Evidence map›Paper›PMID 42897745›Full record

ArticleEndocrinology, diabetes & metabolism2026

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

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Article in Endocrinology, diabetes & metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Xintong ZhangBeijing Shijitan Hospital, Capital Medical University, Beijing, China.
Rui ZhangBeijing Shijitan Hospital, Capital Medical University, Beijing, China.
Tao JiangBeijing Shijitan Hospital, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0003-2081-1997

Funding

Project of Science and Technology Research and Development of China Railway Corporation J2021Z608
6 · The paper itself

Abstract

BACKGROUND AND

aimsWe 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.

methodsK-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.

resultsFour 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.

conclusionsDXA-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.

Indexed as

Body CompositionDiabetes Mellitus, Type 2Absorptiometry, PhotonAgedCluster AnalysisClustering AlgorithmsFemaleHumansMaleMiddle AgedOsteoporosisPhenotypebody composition analysisbody composition phenotypediabetesdiabetes heterogeneitydual‐energy X‐ray absorptiometrylow muscle massobesity

Identifiers

PMID42897745
PMCPMC13680683

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