Body Composition Phenotypes and Their Metabolic Associations in Treatment‐Naïve Patients With Newly Diagnosed Type 2 Diabetes: A
DXA
‐Based Cluster Analysis
Xintong Zhang, Rui Zhang, Tao Jiang ABSTRACT
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.