| Title: | ProtPhenoAge: Integrating plasma proteomics to predict Aging-Related disease Risks |
| Journal: | Journal of Advanced Research |
| Published: | 11 May 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42119967/ |
| DOI: | https://doi.org/10.1016/j.jare.2026.05.022 |
Publication 20106
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Abstract
INTRODUCTION: Plasma proteins reflect the combined influence of both internal and external factors, making proteomics-based aging clocks a promising approach for quantifying the aging process.</p>
OBJECTIVE: This study aims to develop and validate a novel proteomics-based aging clock by integrating plasma proteomics with composite biomarkers.</p>
METHODS: We used a prospective cohort of 37,433 participants (median follow-up: 164.73 months) from the UK Biobank (UKB) with Olink Explore data. We calculated biological age (PhenoAge) and used the Boruta-SHAP (SHapley Additive exPlanations) algorithm to select PhenoAge-related proteins. Based on these proteins, six machine learning models were trained to develop a proteomics-based PhenoAge (ProtPhenoAge). We selected the best model as ProtPhenoAge based on the predictive capabilities of each model for PhenoAge and all-cause mortality. Phenome-wide association study (PheWAS) and Mendelian randomization (MR) explored associations between ProtPhenoAge Acceleration (ProtPhenoAgeAccel) and phenotypes. Genome-wide association study (GWAS) and colocalization analysis identified aging-associated loci.</p>
RESULTS: A total of 185 PhenoAge-related plasma proteins were used to develop ProtPhenoAge. The ProtPhenoAge model, using extreme gradient boosting (XGBoost), showed strong correlation with PhenoAge (r = 0.96, R2 = 0.92) and performed well in predicting all-cause mortality [area under the curve (AUC) = 0.76], outperforming previous aging clocks: chronological age (CA), PhenoAge and ProtAge. ProtPhenoAgeAccel was significantly associated with 313 disease phenotypes, covering a broad range of aging-related phenotypes. Compared with previous clocks, it identified more age-independent but aging-related phenotypes. In the GWAS, we identified 10 aging-associated loci. Among them, rs1045929 (P = 2.61 × 10-9) and rs429358 (P = 7.96 × 10-12) are respectively related to epigenetic aging and the well-recognized aging gene APOE.</p>
CONCLUSION: Based on genomic and phenomic evidences, ProtPhenoAge was regarded to better quantifies the aging process by overcoming the limitations of previous clocks, which failed to detect time-independent aging features. These findings suggested that ProtPhenoAge is a reliable tool to assess aging and supporting aging research.</p>
13 Authors
- Yifan Wang
- Yue Sun
- Fan Yang
- Musu Li
- Tianchen Qi
- Zixuan Lu
- Qian Wang
- Qingyin Bu
- Longhu Sun
- Hongmei Wo
- Yang Zhao
- Honggang Yi
- Juncheng Dai
1 Application
| Application ID | Title |
|---|---|
| 79151 | Uncover the associations of lifestyle behaviors, telomere length, metabolic markers and PhenoAge. |
Enabling scientific discoveries that improve human health