Association of Metabolomic Biomarkers with Sleeve Gastrectomy Weight Loss Outcomes

Abstract

This prospective observational study aimed to evaluate the association of metabolomic alterations with weight loss outcomes following sleeve gastrectomy (SG). We evaluated the metabolomic profile of serum and feces prior to SG and three months post-SG, along with weight loss outcomes in 45 adults with obesity. The percent total weight loss for the highest versus the lowest weight loss tertiles (T3 vs. T1) was 17.0 ± 1.3% and 11.1 ± 0.8%, p < 0.001. Serum metabolite alterations specific to T3 at three months included a decrease in methionine sulfoxide concentration as well as alterations to tryptophan and methionine metabolism (p < 0.03). Fecal metabolite changes specific to T3 included a decrease in taurine concentration and perturbations to arachidonic acid metabolism, and taurine and hypotaurine metabolism (p < 0.002). Preoperative metabolites were found to be highly predictive of weight loss outcomes in machine learning algorithms, with an average area under the curve of 94.6% for serum and 93.4% for feces. This comprehensive metabolomics analysis of weight loss outcome differences post-SG highlights specific metabolic alterations as well as machine learning algorithms predictive of weight loss. These findings could contribute to the development of novel therapeutic targets to enhance weight loss outcomes after SG.

Keywords: bariatric surgery; metabolites; obesity; sleeve gastrectomy; weight loss.

Figure 1

Metabolite set-enrichment analysis (MSEA) of the highest %TWL tertile (T3) at three months post-sleeve gastrectomy compared with all patients at baseline. (A) Serum. (B) Fecal. Note: A total of 209 fecal and 157 serum metabolites were used for the MSEA.

Figure 2

(A) Serum metabolite predictive models and (B) receiver-operating characteristic (ROC) plots for three-month post-sleeve gastrectomy weight loss outcomes: highest %TWL tertile (T3) versus all patients at baseline. Note: %TWL = percent total weight loss; logistic reg = logistic regression; ridge clf = ridge classifier; discr linear = linear discrimination; discr quad = discrete quad; gaussian nb = Gaussian Naive Bayes; svm linear = support vector machine linear; svm kernel = support vector machine kernel; knn = K-nearest neighbors; gbm = generalized boosted regression modeling; xgboost = extreme gradient boosting.

Figure 3

(A) Fecal metabolite predictive models and (B) receiver-operating characteristic (ROC) plots for three-month post-sleeve gastrectomy weight loss outcomes: highest %TWL tertile (T3) versus all patients at baseline. Note: %TWL = percent total weight loss; logistic reg = logistic regression; ridge clf = ridge classifier; discr linear = linear discrimination; discr quad = discrete quad; gaussian nb = Gaussian Naive Bayes; svm linear = support vector machine linear; svm kernel = support vector machine kernel; knn = K-nearest neighbors; gbm = generalized boosted regression modeling; xgboost = extreme gradient boosting.