A Biomarker-based Biological Age in UK Biobank: Composition and Prediction of Mortality and Hospital Admissions

Abstract

Background: Chronological age is the strongest risk factor for most chronic diseases. Developing a biomarker-based age and understanding its most important contributing biomarkers may shed light on the effects of age on later-life health and inform opportunities for disease prevention.

Methods: A subpopulation of 141 254 individuals healthy at baseline were studied, from among 480 019 UK Biobank participants aged 40-70 recruited in 2006-2010, and followed up for 6-12 years via linked death and secondary care records. Principal components of 72 biomarkers measured at baseline were characterized and used to construct sex-specific composite biomarker ages using the Klemera Doubal method, which derived a weighted sum of biomarker principal components based on their linear associations with chronological age. Biomarker importance in the biomarker ages was assessed by the proportion of the variation in the biomarker ages that each explained. The proportions of the overall biomarker and chronological age effects on mortality and age-related hospital admissions explained by the biomarker ages were compared using likelihoods in Cox proportional hazard models.

Results: Reduced lung function, kidney function, reaction time, insulin-like growth factor 1, hand grip strength, and higher blood pressure were key contributors to the derived biomarker age in both men and women. The biomarker ages accounted for >65% and >84% of the apparent effect of age on mortality and hospital admissions for the healthy and whole populations, respectively, and significantly improved prediction of mortality (p < .001) and hospital admissions (p < 1 × 10-10) over chronological age alone.

Conclusions: This study suggests that a broader, multisystem approach to research and prevention of diseases of aging warrants consideration.

Keywords: Epidemiology; Outcomes; Preventative health care; Risk factors.

Figure 1.

Importance of the top 15 biomarker principal components in the biomarker ages for healthy men and women. The percentage of R² denotes the percentage of variation in the biomarker age explained by each biomarker.

Figure 2.

Relative contribution of biomarker ages and chronological age in explaining each health outcome, in the (A) main analysis and when (B) using the reduced biomarker panel, for healthy men and women. The reduced biomarker panel consists of: forced expiratory volume in 1 second/height, forced vital capacity/height, reaction time, insulin growth factor-1, cystatin C, hand grip strength/height, systolic and diastolic blood pressure in both sexes; albumin, sex hormone-binding globulin, fat-free mass, standing height and sitting height in men; and low-density lipoprotein cholesterol, alkaline phosphatase, HbA1c, and urea in women. These were the primary biomarkers that loaded most strongly onto the 10 principal component biomarkers that were most important contributors to biomarker ages for each sex, plus diastolic blood pressure, forced vital capacity, and sitting height because they were strongly loaded onto the same components (rotated factor loading >0.5) and could be measured at the same instance as the primary biomarkers.

Figure 3.

Outcome-free survival of healthy men and healthy women for (A) mortality from chronic disease and (B) age-related hospital admissions, according to whether their biomarker age is younger, similar to or older than their chronological age. Note: BA = biomarker age; CA = chronological age.