biological age brain age machine learning mental health multimorbidity multimorbidity
Biological age and brain age estimated using biological and neuroimaging measures have recently emerged as surrogate aging biomarkers shown to be predictive of diverse health outcomes. As aging underlies the development of many chronic conditions, surrogate aging biomarkers capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Our study investigates whether elevated biological age and brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using Gaussian Processes regression and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of disease accumulation and their relationship with aging.
•Elevated biological age is associated with an increased risk of multimorbidity.•A set of modifiable biological measures is found to influence biological age.•The effect of elevated brain age on risk of multimorbidity is weaker among females.•Both biological age and brain age are related to mental health multimorbidity.•Contributing to the understanding of multimorbidity and its major risk factor aging.