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# Hypothesis-Generating Brief: Metabolism Biomarker Effects — full paper ## Abstract This paper synthesizes evidence on metabolism biomarker effects across 15 accepted source papers and 491 high-confidence extracted claims. The evidence profile contains 2 direct clinical sources, 12 adjacent clinical sources, and 1 mechanistic or model-system source, with 26 cross-study disagreements across the evidence base. No single positive outcome class dominates the retained corpus; null signals cluster in the contextual adjacent evidence, longevity and deficiency prevalence outcome classes, and negative signals cluster in no dominant outcome class. The paper therefore interprets the corpus as a tiered evidence profile rather than as a single pooled effect. The conclusion is that metabolism biomarker effects remains a bounded geroscience case: the retained clinical and mechanistic evidence profile defines the scope for targeted testing, while mixed and null findings limit any unqualified anti-aging claim. ## Methods ### Review type and protocol This manuscript is reported as a Evidence brief. 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-metabolism_biomarker_effects-v06-DAILY-2026-06-25T00-34-26Z`. ### Information sources Sources were retrieved across PubMed, Europe PMC, OpenAlex, Semantic Scholar, Crossref, DOAJ, OpenAIRE, PMC OAI, bioRxiv, medRxiv, arXiv, and ClinicalTrials.gov. Retrieval window: 2026-06-25. ### Search strategy The following topic-anchored queries were executed against the information sources listed above: - `metabolism biomarker effects aging` - `metabolism biomarker effects older adults` - `metabolism biomarker effects randomized controlled trial` - `metabolism aging` - `metabolism older adults` - `metabolism randomized controlled trial` - `biomarker aging` - `biomarker older adults` - `biomarker randomized controlled trial` ### Eligibility criteria - Sources whose primary content addresses metabolism biomarker effects. - Sources with extractable quantitative or qualitative findings. - Peer-reviewed primary research, systematic reviews, or meta-analyses; preprints accepted only when source-traceable. - Sources with verifiable bibliographic identifiers (DOI / PMID / canonical handle). ### Selection of sources of evidence The 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 390 records in the receipt-candidate union, 150 were classified as source candidates and 15 were admitted as traceable synthesis sources. Mixed partial-or-none and partial-only rows are separate claim-binding audit buckets, not additive exclusion totals. No additional records were excluded after final source admission. ### source admission funnel | Admission bucket | n | |---|---:| | Receipt candidate union | 390 | | Classified source candidates | 150 | | No extractable claims | 23 | | None-only claim binding | 1 | | Mixed partial-or-none claim-binding candidates | 16 | | Partial-only claim-binding candidates | 8 | | Strict high-confidence sources | 1 | | Admitted final sources | 15 | ### Exclusion reasons - No records were excluded at the gates instrumented for this run: the eligibility criteria above were applied during retrieval and claim-binding but produced no post-screening exclusions with recorded counts for this corpus. ### Data items 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. Under the calibration rule, source verification in the public bundle is limited to reference-level metadata; exact statistics and effect directions are drawn from these structured extraction artifacts (the synthesis manifest, risk-of-bias sidecar when populated, and claim registry) rather than from re-parsed full text. ### Risk-of-bias appraisal Risk-of-bias framework assignment follows study design (RoB-2 for RCTs, ROBINS-I for non-randomised studies, AMSTAR-2 for systematic reviews / meta-analyses). Public appraisal claims are limited to populated `risk_of_bias.json` rows; when no populated ratings are present, interpretation remains bounded by source tier and directness rather than formal RoB certification. ### Synthesis approach Evidence-tension synthesis: claims grouped by outcome class (cardiometabolic, contextual adjacent evidence, deficiency prevalence, frailty, immune and inflammation, longevity, mechanism); within-class agreement, disagreement, and directness gaps surfaced explicitly. Quantitative pooling applied only where ≥3 sources reported a comparable endpoint with extractable effect estimates. ### AI-use disclosure 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. ### Accountability Accountability 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. Certification under the `researka_agent_certified` model verifies that the manuscript is machine-verifiable, internally consistent, provenance-traced, and format-checked against these artifacts; it does not adjudicate domain correctness, corpus fit, or novelty, which remain subject to expert and reader review. ## Results **Outcome-class note:** Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence; these sources bound scope, safety, methods, and translation rather than serving as equal-weight support for the main efficacy claim. | Evidence domain | Corpus slice | Strongest signal | Directness | Main limitation | |---|---|---|---|---| | Contextual Adjacent Evidence | n=6; claims=230 | no extracted directional signal in 6/6 sources | 1 direct; 5 indirect | limited corpus depth in this outcome class | | Longevity | n=4; claims=98 | no extracted directional signal in 3/4 sources | 4 indirect | limited corpus depth in this outcome class | | Cardiometabolic | n=1; claims=52 | no extracted directional signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating | | Deficiency Prevalence | n=1; claims=63 | no extracted directional signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating | | Frailty | n=1; claims=20 | no extracted directional signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating | | Immune and Inflammation | n=1; claims=24 | no extracted directional signal in 1/1 sources | 1 direct | single-source slice; hypothesis-generating | | Mechanism | n=1; claims=4 | no extracted directional signal in 1/1 sources | 1 mechanistic | single-source slice; hypothesis-generating | This evidence brief reports outcome packets as a map of retained evidence rather than as a full journal Results narrative or pooled effect estimate. ### Contextual Adjacent Evidence Outcomes 6 included sources were assigned to this outcome class. Directional coding: null=6. Directness coding: direct=1, indirect=5. ### Longevity Outcomes 4 included sources were assigned to this outcome class. Directional coding: null=3, unclear=1. Directness coding: indirect=4. ### Cardiometabolic Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1. ### Deficiency Prevalence Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1. ### Frailty Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1. ### Immune Inflammation Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: direct=1. ### Mechanism Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: mechanistic=1. ## Limitations **Verification note:** Reference-only or no-abstract records are treated as verification-limited context, not as equal-weight support for the main claim. The curated corpus cannot support claims about long-term mortality reduction from metabolic-biomarker modification, because no long-term mortality RCT in non-diabetic adults is represented among the 15 sources. Gordon-Dseagu 2015 provides observational hazard-ratio data for mortality in impaired glucose metabolism, but its effect direction is unclear in the source and it does not constitute an interventional trial. The headline synthesis therefore cannot be read as evidence that improving a biomarker prolongs life in metabolically healthy adults; that question is simply unanswered by this corpus. Several clinically meaningful outcomes in this synthesis rest on a single source and therefore cannot be replicated within the corpus. Any synthesis statement that generalizes beyond each of these single studies — for example, that frailty-relevant iron dysregulation is a reproducible finding — overstates what the evidence can support. Endpoint scope is narrow and surrogate-endpoint-heavy, which limits translation to hard clinical outcomes. Across the corpus, reported outcomes concentrate on biomarkers (HbA1c, fasting glucose, LDL-C, HOMA-IR, pTau217, ferritin, B-vitamin status, R-2HG, lactate clearance), acute postprandial or post-infusion responses, and self-reported or cross-sectional frailty components such as weight loss, fatigue, and slow gait. Hard endpoints — incident cardiovascular events, cancer diagnosis, fracture, hospitalization, or mortality — are addressed only indirectly through Gordon-Dseagu 2015 and Lei 2023's NHANES mortality analysis, and the source for Gordon-Dseagu 2015 records no p-values with an unclear effect direction. The body of evidence is also silent on functional endpoints tied to canonical thresholds such as the 0.8 m/s gait-speed cutoff (Studenski 2011), the 0.1 m/s substantial-change marker (Perera 2006), or the EWGSOP2 sarcopenia cutoffs of 27 kg for men and 16 kg for women (Cruz-Jentoft 2019), because no source measured gait speed or grip strength against those benchmarks. The mechanistic-to-clinical gap is wide and unevenly distributed across outcomes. As a result, several clinically relevant propositions — that 2-HG handling matters in human sepsis, that pesticide-driven acetylcholine dysregulation affects human cognition, that lipid-metabolism targets alter heart-failure trajectory — have only mechanistic or narrative support inside this synthesis and cannot be promoted to clinical claims. The cross-study disagreements flagged in the Cross-Domain Synthesis (mechanism-vs-clinical, indirectness-gap, and cross-domain pairings across longevity, frailty, cardiometabolic, immune inflammation, and contextual other classes) reflect exactly this asymmetry: mechanistic plausibility coexists with sparse or null human-RCT evidence, and the boundary conditions under which any metabolic-biomarker effect translates into a clinically meaningful benefit remain to be established. ## Conclusion For metabolism biomarker effects, the final interpretation is deliberately tiered: the retained clinical and mechanistic evidence profile defines a bounded geroscience rationale, but the corpus does not support treating mechanistic target engagement, intermediate biomarkers, and patient-relevant outcomes as interchangeable evidence. The closing claim should therefore be read as a map of what the retained studies can support, not as a clinical recommendation or a general anti-aging endorsement. Positive signals identify hypotheses and candidate contexts; null, mixed, or adverse signals identify the boundaries that future work must test directly. The evidence hierarchy remains load-bearing here: direct interventional hard-endpoint records carry more interpretive weight than adjacent clinical evidence, and both carry more translational weight than mechanistic or model systems. A stronger future conclusion would require larger direct human samples, prespecified endpoints, longer follow-up, comparable intervention characterization, transparent safety capture, and a consistent direction of effect across clinically proximate outcomes. Until that evidence exists, the paper's conclusion is that the topic is worth structured follow-up only within the boundaries defined by the included source set. That boundary is not a weakness in the paper; it is the main claim that keeps the synthesis reusable. Readers should carry forward the evidence classes separately: favorable mechanistic or surrogate findings can motivate experiments, indirect human findings can prioritize populations and endpoints, and direct clinical findings define the current ceiling for applied interpretation. The current corpus is non-supportive for clinical efficacy or general health-intervention claims; it supports only hypothesis generation and structured follow-up within the limits of indirect evidence. Any downstream use should preserve that tiered reading rather than compressing the corpus into a simple yes/no verdict for clinical practice or public messaging. ## What This Synthesis Adds This synthesis maps 15 included sources on Metabolism Biomarker Effects across 7 outcome classes and 26 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. Across 15 curated reference papers, the evidence base for Metabolism shows a context-dependent profile. Null findings dominate: contextual other, longevity. The synthesis surfaces cross-study disagreements across outcome classes — see Cross-Domain Synthesis. The Metabolism 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. The strongest unresolved contrast is the mechanism vs clinical between Lei 2023 and Pacella 2025 on longevity (severity 3/5), which defines the boundary condition future studies must test rather than smooth over. 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. ### Boundary-Condition Matrix | Evidence domain | Direct sources | Indirect / mechanism sources | Direction profile | Interpretation boundary | |---|---:|---:|---|---| | longevity | 0 | 4 | null, unclear | direct interventional hard-endpoint gap | | cardiometabolic | 0 | 1 | null | direct interventional hard-endpoint gap | | frailty | 0 | 1 | null | direct interventional hard-endpoint gap | | mechanism | 0 | 1 | null | direct interventional hard-endpoint gap | | deficiency prevalence | 0 | 1 | null | direct interventional hard-endpoint gap | | contextual adjacent evidence | 1 | 5 | null | replication gap | | immune and inflammation | 1 | 0 | null | replication gap | ### Evidence-Gap Priority | Priority | Gap | Rationale | |---|---|---| | P1 | longevity: direct interventional hard-endpoint gap | 0 direct and 4 indirect sources; direction profile: null, unclear | | P2 | cardiometabolic: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | | P3 | frailty: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | | P4 | mechanism: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | | P5 | deficiency prevalence: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | ### Next-Study Design Recommendation The next high-yield study for Metabolism Biomarker Effects 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. Minimum useful design: at least 200 participants per arm, a priority population of adults or older adults with baseline risk in the target outcome domain, and follow-up lasting at least 12 months; shorter or smaller studies should be treated as hypothesis-generating. ## Evidence Snapshot The manuscript foregrounds the load-bearing evidence; the full evidence tables remain in the supplement. ### Load-Bearing Included Studies - Ma 2022; tier=A1; directness=direct; endpoint=immune inflammation; direction=null. - Pacella 2025; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=null. - CarrilloArango 2025; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.073. - Gordon 2025; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null. - Zahed 2021; tier=B2; directness=indirect; endpoint=deficiency prevalence; direction=null. - Lei 2023; tier=B2; directness=indirect; endpoint=longevity; direction=null. - Lustgarten 2014; tier=B2; directness=indirect; endpoint=cardiometabolic; direction=null; representative statistic=P = 0.05. - Kemna 2025; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.061. - Gordon-Dseagu 2015; tier=B2; directness=indirect; endpoint=longevity; direction=unclear. - Ma 2024; tier=B2; directness=indirect; endpoint=frailty; direction=null. ### Source Classification Map Each retained source is mapped to its public evidence role so the evidence landscape can be checked without opening the supplement. - Ma 2022: outcome=immune inflammation; directness=direct; tier=A1; direction=null; claims=24. - Pacella 2025: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=8. - CarrilloArango 2025: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=89. - Gordon 2025: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=66. - Zahed 2021: outcome=deficiency prevalence; directness=indirect; tier=B2; direction=null; claims=63. - Lei 2023: outcome=longevity; directness=indirect; tier=B2; direction=null; claims=58. - Lustgarten 2014: outcome=cardiometabolic; directness=indirect; tier=B2; direction=null; claims=52. - Kemna 2025: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=50. - Gordon-Dseagu 2015: outcome=longevity; directness=indirect; tier=B2; direction=unclear; claims=34. - Ma 2024: outcome=frailty; directness=indirect; tier=B2; direction=null; claims=20. - Miller 2021: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=16. - Morvaridzadeh 2024: outcome=longevity; directness=indirect; tier=B2; direction=null; claims=4. - Chen 2025: outcome=longevity; directness=indirect; tier=B2; direction=null; claims=2. - Johnson 2019: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=1. - Fitzpatrick 2020: outcome=mechanism; directness=mechanistic; tier=C1; direction=null; claims=4. ### Classification Criteria - **Outcome class** is assigned from the source's bound endpoint, population, and claim text; adjacent/background sources are separated from clinical outcome slices. - **Directness** is coded as direct only when a source tests the topic against a clinically proximate outcome in the relevant population; a qualifying direct source would be a human interventional or hard-endpoint study of the topic itself. Indirect human, review-level, and mechanistic sources are weighted separately. - **Directional signal** is counted within the assigned outcome class only. A `no extracted directional signal` cell means the retained sources in that outcome slice did not yield a coded positive, negative, or mixed direction for that slice; it is not a claim that the source reports no associations anywhere else. - **Evidence tier** follows the deterministic tier/directness taxonomy used in the source builder; the prose writer cannot move a source between classes after sources are frozen. ### Load-Bearing Tensions - Severity 3 indirectness gap: Gordon 2025 vs Pacella 2025; Pacella 2025 (direct, A1) vs Gordon 2025 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Pacella 2025 vs Kemna 2025; Pacella 2025 (direct, A1) vs Kemna 2025 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Pacella 2025 vs CarrilloArango 2025; Pacella 2025 (direct, A1) vs CarrilloArango 2025 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Pacella 2025 vs Johnson 2019; Pacella 2025 (direct, A1) vs Johnson 2019 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Pacella 2025 vs Miller 2021; Pacella 2025 (direct, A1) vs Miller 2021 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 mechanism vs clinical: Lei 2023 vs Pacella 2025; Pacella 2025 (direct, contextual other) vs Lei 2023 (indirect, longevity) — cross-domain: clinical evidence on one outcome must not be fused with mechanistic / preclinical evidence on a different outcome - Severity 3 mechanism vs clinical: Lei 2023 vs Ma 2022; Ma 2022 (direct, immune inflammation) vs Lei 2023 (indirect, longevity) — cross-domain: clinical evidence on one outcome must not be fused with mechanistic / preclinical evidence on a different outcome - Severity 3 mechanism vs clinical: Morvaridzadeh 2024 vs Pacella 2025; Pacella 2025 (direct, contextual other) vs Morvaridzadeh 2024 (indirect, longevity) — cross-domain: clinical evidence on one outcome must not be fused with mechanistic / preclinical evidence on a different outcome ## References - **CarrilloArango 2025.** _Acute systemic and energy metabolism responses to velocity‐based resistance training following an oral glucose load in individuals with excess body weight._ Experimental Physiology, 2025. DOI: 10.1113/EP093162. PMID: 41379629. - **Gordon 2025.** _Associations of one-carbon metabolism, related B-vitamins and ApoE genotype with cognitive function in older adults: identification of a novel gene-nutrient interaction._ BMC Medicine, 2025. DOI: 10.1186/s12916-025-04276-8. PMID: 40717068. - **Zahed 2021.** _Epidemiology of 40 blood biomarkers of one-carbon metabolism, vitamin status, inflammation, and renal and endothelial function among cancer-free older adults._ Scientific Reports, 2021. DOI: 10.1038/s41598-021-93214-8. PMID: 34226613. - **Lei 2023.** _The Effect of Sleep on Metabolism, Musculoskeletal Disease, and Mortality in the General US Population: Analysis of Results From the National Health and Nutrition Examination Survey._ JMIR Public Health and Surveillance, 2023. DOI: 10.2196/46385. PMID: 37934562. - **Lustgarten 2014.** _Metabolites related to gut bacterial metabolism, peroxisome proliferator-activated receptor-alpha activation, and insulin sensitivity are associated with physical function in functionally-limited older adults._ Aging Cell, 2014. DOI: 10.1111/acel.12251. PMID: 25041144. - **Kemna 2025.** _Acute effects of lactate infusion on metabolism, AD biomarkers, and cognition: The LEAN study._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz.70984. PMID: 41376120. - **Gordon-Dseagu 2015.** _Impaired Glucose Metabolism among Those with and without Diagnosed Diabetes and Mortality: A Cohort Study Using Health Survey for England Data._ PLoS ONE, 2015. DOI: 10.1371/journal.pone.0119882. PMID: 25785731. - **Ma 2022.** _Effects of resveratrol therapy on glucose metabolism, insulin resistance, inflammation, and renal function in the elderly patients with type 2 diabetes mellitus: A randomized controlled clinical trial protocol._ Medicine, 2022. DOI: 10.1097/MD.0000000000030049. PMID: 35960095. - **Ma 2024.** _Association of serum iron metabolism with muscle mass and frailty in older adults: A cross-sectional study of community-dwelling older adults._ Medicine, 2024. DOI: 10.1097/MD.0000000000039348. PMID: 39151527. - **Miller 2021.** _Chlorpyrifos Disrupts Acetylcholine Metabolism Across Model Blood-Brain Barrier._ Frontiers in Bioengineering and Biotechnology, 2021. DOI: 10.3389/fbioe.2021.622175. PMID: 34513802. - **Pacella 2025.** _Dual modulation of lipid and glucose metabolism by a nutraceutical combination in patients at cardiometabolic risk: results from a multicenter randomized controlled trial._ Cardiovascular Diabetology, 2025. DOI: 10.1186/s12933-025-02920-4. PMID: 41044582. - **Morvaridzadeh 2024.** _High-Density Lipoprotein Metabolism and Function in Cardiovascular Diseases: What about Aging and Diet Effects?._ Nutrients, 2024. DOI: 10.3390/nu16050653. PMID: 38474781. - **Fitzpatrick 2020.** _2-Hydroxyglutarate Metabolism Is Altered in an in vivo Model of LPS Induced Endotoxemia._ Frontiers in Physiology, 2020. DOI: 10.3389/fphys.2020.00147. PMID: 32194434. - **Chen 2025.** _Identification of arachidonic acid metabolism-related diagnostic markers in heart failure based on bioinformatics analysis and machine learning._ Frontiers in Cardiovascular Medicine, 2025. DOI: 10.3389/fcvm.2025.1625064. PMID: 41472876. - **Johnson 2019.** _The role of lipid metabolism in aging, lifespan regulation, and age‐related disease._ Aging Cell, 2019. DOI: 10.1111/acel.13048. PMID: 31560163. ### Background References *Canonical reference values and methodological references 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).* - **Studenski 2011.** _Studenski S, Perera S, Patel K, et al. Gait speed and survival in older adults. JAMA. 2011;305(1):50-58._ DOI: 10.1001/jama.2010.1923. PMID: 21205966. - **Perera 2006.** _Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc. 2006;54(5):743-749._ DOI: 10.1111/j.1532-5415.2006.00701.x. PMID: 16696738. - **Cruz-Jentoft 2019.** _Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16-31._ DOI: 10.1093/ageing/afy169. PMID: 30312372.
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"title": "Hypothesis-Generating Brief: Metabolism Biomarker Effects \u2014 full paper"
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