ArticlePediatric research2026
Cluster analysis with body composition data for health risk assessment in children.
Article in Pediatric research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Overview of research and practice progress in body composition measurement techniques.Frontiers in medical technology · 2026Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study investigated populations of children at increased health risks using an integrated analysis of anthropometric/body composition data.
methodsA cross-sectional study of elementary school students (first-sixth grade) was conducted from 2020 onward. Body composition measurements using bioelectrical impedance method, anthropometric measurement, and sub-measures (abdominal circumference, serum lipid levels, activity level, and sleep duration) were performed. Measurements were repeated at 1 and 2 years. Body composition data were standardized using polynomial regression models, and hierarchical clustering analysis was performed to identify subpopulations.
resultsReference value models were constructed using 917 standardized body composition data. Cluster analysis of standardized body composition data with body mass index (BMI) identified five clusters. Two high BMI clusters were identified: one characterized by high fat and muscle mass, and the other by high fat but average muscle mass. Cluster classification revealed significant effects for body fat percentage, abdominal circumference, lipid-related indicators, and sleep duration. The subpopulation with high fat/average muscle mass had high body fat percentage, large abdominal circumference, high lipid-related indices, and short sleep duration.
conclusionsAnthropometric and body composition data integration identified a subgroup of children at increased health risk, highlighting the importance of incorporating body composition assessment into routine childhood physical examinations. IMPACT: We established reference data for fat mass, lean body mass, muscle mass, bone mass, and total water mass indices in children aged 7 to 14 years. Integrating anthropometric and body composition data allowed effective identification of pediatric subpopulations at increased health risks, even in generally healthy populations. Children with a high fat mass but average muscle mass had a high body fat percentage, large abdominal circumference, poor lipid profiles, and shorter sleep duration. Incorporating body composition analysis into standard health checkups for children could facilitate the identification of groups at high risk of health problems.
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