Association of Body Fat Distribution Patterns at MRI with Brain Structure, Cognition, and Neurologic Diseases

 Have your competent? doctor identify where your body fat exists AND THE EXACT PROTOCOLS THAT WILL FIX THAT!

Association of Body Fat Distribution Patterns at MRI with Brain Structure, Cognition, and Neurologic Diseases

Authors:

• Authors: Miao Yu, MD https://orcid.org/0009-0001-2012-5586, Libin Yao, MD https://orcid.org/0000-0001-8274-2857, Authors Info & AffiliationsInformation & Authors
• Sanjeev Shahi, MM https://orcid.org/0009-0009-5186-315X, Yingqianxi Xu, MM https://orcid.org/0009-0002-4264-0666, 
• Meizi Li, MM https://orcid.org/0009-0008-0763-6833, Qingtong Zheng, MM https://orcid.org/0009-0009-3303-7436, 
• Di Ma, MM https://orcid.org/0009-0002-5329-3997, … Show All … , and 
• Kai Liu, MD, PhD https://orcid.org/0000-0003-3003-4685 liu-k@hotmail.com
• Metrics & Citations
• View Options
• References
• Figures
• Tables
• Share

 Background

 Although substantial evidence has demonstrated the impact of obesity on brain structure and cognition, the heterogeneity of adiposity—particularly in terms of fat distribution patterns—and its differential neurologic effects remain poorly understood.

Purpose

To identify body fat distribution patterns with MRI and latent profile analysis (LPA) and their associations with brain structure measurements, cognition, and neurologic diseases.

Materials and Methods

This secondary analysis used prospective data from the UK Biobank, including health records and MRI scans of the brain, heart, and abdomen. Fat distribution profiles were classified using LPA based on eight body mass index (BMI)–adjusted MRI-derived fat quantification metrics. Differences in brain volume, white matter properties, cognition, and the risk of neurologic disorders were analyzed across profiles and relative to a benchmark lean profile; analyses were stratified by sex. Group differences were examined using analysis of covariance (ANCOVA) or rank-based ANCOVA.

Results

Among 25 997 participants (mean age, 55 years ± 7.4 [SD]; 13 536 female participants), LPA identified six profiles of body fat distribution in both sexes. Four high-adiposity patterns were identified, including the pancreatic-predominant profile (profile 1), with elevated proton density fat fraction (mean BMI-adjusted z score, 2.38 ± 0.74 for male participants and 3.01 ± 1.08 for female participants; P < .001 for a difference across profiles for both sexes), and the skinny-fat profile (profile 3), with the highest adiposity burden in the majority of depots despite moderate BMI (six of eight depots for male participants and five of eight depots for female participants; P < .001 for a difference across profiles for each depot for both sexes). Compared with the lean profile, profiles 1 and 3 were associated with extensive gray matter atrophy (profile 1: Cohen d, −0.63 for male participants and −0.58 for female participants; profile 3: Cohen d, −0.56 for male participants and −0.12 for female participants; P < .001 for a difference across profiles for both sexes), elevated white matter hyperintensity load (profile 1: Cohen d, 0.47 for male participants and 0.42 for female participants; profile 3: Cohen d, 0.42 for male participants and 0.20 for female participants; P < .001 for a difference across profiles for both sexes), accelerated brain aging (Cohen d, 0.25 for male participants with profile 1 and 0.32 for male participants with profile 3; P < .001 for a difference across profiles), cognitive decline, and increased risk of neurologic disease.

Conclusion

LPA revealed distinct patterns of body fat distribution, where pancreatic-predominant and skinny-fat patterns, in particular, were associated with adverse neurologic outcomes.

© The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.

Supplemental material is available for this article.

Visual Abstract

• Download as PowerPoint