Higher Muscle Volume is Inversely Related to Chronological and Brain Age While Increased Visceral to Muscle Fat Ratio is Positively Related to Chronological and Brain Age

Abstract Background Brain age predicted from structural brain images on T1 weighted scans can lend insight to Alzheimer's risk factors such as muscle loss with sarcopenia. We thus investigated the link between body MRI measured muscle mass, muscle to fat ratio and brain age. Method In all, 1,164 healthy participants from four sites (mean chronological age 55.17 ± 12.37 years, 52% women; 48% men; 39% non‐white) were scanned on 1.5T MR machines with a whole‐body protocol. Whole body sequences utilized in the quantitative analyses of muscle mass were coronal T1 were used to segment total muscle volume normalized to participant height, visceral adipose tissue (VAT) and subcutaneous adipose tissue (VAT). In this process, a nnU‐Net model was used for fully supervised segmentation and ITK‐SNAP was used for manual annotation. Brain age was computed from T1 MPRAGE scans using a regression‐based 3D Simple Fully Convolutional Network. The model was trained on in‐house T1‐weighted MRI scans collected from 5,500 healthy individuals, aged 18 to 89 years. Brain age gap (BAG) was calculated by subtracting chronological age from brain age. Bivariate correlations between total normalized muscle volume (TNMV) as well as VAT and SAT normalized to total muscle volume to chronological and brain age were done with partial correlations adjusted for sex with brain age analyses. Result Mean brain age was higher than chronological age (56.04 ± 12.65, mean BAG = 0.69). Higher TNMV was related to both decreased chronological age (rp=‐0.2579, p = 2.524e‐17) and brain age (rp =‐0.2497, p = 2.65e‐16). VAT normalized to total muscle volume was linked to higher chronological (rp=0.3755, p = 2.615e‐36) and brain age (rp=0.3797, p = 3.871e‐37) adjusting for sex. No statistically significant links were noted with TNMV, VAT, SAT or and BAG. SAT was also not correlated in a statistically significant way to chronological or brain age. Conclusion Increasing muscle mass is related to lower chronological and brain age while visceral fat normalized to muscle volume is related to increased chronological and brain age. Lack of correlation to BAG may be due to the relatively low BAG in this sample. This work suggests improving muscle mass and reducing visceral fat may improve brain aging.

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Journal
Alzheimer s & Dementia
Published
2025-12-01
DOI
https://doi.org/10.1002/alz70856_106692
Primary Topic
Nutrition and Health in Aging
Type
article
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article

Higher Muscle Volume is Inversely Related to Chronological and Brain Age While Increased Visceral to Muscle Fat Ratio is Positively Related to Chronological and Brain Age

Rodrigo Solis Pompa, Saurabh Garg, Yosef Gavriel Chodakiewitz, Ahmed Gouda et al.
Alzheimer s & Dementia
Nutrition and Health in Aging
article

Higher Muscle Volume is Inversely Related to Chronological and Brain Age While Increased Visceral to Muscle Fat Ratio is Positively Related to Chronological and Brain Age

Rodrigo Solis Pompa, Saurabh Garg, Yosef Gavriel Chodakiewitz, Ahmed Gouda, Somayeh Meysami, David A. Merrill, Cyrus A. Raji, Thanh D. Nguyen, Nasrin Akbari, Soojin Lee, Amar Patel, Sam Hashemi, Saqib Basar, Daniel J. Durand
article en

Abstract

Abstract Background Brain age predicted from structural brain images on T1 weighted scans can lend insight to Alzheimer's risk factors such as muscle loss with sarcopenia. We thus investigated the link between body MRI measured muscle mass, muscle to fat ratio and brain age. Method In all, 1,164 healthy participants from four sites (mean chronological age 55.17 ± 12.37 years, 52% women; 48% men; 39% non‐white) were scanned on 1.5T MR machines with a whole‐body protocol. Whole body sequences utilized in the quantitative analyses of muscle mass were coronal T1 were used to segment total muscle volume normalized to participant height, visceral adipose tissue (VAT) and subcutaneous adipose tissue (VAT). In this process, a nnU‐Net model was used for fully supervised segmentation and ITK‐SNAP was used for manual annotation. Brain age was computed from T1 MPRAGE scans using a regression‐based 3D Simple Fully Convolutional Network. The model was trained on in‐house T1‐weighted MRI scans collected from 5,500 healthy individuals, aged 18 to 89 years. Brain age gap (BAG) was calculated by subtracting chronological age from brain age. Bivariate correlations between total normalized muscle volume (TNMV) as well as VAT and SAT normalized to total muscle volume to chronological and brain age were done with partial correlations adjusted for sex with brain age analyses. Result Mean brain age was higher than chronological age (56.04 ± 12.65, mean BAG = 0.69). Higher TNMV was related to both decreased chronological age (rp=‐0.2579, p = 2.524e‐17) and brain age (rp =‐0.2497, p = 2.65e‐16). VAT normalized to total muscle volume was linked to higher chronological (rp=0.3755, p = 2.615e‐36) and brain age (rp=0.3797, p = 3.871e‐37) adjusting for sex. No statistically significant links were noted with TNMV, VAT, SAT or and BAG. SAT was also not correlated in a statistically significant way to chronological or brain age. Conclusion Increasing muscle mass is related to lower chronological and brain age while visceral fat normalized to muscle volume is related to increased chronological and brain age. Lack of correlation to BAG may be due to the relatively low BAG in this sample. This work suggests improving muscle mass and reducing visceral fat may improve brain aging.

Alzheimer s & DementiaVol. 21(S2)
Palo Alto University (US), Washington University in St. Louis (US), Mallinckrodt (United States) (US), Saint John's Health Center (US), Pacific Heart Institute (US), British Columbia Academic Health Science Network (CA)
Openalex Percentile: Top 31%
Nutrition and Health in Aging
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