Population-Level Heterogeneity in People with Obesity: A Cross-Sectional Cluster Analysis of a Population-Based Registry.

Background: Obesity is a heterogeneous chronic disease, yet it is still commonly defined and managed using body mass index alone. Identifying clinically meaningful subgroups may support more efficient, precise and practical care. Objective: To identify and characterize population-level heterogeneity among adults with obesity in a large Israeli healthcare cohort. Methods: In this cross-sectional study, we analyzed deidentified electronic health record data from the Leumit Obesity Registry. Adults with obesity who had at least one documented weight measurement and height or BMI record during 2024 were included. Demographic, socioeconomic, lifestyle, clinical, and treatment variables were analyzed using K-prototypes cluster analysis to identify subgroups within a mixed-data population. Results: The study included 68,203 adults with obesity. Five distinct patient clusters were identified: metabolically healthy young adults, 19% of the cohort; adults with low comorbidity, 18%; middle-aged adults with moderate comorbidity, 23%; multimorbid seniors with socioeconomic disadvantage, 24%; and higher socioeconomic status advanced age with high clinical burden, 16%. The clusters differed substantially in age, comorbidity burden, socioeconomic status, and obesity-treatment utilization. GLP-1 use was most common in the older multimorbid clusters, particularly among low SES. Dietitian use was lower in the oldest and sickest cluster, despite high disease burden. Bariatric surgery was overall relatively rare and was concentrated mainly in the younger clusters. Conclusions: Adults with obesity in this large population-based registry did not represent a single clinical group, but rather several distinct phenotypes with different clinical and treatment patterns. These findings support a shift toward more phenotype-informed obesity care and resource planning.