ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2024
Abdominal Body Composition Reference Ranges and Association With Chronic Conditions in an Age- and Sex-Stratified Representative Sample of a Geographically Defined American Population.
Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Defining Reference Values for Skeletal Muscle Metrics on Abdominal CT Using Data From Healthy Young Adult Populations: A Systematic Review and Meta-Analysis.AJR. American journal of roentgenology · 2025Pooled it
- Influence of rapamycin on safety and healthspan metrics after one year: PEARL trial results.Aging · 2025Trial
- Volumetric assessment from L2 to L4 improves the reliability and validity of body composition by CT.European journal of radiology · 2026Article
- AI-enhanced routine CT for opportunistic osteoporosis screening and risk assessment.Skeletal radiology · 2026Review
- Future of CT body composition research: Methodological discrepancies and advances.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2026Review
- Bridging the gap in nutrition assessment: Barriers and knowledge impact of an educational session on body composition and energy metabolism.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2026Article
- Association Between Computed Tomography-Based AI-Derived Body Composition and Survival in Patients With Pancreatic Ductal Adenocarcinoma.The American journal of gastroenterology · 2026Article
- Granular Machine Learning-Based Computed Tomography Contrast Phase Prediction.Mayo Clinic proceedings. Digital health · 2026Article
- Independent validation of the Mosamatic deep learning automated skeletal muscle and adipose tissue segmentation tool in an external Chinese cancer patient cohort.BJR artificial intelligence · 2026Article
- Associations Between Deep Learning-Derived Fat, Muscle, and Bone Measures From Abdominal Computed Tomography Scans and Fall Risk in Persons Aged 20 Years or Older.Mayo Clinic proceedings. Digital health · 2025Article
- Epicardial, visceral and subcutaneous adipose tissue in heart failure with preserved ejection fraction.ESC heart failure · 2025Article
- The relationship between visceral adipose tissue and osteoarthritis among older adults: evidence from the NHANES 1999-2018.Frontiers in nutrition · 2025Article
- Relative muscle indices and healthy reference values for sarcopenia assessment using T10 through L5 computed tomography skeletal muscle area.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundBody composition can be accurately quantified from abdominal computed tomography (CT) exams and is a predictor for the development of aging-related conditions and for mortality. However, reference ranges for CT-derived body composition measures of obesity, sarcopenia, and bone loss have yet to be defined in the general population.
methodsWe identified a population-representative sample of 4 900 persons aged 20 to 89 years who underwent an abdominal CT exam from 2010 to 2020. The sample was constructed using propensity score matching an age and sex stratified sample of persons residing in the 27-county region of Southern Minnesota and Western Wisconsin. The matching included race, ethnicity, education level, region of residence, and the presence of 20 chronic conditions. We used a validated deep learning based algorithm to calculate subcutaneous adipose tissue area, visceral adipose tissue area, skeletal muscle area, skeletal muscle density, vertebral bone area, and vertebral bone density from a CT abdominal section.
resultsWe report CT-based body composition reference ranges on 4 649 persons representative of our geographic region. Older age was associated with a decrease in skeletal muscle area and density, and an increase in visceral adiposity. All chronic conditions were associated with a statistically significant difference in at least one body composition biomarker. The presence of a chronic condition was generally associated with greater subcutaneous and visceral adiposity, and lower muscle density and vertebrae bone density.
conclusionsWe report reference ranges for CT-based body composition biomarkers in a population-representative cohort of 4 649 persons by age, sex, body mass index, and chronic conditions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.