Aktuelle Ernährungsmedizin 2025; 50(02): e13-e14
DOI: 10.1055/s-0045-1809110
Abstracts
POSTERS

Data-Driven Clustering of patients with Obesity in a Four Years Behavioral Weight-Loss Program

H Schlögl
1   Endocrinology, University Hospital Leipzig
2   Helmholtz Institute for Metabolic, Obesity and Vascular Research at Helmholtz Munich and the University of Leipzig and the University Hospital Leipzig
,
A Kühnapfel
3   Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
,
S V Frenzel
2   Helmholtz Institute for Metabolic, Obesity and Vascular Research at Helmholtz Munich and the University of Leipzig and the University Hospital Leipzig
,
M Blüher
2   Helmholtz Institute for Metabolic, Obesity and Vascular Research at Helmholtz Munich and the University of Leipzig and the University Hospital Leipzig
,
M Stumvoll
1   Endocrinology, University Hospital Leipzig
,
T Ebert
1   Endocrinology, University Hospital Leipzig
› Institutsangaben
› Weitere Informationen
Auch verfügbar auf
 

Introduction: In patients with obesity and type 2 diabetes undergoing bariatric surgery, a data-driven clustering approach revealed that people with insulin resistance in particular benefit from obesity surgery (Raverdy et al. 2022).

Objectives: However, it is not known whether a data-driven cluster analysis can also subphenotype patients participating in a behavioral weight loss program and whether identified subclusters associate with differential therapeutic outcomes.

Methods: We performed k-means clustering using the variables age, body mass index (BMI), and c-peptide-based Homeostasis Model Assessment of beta cell function (HOMA2-%B) and insulin resistance (HOMA2-IR) at baseline in patients with obesity undergoing a four year behavioral weight loss program at the University Hospital Leipzig, Germany. A total of 239 (170 female; 74 with type-2-diabetes) patients were included in the analysis.

Results: At baseline, the median (interquartile range) BMI was 43.2 (8.9) kg/m². After four years, mean weight loss was 3.1 (10.1) kg (p<0.001), and glucose and lipid parameters significantly improved. Based on descriptive cluster characteristics, the clusters “Insulin-deficient Elderly Obesity” (IDEO, N=114), “Insulin-resistant Severe Obesity” (IRSO, N=46) and “Young Glucose-tolerant Obesity” (YGTO, N=79) were formed. At baseline, the three identified clusters significantly differed in all parameters used for clustering (p<0.001), as well as in fasting c-peptide and glucose, hemoglobin A1c, high-density lipoprotein cholesterol and estimated glomerular filtration rate (all p<0.001). Patients from the IRSO cluster showed the highest BMI reductions over the four years of the program, whereas the YGTO cluster had the smallest weight reduction.

Conclusion: Distinct subphenotypes of patients with obesity undergoing a behavioral weight-loss program were associated with differential metabolic effects of the weight-loss intervention. Using a data-driven clustering approach based on anthropometric and metabolic patient characteristics, the most favorable effects of the four year behavioral weight loss program were found in the IRSO cluster. Future prospective studies need to test whether these clusters can help to improve therapy in a personalized medicine approach for patients with obesity [1].



Publikationsverlauf

Artikel online veröffentlicht:
25. Mai 2025

© 2025. Thieme. All rights reserved.

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 

  • References

  • 1 Raverdy V, Cohen RV, Caiazzo R. et al. Data-driven subgroups of type 2 diabetes, metabolic response, and renal risk profile after bariatric surgery: a retrospective cohort study. Lancet Diabetes Endocrinol 2022; 10: 167-76
    CrossrefPubMedSuche in Google Scholar