Loading
Life sciences · Journal article
Endocrinology Diabetes & Metabolism · October 19, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Journal article.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
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.