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Updated: Mar 6, 2026
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Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
Published on: June 7, 2024
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Longitudinal evaluation of intra-patient changes in computed tomography-based body composition measures
B Dustin Pooler1, John W Garrett2, Matthew H Lee2
1School of Medicine and Public Health, University of Wisconsin-Madison, Madison, USA. bpooler@uwhealth.org.
Abdominal Radiology (New York)
|March 5, 2026
Summary
Longitudinal body composition changes significantly differ between sexes and age groups. Automated AI tools reveal age and sex-specific variations in CT-measured body composition over time.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Body composition changes with age and sex.
- Accurate assessment of these changes is crucial for health monitoring.
- Automated tools offer potential for efficient analysis of body composition.
Purpose of the Study:
- To analyze longitudinal, intra-patient changes in CT-based body composition.
- To utilize fully automated artificial intelligence (AI) tools for body composition assessment.
- To investigate age and sex as predictors of these changes.
Main Methods:
- Retrospective study of 15,616 adult patients with at least two abdominal CT scans.
- AI tools quantified vertebral trabecular attenuation, skeletal muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT).
- Longitudinal changes were analyzed over a mean interval of 9.1 years, with statistical analysis for age and sex predictors.
Main Results:
- Significant sex-specific differences in body composition change rates were observed for most measures (p < 0.05), excluding muscle attenuation.
- Age predicted body composition measures, though with small effect sizes (R² 0.002-0.040), varying across age groups.
- Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) changes showed distinct patterns influenced by age and sex.
Conclusions:
- Longitudinal body composition changes exhibit significant age- and sex-specific variations.
- Automated AI analysis provides valuable insights into intra-patient body composition dynamics over time.
- Findings highlight the importance of considering demographic factors in interpreting body composition data.
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