Endocrinology, diabetes & metabolism · 2026
Body Composition Phenotypes and Their Metabolic Associations in Treatment-Naïve Patients With Newly Diagnosed Type 2 Diabetes: A DXA-Based Cluster Analysis.
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Zhang Xintong, Zhang Rui, Jiang Tao
Аннотация
BACKGROUND AND AIMS: We characterized body-composition heterogeneity in treatment-naive type 2 diabetes (T2DM) using dual-energy X-ray absorptiometry (DXA), and assessed whether cluster-metabolic associations persisted after accounting for sex-related body-composition differences.
METHODS: K-means clustering of 50 DXA-derived body-composition variables classified 185 treatment-naive T2DM patients. Variance decomposition, sex-stratified analyses, sex-stratified clustering, sex-specific standardization clustering and multiple imputation (20 datasets) addressed sex confounding and missing data.
RESULTS: Four exploratory body-composition clusters were identified: Low Fat-Low Muscle, Male-Predominant Central Obesity (98.6% male), Female-Predominant Low-Muscle High-Adiposity (98.0% female) and High Fat-High Muscle. Two clusters showed strong sex predominance; sex-specific normalization reduced sex-cluster dependence while preserving metabolic associations. In multiple-imputation-adjusted models, Clusters 2 and 4 had markedly higher MASLD odds (OR = 9.69 and 8.55, respectively), while Cluster 4 had lower osteoporosis (OR = 0.16) and higher hyperuricemia (OR = 6.36) odds versus Cluster 1. Bone turnover markers, diabetic retinopathy and diabetic kidney disease showed no significant associations.
CONCLUSIONS: DXA-based body-composition clusters are associated with distinct metabolic and comorbidity profiles-particularly MASLD, osteoporosis and hyperuricemia-that persisted after sex-specific standardization and multiple imputation. Given the sex-cluster overlap and modest silhouette width, these findings are hypothesis-generating and require prospective validation.
METHODS: K-means clustering of 50 DXA-derived body-composition variables classified 185 treatment-naive T2DM patients. Variance decomposition, sex-stratified analyses, sex-stratified clustering, sex-specific standardization clustering and multiple imputation (20 datasets) addressed sex confounding and missing data.
RESULTS: Four exploratory body-composition clusters were identified: Low Fat-Low Muscle, Male-Predominant Central Obesity (98.6% male), Female-Predominant Low-Muscle High-Adiposity (98.0% female) and High Fat-High Muscle. Two clusters showed strong sex predominance; sex-specific normalization reduced sex-cluster dependence while preserving metabolic associations. In multiple-imputation-adjusted models, Clusters 2 and 4 had markedly higher MASLD odds (OR = 9.69 and 8.55, respectively), while Cluster 4 had lower osteoporosis (OR = 0.16) and higher hyperuricemia (OR = 6.36) odds versus Cluster 1. Bone turnover markers, diabetic retinopathy and diabetic kidney disease showed no significant associations.
CONCLUSIONS: DXA-based body-composition clusters are associated with distinct metabolic and comorbidity profiles-particularly MASLD, osteoporosis and hyperuricemia-that persisted after sex-specific standardization and multiple imputation. Given the sex-cluster overlap and modest silhouette width, these findings are hypothesis-generating and require prospective validation.