While longitudinal changes in biomarker levels and their impact on health have been characterized for individual markers, little is known about how overall marker profiles may change during aging and affect mortality risk. We implemented the recently developed measure of physiological dysregulation based on the statistical distance of biomarker profiles in the framework of the stochastic process model of aging, using data on blood pressure, heart rate, cholesterol, glucose, hematocrit, body mass index, and mortality in the Framingham original cohort. This allowed us to evaluate how physiological dysregulation is related to different aging-related characteristics such as decline in stress resistance and adaptive capacity (which typically are not observed in the data and thus can be analyzed only indirectly), and, ultimately, to estimate how such dynamic relationships increase mortality risk with age. We found that physiological dysregulation increases with age; that increased dysregulation is associated with increased mortality, and increasingly so with age; and that, in most but not all cases, there is a decreasing ability to return quickly to baseline physiological state with age. We also revealed substantial sex differences in these processes, with women becoming dysregulated more quickly but with men showing a much greater sensitivity to dysregulation in terms of mortality risk.
Keywords: Mahalanobis distance; longitudinal data; mortality; physiological dysregulation; sex differences; stochastic process model.