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Integrated multi-modal brain signatures predict sex-specific obesity status
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 ima...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116578/ https://www.ncbi.nlm.nih.gov/pubmed/37091587 http://dx.doi.org/10.1093/braincomms/fcad098 |
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author | Bhatt, Ravi R Todorov, Svetoslav Sood, Riya Ravichandran, Soumya Kilpatrick, Lisa A Peng, Newton Liu, Cathy Vora, Priten P Jahanshad, Neda Gupta, Arpana |
author_facet | Bhatt, Ravi R Todorov, Svetoslav Sood, Riya Ravichandran, Soumya Kilpatrick, Lisa A Peng, Newton Liu, Cathy Vora, Priten P Jahanshad, Neda Gupta, Arpana |
author_sort | Bhatt, Ravi R |
collection | PubMed |
description | 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/m(2) and non-obese body mass index had a body mass index between 19 and 20 kg/m(2). 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. |
format | Online Article Text |
id | pubmed-10116578 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-101165782023-04-21 Integrated multi-modal brain signatures predict sex-specific obesity status Bhatt, Ravi R Todorov, Svetoslav Sood, Riya Ravichandran, Soumya Kilpatrick, Lisa A Peng, Newton Liu, Cathy Vora, Priten P Jahanshad, Neda Gupta, Arpana Brain Commun Original Article 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/m(2) and non-obese body mass index had a body mass index between 19 and 20 kg/m(2). 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. Oxford University Press 2023-04-04 /pmc/articles/PMC10116578/ /pubmed/37091587 http://dx.doi.org/10.1093/braincomms/fcad098 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Bhatt, Ravi R Todorov, Svetoslav Sood, Riya Ravichandran, Soumya Kilpatrick, Lisa A Peng, Newton Liu, Cathy Vora, Priten P Jahanshad, Neda Gupta, Arpana Integrated multi-modal brain signatures predict sex-specific obesity status |
title | Integrated multi-modal brain signatures predict sex-specific obesity status |
title_full | Integrated multi-modal brain signatures predict sex-specific obesity status |
title_fullStr | Integrated multi-modal brain signatures predict sex-specific obesity status |
title_full_unstemmed | Integrated multi-modal brain signatures predict sex-specific obesity status |
title_short | Integrated multi-modal brain signatures predict sex-specific obesity status |
title_sort | integrated multi-modal brain signatures predict sex-specific obesity status |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116578/ https://www.ncbi.nlm.nih.gov/pubmed/37091587 http://dx.doi.org/10.1093/braincomms/fcad098 |
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