Biological ageing : statistical analysis of physical and biochemical biomarkers in UK Biobank

Background: Age is the strongest risk factor for most chronic diseases, yet individuals age biologically at different rates. Summarising patterns of biomarkers that contribute strongly to overall and body system-specific ageing into biological and body system (bodily) ages may aid health risk communication and disease prevention. A systematic review (undertaken within this thesis) found that biological ages indicated or predicted poorer health, but few studies conducted validation or followed good practice for estimating and reporting biological ages. Methods: Among 480,019 UK Biobank participants aged 40-70 followed up for 6-12 years via linked death registry and hospital records, analyses focused on 141,254 (29.4%) participants healthy at baseline. Sex-specific biological ages were estimated from biomarker principal components (characterised from 72 physical and biochemical biomarkers) via two main approaches: (1) the age-based Klemera Doubal method, which emphasised biomarkers strongly related to chronological age, and (2) a novel disease risk-based approach of aggregating 8 body system ages (artery, musculoskeletal, gut, cardiac, metabolic disease, inflammatory, neurological and lung ages, each estimated using Cox lasso models) using a multi-state model. The proportions of the overall biological and chronological age effects on mortality from chronic disease, age-related frailty and the 8 groups of diseases explained by bodily ages were assessed using likelihood-based measures. Results: In healthy participants, reduced lung function, reduced kidney function, slower reaction time, lower insulin-like-growth factor 1 and lower hand grip strength strongly featured in the age-based biological age, while higher general adiposity was a shared risk factor across body system ages and therefore featured strongest in the disease risk-based biological age (together with lower central adiposity in men and lower hand grip strength in women). Although key biomarkers of body system ages were generally different from each other and from biomarkers strongly related to chronological age, biomarker patterns of body system ages apart from neurological age were moderately correlated (p=0.15-0.69). Gut, cardiac and neurological ages for men, and musculoskeletal and neurological ages for women contributed substantially to the prediction of mortality and frailty in the disease risk-based biological age. The age-based biological age overlapped with chronological age to explain two-thirds of the overall biological and chronological age effects on mortality and frailty. Biomarker constituents of the disease risk-based vs age-based biological ages alone explained 11-17% vs 2-8% of the overall effects on mortality and frailty, and the largest improvement was for frailty in healthy women. These proportions were higher when the analysis was repeated in the whole UK Biobank population (25-38% vs 15-35% for disease risk-based vs age-based biological ages). Biomarker constituents of body system ages explained 24-81% of the overall effects for their respective diseases in healthy participants. Conclusions: Bodily ages, particularly the novel disease risk-based ages, improved our understanding of the biomarker-disease relationships represented by chronological age and improved prognosis of later life health outcomes. Biomarkers across a range of body systems described a common ageing effect and substantial proportions of age effects on later life health, supporting a broader, multi-system risk-based approach to research and prevention of diseases of ageing.