ArticleClinical nutrition (Edinburgh, Scotland)2021
Establishment of normative biometric data for body composition based on computed tomography in a North American cohort.
Article in Clinical nutrition (Edinburgh, Scotland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Volumetric assessment from L2 to L4 improves the reliability and validity of body composition by CT.European journal of radiology · 2026Article
- 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
- Association between pre-operative computed tomography-based adipose tissue quantification and post-transplant dyslipidemia in renal transplantation recipients.Frontiers in nutrition · 2026Article
- Relative muscle indices and healthy reference values for sarcopenia assessment using T10 through L5 computed tomography skeletal muscle area.Scientific reports · 2024Article
- Abdominal Body Composition Reference Ranges and Association With Chronic Conditions in an Age- and Sex-Stratified Representative Sample of a Geographically Defined American Population.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
BACKGROUND &
aimsAccurate and reproducible biomarkers are required to allow a more personalized approach to patient care. Body composition is one such biomarker affecting outcomes in a range of surgical and oncological conditions. The aim of this study is to determine the age and sex specific distribution of body composition data, based on information gathered from computed tomography (CT).
methodsThis prospective study used healthy subjects from the medical records linkage of the Rochester Epidemiology Project, based in Minnesota, USA. Each patient had a CT scan without intravenous contrast performed between 1999 and 2001. Quantification was performed using previously validated semi-automated in-house developed software for body composition analysis. Subcutaneous adipose tissue area, visceral adipose tissue area, intermuscular adipose tissue area and skeletal muscle area were measured and indexed to subject height. Generalized Additive Models for Location, Scale and Shape were used to assess the location, scale, and shape of each variable across age, stratified by sex. Z-scores specific to sex were assessed for each of the parameters analyzed. Age-specific z-scores were calculated using the formula: Z = (Index Variable - μ)/σ or Z = (√ (Index Variable) - μ)/σ.
resultsThere were 692 subjects enrolled in the study. The fitted model equation was offered for each variable with values presented for μ and σ. Modelling with penalized splines was performed for VAT index, IMAT index and total adipose tissue index. Scatterplots of each variable were produced with lines of Z-scores as a visual representation.
conclusionThis study offers comparative data to allow comparison amongst multiple populations. This will form an important reference for future research and clinical practice.
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