Objective: To compare different field methods for estimating body fat mass with a reference value derived by a three-component (3C) model in pre-school and school children across Europe.
Design: Multicentre validation study.
Subjects: Seventy-eight preschool/school children aged 4-10 years from four different European countries.
Methods: A standard measurement protocol was carried out in all children by trained field workers. A 3C model was used as the reference method. The field methods included height and weight measurement, circumferences measured at four sites, skinfold measured at two-six sites and foot-to-foot bioelectrical resistance (BIA) via TANITA scales.
Results: With the exception of height and neck circumference, all single measurements were able to explain at least 74% of the fat-mass variance in the sample. In combination, circumference models were superior to skinfold models and height-weight models. The best predictions were given by trunk models (combining skinfold and circumference measurements) that explained 91% of the observed fat-mass variance. The optimal data-driven model for our sample includes hip circumference, triceps skinfold and total body mass minus resistance index, and explains 94% of the fat-mass variance with 2.44 kg fat mass limits of agreement. In all investigated models, prediction errors were associated with fat mass, although to a lesser degree in the investigated skinfold models, arm models and the data-driven models.
Conclusion: When studying total body fat in childhood populations, anthropometric measurements will give biased estimations as compared to gold standard measurements. Nevertheless, our study shows that when combining circumference and skinfold measurements, estimations of fat mass can be obtained with a limit of agreement of 1.91 kg in normal weight children and of 2.94 kg in overweight or obese children.
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http://dx.doi.org/10.1038/ijo.2013.13 | DOI Listing |
Endocrine
January 2025
Department of Health Management, Chronic Health Management Laboratory, Henan Provincial People's Hospital, Zhengzhou, 450003, China.
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January 2025
Research Laboratory CoreLab of the Medical University of Lodz, Łódź, Poland.
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January 2025
Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.
Background: Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.
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Material And Methods: We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.
Physiol Rep
February 2025
Quebec Heart and Lung Institute - Laval University, Quebec, Quebec, Canada.
Metabolic dysfunction-associated steatotic liver disease (MASLD) describes liver diseases caused by the accumulation of triglycerides in hepatocytes (steatosis) as well as the resulting inflammation and fibrosis. Previous studies have demonstrated that accumulation of fat in visceral adipose tissue compartments and the liver is associated with alterations in the circulating levels of some amino acids, notably glutamate. This study aimed to investigate the associations between circulating amino acids, particularly glutamate, and MASLD.
View Article and Find Full Text PDFJ Cachexia Sarcopenia Muscle
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Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan.
Background: Chest computed tomography (CT) is a valuable tool for diagnosing and predicting the severity of coronavirus disease 2019 (COVID-19) and assessing extrapulmonary organs. Reduced muscle mass and visceral fat accumulation are important features of a body composition phenotype in which obesity and muscle loss coexist, but their relationship with COVID-19 outcomes remains unclear. In this study, we aimed to investigate the association between the erector spinae muscle (ESM) to epicardial adipose tissue (EAT) ratio (ESM/EAT) on chest CT and disease severity in patients with COVID-19.
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