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Obesity and Health Practices / Diabetes, Cardiovascular Risks, and Lipoproteins · Journal article
Journal of Clinical Medicine · September 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This cross-sectional cluster analysis of 68,203 adults with obesity from an Israeli healthcare registry identified five distinct phenotypes using K-prototypes clustering, differing in age, comorbidity, socioeconomic status, and treatment use. The work is descriptive and hypothesis-generating, suggesting that obesity management may benefit from phenotype-informed stratification rather than BMI-alone classification, but does not test this hypothesis or measure clinical outcomes.
Cross-sectional cluster analysis. Adults with obesity from the Leumit Obesity Registry in Israel with at least one documented weight measurement and height or BMI record during 2024.. n = 68,203. Israel.
Five distinct patient clusters identified: metabolically healthy young adults (19% of cohort), low comorbidity adults (18%), middle-aged moderate comorbidity (23%), multimorbid seniors with socioeconomic disadvantage (24%), and higher SES advanced age with high clinical burden (16%) GLP-1 use most common in older multimorbid clusters, particularly among low SES Dietitian use lower in oldest and sickest cluster despite high disease burden
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
This descriptive analysis suggests that routine obesity care stratified by phenotype (age, comorbidity, socioeconomic status) rather than BMI alone could inform resource allocation and treatment choice. However, the findings do not yet demonstrate that phenotype-informed care improves outcomes, and clinicians should view this as a proof-of-concept to guide future prospective evaluation.
A cross-sectional cluster analysis of registry data identifying five obesity phenotypes; descriptive and hypothesis-generating rather than testing a causal relationship or clinical outcome, and without a comparator or intervention effect.
As stated by the source record.
Quoted from the source exactly as published.
This descriptive analysis suggests that routine obesity care stratified by phenotype (age, comorbidity, socioeconomic status) rather than BMI alone could inform resource allocation and treatment choice. However, the findings do not yet demonstrate that phenotype-informed care improves outcomes, and clinicians should view this as a proof-of-concept to guide future prospective evaluation.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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.
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