Observational studyArchives of endocrinology and metabolism2026
Development and validation of a clinical prediction model for postoperative weight regain after bariatric surgery using inflammatory, metabolic, and ferritin biomarkers.
Observational study in Archives of endocrinology and metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivePostoperative weight regain remains a challenge after bariatric surgery and affects long-term outcomes. This study aimed to develop a clinical model to predict weight regain within 12 months, prior to surgery by using preoperative inflammatory, metabolic, and ferritin as biomarkers. SUBJECTS AND
methodsThis retrospective observational study included 394 patients with obesity who underwent bariatric surgery (2020-2023), including laparoscopic sleeve gastrectomy (LSG) and laparoscopic Roux-en-Y gastric bypass (LRYGB). Patients were divided into a training set (70%, n = 276) and a validation set (30%, n = 118) using a random number table. Weight regain was defined as a ≥ 10% increase from the postoperative nadir (median time to regain: 8.2 months). Key variables included peripheral blood inflammatory markers [systemic immune-inflammation index (SII, calculated as platelet count × neutrophil count/lymphocyte count), neutrophil-to-lymphocyte ratio (NLR)], glycolipid metabolism indicators [low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C)], and ferritin levels. Multivariate logistic regression was used to identify independent predictive variables, and the nomogram model was validated via calibration, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA).
resultsThe weight regain rate was 19.9% (55/276) in the training set. Independent predictive variables included elevated SII (OR=1.004; 95% CI = 1.000-1.007), LDL-C (OR = 1.873; 95% CI = 1.054-3.329), ferritin (OR = 1.005; 95% CI = 1.003-1.008), and reduced HDL-C (OR = 0.103; 95% CI = 0.013-0.844) (all P < 0.05). The model showed strong discrimination (training AUC = 0.852, 95% CI = 0.795-0.910; validation AUC = 0.812, 95% CI = 0.709-0.915) and good calibration (Hosmer-Lemeshow P > 0.05). DCA confirmed the model's clinical utility across threshold probabilities.
conclusionPreoperative SII, LDL-C, ferritin, and HDL-C levels effectively predict postoperative weight regain. Early monitoring of these biomarkers may guide personalized interventions to improve long-term outcomes.
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