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Association Between Body Composition and Cardiometabolic Outcomes : A Prospective Cohort Study.

Matthias Jung1, Marco Reisert2, Hanna Rieder3

  • 1Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, and Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts (M.J.).

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This summary is machine-generated.

Artificial intelligence (AI) models can estimate body composition (BC) from MRI scans. Elevated visceral adipose tissue (VAT) and high SM fat fraction (SMFF) predict cardiometabolic risk in both sexes, while low skeletal muscle (SM) proportion is a risk factor in males.

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Cardiovascular and metabolic disease research

Background:

  • Traditional adiposity measures have limitations in predicting health outcomes.
  • Artificial intelligence (AI) offers a promising approach for detailed body composition (BC) estimation from routine imaging.
  • Magnetic resonance imaging (MRI) combined with AI can provide accurate and efficient BC assessments.

Purpose of the Study:

  • To evaluate the association between AI-derived body composition (BC) compartments and cardiometabolic outcomes.
  • To investigate the prognostic value of specific BC measures beyond traditional metrics like BMI and waist circumference.
  • To explore sex-specific differences in the relationship between BC and cardiometabolic risk.

Main Methods:

  • A prospective cohort study utilizing UK Biobank (UKB) data from 33,432 participants.
  • AI applied to whole-body MRI scans to quantify 3D body composition (BC) measures: subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), skeletal muscle (SM), and SM fat fraction (SMFF).
  • Sex-stratified analyses using restricted cubic splines to assess associations of BC compartments with incident diabetes mellitus (DM) and major adverse cardiovascular events (MACE), adjusting for various confounders.

Main Results:

  • Greater adiposity and lower skeletal muscle (SM) proportion were initially associated with increased risk of diabetes mellitus (DM) and major adverse cardiovascular events (MACE).
  • After adjusting for body mass index (BMI) and waist circumference (WC), only elevated visceral adipose tissue (VAT) proportion and high SM fat fraction (SMFF) remained significant predictors of DM and MACE in both sexes.
  • In males, a low SM proportion was also associated with increased risk for DM and MACE, independent of BMI and WC.

Conclusions:

  • AI-derived body composition (BC) proportions are strongly linked to cardiometabolic risk.
  • Visceral adipose tissue (VAT) proportion and SM fat fraction (SMFF) provide prognostic information for cardiometabolic outcomes in both sexes, even after accounting for BMI and WC.
  • Skeletal muscle (SM) proportion offers additional prognostic value for males regarding cardiometabolic risk.