{
  "abstract": "Background and aims\n\nWe 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\n\nK-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\n\nFour 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\n\nDXA-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.",
  "abstract_shown": true,
  "affiliations": [
    "Beijing Shijitan Hospital, Capital Medical University, Beijing, China.",
    "Department of Endocrinology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xintong Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Rui Zhang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Tao Jiang",
      "orcid": "0000-0003-2081-1997"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2k86amv1n501x.bib",
  "cited_by": 0,
  "doi": "10.1002/edm2.70366",
  "fulltext_url": "https://europepmc.org/articles/PMC13680683",
  "id": "2k86amv1n501x",
  "issns": [
    "2398-9238"
  ],
  "landing_url": "https://europepmc.org/article/MED/42897745",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2712|Diabetes, Cardiovascular Risks, and Lipoproteins"
    ]
  ],
  "openalex": "W7223031284",
  "pdf_url": "https://europepmc.org/articles/PMC13680683?pdf=render",
  "pmcid": "PMC13680683",
  "pmid": "42897745",
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2k86amv1n501x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:32Z",
      "native_id": "MED:42897745",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42897745"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2712|Diabetes, Cardiovascular Risks, and Lipoproteins",
      "subfield": "endocrinology-metabolism"
    }
  ],
  "title": "Body Composition Phenotypes and Their Metabolic Associations in Treatment-Naïve Patients With Newly Diagnosed Type 2 Diabetes: A DXA-Based Cluster Analysis.",
  "title_text": "Body Composition Phenotypes and Their Metabolic Associations in Treatment-Naïve Patients With Newly Diagnosed Type 2 Diabetes: A DXA-Based Cluster Analysis.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:32Z",
  "url": "https://research.draconitic.com/paper/2k86amv1n501x",
  "venue": "Endocrinology, diabetes & metabolism",
  "versions": []
}