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Journal of Peking University (Health Sciences) ›› 2026, Vol. 58 ›› Issue (3): 543-550. doi: 10.19723/j.issn.1671-167X.2026.03.014

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Application of biological age for cardiovascular risk prediction in a community-based Chinese cohort

Mengxi LU1, Binghan WANG1, Jiali KANG1, Qiuping LIU1, Yifan ZHOU1, Yexiang SUN2, Peng SHEN2, Hongbo LIN2, Xun TANG1,3,4,*(), Pei GAO1,3,4,*()   

  1. 1. Department of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing 100191, China
    2. Yinzhou District Center for Disease Control and Prevention, Ningbo 315101, Zhejiang, China
    3. Key Laboratory of Epidemiology of Major Disease (Peking University), Ministry of Education, Beijing 100191, China
    4. Center for Real-world Evidence Evaluation, Peking University Clinical Research Institute, Beijing 100191, China
  • Received:2026-02-24 Online:2026-06-18 Published:2026-04-29
  • Contact: Xun TANG, Pei GAO
  • Supported by:
    the Noncommunicable Chronic Diseases-National Science and Technology Major Project(2024ZD0527406); the National Natural Science Foundation of China(82373662); the Beijing Natural Science Foundation(IS24047)

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Abstract:

Objective: To independently evaluate the discrimination of the light version of biological age (Light BioAge) model for predicting all-cause mortality, to explore the association of the difference on Light BioAge and chronological age (AgeDiff) with the composite outcomes of cardiovascular disease (CVD), and to assess the performance of CVD risk prediction using the Light BioAge instead of chronological age in a large Chinese population-based cohort. Methods: Participants aged 40-79 years without a history of CVD at baseline were drawn from the CHinese Electronic health Records Research in Yinzhou (CHERRY) study. Harrell' s concordance index (C-index) was employed to assess the discrimination of Light BioAge in predicting all-cause mortality across the overall population and sex-specific subgroups. Cox proportional hazards models were used to assess the association between AgeDiff and the composite outcome of CVD onset and death, adjusting for chronological age, sex, education, region, smoking status, body mass index, systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated. Restricted cubic spline (RCS) regression was used to further analyze the potential nonlinear association between AgeDiff and CVD outcomes. Light BioAge was introduced to replace chronological age in the World Health Organization (WHO) non-laboratory CVD risk model to evaluate the discrimination and calibration in predicting 10-year CVD risk. Results: A total of 226 406 adults were included, with a mean age of 55.0 years at baseline, 53.2% of whom were women. During a median follow-up of 7.39 years (cumulative 1 562 141 person-years), 11 703 deaths (7.49 per 1 000 person-years) and 9 815 CVD events (6.30 per 1 000 person-years) occurred. The median Light BioAge and AgeDiff were 49.31 and -5.19 years, respectively, suggesting an underestimation of chronological age. Although the Light BioAge model demonstrated good discrimination for predicting all-cause mortality in the overall population (C-index: 0.742, 95% CI: 0.738-0.746), discrimination was lower in men (0.722, 95%CI: 0.714-0.730) than in women (0.755, 95%CI: 0.749-0.761). After adjusting for confounders, the risk of CVD events showed an elevated trend with increasing AgeDiff (Ptrend < 0.001). Compared with the lowest quartile of AgeDiff, the risk of CVD events in the highest quartile increased by 21% in men (HR=1.21, 95%CI: 1.12-1.30) and 27% in women (HR=1.27, 95%CI: 1.17-1.38). RCS regression further indicated that in the overall population, the risk of CVD events increased with AgeDiff. Besides, no significant thre-shold effect was observed in sex-specific subgroups (P for non-linearity >0.05). Replacing chronological age with the Light BioAge in the WHO model did not improve discrimination; however, it significantly enhanced calibration. Calibration improvement was especially evident in women: while chronological age overestimated risk by 20.5% [expected/observed ratio (EOR)=1.205, 95%CI: 1.167-1.246), the Light BioAge reduced this to a marginal 2.1% underestimation (EOR=0.979, 95%CI: 0.948-1.012). Conclusion: The discrimination of the Light BioAge in predicting all-cause mortality seems good, and a wider AgeDiff indicates higher cardiovascular risk in this large population-based Chinese cohort. Replacing chronological age with biological age in the WHO non-laboratory model significantly improved calibration for women.

Key words: Biological age, Cardiovascular disease, Cohort study, Risk assessment, Sex disparity

CLC Number: 

  • R181.2

Table 1

Baseline characteristics of study participants"

Characteristics Total (n=226 406) Men (n=105 848) Women (n=120 558) P value
Age/years, $\bar x \pm s$ 55.0±9.3 55.5±9.9 54.5±9.9 < 0.001
Region (urban), n(%) 154 444 (68.2) 73 345 (69.3) 81 099 (67.3) < 0.001
Education (senior high school or higher), n(%) 34 238 (15.1) 19 442 (18.4) 14 796 (12.3) < 0.001
Smoker, n(%) 41 861 (18.5) 40 238 (38.0) 1 623 (1.3) < 0.001
Hypertension, n(%) 74 319 (32.8) 35 014 (33.1) 39 305 (32.6) 0.016
Diabetes, n(%) 19 701 (8.7) 9 226 (8.7) 10 475 (8.7) 0.823
SBP/mmHg, $\bar x \pm s$ 131.2±16.3 131.9±15.9 130.6±16.7 < 0.001
DBP/mmHg, $\bar x \pm s$ 81.6±9.6 82.5±9.5 80.8±9.6 < 0.001
BMI/(kg/m2), $\bar x \pm s$ 23.3±2.8 23.3±2.7 23.2±2.9 < 0.001
TG/(mg/dL), M (IQR) 145.5
(111.5, 179.1)		 141.6
(106.4, 172.8)		 151.2
(116.4, 185.7)		 < 0.001
TC/(mg/dL), $\bar x \pm s$ 190.2±37.8 185.6±37.3 194.3±37.7 0.030
HDL-C/(mg/dL), $\bar x \pm s$ 50.3±12.9 48.5±13.1 52.0±12.4 < 0.001
LDL-C/(mg/dL), $\bar x \pm s$ 109.7±31.0 111.5±31.3 107.2±30.6 < 0.001
GLU/(mmol/L), M (IQR) 5.3 (4.9, 5.9) 5.4 (5.0, 6.0) 5.3 (4.9, 5.8) < 0.001
Creatinine/(mg/dL), $\bar x \pm s$ 0.7±0.2 0.8±0.2 0.7±0.2 < 0.001
hs-CRP/(mg/dL), M (IQR) 0.6 (0.3, 1.7) 0.7 (0.3, 1.8) 0.6 (0.2, 1.7) < 0.001
Light BioAge, M (IQR) 49.3
(40.3, 58.9)		 50.9
(41.7, 60.6)		 47.9
(39.0, 57.4)		 < 0.001
AgeDiff, $\bar x \pm s$ -5.1±8.3 -4.1±8.4 -6.0±8.3 < 0.001

Figure 1

Distribution of cardiovascular composite outcome and Light BioAge of study participants A, Kaplan-Meier curve of cardiovascular composite outcome by sex; B, correlations of Light BioAge with chronological age; C, the distribution of age difference on Light BioAge and chronological age(AgeDiff). MAE, mean absolute error; SD, standard deviation."

Figure 2

Association of age difference on Light BioAge and chronological age with cardiovascular composite outcome, by sex *Adjusted for chronological age, education, region, smoking status, body mass index, systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. HR, hazard ratio; CI, confidence interval."

Figure 3

Nonlinear association of age difference on Light BioAge and chronological age with cardiovascular composite outcome, by sex P for all tested whether using splines improves the model compared to a simple linear term; P for nonlinear tested whether the nonlinear part of the spline model is statistically significant. Graphs show multivariate adjusted HR (solid lines) and 95%CI (shaded areas). Dashed lines represent the proportion of the population with different levels of age difference (AgeDiff) on Light BioAge and chronological age. Arrows indicate the age difference at the point where risk crosses the line (HR=1). Analyses used variables in the Model 2 (adjusted for chronological age, education, region, smoking status, body mass index, systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol). HR, hazard ratio; CI, confidence interval."

Figure 4

Calibration plots of the WHO cardiovascular risk models using chronological age or Light BioAge A, WHO model by chronological age (women); B, WHO model by Light BioAge (women); C, WHO model by chronological age (men); D, WHO model by Light BioAge (men). WHO, World Health Organization; CVD, cardiovascular disease."

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