Development and Application of Children's Sex‐ and Age‐Specific Fat‐Mass and Muscle‐Mass Reference Curves From Dual‐Energy X‐Ray Absorptiometry Data for Predicting Cardiometabolic Risk
Year: 2025
Type: article
Abstract: BACKGROUND: A dual-energy x-ray absorptiometry (DXA)-derived phenotype classification based on fat mass and muscle mass has been developed for adults. We extended this to a paediatric population. METHODS: Children's (≤ 17 years) DXA data in NHANES (n = 6120) were used to generate sex- and age-specific deciles of appendicular skeletal muscle mass index and fat mass index with the Lambda Mu Sigma method. Four phenotypes (high [H] or low [L], adiposity [A] and muscle mass [M]: HA-HM, HA-LM, LA-HM, LA-LM) were identified based on being above/below the median compared to same-sex and same-age peers. These reference curves were applied to the QUALITY cohort (n = 630, 8-10 years of age in 2005) to assess whether the phenotypes correctly identified cardiometabolic risk at baseline, follow-up (2008-2010), and their longitudinal changes. Multiple linear regression models were adjusted for age, sex, and Tanner's stage. RESULTS: Compared to the LA-HM reference group, the HA-HM phenotype was associated with less favourable HDL, triglycerides, and HOMA-IR at baseline and first follow-up, but not in their changes. The HA-LM phenotype was associated with less favourable HOMA-IR at baseline and first follow-up. CONCLUSIONS: Results suggest that phenotypes based on fat and muscle mass may have clinical utility in children and should be further investigated.
Source: Pediatric Obesity
DOI: 10.1111/ijpo.70051
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,880
Funders: Heart and Stroke Foundation of Canada, Université Laval, McGill University, Concordia University, Université de Montréal, Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, Fonds de Recherche du Québec - Santé, Fonds de recherche du Québec – Nature et technologies
Awards: G2271590008, G2808990671, Towards the prevention of type 2 diabetes: improving our understanding of longitudinal changes in glucose homeostatis in youth, Établissement de la relève professorale — Analyse numérique, Modélisation et simulation, Robust sparse recovery and deep learning algorithms in computational mathematics, G5060859382, Predictors and modifiers of the cardiometabolic risk profile in children, Predictors and modifiers of the cardiometabolic risk profile in children
OpenAlex ID: W4413831740
