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Maskarinec G, Shvetsov Y, Wong MC, Cataldi D, Bennett J, Garber AK, Buchthal SD, Heymsfield SB, Shepherd JA. Predictors of visceral and subcutaneous adipose tissue and muscle density: The ShapeUp! Kids study. Nutr Metab Cardiovasc Dis 2024; 34:799-806. [PMID: 38218711 PMCID: PMC10922397 DOI: 10.1016/j.numecd.2023.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 12/02/2023] [Accepted: 12/14/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND AND AIMS Body fat distribution, i.e., visceral (VAT), subcutaneous adipose tissue (SAT) and intramuscular fat, is important for disease prevention, but sex and ethnic differences are not well understood. Our aim was to identify anthropometric, demographic, and lifestyle predictors for these outcomes. METHODS AND RESULTS The cross-sectional ShapeUp!Kids study was conducted among five ethnic groups aged 5-18 years. All participants completed questionnaires, anthropometric measurements, and abdominal MRI scans. VAT and SAT areas at four lumbar levels and muscle density were assessed manually. General linear models were applied to estimate coefficients of determination (R2) and to compare the fit of VAT and SAT prediction models. After exclusions, the study population had 133 male and 170 female participants. Girls had higher BMI-z scores, waist circumference (WC), and SAT than boys but lower VAT/SAT and muscle density. SAT, VAT, and VAT/SAT but not muscle density differed significantly by ethnicity. R2 values were higher for SAT than VAT across groups and improved slightly after adding WC. For SAT, R2 increased from 0.85 to 0.88 (girls) and 0.62 to 0.71 (boys) when WC was added while VAT models improved from 0.62 to 0.65 (girls) and 0.57 to 0.62 (boys). VAT values were significantly lower among Blacks than Whites with little difference for the other groups. CONCLUSION This analysis in a multiethnic population identified BMI-z scores and WC as the major predictors of MRI-derived SAT and VAT and highlights the important ethnic differences that need to be considered in diverse populations.
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Affiliation(s)
| | | | | | - Devon Cataldi
- University of Hawaii Cancer Center, Honolulu, HI, USA
| | | | - Andrea K Garber
- University of California at San Francisco, San Francisco, CA, USA
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Lu Y, Shan Y, Dai L, Jiang X, Song C, Chen B, Zhang J, Li J, Zhang Y, Xu J, Li T, Xiong Z, Bai Y, Huang X. Sex-specific equations to estimate body composition: Derivation and validation of diagnostic prediction models using UK Biobank. Clin Nutr 2023; 42:511-518. [PMID: 36857960 DOI: 10.1016/j.clnu.2023.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/21/2023] [Accepted: 02/09/2023] [Indexed: 02/18/2023]
Abstract
BACKGROUND & AIMS Body mass index and waist circumference are simple measures of obesity. However, they do not distinguish between visceral and subcutaneous fat, or muscle, potentially leading to biased relationships between individual body composition parameters and adverse health outcomes. The purpose of this study was to develop and validate prediction models for volumetric adipose and muscle. METHODS Based on cross-sectional data of 18,457, 18,260, and 17,052 White adults from the UK Biobank, we developed sex-specific equations to estimate visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), and total thigh fat-free muscle (FFM) volumes, respectively. Volumetric magnetic resonance imaging served as the reference. We used the least absolute shrinkage and selection operator and the extreme gradient boosting methods separately to fit three sequential models, the inputs of which included demographics and anthropometrics and, in some, bioelectrical impedance analysis parameters. We applied comprehensive metrics to assess model performance in the temporal validation set. RESULTS The equations that included more predictors generally performed better. Accuracy of the equations was moderate for VAT (percentage of estimates that differed <30% from the measured values, 70 to 78 in males, 64 to 69 in females) and good for ASAT (85 to 91 in males, 90 to 95 in females) and FFM (99 to 100 in both sexes). All the equations appeared precise (interquartile range of the difference, 0.89 to 1.76 L for VAT, 1.16 to 1.61 L for ASAT, 0.81 to 1.39 L for FFM). Bias of all the equations was negligible (-0.17 to 0.05 L for VAT, -0.10 to 0.12 L for ASAT, -0.07 to 0.09 L for FFM). The equations achieved superior cardiometabolic correlations compared with body mass index and waist circumference. CONCLUSIONS The developed equations to estimate VAT, ASAT, and FFM volumes achieved moderate to good performance. They may be cost-effective tools to revisit the implications of diverse body components.
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Affiliation(s)
| | - Ying Shan
- Clinical Research Academy, Peking University Shenzhen Hospital, Peking University, Shenzhen, China
| | - Liang Dai
- Clinical Research Academy, Peking University Shenzhen Hospital, Peking University, Shenzhen, China
| | | | - Congying Song
- Clinical Research Academy, Peking University Shenzhen Hospital, Peking University, Shenzhen, China
| | - Bangwei Chen
- BGI-Shenzhen, Shenzhen, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Jingwen Zhang
- Renal Division, Peking University Shenzhen Hospital, Peking University, Shenzhen, China
| | - Jing Li
- Clinical Research Academy, Peking University Shenzhen Hospital, Peking University, Shenzhen, China; Renal Division, Peking University Shenzhen Hospital, Peking University, Shenzhen, China
| | - Yue Zhang
- Renal Division, Peking University Shenzhen Hospital, Peking University, Shenzhen, China
| | - Junjie Xu
- Clinical Research Academy, Peking University Shenzhen Hospital, Peking University, Shenzhen, China
| | - Tao Li
- BGI-Shenzhen, Shenzhen, China
| | - Zuying Xiong
- Renal Division, Peking University Shenzhen Hospital, Peking University, Shenzhen, China
| | | | - Xiaoyan Huang
- Clinical Research Academy, Peking University Shenzhen Hospital, Peking University, Shenzhen, China; Renal Division, Peking University Shenzhen Hospital, Peking University, Shenzhen, China.
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