Inferring a measure of physiological age from multiple ageing related phenotypes

What is ageing? One definition is simultaneous degradation of multiple organ systems. Can an individual be said to be “old” or “young” for their (chronological) age in a scientifically meaningful way? We investigate these questions statistically using ageing related phenotypes measured on the 12,000 female twins in the Twins UK study [4]. We propose a simple linear model of ageing, which allows a latent adjustment ∆ to be made to an individual’s chronological age to give their “physiological age”, shared across the observed phenotypes. We note problems with the analysis resulting from the linearity assumption and show how to alleviate these issues using a non-linear extension. We find more gene expression probes are significantly associated with our measurement of physiological age than to chronological age. The aim of this work is to infer a measure of the “physiological age” of an individual, in this case using the ten ageing related phenotypes shown in Table 1. Phenotype Correlation Lens nuclear dip (cataract) 0.63 FEV (lung function) -0.61 Grip strength -0.38 Telomere length -0.33 Bone mineral density -0.38 Total moles -0.22 DHEAS -0.19 Eye wrinkles 0.16 Neck wrinkles 0.21 Hair loss 0.18 Table 1: Phenotypes used for inferring our measure of physiological age, and Pearson correlation coefficient to chronological age. Most are self explanatory; DHEAS is an important hormone. We are interested in whether this measure correlates more strongly to low level phenotypes, such as gene expression measurements, than chronological age, or the individual phenotypes used. This continues our previous work of finding low dimensional summaries of high dimensional phenotypes, but with the added characteristic of the summary being interpretable as ageing. In fact our model represents “physiological age” as a + ∆, where a is chronological age, and ∆ is the unobserved variable representing how much older this individual appears biologically than the average. This work is being performed in collaboration with the Department of Twin Research and Genetic Epidemiology (DTR) at King’s College London. The DTR manages the largest UK adult twin