Hypothesis-Generating Brief: Metabolism Biomarker Effects

Hypothesis-Generating Brief: Metabolism Biomarker Effects

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 Thin-corpus 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-2026-06-25T01-34-20Z-R2.

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
Population / 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.

Population / 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.

A first limitation concerns corpus scope. Studies such as Gordon-Dseagu 2015 and Lei 2023 provide longitudinal mortality associations, but they are observational and cannot substitute for a randomized evaluation. The headline inferences about the metabolism–biomarker–anti-aging case are therefore drawn from indirect, cross-sectional, or short-term mechanistic data, and any extension to disease prevention or lifespan in healthy adults is not supported by the admitted evidence. The four FDA-grade, hard-endpoint RCTs that would normally anchor such a synthesis are absent from this corpus.

A second limitation is single-trial generalization risk. Findings anchored on a single trial cannot be replicated within this corpus, and the absence of a corroborating second study is itself a limitation. Citing such findings as evidence for a synthesis-level claim is not warranted.

A third limitation concerns population specificity. Younger adults, healthy community-dwelling older adults, and racial or geographic groups not represented in these cohorts are absent. The synthesis cannot be generalized to populations outside the enrolled ones, and the WHO 2000 overweight (25 kg/m²) and obesity (30 kg/m²) thresholds — used here only as contextual anchors for the enrolled cohorts — do not transfer to groups with different body-composition norms or dietary backgrounds.

A fourth limitation is endpoint scope. The admitted sources overwhelmingly report biomarker or surrogate endpoints, which carry known validity concerns as proxies for hard clinical outcomes (Ioannidis 2005). For example, Pacella 2025 reports reductions in LDL-C, fasting plasma glucose, HbA1c, and HOMA-IR, and Ma 2022 reports resveratrol effects on glucose metabolism, insulin resistance, inflammation, and renal function; the ADA 2024 HbA1c targets of 7% (general adults) and 6.5% (younger / lower-risk patients) appear in the corpus only as contextual thresholds, not as achieved outcomes. None of the sources reports adjudicated cardiovascular events, incident dementia, fractures, or all-cause mortality as a randomized comparison. Surrogate-endpoint results dominate the evidence base, and the inference that biomarker improvement will translate into reduced hard-outcome incidence in this population is not supported by any trial in the corpus.

A fifth limitation is the mechanism-to-clinic gap. The 0.8 m/s gait-speed threshold (Studenski 2011), the 0.1 m/s substantial-improvement marker (Perera 2006), and the EWGSOP2 grip-strength sarcopenia cutoffs of 27 kg for men and 16 kg for women (Cruz-Jentoft 2019) appear in the corpus only as analytical anchors; the trials themselves did not test whether modifying a metabolism biomarker shifts any of these functional endpoints. Translational inference from mechanistic source to clinical recommendation is therefore not warranted by the admitted evidence.

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.
• ADA 2024. American Diabetes Association. Standards of Care in Diabetes. Diabetes Care. 2024;47(Suppl 1). DOI: 10.2337/dc24-S006.
• WHO 2000. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. WHO Technical Report Series 894. 2000. PMID: 11234459.
• 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.
• Ioannidis 2005. Ioannidis JPA. Why most published research findings are false. PLoS Med. 2005;2(8):e124. (methodological reference) DOI: 10.1371/journal.pmed.0020124. PMID: 16060722.