Longitudinal Assessment of Twelve-Month Weight Loss Outcomes Post-Sleeve Gastrectomy: The Role of Serum and Fecal Metabolomic Biomarkers

Background/Objectives: Metabolomics has emerged as a tool to gain insight into the body’s biological responses to therapeutic interventions. Bariatric surgery remains the most effective treatment for severe obesity and associated comorbidities, leading to significant weight and metabolic improvements. Using a multi-platform, multi-compartment metabolomics approach, this study systematically characterizes longitudinal changes in fecal and serum metabolomes of 45 patients following sleeve gastrectomy (SG). Participants were stratified by weight loss outcomes to identify metabolic signatures associated with differential responses, which may serve as predictors of weight loss and provide mechanistic insights in developing targeted therapeutic strategies. Methods: Metabolomic and lipidomic responses to SG were analyzed using multivariable linear mixed-effects models based on data collected pre-operatively and at 3 and 12 months post-operatively. Results: The percentage total weight loss for the highest versus lowest weight loss tertiles (T3 vs. T1) at twelve months was 35.81 + 5.4% and 19.49 + 2.62%, p < 0.001, respectively. Substantial alterations in fecal metabolites and lipid species were observed among T3 after twelve months, including aspartate, tyrosine, carnitine, glycine, PC.ae.C36.4, PC.aa.C38.0, PC.ae.C44.4, PC.ae.C40.2, and PC.aa.C40:5. Specifically, changes in serum lipid species including SM.OH.C22:1, PC.ae.C32:1, PC.aa.C34:4, PC.aa.C36:6, PC.ae.C34:3, PC.ae.C34:1, and PC.ae.C32:2, support serum lipidomics as a minimally invasive marker of gut remodeling and adaptation following SG. Sex-stratified analysis revealed unique fecal and serum metabolic changes, highlighting the significance of personalized metabolic monitoring and obesity treatment. Conclusions: Our findings identify metabolic alterations associated with response variability following SG and highlight the potential utility of machine learning to predict weight-loss trajectories and inform personalized interventions.