Brain signatures of body mass index predict cardiometabolic and respiratory disease status

Despite advances in brain biomarkers using neural networks, the effects of body mass on brain structure have been neglected, particularly in connection with noncommunicable diseases. Here, we isolated brain biomarkers of body mass index (BMI) and evaluated their association with disease states. We applied deep learning on T1-weighted MRI scans to predict BMI from six independent cohorts and achieved strong within-cohort and reduced external performance.

In a longitudinal follow-up subset, the model successfully tracked BMI changes over 2.3 years, with stronger sensitivity to BMI increases, and for obese participants. Next, we used the learned brain biomarkers to infer lifestyle factors and diagnoses related to cardiometabolic and pulmonary conditions. Strikingly, brain-based models showed superior discriminative power compared to BMI itself for detecting disorders without a primary neurological etiology.

Inspection of learned patterns revealed that predictions were driven by white matter signals in the cerebellum, corpus callosum and brainstem, which on their own detected disorders as well as the full model. The existence of dynamic brain BMI signatures, and their detection of systemic disease consistently above BMI, suggest the possibility of shared mechanisms linking metabolic state and brain structure. No funding was received for this research.

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu); the HCP Young Adult and HCP-Aging studies; the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data); PREDICT-HD (NS040068); and the UK Biobank Resource (www.ukbiobank.ac.uk/). The investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. Our research has been conducted in part using the UK Biobank Resource under Application Number 95318.

This work uses data provided by patients and collected by the NHS as part of their care and support. The HCP project (Principal Investigators: Bruce Rosen, M.D., Ph.D., Martinos Center at Massachusetts General Hospital; Arthur W. Toga, Ph.D., University of Southern California; Van J.

Weeden, MD, Martinos Center at Massachusetts General Hospital) is supported by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP is the result of efforts of co-investigators from the University of Southern California, Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH), Washington University, and the University of Minnesota. HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California.