Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II

Abstract

From a biological perspective, humans differ in the speed they age, and this may manifest in both mental and physical health disparities. The discrepancy between an individual’s biological and chronological age of the brain (“brain age gap”) can be assessed by applying machine learning techniques to Magnetic Resonance Imaging (MRI) data. Here, we examined the links between brain age gap and a broad range of cognitive, affective, socioeconomic, lifestyle, and physical health variables in up to 335 adults of the Berlin Aging Study II. Brain age gap was assessed using a validated prediction model that we previously trained on MRI scans of 32,634 UK Biobank individuals. Our statistical analyses revealed overall stronger evidence for a link between higher brain age gap and less favorable health characteristics than expected under the null hypothesis of no effect, with 80% of the tested associations showing hypothesis-consistent effect directions and 23% reaching nominal significance. The most compelling support was observed for a cluster covering both cognitive performance variables (episodic memory, working memory, fluid intelligence, digit symbol substitution test) and socioeconomic variables (years of education and household income). Furthermore, we observed higher brain age gap to be associated with heavy episodic drinking, higher blood pressure, and higher blood glucose. In sum, our results point toward multifaceted links between brain age gap and human health. Understanding differences in biological brain aging may therefore have broad implications for future informed interventions to preserve mental and physical health in old age.

Explaining Variation in Individual Aging, Its Sources, and Consequences: A Comprehensive Conceptual Model of Human Aging

“…Thus, although it is possible to describe the aging of cells or organs, this is not what is typically meant when researchers identify a phenomenon as cell aging or organ aging (e.g., brain age). Instead, this research typically identifies changes in cell or organ functioning or in the regenerative capacity of cells [2][3][4] that are unrelated to the "life expectancy" of the cells or organs themselves but instead accelerate aging of the entire organism and thus should be labeled as sources of aging according to our model.…”

Genome-wide analysis of brain age identifies 25 associated loci and unveils relationships with mental and physical health

“…6,7 A positive BAG (interpreted as accelerated aging) has been linked to reduced mental and physical fitness; 5 including weaker grip strength, higher blood pressure, diabetes, adverse drinking and smoking behavior, poorer cognitive abilities, and depressive symptoms. [8][9][10][11][12][13] Enhanced BAG is also evident in neurological and psychiatric disorders such as Alzheimer's disease, schizophrenia, and bipolar disorder. 14,15 While previous genetic studies suggest that BAG exhibits a substantial heritable component, only few studies have identified specific genetic variants that contribute to BAG.…”

Extensive MEG time-series phenotyping unveils neural markers predictive of age

“…In the last decade, openly accessible neuroimaging datasets have enabled predictive modeling using large sample sizes, mostly focusing on MRI-derived features in the context of development and aging (for an overview see 14 ). Abnormal brain aging, quantified by the difference between chronological age and predicted age of an individual, has been observed in several neurological and psychiatric disorders [15][16][17][18][19] , linked to cognition 20 and physical health 21,22 . These so-called brain age gaps likely underlie genetic influence 15,23 and represent specific biophysical processes 24 , making it a suitable paradigm to study the interaction between normal aging and pathology 25 .…”