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      Metabolomics profiles associated with HbA1c levels in patients with type 2 diabetes

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          Abstract

          Glycated hemoglobin (HbA1c) is an indicator of the average blood glucose concentration. Failing to control HbA1c levels can accelerate the development of complications in patients with diabetes. Although metabolite profiles associated with HbA1c level in diabetes patients have been characterized using different platforms, more studies using high-throughput technology will be helpful to identify additional metabolites related to diabetes. Type 2 diabetes (T2D) patients were divided into two groups based on the HbA1c level: normal (HbA1c ≤6%) and high (HbA1c ≥9%) in both discovery and replication sets. A targeted metabolomics approach was used to quantify serum metabolites and multivariate logistic regression was used to identify significant differences between groups. The concentrations of 22 metabolites differed significantly between the two groups in the discovery set. In the replication set, the levels of 21 metabolites, including 16 metabolites identified in the discovery set, differed between groups. Among these, concentrations of eleven amino acids and one phosphatidylcholine (PC), lysoPC a C16:1, were higher and four metabolites, including three PCs (PC ae C36:1, PC aa C26:0, PC aa C34:2) and hexose, were lower in the group with normal HbA1c group than in the group with high HbA1c. Metabolites with high concentrations in the normal HbA1c group, such as glycine, valine, and PCs, may contribute to reducing HbA1c levels in patients with T2D. The metabolite signatures identified in this study provide insight into the mechanisms underlying changes in HbA1c levels in T2D.

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          Glutathione Synthesis Is Diminished in Patients With Uncontrolled Diabetes and Restored by Dietary Supplementation With Cysteine and Glycine

          OBJECTIVE Sustained hyperglycemia is associated with low cellular levels of the antioxidant glutathione (GSH), which leads to tissue damage attributed to oxidative stress. We tested the hypothesis that diminished GSH in adult patients with uncontrolled type 2 diabetes is attributed to decreased synthesis and measured the effect of dietary supplementation with its precursors cysteine and glycine on GSH synthesis rate and oxidative stress. RESEARCH DESIGN AND METHODS We infused 12 diabetic patients and 12 nondiabetic control subjects with [2H2]-glycine to measure GSH synthesis. We also measured intracellular GSH concentrations, reactive oxygen metabolites, and lipid peroxides. Diabetic patients were restudied after 2 weeks of dietary supplementation with the GSH precursors cysteine and glycine. RESULTS Compared with control subjects, diabetic subjects had significantly higher fasting glucose (5.0 ± 0.1 vs. 10.7 ± 0.5 mmol/l; P < 0.001), lower erythrocyte concentrations of glycine (514.7 ± 33.1 vs. 403.2 ± 18.2 μmol/l; P < 0.01), and cysteine (25.2 ± 1.5 vs. 17.8 ± 1.5 μmol/l; P < 0.01); lower concentrations of GSH (6.75 ± 0.47 vs. 1.65 ± 0.16 μmol/g Hb; P < 0.001); diminished fractional (79.21 ± 5.75 vs. 44.86 ± 2.87%/day; P < 0.001) and absolute (5.26 ± 0.61 vs. 0.74 ± 0.10 μmol/g Hb/day; P < 0.001) GSH synthesis rates; and higher reactive oxygen metabolites (286 ± 10 vs. 403 ± 11 Carratelli units [UCarr]; P < 0.001) and lipid peroxides (2.6 ± 0.4 vs. 10.8 ± 1.2 pg/ml; P < 0.001). Following dietary supplementation in diabetic subjects, GSH synthesis and concentrations increased significantly and plasma oxidative stress and lipid peroxides decreased significantly. CONCLUSIONS Patients with uncontrolled type 2 diabetes have severely deficient synthesis of glutathione attributed to limited precursor availability. Dietary supplementation with GSH precursor amino acids can restore GSH synthesis and lower oxidative stress and oxidant damage in the face of persistent hyperglycemia.
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            Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma.

            A critical question facing the field of metabolomics is whether data obtained from different centers can be effectively compared and combined. An important aspect of this is the interlaboratory precision (reproducibility) of the analytical protocols used. We analyzed human samples in six laboratories using different instrumentation but a common protocol (the AbsoluteIDQ p180 kit) for the measurement of 189 metabolites via liquid chromatography (LC) or flow injection analysis (FIA) coupled to tandem mass spectrometry (MS/MS). In spiked quality control (QC) samples 82% of metabolite measurements had an interlaboratory precision of <20%, while 83% of averaged individual laboratory measurements were accurate to within 20%. For 20 typical biological samples (serum and plasma from healthy individuals) the median interlaboratory coefficient of variation (CV) was 7.6%, with 85% of metabolites exhibiting a median interlaboratory CV of <20%. Precision was largely independent of the type of sample (serum or plasma) or the anticoagulant used but was reduced in a sample from a patient with dyslipidaemia. The median interlaboratory accuracy and precision of the assay for standard reference plasma (NIST SRM 1950) were 107% and 6.7%, respectively. Likely sources of irreproducibility were the near limit of detection (LOD) typical abundance of some metabolites and the degree of manual review and optimization of peak integration in the LC-MS/MS data after acquisition. Normalization to a reference material was crucial for the semi-quantitative FIA measurements. This is the first interlaboratory assessment of a widely used, targeted metabolomics assay illustrating the reproducibility of the protocol and how data generated on different instruments could be directly integrated in large-scale epidemiological studies.
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              Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics.

              Metabolic profiling of small molecules offers a snapshot of physiological processes. To identify metabolic signatures associated with type 2 diabetes and impaired fasting glucose (IFG) beyond differences in glucose, we used mass spectrometry-based metabolic profiling. Individuals attending an institutional health screen were enrolled. IFG (n = 24) was defined as fasting glucose (FG) of 6.1 to 6.9 mmol/L and 2-hour post glucose load <11.1 mmol/L or glycosylated hemoglobin <6.5%, type 2 diabetes (n = 27), FG ≥7.0 mmol/L, or 2-hour post glucose load ≥11.1 mmol/L, or glycosylated hemoglobin ≥6.5%, and healthy controls (n = 60), FG <6.1 mmol/L. Fasting serum metabolomes were profiled and compared using gas chromatography/mass spectrometry and liquid chromatography/mass spectrometry. Compared to healthy controls, those with IFG and type 2 diabetes had significantly raised fructose, α-hydroxybutyrate, alanine, proline, phenylalanine, glutamine, branched-chain amino acids (leucine, isoleucine, and valine), low carbon number lipids (myristic, palmitic, and stearic acid), and significantly reduced pyroglutamic acid, glycerophospohlipids, and sphingomyelins, even after adjusting for age, gender, and body mass index. Using 2 highly sensitive metabolomic techniques, we report distinct serum profile change of a wide range of metabolites from healthy persons to type 2 diabetes mellitus. Apart from glucose, IFG and diabetes mellitus are characterized by abnormalities in amino acid, fatty acids, glycerophospholipids, and sphingomyelin metabolism. These early broad-spectrum metabolic changes emphasize the complex abnormalities present in a disease defined mainly by elevated blood glucose levels.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: Writing – original draft
                Role: Formal analysisRole: Methodology
                Role: Methodology
                Role: Methodology
                Role: Resources
                Role: Resources
                Role: ConceptualizationRole: Project administrationRole: Resources
                Role: ConceptualizationRole: Project administrationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Project administrationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                7 November 2019
                2019
                : 14
                : 11
                : e0224274
                Affiliations
                [1 ] Division of Genome Research, Center for Genome Science, Korea National Institute of Health, Cheongju, Chungbuk, Republic of Korea
                [2 ] College of Pharmacy, Chungbuk National University, Cheongju, Chungbuk, Republic of Korea
                [3 ] Department of Internal Medicine, Chungbuk National University College of Medicine, Cheongju, Chungbuk, Republic of Korea
                [4 ] Department of Biomedical Sciences & Department of Anatomy and Cell Biology, Wide River Institute of Immunology, Seoul National University College of Medicine, Seoul, Republic of Korea
                International University of Health and Welfare, School of Medicine, JAPAN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-2841-0688
                http://orcid.org/0000-0003-0593-6978
                http://orcid.org/0000-0002-4305-5162
                Article
                PONE-D-19-17983
                10.1371/journal.pone.0224274
                6837371
                31697702
                01f39fd3-2570-4aea-b4d0-330ec8375a3f
                © 2019 Yun et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 9 July 2019
                : 9 October 2019
                Page count
                Figures: 1, Tables: 3, Pages: 9
                Funding
                This study was supported by intramural grants from the Korea National Institute of Health (Nos. 2013-NG73001-00). Biospecimens and data were provided by the Korean Genome Analysis Project (4845-301), the Korean Genome and Epidemiology Study (4851-302), and the Korea Biobank Projects (4851-307), which were supported by the Korea Centers for Disease Control and Prevention, Republic of Korea. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and health sciences
                Diagnostic medicine
                Diabetes diagnosis and management
                HbA1c
                Biology and life sciences
                Biochemistry
                Proteins
                Hemoglobin
                HbA1c
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolites
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Carbohydrate Metabolism
                Glucose Metabolism
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Pharmacology
                Pharmacokinetics
                Drug Metabolism
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolomics
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Amino Acid Metabolism
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Amino Acids
                Aliphatic Amino Acids
                Valine
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Amino Acids
                Aliphatic Amino Acids
                Valine
                Biology and Life Sciences
                Biochemistry
                Proteins
                Amino Acids
                Aliphatic Amino Acids
                Valine
                Custom metadata
                All data are within the manuscript and its supporting information file. Additional raw and processed data are uploaded to Open Science Framework ( https://osf.io/57kas).

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