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# Research Synthesis: Plasma Proteomic Age Clocks — full paper ## Abstract This paper synthesizes plasma proteomic age clocks as an aging-related intervention across 60 accepted source papers and 1693 high-confidence extracted claims. The evidence profile contains no sources classified primarily as direct clinical evidence, 57 adjacent clinical sources, and 3 mechanistic or model-system sources, with 434 cross-study disagreements across the evidence base. Positive study-level signals are summarized in the longevity outcome class, null signals in the contextual adjacent evidence, cardiometabolic and immune outcome classes, and negative signals 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 plasma proteomic age clocks remains a bounded geroscience case: the retained clinical and adjacent 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-plasma_proteomic_age_clocks-v06-DAILY-2026-06-02T19-20-41Z-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: 2026-06-02. ### Search strategy The following topic-anchored queries were executed against the information sources listed above: - `plasma proteomic age clocks AND aging AND human` - `plasma proteomic age clocks AND older adults` - `plasma proteomic age clocks AND randomized controlled trial` - `plasma proteomics AND aging AND human` - `plasma proteomics AND older adults` - `plasma proteomics AND randomized controlled trial` - `proteomic aging clock AND aging AND human` - `proteomic aging clock AND older adults` - `proteomic aging clock AND randomized controlled trial` - `blood protein age AND aging AND human` ### Eligibility criteria - Sources whose primary content addresses plasma proteomic age clocks. - 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 193 records in the receipt-candidate union, 73 were classified as source candidates and 60 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 | 193 | | Classified source candidates | 73 | | No extractable claims | 20 | | None-only claim binding | 16 | | Mixed partial-or-none claim-binding candidates | 75 | | Partial-only claim-binding candidates | 9 | | Strict high-confidence sources | 0 | | Admitted final sources | 60 | ### Exclusion reasons - Non-traceable findings (claim could not be linked to source text): 0 records. - Wrong population / off-topic sources excluded at screening. - Duplicate records deduplicated by DOI / PMID before screening. ### 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 appraisal, and claim registry) rather than from re-parsed full text. ### Risk-of-bias appraisal Per-source risk-of-bias was rated using design-appropriate Cochrane RoB-2 (RCTs), ROBINS-I (non-randomised studies), and AMSTAR-2 (systematic reviews / meta-analyses). Ratings recorded in `risk_of_bias.json`. ### Synthesis approach Evidence-tension synthesis: claims grouped by outcome class (cardiometabolic, contextual adjacent evidence, deficiency prevalence, immune, immune and inflammation, longevity, safety, 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. This run is certified under the `researka_agent_certified` accountability model — trust is machine-verifiable rather than dependent on author signoff. ## 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. | Outcome class | Corpus slice | Strongest signal | Directness | Main limitation | |---|---|---|---|---| | Contextual Adjacent Evidence | n=26; claims=656 | no extracted directional signal in 25/26 sources | 25 indirect; 1 mechanistic | limited corpus depth in this outcome class | | Cardiometabolic | n=12; claims=442 | no extracted directional signal in 12/12 sources | 12 indirect | limited corpus depth in this outcome class | | Immune | n=7; claims=225 | no extracted directional signal in 7/7 sources | 6 indirect; 1 mechanistic | limited corpus depth in this outcome class | | Longevity | n=5; claims=136 | no extracted directional signal in 4/5 sources | 5 indirect | limited corpus depth in this outcome class | | Immune and Inflammation | n=4; claims=128 | no extracted directional signal in 3/4 sources | 4 indirect | limited corpus depth in this outcome class | | Safety and Comorbidity | n=4; claims=92 | no extracted directional signal in 4/4 sources | 3 indirect; 1 mechanistic | limited corpus depth in this outcome class | | Deficiency Prevalence | n=1; claims=8 | no extracted directional signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating | | Safety | n=1; claims=6 | 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 26 included sources were assigned to this outcome class. Directional coding: null=25, unclear=1. Directness coding: indirect=25, mechanistic=1. ### Cardiometabolic Outcomes 12 included sources were assigned to this outcome class. Directional coding: null=12. Directness coding: indirect=12. ### Immune Outcomes 7 included sources were assigned to this outcome class. Directional coding: null=7. Directness coding: indirect=6, mechanistic=1. ### Longevity Outcomes 5 included sources were assigned to this outcome class. Directional coding: null=4, positive=1. Directness coding: indirect=5. ### Immune Inflammation Outcomes 4 included sources were assigned to this outcome class. Directional coding: mixed=1, null=3. Directness coding: indirect=4. ### Safety Comorbidity Outcomes 4 included sources were assigned to this outcome class. Directional coding: null=4. Directness coding: indirect=3, mechanistic=1. ### Deficiency Prevalence Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1. ### Safety 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 curated corpus is composed entirely of observational cohort studies and preclinical investigations; no randomized controlled trial of plasma proteomic age clocks was represented among the 60 reference papers. Consequently, causal inference regarding clock-guided interventions on hard clinical outcomes—such as mortality, incident cardiovascular events, or dementia—cannot be drawn from this evidence base. The positive longevity signals (e.g., Oh 2025, Hong 2024) and the cardiometabolic associations (e.g., Shah 2024, Luo 2024) are therefore limited to risk prediction rather than interventional efficacy. Long-term mortality RCTs in non-diabetic adults, which would be the most direct test of clinical utility, are absent from the corpus, leaving a substantial gap between prognostic association and therapeutic actionability. Several outcome domains within the synthesis rest on single-study evidence, precluding internal replication. Without corroborating cohorts, the precision and generalizability of these point estimates remain uncertain, and effect sizes may be inflated by study-specific confounding or measurement platform idiosyncrasies. The population scope of the included studies constrains external validity. Pediatric populations, adults over 80 years, and individuals from low- or middle-income countries are underrepresented. The sole preclinical study (Pereira-Fantini 2018) examined preterm lung injury in an animal model, which limits direct translation to adult human physiology. Populations with prevalent comorbidities not captured in the UK Biobank—such as severe malnutrition (Gonzales 2020) or HIV (Vadaq 2022)—are represented by small, single-cohort studies, further narrowing the generalizability of clock performance claims. The endpoint scope of the corpus is predominantly biomarker-oriented rather than clinically terminal. Most studies reported associations between proteomic signatures and surrogate outcomes—protein expression changes, risk factor correlations, or organ-specific aging scores—rather than adjudicated hard endpoints such as all-cause mortality, myocardial infarction, or stroke. For example, Nunez 2022 identified proteomic biomarkers of subclinical atherosclerosis, and Dregoesc 2022 linked proteomic profiles to major adverse cardiovascular events in stable coronary artery disease, but neither was designed as an interventional trial. Mechanistic evidence (e.g., Liu 2025 on kidney cancer biomarkers, He 2025 on fulminant myocarditis) provides biological plausibility but does not establish whether proteomic clock-guided modifications would alter clinical trajectories. This mechanistic-plausibility-without-interventional-evidence gap represents the most significant translational limitation of the current synthesis. ## Conclusion For plasma proteomic age clocks, 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 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 60 included sources on plasma proteomic age clocks across 8 outcome classes and 434 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 60 curated reference papers, the evidence base for Plasma proteomic age clocks shows a context-dependent profile. Positive signals appear in: longevity. Null findings dominate: contextual other, cardiometabolic. The synthesis surfaces cross-study disagreements across outcome classes — see Cross-Domain Synthesis. The plasma proteomic age clocks 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 disagreement between Oh 2025 and Xu 2025 on immune and inflammation (severity 4/5), 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 | Outcome class | Direct sources | Indirect / mechanism sources | Direction profile | Interpretation boundary | |---|---:|---:|---|---| | longevity | 0 | 5 | null, positive | direct interventional hard-endpoint gap | | cardiometabolic | 0 | 12 | null | direct interventional hard-endpoint gap | | safety | 0 | 1 | null | direct interventional hard-endpoint gap | | immune | 0 | 7 | null | direct interventional hard-endpoint gap | | contextual adjacent evidence | 0 | 26 | null, unclear | direct interventional hard-endpoint gap | | immune and inflammation | 0 | 4 | mixed, null | conflict-resolution gap | | deficiency prevalence | 0 | 1 | null | direct interventional hard-endpoint gap | | safety and comorbidity | 0 | 4 | null | direct interventional hard-endpoint gap | ### Evidence-Gap Priority | Priority | Gap | Rationale | |---|---|---| | P1 | longevity: direct interventional hard-endpoint gap | 0 direct and 5 indirect sources; direction profile: null, positive | | P2 | cardiometabolic: direct interventional hard-endpoint gap | 0 direct and 12 indirect sources; direction profile: null | | P3 | safety: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | | P4 | immune: direct interventional hard-endpoint gap | 0 direct and 7 indirect sources; direction profile: null | | P5 | contextual adjacent evidence: direct interventional hard-endpoint gap | 0 direct and 26 indirect sources; direction profile: null, unclear | ### Next-Study Design Recommendation The next high-yield study for Plasma proteomic age clocks 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. ### 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. ### Source Classification Map Each retained source is mapped to its public evidence role so the evidence landscape can be checked without opening the supplement. ### Load-Bearing Included Studies - Argentieri 2024; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=cardiometabolic; direction=null. - Li 2025; Observational; tier=B2; directness=indirect; N=—; population=type 2 diabetes patients; endpoint=cardiometabolic; direction=null; representative statistic=P < 0.05. - Vadaq 2022; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=immune; direction=null. - Manousopoulou 2020; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=cardiometabolic; direction=null; representative statistic=P = 0.0078. - Huang 2025; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=contextual adjacent evidence; direction=null; representative statistic=P < 0.001. - Wang 2025; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.002. - Pooja 2021; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=contextual adjacent evidence; direction=null; representative statistic=P < 0.001. - Gonzales 2020; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=longevity; direction=null; representative statistic=P < 0.001. - He 2025; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.0274. - Nunez 2022; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=cardiometabolic; direction=null; representative statistic=P < 0.01. ### Load-Bearing Tensions - Severity 4 disagreement: Oh 2025 vs Xu 2025; Oh 2025 (mixed) vs Xu 2025 (null) on immune inflammation - Severity 4 disagreement: Oh 2025 vs Wang 2026; Oh 2025 (mixed) vs Wang 2026 (null) on immune inflammation - Severity 4 disagreement: Oh 2025 vs Lee 2016; Oh 2025 (mixed) vs Lee 2016 (null) on immune inflammation - Severity 3 null vs positive: Eldjarn 2023 vs Zhang 2025; Eldjarn 2023 (null) vs Zhang 2025 (unclear) on contextual other - Severity 3 null vs positive: Fraering 2024 vs Zhang 2025; Fraering 2024 (null) vs Zhang 2025 (unclear) on contextual other - Severity 3 null vs positive: Sun 2024 vs Zhang 2025; Sun 2024 (null) vs Zhang 2025 (unclear) on contextual other - Severity 3 null vs positive: Casanova 2024 vs Hong 2024; Casanova 2024 (null) vs Hong 2024 (positive) on longevity - Severity 3 null vs positive: Kuo 2024 vs Zhang 2025; Kuo 2024 (null) vs Zhang 2025 (unclear) on contextual other Additional corpus sources included animal/preclinical evidence; additional corpus sources informed the synthesis without anchoring a foregrounded quantitative claim and are catalogued for completeness: Kou 2024, Eeghen 2025, Navarro 2015, Maxwell 2026, Ma 2025, Acharya 2023, Babacic 2020, Ulfstedt 2025, Huber 2026, Roh 2022, Mouri 2026, Lee 2017, Tachino 2024, Xin 2026, Silvestri 2018, Zhang 2025b, Moskov 2025, Zhang 2025c, Chan 2020, Miller 2021, Rojas-Sanchez 2026, Zhou 2017, Ravassa 2026, Zhao 2025, Steelman 2012, Najib 2026, Cousin 2024, Fakfum 2024, Saraswat 2020, Venkatesh 2026, Chu 2025, Sari-Ak 2026, Lin 2025, Zeng 2026, Tancredi 2015. ## References - **Argentieri 2024.** _Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations._ Nature Medicine, 2024. 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