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Metabolomics and correlation network analyses of core biomarkers in type 2 diabetes

Amino Acids volume 52, pages 1307–1317 (2020)Cite this article

Abstract

The identification of metabolic pathways and the core metabolites provide novel molecular targets for the prevention and treatment of diseases. Diabetes is often accompanied with multiple metabolic disorders including hyperglycemia and dyslipidemia. Analysis of the variances of plasma metabolites is critical for identifying potential therapeutic targets for diabetes. In the current study, non-diabetic subjects with normal glucose tolerance and diabetics (age 40–60 years; n = 42 per group) were selected and plasma samples were analyzed by GC–MS for various metabolites profiling followed by network analysis. Our study identified 24 differential metabolites that were mainly enriched in protein synthesis, lipid and amino acid metabolism. Furthermore, we applied the correlation network analysis on these differential metabolites in fatty acid and amino acid metabolism and identified glycerol, alanine and serine as the hub metabolites in diabetic group. In addition, we measured the activities of enzymes in gluconeogenesis and amino acid metabolism and found significant higher activities of fructose 1,6-bisphosphatase, pyruvate carboxylase, lactate dehydrogenase, aspartate aminotransferase and alanine aminotransferase in diabetic patients. In contrast, the enzyme activities of glycolysis pathway (e.g., hexokinase, phosphofructokinase and pyruvate kinase) and TCA cycle (e.g., isocitrate dehydrogenase, succinate dehydrogenase, fumarate hydratase and malate dehydrogenase) were reduced in diabetes. Together, our studies showed that the linoleic acid and amino acid metabolism were the most affected metabolic pathways and glycerol, alanine and serine could play critical role in diabetes. The integration of network analysis and metabolic data could provide novel molecular targets or biomarkers for diabetes.

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References

  • Bogdanov M, Matson WR, Wang L, Matson T, Saunders-Pullman R, Bressman SS, Flint BM (2008) Metabolomic profiling to develop blood biomarkers for Parkinson's disease. Brain J Neurol 131(Pt 2):389–396

    Article  Google Scholar 

  • Borgatti SP (2005) Centrality and network flow. Soc Netw 27(1):55–71

    Article  Google Scholar 

  • Brugnara L, Vinaixa M, Murillo S, Samino S, Rodriguez MA, Beltran A, Lerin C, Davison G, Correig X, Novials A (2012) Metabolomics approach for analyzing the effects of exercise in subjects with type 1 diabetes mellitus. PLoS ONE 7(7):e40600

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Burns SP, Cohen RD (2004) To: Krebs M, Brehm A, Krssak M et al. (2003) Direct and indirect effects of amino acids on hepatic glucose metabolism in humans. Diabetologia 46:917–925

    Google Scholar 

  • Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D, Palma MJ, Roberts LD, Dejam A, Souza AL, Deik AA, Magnusson M, Fox CS, O'Donnell CJ, Vasan RS, Melander O, Clish CB, Gerszten RE, Wang TJ (2012) Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 125(18):2222–2231

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, Wishart DS, Xia J (2018) MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 46((Web Server issue)):W486–W494

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Choque B, Catheline D, Rioux V, Legrand P (2014) Linoleic acid: between doubts and certainties. Biochimie 96:14–21. https://doi.org/10.1016/j.biochi.2013.07.012

    CAS  PubMed  Article  Google Scholar 

  • Consoli A, Nurjhan N, Reilly JJ, Bier DM, Gerich JE (1990) Mechanism of increased gluconeogenesis in noninsulin-dependent diabetes mellitus. Role of alterations in systemic, hepatic, and muscle lactate and alanine metabolism. J Clin Investig 86(6):2038–2045

    CAS  PubMed  Article  Google Scholar 

  • Crabtree B, Newsholme EA (1972) The activities of phosphorylase, hexokinase, phosphofructokinase, lactate dehydrogenase and the glycerol 3-phosphate dehydrogenases in muscles from vertebrates and invertebrates. Biochem J 126(1):49–58

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Deng J, Kaweng S, Zhang Y, Yan R, Hu Y (2016) Analyzing the Chinese landscape in anti-diabetic drug research: leading knowledge production institutions and thematic communities. Chin Med 11(1):13–23

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • Do L, Geladi P, Haglund PJJOCA (2014) Multivariate data analysis to characterize gas chromatography columns for dioxin analysis. J Chromatogr A 1347:137–145. https://doi.org/10.1016/j.chroma.2014.04.008

    CAS  PubMed  Article  Google Scholar 

  • Felig P (1973) The glucose-alanine cycle. Metab Clin Exp 22(2):179–207

    CAS  PubMed  Article  Google Scholar 

  • Floegel A, Stefan N, Yu Z, Muhlenbruch K, Drogan D, Joost HG, Fritsche A, Haring HU, Hrabe de Angelis M, Peters A, Roden M, Prehn C, Wang-Sattler R, Illig T, Schulze MB, Adamski J, Boeing H, Pischon T (2013) Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 62(2):639–648

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Foy JJ, Bhattacharjee JK (1977) Gluconeogenesis in Saccharomyces cerevisiae: determination of fructose-1,6-bisphosphatase activity in cells grown in the presence of glycolytic carbon sources. J Bacteriol 129(2):978–982

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Ghosh S, O'Connell JF, Carlson OD, Gonzalez-Mariscal I (2019) Linoleic acid in diets of mice increases total endocannabinoid levels in bowel and liver: modification by dietary glucose. Obes Sci Pract 5(4):383–394. https://doi.org/10.1002/osp4.344

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Guasch-Ferre M, Hruby A, Toledo E, Clish CB, Martinez-Gonzalez MA, Salas-Salvado J, Hu FB (2016) Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care 39(5):833–846. https://doi.org/10.2337/dc15-2251

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Harrigan GG, LaPlante RH, Cosma GN, Cockerell G, Goodacre R, Maddox JF, Luyendyk JP, Ganey PE, Roth RA (2004) Application of high-throughput Fourier-transform infrared spectroscopy in toxicology studies: contribution to a study on the development of an animal model for idiosyncratic toxicity. Toxicol Lett 146(3):197–205

    CAS  PubMed  Article  Google Scholar 

  • Helen EJ, David B, Douglas BK, Michael KT, Roger JM, Gareth WG (2004) High-throughput metabolic fingerprinting of legume silage fermentations via Fourier transform infrared spectroscopy and chemometrics. Appl Environ Microbiol 70(3):1583–1592

    Article  CAS  Google Scholar 

  • Kankainen M, Gopalacharyulu P, Holm L, Orešič M (2011) MPEA—metabolite pathway enrichment analysis. Bioinformatics 27(13):1878–1879

    CAS  PubMed  Article  Google Scholar 

  • Krebs HA (1970) The history of the tricarboxylic acid cycle. Perspect Biol Med 14(1):154–170

    CAS  PubMed  Article  Google Scholar 

  • Kreisberg RA (1972) Glucose-lactate inter-relations in man. N Engl J Med 287(3):132–137

    CAS  PubMed  Article  Google Scholar 

  • Lanza IR, Zhang S, Ward LE, Karakelides H, Raftery D, Nair KS (2010) Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes. PLoS ONE 5(5):e10538

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • Lerin C, Goldfine AB, Boes T, Liu M, Patti MEJMM (2016) Defects in muscle branched-chain amino acid oxidation contribute to impaired lipid metabolism. Mol Metab 5(10):926–936. https://doi.org/10.1016/j.molmet.2016.08.001

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Li JP, Yuan Y, Zhang WY, Jiang Z, Hu TJ, Feng YT, Liu MX (2019) Effect of Radix isatidis polysaccharide on alleviating insulin resistance in type 2 diabetes mellitus cells and rats. J Pharm Pharmacol 71(2):220–229. https://doi.org/10.1111/jphp.13023

    CAS  PubMed  Article  Google Scholar 

  • Lienert DJPA (1998) Gas chromatography-mass spectral analysis of roots of Echinacea species and classification by multivariate data analysis. Phytochem Anal 9(2):88–98

    CAS  Article  Google Scholar 

  • Liu Q, Adams L, Broyde A, Fernandez R, Baron AD, Parkes DG (2010) The exenatide analogue AC3174 attenuates hypertension, insulin resistance, and renal dysfunction in Dahl salt-sensitive rats. Cardiovasc Diabetol 9:32–42. https://doi.org/10.1186/1475-2840-9-32

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Liu T, Li J, Xu F, Wang M, Ding S, Xu H, Dong F (2016) Comprehensive analysis of serum metabolites in gestational diabetes mellitus by UPLC/Q-TOF-MS. Anal Bioanal Chem 408(4):1125–1135

    CAS  PubMed  Article  Google Scholar 

  • Liyan L, Maoqing W, Xue Y, Mingxin B, Lixin N, Yucun N, Ying L, Changhao S (2013) Fasting serum lipid and dehydroepiandrosterone sulfate as important metabolites for detecting isolated postchallenge diabetes: serum metabolomics via ultra-high-performance LC–MS. Clin Chem 59(9):1338–1348

    Article  CAS  Google Scholar 

  • Lu Y, Wang Y, Liang X, Zou L, Ong CN, Yuan JM, Koh WP, Pan A (2019) Serum amino acids in association with prevalent and incident type 2 diabetes in a Chinese population. Metabolites 9(1):14–24. https://doi.org/10.3390/metabo9010014

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  • Ma X, Zhang YL, Ji Q, Xing Y, Pan H, Chen S, Tang JL, Zhu S (2017) Diagnostic criteria for diabetes in China: are we pushing too much beyond evidence? Eur J Clin Nutr 71(7):812–815. https://doi.org/10.1038/ejcn.2017.51

    CAS  PubMed  Article  Google Scholar 

  • Mahbub MH, Yamaguchi N, Takahashi H, Hase R, Ishimaru Y, Sunagawa H, Amano H, Kobayashi-Miura M, Kanda H, Fujita Y, Yamamoto H, Yamamoto M, Kikuchi S, Ikeda A, Kageyama N, Nakamura M, Tanabe T (2017) Association of plasma free amino acids with hyperuricemia in relation to diabetes mellitus, dyslipidemia, hypertension and metabolic syndrome. Sci Rep 7(1):17616–17628. https://doi.org/10.1038/s41598-017-17710-6

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Mahendran Y, Cederberg H, Vangipurapu J, Kangas AJ, Soininen P, Kuusisto J, Uusitupa M, Ala-Korpela M, Laakso M (2013) Glycerol and fatty acids in serum predict the development of hyperglycemia and type 2 diabetes in Finnish men. Diabetes Care 36(11):3732–3738

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Nicholson J, Wilson I (2003) Opinion: understanding 'global' systems biology: metabonomics and the continuum of metabolism. Nat Rev Drug Discov 8:668–676

    Article  CAS  Google Scholar 

  • Nicholson JK, Connelly J, Lindon JC, Holmes E (2002) INNOVATIONMetabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 1(2):153–161

    CAS  PubMed  Article  Google Scholar 

  • Nicholson JK, Lindon JC, Holmes E (1999) 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29(11):1181–1189

    CAS  PubMed  Article  Google Scholar 

  • Palomer X, Pizarro-Delgado J, Barroso E, Vázquez-Carrera MJTIE (2017) Palmitic and oleic acid: the yin and yang of fatty acids in type 2 diabetes mellitus. Metabolism 29(3):178–190. https://doi.org/10.1016/j.tem.2017.11.009

    CAS  Article  Google Scholar 

  • Peddinti G, Cobb J, Yengo L, Froguel P, Kravic J, Balkau B, Tuomi T, Aittokallio T, Groop L (2017) Early metabolic markers identify potential targets for the prevention of type 2 diabetes. Diabetologia 60(9):1740–1750. https://doi.org/10.1007/s00125-017-4325-0

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Qian K, Zhong S, Xie K, Yu D, Yang R, Gong DW (2015) Hepatic ALT isoenzymes are elevated in gluconeogenic conditions including diabetes and suppressed by insulin at the protein level. Diabetes Metab Res Rev 31(6):562–571. https://doi.org/10.1002/dmrr.2655

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Ritz P, Berrut G (2005) Mitochondrial function, energy expenditure, aging and insulin resistance. Diabetes Metab 31(6):5S67–65S73

    CAS  PubMed  Article  Google Scholar 

  • Sato T, Yoshida Y, Morita A, Mori N, Miura S (2016) Glycerol-3-phosphate dehydrogenase 1 deficiency induces compensatory amino acid metabolism during fasting in mice. Metab Clin Exp 65(11):1646–1656. https://doi.org/10.1016/j.metabol.2016.08.005

    CAS  PubMed  Article  Google Scholar 

  • Sunny NE, Bequette BJ (2011) Glycerol is a major substrate for glucose, glycogen, and nonessential amino acid synthesis in late-term chicken embryos. J Anim Sci 89(12):3945–3953

    CAS  PubMed  Article  Google Scholar 

  • Tziomalos K, Athyros VG, Karagiannis A (2012) Non-alcoholic fatty liver disease in type 2 diabetes: pathogenesis and treatment options. Curr Vasc Pharmacol 10(2):162–172

    CAS  PubMed  Article  Google Scholar 

  • Villas-Boas SG, Mas S, Akesson M, Smedsgaard J, Nielsen J (2005) Mass spectrometry in metabolome analysis. Mass Spectrom Rev 24(5):613–646

    CAS  PubMed  Article  Google Scholar 

  • Vozarova B, Stefan N, Lindsay RS, Saremi A, Pratley RE, Bogardus C, Tataranni PA (2002) High alanine aminotransferase is associated with decreased hepatic insulin sensitivity and predicts the development of type 2 diabetes. Diabetes 51(6):1889–1895

    CAS  PubMed  Article  Google Scholar 

  • Walford GA, Ma Y, Clish C, Florez JC, Wang TJ, Gerszten RE (2016) Metabolite profiles of diabetes incidence and intervention response in the diabetes prevention program. Diabetes 65(5):1424–1433

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A (2012) Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol 8:615–626

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • Wang X, Yang B, Sun H, Zhang A (2012) Pattern recognition approaches and computational systems tools for ultra performance liquid chromatography-mass spectrometry-based comprehensive metabolomic profiling and pathways analysis of biological data sets. Anal Chem 84(1):428–439

    CAS  PubMed  Article  Google Scholar 

  • Westerbacka J, Cornér A, Tiikkainen M, Tamminen M, Vehkavaara S, Häkkinen A-M, Fredriksson J, Yki-Järvinen H (2004) Women and men have similar amounts of liver and intra-abdominal fat, despite more subcutaneous fat in women: implications for sex differences in markers of cardiovascular risk. Diabetologia 47(8):1360–1369

    CAS  PubMed  Article  Google Scholar 

  • Wikoff WR, Gangoiti JA, Barshop BA, Siuzdak G (2007) Metabolomics identifies perturbations in human disorders of propionate metabolism. Clin Chem 53(12):2169–2176

    CAS  PubMed  Article  Google Scholar 

  • Wozniak LA, Szczesna D, Chmielewska-Kassassir M, Bukowiecka-Matusiak MJMRIMC (2016) Metabolomic insight into lipid and protein profile in diabetes using mass spectrometry. Mini Rev Med 16(14):1167–1174. https://doi.org/10.2174/1389557516666160722133534

    CAS  Article  Google Scholar 

  • Wu Y, Ding Y, Tanaka Y, Zhang W (2014) Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int J Med Sci 11(11):1185–1200. https://doi.org/10.7150/ijms.10001

    PubMed  PubMed Central  Article  Google Scholar 

  • Yamaguchi M, Kura M (1981) Calcitonin increases pyruvate carboxylase activity in hepatic mitochondria of rats. Endocrinologia Japonica 28(6):709–714

    CAS  PubMed  Article  Google Scholar 

  • Yang H, Wu JW, Wang SP, Severi I, Sartini L, Frizzell N, Cinti S, Yang G, Mitchell GA (2016) Adipose-specific deficiency of fumarate hydratase in mice protects against obesity, hepatic steatosis, and insulin resistance. Diabetes 65(11):3396–3409. https://doi.org/10.2337/db16-0136

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Yarian CS, Toroser D, Sohal RS (2006) Aconitase is the main functional target of aging in the citric acid cycle of kidney mitochondria from mice. Mech Ageing Dev 127(1):79–84

    CAS  PubMed  Article  Google Scholar 

  • Zhang AD, Dai SX, Huang JF (2013) Reconstruction and analysis of human kidney-specific metabolic network based on omics data. Biomed Res Int 1:187509–187520

    Google Scholar 

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Funding

This study was supported by National Natural Science Foundation of China (NSFC) (Grant nos. 81570655, 81770728).

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Affiliations

  1. The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China

    Wenjuan Lin, Meng Chen, Xuewei Zheng, Di Gao, Zhe Yang & Zhongmin Tian

  2. School of Life Sciences, Northwestern Polytechnical University, Xi’an, 710072, China

    Wenjuan Lin

  3. Department of Equipment, Shaanxi Second Provincial People’s Hospital, Xi’an, 710005, China

    Meng Wang

  4. Xi’an Gao Xin No. 1 High School, Xi’an, 710061, China

    Yutong Wu

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  1. Wenjuan Lin
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  2. Meng Wang
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  3. Meng Chen
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  4. Xuewei Zheng
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  5. Yutong Wu
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  6. Di Gao
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  7. Zhe Yang
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  8. Zhongmin Tian
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Correspondence to Zhe Yang or Zhongmin Tian.

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This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by Xi’an Jiaotong University (Xjtu (2017)/2).

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Lin, W., Wang, M., Chen, M. et al. Metabolomics and correlation network analyses of core biomarkers in type 2 diabetes. Amino Acids 52, 1307–1317 (2020). https://doi.org/10.1007/s00726-020-02891-8

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  • DOI: https://doi.org/10.1007/s00726-020-02891-8

Keywords

  • Diabetes
  • GC–MS
  • Metabolomics and correlation network
  • Disorders of amino acid and fatty acid metabolism