An Untargeted Metabolomics Approach to Characterize Short-Term and Long-Term Metabolic Changes after Bariatric Surgery

Abstract

Bariatric surgery is currently one of the most effective treatments for obesity and leads to significant weight reduction, improved cardiovascular risk factors and overall survival in treated patients. To date, most studies focused on short-term effects of bariatric surgery on the metabolic profile and found high variation in the individual responses to surgery. The aim of this study was to identify relevant metabolic changes not only shortly after bariatric surgery (Roux-en-Y gastric bypass) but also up to one year after the intervention by using untargeted metabolomics. 132 serum samples taken from 44 patients before surgery, after hospital discharge (1-3 weeks after surgery) and at a 1-year follow-up during a prospective study (NCT01271062) performed at two study centers (Austria and Switzerland). The samples included 24 patients with type 2 diabetes at baseline, thereof 9 with diabetes remission after one year. The samples were analyzed by using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS, HILIC-QExactive). Raw data was processed with XCMS and drift-corrected through quantile regression based on quality controls. 177 relevant metabolic features were selected through Random Forests and univariate testing and 36 metabolites were identified. Identified metabolites included trimethylamine-N-oxide, alanine, phenylalanine and indoxyl-sulfate which are known markers for cardiovascular risk. In addition we found a significant decrease in alanine after one year in the group of patients with diabetes remission relative to non-remission. Our analysis highlights the importance of assessing multiple points in time in subjects undergoing bariatric surgery to enable the identification of biomarkers for treatment response, cardiovascular benefit and diabetes remission. Key-findings include different trend pattern over time for various metabolites and demonstrated that short term changes should not necessarily be used to identify important long term effects of bariatric surgery.

Fig 1. Scheme of untargeted metabolomics approach (CVR = cardiovascular risk), 4 CVD metabolites out of 36 were searched explicitly in the data.

Fig 2. Multidimensional scaling plots from supervised Random Forests of initial 923 metabolic features show distinct clustering of samples taken before (PRE) and after the surgery for both points in time (POST, FU).

Fig 3. Multidimensional scaling plot of unsupervised Random Forests using 177 selected metabolic features from all three sampling points.

Fig 4. Boxplots of peak-AUC metabolites related to CVR for three different sampling points.

Fig 5. Metabolites showing a significant decline (FU/PRE) in diabetes remission (R) patients compared to non-remission (N-R).

Above: Metabolite changes over time, below: different metabolite levels for diabetes remission (R), non-remission (N-R) and non-diabetes patients (N-DM)