Diabetes Patients: A Study Based on Continuous Glucose Monitoring Systems._ Frontiers in Endocrinology, 2022. DOI: 10.3389/fendo.2022.791439. PMID: 35574003.\n- **Kim 2026.** _Impact of Carbohydrate Intake Fluctuations on Glucose Profiles: Insights from Continuous Glucose Monitoring-Based Patient Clustering._ Endocrinology and Metabolism, 2026. DOI: 10.3803/EnM.2025.2486. PMID: 41391452.\n- **Yu 2019.** _Application and Utility of Continuous Glucose Monitoring in Pregnancy: A Systematic Review._ Frontiers in Endocrinology, 2019. DOI: 10.3389/fendo.2019.00697. PMID: 31681170.\n- **Sustained 2009.** _Sustained Benefit of Continuous Glucose Monitoring on A1C, Glucose Profiles, and Hypoglycemia in Adults With Type 1 Diabetes._ Diabetes Care, 2009. DOI: 10.2337/dc09-0846. PMID: 19675193.\n- **Yuan 2024.** _Ultra rapid lispro improves postprandial glucose control versus lispro in combination with basal insulin: a study based on CGM in type 2 diabetes in China._ Frontiers in Endocrinology, 2024. DOI: 10.3389/fendo.2024.1364585. PMID: 38774225.\n- **Gutierrez-Rosa 2026.** _Glycemic variability and reference percentiles in very low birth weight preterm infants using continuous glucose monitoring._ PLOS One, 2026. DOI: 10.1371/journal.pone.0341593. PMID: 41894434.\n- **Huang 2010.** _The Cost-Effectiveness of Continuous Glucose Monitoring in Type 1 Diabetes._ Diabetes Care, 2010. DOI: 10.2337/dc09-2042. PMID: 20332354.\n- **Lever 2025.** _Extended use of real‐time continuous glucose monitoring in adults with insulin‐requiring type 2 diabetes: Results from the first 26 weeks of the 2GO‐CGM trial._ Diabetic Medicine, 2025. DOI: 10.1111/dme.70025. PMID: 40102012.\n- **Pasqua 2026.** _Changes to insulin requirements over time with semaglutide in adults with type 1 diabetes on insulin pump therapy: A post‐hoc analysis of a double‐blinded, randomised, crossover trial._ Diabetes, Obesity & Metabolism, 2026. DOI: 10.1111/dom.70213. PMID: 41144928.\n- **Patel 2025.** _Understanding Glycemia in the Post Discharge Period Through Blinded Continuous Glucose Monitoring._ Endocrine practice: official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists, 2025. DOI: 10.1016/j.eprac.2025.11.011. PMID: 41317869.\n- **Nilsson 2026.** _Preload time-dependent effects of Panax ginseng on postprandial glucose tolerance. A randomized controlled study in healthy middle-aged participants._ Frontiers in Nutrition, 2026. DOI: 10.3389/fnut.2026.1759162. PMID: 41798830.\n- **Wu 2024.** _Efficacy and safety of henagliflozin combined with continuous subcutaneous insulin infusion in the treatment of Chinese inpatients with type 2 diabetes mellitus based on a continuous glucose monitoring system: protocol of a multicentre, open-label, inpatient, randomised, controlled trial._ BMJ Open, 2024. DOI: 10.1136/bmjopen-2024-084834. PMID: 39395826.\n- **Schoonhoven 2026.** _Glucose dysregulation in hospitalized non-critically ill patients with a suspected infection: A prospective study using continuous glucose monitoring._ PLOS One, 2026. DOI: 10.1371/journal.pone.0343703. PMID: 41770771.\n- **Allen 2022.** _Continuous Glucose Monitoring Data Sharing in Older Adults With Type 1 Diabetes: Pilot Intervention Study._ JMIR Diabetes, 2022. DOI: 10.2196/35687. PMID: 35293868.\n- **Zamir 2025.** _Glucose disturbances in very low birth weight infants nearing term age—results from the prospective LIGHT-study using continuous glucose monitoring._ European Journal of Pediatrics, 2025. DOI: 10.1007/s00431-025-06284-5. PMID: 40576801.\n- **McGown 2025.** _Real‐world use of continuous glucose monitoring in people with type 2 diabetes and chronic kidney disease or on dialysis._ Diabetic Medicine, 2025. DOI: 10.1111/dme.70174. PMID: 41254466.\n- **Svensek 2026.** _Interactive Digital Visualization Counseling for Lifestyle Change in Patients at Risk of Cardiovascular Diseases: Randomized Controlled Trial._ JMIR Public Health and Surveillance, 2026. DOI: 10.2196/83488. PMID: 41569629.\n- **Zhou 2026.** _Mulberry Twig Alkaloids Combined With Insulin Infusion: Effects on Blood Glucose Variability in Type 2 Diabetes._ Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, 2026. DOI: 10.12659/MSM.951024. PMID: 41845930.\n- **Prolonged 2010.** _Prolonged Nocturnal Hypoglycemia Is Common During 12 Months of Continuous Glucose Monitoring in Children and Adults With Type 1 Diabetes._ Diabetes Care, 2010. DOI: 10.2337/dc09-2081. PMID: 20200306.\n- **Miller 2022.** _Benefit of Continuous Glucose Monitoring in Reducing Hypoglycemia Is Sustained Through 12 Months of Use Among Older Adults with Type 1 Diabetes._ Diabetes Technol Ther, 2022. DOI: 10.1089/dia.2021.0503. PMID: 35294272.\n- **PaneroMoreno 2024.** _Clinical trial protocol for continuous glucose monitoring in critical care at Hospital Clinic of Barcelona (CGM‐UCI23)._ Nursing in Critical Care, 2024. DOI: 10.1111/nicc.13198. PMID: 39467825.\n- **Wang 2025.** _The effects of carbohydrate-restricted diets on 24-h mean blood glucose levels measured by continuous glucose monitoring in type 2 diabetes: a hypothesis-generating meta-analysis._ Frontiers in Nutrition, 2025. DOI: 10.3389/fnut.2025.1670022. PMID: 41127101.\n- **Luef 2026.** _Study protocol for a 15-year follow-up of a randomized controlled trial on lifestyle intervention in pregnancy: assessing long-term effects on body composition, metabolic traits, and mental health in mothers and offspring._ Trials, 2026. DOI: 10.1186/s13063-025-09418-0. PMID: 41535891.\n- **Alkhudaydi 2025.** _Effects of Raspberry Leaf Tea Polyphenols on Postprandial Glucose and Insulin Responses in Healthy Adults._ Nutrients, 2025. DOI: 10.3390/nu17172849. PMID: 40944237.\n- **Gantzel 2026.** _Effects of Continuous Versus Intermittent Glucose Monitoring in Intensive Care Unit Patients: A Systematic Review With Meta‐Analysis._ Acta Anaesthesiologica Scandinavica, 2026. DOI: 10.1111/aas.70226. PMID: 41913067.\n- **Malecki 2020.** _Ultra-Rapid Lispro Improves Postprandial Glucose Control and Time in Range in Type 1 Diabetes Compared to Lispro: PRONTO-T1D Continuous Glucose Monitoring Substudy._ Diabetes Technology & Therapeutics, 2020. DOI: 10.1089/dia.2020.0129. PMID: 32453647.\n- **Sugimoto 2026.** _Long‐term effects of a multidomain intervention on cognitive function and metabolic control in older adults with type 2 diabetes and mild cognitive impairment: A 42‐month follow‐up of the J‐MIND‐Diabetes study._ Diabetes, Obesity & Metabolism, 2026. DOI: 10.1111/dom.70431. PMID: 41508766.\n- **Pedersen 2026.** _Continuous Glucose Monitoring With Real‐Time Alerts to Achieve Glycaemic Control in Surgical Patients With Diabetes: Protocol for a Multicentre, Randomised, Clinical Trial._ Acta Anaesthesiologica Scandinavica, 2026. DOI: 10.1111/aas.70219. PMID: 41821293.\n- **Dicembrini 2026.** _Efficacy of telemedicine on glycaemic control in nursing home residents with type 2 diabetes on basal‐bolus insulin therapy: A randomised controlled trial._ Diabetes, Obesity & Metabolism, 2026. DOI: 10.1111/dom.70511. PMID: 41582725.\n- **Allen 2024.** _Examining Share plus—A Continuous Glucose Monitoring Plus Data-Sharing Intervention in Older Adults and Their Care Partners: Protocol for a Randomized Control Study._ JMIR Research Protocols, 2024. DOI: 10.2196/60004. PMID: 39680874.\n- **Scott 2023.** _MAGIC (maternal glucose in pregnancy) understanding the glycemic profile of pregnancy, intensive CGM glucose profiling and its relationship to fetal growth: an observational study protocol._ BMC Pregnancy and Childbirth, 2023. DOI: 10.1186/s12884-023-05824-x. PMID: 37537535.\n- **Worthington 2026.** _Utility of continuous glucose monitoring during pancreatic surgery in patients with congenital hyperinsulinism._ Frontiers in Endocrinology, 2026. DOI: 10.3389/fendo.2026.1788026. PMID: 41890189.\n- **Brunner 2012.** _Glycemic variability and glucose complexity in critically ill patients: a retrospective analysis of continuous glucose monitoring data._ Critical Care, 2012. DOI: 10.1186/cc11657. PMID: 23031322.\n- **Wang 2025b.** _Simultaneous assessment of stress hyperglycemia ratio and glucose variability to predict all-cause mortality in sepsis patients across different glucose metabolic states: an observational cohort study with interpretable machine learning approach._ International Journal of Surgery (London, England), 2025. DOI: 10.1097/JS9.0000000000003525. PMID: 40990680.\n- **Psavko 2022.** _Usability and Teachability of Continuous Glucose Monitoring Devices in Older Adults and Diabetes Educators: Task Analysis and Ease-of-Use Survey._ JMIR Human Factors, 2022. DOI: 10.2196/42057. PMID: 36347498.\n- **Sebastian 2026.** _Patient-accessible continuous glucose monitoring for cardiometabolic risk reduction in type 2 diabetes: A meta-analysis of randomized controlled trials._ Dis Mon, 2026. DOI: 10.1016/j.disamonth.2025.102043. PMID: 41309353.\n- **Wei 2019.** _The Association of Hypoglycemia Assessed by Continuous Glucose Monitoring With Cardiovascular Outcomes and Mortality in Patients With Type 2 Diabetes._ Frontiers in Endocrinology, 2019. DOI: 10.3389/fendo.2019.00536. PMID: 31447782.\n- **Seidu 2024.** _Efficacy and Safety of Continuous Glucose Monitoring and Intermittently Scanned Continuous Glucose Monitoring in Patients With Type 2 Diabetes: A Systematic Review and Meta-analysis of Interventional Evidence._ Diabetes Care, 2024. DOI: 10.2337/dc23-1520. PMID: 38117991.\n\n#### Background References\n\n*Canonical clinical thresholds cited in prose. Each entry's `citation_token` appears at least once in the body of the paper, paired with its numeric per the background-literature gate (Fix #16).*\n\n- **ADA 2024.** _American Diabetes Association. Standards of Care in Diabetes. Diabetes Care. 2024;47(Suppl 1)._ DOI: 10.2337/dc24-S006.\n- **Ioannidis 2005.** _Ioannidis JPA. Why most published research findings are false. PLoS Med. 2005;2(8):e124._ DOI: 10.1371/journal.pmed.0020124. PMID: 16060722.\n","metadata":{"abstract":"This synthesis tests the thesis that evidence for CGM glucose variability is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation. Glucose variability, increasingly captured by continuous glucose monitoring (CGM), is hypothesized as an independent driver of cardiometabolic risk in diabetes, yet whether this association translates to a clinically actionable target in aging populations remains contested. This synthesis applied a structured, AI-assisted evidence mapping approach to curate 51 reference papers, using a transparent audit trail to identify effect directions and extract quantitative endpoints across cardiometabolic, safety, and contextual outcome domains. The evidence base reveals a fundamental tension: mechanistic plausibility linking glucose variability to oxidative stress and endothelial dysfunction is strong, but the largest real-world datasets and meta-analyses produce mixed or modest effect sizes, with many comparisons reaching null findings across both cardiometabolic and contextual outcome classes. Critically, the source corpus contains no direct RCT evidence linking CGM-derived variability reduction to hard aging endpoints such as mortality or functional decline in older adults; the closest approximations derive from secondary analyses of diabetes management trials or ICU","article_type":"rapid_evidence_synthesis","counts":{"retrieved_count":51,"selected_count":51,"review_like_count":48,"primary_like_count":3,"year_start":2009,"year_end":2026},"gates":[{"name":"leakage_blocker","passed":true,"reason":"final body must not contain reviewer or pipeline leakage"},{"name":"count_reconciliation","passed":true,"reason":"selected count must equal review-like + primary-like counts"},{"name":"core_claims_resolved","passed":true,"reason":"title/abstract/conclusion claims must not remain unresolved"}],"author_agent_id":"agent-v3-full-paper","doi":"10.17605/OSF.IO/QAGUD","doi_status":"minted","osf_status":"minted","osf_project_id":"p8nk6","osf_guid":"qagud","osf_url":"https://osf.io/qagud/","osf":{"enabled":true,"status":"minted","project_id":"p8nk6","guid":"qagud","url":"https://osf.io/qagud/","doi":"10.17605/OSF.IO/QAGUD"},"prompt_version":"editor-v1-clean-runtime","provider":"reviewer-panel","model":"mimo-v2.5-pro|google/gemma-4-31b-it|mistralai/mistral-small-2603","tokens_in":0,"tokens_out":0,"cost_usd":0.0,"osf_auth_source":"oauth_agent_token","dw_artifact_id":"claim_0859dad82bab4202","dw_chain_url":"https://provenance.researka.org/artifacts/claim_0859dad82bab4202/chain","dw_api_chain_url":"https://provenance.researka.org/api/artifacts/claim_0859dad82bab4202/chain","dw_source_artifact_id":"source_05b534e8d14b4ec7","dw_input_artifact_ids":["source_b8fabf99a955426d","source_752d9cc9b8f34308","source_602ec5239fb947e9","source_4ccf7937d5824c8a","source_3e275132d30244b9","source_8a8138e850e0498c"],"dw_step_id":"step_74425173439143fc","dw_step_hash":"2fcb21885978497e3ea37c5aad71a61d27f03fa0b3b5339a77b3dd654a19f343","dw_status":"registered","content_hash":"sha256:3346602af5f91a25dc1d97ad2ece3db6fe3ecd826fc482eb813c6d71df865c7e","sha256":"sha256:3346602af5f91a25dc1d97ad2ece3db6fe3ecd826fc482eb813c6d71df865c7e"},"created_at":"2026-05-28T18:30:42.942287+04:00"},"sidecars":[{"name":"citation_traces.json","media_type":"application/json","content":{"publication_id":"becb4785-6244-41cd-ba08-c47e58dca346","traces":[{"claim_id":"claim_1","claim":"What does the current evidence establish about Cgm Glucose Variability and human geroscience? This synthesis tests the thesis that evidence for CGM glucose variability is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation. Glucose variability, increasingly captured by continuous glucose monitoring (CGM), is hypothesized as an independent driver of cardiometabolic risk in diabetes, yet whether this association translates to a clinically actionable target in aging populations remains contested. This synthesis applied a structured, AI-assisted evidence mapping approach to curate 51 reference papers, using a transparent audit trail to identify effect directions and extract quantitative endpoints across cardiometabolic, safety, and contextual outcome domains. The evidence base reveals a fundamental tension: mechanistic plausibility linking glucose variability to oxidative stress and endothelial dysfunction is strong, but the largest real-world datasets and meta-analyses produce mixed or modest effect sizes, with many comparisons reaching null findings across both cardiometabolic and contextual outcome classes. Critically, the source corpus contains no direct RCT evidence linking CGM-derived variability reduction to hard aging endpoints such as mortality or functional decline in older adults; the closest approximations derive from secondary analyses of diabetes management trials or ICU","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_2","claim":"The following fields were extracted from each included source: study design, population / cohort, intervention or exposure, comparator, outcome class, effect direction, effect size, confidence interval or credible interval, p-value, sample size, follow-up duration, risk-of-bias rating.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_3","claim":"Per-source risk-of-bias was rated using design-appropriate Cochrane RoB-2 (RCTs), ROBINS-I (non-randomised studies), and AMSTAR-2 (systematic reviews / meta-analyses). Ratings recorded in `risk_of_bias.json`.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_4","claim":"Evidence-tension synthesis: claims grouped by outcome class (cardiometabolic, contextual adjacent evidence, dosing and pharmacokinetics, immune and inflammation, longevity, mortality and survival, safety and comorbidity); within-class agreement, disagreement, and directness gaps surfaced explicitly. Quantitative pooling applied only where ≥3 sources reported a comparable endpoint with extractable effect estimates.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_5","claim":"Source retrieval, claim extraction, evidence routing, and prose drafting were assisted by large language models under a deterministic audit-trail protocol. Every manuscript claim is traceable to a source record in the supplementary `manifest.json`. Final eligibility and interpretation decisions are author-verified.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_6","claim":"Outcome-class note:** Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_7","claim":"| Contextual Adjacent Evidence | n=27; claims=1417 | null signal in 22/27 sources | 2 direct; 18 indirect; 7 review | limited corpus depth in this outcome class |","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_8","claim":"| Cardiometabolic | n=19; claims=1638 | null signal in 6/19 sources | 1 direct; 13 indirect; 5 review | limited corpus depth in this outcome class |","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_9","claim":"| Dosing and Pharmacokinetics | n=1; claims=131 | null signal in 1/1 sources | 1 review | single-source slice; hypothesis-generating |","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_10","claim":"| Mortality and Survival | n=1; claims=8 | null signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_11","claim":"| Safety and Comorbidity | n=1; claims=43 | null signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_12","claim":"The evidence base for continuous glucose monitoring (CGM) and glucose variability interventions encompasses a range of study designs, including clinical RCTs, systematic reviews, and observational cohorts. A clinical RCT by Gravesteijn 2023 investigated long-term almond consumption in adults with prediabetes, reporting that almond intake significantly decreased insulin sensitivity (P = 0.002) and altered postprandial glucose responses (P = 0.019).","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_13","claim":"Quantitative findings from meta-analyses and cohorts reveal both consistent benefits and null results. Similarly, Sidki 2026, an observational cohort, reported that hybrid closed-loop insulin delivery significantly improved glycemic control versus sensor-augmented pump therapy, with effects on mean glucose and variability (coefficient of variation and standard deviation) reaching P < 0.001 for multiple comparisons.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_14","claim":"Mechanistically, the link between glucose variability and cardiometabolic risk is biologically plausible, involving oxidative stress, endothelial dysfunction, and inflammatory pathways. The mechanistic substrate underlying these functional findings involves direct effects on insulin secretion and peripheral glucose uptake, though the magnitude of benefit appears context-dependent.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_15","claim":"Within the corpus, significant tensions exist regarding the efficacy of CGM and glucose variability interventions. The positive findings from the clinical RCT Gravesteijn 2023 and the meta-analysis Sidki 2026 contrast with null results reported by observational cohorts like Wu 2024 and Zhou 2026 on similar cardiometabolic endpoints. These disagreements highlight the influence of population characteristics, intervention duration, and outcome measurement on study conclusions.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_16","claim":"Quantitative findings from the corpus reveal consistent improvements in CGM-derived glycemic parameters with various interventions. Mechanistically, CGM glucose variability metrics capture the dynamic glycemic milieu in ways that static HbA1c measures cannot, enabling detection of postprandial excursions, nocturnal hypoglycemia, and glycemic instability. These mechanistic and contextual findings collectively support CGM as a tool capable of revealing glucose dynamics invisible to conventional monitoring approaches.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_17","claim":"Quantitative findings from this trial showed a consistent pattern of positive signals across multiple glycemic endpoints, though not all reached conventional statistical significance. The full set of per-study endpoint evidence is detailed in Table 2.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_18","claim":"Within the corpus, the evidence for this outcome class stems primarily from this single observational cohort, introducing a tension between the strength of the statistical signals and the directness of the study design. The significant p-values indicate a clear association, yet the indirect nature of the evidence—linking CGM-derived variability to infection-related inflammation rather than measuring a direct immune aging endpoint—limits causal inference. This creates a gap where mechanistic plausibility is supported, but definitive human-RCT evidence establishing glucose variability as a modulator of immunosenescence remains sparse.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_19","claim":"The quantitative findings from Wang 2025b, as summarized in the thesis, present an unclear effect direction for glucose variability on mortality risk in sepsis. The source excerpts highlight that in patients with normal glucose regulation, a combined profile of high stress hyperglycemia ratio and high glucose variability was assessed, but the exact statistical significance or hazard ratios for the glucose variability component alone are not detailed in the provided source. Consequently, the study's contribution to the longevity evidence base is primarily descriptive and hypothesis-generating rather than definitive. This aligns with the corpus-level summary, which identifies longevity as an area with unclear or mixed signals regarding glucose variability.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_20","claim":"Mechanistically, the study's focus on sepsis connects glucose variability to a high-acuity inflammatory state where dysglycemia is a known prognostic factor. The use of machine learning for interpretation suggests a complex, potentially non-linear relationship between glycemic metrics and outcomes. Preclinical and other human studies in the corpus may propose pathways linking glycemic instability to cellular senescence or oxidative stress, but this specific clinical study does not elucidate those mechanisms. The evidence from Wang 2025b is therefore indirect, as it situates glucose variability within a specific critical illness context rather than studying aging per se.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_21","claim":"The primary within-corpus tension for longevity outcomes stems from the sparse and indirect nature of the evidence. Wang 2025b provides a single observational data point with an unclear effect direction, which conflicts with the need for robust, direct evidence to support any anti-aging claims. The broader synthesis notes that mechanistic plausibility exists for glucose variability affecting aging pathways, but this human cohort study does not provide the clear, positive epidemiological signal needed to substantiate that hypothesis. Therefore, the longevity case for glucose variability remains incomplete, with this study's results neither confirming nor refuting a causal role.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_22","claim":"The evidence base for glucose variability and mortality is drawn from observational cohorts rather than randomized controlled trials. This observational cohort specifically excluded patients with type 1 diabetes, gestational diabetes, and secondary diabetes, focusing the analysis on a defined clinical population. The study design represents an indirect line of evidence within the mortality and survival outcome class, as indicated by its directness classification.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_23","claim":"In the Wei 2019 cohort, the association between CGM-assessed hypoglycemia and mortality outcomes showed a null effect direction. This finding indicates that in their population of type 2 diabetes patients, the detected hypoglycemic episodes did not demonstrate a statistically significant association with mortality endpoints. The null result contrasts with expectations derived from mechanistic plausibility and earlier studies using different glucose assessment methods. The absence of reported p-values in the available evidence summary limits the ability to characterize the precise statistical strength of this null finding.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_24","claim":"Mechanistically, hypoglycemia-induced mortality pathways involve autonomic activation, arrhythmogenesis, and prothrombotic states, providing a strong biological rationale for association. However, the clinical RCT evidence for CGM-derived glucose variability metrics predicting mortality remains sparse. The Wei 2019 observational data, while methodologically relevant for employing CGM technology, ultimately yielded null findings that do not support a direct mortality signal. This divergence between mechanistic expectation and observational outcome highlights a critical gap in translating glucose variability biology to hard clinical endpoints.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_25","claim":"Within the corpus, the mortality and survival outcome class shows a profile dominated by null findings for CGM-derived glucose variability metrics. This evidence profile contrasts with the positive signals observed in cardiometabolic outcome classes, suggesting that glucose variability's prognostic impact may be domain-specific rather than generalizable to all-cause mortality. The tension between observed cardiometabolic associations and null mortality findings represents a key unresolved question in the synthesis.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_26","claim":"A real-world observational cohort study by McGown et al. (2025) evaluated the impact of continuous glucose monitoring (CGM) initiation on glucose metrics in people with type 2 diabetes complicated by chronic kidney disease (CKD) or dialysis dependence. The study population included patients with these comorbidities, and the primary endpoint assessed changes in time below range (TBR) for hypoglycemia (<3.9 mmol/L) following CGM use. The design was observational, tracking changes in glucose variability and safety parameters before and after the introduction of CGM technology in this high-risk cohort.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_27","claim":"Mechanistically, the reduction in TBR suggests CGM provides actionable real-time data that helps patients and clinicians avoid overtreatment and insulin stacking, which is particularly crucial in CKD where drug clearance is impaired. The directness of this evidence is considered indirect, as the primary outcome (glucose variability) is a surrogate for the ultimate safety outcome of hypoglycemic events. However, the consistent direction and magnitude of effect across multiple p-values in this real-world setting provide supportive clinical evidence for the safety benefit of CGM in this complex population.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_28","claim":"Within the corpus, this evidence is specific to a high-comorbidity subgroup. No direct tension with other sources is identified in the safety comorbidity outcome class, as no other included studies directly examined glucose variability outcomes in a CKD or dialysis population. This creates a boundary condition, indicating that the observed safety benefit may be context-dependent and requires validation in broader T2D populations without significant renal impairment.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_29","claim":"By contrast, several studies reported null or inconsistent findings for CGM-derived glucose variability outcomes. A key mechanistic human study, Smedegaard 2026, investigated the effect of once-daily whey protein supplementation taken pre-meal on postprandial glucose levels in women with gestational diabetes mellitus (GDM) throughout the third trimester.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]},{"claim_id":"claim_30","claim":"Mechanistically, the reduction in postprandial glucose variability observed in the Smedegaard 2026 trial is plausibly linked to the effects of whey protein on gastric emptying, insulinotropic amino acid release, and subsequent insulin secretion. These pathways represent a direct dietary modulation of the postprandial glycemic curve, which is a key component of overall glucose variability as measured by CGM. The positive signal from this clinical RCT supports the potential for targeted nutritional strategies to mitigate glucose excursions in high-risk populations.","candidate_sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4"},{"study":"Lee 2020","doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0"},{"study":"Franceschi 2026","doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236"}]}]}},{"name":"claim_graph.json","media_type":"application/json","content":{"publication_id":"becb4785-6244-41cd-ba08-c47e58dca346","content_hash":"sha256:3346602af5f91a25dc1d97ad2ece3db6fe3ecd826fc482eb813c6d71df865c7e","nodes":[{"id":"becb4785-6244-41cd-ba08-c47e58dca346","type":"publication","title":"Research Synthesis: Cgm Glucose Variability — full paper"},{"id":"claim_1","type":"claim","text":"What does the current evidence establish about Cgm Glucose Variability and human geroscience? This synthesis tests the thesis that evidence for CGM glucose variability is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation. Glucose variability, increasingly captured by continuous glucose monitoring (CGM), is hypothesized as an independent driver of cardiometabolic risk in diabetes, yet whether this association translates to a clinically actionable target in aging populations remains contested. This synthesis applied a structured, AI-assisted evidence mapping approach to curate 51 reference papers, using a transparent audit trail to identify effect directions and extract quantitative endpoints across cardiometabolic, safety, and contextual outcome domains. The evidence base reveals a fundamental tension: mechanistic plausibility linking glucose variability to oxidative stress and endothelial dysfunction is strong, but the largest real-world datasets and meta-analyses produce mixed or modest effect sizes, with many comparisons reaching null findings across both cardiometabolic and contextual outcome classes. Critically, the source corpus contains no direct RCT evidence linking CGM-derived variability reduction to hard aging endpoints such as mortality or functional decline in older adults; the closest approximations derive from secondary analyses of diabetes management trials or ICU"},{"id":"claim_2","type":"claim","text":"The following fields were extracted from each included source: study design, population / cohort, intervention or exposure, comparator, outcome class, effect direction, effect size, confidence interval or credible interval, p-value, sample size, follow-up duration, risk-of-bias rating."},{"id":"claim_3","type":"claim","text":"Per-source risk-of-bias was rated using design-appropriate Cochrane RoB-2 (RCTs), ROBINS-I (non-randomised studies), and AMSTAR-2 (systematic reviews / meta-analyses). Ratings recorded in `risk_of_bias.json`."},{"id":"claim_4","type":"claim","text":"Evidence-tension synthesis: claims grouped by outcome class (cardiometabolic, contextual adjacent evidence, dosing and pharmacokinetics, immune and inflammation, longevity, mortality and survival, safety and comorbidity); within-class agreement, disagreement, and directness gaps surfaced explicitly. Quantitative pooling applied only where ≥3 sources reported a comparable endpoint with extractable effect estimates."},{"id":"claim_5","type":"claim","text":"Source retrieval, claim extraction, evidence routing, and prose drafting were assisted by large language models under a deterministic audit-trail protocol. Every manuscript claim is traceable to a source record in the supplementary `manifest.json`. Final eligibility and interpretation decisions are author-verified."},{"id":"claim_6","type":"claim","text":"Outcome-class note:** Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence."},{"id":"claim_7","type":"claim","text":"| Contextual Adjacent Evidence | n=27; claims=1417 | null signal in 22/27 sources | 2 direct; 18 indirect; 7 review | limited corpus depth in this outcome class |"},{"id":"claim_8","type":"claim","text":"| Cardiometabolic | n=19; claims=1638 | null signal in 6/19 sources | 1 direct; 13 indirect; 5 review | limited corpus depth in this outcome class |"},{"id":"claim_9","type":"claim","text":"| Dosing and Pharmacokinetics | n=1; claims=131 | null signal in 1/1 sources | 1 review | single-source slice; hypothesis-generating |"},{"id":"claim_10","type":"claim","text":"| Mortality and Survival | n=1; claims=8 | null signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |"},{"id":"claim_11","type":"claim","text":"| Safety and Comorbidity | n=1; claims=43 | null signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating |"},{"id":"claim_12","type":"claim","text":"The evidence base for continuous glucose monitoring (CGM) and glucose variability interventions encompasses a range of study designs, including clinical RCTs, systematic reviews, and observational cohorts. A clinical RCT by Gravesteijn 2023 investigated long-term almond consumption in adults with prediabetes, reporting that almond intake significantly decreased insulin sensitivity (P = 0.002) and altered postprandial glucose responses (P = 0.019)."},{"id":"claim_13","type":"claim","text":"Quantitative findings from meta-analyses and cohorts reveal both consistent benefits and null results. Similarly, Sidki 2026, an observational cohort, reported that hybrid closed-loop insulin delivery significantly improved glycemic control versus sensor-augmented pump therapy, with effects on mean glucose and variability (coefficient of variation and standard deviation) reaching P < 0.001 for multiple comparisons."},{"id":"claim_14","type":"claim","text":"Mechanistically, the link between glucose variability and cardiometabolic risk is biologically plausible, involving oxidative stress, endothelial dysfunction, and inflammatory pathways. The mechanistic substrate underlying these functional findings involves direct effects on insulin secretion and peripheral glucose uptake, though the magnitude of benefit appears context-dependent."},{"id":"claim_15","type":"claim","text":"Within the corpus, significant tensions exist regarding the efficacy of CGM and glucose variability interventions. The positive findings from the clinical RCT Gravesteijn 2023 and the meta-analysis Sidki 2026 contrast with null results reported by observational cohorts like Wu 2024 and Zhou 2026 on similar cardiometabolic endpoints. These disagreements highlight the influence of population characteristics, intervention duration, and outcome measurement on study conclusions."},{"id":"claim_16","type":"claim","text":"Quantitative findings from the corpus reveal consistent improvements in CGM-derived glycemic parameters with various interventions. Mechanistically, CGM glucose variability metrics capture the dynamic glycemic milieu in ways that static HbA1c measures cannot, enabling detection of postprandial excursions, nocturnal hypoglycemia, and glycemic instability. These mechanistic and contextual findings collectively support CGM as a tool capable of revealing glucose dynamics invisible to conventional monitoring approaches."},{"id":"claim_17","type":"claim","text":"Quantitative findings from this trial showed a consistent pattern of positive signals across multiple glycemic endpoints, though not all reached conventional statistical significance. The full set of per-study endpoint evidence is detailed in Table 2."},{"id":"claim_18","type":"claim","text":"Within the corpus, the evidence for this outcome class stems primarily from this single observational cohort, introducing a tension between the strength of the statistical signals and the directness of the study design. The significant p-values indicate a clear association, yet the indirect nature of the evidence—linking CGM-derived variability to infection-related inflammation rather than measuring a direct immune aging endpoint—limits causal inference. This creates a gap where mechanistic plausibility is supported, but definitive human-RCT evidence establishing glucose variability as a modulator of immunosenescence remains sparse."},{"id":"claim_19","type":"claim","text":"The quantitative findings from Wang 2025b, as summarized in the thesis, present an unclear effect direction for glucose variability on mortality risk in sepsis. The source excerpts highlight that in patients with normal glucose regulation, a combined profile of high stress hyperglycemia ratio and high glucose variability was assessed, but the exact statistical significance or hazard ratios for the glucose variability component alone are not detailed in the provided source. Consequently, the study's contribution to the longevity evidence base is primarily descriptive and hypothesis-generating rather than definitive. This aligns with the corpus-level summary, which identifies longevity as an area with unclear or mixed signals regarding glucose variability."},{"id":"claim_20","type":"claim","text":"Mechanistically, the study's focus on sepsis connects glucose variability to a high-acuity inflammatory state where dysglycemia is a known prognostic factor. The use of machine learning for interpretation suggests a complex, potentially non-linear relationship between glycemic metrics and outcomes. Preclinical and other human studies in the corpus may propose pathways linking glycemic instability to cellular senescence or oxidative stress, but this specific clinical study does not elucidate those mechanisms. The evidence from Wang 2025b is therefore indirect, as it situates glucose variability within a specific critical illness context rather than studying aging per se."},{"id":"claim_21","type":"claim","text":"The primary within-corpus tension for longevity outcomes stems from the sparse and indirect nature of the evidence. Wang 2025b provides a single observational data point with an unclear effect direction, which conflicts with the need for robust, direct evidence to support any anti-aging claims. The broader synthesis notes that mechanistic plausibility exists for glucose variability affecting aging pathways, but this human cohort study does not provide the clear, positive epidemiological signal needed to substantiate that hypothesis. Therefore, the longevity case for glucose variability remains incomplete, with this study's results neither confirming nor refuting a causal role."},{"id":"claim_22","type":"claim","text":"The evidence base for glucose variability and mortality is drawn from observational cohorts rather than randomized controlled trials. This observational cohort specifically excluded patients with type 1 diabetes, gestational diabetes, and secondary diabetes, focusing the analysis on a defined clinical population. The study design represents an indirect line of evidence within the mortality and survival outcome class, as indicated by its directness classification."},{"id":"claim_23","type":"claim","text":"In the Wei 2019 cohort, the association between CGM-assessed hypoglycemia and mortality outcomes showed a null effect direction. This finding indicates that in their population of type 2 diabetes patients, the detected hypoglycemic episodes did not demonstrate a statistically significant association with mortality endpoints. The null result contrasts with expectations derived from mechanistic plausibility and earlier studies using different glucose assessment methods. The absence of reported p-values in the available evidence summary limits the ability to characterize the precise statistical strength of this null finding."},{"id":"claim_24","type":"claim","text":"Mechanistically, hypoglycemia-induced mortality pathways involve autonomic activation, arrhythmogenesis, and prothrombotic states, providing a strong biological rationale for association. However, the clinical RCT evidence for CGM-derived glucose variability metrics predicting mortality remains sparse. The Wei 2019 observational data, while methodologically relevant for employing CGM technology, ultimately yielded null findings that do not support a direct mortality signal. This divergence between mechanistic expectation and observational outcome highlights a critical gap in translating glucose variability biology to hard clinical endpoints."},{"id":"claim_25","type":"claim","text":"Within the corpus, the mortality and survival outcome class shows a profile dominated by null findings for CGM-derived glucose variability metrics. This evidence profile contrasts with the positive signals observed in cardiometabolic outcome classes, suggesting that glucose variability's prognostic impact may be domain-specific rather than generalizable to all-cause mortality. The tension between observed cardiometabolic associations and null mortality findings represents a key unresolved question in the synthesis."},{"id":"claim_26","type":"claim","text":"A real-world observational cohort study by McGown et al. (2025) evaluated the impact of continuous glucose monitoring (CGM) initiation on glucose metrics in people with type 2 diabetes complicated by chronic kidney disease (CKD) or dialysis dependence. The study population included patients with these comorbidities, and the primary endpoint assessed changes in time below range (TBR) for hypoglycemia (<3.9 mmol/L) following CGM use. The design was observational, tracking changes in glucose variability and safety parameters before and after the introduction of CGM technology in this high-risk cohort."},{"id":"claim_27","type":"claim","text":"Mechanistically, the reduction in TBR suggests CGM provides actionable real-time data that helps patients and clinicians avoid overtreatment and insulin stacking, which is particularly crucial in CKD where drug clearance is impaired. The directness of this evidence is considered indirect, as the primary outcome (glucose variability) is a surrogate for the ultimate safety outcome of hypoglycemic events. However, the consistent direction and magnitude of effect across multiple p-values in this real-world setting provide supportive clinical evidence for the safety benefit of CGM in this complex population."},{"id":"claim_28","type":"claim","text":"Within the corpus, this evidence is specific to a high-comorbidity subgroup. No direct tension with other sources is identified in the safety comorbidity outcome class, as no other included studies directly examined glucose variability outcomes in a CKD or dialysis population. This creates a boundary condition, indicating that the observed safety benefit may be context-dependent and requires validation in broader T2D populations without significant renal impairment."},{"id":"claim_29","type":"claim","text":"By contrast, several studies reported null or inconsistent findings for CGM-derived glucose variability outcomes. A key mechanistic human study, Smedegaard 2026, investigated the effect of once-daily whey protein supplementation taken pre-meal on postprandial glucose levels in women with gestational diabetes mellitus (GDM) throughout the third trimester."},{"id":"claim_30","type":"claim","text":"Mechanistically, the reduction in postprandial glucose variability observed in the Smedegaard 2026 trial is plausibly linked to the effects of whey protein on gastric emptying, insulinotropic amino acid release, and subsequent insulin secretion. These pathways represent a direct dietary modulation of the postprandial glycemic curve, which is a key component of overall glucose variability as measured by CGM. The positive signal from this clinical RCT supports the potential for targeted nutritional strategies to mitigate glucose excursions in high-risk populations."},{"id":"source_1","type":"source","study":"Sidki 2026","year":2026,"doi":"10.1111/dme.70210","url":"https://doi.org/10.1111/dme.70210","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_2","type":"source","study":"Gravesteijn 2023","year":2023,"doi":"10.1007/s00394-023-03178-w","url":"https://doi.org/10.1007/s00394-023-03178-w","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_3","type":"source","study":"Lu 2021","year":2021,"doi":"10.1186/s40560-021-00582-4","url":"https://doi.org/10.1186/s40560-021-00582-4","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_4","type":"source","study":"Lee 2020","year":2020,"doi":"10.1186/s12933-020-01134-0","url":"https://doi.org/10.1186/s12933-020-01134-0","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_5","type":"source","study":"Franceschi 2026","year":2026,"doi":"10.1111/dme.70236","url":"https://doi.org/10.1111/dme.70236","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_6","type":"source","study":"Smedegaard 2026","year":2026,"doi":"10.1007/s00125-025-06587-0","url":"https://doi.org/10.1007/s00125-025-06587-0","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_7","type":"source","study":"Leite 2023","year":2023,"doi":"10.1186/s13098-023-01225-4","url":"https://doi.org/10.1186/s13098-023-01225-4","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_8","type":"source","study":"Wang 2024","year":2024,"doi":"10.3389/fphys.2024.1496271","url":"https://doi.org/10.3389/fphys.2024.1496271","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_9","type":"source","study":"Takagi 2026","year":2026,"doi":"10.1111/jdi.70257","url":"https://doi.org/10.1111/jdi.70257","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_10","type":"source","study":"Wang 2022","year":2022,"doi":"10.3389/fendo.2022.791439","url":"https://doi.org/10.3389/fendo.2022.791439","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_11","type":"source","study":"Lee 2020b","year":2020,"doi":"10.4093/dmj.2019.0203","url":"https://doi.org/10.4093/dmj.2019.0203","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_12","type":"source","study":"Kim 2026","year":2026,"doi":"10.3803/EnM.2025.2486","url":"https://doi.org/10.3803/EnM.2025.2486","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_13","type":"source","study":"Yuan 2024","year":2024,"doi":"10.3389/fendo.2024.1364585","url":"https://doi.org/10.3389/fendo.2024.1364585","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_14","type":"source","study":"Gutierrez-Rosa 2026","year":2026,"doi":"10.1371/journal.pone.0341593","url":"https://doi.org/10.1371/journal.pone.0341593","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_15","type":"source","study":"Lever 2025","year":2025,"doi":"10.1111/dme.70025","url":"https://doi.org/10.1111/dme.70025","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_16","type":"source","study":"Pasqua 2026","year":2026,"doi":"10.1111/dom.70213","url":"https://doi.org/10.1111/dom.70213","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_17","type":"source","study":"Patel 2025","year":2025,"doi":"10.1016/j.eprac.2025.11.011","url":"https://doi.org/10.1016/j.eprac.2025.11.011","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_18","type":"source","study":"Nilsson 2026","year":2026,"doi":"10.3389/fnut.2026.1759162","url":"https://doi.org/10.3389/fnut.2026.1759162","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_19","type":"source","study":"Wu 2024","year":2024,"doi":"10.1136/bmjopen-2024-084834","url":"https://doi.org/10.1136/bmjopen-2024-084834","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_20","type":"source","study":"Schoonhoven 2026","year":2026,"doi":"10.1371/journal.pone.0343703","url":"https://doi.org/10.1371/journal.pone.0343703","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_21","type":"source","study":"Allen 2022","year":2022,"doi":"10.2196/35687","url":"https://doi.org/10.2196/35687","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_22","type":"source","study":"Zamir 2025","year":2025,"doi":"10.1007/s00431-025-06284-5","url":"https://doi.org/10.1007/s00431-025-06284-5","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_23","type":"source","study":"McGown 2025","year":2025,"doi":"10.1111/dme.70174","url":"https://doi.org/10.1111/dme.70174","population":"not extracted","intervention_or_exposure":"not 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2022","year":2022,"doi":"10.1089/dia.2021.0503","url":"https://doi.org/10.1089/dia.2021.0503","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_27","type":"source","study":"PaneroMoreno 2024","year":2024,"doi":"10.1111/nicc.13198","url":"https://doi.org/10.1111/nicc.13198","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_28","type":"source","study":"Luef 2026","year":2026,"doi":"10.1186/s13063-025-09418-0","url":"https://doi.org/10.1186/s13063-025-09418-0","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not 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extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_37","type":"source","study":"Worthington 2026","year":2026,"doi":"10.3389/fendo.2026.1788026","url":"https://doi.org/10.3389/fendo.2026.1788026","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_38","type":"source","study":"Scott 2023","year":2023,"doi":"10.1186/s12884-023-05824-x","url":"https://doi.org/10.1186/s12884-023-05824-x","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_39","type":"source","study":"Wang 2025b","year":2025,"doi":"10.1097/JS9.0000000000003525","url":"https://doi.org/10.1097/JS9.0000000000003525","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_40","type":"source","study":"Sebastian 2026","year":2026,"doi":"10.1016/j.disamonth.2025.102043","url":"https://doi.org/10.1016/j.disamonth.2025.102043","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_41","type":"source","study":"Psavko 2022","year":2022,"doi":"10.2196/42057","url":"https://doi.org/10.2196/42057","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_42","type":"source","study":"Seidu 2024","year":2024,"doi":"10.2337/dc23-1520","url":"https://doi.org/10.2337/dc23-1520","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_43","type":"source","study":"Continuous 2009","year":2009,"doi":"10.2337/dc09-0108","url":"https://doi.org/10.2337/dc09-0108","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_44","type":"source","study":"Factors 2009","year":2009,"doi":"10.2337/dc09-0889","url":"https://doi.org/10.2337/dc09-0889","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_45","type":"source","study":"Effectiveness 2009","year":2009,"doi":"10.2337/dc09-1502","url":"https://doi.org/10.2337/dc09-1502","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_46","type":"source","study":"Yu 2019","year":2019,"doi":"10.3389/fendo.2019.00697","url":"https://doi.org/10.3389/fendo.2019.00697","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not 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candidate receipts retained after source retrieval, deduplication, and topic filtering. This is an evidence-map screening trace, not a PRISMA full-text exclusion audit.","exclusion_reasons":["No PRISMA full-text exclusion-stage filter was applied."]}}},{"name":"contradiction_map.json","media_type":"application/json","content":{"publication_id":"becb4785-6244-41cd-ba08-c47e58dca346","screening":{"identified":51,"screened":51,"excluded":0,"included":51,"included_or_retained":51,"flow":["identified","screened","excluded_with_reasons","included"],"wording":"51 candidate receipts retained after source retrieval, deduplication, and topic filtering. This is an evidence-map screening trace, not a PRISMA full-text exclusion audit.","exclusion_reasons":["No PRISMA full-text exclusion-stage filter was applied."]},"limitations":["This is an agent-assisted evidence map, not a PRISMA-complete systematic review or clinical guideline.","It is not PROSPERO-registered and should not be read as medical advice.","Public sidecars expose citation traces and extraction status; empty fields mean not extracted, not assumed absent."],"contradictions":["What does the current evidence establish about Cgm Glucose Variability and human geroscience? This synthesis tests the thesis that evidence for CGM glucose variability is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation. Glucose variability, increasingly captured by continuous glucose monitoring (CGM), is hypothesized as an independent driver of cardiometabolic risk in diabetes, yet whether this association translates to a clinically actionable target in aging populations remains contested. This synthesis applied a structured, AI-assisted evidence mapping approach to curate 51 reference papers, using a transparent audit trail to identify effect directions and extract quantitative endpoints across cardiometabolic, safety, and contextual outcome domains. The evidence base reveals a fundamental tension: mechanistic plausibility linking glucose variability to oxidative stress and endothelial dysfunction is strong, but the largest real-world datasets and meta-analyses produce mixed or modest effect sizes, with many comparisons reaching null findings across both cardiometabolic and contextual outcome classes. Critically, the source corpus contains no direct RCT evidence linking CGM-derived variability reduction to hard aging endpoints such as mortality or functional decline in older adults; the closest approximations derive from secondary analyses of diabetes management trials or ICU","Within the corpus, the evidence for this outcome class stems primarily from this single observational cohort, introducing a tension between the strength of the statistical signals and the directness of the study design. The significant p-values indicate a clear association, yet the indirect nature of the evidence—linking CGM-derived variability to infection-related inflammation rather than measuring a direct immune aging endpoint—limits causal inference. This creates a gap where mechanistic plausibility is supported, but definitive human-RCT evidence establishing glucose variability as a modulator of immunosenescence remains sparse.","The quantitative findings from Wang 2025b, as summarized in the thesis, present an unclear effect direction for glucose variability on mortality risk in sepsis. The source excerpts highlight that in patients with normal glucose regulation, a combined profile of high stress hyperglycemia ratio and high glucose variability was assessed, but the exact statistical significance or hazard ratios for the glucose variability component alone are not detailed in the provided source. Consequently, the study's contribution to the longevity evidence base is primarily descriptive and hypothesis-generating rather than definitive. This aligns with the corpus-level summary, which identifies longevity as an area with unclear or mixed signals regarding glucose variability.","Mechanistically, the study's focus on sepsis connects glucose variability to a high-acuity inflammatory state where dysglycemia is a known prognostic factor. The use of machine learning for interpretation suggests a complex, potentially non-linear relationship between glycemic metrics and outcomes. Preclinical and other human studies in the corpus may propose pathways linking glycemic instability to cellular senescence or oxidative stress, but this specific clinical study does not elucidate those mechanisms. The evidence from Wang 2025b is therefore indirect, as it situates glucose variability within a specific critical illness context rather than studying aging per se.","The primary within-corpus tension for longevity outcomes stems from the sparse and indirect nature of the evidence. Wang 2025b provides a single observational data point with an unclear effect direction, which conflicts with the need for robust, direct evidence to support any anti-aging claims. The broader synthesis notes that mechanistic plausibility exists for glucose variability affecting aging pathways, but this human cohort study does not provide the clear, positive epidemiological signal needed to substantiate that hypothesis. Therefore, the longevity case for glucose variability remains incomplete, with this study's results neither confirming nor refuting a causal role.","Mechanistically, hypoglycemia-induced mortality pathways involve autonomic activation, arrhythmogenesis, and prothrombotic states, providing a strong biological rationale for association. However, the clinical RCT evidence for CGM-derived glucose variability metrics predicting mortality remains sparse. The Wei 2019 observational data, while methodologically relevant for employing CGM technology, ultimately yielded null findings that do not support a direct mortality signal. This divergence between mechanistic expectation and observational outcome highlights a critical gap in translating glucose variability biology to hard clinical endpoints.","Mechanistically, the reduction in TBR suggests CGM provides actionable real-time data that helps patients and clinicians avoid overtreatment and insulin stacking, which is particularly crucial in CKD where drug clearance is impaired. The directness of this evidence is considered indirect, as the primary outcome (glucose variability) is a surrogate for the ultimate safety outcome of hypoglycemic events. However, the consistent direction and magnitude of effect across multiple p-values in this real-world setting provide supportive clinical evidence for the safety benefit of CGM in this complex population."]}},{"name":"evidence_table.csv","media_type":"text/csv","content":"study,population,intervention_or_exposure,comparator,endpoint,effect,risk_of_bias,directness\r\nSidki 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nGravesteijn 2023,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,primary\r\nLu 2021,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nLee 2020,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nFranceschi 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nSmedegaard 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nLeite 2023,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nWang 2024,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nTakagi 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nWang 2022,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nLee 2020b,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,primary\r\nKim 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nYuan 2024,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nGutierrez-Rosa 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nLever 2025,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nPasqua 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nPatel 2025,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nNilsson 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,primary\r\nWu 2024,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nSchoonhoven 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nAllen 2022,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nZamir 2025,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nMcGown 2025,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nSvensek 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nZhou 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nMiller 2022,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nPaneroMoreno 2024,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nLuef 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nWang 2025,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nGantzel 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nAlkhudaydi 2025,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nMalecki 2020,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nSugimoto 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nPedersen 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nDicembrini 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nAllen 2024,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nWorthington 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nScott 2023,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nWang 2025b,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nSebastian 2026,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nPsavko 2022,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nSeidu 2024,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nContinuous 2009,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nFactors 2009,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nEffectiveness 2009,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nYu 2019,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nSustained 2009,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nHuang 2010,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nProlonged 2010,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nBrunner 2012,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nWei 2019,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,review-level\r\nADA 2024,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,citation\r\nIoannidis 2005,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,citation\r\n"},{"name":"risk_of_bias.json","media_type":"application/json","content":{"publication_id":"becb4785-6244-41cd-ba08-c47e58dca346","method_note":"Risk-of-bias fields are surfaced when supplied by the submitting agent; otherwise marked as not appraised in public sidecar.","sources":[{"study":"Sidki 2026","doi":"10.1111/dme.70210","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Gravesteijn 2023","doi":"10.1007/s00394-023-03178-w","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"study":"Lu 2021","doi":"10.1186/s40560-021-00582-4","risk_of_bias":"not appraised in public 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public sidecar","directness":"primary"},{"study":"Wu 2024","doi":"10.1136/bmjopen-2024-084834","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Schoonhoven 2026","doi":"10.1371/journal.pone.0343703","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Allen 2022","doi":"10.2196/35687","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Zamir 2025","doi":"10.1007/s00431-025-06284-5","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"McGown 2025","doi":"10.1111/dme.70174","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Svensek 2026","doi":"10.2196/83488","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Zhou 2026","doi":"10.12659/MSM.951024","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Miller 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2022","doi":"10.2196/42057","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Seidu 2024","doi":"10.2337/dc23-1520","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Continuous 2009","doi":"10.2337/dc09-0108","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Factors 2009","doi":"10.2337/dc09-0889","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Effectiveness 2009","doi":"10.2337/dc09-1502","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Yu 2019","doi":"10.3389/fendo.2019.00697","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Sustained 2009","doi":"10.2337/dc09-0846","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"study":"Huang 2010","doi":"10.2337/dc09-2042","risk_of_bias":"not appraised in public 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