A Blood-Based Biological Age Model Derived from Routine Laboratory Biomarkers in the Singapore Longitudinal Ageing Study
Ye, J.; Lim, X.; Franck, M.; Li, W.; Goh, D.; Chen, H.; Chung, Z.; Yang, W.; Zhu, Z.; Sitjar, P. H.; Wang, T. O.; Wee, F.; Fincham, R. E. A.; Tan, D. S. W.; Chua, K.; Hong-Wen, T.; Goh, J.; Lim, D. W.-T.; Lim, E.; Cohen, A. A.; Ho, R. C. M.; Tan, M.-H.; Jia, G.; Fülöp, T.; Yeong, J.
Show abstract
Risk assessment in clinical practice depends largely on clinical phenotypes, including age, sex, body mass index, blood pressure and comorbidities. Routine laboratory data remain underutilised despite their accessibility and low cost. Using data from the Singapore Longitudinal Ageing Studies (n = 5,409; follow-up median 11.4 years), we developed a mortality prediction model based on routine laboratory biomarkers. We derived a biological age (age quotient, or AQ) score, and investigated its role as a mediator between lifestyle risk factors and mortality. Both models and association analyses were validated in the US National Health and Nutrition Examination Survey (n = 6,593) and UK Biobank (n = 290,949) cohorts. AQ was significantly elevated in deceased individuals (P<0.0001). AQ acceleration was also observed (P<0.0001). In overall survival discrimination, AQ outperformed chronological age (C-index 0.629 [SE 0.011] vs 0.606 [SE 0.011]), indicating superior prognostic prediction. Additionally, incorporation of AQ into a baseline model containing chronological age resulted in an improvement in model fit (likelihood ratio test, P<0.0001), consistent with incremental predictive value for mortality beyond chronological age alone. Mediation analysis supports a partial mediating role for AQ in the relationship between lifestyle factors and mortality. In a 57-patient subset, higher AQ was associated with increased TET2 clonal hematopoiesis burden ({beta}{approx}0.016 per +1 AQ year), suggesting a potential link between AQ acceleration, CH risk and diseases of aging, requiring validation in larger cohorts. We identified differential associations between lifestyle factors and groups of biological age components, indicating selective effects across biological systems. These findings provide an evidence-based framework for earlier and more accurate identification of high-risk individuals, offering a practical and easy-to-implement tool to inform preventive strategies.
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