Personalizing bariatric metabolic surgery : Predictors of weight-loss success and risk of weight recurrence
(2026) In Metabolism: Clinical and Experimental 177.- Abstract
Background Bariatric metabolic surgery (Roux-en-Y gastric bypass [RYGB] and sleeve gastrectomy [SG]) effectively treats obesity and type 2 diabetes; however, weight loss varies, necessitating predictive factors. Methods We analysed 12- and 24-month weight loss data from 811 patients (RYGB or SG). Factor Analysis of Mixed Data and neural network (NN) modelling identified distinct patient phenotypes and predicted weight-loss patterns. A comparative analysis evaluated weight loss and recurrence between the two procedures. Findings RYGB showed significantly greater weight loss than SG at both 12 (30.3% vs. 25.4%; p < 0.001) and 24 months (26.3% vs. 21.4%; p < 0.001). SG revealed greater variability with bimodal weight loss... (More)
Background Bariatric metabolic surgery (Roux-en-Y gastric bypass [RYGB] and sleeve gastrectomy [SG]) effectively treats obesity and type 2 diabetes; however, weight loss varies, necessitating predictive factors. Methods We analysed 12- and 24-month weight loss data from 811 patients (RYGB or SG). Factor Analysis of Mixed Data and neural network (NN) modelling identified distinct patient phenotypes and predicted weight-loss patterns. A comparative analysis evaluated weight loss and recurrence between the two procedures. Findings RYGB showed significantly greater weight loss than SG at both 12 (30.3% vs. 25.4%; p < 0.001) and 24 months (26.3% vs. 21.4%; p < 0.001). SG revealed greater variability with bimodal weight loss distributions. Unsupervised clustering of SG patients highligheted three phenotypes: the highest responders were women with favourable metabolic profiles; the lowest responders were mostly men with insulin resistance and diabetes. A NN achieved an overall accuracy of 72.5% in predicting 12-month weight loss from baseline characteristics. In RYGB, clustering was less distinct, though baseline metabolic health influenced weight trajectories. A NN predicted weight recurrence versus sustained loss with 74% accuracy. Poor outcomes were associated with higher baseline glucose, insulin resistance, and dyslipidemia; younger age and absence of diabetes predicted better responses. RYGB was superior to SG, even for metabolic high-risk individuals. Interpretation Baseline metabolic health predicts weight-loss outcomes and recurrence risk. RYGB offered greater and more consistent mid-term weight loss, especially benefiting metabolically high-risk patients. Procedure choice must be individualized accounting for specific risk profile and potential complications. These results advocate for a precision-medicine approach in bariatric procedure selection.
(Less)
- author
- organization
- publishing date
- 2026-04
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bariatric metabolic surgery, Longitudinal clustering, Mathematical modelling, Neural network, Weight loss
- in
- Metabolism: Clinical and Experimental
- volume
- 177
- article number
- 156495
- publisher
- Elsevier
- external identifiers
-
- pmid:41539472
- scopus:105027886175
- ISSN
- 0026-0495
- DOI
- 10.1016/j.metabol.2026.156495
- language
- English
- LU publication?
- yes
- id
- 1b30d29a-bb62-402b-824c-f51e0b5c6cb2
- date added to LUP
- 2026-02-18 11:38:19
- date last changed
- 2026-02-18 11:38:35
@article{1b30d29a-bb62-402b-824c-f51e0b5c6cb2,
abstract = {{<p>Background Bariatric metabolic surgery (Roux-en-Y gastric bypass [RYGB] and sleeve gastrectomy [SG]) effectively treats obesity and type 2 diabetes; however, weight loss varies, necessitating predictive factors. Methods We analysed 12- and 24-month weight loss data from 811 patients (RYGB or SG). Factor Analysis of Mixed Data and neural network (NN) modelling identified distinct patient phenotypes and predicted weight-loss patterns. A comparative analysis evaluated weight loss and recurrence between the two procedures. Findings RYGB showed significantly greater weight loss than SG at both 12 (30.3% vs. 25.4%; p < 0.001) and 24 months (26.3% vs. 21.4%; p < 0.001). SG revealed greater variability with bimodal weight loss distributions. Unsupervised clustering of SG patients highligheted three phenotypes: the highest responders were women with favourable metabolic profiles; the lowest responders were mostly men with insulin resistance and diabetes. A NN achieved an overall accuracy of 72.5% in predicting 12-month weight loss from baseline characteristics. In RYGB, clustering was less distinct, though baseline metabolic health influenced weight trajectories. A NN predicted weight recurrence versus sustained loss with 74% accuracy. Poor outcomes were associated with higher baseline glucose, insulin resistance, and dyslipidemia; younger age and absence of diabetes predicted better responses. RYGB was superior to SG, even for metabolic high-risk individuals. Interpretation Baseline metabolic health predicts weight-loss outcomes and recurrence risk. RYGB offered greater and more consistent mid-term weight loss, especially benefiting metabolically high-risk patients. Procedure choice must be individualized accounting for specific risk profile and potential complications. These results advocate for a precision-medicine approach in bariatric procedure selection.</p>}},
author = {{Panunzi, Simona and Russo, Sara and Pompa, Marcello and De Gaetano, Andrea and Verrastro, Ornella and Tuccinardi, Dario and Guidone, Caterina and Gissey, Lidia Castagneto and Casella, Giovanni and Casella Mariolo, James R. and Angelini, Giulia and Pattou, Francois and Sabatini, Silvia and Gastaldelli, Amalia and Franks, Paul W. and Al Ozairi, Ebaa and Sparso, Thomas and Bornstein, Stefan and Le Roux, Carel W. and Mingrone, Geltrude}},
issn = {{0026-0495}},
keywords = {{Bariatric metabolic surgery; Longitudinal clustering; Mathematical modelling; Neural network; Weight loss}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Metabolism: Clinical and Experimental}},
title = {{Personalizing bariatric metabolic surgery : Predictors of weight-loss success and risk of weight recurrence}},
url = {{http://dx.doi.org/10.1016/j.metabol.2026.156495}},
doi = {{10.1016/j.metabol.2026.156495}},
volume = {{177}},
year = {{2026}},
}