{
  "abstract": "Purpose\n\nRosacea has been linked with systemic metabolic abnormalities in case-control studies, but most such evidence compares rosacea patients with healthy controls rather than asking which patients within a rosacea population carry the greatest metabolic burden. We aimed to describe the metabolic profile of a real-world rosacea outpatient cohort and build a time-stratified, outcome-independent machine-learning model ranking which routine metabolic indicators predict metabolic syndrome status.Patients and methods\n\nWe conducted a combined retrospective-prospective study of 298 outpatients with rosacea, seen at a tertiary dermatology clinic from March 2021 to March 2026, contributing 5420 laboratory records (repeated measurements; 298 unique patients) across nine metabolic indicators. The outcome was metabolic syndrome status, defined with a modified NCEP-ATP III definition that includes fasting glucose, triglycerides, blood pressure and waist circumference (Laboratory Variables); fasting glucose and triglycerides were then excluded from the predictors; a random forest classifier was built from the remaining seven indicators, trained on patients enrolled in 2021-2022 (n=164) and evaluated on a time-stratified holdout enrolled in 2022-2023 (n=134). A subset of 83 patients with ≥2 follow-up visits was used to examine within-patient change over time.Results\n\nAbnormal fasting glucose occurred in 23.7% of records and abnormal triglycerides in 31.4%. Patients with metabolic syndrome (n=64, 21.5%) had higher CRP (6.8±3.1 vs 2.4±1.6 mg/L, P<0.001) and higher severity scores (14.2±3.5 vs 10.1±3.0, P=0.003). On the holdout, the random forest model reached an AUC of 0.734 (95% CI 0.628-0.824), with CRP, total cholesterol, ALT, and HbA1c as leading contributors. Change in fasting glucose correlated with change in severity score on follow-up (ρ=0.31, P=0.004).Conclusion\n\nOutpatients with rosacea frequently show systemic metabolic disturbances aligned with metabolic syndrome status and clinical severity. A machine-learning model built from routinely available, non-diagnostic indicators retained discrimination on a time-stratified holdout, suggesting a low-cost approach to flagging metabolic risk in this population, which requires external validation in independent, multicenter cohorts before clinical use.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Dermatology, Hunan Provincial People's Hospital/The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, People's Republic of China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Meng Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Ke Li",
      "orcid": "0000-0003-4805-8677"
    },
    {
      "affs": [
        0
      ],
      "name": "Yangying Liao"
    },
    {
      "affs": [
        0
      ],
      "name": "Lan Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiao Xiao"
    },
    {
      "affs": [
        0
      ],
      "name": "Junweichen Guo"
    },
    {
      "affs": [
        0
      ],
      "name": "Hao Feng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6nqa9298vppn5.bib",
  "cited_by": 0,
  "doi": "10.2147/ccid.s634954",
  "fulltext_url": "https://europepmc.org/articles/PMC13718322",
  "id": "6nqa9298vppn5",
  "issns": [
    "1178-7015"
  ],
  "landing_url": "https://europepmc.org/article/MED/42960137",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2708|Acne and Rosacea Treatments and Effects"
    ]
  ],
  "openalex": "W7223558950",
  "pdf_url": "https://europepmc.org/articles/PMC13718322?pdf=render",
  "pmcid": "PMC13718322",
  "pmid": "42960137",
  "published": "2026-10-25",
  "publisher": "Dove Press",
  "ris": "https://research.draconitic.com/paper/6nqa9298vppn5.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T00:20:50Z",
      "native_id": "MED:42960137",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42960137"
    },
    {
      "first_seen": "2026-11-02T12:22:22Z",
      "native_id": "PMC13718322",
      "source": "pmc",
      "url": "https://doi.org/10.2147/ccid.s634954"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2708|Acne and Rosacea Treatments and Effects",
      "subfield": "general"
    }
  ],
  "title": "Routine Metabolic Laboratory Profile and Time-Stratified Machine-Learning Risk Stratification of Rosacea: A Combined Retrospective-Prospective Cohort Study.",
  "title_text": "Routine Metabolic Laboratory Profile and Time-Stratified Machine-Learning Risk Stratification of Rosacea: A Combined Retrospective-Prospective Cohort Study.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:22:22Z",
  "url": "https://research.draconitic.com/paper/6nqa9298vppn5",
  "venue": "Clinical, cosmetic and investigational dermatology",
  "versions": []
}