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BMC Endocrine Disorders
Published in: BMC Endocrine Disorders 1/2020

01-12-2020 | Diabetes | Research article

Metabolite biomarkers of type 2 diabetes mellitus and pre-diabetes: a systematic review and meta-analysis

Authors: Jianglan Long, Zhirui Yang, Long Wang, Yumei Han, Cheng Peng, Can Yan, Dan Yan

Published in: BMC Endocrine Disorders | Issue 1/2020

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Abstract

Background

We aimed to explore metabolite biomarkers that could be used to identify pre-diabetes and type 2 diabetes mellitus (T2DM) using systematic review and meta-analysis.

Methods

Four databases, the Cochrane Library, EMBASE, PubMed and Scopus were selected. A random effect model and a fixed effect model were applied to the results of forest plot analyses to determine the standardized mean difference (SMD) and 95% confidence interval (95% CI) for each metabolite. The SMD for every metabolite was then converted into an odds ratio to create an metabolite biomarker profile.

Results

Twenty-four independent studies reported data from 14,131 healthy individuals and 3499 patients with T2DM, and 14 included studies reported 4844 healthy controls and a total of 2139 pre-diabetes patients. In the serum and plasma of patients with T2DM, compared with the healthy participants, the concentrations of valine, leucine, isoleucine, proline, tyrosine, lysine and glutamate were higher and that of glycine was lower. The concentrations of isoleucine, alanine, proline, glutamate, palmitic acid, 2-aminoadipic acid and lysine were higher and those of glycine, serine, and citrulline were lower in prediabetic patients. Metabolite biomarkers of T2DM and pre-diabetes revealed that the levels of alanine, glutamate and palmitic acid (C16:0) were significantly different in T2DM and pre-diabetes.

Conclusions

Quantified multiple metabolite biomarkers may reflect the different status of pre-diabetes and T2DM, and could provide an important reference for clinical diagnosis and treatment of pre-diabetes and T2DM.
Appendix
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Literature
1.
Kostraba JN, Klein R, Dorman JS, et al. The epidemiology of diabetes complications study. IV. Correlates of diabetic background and proliferative retinopathy. Am J Epidemiol. 1991;133(4):381–91.PubMedCrossRef
2.
Roberts MD, Slover RH, Chase HP. Diabetic ketoacidosis with intracerebral complications. Pediatr Diabetes. 2001;2(3):109–14.PubMedCrossRef
3.
Collaboration NRF. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet. 2016;387(10027):1513–30.CrossRef
4.
Howarth FC, Shafiullah M, Qureshi MA. Chronic effects of type 2 diabetes mellitus on cardiac muscle contraction in the Goto-Kakizaki rat. Exp Physiol. 2007;92(6):1029–36..PubMedCrossRef
5.
Li G, Zhang P, Wang J, et al. Cardiovascular mortality, all-cause mortality, and diabetes incidence after lifestyle intervention for people with impaired glucose tolerance in the Da Qing diabetes prevention study: a 23-year follow-up study. Lancet Diabetes Endocrinol. 2014;2(6):474–80.PubMedCrossRef
6.
7.
Tessari P, Cecchet D, Cosma A, et al. Insulin resistance of amino acid and protein metabolism in type 2 diabetes. Clin Nutr. 2011;30(3):267–72.PubMedCrossRef
8.
Michaliszyn SF, Sjaarda LA, Mihalik SJ, et al. Metabolomic profiling of amino acids and beta-cell function relative to insulin sensitivity in youth. J Clin Endocrinol Metab. 2012;97(11):E2119–24.PubMedPubMedCentralCrossRef
9.
Wurtz P, Soininen P, Kangas AJ, et al. Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care. 2013;36(3):648–55.PubMedPubMedCentralCrossRef
10.
Zhu C, Liang QL, Hu P, et al. Phospholipidomic identification of potential plasma biomarkers associated with type 2 diabetes mellitus and diabetic nephropathy. Talanta. 2011;85(4):1711–20.PubMedCrossRef
11.
Liu X, Gao J, Chen J, et al. Identification of metabolic biomarkers in patients with type 2 diabetic coronary heart diseases based on metabolomic approach. Sci Rep. 2016;6:30785.PubMedPubMedCentralCrossRef
12.
Guasch-Ferré M, Hruby A, Toledo E, et al. Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care. 2016;39(5):833–46.PubMedPubMedCentralCrossRef
13.
14.
American Diabetes A. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;34(Suppl 1):S62–9.CrossRef
15.
World Health Organisation. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: Report of a WHO/IDF consultation. Geneva: World Health Organization; 2006.
16.
Hadwin JA, Lee E, Kumsta R, et al. Cortisol awakening response in children and adolescents with autism spectrum disorder: a systematic review and meta-analysis. Evid Based Ment Health. 2019;22(3):118–24.PubMedCrossRef
17.
Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.PubMedCrossRef
18.
Wan X, Wang W, Liu J, et al. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14(1):135.PubMedPubMedCentralCrossRef
19.
Luo D, Wan X, Liu J, et al. Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Stat Methods Med Res. 2018;27(6):1785–805.PubMedCrossRef
20.
Hasselblad V, Hedges LV. Meta-analysis of screening and diagnostic tests. Psychol Bull. 1995;117(1):167–78.PubMedCrossRef
21.
da Costa BR, Rutjes AW, Johnston BC, et al. Methods to convert continuous outcomes into odds ratios of treatment response and numbers needed to treat: meta-epidemiological study. Int J Epidemiol. 2012;41(5):1445–59.PubMedCrossRef
22.
23.
Ha CY, Kim JY, Paik JK, et al. The association of specific metabolites of lipid metabolism with markers of oxidative stress, inflammation and arterial stiffness in men with newly diagnosed type 2 diabetes. Clin Endocrinol. 2012;76(5):674–82.CrossRef
24.
Menni C, Fauman E, Erte I, et al. Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes. 2013;62(12):4270–6.PubMedPubMedCentralCrossRef
25.
Kujala U, Peltonen M, Laine M, et al. Branched-chain amino acid levels are related with surrogates of disturbed lipid metabolism among older men. Front Med. 2016;3:57.CrossRef
26.
Peddinti G, Cobb J, Yengo L, et al. Early metabolic markers identify potential targets for the prevention of type 2 diabetes. Diabetologia. 2017;60(9):1740–50.PubMedPubMedCentralCrossRef
27.
Okekunle AP, Li Y, Liu L, et al. Abnormal circulating amino acid profiles in multiple metabolic disorders. Diabetes Res Clin Pract. 2017;132:45–58.PubMedCrossRef
28.
Abu Bakar MH, Sarmidi MR. Association of cultured myotubes and fasting plasma metabolite profiles with mitochondrial dysfunction in type 2 diabetes subjects. Mol BioSyst. 2017;13(9):1838–53.PubMedCrossRef
29.
Andersson-Hall U, Gustavsson C, Pedersen A, et al. Higher concentrations of BCAAs and 3-HIB are associated with insulin resistance in the transition from gestational diabetes to type 2 diabetes. J Diabetes Res. 2018;2018:1–12.CrossRef
30.
Lee HS, Xu T, Lee Y, et al. Identification of putative biomarkers for type 2 diabetes using metabolomics in the Korea association REsource (KARE) cohort. Metabolomics. 2016;12:178.CrossRef
31.
Floegel A, Stefan N, Yu Z, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62(2):639–48.PubMedPubMedCentralCrossRef
32.
Gogna N, Krishna M, Oommen AM, et al. Investigating correlations in the altered metabolic profiles of obese and diabetic subjects in a south Indian Asian population using an NMR-based metabolomic approach. Mol BioSyst. 2015;11(2):595–606.PubMedCrossRef
33.
Lu Y, Wang Y, Ong CN, et al. Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS. Diabetologia. 2016;59(11):2349–59.PubMedCrossRef
34.
Palmer ND, Stevens RD, Antinozzi PA, et al. Metabolomic profile associated with insulin resistance and conversion to diabetes in the insulin resistance atherosclerosis study. J Clin Endocrinol Metab. 2015;100(3):E463–8.PubMedCrossRef
35.
Ng DP, Salim A, Liu Y, et al. A metabolomic study of low estimated GFR in non-proteinuric type 2 diabetes mellitus. Diabetologia. 2012;55(2):499–508.PubMedCrossRef
36.
37.
Merino J, Leong A, Liu CT, et al. Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose. Diabetologia. 2018;61(6):1315–24.PubMedPubMedCentralCrossRef
38.
Li L, Wang C, Yang H, et al. Metabolomics reveal mitochondrial and fatty acid metabolism disorders that contribute to the development of DKD in T2DM patients. Mol BioSyst. 2017;13(11):2392–400.PubMedCrossRef
39.
Chou J, Liu R, Yu J, et al. Fasting serum αhydroxybutyrate and pyroglutamic acid as important metabolites for detecting isolated post-challenge diabetes based on organic acid profiles. J Chromatogr B. 2018;15:6–16.CrossRef
40.
Wolak-Dinsmore J, Gruppen EG, Shalaurova I, et al. A novel NMR-based assay to measure circulating concentrations of branched-chain amino acids: elevation in subjects with type 2 diabetes mellitus and association with carotid intima media thickness. Clin Biochem. 2018;54:92–9.PubMedCrossRef
41.
Lu Y, Wang Y, Liang X, et al. Serum amino acids in association with prevalent and incident type 2 diabetes in a Chinese population. J Chromatogr B. 2019;9:14.
42.
Lu Y, Wang Y, Zou L, et al. Serum lipids in association with type 2 diabetes risk and prevalence in a Chinese population. J Clin Endocrinol Metab. 2018;103(2):671–80.PubMedCrossRef
43.
Friedrich N, Budde K, Suhre K, et al. Sex differences in urine metabolites related with risk of diabetes using NMR spectroscopy: results of the study of health in pomerania. Metabolomics. 2015;11:1405–15.CrossRef
44.
Cobb J, Eckhart A, Motsinger-Reif A, et al. Alpha-Hydroxybutyric acid is a selective metabolite biomarker of impaired glucose tolerance. Diabetes Care. 2016;39(6):988–95.PubMedCrossRef
45.
Tulipani S, Palau-Rodriguez M, Minarro Alonso A, et al. Biomarkers of morbid obesity and prediabetes by metabolomic profiling of human discordant phenotypes. Clin Chim Acta. 2016;463:53–61.PubMedCrossRef
46.
Zeng M, Xiao Y, Liang YZ, et al. Metabolic alterations of impaired fasting glucose by GC/MS based plasma metabolic profiling combined with chemometrics. Metabolomics. 2010;6:303–11.CrossRef
47.
Butte NF, Liu Y, Zakeri IF, et al. Global metabolomic profiling targeting childhood obesity in the Hispanic population. Am J Clin Nutr. 2015;102(2):256–67.PubMedPubMedCentralCrossRef
48.
Gao X, Zhang W, Wang Y, et al. Serum metabolic biomarkers distinguish metabolically healthy peripherally obese from unhealthy centrally obese individuals. Nutr Metab. 2016;13:33.CrossRef
49.
Kim JY, Park JY, Kim OY, et al. Metabolic profiling of plasma in overweight/obese and lean men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC-Q-TOF MS). J Proteome Res. 2010;9(9):4368–75.PubMedCrossRef
50.
Lee A, Jang HB, Ra M, et al. Prediction of future risk of insulin resistance and metabolic syndrome based on Korean boy's metabolite profiling. Obes Res Clin Pract. 2015;9(4):336–45.PubMedCrossRef
51.
Mastrangelo A, Martos-Moreno GÁ, García A, et al. Insulin resistance in prepubertal obese children correlates with sex-dependent early onset metabolomic alterations. Int J Obes. 2016;40(10):1494–502.CrossRef
52.
Newgard CB, An J, Bain JR, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009;9(4):311–26.PubMedPubMedCentralCrossRef
53.
Ni Y, Zhao L, Yu H, et al. Circulating unsaturated fatty acids delineate the metabolic status of obese individuals. EBioMedicine. 2015;2(10):1513–22.PubMedPubMedCentralCrossRef
54.
55.
Vaishya S, Sarwade R, Seshadri V. MicroRNA, proteins, and metabolites as novel biomarkers for prediabetes, diabetes, and related complications. Front Endocrinol. 2018;9:180.CrossRef
56.
Metadata
Title
Metabolite biomarkers of type 2 diabetes mellitus and pre-diabetes: a systematic review and meta-analysis
Authors
Jianglan Long
Zhirui Yang
Long Wang
Yumei Han
Cheng Peng
Can Yan
Dan Yan
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Endocrine Disorders / Issue 1/2020
Electronic ISSN: 1472-6823
DOI
https://doi.org/10.1186/s12902-020-00653-x

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