Abstract
AIMS: The growing epidemic of overweight and obesity elevates disease risks, with metabolic disorders and inflammation critically involved in the pathogenic mechanisms. This study refines the subtyping of overweight and obesity using metabolic and inflammatory markers to enhance risk assessment and personalized prevention.
MATERIALS AND METHODS: Based on the UK Biobank, this retrospective study included participants classified as overweight or obese (BMI ≥25 kg/m2). K-means clustering was performed using metabolic and inflammatory biomarkers. Multivariate Cox regression analysis assessed the risk of complications and mortality over a follow-up period of 13.5 years. Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) explored cluster-specific genetic traits.
RESULTS: Among 126 145 participants (mean [IQR] age: 55.0 [14.0] years; 61 983 males [49.1%]), five clusters were identified: (1) Low Metabolic Risk-related, (2) Hypertension-Related, (3) Mixed Hyperlipidemia-Related, (4) Elevated Lipoprotein(a)-Related and (5) High BMI and Inflammation-Related. Cluster 1 exhibited a lower risk of complications than other clusters. Cluster 2 had the highest incidence of stroke, linked to variants affecting blood circulation. Cluster 3 showed the highest risks for ischaemic heart disease, characterized by variants enriched in cholesterol metabolism pathways. Cluster 4 was associated with high cardiovascular risks. Cluster 5 had the highest risks for diabetes, asthma, chronic obstructive pulmonary disease, osteoarthritis and mortality, linked to obesity-related genetic variants. We also proposed a method for applying this classification in clinical settings.
CONCLUSIONS: This classification provides insights into the heterogeneity of individuals with overweight and obesity, aiding in the identification of high-risk patients who may benefit from targeted interventions.