Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome

• DOI: 10.1016/j.xcrm.2023.101109
• Publication Date: 2023-07-18T14:35:33Z
• Authors: Yifan Chen; Wei Xu; Wei Zhang; Renyang Tong; Ancai Yuan; Zheng Li; Huiru Jiang; Liuhua Hu; Lin Huang; Yudian Xu; Ziyue Zhang; Mingze Sun; Xiaoxiang Yan; Alex F. Chen; Kun Qian; Jun Pu
• Keywords: Metabolic Syndrome; Risk Factors; Hypertension; Humans; Obesity; Risk Assessment; Article; 3. Good health

Direct diagnosis and accurate assessment of metabolic syndrome (MetS) allow for prompt clinical interventions. However, traditional diagnostic strategies overlook the complex heterogeneity of MetS. Here, we perform metabolomic analysis in 13,554 participants from the natural cohort and identify 26 hub plasma metabolic fingerprints (PMFs) associated with MetS and its early identification (pre-MetS). By leveraging machine-learning algorithms, we develop robust diagnostic models for pre-MetS and MetS with convincing performance through independent validation. We utilize these PMFs to assess the relative contributions of the four major MetS risk factors in the general population, ranked as follows: hyperglycemia, hypertension, dyslipidemia, and obesity. Furthermore, we devise a personalized three-dimensional plasma metabolic risk (PMR) stratification, revealing three distinct risk patterns. In summary, our study offers effective screening tools for identifying pre-MetS and MetS patients in the general community, while defining the heterogeneous risk stratification of metabolic phenotypes in real-world settings.