Establishment of normative biometric data for body composition based on computed tomography in a North American cohort

• Published in: Clinical nutrition (Edinburgh, Scotland) Vol. 40; no. 4; pp. 2435 - 2442
• Main Authors: Navin, P.J., Moynagh, M.R., Atkinson, E.J., Tirumanisetty, P., LeBrasseur, N.K., Kumar, A., Khosla, S., Takahashi, N.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.04.2021
• Subjects: adipose tissue; age; area; biomarkers; biometry; Body composition; clinical nutrition; Computed tomography; computer software; equations; height; information; intravenous injection; medical records; Minnesota; Normative data; patient care; patients; population; prospective studies; Sarcopenia; shape; skeletal muscle; Minnesota; Sarcopenia; Normative data; Computed tomography; Body composition
• ISSN: 0261-5614; 1532-1983; 1532-1983
• DOI: 10.1016/j.clnu.2020.10.046

Accurate 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). This 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. There 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. This study offers comparative data to allow comparison amongst multiple populations. This will form an important reference for future research and clinical practice. •Measures of body composition are important biomarkers to guide treatment and assess response.•Normative data is required to allow adequate comparison within a larger population.•Normative data on body composition from a population-specific cohort of North Americans is available for future practice.•Normative data on body composition derived from a population-specific cohort of North Americans is available for research and clinical practice.