Quantification of biological age as a determinant of age-related diseases in the Rotterdam Study: a structural equation modeling approach
Chronological age alone is not a sufficient measure of the true physiological state of the body. The aims of the present study were to: (1) quantify biological age based on a physiological biomarker composite model; (2) and evaluate its association with death and age-related disease onset in the setting of an elderly population. Using structural equation modeling we computed biological age for 1699 individuals recruited from the first and second waves of the Rotterdam study. The algorithm included nine physiological parameters (c-reactive protein, creatinine, albumin, total cholesterol, cytomegalovirus optical density, urea nitrogen, alkaline phosphatase, forced expiratory volume and systolic blood pressure). We assessed the association between biological age, all-cause mortality, all-cause morbidity and specific age-related diseases over a median follow-up of 11 years. Biological age, compared to chronological age or the traditional biomarkers of age-related diseases, showed a stronger association with all-cause mortality (HR 1.15 vs. 1.13 and 1.10), all-cause morbidity (HR 1.06 vs. 1.05 and 1.03), stroke (HR 1.17 vs. 1.08 and 1.04), cancer (HR 1.07 vs. 1.04 and 1.02) and diabetes mellitus (HR 1.12 vs. 1.01 and 0.98). Individuals who were biologically younger exhibited a healthier life-style as reflected in their lower BMI (P < 0.001) and lower incidence of stroke (P < 0.001), cancer (P < 0.01) and diabetes mellitus (P = 0.02). Collectively, our findings suggest that biological age based on the biomarker composite model of nine physiological parameters is a useful construct to assess individuals 65 years and older at increased risk for specific age-related diseases.
|Keywords||Biological age, Morbidity, Mortality, Biologically young, Elderly|
|Persistent URL||dx.doi.org/10.1007/s10654-019-00497-3, hdl.handle.net/1765/118961|
|Journal||European Journal of Epidemiology|
Waziry, R., Gras, L.A, Sedaghat, S, Tiemeier, H.W, Weverling, G.J, Ghanbari, M., … Goudsmit, J. (2019). Quantification of biological age as a determinant of age-related diseases in the Rotterdam Study: a structural equation modeling approach. European Journal of Epidemiology, 34(8), 793–799. doi:10.1007/s10654-019-00497-3