Metabolomics analyses in non-diabetic middle-aged individuals reveal metabolites impacting early glucose disturbances and insulin sensitivity

Bos, Maxime M.; Noordam, Raymond; Bennett, Kate; Beekman, Marian; Mook‐Kanamori, Dennis O.; Dijk, Ko Willems van; Slagboom, P. Eline; Lundstedt, Torbjörn; Surowiec, Izabella; Heemst, Diana van

doi:10.1007/s11306-020-01653-7

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…According to recent studies, it has been found that there is a strong association between the chronic elevation of BCAA in serum and insulin resistance ( 32 , 33 ). In a study of serum metabolites based on non-diabetic middle-aged people ( 34 ), serum tryptophan and tyrosine concentrations are found to be related to glucose intolerance and insulin resistance, and these two amino acids are also higher in patients with diabetics with decreased insulin secretion ( 34 , 35 ). Serum L-homocysteine concentrations are also associated with insulin resistance and diabetes.…”

Section: Discussionmentioning

confidence: 99%

Beneficial Effects of a Low-Glycemic Diet on Serum Metabolites and Gut Microbiota in Obese Women With Prevotella and Bacteriodes Enterotypes: A Randomized Clinical Trial

Hur

Yang

et al. 2022

Front. Nutr.

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Generalized healthy eating patterns may not benefit everyone due to different genetics and enterotypes. We aimed to compare the effects of a low-glycemic diet representing the Korean traditional balanced diet (Low-GID) and westernized diet as a control diet (CD) on anthropometry, serum metabolites, and fecal bacteria in a randomized clinical trial according to enterotypes. We recruited 52 obese women aged 30–50 years, and they consumed Low-GID and CD meals for 1 month, with a 1-month washout period, in a crossover randomized clinical trial. The Low-GID was mainly composed of whole grains with fish, vegetables, seaweeds, and perilla oil, whereas CD contained refined rice, bread, noodles, meats, and processed foods. Serum lipid profiles, metabolomics, serum short-chain fatty acids, and fecal bacteria were analyzed. The important variables influenced by Low-GID and CD were determined by SHAP value in the XGBoost algorithm according to Bacteroides (ET-B) and Prevotella (ET-P). Low-GID and CD interventions did not change the enterotypes, but they modified serum metabolites and some fecal bacterial species differently according to enterotypes. The 10-fold cross-validation of the XGBoost classifier in the ET-P and ET-B clusters was 0.91 ± 0.04 and 0.8 ± 0.07, respectively. In the ET-P cluster, serum L-homocysteine, glutamate, leucine concentrations, and muscle mass were higher in the CD group than in the Low-GID group, whereas serum 3-hydroxybutyric acid concentration was significantly higher in the Low-GID group than in the CD group (p < 0.05). In fecal bacteria, Gemmiger formicilis, Collinsella aerofaciens, and Escherichia coli were higher in the CD group than in the Low-GID group. In the ET-B cohort, serum tryptophan and total cholesterol concentrations were higher in the CD group than in the Low-GID group, whereas serum glutathione and 3-hydroxybutyric acid concentrations were significantly higher in the Low-GID group than in the CD group (p < 0.05). However, Bifidobacterium longum was higher in CD than Low-GID in the ET-B cluster, but serum butyric acid levels were higher in the Low-GID than in the CD group. In conclusion, Low-GID can be recommended in obese women with both ET-P and ET-B enterotypes, although its efficacy was more effective in ET-P.Clinical Trial Registration[https://cris.nih.go.kr/cris/search/detailSearch.do/17398], identifier [KCT0005340].

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Section: Discussionmentioning

confidence: 99%

Beneficial Effects of a Low-Glycemic Diet on Serum Metabolites and Gut Microbiota in Obese Women With Prevotella and Bacteriodes Enterotypes: A Randomized Clinical Trial

Hur

Yang

et al. 2022

Front. Nutr.

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“…DC. C4.OH, [ 28 ] Pre-diabetes GC–MS Tyrosine, lysine, alanine, valine, proline, phenylalanine, tryptophan, hexadecanoic acid, alpha-ketoglutaric acid, myristic acid, octadecanoic acid, uric acid [ 29 ] Pre-diabetes and T2DM LC-MS Ceramides, saturated sphingomyelins, unsaturated sphingomyelins, hydroxyl-sphingomyelins, and a hexosylceramide [ 30 ] Pre-diabetes LC-MS N2, N2-dimethylguanosine, 7-methylguanine and 3-hydroxytrimethyllysine [ 31 ] Pre-diabetes LC-MS A cluster of saturated sphingomyelins [ 32 ] Pre-diabetes 1H-NMR Lipids in HDL subtypes, citrate, glycoprotein acetyls, branched-chain amino acids, VLDL lipids [ 33 ] Pre-diabetes LC-MS Alanine, aspartate, glutamate, isoleucine, leucine, phenylalanine, tyrosine, tryptophan, and valine [ 34 ] Pre-diabetes GC-MS and LC-MS 1,5-anhydroglucitol [ 35 ] Pre-diabetes HPLC-MRM TAGs, lyso-phosphatidylinositols, phosphatidylcholines, PUFA-PEps, cholesteryl esters [ 36 ] Pre-diabetes CIL-LC-MS Methionine (Met) sulfoxide, amino acids (Asn, Gln, and His), 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, L-2-amino-3-oxobutanoic acid, serotonin, and 4,6-dihydroxyquinoline. [ 37 ] Pre-diabetes GC-MS and LC-MS Lysophosphatidylcholines (muscle), glycodeoxycholic acid (liver) …”

Section: Application Of Metabolomics In the Diagnosis Of T2dmmentioning

confidence: 99%

“… [ 37 ] Pre-diabetes GC-MS and LC-MS Lysophosphatidylcholines (muscle), glycodeoxycholic acid (liver) [ 38 ] Pre-diabetes and diabetes GC-MS Maltose, glucose, trehalose, an unknown sugar compound (U15), mannose, fructose, sedoheptulose, and 1,5 anhydroglucitol, [ 39 ] T2DM CIL-LC-MS Amino acids, amino acids metabolites, and dipeptides. [ 37 ] T2DM GC-MS and LC-MS Carnitines (liver), lysophosphatidylcholines (muscle and serum) [ 38 ] T2DM LC-MS Alanine, 3-methyl histidine, glutamic acid, arginine, tryptophan, and ethanolamine sarcosine [ 40 ] T2DM 1H-NMR and 2D-NMR 61 distinct metabolites [ 41 ] Pre-diabetes and diabetes LC-MS Oxidized glycerophosphatidylcholines [ 42 ] Obese insulin sensitive (OIS) LC-MS Phospholipid metabolites, including choline, glycerophosphoethanolamine, and glycerophosphorylcholine [ 43 ] T2DM GC-MS Tyrosine, alanine, valine, tryptophan, and alpha-ketoglutaric acid [ 29 ] T2DM LC-MS Organophosphate flame retardant (OPFR) diesters [ 44 ] T2DM LC-MS …”

Section: Application Of Metabolomics In the Diagnosis Of T2dmmentioning

confidence: 99%

“…53 Recent advancements in metabolomics have significantly contributed to pre-diabetes biomarker recognition. 26,27,[29][30][31][32][33][34]38 Li et al utilized non-targeted and targeted metabolomics to identify novel plasma biomarkers in lean β-Phb2-/-mice and obese db/db mice. The study highlighted 1,5-anhydroglucitol's association with the loss of functional β-cell mass, revealing metabolic similarities between the liver and plasma.…”

Section: Dovepressmentioning

confidence: 99%

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Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Li,

Zhu,

Wang

et al. 2023

DMSO

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Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.

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“…(5) The application of metabolomics in observational epidemiological studies has helped to identify metabolites involved in the pathogenesis of T2D, or that might contribute to T2D incidence. (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) For example, a recent meta-analysis identified associations of incident T2D with differences in several lipid and non-lipid metabolites, including higher levels of branched chain amino acids (BCAAs), some fatty acids and acylcarnitines, amongst others. (17) For CAD, metabolomics in observational studies has helped to identify key metabolic characteristics of the disease, (18)(19)(20)(21)(22)(23)(24)(25) to predict and diagnose CAD, (26)(27)(28)(29) and to predict outcomes and mortality in CAD patients.…”

Section: Introductionmentioning

confidence: 99%

Distinct metabolic features of genetic liability to type 2 diabetes and coronary artery disease: a reverse Mendelian randomization study

Smith

Bull

Holmes

et al. 2022

Preprint

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BackgroundType 2 diabetes (T2D) and coronary artery disease (CAD) both have roots in disordered metabolism and known genetic determinants. The mechanisms through which their associated genetic variants lead to disease onset remain poorly understood. Here, we used large-scale metabolomics data to directly compare the metabolic features of genetic liability to T2D and to CAD.MethodsWe performed two-sample reverse Mendelian randomization (MR) to estimate effects of genetic liability to T2D and CAD on 249 circulating metabolites from targeted nuclear magnetic resonance spectroscopy in the UK Biobank (N=118,466) using 167 and 145 genetic instruments, respectively. We examined the potential for medication use to distort effect estimates by examining effects of disease liability on metformin and statin use and by conducting age-stratified metabolite analyses.ResultsUsing inverse variance weighted (IVW) models, higher genetic liability to T2D was estimated to decrease high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) (e.g., HDL-C: -0.05 SD; 95% CI -0.07, -0.03, per doubling of liability), whilst increasing all triglyceride groups and branched chain amino acids (BCAAs). Estimates for CAD liability suggested an effect on reducing HDL-C as well as raising very-low density lipoprotein cholesterol (VLDL-C) and LDL-C, and LDL triglycerides. Liability to each disease was estimated to decrease apolipoprotein-A1, whilst only CAD liability was estimated with IVW to increase apolipoprotein-B (0.10 SD; 95% CI 0.03, 0.17). In pleiotropy-robust sensitivity models, T2D liability was still estimated to increase BCAAs, but several effect estimates for higher CAD liability reversed and supported decreased LDL-C and apolipoprotein-B. Moreover, higher T2D liability uniquely increased the odds of using metformin, whereas higher CAD liability uniquely increased the odds of using statins. Estimated effects of CAD liability differed uniquely and substantially by age for non-HDL-C traits in particular, with, e.g., pleiotropy-robust models suggesting that higher CAD liability lowers LDL-C only at older ages when use of statins is common.ConclusionsOur results support largely distinct metabolic features of genetic liability to T2D and to CAD, particularly higher BCAAs in T2D and lower LDL-C and apolipoprotein-B in CAD. Such apparently protective effects of CAD liability differ substantially by age and likely reflect mediation by statin use in adulthood.

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Metabolomics analyses in non-diabetic middle-aged individuals reveal metabolites impacting early glucose disturbances and insulin sensitivity

Cited by 9 publications

References 29 publications

Beneficial Effects of a Low-Glycemic Diet on Serum Metabolites and Gut Microbiota in Obese Women With Prevotella and Bacteriodes Enterotypes: A Randomized Clinical Trial

Beneficial Effects of a Low-Glycemic Diet on Serum Metabolites and Gut Microbiota in Obese Women With Prevotella and Bacteriodes Enterotypes: A Randomized Clinical Trial

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Distinct metabolic features of genetic liability to type 2 diabetes and coronary artery disease: a reverse Mendelian randomization study

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