Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Oct 22;23(1):1158.
doi: 10.1186/s12967-025-07241-4.

Magnetic resonance imaging quantifies causal body compartment effects on metabolic traits via immune phenotypes-a Mendelian randomisation study

Di Zhang #  1 , Liang-Ping Li #  1 , Ying-Hui Zhang #  1 , Pu Wang  1 , Lei Lei  1 , Yun Hu  1 , Chao Zhou  1 , Shu-Shu Yang  1 , Wei-Hui Liu  2 , Xin-Yu Huang  3
Affiliations

Magnetic resonance imaging quantifies causal body compartment effects on metabolic traits via immune phenotypes-a Mendelian randomisation study

Di Zhang et al. J Transl Med. .

Abstract
in English, German

Background: Metabolic diseases, including type 2 diabetes (T2D), hypertension, hyperlipidemia and metabolic dysfunction-associated fatty liver disease (MAFLD), are globally prevalent. However, traditional measures, such as body mass index (BMI), fail to capture body composition heterogeneity to accurately predict metabolic diseases. Magnetic resonance imaging (MRI) provides a precise quantification of body composition; however, the causal links among these compartments, immune phenotypes, and metabolic traits remain unclear. This Mendelian randomisation (MR) study aimed to investigate the compartment-specific effects of MRI-derived body composition measures on metabolic diseases and assess the mediating role of immune phenotypes.

Methods: Using a two-sample MR framework, genetic instruments for 10 MRI-assessed body composition traits were derived from GWAS data. The outcomes included metabolic diseases (T2D, MAFLD, and hypertension) and glycolipid metabolism traits. Mediation analysis integrated 731 immune cell types. Robustness was assessed using the MR-Egger, Weighted Median, and MR-PRESSO methods, addressing pleiotropy and heterogeneity.

Results: Visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) exerted strong causal effects on BMI (odds ratio [OR] = 1.136). The strongest causal effect on MAFLD risk was observed for liver proton density fat fraction (LPDFF) (OR = 1.833). VAT, weight-muscle ratio and ASAT positively influenced levels of low-density lipoprotein cholesterol (LDL-C) and total cholesterol, whereas they negatively influenced triglyceride levels. Immune phenotypes mediated 28 causal pathways: ectopic fat promoted pro-inflammatory immune profiles, transitional B cells and Memory-CD8+ T cells expanded positive correlation with BMI. CD14+ monocytes and CD16+ monocytes positively regulate Cholesterol and LDL-C levels, respectively.

Conclusion: Our study emphasised the correlation between MRI-derived body composition measures and metabolic diseases, particularly revealing VAT, subcutaneous adipose tissue, and liver proton density fat fraction as key causal drivers of metabolic diseases while uncovering the pivotal role of immune phenotypes in mediating these metabolic pathways. These findings support the integration of MRI-based body composition profiling into clinical screening for metabolic diseases of high-risk cohorts, and provide a novel theoretical foundation for immune-targeted therapies to combat metabolic dysregulation.

Keywords: Immune phenotypes; MRI-profiled body composition; Mendelian randomisation; Metabolic diseases.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: Since this study relied on publicly available summary statistics from GWAS, ethics approval was not required. The respective ethics approvals for the GWAS were obtained by the relevant investigators. Consent for publication: Not applicable. Competing interests: The authors declare that they have no other competing interests.

Figures

Fig. 1
Fig. 1
Flow chart of the study. We obtained summary data from published genome-wide association studies with 10 MRI-derived body composition traits as exposures, metabolic traits (Hypertension, Body Mass Index, Lipids traits, Glucose traits, Metabolic dysfunction-associated fatty liver disease) as outcomes, and 731 immune cell types as mediators. Causal relationships between features associated with MRI-derived body composition and metabolic traits and immune cell types were analyzed using a two-sample MR framework
Fig. 2
Fig. 2
Causal effects of Mendel Randomisation (MR) between magnetic resonance imaging (MRI)-derived body composition measures and metabolic traits. (A) Dot chart of the MR between MRI-derived body composition measures and metabolic traits. (B) Forest plots of the MR between MRI-derived body composition measures and metabolic traits. MAFLD, metabolic dysfunction-associated fatty liver disease; BMI, Body Mass Index; ASAT, Abdominal subcutaneous adipose tissue; VAT, Visceral adipose tissue; ATMV, Anterior thigh muscle volume; PTMV, Posterior thigh muscle volume; ATMFI, Anterior thigh muscle fat infiltration (%); PTMFI, Posterior thigh muscle fat infiltration (%); WMR, Weight-muscle-ratio; AFR, Abdominal fat ratio; LPDFF, Liver proton density fat fraction (%); TTMVz, Total thigh muscle volume z-score
Fig. 3
Fig. 3
Reverse MR between metabolic traits and MRI-derived body composition assessments. A. Dot chart of the reverse MR between metabolic traits and MRI-derived body composition measures. B. Forest plots of the reverse MR between metabolic traits and MRI-derived body composition measures
Fig. 4
Fig. 4
Causal effects of the MR between MRI-derived body composition measures and immune phenotypes with metabolic traits. A. Dot chart of the MR between MRI-derived body composition measures and immune phenotypes with metabolic traits. B. Forest plots of the MR between MRI-derived body composition measures and immune phenotypes with metabolic traits
Fig. 5
Fig. 5
Causal effects of the MR between immune phenotypes and metabolic traits with MRI-derived body composition measures. A. Dot chart of the MR between immune phenotypes and metabolic traits with MRI-derived body composition measures. B. Forest plots of the MR between immune phenotypes and metabolic traits with MRI-derived body composition measures
Fig. 6
Fig. 6
Mediation effect of MRI-derived body composition measures on metabolic traits via immune phenotypes
See this image and copyright information in PMC

References

    1. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet (London, England). 2020;396(10258):1204-22. - PMC - PubMed
    1. Neeland IJ, Ross R, Després JP, Matsuzawa Y, Yamashita S, Shai I, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol. 2019;7(9):715–25. - PubMed
    1. Linge J, Borga M, West J, Tuthill T, Miller MR, Dumitriu A, et al. Body composition profiling in the UK biobank imaging Study. Obesity (Silver spring. Md). 2018;26(11):1785–95. - PMC - PubMed
    1. Hotamisligil GS. Inflammation and metabolic disorders. Nature. 2006;444(7121):860–7. - PubMed
    1. Rocha VZ, Libby P. Obesity, inflammation, and atherosclerosis. Nat Reviews Cardiol. 2009;6(6):399–409. - PubMed