{
  "abstract": "Background: Cardiometabolic multimorbidity (CMM) is an increasing burden in ageing populations. Cardiovascular-kidney-metabolic (CKM) stages 0–3 provide an upstream framework for identifying metabolic, renal, and cardiovascular vulnerability before overt multimorbidity. We evaluated whether routinely derived metabolic–adiposity indices add useful risk-phenotype information beyond established clinical factors.\n\nMethods: This prospective CHARLS cohort included 7,062 adults aged ≥ 45 years who were free of CMM and had CKM stages 0–3 at baseline. Twenty-nine cholesterol–high-density lipoprotein cholesterol–glucose (CHG)-derived, triglyceride–glucose (TyG)-derived, and adiposity/body-shape indices were evaluated; the main text focused on RFM, CHG-RFM, TyG-RFM, BMI, and WC. Model 3 was used to anchor the primary association interpretation, while Model 4 was retained as an extended clinical-adjustment model to assess robustness after broader adjustment. Benjamini–Hochberg correction, inter-index correlations, paired CHG–TyG models, proportional-hazards diagnostics, prediction analyses, and sensitivity analyses were performed.\n\nResults: Among 7,062 participants, 613 developed incident CMM (8.7%). In Model 3, the per-SD HRs (95% CIs) were 1.85 (1.52–2.25) for RFM, 1.72 (1.47–2.03) for CHG-RFM, 1.67 (1.41–1.98) for TyG-RFM, 1.30 (1.20–1.41) for BMI, and 1.34 (1.23–1.46) for WC. The corresponding estimates remained broadly consistent in Model 4. After Benjamini–Hochberg correction, 28 of 29 Model 3 per-SD associations remained supported; ABSI was the exception. Correlations were substantial among structurally related indices, and paired CHG–TyG models showed marked mutual attenuation. The clinical baseline AUC was 0.787, whereas selected index-extended models achieved AUCs of approximately 0.799–0.800, indicating modest absolute improvement.\n\nConclusions: Selected routine metabolic–adiposity indices were associated with incident CMM among adults with CKM stages 0–3. RFM-related indices showed stronger association signals, whereas BMI and WC remain more accessible measures. The broadly consistent Model 3 and Model 4 estimates supported the overall association pattern. However, because inter-index overlap was substantial and incremental predictive gains were modest, these indices should be viewed as candidate adjunctive risk markers rather than independent biomarkers or clinically validated decision tools.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12872-026-06385-5.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Brain Injury Laboratory, The Affiliated Taian City Central Hospital of Qingdao University, Taian, Shandong Province China",
    "Department of Neuroimmunology and Infectious Diseases, The Affiliated Taian City Central Hospital of Qingdao University, Taian, China",
    "Department of Neurosurgery, The Affiliated Taian City Central Hospital of Qingdao University, Taian, China",
    "Cerebrovascular Disease Screening and Follow-up Office, The Affiliated Taian City Central Hospital of Qingdao University, Taian, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Zhenyu Geng"
    },
    {
      "affs": [
        3
      ],
      "name": "Yawei Rong"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Jiahao Liu"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Haochen Yang"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Yulun Wang"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Xunlu Yin"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Xiangning He"
    },
    {
      "affs": [
        0
      ],
      "name": "Qian Chen"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Fangfang Zhao",
      "orcid": "0000-0002-0824-1410"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Guojun Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3d6gps2gccfw8.bib",
  "cited_by": 0,
  "doi": "10.1186/s12872-026-06385-5",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13732876/",
  "id": "3d6gps2gccfw8",
  "issns": [
    "1471-2261"
  ],
  "landing_url": "https://doi.org/10.1186/s12872-026-06385-5",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2712|Diabetes, Cardiovascular Risks, and Lipoproteins"
    ]
  ],
  "openalex": "W7201981535",
  "pdf_url": null,
  "pmcid": "PMC13732876",
  "pmid": "42983283",
  "published": "2026-08-08",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/3d6gps2gccfw8.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T12:22:46Z",
      "native_id": "PMC13732876",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12872-026-06385-5"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2712|Diabetes, Cardiovascular Risks, and Lipoproteins",
      "subfield": "endocrinology-metabolism"
    }
  ],
  "title": "Routine metabolic–adiposity indices and incident cardiometabolic multimorbidity among adults with CKM stages 0–3: a prospective cohort study",
  "title_text": "Routine metabolic–adiposity indices and incident cardiometabolic multimorbidity among adults with CKM stages 0–3: a prospective cohort study",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:22:46Z",
  "url": "https://research.draconitic.com/paper/3d6gps2gccfw8",
  "venue": "BMC Cardiovascular Disorders",
  "versions": []
}