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

Aug 5, 2026

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Machine learning is applied to microstructural neuroimaging and fecal metabolomic datasets to clarify drivers separating obese from overweight individuals. Findings show that while gut-derived factors contribute, brain-directed changes primarily distinguish obesity. Key gut metabolites identified by the model appear partly influenced by gut microbiota, including amino-acid derivatives. Beyond the central nervous system, regions within the extended reward network emerge as important differentiators, implicating previously underexplored neural pathways in obesity pathogenesis.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Brain-gut-microbiota interactions and obesity

Study Approach

Machine learning using neuroimaging and fecal metabolomics

Results and Key Findings

Gut vs brain contributions

Gut metabolites linked to microbiota

Extended reward network differentiators

UCLA UCLA Previously Published Works Title Machine learning model to predict obesity using gut metabolite and brain microstructure data Permalink [https://escholarship.org/uc/item/4ts256z5](https://escholarship.org/uc/item/4ts256z5) Journal Scientific Reports, 13(1) ISSN 2045-2322 Authors Osadchiy, Vadim Bal, Roshan Mayer, Emeran A et al. Publication Date 2023 DOI 10.1038/s41598-023-32713-2 Peer reviewed [eScholarship.org](eScholarship.org) Powered by the California Digital Library University of California [www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Machine learning model to predict obesity using gut metabolite and brain microstructure data Vadim Osadchiy1,2,3,4, Roshan Bal1, EmeranA. Mayer1,2,3, Rama Kunapuli1, Tien Dong 1,2 , Priten Vora1,3,5, Danny Petrasek6, Cathy Liu1,3, Jean Stains1,3,5 & Arpana Gupta1,2,3,7* 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. The obesity epidemic has emerged as a major public health crisis nationally and internationally1,2. In addition to costing the healthcare system hundreds of billions of dollars, there are countless associated negative health outcomes including cancers, endocrinological disorders, musculoskeletal disorders, and a well-documented increase in premature mortality from cardiovascular disease3. Additionally, the distinction between overweight and obese is becoming increasingly important as many studies have demonstrated a dose-dependent relationship between excess weight or body mass index (BMI) and health outcomes4,5. The pathophysiology of obesity remains complex, representing a derangement of energy homeostasis and gut endocrine signaling, especially within the context of aberrant insulin sensitivity and regulation, in addition to disruptions in the fine balance of pro-and anti-satiety signals in the gut6,7. In brief, gut hormones such as ghrelin produce hunger and cravings8,9, while hormones such as glucagon like peptide (GLP)-110 and peptide tyrosine tyrosine (PYY)11 trigger satiety. External factors, such as the gut microbiota, can disrupt this carefully orchestrated homeostatic energy balance. For example, spore forming microbes found in the human gut microbiome can influence enteroendocrine cells of the gut to release more or less GLP-1 in response to microbiota-derived secondary bile acids12. Obesity, however, is just as much a disorder of the endocrine system as it is of the brain, especially with respect to the extended reward network, which is responsible for processing rewarding stimuli and food-seeking behaviors. Key regions of the extended reward network that have been implicated include those related to salience, executive control, core reward, sensorimotor, and emotional regulation-related processes13–17. Despite the robust body of neuroscience research on obesity, investigations have almost exclusively focused on understanding how the obese brain differs from the non-obese brain; no studies have investigated central nervous system (CNS) changes that differentiate obese from overweight individuals. In a

FAQs

What datasets does the machine learning model use to study obesity?

The model leverages microstructural neuroimaging data and fecal metabolomic data to compare obese and overweight phenotypes.

Does the model indicate gut factors or brain factors are more important for distinguishing obesity?

Gut-derived factors play a role, but primarily brain-directed changes differentiate obese from overweight individuals.

What kinds of gut metabolites does the model highlight?

Several key gut metabolites emerge, including amino-acid derivatives that are likely partly derived from or influenced by gut microbiota.

Which brain network is implicated by the findings?

Regions outside the central nervous system within the extended reward network appear as important differentiators, suggesting a role for previously unexplored neural pathways.

Aug 5, 2026

Upload by Aurora