#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

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


Autoři: Jun Ho Yun aff001;  Heun-Sik Lee aff001;  Ho-Yeong Yu aff001;  Yeon-Jung Kim aff001;  Hyun Jeong Jeon aff003;  Taekeun Oh aff003;  Bong-Jo Kim aff001;  Hyung Jin Choi aff004;  Jeong-Min Kim aff001
Působiště autorů: Division of Genome Research, Center for Genome Science, Korea National Institute of Health, Cheongju, Chungbuk, Republic of Korea aff001;  College of Pharmacy, Chungbuk National University, Cheongju, Chungbuk, Republic of Korea aff002;  Department of Internal Medicine, Chungbuk National University College of Medicine, Cheongju, Chungbuk, Republic of Korea aff003;  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 aff004
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0224274

Souhrn

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.

Klíčová slova:

Amino acid metabolism – Drug metabolism – Glucose metabolism – Metabolites – Metabolomics – Valine


Zdroje

1. Nicholson JK. Global systems biology, personalized medicine and molecular epidemiology. Mol Syst Biol. 2006;2:52. Epub 2006/10/04. doi: 10.1038/msb4100095 17016518; PubMed Central PMCID: PMC1682018.

2. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB. Metabolomics applied to diabetes research: moving from information to knowledge. Diabetes. 2009;58(11):2429–43. Epub 2009/10/31. doi: 10.2337/db09-0580 19875619; PubMed Central PMCID: PMC2768174.

3. Preet A. Metabolomics: Approaches and Applications to Diabetes Research. Journal of Diabetes & Metabolism. 2013;01(S6). doi: 10.4172/2155-6156.S6-001

4. Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, et al. Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol. 2012;8:615. Epub 2012/09/27. doi: 10.1038/msb.2012.43 23010998; PubMed Central PMCID: PMC3472689.

5. Floegel A, Stefan N, Yu Z, Muhlenbruch K, Drogan D, Joost HG, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62(2):639–48. Epub 2012/10/09. doi: 10.2337/db12-0495 23043162; PubMed Central PMCID: PMC3554384.

6. Lee H-S, Xu T, Lee Y, Kim N-H, Kim Y-J, Kim J-M, et al. Identification of putative biomarkers for type 2 diabetes using metabolomics in the Korea Association REsource (KARE) cohort. Metabolomics. 2016;12(12). doi: 10.1007/s11306-016-1103-9

7. Koenig RJ, Peterson CM, Jones RL, Saudek C, Lehrman M, Cerami A. Correlation of glucose regulation and hemoglobin AIc in diabetes mellitus. N Engl J Med. 1976;295(8):417–20. Epub 1976/08/19. doi: 10.1056/NEJM197608192950804 934240.

8. Sherwani SI, Khan HA, Ekhzaimy A, Masood A, Sakharkar MK. Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients. Biomark Insights. 2016;11:95–104. Epub 2016/07/12. doi: 10.4137/BMI.S38440 27398023; PubMed Central PMCID: PMC4933534.

9. International Expert C. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care. 2009;32(7):1327–34. Epub 2009/06/09. doi: 10.2337/dc09-9033 19502545; PubMed Central PMCID: PMC2699715.

10. Organization WH. Use of Glycated Haemoglobin (HbA1c) in the Diagnosis of Diabetes Mellitus: Abbreviated Report of a WHO Consultation. WHO Press: 2011.

11. Qiu Y, Rajagopalan D, Connor S, Damian D, Zhu L, Handzel A, et al. Multivariate classification analysis of metabolomic data for candidate biomarker discovery in type 2 diabetes mellitus. Metabolomics. 2008;4:337–46. Epub 2008/08/14. https://doi.org/10.1007/s11306-008-0123-5.

12. Safai N, Suvitaival T, Ali A, Spegel P, Al-Majdoub M, Carstensen B, et al. Effect of metformin on plasma metabolite profile in the Copenhagen Insulin and Metformin Therapy (CIMT) trial. Diabet Med. 2018;35(7):944–53. Epub 2018/04/11. doi: 10.1111/dme.13636 29633349.

13. den Ouden H, Pellis L, Rutten G, Geerars-van Vonderen IK, Rubingh CM, van Ommen B, et al. Metabolomic biomarkers for personalised glucose lowering drugs treatment in type 2 diabetes. Metabolomics. 2016;12:27. Epub 2016/01/16. doi: 10.1007/s11306-015-0930-4 26770180; PubMed Central PMCID: PMC4703625.

14. Deja S, Barg E, Mlynarz P, Basiak A, Willak-Janc E. 1H NMR-based metabolomics studies of urine reveal differences between type 1 diabetic patients with high and low HbAc1 values. J Pharm Biomed Anal. 2013;83:43–8. Epub 2013/05/25. doi: 10.1016/j.jpba.2013.04.017 23702564.

15. Siskos AP, Jain P, Romisch-Margl W, Bennett M, Achaintre D, Asad Y, et al. Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma. Anal Chem. 2017;89(1):656–65. Epub 2016/12/14. doi: 10.1021/acs.analchem.6b02930 27959516; PubMed Central PMCID: PMC6317696.

16. t Hart LM, Vogelzangs N, Mook-Kanamori DO, Brahimaj A, Nano J, van der Heijden A, et al. Blood Metabolomic Measures Associate With Present and Future Glycemic Control in Type 2 Diabetes. J Clin Endocrinol Metab. 2018;103(12):4569–79. Epub 2018/08/17. doi: 10.1210/jc.2018-01165 30113659.

17. Sekhar RV, McKay SV, Patel SG, Guthikonda AP, Reddy VT, Balasubramanyam A, et al. Glutathione synthesis is diminished in patients with uncontrolled diabetes and restored by dietary supplementation with cysteine and glycine. Diabetes Care. 2011;34(1):162–7. Epub 2010/10/12. doi: 10.2337/dc10-1006 20929994; PubMed Central PMCID: PMC3005481.

18. Wijekoon EP, Skinner C, Brosnan ME, Brosnan JT. Amino acid metabolism in the Zucker diabetic fatty rat: effects of insulin resistance and of type 2 diabetes. Can J Physiol Pharmacol. 2004;82(7):506–14. Epub 2004/09/25. doi: 10.1139/y04-067 15389298.

19. Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D, et al. Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation. 2012;125(18):2222–31. Epub 2012/04/13. doi: 10.1161/CIRCULATIONAHA.111.067827 22496159; PubMed Central PMCID: PMC3376658.

20. Xu F, Tavintharan S, Sum CF, Woon K, Lim SC, Ong CN. Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics. J Clin Endocrinol Metab. 2013;98(6):E1060–5. Epub 2013/05/02. doi: 10.1210/jc.2012-4132 23633210.

21. Nagata C, Nakamura K, Wada K, Tsuji M, Tamai Y, Kawachi T. Branched-chain amino acid intake and the risk of diabetes in a Japanese community: the Takayama study. Am J Epidemiol. 2013;178(8):1226–32. Epub 2013/09/07. doi: 10.1093/aje/kwt112 24008908.

22. Suvitaival T, Bondia-Pons I, Yetukuri L, Poho P, Nolan JJ, Hyotylainen T, et al. Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men. Metabolism. 2018;78:1–12. Epub 2017/09/25. doi: 10.1016/j.metabol.2017.08.014 28941595.

23. Natarajan Sulochana K, Lakshmi S, Punitham R, Arokiasamy T, Sukumar B, Ramakrishnan S. Effect of oral supplementation of free amino acids in type 2 diabetic patients—a pilot clinical trial. Med Sci Monit. 2002;8(3):CR131–7. Epub 2002/03/12. 11887024.

24. Cruz M, Maldonado-Bernal C, Mondragon-Gonzalez R, Sanchez-Barrera R, Wacher NH, Carvajal-Sandoval G, et al. Glycine treatment decreases proinflammatory cytokines and increases interferon-gamma in patients with type 2 diabetes. J Endocrinol Invest. 2008;31(8):694–9. Epub 2008/10/15. doi: 10.1007/bf03346417 18852529.


Článek vyšel v časopise

PLOS One


2019 Číslo 11
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Svět praktické medicíny 1/2024 (znalostní test z časopisu)
nový kurz

Koncepce osteologické péče pro gynekology a praktické lékaře
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Význam metforminu pro „udržitelnou“ terapii diabetu
Autoři: prof. MUDr. Milan Kvapil, CSc., MBA

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#