Abstract
Biological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to compare fifteen omics ageing clocks, with correlations of 0.21-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 95% of the variance in chronAge being shared among clocks. The difference between BA and chronAge - omics clock age acceleration (OCAA) - often associates with health measures. One year’s OCAA typically has the same effect on risk factors/10-year disease incidence as 0.09/0.25 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge.
Highlights
Age is a phenotype that we are all familiar with, and is a major risk factor for numerous diseases including the largest causes of mortality [1]
Despite finding only modest positive correlations between our omics clock age acceleration (OCAA), we showed that different clocks overlap in the variation they explain in chronAge more than would be expected by chance if they were independently sampling from a latent set of complete predictors
We found associations of OCAAs with total cholesterol, C-reactive protein, BMI, creatinine, cortisol, forced expiratory volume in 1 second (FEV1) and systolic blood pressure
Summary
Age is a phenotype that we are all familiar with, and is a major risk factor for numerous diseases including the largest causes of mortality [1]. Measuring BA has the potential to be more prognostic of health and functional capacity than chronAge and, as importantly, BA may be reversible [3], unlike chronAge [4]. Since this concept was proposed, there has been a push to construct models of BA, using a variety of both statistical methods and types of biomarkers; the resultant estimates we shall term omics clock ages (OCAs). The excess of OCA over chronAge being omics clock age acceleration (OCAA), hopefully measuring an underlying biological effect. There has been limited comparison of the performance, for example accuracy and correlation, of different omics ageing clocks, in the same set of individuals
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