Integrated multi-modal brain signatures predict sex-specific obesity status

Author:

Bhatt Ravi R1ORCID,Todorov Svetoslav2,Sood Riya2,Ravichandran Soumya2,Kilpatrick Lisa A2,Peng Newton2,Liu Cathy2,Vora Priten P2,Jahanshad Neda1,Gupta Arpana2

Affiliation:

1. Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California , Marina del Rey, CA, 90089 , USA

2. Goodman-Luskin Microbiome Center, G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases, Ingestive Behavior and Obesity Program, David Geffen School of Medicine at UCLA , Los Angeles, CA, 90095 , USA

Abstract

Abstract Investigating sex as a biological variable is key to determine obesity manifestation and treatment response. Individual neuroimaging modalities have uncovered mechanisms related to obesity and altered ingestive behaviours. However, few, if any, studies have integrated data from multi-modal brain imaging to predict sex-specific brain signatures related to obesity. We used a data-driven approach to investigate how multi-modal MRI and clinical features predict a sex-specific signature of participants with high body mass index (overweight/obese) compared to non-obese body mass index in a sex-specific manner. A total of 78 high body mass index (55 female) and 105 non-obese body mass index (63 female) participants were enrolled in a cross-sectional study. All participants classified as high body mass index had a body mass index greater than 25 kg/m2 and non-obese body mass index had a body mass index between 19 and 20 kg/m2. Multi-modal neuroimaging (morphometry, functional resting-state MRI and diffusion-weighted scan), along with a battery of behavioural and clinical questionnaires were acquired, including measures of mood, early life adversity and altered ingestive behaviours. A Data Integration Analysis for Biomarker discovery using Latent Components was conducted to determine whether clinical features, brain morphometry, functional connectivity and anatomical connectivity could accurately differentiate participants stratified by obesity and sex. The derived models differentiated high body mass index against non-obese body mass index participants, and males with high body mass index against females with high body mass index obtaining balanced accuracies of 77 and 75%, respectively. Sex-specific differences within the cortico-basal-ganglia-thalamic-cortico loop, the choroid plexus-CSF system, salience, sensorimotor and default-mode networks were identified, and were associated with early life adversity, mental health quality and greater somatosensation. Results showed multi-modal brain signatures suggesting sex-specific cortical mechanisms underlying obesity, which fosters clinical implications for tailored obesity interventions based on sex.

Funder

National Institutes of Health

National Science Foundation

Ahmanson-Lovelace Brain Mapping Center

Publisher

Oxford University Press (OUP)

Subject

Neurology,Cellular and Molecular Neuroscience,Biological Psychiatry,Psychiatry and Mental health

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