CaltechAUTHORS
Published April 4, 2023 | Version Supplemental Material + Published
Journal Article Open

Machine learning model to predict obesity using gut metabolite and brain microstructure data

Creators

  • 1. ROR icon University of California, Los Angeles
  • 2. ROR icon VA Greater Los Angeles Healthcare System
  • 3. ROR icon California Institute of Technology

Abstract

A growing body of preclinical and clinical literature suggests that brain-gut-microbiota interactions may contribute to obesity pathogenesis. In this study, we use a machine learning approach to leverage the enormous amount of microstructural neuroimaging and fecal metabolomic data to better understand key drivers of the obese compared to overweight phenotype. Our findings reveal that although gut-derived factors play a role in this distinction, it is primarily brain-directed changes that differentiate obese from overweight individuals. Of the key gut metabolites that emerged from our model, many are likely at least in part derived or influenced by the gut-microbiota, including some amino-acid derivatives. Remarkably, key regions outside of the central nervous system extended reward network emerged as important differentiators, suggesting a role for previously unexplored neural pathways in the pathogenesis of obesity.

Additional Information

© Te Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. This research was supported by grants from the National Institutes of Health including K23 DK106528 (AG), ULTR001881/DK041301 (UCLA CURE/CTSI Pilot and Feasibility Study; AG), R01 DK048351 (EAM), and pilot funds were provided for brain scanning by the Ahmanson-Lovelace Brain Mapping Center. Contributions. A.G. contributed to funding, study concept and design, analysis and interpretation of data, critical revision of the manuscript for important intellectual content, study supervision. R.B. contributed to analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript for important intellectual content. E.A.M. contributed to funding, study concept and design, and critical revision of the manuscript for important intellectual content. R.K. contributed to study concept and design, analysis and interpretation of data, drafting of the manuscript, and critical revision of the manuscript for important intellectual content. T.D. contributed to critical revision of the manuscript for important intellectual content. P.V. and J.S. contributed to technical support and analysis and interpretation of data. D.P. contributed to study concept and design. C.L. contributed to technical support, analysis and interpretation of data, and figure creation. V.O. contributed to study concept and design, drafting of the manuscript, critical revision of the manuscript for important intellectual content, and study supervision. All authors read and approved the final manuscript. Data availability. The datasets generated and/or analyzed during the current study are not publicly available due to the fact that the data collected are a part of an ongoing study but are available from the corresponding author on reasonable request. The authors declare no competing interests.

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Additional details

Identifiers

PMCID
PMC10073225
Eprint ID
121626
Resolver ID
CaltechAUTHORS:20230530-441768000.61

Funding

NIH
K23 DK106528
NIH
R01 DK048351
NIH
ULTR001881/DK041301

Dates

Created
2023-06-12
Created from EPrint's datestamp field
Updated
2023-06-12
Created from EPrint's last_modified field