ArticleBMC medicine2024
Estimation of physiological aging based on routine clinical biomarkers: a prospective cohort study in elderly Chinese and the UK Biobank.
Article in BMC medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Liver-specific phenotypic aging, behavior and genetic risks, and long-term liver-related outcomes.GeroScience · 2026Article
- A novel clinical biomarker-based Physiology Healthy Aging Index and risk of all-cause and cause-specific mortality: A 20-year prospective cohort study.GeroScience · 2026Article
- Multidimensional Physiological and Gut Microbiome Profiling Identifies Subtle Physiological Patterns in an Apparently Healthy Aging Indian Cohort: A Cross-Sectional Observational Study.Microorganisms · 2026Article
- Microbiome and aging: Trajectories of microbiome age across human ecosystems and their systemic effects.iMeta · 2026Review
- A clinically practical aging clock (physical clock) for healthy aging: development, validation, and application for health assessment and intervention.Science China. Life sciences · 2026Article
- Impact of biological age versus chronological age on clinical outcomes in patients with atrial fibrillation: insights from the COOL-AF registry.Journal of thrombosis and thrombolysis · 2026Article
- Biological aging, a mediator between smoking and chronic obstructive pulmonary disease: evidence from a hospital-based cohort study in China.BMC pulmonary medicine · 2026Article
- Cardiac and systemic biological age capture complementary aging signals associated with cardiovascular disease.Frontiers in cardiovascular medicine · 2026Article
- Combined association between phenotypic age acceleration and dietary fiber intake with mortality in middle-aged and elderly cancer survivors.Nutrition journal · 2025Article
- Cellular and molecular mechanisms underlying cardiovascular aging.Cellular & molecular biology letters · 2025Review
- How does biological age acceleration mediate the associations of obesity with cardiovascular disease? Evidence from international multi-cohort studies.Cardiovascular diabetology · 2025Article
- Exploring the nonlinear relationship between serum uric acid to high-density lipoprotein cholesterol ratio and obesity in older adults: a cross-sectional study.Frontiers in public health · 2025Article
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9 authors.
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Abstract
backgroundChronological age (CA) does not reflect individual variation in the aging process. However, existing biological age predictors are mostly based on European populations and overlook the widespread nonlinear effects of clinical biomarkers.
methodsUsing data from the prospective Dongfeng-Tongji (DFTJ) cohort of elderly Chinese, we propose a physiological aging index (PAI) based on 36 routine clinical biomarkers to measure aging progress. We first determined the optimal level of each biomarker by restricted cubic spline Cox models. For biomarkers with a U-shaped relationship with mortality, we derived new variables to model their distinct effects below and above the optimal levels. We defined PAI as a weighted sum of variables predictive of mortality selected by a LASSO Cox model. To measure aging acceleration, we defined ΔPAI as the residual of PAI after regressing on CA. We evaluated the predictive value of ΔPAI on cardiovascular diseases (CVD) in the DFTJ cohort, as well as nine major chronic diseases in the UK Biobank (UKB).
resultsIn the DFTJ training set (n = 12,769, median follow-up: 10.38 years), we identified 25 biomarkers with significant nonlinear associations with mortality, of which 11 showed insignificant linear associations. By incorporating nonlinear effects, we selected CA and 17 clinical biomarkers to calculate PAI. In the DFTJ testing set (n = 15,904, 5.87 years), PAI predict mortality with a concordance index (C-index) of 0.816 (95% confidence interval, [0.796, 0.837]), better than CA (C-index = 0.771 [0.755, 0.788]) and PhenoAge (0.799 [0.784, 0.814]). ΔPAI was predictive of incident CVD and its subtypes, independent of traditional risk factors. In the external validation set of UKB (n = 296,931, 12.80 years), PAI achieved a C-index of 0.749 (0.746, 0.752) to predict mortality, remaining better than CA (0.706 [0.702, 0.709]) and PhenoAge (0.743 [0.739, 0.746]). In both DFTJ and UKB, PAI calibrated better than PhenoAge when comparing the predicted and observed survival probabilities. Furthermore, ΔPAI outperformed any single biomarker to predict incident risks of eight age-related chronic diseases.
conclusionsOur results highlight the potential of PAI and ΔPAI as integrative biomarkers to evaluate aging acceleration and facilitate the development of targeted intervention strategies for healthy aging.
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