{
  "abstract": "Hyperuricemia (HUA) imposes a growing public health burden, calling for better risk stratification tools. In this cross-sectional study of 4906 Chinese adults undergoing routine health checks (overall HUA prevalence: 26.0%), we built machine learning-based predictive models using a stratified 80/20 data split. To avoid variable selection bias, we applied LASSO regression with tenfold cross-validation, which identified 12 core predictors from routine clinical and demographic data. Among four algorithms tested, the Gradient Boosting (GB) model showed the best discrimination (AUC = 0.770) and good calibration (slope = 0.982, intercept = − 0.007, Brier score = 0.157). SHAP analysis revealed serum creatinine (Scr), HDL cholesterol (HDL-C), and body mass index (BMI) as the top predictors. Notably, SHAP interaction plots uncovered a nonlinear rise in risk above a Scr threshold and a compounded risk when high Scr coincided with low HDL-C. In summary, the well-calibrated GB model offers a reliable, data-driven tool for HUA risk screening, with insights into marker interactions to guide targeted prevention and early intervention.",
  "abstract_shown": true,
  "affiliations": [
    "Health Management Medicine Center, The Third Xiangya Hospital, Central South University",
    "School of Psychological Sciences, Monash University",
    "Xiangya Nursing School of Central South University",
    "Nursing Department, The Third Xiangya Hospital, Central South University",
    "School of Health Sciences, Western Sydney University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "ChuXia Tan"
    },
    {
      "affs": [
        0
      ],
      "name": "Xue He"
    },
    {
      "affs": [
        1
      ],
      "name": "Lijun Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Ying Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Pingting Yang"
    },
    {
      "affs": [
        2
      ],
      "name": "Yuxuan Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Juan Luo"
    },
    {
      "affs": [
        2
      ],
      "name": "Mingyue Huang"
    },
    {
      "affs": [
        3
      ],
      "name": "Honglian Zhang"
    },
    {
      "affs": [
        4
      ],
      "name": "Andy S. K. Cheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/64hvmtzp3mkst.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-62552-w",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-62552-w",
  "id": "64hvmtzp3mkst",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-62552-w",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2727|Gout, Hyperuricemia, Uric Acid"
    ]
  ],
  "openalex": "W7169833869",
  "pdf_url": "https://www.nature.com/articles/s41598-026-62552-w_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/64hvmtzp3mkst.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:52Z",
      "native_id": "20c324be4ecf4c4084b556e815244d53",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-62552-w"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2727|Gout, Hyperuricemia, Uric Acid",
      "subfield": "general"
    }
  ],
  "title": "Machine learning-based analysis of risk factors and construction of a predictive model for hyperuricemia in Chinese health examination population",
  "title_text": "Machine learning-based analysis of risk factors and construction of a predictive model for hyperuricemia in Chinese health examination population",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:52Z",
  "url": "https://research.draconitic.com/paper/64hvmtzp3mkst",
  "venue": "Scientific Reports",
  "versions": []
}