Metabolomic Fingerprints of Medical Therapy Versus Bariatric Surgery in Patients With Obesity and Type 2 Diabetes: The STAMPEDE Trial

Abstract

Objective: Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) are effective procedures to treat and manage type 2 diabetes (T2D). However, the underlying metabolic adaptations that mediate improvements in glucose homeostasis remain largely elusive. The purpose of this study was to identify metabolic signatures associated with biochemical resolution of T2D after medical therapy (MT) or bariatric surgery.

Research design and methods: Plasma samples from 90 patients (age 49.9 ± 7.6 years; 57.7% female) randomly assigned to MT (n = 30), RYGB (n = 30), or SG (n = 30) were retrospectively subjected to untargeted metabolomic analysis using ultra performance liquid chromatography with tandem mass spectrometry at baseline and 24 months of treatment. Phenotypic importance was determined by supervised machine learning. Associations between change in glucose homeostasis and circulating metabolites were assessed using a linear mixed effects model.

Results: The circulating metabolome was dramatically remodeled after SG and RYGB, with largely overlapping signatures after MT. Compared with MT, SG and RYGB profoundly enhanced the concentration of metabolites associated with lipid and amino acid signaling, while limiting xenobiotic metabolites, a function of decreased medication use. Random forest analysis revealed 2-hydroxydecanoate as having selective importance to RYGB and as the most distinguishing feature between MT, SG, and RYGB. To this end, change in 2-hydroxydecanoate correlated with reductions in fasting glucose after RYGB but not SG or MT.

Conclusions: We identified a novel metabolomic fingerprint characterizing the longer-term adaptations to MT, RYGB, and SG. Notably, the metabolomic profiles of RYGB and SG procedures were distinct, indicating equivalent weight loss may be achieved by divergent effects on metabolism.

Graphical abstract

Figure 1

Differential plasma metabolomic signatures after MT, SG, and RYGB surgery. A: Principal component analysis (PCA) at baseline and 24 months of treatment. B: Volcano plot visualization of differentially expressed metabolites (false discovery rate q < 0.1) from baseline to 24 months of treatment within groups. C: Visualization of differentially expressed metabolites by treatment allocation. D: Differential pathway enrichment analysis from baseline to 24 months of treatment. E: Unsupervised machine learning prediction of pathway significance to metabolome phenotype.

Figure 2

Identification of metabolic regulators of glucose homeostasis after MT, SG, and RYGB surgery. A–D: Random forest analysis of MT (A), SG (B), RYGB (C), and MT vs. SG vs. RYGB (D). E: Venn diagram illustrating overlapping vs. discriminate metabolites in each intervention. F: Venn diagram illustrating overlapping vs. discriminate metabolites correlated with change in glucose and HbA_1c from baseline to 2 years. G: Expression intensity of 2-hydroxydecanoate before and 2 years after MT, SG, or RYGB. H–J: Regression modeling of relationship between 2-hydroxydecanoate expression (baseline expression intensity) and plasma glucose (change in mg/dL from baseline to 2 years) with MT (H), SG (I), and RYGB (J).