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Abstract
There is marked heterogeneity in the response to weight loss interventions with regards to weight loss amount and metabolic improvement. We sought to identify biomarkers predictive of type 2 diabetes remission and amount of weight loss in individuals with severe obesity enrolled in the Longitudinal Assessment of Bariatric Surgery (LABS) and the Look AHEAD (Action for Health in Diabetes) studies. Targeted mass spectrometry-based profiling of 135 metabolites was performed in pre-intervention blood samples using a nested design for diabetes remission over five years (n = 93 LABS, n = 80 Look AHEAD; n = 87 remitters), and for extremes of weight loss at five years (n = 151 LABS; n = 75 with high weight loss). Principal components analysis (PCA) was used for dimensionality reduction, with PCA-derived metabolite factors tested for association with both diabetes remission and weight loss. Metabolic markers were tested for incremental improvement to clinical models, including the DiaRem score. Two metabolite factors were associated with diabetes remission: one primarily composed of branched chain amino acids (BCAA) and tyrosine (odds ratio (95% confidence interval) [OR (95% CI)] = 1.4 [1.0–1.9], p = 0.045), and one with betaine and choline (OR [95% CI] = 0.7 [0.5–0.9], p = 0.02).These results were not significant after adjustment for multiple tests. Inclusion of these two factors in clinical models yielded modest improvements in model fit and performance: in a constructed clinical model, the C-statistic improved from 0.87 to 0.90 (p = 0.02), while the net reclassification index showed improvement in prediction compared to the DiaRem score (NRI = 0.26, p = 0.0013). No metabolite factors associated with weight loss at five years. Baseline levels of metabolites in the BCAA and trimethylamine-N-oxide (TMAO)-microbiome-related pathways are independently and incrementally associated with sustained diabetes remission after weight loss interventions in individuals with severe obesity. These metabolites could serve as clinically useful biomarkers to identify individuals who will benefit the most from weight loss interventions.
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1 Duke Molecular Physiology Institute, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961)
2 Duke Molecular Physiology Institute, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Sarah W. Stedman Nutrition and Metabolism Center, Durham, USA (GRID:grid.26009.3d)
3 Oregon Health & Science University,, Departments of Surgery and Medicine, Portland, USA (GRID:grid.5288.7) (ISNI:0000 0000 9758 5690)
4 Columbia University College of Physicians and Surgeons, New York Obesity Research Center, Division of Endocrinology, Department of Medicine, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729)
5 Wake Forest School of Medicine Medical Center, Department of Biostatistics and Data Science, Winston-Salem, USA (GRID:grid.241167.7) (ISNI:0000 0001 2185 3318)
6 Duke Molecular Physiology Institute, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Sarah W. Stedman Nutrition and Metabolism Center, Durham, USA (GRID:grid.26009.3d); Duke University, Department of Pharmacology & Cancer Biology and Division of Endocrinology, Department of Medicine, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961)
7 Duke Molecular Physiology Institute, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Duke University Medical Center, Division of Cardiology, Department of Medicine, Durham, USA (GRID:grid.189509.c) (ISNI:0000000100241216)