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

Affiliation
Department of Psychology, Humboldt-Universität zu Berlin ,Berlin ,Germany
Jawinski, Philippe;
Affiliation
Department of Psychology, Humboldt-Universität zu Berlin ,Berlin ,Germany
Markett, Sebastian;
Affiliation
Department of Psychology, Humboldt-Universität zu Berlin ,Berlin ,Germany
Drewelies, Johanna;
Affiliation
Center for Lifespan Psychology, Max Planck Institute for Human Development ,Berlin ,Germany
Düzel, Sandra;
Affiliation
Division of Lipid Metabolism, Department of Endocrinology and Metabolic Diseases, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité – Universitätsmedizin Berlin ,Berlin ,Germany
Demuth, Ilja;
Affiliation
Division of Lipid Metabolism, Department of Endocrinology and Metabolic Diseases, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité – Universitätsmedizin Berlin ,Berlin ,Germany
Steinhagen-Thiessen, Elisabeth;
Affiliation
Center for Lifespan Psychology, Max Planck Institute for Human Development ,Berlin ,Germany
Wagner, Gert G.;
Affiliation
Center for Lifespan Psychology, Max Planck Institute for Human Development ,Berlin ,Germany
Gerstorf, Denis;
Affiliation
Center for Lifespan Psychology, Max Planck Institute for Human Development ,Berlin ,Germany
Lindenberger, Ulman;
GND
123460980
Affiliation
Structural Brain Mapping Group, Department of Psychiatry and Neurology, Jena University Hospital ,Jena ,Germany
Gaser, Christian;
Affiliation
Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development ,Berlin ,Germany
Kühn, Simone

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.

Cite

Citation style:
Could not load citation form.

Rights

License Holder: Copyright © 2022 Jawinski, Markett, Drewelies, Düzel, Demuth, Steinhagen-Thiessen, Wagner, Gerstorf, Lindenberger, Gaser and Kühn.

Use and reproduction: