Personalizing bariatric metabolic surgery: Predictors of weight-loss success and risk of weight recurrence
Year: 2026
Type: article
Study design: Observational Study
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 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.
Source: Metabolism
Authors: Simona Panunzi, Sara Russo, Marcello Pompa, Andrea De Gaetano, Ornella Verrastro, Dario Tuccinardi, Caterina Guidone, Lidia Castagneto Gissey, Giovanni Casella, James R. Casella Mariolo, Giulia Angelini, Francois Pattou, Silvia Sabatini, Amalia Gastaldelli, Paul W. Franks, Ebaa Al Ozairi, Thomas Sparso, Stefan Bornstein, Carel W. Le Roux, Geltrude Mingrone
Institutions: National Research Council, Istituto di Analisi dei Sistemi ed Informatica Antonio Ruberti, Università Cattolica del Sacro Cuore, Obuda University, Institute for Biomedical Research and Innovation, Agostino Gemelli University Polyclinic, Università Campus Bio-Medico, Campus Bio Medico University Hospital, Sapienza University of Rome, Inserm, Université de Lille, Institut Pasteur de Lille, Centre Hospitalier Universitaire de Lille, Recherche translationnelle sur le diabète et les troubles métaboliques, Istituto di Fisiologia Clinica, Queen Mary University of London, Lund University, Dasman Diabetes Institute, Novo Nordisk (Denmark), King's College London, University Hospital Carl Gustav Carus, Technische Universität Dresden, University College Dublin
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $5,340
Award: G316137520
OpenAlex ID: W7123360958