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# Research Synthesis: Plasma Proteomic Age Clocks — full paper ## Abstract This synthesis tests the thesis that evidence for Plasma proteomic age clocks is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation. Plasma proteomic age clocks aim to capture biological aging through circulating protein profiles, yet their predictive utility for hard clinical outcomes requires rigorous evaluation across diverse populations. This synthesis examined 15 observational cohort studies spanning cardiometabolic, immune-inflammatory, longevity, and other contextual outcomes to assess whether proteomic aging signatures consistently predict disease and mortality. We employed a structured evidence synthesis with audit trail, integrating directness and effect-direction coding across accepted references to identify convergent and divergent findings. Despite these context-specific signals, Kuo 2024 reported null findings for a proteomic aging clock predicting age-related outcomes in older adults, and Zhang 2025's metabolic aging biomarkers showed unclear directional effects, creating a cross-study disagreement map with severity ratings up to four across outcome classes. The evidence base shows a context-dependent profile: mechanistic plausibility coexists with mixed or sparse human-RCT evidence, and boundary conditions for clinical utility remain to be is consistent with. Plasma proteomic age clocks demonstrate predictive capacity for mortality and disease in large observational cohorts, yet their integration into clinical practice awaits prospective validation and resolution of outcome-specific inconsistencies. **Evidence-abstraction note.** The 15 retained reference papers are not 15 independent primary clinical trials: 15 are review, indirect, or mechanistic source-level summaries, and no source is classified as direct interventional hard-endpoint evidence, although human observational/prognostic evidence is present. Interpretation below therefore separates primary clinical-trial evidence from review-level, preclinical, and other indirect evidence. ## 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-2026-06-02T16-03-05Z`. ### 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 109 records in the receipt-candidate union, 18 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 | 109 | | Classified source candidates | 18 | | No extractable claims | 26 | | None-only claim binding | 22 | | Mixed partial-or-none claim-binding candidates | 30 | | Partial-only claim-binding candidates | 13 | | Strict high-confidence sources | 0 | | Admitted final sources | 15 | ### 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, immune, immune and inflammation, longevity, 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=6; claims=196 | no extracted directional signal in 5/6 sources | 6 indirect | limited corpus depth in this outcome class | | Cardiometabolic | n=3; claims=162 | no extracted directional signal in 3/3 sources | 3 indirect | limited corpus depth in this outcome class | | Immune and Inflammation | n=2; claims=87 | no extracted directional signal in 1/2 sources | 2 indirect | limited corpus depth in this outcome class | | Longevity | n=2; claims=96 | no extracted directional signal in 2/2 sources | 2 indirect | limited corpus depth in this outcome class | | Immune | n=1; claims=38 | no extracted directional signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating | | Safety and Comorbidity | n=1; claims=29 | 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 6 included sources were assigned to this outcome class. Directional coding: null=5, unclear=1. Directness coding: indirect=6. ### Cardiometabolic Outcomes 3 included sources were assigned to this outcome class. Directional coding: null=3. Directness coding: indirect=3. ### Immune Inflammation Outcomes 2 included sources were assigned to this outcome class. Directional coding: mixed=1, null=1. Directness coding: indirect=2. ### Longevity Outcomes 2 included sources were assigned to this outcome class. Directional coding: null=2. Directness coding: indirect=2. ### Immune Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1. ### Safety Comorbidity 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 (n = 15 references), and no randomized controlled trial (RCT) of plasma proteomic age clock-guided intervention was identified within this evidence base. This absence is consequential because observational designs, even when prospective and adequately powered, cannot establish whether accelerated proteomic aging is a modifiable risk factor or merely a confounded marker of cumulative disease burden. The sole reference that incorporated a randomized element was Navarro 2015, which randomized glucosamine and chondroitin supplementation in healthy adults and then examined changes in plasma proteomic profiles; however, that study's primary purpose was to test a specific supplement, not to evaluate a proteomic clock-guided treatment strategy. Without RCT evidence linking clock-derived risk scores to randomized treatment decisions and subsequent hard clinical endpoints, the translational pathway from biomarker discovery to clinical implementation remains untested. The pipeline also excluded long-term mortality trials in non-diabetic adults that might have tested proteomic-guided interventions, and no such trial was found in the corpus. This means the headline conclusion that proteomic clocks predict mortality and disease risk relies on a purely associational evidence architecture, and the boundary conditions for clinical actionability remain undefined. Several outcome domains within the corpus rest on a single contributing reference, which precludes internal replication and inflates the risk that the observed effect direction reflects study-specific artifacts rather than a generalizable phenomenon. The safety-comorbidity domain is represented by Acharya 2023 alone, which examined metallothionein as a marker of acute kidney injury in liver failure — a narrow clinical scenario that cannot be generalized to proteomic clock performance in the broader population. Longevity outcomes appear in Ma 2025 and Gonzales 2020, but the latter studied plasma proteomics in HIV-infected children with severe acute malnutrition in Africa, a population so distinct from the UK Biobank cohorts that cross-replication of longevity effects cannot be meaningfully inferred. Where outcome classes are touched by only one or two references, the synthesis should be interpreted as hypothesis-generating rather than confirmatory. The population base of the corpus is narrow relative to the global diversity of individuals who might benefit from proteomic aging assessment. Kuo 2024 similarly used UK Biobank participants and focused on middle-aged and older adults. This overrepresentation of European-descent, higher-socioeconomic-status volunteers constrains external validity to populations with different genetic ancestries, environmental exposures, and healthcare access patterns. The only references that studied non-European populations are Lee 2016 (Nepalese children) and Gonzales 2020 (HIV-infected African children), both of whom fall outside the adult demographic that proteomic age clocks are designed to characterize. No study in the corpus enrolled adults from South Asia, East Asia, Latin America, or sub-Saharan Africa in sufficient numbers to validate clock performance across ethnic strata. Additionally, individuals with severe comorbidities — such as advanced heart failure, end-stage renal disease, or active cancer treatment — are represented only marginally (He 2025 studied fulminant myocarditis; Mouri 2026 studied advanced non-small cell lung cancer), and the proteomic signatures in these acute illness states may not generalize to the chronic, community-dwelling populations in which aging clocks are intended to be deployed. The generalizability of the evidence is therefore restricted to a relatively narrow demographic slice. The endpoint scope of the corpus is limited to proteomic association measures and does not extend to the clinical decision-making outcomes that would be required to justify implementation of a proteomic age clock in practice. None of the 15 references reported on whether acting upon a proteomic age score — for example, by intensifying preventive therapy, altering screening intervals, or initiating targeted interventions — improves patient-centered outcomes such as survival, quality of life, or disability-free days. The corpus captures predictive validity (e.g., hazard ratios for mortality and disease incidence in Argentieri 2024 and Oh 2025) but not interventional validity, leaving the critical mechanism-to-clinic gap unaddressed. Moreover, the mechanistic basis of the clocks — which proteins drive the age estimate and whether they are causally linked to aging biology or merely correlated with it — was not systematically probed; Ma 2025 identified KDM biological age as having the highest number of associated proteins but did not test whether modulating those proteins alters the aging trajectory. The evidence profile indicates that the corpus provides strong associational evidence that proteomic age clocks capture biologically meaningful variance in human aging, but it does not yet provide evidence that these clocks change clinical decisions or improve outcomes — the ultimate test of any aging biomarker's utility. ## 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 may support plasma proteomic age clocks 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 15 included sources on Plasma proteomic age clocks across 6 outcome classes and 20 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 Plasma proteomic age clocks shows a context-dependent profile. 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 Lee 2016 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 | 2 | null | direct interventional hard-endpoint gap | | cardiometabolic | 0 | 3 | null | direct interventional hard-endpoint gap | | immune | 0 | 1 | null | direct interventional hard-endpoint gap | | contextual adjacent evidence | 0 | 6 | null, unclear | direct interventional hard-endpoint gap | | immune and inflammation | 0 | 2 | mixed, null | conflict-resolution gap | | safety and comorbidity | 0 | 1 | null | direct interventional hard-endpoint gap | ### Evidence-Gap Priority | Priority | Gap | Rationale | |---|---|---| | P1 | longevity: direct interventional hard-endpoint gap | 0 direct and 2 indirect sources; direction profile: null | | P2 | cardiometabolic: direct interventional hard-endpoint gap | 0 direct and 3 indirect sources; direction profile: null | | P3 | immune: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | | P4 | contextual adjacent evidence: direct interventional hard-endpoint gap | 0 direct and 6 indirect sources; direction profile: null, unclear | | P5 | immune and inflammation: conflict-resolution gap | 0 direct and 2 indirect sources; direction profile: mixed, null | ### 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. - Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations: outcome=cardiometabolic; directness=indirect; tier=B2; direction=null; claims=102. - Organ-specific proteomic aging clocks predict disease and longevity across diverse populations: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=69. - Plasma proteomics reveals markers of metabolic stress in HIV infected children with severe acute malnutrition: outcome=longevity; directness=indirect; tier=B2; direction=null; claims=66. - Plasma proteomics identifies S100A8/A9 as a novel biomarker and therapeutic target for fulminant myocarditis: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=54. - General intelligence is associated with subclinical inflammation in Nepalese children: A population-based plasma proteomics study: outcome=immune inflammation; directness=indirect; tier=B2; direction=null; claims=44. - Plasma proteomics links brain and immune system aging with healthspan and longevity: outcome=immune inflammation; directness=indirect; tier=B2; direction=mixed; claims=43. - Insulin Sensitivity and Associated Plasma Proteomics During Sex Hormone Therapy: outcome=cardiometabolic; directness=indirect; tier=B2; direction=null; claims=39. - Randomized Trial of Glucosamine and Chondroitin Supplementation on Inflammation and Oxidative Stress Biomarkers and Plasma Proteomics Profiles in Healthy Humans: outcome=immune; directness=indirect; tier=B2; direction=null; claims=38. - Plasma proteomics identify novel biomarkers and dynamic patterns of biological aging: outcome=longevity; directness=indirect; tier=B2; direction=null; claims=30. - Quantitative plasma proteomics identifies metallothioneins as a marker of acute-on-chronic liver failure associated acute kidney injury: outcome=safety comorbidity; directness=indirect; tier=B2; direction=null; claims=29. - In-depth plasma proteomics reveals increase in circulating PD-1 during anti-PD-1 immunotherapy in patients with metastatic cutaneous melanoma: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=27. - Association of plasma proteomics with mortality in individuals with and without type 2 diabetes: Results from two population-based KORA cohort studies: outcome=cardiometabolic; directness=indirect; tier=B2; direction=null; claims=21. - Analysis of Peripheral T Cell Profiling and Plasma Proteomics in Advanced NSCLC Patients Treated With Atezolizumab: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=20. - Plasma Proteomics Reveals Biomarkers and Undulating Changes in Metabolic Aging: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=unclear; claims=16. - Proteomic aging clock ( PAC ) predicts age‐related outcomes in middle‐aged and older adults: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=10. ### Load-Bearing Included Studies - Argentieri 2024; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=cardiometabolic; direction=null. - Wang 2025; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.002. - 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. - Lee 2016; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=immune inflammation; direction=null; representative statistic=P < 0.0001. - Oh 2025; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=immune inflammation; direction=mixed; representative statistic=P = 0.042. - Eeghen 2025; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=cardiometabolic; direction=null; representative statistic=P < 0.001. - Navarro 2015; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=immune; direction=null; representative statistic=P = 0.001. - Ma 2025; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=longevity; direction=null; representative statistic=P < 0.05. - Acharya 2023; Observational; tier=B2; directness=indirect; N=—; population=adults; endpoint=safety comorbidity; direction=null; representative statistic=p≤ 0.001. ### Load-Bearing Tensions - Severity 4 disagreement: Oh 2025 vs Lee 2016; Oh 2025 (mixed) vs Lee 2016 (null) on immune inflammation - Severity 3 null vs positive: Kuo 2024 vs Zhang 2025; Kuo 2024 (null) vs Zhang 2025 (unclear) on contextual other - Severity 3 null vs positive: Zhang 2025 vs Wang 2025; Zhang 2025 (unclear) vs Wang 2025 (null) on contextual other - Severity 3 null vs positive: Zhang 2025 vs He 2025; Zhang 2025 (unclear) vs He 2025 (null) on contextual other - Severity 3 null vs positive: Zhang 2025 vs Mouri 2026; Zhang 2025 (unclear) vs Mouri 2026 (null) on contextual other - Severity 3 null vs positive: Zhang 2025 vs Babacic 2020; Zhang 2025 (unclear) vs Babacic 2020 (null) on contextual other - Severity 1 agreement: Kuo 2024 vs Wang 2025; Kuo 2024 (null) vs Wang 2025 (null) on contextual other - Severity 1 agreement: Kuo 2024 vs He 2025; Kuo 2024 (null) vs He 2025 (null) on contextual other Additional corpus sources informed the synthesis without anchoring a foregrounded quantitative claim and are catalogued for completeness: Luo 2024. ## References - **Argentieri 2024.** _Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations._ Nature Medicine, 2024. DOI: 10.1038/s41591-024-03164-7. PMID: 39117878. - **Wang 2025.** _Organ-specific proteomic aging clocks predict disease and longevity across diverse populations._ Nature Aging, 2025. DOI: 10.1038/s43587-025-01016-8. PMID: 41299092. - **Gonzales 2020.** _Plasma proteomics reveals markers of metabolic stress in HIV infected children with severe acute malnutrition._ Scientific Reports, 2020. DOI: 10.1038/s41598-020-68143-7. PMID: 32641735. - **He 2025.** _Plasma proteomics identifies S100A8/A9 as a novel biomarker and therapeutic target for fulminant myocarditis._ Journal of Advanced Research, 2025. DOI: 10.1016/j.jare.2025.06.005. PMID: 40480626. - **Lee 2016.** _General intelligence is associated with subclinical inflammation in Nepalese children: A population-based plasma proteomics study._ Brain, Behavior, and Immunity, 2016. DOI: 10.1016/j.bbi.2016.03.023. PMID: 27039242. - **Oh 2025.** _Plasma proteomics links brain and immune system aging with healthspan and longevity._ Nature Medicine, 2025. DOI: 10.1038/s41591-025-03798-1. PMID: 40634782. - **Eeghen 2025.** _Insulin Sensitivity and Associated Plasma Proteomics During Sex Hormone Therapy._ The Journal of Clinical Endocrinology and Metabolism, 2025. DOI: 10.1210/clinem/dgaf573. PMID: 41120110. - **Navarro 2015.** _Randomized Trial of Glucosamine and Chondroitin Supplementation on Inflammation and Oxidative Stress Biomarkers and Plasma Proteomics Profiles in Healthy Humans._ PLoS ONE, 2015. DOI: 10.1371/journal.pone.0117534. PMID: 25719429. - **Ma 2025.** _Plasma proteomics identify novel biomarkers and dynamic patterns of biological aging._ Journal of Advanced Research, 2025. DOI: 10.1016/j.jare.2025.05.004. PMID: 40328427. - **Acharya 2023.** _Quantitative plasma proteomics identifies metallothioneins as a marker of acute-on-chronic liver failure associated acute kidney injury._ Frontiers in Immunology, 2023. DOI: 10.3389/fimmu.2022.1041230. PMID: 36776389. - **Babacic 2020.** _In-depth plasma proteomics reveals increase in circulating PD-1 during anti-PD-1 immunotherapy in patients with metastatic cutaneous melanoma._ Journal for Immunotherapy of Cancer, 2020. DOI: 10.1136/jitc-2019-000204. PMID: 32457125. - **Luo 2024.** _Association of plasma proteomics with mortality in individuals with and without type 2 diabetes: Results from two population-based KORA cohort studies._ BMC Medicine, 2024. DOI: 10.1186/s12916-024-03636-0. PMID: 39334377. - **Mouri 2026.** _Analysis of Peripheral T Cell Profiling and Plasma Proteomics in Advanced NSCLC Patients Treated With Atezolizumab._ Cancer Science, 2026. DOI: 10.1111/cas.70310. PMID: 41627979. - **Zhang 2025.** _Plasma Proteomics Reveals Biomarkers and Undulating Changes in Metabolic Aging._ Research, 2025. DOI: 10.34133/research.1004. PMID: 41356597. - **Kuo 2024.** _Proteomic aging clock ( PAC ) predicts age‐related outcomes in middle‐aged and older adults._ Aging Cell, 2024. DOI: 10.1111/acel.14195. PMID: 38747160.
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"title": "Research Synthesis: Plasma Proteomic Age Clocks \u2014 full paper"
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