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Record W4411305996 · doi:10.1101/2025.06.13.25329572

Plasma Proteomic Signature of Frailty in 50,506 Adults

2025· preprint· en· W4411305996 on OpenAlexaff
Xueqing Jia, W. K. Gao, Yanjie Zhao, Xingqi Cao, Y X Wu, Lina Ma, Liangkai Chen, Liang Sun, Huan Guo, Juulia Jylhävä, Zixin Hu, Hampus Hagelin, Zuyun Liu

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersScience for Life LaboratoryZhejiang UniversityVetenskapsrådetKarolinska InstitutetNational Natural Science Foundation of China
KeywordsSignature (topology)Computational biologyBiologyMathematics

Abstract

fetched live from OpenAlex

Abstract Proteomics enables systematic elucidation of the biological mechanisms underlying health states including frailty. Here, through a large-scale proteome-wide association study (PWAS) encompassing 2,911 plasma proteins in 50,506 UK Biobank participants, we identified 1,339 proteins significantly associated with frailty, revealing novel functional modules implicated in frailty pathogenesis, particularly the one characterized by the collagen-containing extracellular matrix and vesicle lumen pathways. Replication analyses in an independent external cohort (TwinGene study) confirmed partial but consistent associations at both protein and pathway levels, supporting the reliability of these findings. Mendelian randomization analyses supported causal associations of 50 proteins with frailty. Protein-protein interaction network and expression quantitative trait loci analyses revealed MMP1 and LGALS8 serving as hub proteins. Moreover, we developed a novel proteome-based frailty measure, termed as Proteomic Frailty Score (PFS), which demonstrated robust predictive performance (C-index > 0.7) for 198 (30.2% = 198/655) incident diseases across 13 categories and broad responsiveness to 85 modifiable risk factors. Incorporating PFS into a conventional risk factors model significantly improved the predictive performance for 510 (77.9% = 510/655) incident diseases. Longitudinal analyses with three assessments (n∼1000) revealed an accelerated progression of the PFS with advancing age and increasing baseline frailty severity. To facilitate public use, we further created a publicly accessible online tool for PFS calculation ( https://zipoa.shinyapps.io/frailty/ ). Finally, we observed a biphasic pattern of frailty-associated proteomic dysregulation across lifespan, with peak transitions occurring at approximately ages 50 and 63, implicating distinct biological pathways. Together, we establish PFS as a robust biomarker of biological aging while identifying critical windows and molecular targets for interventions against frailty progression.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.288
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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