{
  "abstract": "Objective: To characterize the temporal trajectories, geographic heterogeneity, and population-level factors associated with Cardiovascular-Kidney-Metabolic (CKM) syndrome stages in US adults.\n\nMethods: We analyzed 2,179,431 adults from the Behavioral Risk Factor Surveillance System (2011–2023). CKM stages (0–4) were defined per American Heart Association criteria, with Stage 0 representing optimal health (no risk factors), Stage 2 indicating metabolic risk (diabetes, hypertension, or hypercholesterolemia), and Stage 4 denoting clinical cardiovascular disease (myocardial infarction, coronary heart disease, or stroke). Interrupted time series (ITS) assessed pandemic impacts; geospatial Local Indicators of Spatial Association (LISA) identified risk clusters; and Population Attributable Fractions (PAF) quantified modifiable contributors to advanced disease using survey-weighted logistic regression.\n\nResults: The prevalence of optimal health (Stage 0) declined significantly following the pandemic onset, reaching a low of 15.7% in 2023, well below the counterfactual projection of 18.2% ( P < .001). Conversely, metabolic risk (Stage 2) rose to 41.9%. Geospatial analysis revealed divergent patterns: while clinical CVD (Stage 4) remained entrenched in the traditional southeastern “Stroke Belt,” metabolic risk (Stage 2) exhibited significant “High-High” clustering that expanded northward into the Midwest and Rust Belt. Attribution analysis identified physical inactivity (PAF 22.0%; 95% CI, 21.0%-23.0%) and socioeconomic disadvantage (low education and income combined PAF ~ 36%, an approximate upper-bound estimate derived by summing the PAFs of two correlated exposures) as the factors most strongly associated with advanced disease, exceeding the contribution of current smoking (PAF 2.9%).\n\nConclusions: The US CKM epidemic involves post-pandemic deterioration and geographic expansion into the Heartland. Structural socioeconomic barriers and physical inactivity are primary contributors, supporting the need for precision public health interventions targeting these emerging hotspots and social determinants beyond traditional risk factors.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Jianhua Gu",
      "orcid": "0000-0002-1578-6814"
    },
    {
      "name": "Jiqing Li"
    },
    {
      "name": "Shuo Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bps1mt26v7rep.bib",
  "cited_by": 0,
  "doi": "10.1186/s12889-026-30214-1",
  "fulltext_url": null,
  "id": "bps1mt26v7rep",
  "issns": [
    "1471-2458"
  ],
  "landing_url": "https://link.springer.com/article/10.1186/s12889-026-30214-1",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/bps1mt26v7rep.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T12:21:21Z",
      "native_id": "10.1186/s12889-026-30214-1",
      "source": "crossref",
      "url": "https://link.springer.com/article/10.1186/s12889-026-30214-1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:1471-2458",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "The evolving epidemic of cardiovascular-kidney-metabolic syndrome: post-pandemic acceleration and geographic expansion in the US, 2011–2023",
  "title_text": "The evolving epidemic of cardiovascular-kidney-metabolic syndrome: post-pandemic acceleration and geographic expansion in the US, 2011–2023",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T12:21:21Z",
  "url": "https://research.draconitic.com/paper/bps1mt26v7rep",
  "venue": "BMC Public Health",
  "versions": []
}