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# Hypothesis-Generating Brief: Brain age MRI — full paper ## Abstract Evidence-honesty note: 47/48 retained sources are coded as null or no extracted directional signal; this corpus is non-supportive for clinical efficacy claims and hypothesis-generating only. Source-bundle reconciliation note: Directional coding is conservative claim-level coding from extracted claim records, not a statement that the source texts contain no directional findings; source-level positive, negative, or unclear findings should be interpreted through the coded outcome class, directness, and claim-count fields. 47/48 retained sources are indirect, review-level, adjacent, or mechanistic and are used only to bound interpretation. The conclusion therefore does not support broad causal, clinical, or policy claims. This paper synthesizes evidence on Brain age MRI across 48 included source papers and 689 high-confidence extracted claims. The evidence profile contains 1 direct clinical source, 47 adjacent clinical sources, and no sources classified primarily as mechanistic or model-system evidence, with 47 cross-study disagreements across the evidence base. No single positive outcome class dominates the retained corpus; null signals cluster in the contextual adjacent evidence, safety and comorbidity, frailty 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 Brain age MRI should be treated as a bounded geroscience hypothesis: the retained clinical and adjacent evidence profile defines the scope for targeted testing, while mixed and null findings limit any unqualified anti-aging claim. ## 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=38; claims=521 | no extracted directional signal in 37/38 sources | 1 direct; 36 indirect; 1 review | limited corpus depth in this outcome class | | Safety and Comorbidity | n=3; claims=54 | no extracted directional signal in 3/3 sources | 3 indirect | limited corpus depth in this outcome class | | Frailty | n=2; claims=15 | no extracted directional signal in 2/2 sources | 2 indirect | limited corpus depth in this outcome class | | Muscle Function | n=2; claims=2 | no extracted directional signal in 2/2 sources | 2 indirect | limited corpus depth in this outcome class | | Cardiometabolic | n=1; claims=16 | no extracted directional signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating | | Cognitive | n=1; claims=21 | no extracted directional signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating | | Immune and Inflammation | n=1; claims=60 | no extracted directional signal in 1/1 sources | 1 indirect | 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 38 included sources were assigned to this outcome class. Directional coding: null=37, unclear=1. Directness coding: direct=1, indirect=36, review=1. ### Safety Comorbidity Outcomes 3 included sources were assigned to this outcome class. Directional coding: null=3. Directness coding: indirect=3. ### Frailty Outcomes 2 included sources were assigned to this outcome class. Directional coding: null=2. Directness coding: indirect=2. ### Muscle Function Outcomes 2 included sources were assigned to this outcome class. Directional coding: null=2. Directness coding: indirect=2. ### Cardiometabolic Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1. ### Cognitive 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: indirect=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 corpus is dominated by cross-sectional and longitudinal observational cohorts rather than long-term mortality or hard-outcome randomized trials in non-diabetic or non-frail adults. Haudry 2025 is the single source classified as an RCT, and its endpoint is mechanistic/biomarker (multimodal neuroimaging-derived brain age in older meditators and experts) rather than a clinical endpoint such as incident dementia, cardiovascular events, or mortality. No source in the curated set evaluates all-cause mortality, institutionalization, or quality-of-life outcomes against brain-age trajectories, which means that the clinically actionable inferences drawn from the broader pooled signals (for example, the Wang 2025 plasma-based hazard ratio for Alzheimer disease risk, or the Kou 2024 per-unit proteomic brain age gap association) remain extrapolations from indirect, biomarker-level cohorts. The headline synthesis therefore cannot support causal or survival-bounded claims about brain-age modification in the general adult population. Several outcomes in the synthesis rest on a single source and cannot be triangulated within the corpus. Single-source signals of this kind are vulnerable to algorithm-specific bias, scanner-site effects noted in Satpathi 2025 (Model-A 2.17 years vs Model-B 1.71 years mean inter-scanner difference), and unmeasured confounding, and the cross-source agreement needed for replication is structurally absent. Population specificity further narrows the external validity of the pooled conclusions. The trials and cohorts most heavily weighted in this synthesis are mid-to-older adults from UK Biobank–derived samples (Dunk 2025; Motaghi 2025; Heffernan 2025), specialist memory-clinic or AD-research datasets such as Cam-CAN, NACC, and ADNI (Li 2024; Ahmadi 2025; Lu 2024), and clinically enriched subgroups such as knee osteoarthritis (Tanner 2025), schizophrenia spectrum disorders (Yilmaz 2025), traumatic brain injury in veterans (Coetzee 2025), stroke (Liew 2023), and Parkinson's disease with LRRK2 carriers (Teipel 2024). Pediatric, perinatal, and young-adult brain-age evidence is essentially absent, and ethnic and geographic diversity is limited by the heavy reliance on European and North American biobanks. Endpoint coverage is narrow relative to the clinical decision space in which brain-age biomarkers are being proposed. The mechanistic-vs-clinical gap noted by Ioannidis 2005 is therefore unresolved: surrogate-endpoint associations do not, in this corpus, come with hard-outcome validation. Finally, the mechanism-to-clinic gap is acute for intervention-style claims. 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 may support Brain age MRI 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. 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 48 included sources on Brain Age MRI across 7 outcome classes and 47 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 48 curated reference papers, the evidence base for Brain shows a context-dependent profile. Null findings dominate: contextual other, safety comorbidity. The synthesis surfaces cross-study disagreements across outcome classes — see Cross-Domain Synthesis. The Brain 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 indirectness gap between Liew 2023 and Haudry 2025 on contextual adjacent evidence (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 | |---|---:|---:|---|---| | cardiometabolic | 0 | 1 | null | direct interventional hard-endpoint gap | | cognitive | 0 | 1 | null | direct interventional hard-endpoint gap | | frailty | 0 | 2 | null | direct interventional hard-endpoint gap | | muscle function | 0 | 2 | null | direct interventional hard-endpoint gap | | immune and inflammation | 0 | 1 | null | direct interventional hard-endpoint gap | | safety and comorbidity | 0 | 3 | null | direct interventional hard-endpoint gap | | contextual adjacent evidence | 1 | 37 | null, unclear | replication gap | ### Evidence-Gap Priority | Priority | Gap | Rationale | |---|---|---| | P1 | cardiometabolic: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | | P2 | cognitive: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | | P3 | frailty: direct interventional hard-endpoint gap | 0 direct and 2 indirect sources; direction profile: null | | P4 | muscle function: direct interventional hard-endpoint gap | 0 direct and 2 indirect sources; direction profile: null | | P5 | immune and inflammation: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | ### Next-Study Design Recommendation The next high-yield study for Brain Age MRI should target the **cardiometabolic** 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 Source directness breakdown: 1/48 retained sources directly address the stated topic and aging-relevant hard endpoints; 47/48 are adjacent, contextual, review-level, or mechanistic and are used only to bound interpretation. A qualifying direct source would directly test the named exposure or construct in the target population with aging-relevant clinical or hard-endpoint follow-up. Inclusion rationale: adjacent sources are reclassified as contextual rather than used for broad efficacy claims. ### Source Classification Map - Huang 2025: outcome=Immune and Inflammation; directness=indirect; tier=B2. - Tanner 2025: outcome=Safety and Comorbidity; directness=indirect; tier=B2. - Selitser 2025: outcome=Contextual Adjacent Evidence; directness=review; tier=B2. - Narula 2026: outcome=Contextual Adjacent Evidence; directness=indirect; tier=B2. - Liew 2023: outcome=Contextual Adjacent Evidence; directness=indirect; tier=B2. - Gemein 2024: outcome=Contextual Adjacent Evidence; directness=indirect; tier=B2. - Lu 2024: outcome=Contextual Adjacent Evidence; directness=indirect; tier=B2. - Yilmaz 2025: outcome=Contextual Adjacent Evidence; directness=indirect; tier=B2. Topic-fit rationale: Sources are retained only when they operationalize brain age mri directly or provide adjacent/contextual boundary evidence for the same construct. 1/48 retained sources are classified as direct; adjacent, contextual, review-level, or mechanistic sources are reclassified as boundary evidence rather than used for broad efficacy claims. Representative source-fit checks: Huang 2025 (indirect; Immune and Inflammation), Tanner 2025 (indirect; Safety and Comorbidity), Selitser 2025 (review; Contextual Adjacent Evidence), Narula 2026 (indirect; Contextual Adjacent Evidence), Liew 2023 (indirect; Contextual Adjacent Evidence). The manuscript foregrounds the load-bearing evidence; the full evidence tables remain in the supplement. ### Load-Bearing Included Studies - Haudry 2025; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.14. - Huang 2025; tier=B2; directness=indirect; endpoint=immune inflammation; direction=null. - Tanner 2025; tier=B2; directness=indirect; endpoint=safety comorbidity; direction=null; representative statistic=P = 0.061. - Narula 2026; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null; representative statistic=P > 0.05. - Selitser 2025; tier=B2; directness=review; endpoint=contextual adjacent evidence; direction=null. - Liew 2023; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.386. - Gemein 2024; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.13. - Lu 2024; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null. - Yilmaz 2025; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=unclear. - Teipel 2024; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; 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. - Impact of meditation on brain age derived from multimodal neuroimaging in experts and older adults from a randomized trial: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=17. - Association of peripheral immune markers with brain age and dementia risk estimated using deep learning methods: outcome=immune inflammation; directness=indirect; tier=B2; direction=null; claims=60. - More than chronic pain: behavioural and psychosocial protective factors predict lower brain age in adults with/at risk of knee osteoarthritis over two years: outcome=safety comorbidity; directness=indirect; tier=B2; direction=null; claims=50. - The impact of balance exercise on brain age and brain morphometry: insights from MRI analysis: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=43. - Cardiometabolic risk factors and brain age: a meta-analysis to quantify brain structural differences related to diabetes, hypertension, and obesity: outcome=contextual adjacent evidence; directness=review; tier=B2; direction=null; claims=43. - Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=40. - Brain age revisited: Investigating the state vs. trait hypotheses of EEG-derived brain-age dynamics with deep learning: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=34. - MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=32. - Brain age gap reduction following exercise mirrors clinical improvements in schizophrenia spectrum disorders: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=unclear; claims=29. - Brain age in genetic and idiopathic Parkinson's disease: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=27. - Genome-wide analysis of brain age identifies 59 associated loci and unveils relationships with mental and physical health: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=25. - A deep learning model for brain age prediction using minimally preprocessed T1w images as input: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=24. - Association between low‐frequency oscillations in blood pressure variability and brain age derived from neuroimaging: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=22. - Brain age gap, dementia risk factors and cognition in middle age: outcome=cognitive; directness=indirect; tier=B2; direction=null; claims=21. - Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=16. - ASSOCIATIONS BETWEEN CARDIORESPIRATORY FITNESS, BRAIN AGE, AND NEURODEGENERATION AMONG OLDER ADULTS: outcome=cardiometabolic; directness=indirect; tier=B2; direction=null; claims=16. - Decoding MRI-informed brain age using mutual information: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=15. - Longitudinal accelerated brain age in mild cognitive impairment and Alzheimer’s disease: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=14. - Meditation Linked to Enhanced MRI Signal Intensity in the Pineal Gland and Reduced Predicted Brain Age: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=13. - Increased Brain Age Among Psychiatrically Healthy Adults Exposed to Childhood Trauma: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=11. - Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=11. - MRI-based whole-brain elastography and volumetric measurements to predict brain age: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=10. - Investigating the Association of Frailty Score and Diabetes with Relative Brain Age : Insights from the UK Biobank: outcome=frailty; directness=indirect; tier=B2; direction=null; claims=9. - Sleep Patterns in Midlife and Brain Age: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=8. - Plasma‐based Brain Age as a Biomarker for Cognitive Health and Risk of Brain‐Related Diseases: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=8. - Brain age gap estimation using attention-based ResNet method for Alzheimer’s disease detection: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=7. - Advanced brain age prediction using 3D convolutional neural network on structural MRI: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=7. - Examining the reliability of brain age algorithms under varying degrees of participant motion: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=7. - Predicting brain age using Tri-UNet and various MRI scale features: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=7. - A PROTEOMICS-BASED MEASURE OF ACCELERATING AGING IS CORRELATED WITH THE BRAIN AGE GAP IN THE ARIC STUDY: outcome=frailty; directness=indirect; tier=B2; direction=null; claims=6. - The association between a pro‐inflammatory diet and machine learning‐based brain age in middle‐aged and older adults: Findings from the UK Biobank: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=6. - Association between shift work and brain age gap: a neuroimaging study using MRI-based brain age prediction algorithms: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=6. - White Matter Hyperintensities on Brain MRI are Related to Brain Atrophy and Accelerated Brain Age: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=5. - White Matter Hyperintensities on Brain MRI are Related to Brain Atrophy and Accelerated Brain Age: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=5. - Association between cardiovascular disease risk, regional brain age gap, and cognition in healthy adults: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=5. - The Impact of Brain Age versus Chronological Age on Cognitive Fatigue: Novel Metrics and New Insights: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=5. - Evaluating the Impact of Cardiometabolic Risk Factors on Neuroimaging‐Based Brain Age: A Deep Learning Approach: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=5. - PROTEOMIC BRAIN AGE GAP, DEMENTIA RISK, AND BRAIN VOLUME MEASUREMENTS: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=3. - Brain Age Acceleration on MRI Due to Poor Sleep: Associations, Mechanisms, and Clinical Implications: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=3. - Sex Differences in Brain Age Gap Estimation Across Alzheimer's Disease Diagnostic Groups: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=2. ### 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: Liew 2023 vs Haudry 2025; Haudry 2025 (direct, A1) vs Liew 2023 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Dartora 2024 vs Haudry 2025; Haudry 2025 (direct, A1) vs Dartora 2024 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Hanson 2024 vs Haudry 2025; Haudry 2025 (direct, A1) vs Hanson 2024 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Aghaei 2024 vs Haudry 2025; Haudry 2025 (direct, A1) vs Aghaei 2024 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Pang 2024 vs Haudry 2025; Haudry 2025 (direct, A1) vs Pang 2024 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Lu 2024 vs Haudry 2025; Haudry 2025 (direct, A1) vs Lu 2024 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Dorfel 2024 vs Haudry 2025; Haudry 2025 (direct, A1) vs Dorfel 2024 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Li 2024 vs Haudry 2025; Haudry 2025 (direct, A1) vs Li 2024 (indirect) on contextual other — direct vs indirect must be kept separate ## Conclusion For Brain age MRI, the final interpretation is deliberately tiered: the retained clinical and adjacent 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 clinical 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. Pending further trials, the intervention should not be used off-label for geroprotection or anti-aging purposes outside clinical-trial settings given current 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. ## 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-brain_age_mri-v06-DAILY-2026-06-21T13-06-51Z-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: 2026-06-21. ### Search strategy The following topic-anchored queries were executed against the information sources listed above: - `brain age MRI AND aging AND human` - `brain age MRI AND older adults` - `brain age MRI AND randomized controlled trial` - `brain age AND aging AND human` - `brain age AND older adults` - `brain age AND randomized controlled trial` - `MRI brain age AND aging AND human` - `MRI brain age AND older adults` - `MRI brain age AND randomized controlled trial` - `neuroimaging aging AND aging AND human` ### Eligibility criteria - Sources whose primary content addresses brain age mri. - 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 198 records in the receipt-candidate union, 78 were classified as source candidates and 48 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 | 198 | | Classified source candidates | 78 | | No extractable claims | 21 | | None-only claim binding | 10 | | Mixed partial-or-none claim-binding candidates | 73 | | Partial-only claim-binding candidates | 12 | | Strict high-confidence sources | 4 | | Admitted final sources | 48 | ### 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, cognitive, contextual adjacent evidence, frailty, immune and inflammation, muscle function, 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. ### 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. Additional corpus sources informed the synthesis without anchoring a foregrounded quantitative claim and are catalogued for completeness: Jawinski 2025, Stefaniak 2024, Derboghossian 2024, Ly 2024, Plini 2025, Hendrikse 2025, Hu 2025, Claros-Olivares 2024, Cavailles 2025, Casanova 2024, Kim 2025, Tavakoli 2025, Pallapothu 2025, Meysami 2025, Roman 2025, Meysami 2026, Toraih 2025, Yu 2025, Yu 2025b, Rajabli 2025, Rajabli 2026, Aithal 2025, Raji 2025, Raji 2026, Studenski 2011, Perera 2006. ## References - **Huang 2025.** _Association of peripheral immune markers with brain age and dementia risk estimated using deep learning methods._ International Journal of Surgery (London, England), 2025. DOI: 10.1097/JS9.0000000000002746. PMID: 40561180. - **Tanner 2025.** _More than chronic pain: behavioural and psychosocial protective factors predict lower brain age in adults with/at risk of knee osteoarthritis over two years._ Brain Communications, 2025. DOI: 10.1093/braincomms/fcaf344. PMID: 41020178. - **Selitser 2025.** _Cardiometabolic risk factors and brain age: a meta-analysis to quantify brain structural differences related to diabetes, hypertension, and obesity._ Journal of Psychiatry & Neuroscience : JPN, 2025. DOI: 10.1503/jpn.240105. PMID: 40068862. - **Narula 2026.** _The impact of balance exercise on brain age and brain morphometry: insights from MRI analysis._ Aging Clinical and Experimental Research, 2026. DOI: 10.1007/s40520-026-03322-6. PMID: 41566095. - **Liew 2023.** _Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke._ Neurology, 2023. DOI: 10.1212/WNL.0000000000207219. PMID: 37015818. - **Gemein 2024.** _Brain age revisited: Investigating the state vs. trait hypotheses of EEG-derived brain-age dynamics with deep learning._ Imaging Neuroscience, 2024. DOI: 10.1162/imag_a_00210. 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PMID: 41049536. - **Meysami 2025.** _White Matter Hyperintensities on Brain MRI are Related to Brain Atrophy and Accelerated Brain Age._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz70862_110308. - **Roman 2025.** _The Impact of Brain Age versus Chronological Age on Cognitive Fatigue: Novel Metrics and New Insights._ Innovation in Aging, 2025. DOI: 10.1093/geroni/igaf122.4213. - **Meysami 2026.** _White Matter Hyperintensities on Brain MRI are Related to Brain Atrophy and Accelerated Brain Age._ Alzheimer's & Dementia, 2026. DOI: 10.1002/alz70856_106425. - **Kou 2024.** _PROTEOMIC BRAIN AGE GAP, DEMENTIA RISK, AND BRAIN VOLUME MEASUREMENTS._ Innovation in Aging, 2024. DOI: 10.1093/geroni/igae098.3470. - **Toraih 2025.** _Brain Age Acceleration on MRI Due to Poor Sleep: Associations, Mechanisms, and Clinical Implications._ Brain Sciences, 2025. DOI: 10.3390/brainsci15121325. PMID: 41440121. - **Yu 2025.** _Chronic Medical Conditions and Dementia Risk: Brain Age Models for Quantifying Impact and Understanding Mechanisms._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz.093829. - **Yu 2025b.** _Chronic Medical Conditions and Dementia Risk: Brain Age Models for Quantifying Impact and Understanding Mechanisms._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz.089382. - **Rajabli 2025.** _Sex Differences in Brain Age Gap Estimation Across Alzheimer's Disease Diagnostic Groups._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz70862_110227. - **Satpathi 2025.** _Developing scanner change invariant brain age models for aging and dementia studies._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz70856_097891. - **Rajabli 2026.** _Sex Differences in Brain Age Gap Estimation Across Alzheimer's Disease Diagnostic Groups._ Alzheimer's & Dementia, 2026. DOI: 10.1002/alz70856_107437. - **Dorfel 2024.** _Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET-derived features._ GeroScience, 2024. DOI: 10.1007/s11357-024-01148-6. PMID: 38668887. - **Aithal 2025.** _Simple fully convolutional network to estimate Brain Age._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz.088019. - **Raji 2025.** _Higher Muscle Volume is Inversely Related to Chronological and Brain Age While Increased Visceral to Muscle Fat Ratio is Positively Related to Chronological and Brain Age._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz70862_110051. - **Raji 2026.** _Higher Muscle Volume is Inversely Related to Chronological and Brain Age While Increased Visceral to Muscle Fat Ratio is Positively Related to Chronological and Brain Age._ Alzheimer's & Dementia, 2026. DOI: 10.1002/alz70856_106692. ### 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. - **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.
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