A novel method for biological age assessment utilizing hospitalization risk of cardiovascular disease
4 affiliations
- 1Department of Occupational and Environmental Medicine, College of Medicine, Ewha Womans University
- 2Department of Statistics and Data Science, Carnegie Mellon University
- 3Institute of Environmental Medicine, Medical Research Center, Seoul National University
- 4Department of Human Systems Medicine, College of Medicine, Seoul National University
Abstract
Background Aging is a complex process involving physiological changes that gradually impair function in organisms. Chronological age (CA) does not fully account for variability in the aging process, whereas biological age (BA) better reflects disease risk. This study introduces Cardiovascular Disease Probability-based Biological Age (CDPBA), a novel BA algorithm. Methods We analyzed clinical data from 11.7 million individuals aged ≥ 40 years in the National Health Insurance Service database. Using a Cox proportional hazards model, clinical biomarkers, lifestyle factors, fine particulate matter exposure, and family history were used to estimate the 10-year risk of CVD hospitalization. Individual CDPBA was computed by comparing each subject’s CVD risk with the median risk profile of the corresponding age group, thereby capturing the nonlinear relationship between CDPBA and biomarkers while maintaining interpretability. Results Our CDPBA algorithm exhibited a strong correlation with CA (r > 0.92), and the estimated CDPBA distribution was symmetric with respect to CA. All-cause mortality prediction was robust (area under the receiver operating characteristic curve [AUROC]: 0.849 and 0.871 in men and women, respectively), outperforming the regression-based BA approach (AUROC: 0.777 and 0.683 for men and women, respectively). The slow-aging group mostly maintained their health, whereas the accelerated-aging group showed increasingly worse health over time. Using CVD median risk profiles, the CDPBA algorithm shows a high correlation with CA and demonstrates superior performance in predicting all-cause mortality compared to conventional regression methods. Conclusion The CDPBA provides a novel, interpretable approach to biological age estimation based on cardiovascular disease hospitalization risk.
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BibTeX
@article{oh2026novel,
title = {{A novel method for biological age assessment utilizing hospitalization risk of cardiovascular disease}},
author = {Jongmin Oh and Jinwoo Cho and Yun-Chul Hong and Hyung-Jin Yoon and Eunhee Ha},
journal = {BMC Cardiovascular Disorders},
year = {2026},
month = jul,
doi = {10.1186/s12872-026-06325-3},
url = {https://doi.org/10.1186/s12872-026-06325-3},
note = {CC BY},
}
RIS
TY - JOUR
TI - A novel method for biological age assessment utilizing hospitalization risk of cardiovascular disease
AU - Jongmin Oh
AU - Jinwoo Cho
AU - Yun-Chul Hong
AU - Hyung-Jin Yoon
AU - Eunhee Ha
PY - 2026
DA - 2026/07/01
JO - BMC Cardiovascular Disorders
DO - 10.1186/s12872-026-06325-3
UR - https://doi.org/10.1186/s12872-026-06325-3
AB - Background Aging is a complex process involving physiological changes that gradually impair function in organisms. Chronological age (CA) does not fully account for variability in the aging process, whereas biological age (BA) better reflects disease risk. This study introduces Cardiovascular Disease Probability-based Biological Age (CDPBA), a novel BA algorithm. Methods We analyzed clinical data from 11.7 million individuals aged ≥ 40 years in the National Health Insurance Service database. Using a Cox proportional hazards model, clinical biomarkers, lifestyle factors, fine particulate matter exposure, and family history were used to estimate the 10-year risk of CVD hospitalization. Individual CDPBA was computed by comparing each subject’s CVD risk with the median risk profile of the corresponding age group, thereby capturing the nonlinear relationship between CDPBA and biomarkers while maintaining interpretability. Results Our CDPBA algorithm exhibited a strong correlation with CA (r > 0.92), and the estimated CDPBA distribution was symmetric with respect to CA. All-cause mortality prediction was robust (area under the receiver operating characteristic curve [AUROC]: 0.849 and 0.871 in men and women, respectively), outperforming the regression-based BA approach (AUROC: 0.777 and 0.683 for men and women, respectively). The slow-aging group mostly maintained their health, whereas the accelerated-aging group showed increasingly worse health over time. Using CVD median risk profiles, the CDPBA algorithm shows a high correlation with CA and demonstrates superior performance in predicting all-cause mortality compared to conventional regression methods. Conclusion The CDPBA provides a novel, interpretable approach to biological age estimation based on cardiovascular disease hospitalization risk.
SN - 1471-2261
ER -