Abstract
BackgroundChronological 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.MethodsA 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.ResultsReduced 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.ConclusionsThis study suggests that a broader, multisystem approach to research and prevention of diseases of aging warrants consideration.
Highlights
Chronological age is the strongest risk factor for most chronic diseases that limit healthy lifespan, but individuals may age biologically at different rates [1], characterised by differential rates of disease accumulation and frailty onset
In a review of estimation methods for biological ages [5], studies that compared different estimation methods [6,7,8] favoured the r Klemera Doubal method (KDM), which derives a weighted sum of biomarkers based on the c strengths of their associations with chronological age [9]
A substantial middle-aged and apparently healthy p subpopulation of the UK Biobank can be identified, to assess the prognostic capability of a e biomarker age for subsequent health and to reduce reverse causality from prior health or c medication use affecting biomarker levels. c This study aims to focus on healthy individuals and: 1. estimate sex-specific biomarker ages Ain the UK Biobank using the KDM, 2. identify the main biomarker determinants of the biomarker ages, and 3. investigate the relationship between the biomarker age and chronological age in the prediction of mortality from chronic diseases and age-related hospital admissions
Summary
Chronological age is the strongest risk factor for most chronic diseases that limit healthy lifespan, but individuals may age biologically at different rates [1], characterised by differential rates of disease accumulation and frailty onset. A commonly used approach to identifying risk of accelerated ageing and reduced lifespan is to form a risk score by regressing mortality on risk factors [2, 3]. This tends to identify people who have known health conditions [2]. It would be advantageous to be able to identify accelerated ageing in apparently healthy people for primary prevention t of diseases of ageing [4] Another approach involves estimating a person’s biological age ip from the age that their biomarker profile typically reflects. A substantial middle-aged and apparently healthy p subpopulation of the UK Biobank can be identified, to assess the prognostic capability of a e biomarker age for subsequent health and to reduce reverse causality from prior health or c medication use affecting biomarker levels. c This study aims to focus on healthy individuals and: 1. estimate sex-specific biomarker ages Ain the UK Biobank using the KDM, 2. identify the main biomarker determinants of the biomarker ages, and 3. investigate the relationship between the biomarker age and chronological age in the prediction of mortality from chronic diseases and age-related hospital admissions
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