The metabolomic signature of weight loss in the Diabetes Remission Clinical Trial (DiRECT)

Use of high throughput metabolomics technologies in a variety of study designs has demonstrated a strong and consistent metabolomic signature of overweight and type 2 diabetes. However, the extent to which these metabolomic patterns can be recovered with weight loss and diabetes remission has not been investigated. We aimed to characterise the metabolomic consequences of a weight loss intervention in diabetes, within an existing randomised controlled trial, the Diabetes Remission Clinical Trial (DiRECT), to provide insight into how weight loss-induced metabolic changes could lead to improved health. Decreases in branched chain amino acids, sugars and LDL triglycerides, and increases in sphingolipids, plasmalogens and metabolites related to fatty acid metabolism were associated with the intervention. The change in metabolomic pattern with mean 8.8kg weight loss thus reverses many features associated with the development of type 2 diabetes. Furthermore, metabolomic profiling also appears to capture variation in response to treatment seen across patients.

[1]  N. Timpson,et al.  metaboprep: an R package for preanalysis data description and processing , 2022, Bioinform..

[2]  Cholsoon Jang,et al.  Metabolism and Health Impacts of Dietary Sugars , 2022, Journal of lipid and atherosclerosis.

[3]  R. Ma,et al.  Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies , 2021, Cells.

[4]  Deyang Yu,et al.  The adverse metabolic effects of branched-chain amino acids are mediated by isoleucine and valine. , 2021, Cell metabolism.

[5]  Luis Pedro Coelho,et al.  Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology , 2020, Nature Communications.

[6]  F. Bäckhed,et al.  Microbial Imidazole Propionate Affects Responses to Metformin through p38γ-Dependent Inhibitory AMPK Phosphorylation , 2020, Cell metabolism.

[7]  Lyanne M. Kieneker,et al.  Urinary Ethyl Glucuronide as Measure of Alcohol Consumption and Risk of Cardiovascular Disease: A Population‐Based Cohort Study , 2020, Journal of the American Heart Association.

[8]  A. Dömling,et al.  Metabolic signature of obesity-associated insulin resistance and type 2 diabetes , 2019, Journal of Translational Medicine.

[9]  N. Timpson,et al.  Early Metabolic Features of Genetic Liability to Type 2 Diabetes: Cohort Study With Repeated Metabolomics Across Early Life , 2019, Diabetes Care.

[10]  Roy Taylor,et al.  Remission of type 2 diabetes: a position statement from the Association of British Clinical Diabetologists (ABCD) and the Primary Care Diabetes Society (PCDS) , 2019, British Journal of Diabetes.

[11]  M. Shams,et al.  Metabolomics of Type 1 and Type 2 Diabetes , 2019, International journal of molecular sciences.

[12]  P. Meikle,et al.  Lipidomic profiling reveals early-stage metabolic dysfunction in overweight or obese humans. , 2019, Biochimica et biophysica acta. Molecular and cell biology of lipids.

[13]  Elizabeth T. Cirulli,et al.  Profound Perturbation of the Metabolome in Obesity Is Associated with Health Risk , 2019, Cell metabolism.

[14]  G. Bergström,et al.  Microbially Produced Imidazole Propionate Impairs Insulin Signaling through mTORC1 , 2018, Cell.

[15]  Benjamin S. Aribisala,et al.  Remission of Human Type 2 Diabetes Requires Decrease in Liver and Pancreas Fat Content but Is Dependent upon Capacity for β Cell Recovery. , 2018, Cell metabolism.

[16]  S. Peterson,et al.  Mannose Alters Gut Microbiome, Prevents Diet-Induced Obesity, and Improves Host Metabolism. , 2018, Cell reports.

[17]  M. McCullough,et al.  Untargeted Metabolomics Identifies Novel Potential Biomarkers of Habitual Food Intake in a Cross-Sectional Study of Postmenopausal Women. , 2018, The Journal of nutrition.

[18]  Min-Jeong Shin,et al.  Serum metabolite profile associated with incident type 2 diabetes in Koreans: findings from the Korean Genome and Epidemiology Study , 2018, Scientific Reports.

[19]  M. Holeček.  Branched-chain amino acids in health and disease: metabolism, alterations in blood plasma, and as supplements , 2018, Nutrition & Metabolism.

[20]  T. Lehtimäki,et al.  Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four Finnish cohorts , 2019, bioRxiv.

[21]  E. Boerwinkle,et al.  Serum metabolomic profile of incident diabetes , 2018, Diabetologia.

[22]  D. Corella,et al.  Non-targeted metabolomic biomarkers and metabotypes of type 2 diabetes: A cross-sectional study of PREDIMED trial participants. , 2018, Diabetes & metabolism.

[23]  Roy Taylor,et al.  Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial , 2017, The Lancet.

[24]  Angela M Rodrigues,et al.  Clinical and metabolic features of the randomised controlled Diabetes Remission Clinical Trial (DiRECT) cohort , 2017, Diabetologia.

[25]  G. Davey Smith,et al.  Genetic Support for a Causal Role of Insulin Resistance on Circulating Branched-Chain Amino Acids and Inflammation , 2017, Diabetes Care.

[26]  M. Laakso,et al.  Plasma Mannose Levels Are Associated with Incident Type 2 Diabetes and Cardiovascular Disease. , 2017, Cell metabolism.

[27]  T. Hankemeier,et al.  Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study , 2017, Metabolomics.

[28]  F. Tinahones,et al.  Metabolomics‐guided insights on bariatric surgery versus behavioral interventions for weight loss , 2016, Obesity.

[29]  Inês Barroso,et al.  Genetic Predisposition to an Impaired Metabolism of the Branched-Chain Amino Acids and Risk of Type 2 Diabetes: A Mendelian Randomisation Analysis , 2016, PLoS medicine.

[30]  S. A. Arriola Apelo,et al.  Decreased Consumption of Branched-Chain Amino Acids Improves Metabolic Health. , 2016, Cell reports.

[31]  Frank B Hu,et al.  Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis , 2016, Diabetes Care.

[32]  Angela M Rodrigues,et al.  The Diabetes Remission Clinical Trial (DiRECT): protocol for a cluster randomised trial , 2016, BMC Family Practice.

[33]  Clary B. Clish,et al.  Metabolomics: an emerging but powerful tool for precision medicine , 2015, Cold Spring Harbor molecular case studies.

[34]  Chua Lee Suan,et al.  Metabolomics - the complementary field in systems biology: a review on obesity and type 2 diabetes. , 2015, Molecular bioSystems.

[35]  Fabian J Theis,et al.  Multi-omic signature of body weight change: results from a population-based cohort study , 2015, BMC Medicine.

[36]  Pasi Soininen,et al.  Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. , 2015, Circulation. Cardiovascular genetics.

[37]  Samuli Ripatti,et al.  Metabolic Signatures of Adiposity in Young Adults: Mendelian Randomization Analysis and Effects of Weight Change , 2014, PLoS medicine.

[38]  Curtis J. Donskey,et al.  Metabolomics Analysis Identifies Intestinal Microbiota-Derived Biomarkers of Colonization Resistance in Clindamycin-Treated Mice , 2014, PloS one.

[39]  J. Martínez,et al.  Metabolomics identifies changes in fatty acid and amino acid profiles in serum of overweight older adults following a weight loss intervention , 2014, Journal of Physiology and Biochemistry.

[40]  S. Zeisel,et al.  Choline metabolism provides novel insights into nonalcoholic fatty liver disease and its progression , 2012, Current opinion in gastroenterology.

[41]  Bryan C. Batch,et al.  Branched-chain amino acid levels are associated with improvement in insulin resistance with weight loss , 2012, Diabetologia.

[42]  G. Schmitz,et al.  Plasmalogens the neglected regulatory and scavenging lipid species. , 2011, Chemistry and physics of lipids.

[43]  Benjamin S. Aribisala,et al.  Reversal of type 2 diabetes: normalisation of beta cell function in association with decreased pancreas and liver triacylglycerol , 2011, Diabetologia.

[44]  Christian Gieger,et al.  Metabolic Footprint of Diabetes: A Multiplatform Metabolomics Study in an Epidemiological Setting , 2010, PloS one.

[45]  Anne M. Evans,et al.  Organization of GC/MS and LC/MS metabolomics data into chemical libraries , 2010, J. Cheminformatics.

[46]  Reino Laatikainen,et al.  High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. , 2009, The Analyst.

[47]  Corey D. DeHaven,et al.  Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. , 2009, Analytical chemistry.

[48]  Roy Taylor,et al.  Pathogenesis of type 2 diabetes: tracing the reverse route from cure to cause , 2008, Diabetologia.

[49]  Joachim Selbig,et al.  pcaMethods - a bioconductor package providing PCA methods for incomplete data , 2007, Bioinform..

[50]  F. Wurst,et al.  Detection of recent ethanol intake with new markers: comparison of fatty acid ethyl esters in serum and of ethyl glucuronide and the ratio of 5-hydroxytryptophol to 5-hydroxyindole acetic acid in urine. , 2005, Alcoholism, clinical and experimental research.

[51]  W. März,et al.  Low-Density Lipoprotein Triglycerides Associated With Low-Grade Systemic Inflammation, Adhesion Molecules, and Angiographic Coronary Artery Disease: The Ludwigshafen Risk and Cardiovascular Health Study , 2004, Circulation.

[52]  Ronald J A Wanders,et al.  Functions and biosynthesis of plasmalogens in health and disease. , 2004, Biochimica et biophysica acta.

[53]  R. T. Rosen,et al.  Determination of allicin, S-allylcysteine and volatile metabolites of garlic in breath, plasma or simulated gastric fluids. , 2001, The Journal of nutrition.

[54]  U. Smith,et al.  Mannose is an insulin-regulated metabolite reflecting whole-body insulin sensitivity in man. , 2019, Metabolism: clinical and experimental.

[55]  Tommi Suvitaival,et al.  Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men. , 2018, Metabolism: clinical and experimental.

[56]  J. Griffin,et al.  Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. , 2014, The lancet. Diabetes & endocrinology.

[57]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[58]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .