Metabotype Risk Clustering Based on Metabolic Disease Biomarkers and Its Association with Metabolic Syndrome in Korean Adults: Findings from the 2016-2023 Korea National Health and Nutrition Examination Survey (KNHANES).

Abstract

Background: Metabolic syndrome (MetS) is a multifactorial condition involving central obesity, dyslipidemia, hypertension, and impaired glucose metabolism, significantly increasing the risk of type 2 diabetes and cardiovascular disease.

Objectives: Given the clinical heterogeneity of MetS, this study aimed to identify distinct metabolic phenotypes, referred to as metabotypes, using validated biomarkers and to examine their association with MetS.

Materials and methods: A total of 1245 Korean adults aged 19-79 years were selected from the 2016-2023 Korea National Health and Nutrition Examination Survey. Metabotype risk clusters were derived using k-means clustering based on five biomarkers: body mass index (BMI), uric acid, fasting blood glucose (FBG), high-density lipoprotein cholesterol (HDLc), and non-HDL cholesterol (non-HDLc). Multivariable logistic regression was used to assess associations with MetS.

Results: Three distinct metabotype risk clusters (low, intermediate, and high risk) were identified. The high-risk cluster exhibited significantly worse metabolic profiles, including elevated BMI, FBG, HbA1c, triglyceride, and reduced HDLc. The prevalence of MetS increased progressively across metabotype risk clusters (OR: 5.46, 95% CI: 2.89-10.30, p < 0.001). In sex-stratified analyses, the high-risk cluster was strongly associated with MetS in both men (OR: 9.22, 95% CI: 3.49-24.36, p < 0.001) and women (OR: 3.70, 95% CI: 1.56-8.75, p = 0.003), with notable sex-specific differences in lipid profiles, particularly in HDLc.

Conclusion: These findings support the utility of metabotyping using routine biomarkers as a tool for early identification of high-risk individuals and the development of personalized prevention strategies in clinical and public health settings.