Summary There is a recognized link between risk factors for non-communicable diseases and brain health. However, the specific effects that they have on brain health are still poorly understood, preventing its implementation in clinical practice. For instance, the association between such risk factors and cortical thickness (CT) has been primarily explored using univariate/bivariate methods and global/lobar measures of CT and has yielded inconsistent results. In this work, we aim to study the relationship between risk factors for non-communicable diseases and CT. In addition, we adopt a systems-level perspective to understand such relationship, by integrating several brain features including brain structure and function as well as neurotransmitter systems. Here, we analyzed latent dimensions linking a broad set of risk factors for non-communicable diseases to parcel-wise CT across the whole cortex (including raw, proportional, and brain size- corrected measures). We used a multivariate approach (regularized canonical correlation analysis (RCCA)) embedded in a machine learning framework that allows to capture inter- individual variability and to assess the generalizability of the model. The brain patterns (captured in association with risk factors) were characterized from a multi-level perspective, by comparing them with patterns of brain structure, function, and neurotransmitter systems. Analyses were performed separately in women (n=3685, 46-81 years) and in age-matched men (n=3685, 46-81 years) to avoid sex-bias on the results. We found one significant latent dimension (women: r range =0.25-0.30, p=0.005-0.005; men: r range =0.31-0.34, p=0.005-0.005), capturing variability in cardiometabolic health, including physical activity, body morphology/composition, basal metabolic rate, and blood pressure. This cardiometabolic health dimension was linked to a CT axis of inter-individual variability from the insula and cingulate cortex to occipital and parietal areas. Interestingly, this brain pattern was associated with the binding potentials of several neurotransmitter systems, including serotoninergic, dopaminergic, cholinergic, and GABAergic systems. Of note, this latent dimension was similar across sexes and across CT measures (raw, proportional, and brain-size corrected). We observed a robust, multi-level and multivariate link between cardiometabolic health, CT, and neurotransmitter systems. These findings support the urgency of further investigation into the interaction between brain health and physical health and contributes to the challenge to the classical conceptualization of neuropsychiatric and physical illnesses as categorical entities. Therefore, regular monitoring of cardiometabolic risk factors may reduce their adverse effects on brain health and prevent the development of brain diseases.

Cardiometabolic health, cortical thickness, and neurotransmitter systems: a large-scale multivariate study