Plasma Proteomic Signature of Frailty in 50,506 Adults
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".