ArticleScientific reports2024
Risk factors for metabolic syndrome in the premetabolic state assessed using hierarchical clustering study in a health screening group.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Metabolic Syndrome and Periodontitis-From Shared Mechanisms to Interdisciplinary Care: A Narrative Review of Clinical Evidence.Journal of clinical medicine · 2026Review
- Dietary Antioxidant Capacity, Inflammation, and Insulin Resistance in Women with Polycystic Ovary Syndrome: An Integrated Metabolic and Nutritional Assessment.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Article
- Machine learning for prediction ofFrontiers in medicine · 2025Article
- The role of cognitive function in predicting metabolic risk in schizophrenia: a multi-model comparison incorporating clinical features.Frontiers in psychiatry · 2025Article
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4 authors.
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Abstract
Early detection of a premetabolic status that is at risk for metabolic syndrome (MetS) but not meeting the criteria is crucial. This study examined 27,623 participants aged 20-50 (mean: 40.7) years who underwent initial health screening at Kangbuk Samsung Hospital (2011-2019), focusing on individuals with one or two MetS components. Hierarchical agglomerative clustering was used to form MetS risk clusters based on initial and follow-up data, including age, resting heart rate (rHR), serum uric acid (UA), C-reactive protein (CRP), gamma-glutamyl transpeptidase, and ferritin levels, and nonalcoholic fatty liver disease (NAFLD), periodontal disease, and Helicobacter pylori infection duration. Kaplan-Meier and generalized additive models were used to present the restricted mean survival time (RMST) and identify onset contributors. Clusters with NAFLD and elevated UA levels had the highest MetS risk, whereas those with uniformly low biomarker levels revealed the lowest risk. During follow-up, a cluster initially comprising 60.2% moderate-risk patients exhibited high biomarker levels and had the worst MetS prognosis (RMST: 211 days). UA, CRP levels, and rHR contributed to the incidence of MetS in the fitted model. Machine learning can predict the premetabolic state at MetS risk in a population-based cohort.
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