UPLC-based metabonomic applications for discovering biomarkers of diseases in clinical chemistry

Clinical Biochemistry

Volumn 47, Issue 15, 2015, Pages 16-26

  Zhao, Ying Yong ^a,b   Cheng, Xian Long ^d   Vaziri, Nosratola D ^b   Liu, Shuman ^b   Lin, Rui Chao ^c  

^a NORTHWEST UNIVERSITY   (China)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c BEIJING UNIVERSITY OF CHINESE MEDICINE   (China)

^d NATIONAL INSTITUTES FOR FOOD AND DRUG CONTROL   (China)

Author keywords

Clinical chemistry; Disease biomarkers; Mass spectrometry; Metabonomics; Ultra performance liquid chromatography

Indexed keywords

2 OXOGLUTARIC ACID; ALPHA FETOPROTEIN; AMINO ACID; ARACHIDONIC ACID; BILE ACID; BIOLOGICAL MARKER; CARNITINE; CHENODEOXYCHOLIC ACID; CHOLESTEROL; CREATININE; ERYTHRITOL; FATTY ACID; FOLIC ACID; GLUTAMINE; GLYCEROPHOSPHORYLCHOLINE; GLYCOCHENODEOXYCHOLIC ACID; GLYCODEOXYCHOLIC ACID; PALMITIC ACID; PHENYLALANINE; PREGNANEDIOL; PROPIONYLCARNITINE; PROSTACYCLIN; PROSTAGLANDIN E2; S ADENOSYLHOMOCYSTEINE; SARCOSINE; SPHINGOLIPID; TAUROCHENODEOXYCHOLIC ACID; TAUROCHOLIC ACID; UROBILIN; UROBILINOGEN;

ALZHEIMER DISEASE; AMINO ACID METABOLISM; ANALYTIC METHOD; BIOSYNTHESIS; CEREBROVASCULAR ACCIDENT; CHRONIC KIDNEY DISEASE; CHRONIC LIVER DISEASE; CLINICAL CHEMISTRY; DATA ANALYSIS; FATTY ACID METABOLISM; FECES; GASTROINTESTINAL DISEASE; HUMAN; INSULIN DEPENDENT DIABETES MELLITUS; LIVER; LIVER CELL CARCINOMA; LIVER CIRRHOSIS; LUNG CANCER; MASS SPECTROMETRY; MEDICAL TECHNOLOGY; METABOLITE; METABOLOME; METABONOMICS; NON INSULIN DEPENDENT DIABETES MELLITUS; OVARY CANCER; PNEUMONIA; PROSTATE CANCER; PROTON NUCLEAR MAGNETIC RESONANCE; REVIEW; SCHIZOPHRENIA; SERUM; TIME OF FLIGHT MASS SPECTROMETRY; ULTRA PERFORMANCE LIQUID CHROMATOGRAPHY; URINE; UROGENITAL TRACT DISEASE; CHEMISTRY; DIAGNOSIS; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; MEDICAL RESEARCH; METABOLISM; METABOLOMICS; MOLECULAR WEIGHT; MOLECULARLY TARGETED THERAPY; PROCEDURES;

BIOLOGICAL MARKERS; BIOMEDICAL RESEARCH; CHEMISTRY, CLINICAL; CHROMATOGRAPHY, HIGH PRESSURE LIQUID; DIAGNOSIS; HUMANS; MASS SPECTROMETRY; METABOLOMICS; MOLECULAR TARGETED THERAPY; MOLECULAR WEIGHT;

EID: 84910064935     PISSN: 00099120     EISSN: 18732933     Source Type: Journal    
DOI: 10.1016/j.clinbiochem.2014.07.019     Document Type: Review

References (120)

1. Mamas M., Dunn W.B., Neyses L., Goodacre R. The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Arch Toxicol 2011, 85:5-17.
2. Nicholson J.K., Lindon J.C., Holmes E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999, 29:1181-1189.
3. Lindon J.C., Holmes E., Bollard M.E., Stanley E.G., Nicholson J.K. Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers 2004, 9:1-31.
4. Zhang A., Sun H., Wang P., Han Y., Wang X. Modern analytical techniques in metabolomics analysis. Analyst 2012, 137:293-300.
5. Griffin J.L., Shockcor J.P. Metabolic profiles of cancer cells. Nat Rev Cancer 2004, 4:551-561.
6. Zhao Y.Y. Metabolomics in chronic kidney disease. Clin Chim Acta 2013, 422:59-69.
7. 1H spin echo NMR. FEBS Lett 1977, 82:12-16.
8. Reo N.V. NMR-based metabolomics. Drug Chem Toxicol 2002, 25:375-382.
9. Draper J., Enot D.P., Parker D., Beckmann M., Snowdon S., Lin W., et al. Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'. BMC Bioinformatics 2009, 10:227.
10. Wilson I.D., Nicholson J.K., Castro-Perez J., Granger J.H., Johnson K.A., Smith B.W., et al. High resolution "ultra performance" liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. J Proteome Res 2005, 4:591-598.
11. E; a new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Commun Mass Spectrom 2006, 20:1989-1994.
12. E with mass defect filtering for in vitro and in vivo metabolite identification. Rapid Commun Mass Spectrom 2007, 21:1485-1496.
13. Rainville P.D., Stumpf C.J., Shockcor J.P., Plumb R.S., Nicholson J.K. Novel application of reversed-phase UPLC-oaTOFMS for lipid analysis in complex biological mixture: a new tool for lipidomics. J Proteome Res 2007, 6:552-558.
14. E application in disease biomarker discovery: the discoveries in proteomics to metabolomics. Chem Biol Interact 2014, 215:7-16.
15. Zhao Y.Y., Liu J., Cheng X.L., Bai X., Lin R.C. Urinary metabonomics study on biochemical changes in an experimental model of chronic renal failure by adenine based on UPLC Q-TOF/MS. Clin Chim Acta 2012, 413:642-649.
16. E data collection technique. Biomarkers 2012, 17:721-729.
17. Zhao Y.Y., Feng Y.L., Bai X., Tan X.J., Lin R.C., Mei Q. Ultra performance liquid chromatography-based metabonomic study of therapeutic effect of the surface layer of Poria cocos on adenine-induced chronic kidney disease provides new insight into anti-fibrosis mechanism. PLoS One 2013, 8:e59617.
18. Zhao Y.Y., Li H.T., Feng Y.L., Bai X., Lin R.C. Urinary metabonomic study of the surface layer of Poria cocos as an effective treatment for chronic renal injury in rats. J Ethnopharmacol 2013, 148:403-410.
19. Zhao Y.Y., Cheng X.L., Wei F., Xiao X.Y., Sun W.J., Zhang Y., et al. Serum metabonomics study of adenine-induced chronic renal failure rat by ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Biomarkers 2012, 17:48-55.
20. Dillon W.R., Goldstein M. Multivariate Analysis, Methods and Application 1989, John Wiley and Sons Chichester, UK.
21. Nyamundanda G., Brennan L., Gormley I.C. Probabilistic principal component analysis for metabolomic data. BMC Bioinformatics 2010, 11:571.
22. Pérez-Enciso M., Tenenhaus M. Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Hum Genet 2003, 112:581-592.
23. Miao H., Chen H., Zhang X., Yin L., Chen D.Q., Cheng X.L., et al. Urinary metabolomics on the biochemical profiles in diet-induced hyperlipidemia rat using ultraperformance liquid chromatography coupled with quadrupole time-of-flight SYNAPT high-definition mass spectrometry. J Anal Methods Chem 2014, 2014:184162.
24. Bylesjo M., Rantalainen M., Cloarec O., Nicholson J.K., Holmes E., Trygg J. OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J Chemom 2006, 20:341-351.
25. Trygg J., Wold S. Orthogonal projections to latent structures (O-PLS). J Chemom 2002, 16:119-128.
26. Katajamaa M., Oresic M. Data processing for mass spectrometry-based metabolomics. J Chromatogr A 2007, 1158:318-328.
27. Ressom H.W., Xiao J.F., Tuli L., Varghese R.S., Zhou B., Tsai T.H., et al. Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis. Anal Chim Acta 2012, 743:90-100.
28. Chen F., Xue J., Zhou L., Wu S., Chen Z. Identification of serum biomarkers of hepatocarcinoma through liquid chromatography/mass spectrometry-based metabonomic method. Anal Bioanal Chem 2011, 401:1899-1904.
29. Chen T., Xie G., Wang X., Fan J., Qiu Y., Zheng X., et al. Serum and urine metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma. Mol Cell Proteomics 2011, 10. [M110.004945].
30. Chen S., Kong H., Lu X., Li Y., Yin P., Zeng Z., et al. Pseudotargeted metabolomics method and its application in serum biomarker discovery for hepatocellular carcinoma based on ultra high-performance liquid chromatography/triple quadrupole mass spectrometry. Anal Chem 2013, 85:8326-8333.
31. Xiao J.F., Varghese R.S., Zhou B., Nezami Ranjbar M.R., Zhao Y., Tsai T.H., et al. LC-MS based serum metabolomics for identification of hepatocellular carcinoma biomarkers in Egyptian cohort. J Proteome Res 2012, 11:5914-5923.
32. Wang B., Chen D., Chen Y., Hu Z., Cao M., Xie Q., et al. Metabonomic profiles discriminate hepatocellular carcinoma from liver cirrhosis by ultra performance liquid chromatography-mass spectrometry. J Proteome Res 2012, 11:1217-1227.
33. Zhou L., Wang Q., Yin P., Xing W., Wu Z., Chen S., et al. Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases. Anal Bioanal Chem 2012, 403:203-213.
34. Chen J., Wang W., Lv S., Yin P., Zhao X., Lu X., et al. Metabonomics study of liver cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations. Anal Chim Acta 2009, 650:3-9.
35. Zhou L., Ding L., Yin P., Lu X., Wang X., Niu J., et al. Serum metabolic profiling study of hepatocellular carcinoma infected with hepatitis B or hepatitis C virus by using liquid chromatography-mass spectrometry. J Proteome Res 2012, 11:5433-5442.
36. Zhang A., Sun H., Yan G., Han Y., Ye Y., Wang X. Urinary metabolic profiling identifies a key role for glycocholic acid in human liver cancer by ultra-performance liquid-chromatography coupled with high-definition mass spectrometry. Clin Chim Acta 2013, 418:86-90.
37. Cao H., Huang H., Xu W., Chen D., Yu J., Li J., et al. Fecal metabolome profiling of liver cirrhosis and hepatocellular carcinoma patients by ultra performance liquid chromatography-mass spectrometry. Anal Chim Acta 2011, 691:68-75.
38. Liu S.Y., Zhang R.L., Kang H., Fan Z.J., Du Z. Human liver tissue metabolic profiling research on hepatitis B virus-related hepatocellular carcinoma. World J Gastroenterol 2013 Jun 14, 19(22):3423-3432.
39. 1H NMR spectroscopy in conjunction with multivariate data analysis. J Proteome Res 2007, 6:2605-2614.
40. Tan Y., Yin P., Tang L., Xing W., Huang Q., Cao D., et al. Metabolomics study of stepwise hepatocarcinogenesis from the model rats to patients: potential biomarkers effective for small hepatocellular carcinoma diagnosis. Mol Cell Proteomics 2012, 11. [M111.010694].
41. Patterson A.D., Maurhofer O., Beyoglu D., Lanz C., Krausz K.W., Pabst T., et al. Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling. Cancer Res 2011, 71:6590-6600.
42. Wang X., Wang X., Xie G., Zhou M., Yu H., Lin Y., et al. Urinary metabolite variation is associated with pathological progression of the post-hepatitis B cirrhosis patients. J Proteome Res 2012, 11:3838-3847.
43. Hao S., Xin J., Lian J., Xie Q., Chen D., Guo Y., et al. Establishing a metabolomic model for the prognosis of hepatitis B virus-induced acute-on-chronic liver failure treated with different liver support systems. Metabolomics 2011, 7:400-412.
44. Lian J.S., Liu W., Hao S.R., Guo Y.Z., Huang H.J., Chen D.Y., et al. A serum metabonomic study on the difference between alcohol- and HBV-induced liver cirrhosis by ultra performance liquid chromatography coupled to mass spectrometry plus quadrupole time-of-flight mass spectrometry. Chin Med J (Engl) 2011, 124:1367-1373.
45. Zhang A., Sun H., Han Y., Yan G., Wang X. Urinary metabolic biomarker and pathway study of hepatitis B virus infected patients based on UPLC-MS system. PLoS One 2013, 8:e64381.
46. Pares A., Perez-Cormenzana M., Mayo R., Bennasar A., Mas A., Castro A. Lipidic and bile acid metabolomic profile in patients with primary biliary cirrhosis and pruritus. Hepatology 2012, 56:1129A.
47. Legido-Quigley C., McDermott L., Vilca-Melendez H., Murphy G.M., Heaton N., Lindon J.C., et al. Bile UPLC-MS fingerprinting and bile acid fluxes during human liver transplantation. Electrophoresis 2011, 32:2063-2070.
48. Yang J., Zhao X., Liu X., Wang C., Gao P., Wang J., et al. High performance liquid chromatography-mass spectrometry for metabonomics: potential biomarkers for acute deterioration of liver function in chronic hepatitis B. J Proteome Res 2006, 5:554-561.
49. Goto T., Myint K.T., Sato K., Wada O., Kakiyama G., Iida T., et al. LC/ESI-tandem mass spectrometric determination of bile acid 3-sulfates in human urine 3β-Sulfooxy-12α-hydroxy-5β-cholanoic acid is an abundant nonamidatedsulfate. J Chromatogr B 2007, 846:69-77.
50. Park J.Y., Park B.K., Ko J.S., Bang S., Song S.Y., Chung J.B. Bile acid analysis in biliary tract cancer. Yonsei Med J 2006, 47:817-825.
51. Sokol R.J., Dahl R., Devereaux M.W., Yerushalmi B., Kobak G.E., Gumpricht E. Human hepatic mitochondria generate reactive oxygen species and undergo the permeability transition in response to hydrophobic bile acids. J Pediatr Gastroenterol Nutr 2005, 41(2):235-243.
52. Gowda G.A. Human bile as a rich source of biomarkers for hepatopancreatobiliary cancers. Biomark Med 2010, 4:299-314.
53. Dong J., Cai X., Zou L., Chen C., Xue X., Zhang X., et al. Lysophosphatidylcholine biomarkers of lung cancer detected by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Chem Res Chin U 2011, 27:750-755.
54. Dong J., Cai X., Zhao L., Xue X., Zou L., Zhang X., et al. Lysophosphatidylcholine profiling of plasma: discrimination of isomers and discovery of lung cancer biomarkers. Metabolomics 2010, 6:478-488.
55. An Z., Chen Y., Zhang R., Song Y., Sun J., He J., et al. Integrated ionization approach for RRLC-MS/MS-based metabonomics: finding potential biomarkers for lung cancer. J Proteome Res 2010, 9:4071-4081.
56. Laviano A., Cascino A., Muscaritoli M., Fanfarillo F., Rossi Fanelli F. Tumor-induced changes in host metabolism: a possible role for free tryptophan as a marker of neoplastic disease. Adv Exp Med Biol 2003, 527:363-366.
57. Frickenschmidt A., Frohlich H., Bullinger D., Zell A., Laufer S., Gleiter C.H., et al. Metabonomics in cancer diagnosis: mass spectrometry-based profiling of urinary nucleosides from breast cancer patients. Biomarkers 2008, 13:435-449.
58. Cai X., Dong J., Zou L., Xue X., Zhang X., Liang X. Metabonomic study of lung cancer and the effects of radiotherapy on lung cancer patients: analysis of highly polar metabolites by ultra performance HILIC coupled with Q-TOF MS. Chromatographia 2011, 74:391-398.
59. Wedge D.C., Allwood J.W., Dunn W., Vaughan A.A., Simpson K., Brown M., et al. Is serum or plasma more appropriate for intersubject comparisons in metabolomic studies? An assessment in patients with small-cell lung cancer. Anal Chem 2011, 83:6689-6697.
60. Laiakis E.C., Morris G.A., Fornace A.J., Howie S.R. Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study. PLoS One 2010, 5. [pii: e12655].
61. Mene P., Punzo G. Uric acid: bystander or culprit in hypertension and progressive renal disease?. J Hypertens 2008, 26:2085-2092.
62. Rodriguez-Nunez A., Camina F., Lojo S., Rodriguez-Segade S., Castro-Gago M. Concentrations of nucleotides, nucleosides, purine bases and urate in cerebrospinal fluid of children with meningitis. Acta Paediatr 1993, 82:849-852.
63. Fabre J.E., Nguyen M., Latour A., Keifer J.A., Audoly L.P., Coffman T.M., et al. Decreased platelet aggregation, increased bleeding time and resistance to thromboembolism in P2Y1-deficient mice. Nat Med 1999, 5:1199-1202.
64. Ma Y.L., Qin H.L., Liu W.J., Peng J.Y., Huang L., Zhao X.P., et al. Ultra-high performance liquid chromatography-mass spectrometry for the metabolomic analysis of urine in colorectal cancer. Dig Dis Sci 2009, 54:2655-2662.
65. Qiu Y., Cai G., Su M., Chen T., Zheng X., Xu Y., et al. Serum metabolite profiling of human colorectal cancer using GC-TOFMS and UPLC-QTOFMS. J Proteome Res 2009, 8:4844-4850.
66. Tan B., Qiu Y., Zou X., Chen T., Xie G., Cheng Y., et al. Metabonomics identifies serum metabolite markers of colorectal cancer. J Proteome Res 2013, 12:3000-3009.
67. Qiu Y., Cai G., Su M., Chen T., Liu Y., Xu Y., et al. Urinary metabonomic study on colorectal cancer. J Proteome Res 2010, 9:1627-1634.
68. Wang W., Feng B., Li X., Yin P., Gao P., Zhao X., et al. Urinary metabolic profiling of colorectal carcinoma based on online affinity solid phase extraction-high performance liquid chromatography and ultra performance liquid chromatography-mass spectrometry. Mol Biosyst 2010, 6:1947-1955.
69. Mal M., Koh P.K., Cheah P.Y., Chan E.C. Ultra-pressure liquid chromatography/tandem mass spectrometry targeted profiling of arachidonic acid and eicosanoids in human colorectal cancer. Rapid Commun Mass Spectrom 2011, 25:755-764.
70. Sreekumar A., Poisson L.M., Rajendiran T.M., Khan A.P., Cao Q., Yu J., et al. Metabolomic prodiles delineate potential role for sarcosine in prostate cancer progression. Nature 2009, 457:910-914.
71. Saylor P.J., Karoly E.D., Smith M.R. Prospective study of changes in the metabolomic profiles of men during their first three months of androgen deprivation therapy for prostate cancer. Clin Cancer Res 2012, 18:3677-3685.
72. Chen J., Zhang X., Cao R., Lu X., Zhao S., Fekete A., et al. Serum 27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide discovered by metabolomics as potential diagnostic biomarker for epithelium ovarian cancer. J Proteome Res 2011, 10:2625-2632.
73. Zhang T., Wu X., Yin M., Fan L., Zhang H., Zhao F., et al. Discrimination between malignant and benign ovarian tumors by plasma metabolomic profiling using ultra performance liquid chromatography/mass spectrometry. Clin Chim Acta 2012, 413:861-868.
74. Sperner-Unterweger B., Neurauter G., Klieber M., Kurz K., Meraner V., Zeimet A., et al. Enhanced tryptophan degradation in patients with ovarian carcinoma correlates with several serum soluble immune activation markers. Immunobiology 2011, 216:296-301.
75. Murph M., Tanaka T., Pang J., Felix E., Liu S., Trost R., et al. Liquid chromatography mass spectrometry for quantifying plasma lysophospholipids: potential biomarkers for cancer diagnosis. Methods Enzymol 2007, 433:1-25.
76. Jia L., Chen J., Yin P., Lu X., Xu G. Serum metabonomics study of chronic renal failure by ultra performance liquid chromatography coupled with Q-TOF mass spectrometry. Metabolomics 2008, 4:183-189.
77. Sato E., Kohno M., Yamamoto M., Fujisawa T., Fujiwara K., Tanaka N. Metabolomic analysis of human plasma from haemodialysis patients. Eur J Clin Invest 2011, 41:241-255.
78. Duan H., Guan N., Wu Y., Zhang J., Ding J., Shao B. Identification of biomarkers for melamine-induced nephrolithiasis in young children based on ultra high performance liquid chromatography coupled to time-of-flight mass spectrometry (U-HPLC-Q-TOF/MS). J Chromatogr B 2011, 879:3544-3550.
79. Sysi-Aho M., Ermolov A., Gopalacharyulu P.V., Tripathi A., Seppänen-Laakso T., Maukonen J., et al. Metabolic regulation in progression to autoimmune diabetes. PloS Comput Biol 2011, 7:e1002257.
80. Posada-Ayala M., Zubiri I., Martin-Lorenzo M., Sanz-Maroto A., Molero D., Gonzalez-Calero L., et al. Identification of a urine metabolomic signature in patients with advanced-stage chronic kidney disease. Kidney Int 2014, 85:103-111.
81. 1H NMR and LC-MS/MS confirms altered metabolic pathways in diabetes. PLoS One 2010, 5:e10538.
82. Felig P., Marliss E., Ohman J.L., Cahill C.F. Plasma amino acid levels in diabetic ketoacidosis. Diabetes 1970, 19:727-728.
83. Pacy P.J., Nair K.S., Ford C., Halliday D. Failure of insulin infusion to stimulate fractional muscle protein synthesis in type I diabetic patients. Anabolic effect of insulin and decreased proteolysis. Diabetes 1989, 38:618-662.
84. Nair K.S., Ford G.C., Ekberg K., Fernqvist-Forbes E., Wahren J. Protein dynamics in whole body and in splanchnic and leg tissues in type I diabetic patients. J Clin Invest 1995, 95:2926-2937.
85. Gu Y., Zhang Y., Shi X., Li X., Hong J., Chen J. Effect of traditional Chinese medicine berberine on type 2 diabetes based on comprehensive metabonomics. Talanta 2010, 81:766-772.
86. Huo T., Cai S., Lu X., Sha Y., Yu M., Li F. Metabonomic study of biochemical changes in the serum of type 2 diabetes mellitus patients after the treatment of metformin hydrochloride. J Pharm Biomed 2009, 49:976-982.
87. Suhre K., Meisinger C., Döring A., Altmaier E., Belcredi P., Gieger C., et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One 2010, 5:e13953.
88. Boden G., Chen X., Iqbal N. Acute lowering of plasma fatty acids lowers basal insulin secretion in diabetic and nondiabetic subjects. Diabetes 1998, 47:1609-1612.
89. Boden G., Shulman G.I. Free fatty acids in obesity and type 2 diabetes: defining their role in the development of insulin resistance and beta-cell dysfunction. Eur J Clin Invest 2002, 32:14-23.
90. Houstis N., Rosen E.D., Lander E.S. Reactive oxygen species have a causal role in multiple forms of insulin resistance. Nature Apr 13 2006, 440:944-948.
91. Schinner S., Scherbaum W.A., Bornstein S.R., Barthel A. Molecular mechanisms of insulin resistance. Diabet Med 2005, 22:674-682.
92. Jové M., Portero-Otín M., Naudí A., Ferrer I., Pamplona R. Metabolomics of human brain aging and age-related neurodegenerative diseases. J Neuropathol Exp Neurol 2014, 73:640-657.
93. Trushina E., Mielke M.M. Recent advances in the application of metabolomics to Alzheimer's disease. Biochim Biophys Acta-Mol Basis Dis 2014, 1842:1232-1239.
94. Ibáñez C., Simó C., Barupal D.K., Fiehn O., Kivipelto M., Cedazo-Mínguez A., et al. A new metabolomic workflow for early detection of Alzheimer's disease. J Chromatogr A 2013, 1302:65-71.
95. Trushina E., Dutta T., Persson X.M., Mielke M.M., Petersen R.C. Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer's disease using metabolomics. PLoS One 2013, 8:e63644.
96. Selkoe D.J. Alzheimer's disease is a synaptic failure. Science 2002, 298:789-791.
97. Rodriguez J.J., Noristani H.N., Verkhratsky A. The serotonergic system in ageing and Alzheimer's disease. Prog Neurobiol 2012, 99:15-41.
98. Ibáñez C., Simó C., Martín-Álvarez P.J., Kivipelto M., Winblad B., Cedazo-Mínguez A., et al. Toward a predictive model of Alzheimer's disease progression using capillary electrophoresis-mass spectrometry metabolomics. Anal Chem 2012, 84(20):8532-8540.
99. Jiang Z., Sun J., Liang Q., Cai Y., Li S., Huang Y., et al. A metabonomic approach applied to predict patients with cerebral infarction. Talanta 2011, 84:298-304.
100. Rafii M., Elango R., House J.D., Courtney-Martin G., Darling P., Fisher L., et al. Measurement of homocysteine and related metabolites in human plasma and urine by liquid chromatography electrospray tandem mass spectrometry. J Chromatogr B 2009, 877:3282-3291.
101. Cha M.H., Jones A.D., Ko M.M., Zhang C., Lee M.S. Metabolic profiles distinguish non-dampness-phlegm and dampness-phlegm patterns among Korean patients with acute cerebral infarction. Evid Based Complement Altern Med 2013, 2013:517018.
102. Orešič M., Tang J., Seppänen-Laakso T., Mattila I., Saarni S.E., Saarni S.I., et al. Metabolome in schizophrenia and other psychotic disorders: a general population-based study. Genome Med 2011, 3:19.
103. Ko M.M., Kang B.K., Lim J.H., Lee M.S., Cha M.H. Genetic association of NPY gene polymorphisms with dampness-phlegm pattern in Korean stroke patients. Evid Based Complement Altern Med 2012, 2012:109796.
104. Kim H.J., Bae H.S., Park S.U., Moon S.K., Park J.M., Jung W.S. Clinical approach to the standardization of oriental medical diagnostic pattern identification in stroke patients. Evid Based Complement Altern Med 2011, 2011:768492.
105. Pietiläinen K.H., Sysi-Aho M., Rissanen A., Seppänen-Laakso T., Yki-Järvinen H., Kaprio J., et al. Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects-a monozygotic twin study. PLoS One 2007, 2:e218.
106. Barber M.N., Risis S., Yang C., Meikle P.J., Staples M., Febbraio M.A., et al. Plasma lysophosphatidylcholine levels are reduced in obesity and type 2 diabetes. PLoS One 2012, 7:e41456.
107. Orešič M., Tang J., Seppänen-Laakso T., Mattila I., Saarni S.E., Saarni S.I., et al. Metabolome in schizophrenia and other psychotic disorders: a general population-based study. Genome Med 2011, 3:19.
108. Cherlyn S.Y., Woon P.S., Liu J.J., Ong W.Y., Tsai G.C., Sim K. Genetic association studies of glutamate, GABA and related genes in schizophrenia and bipolar disorder: a decade of advance. Neurosci Biobehav Rev 2010, 34:958-977.
109. Liu H., Heath S.C., Sobin C., Roos J.L., Galke B.L., Blundell M.L., et al. Genetic variation at the 22q11 PRODH2/DGCR6 locus presents an unusual pattern and increases susceptibility to schizophrenia. Proc Natl Acad Sci U S A 2002, 99:3717-3722.
110. Kempf L., Nicodemus K.K., Kolachana B., Vakkalanka R., Verchinski B.A., Egan M.F., et al. Functional polymorphisms in PRODH are associated with risk and protection for schizophrenia and fronto-striatal structure and function. PloS Genet 2008, 4:e1000252.
111. Xuan J., Pan G., Qiu Y., Yang L., Su M., Liu Y., et al. Metabolomic profiling to identify potential serum biomarkers for schizophrenia and risperidone action. J Proteome Res 2011, 10:5433-5443.
112. Zhao Y.Y., Lin R.C. Metabolomics in nephrotoxicity. Adv Clin Chem 2014, 65:69-89.
113. Chen H., Miao H., Feng Y.L., Zhao Y.Y., Lin R.C. Metabolomics in dyslipidemia. Adv Clin Chem 2014, 66:101-119.
114. Zhao Y.Y., Wu S.P., Liu S., Zhang Y., Lin R.C. Ultra-performance liquid chromatography-mass spectrometry as a sensitive and powerful technology in lipidomic applications. Chem Biol Interact 2014, [in press]. 10.1016/j.cbi.2014.06.029.
115. E data collection technique. Process Biochem 2012, 47:1980-1987.
116. Zhang Z.H., Zhao Y.Y., Cheng X.L., Dai Z., Zhou C., Bai X., et al. General toxicity of Pinellia ternate (Thunb.) Berit. in rat: a metabonomic method for profiling of serum metabolic changes. J Ethnopharmacol 2013, 149:303-310.
117. E-based metabonomics for adenine-induced changes in metabolic profiles of rat faeces and intervention effects of ergosta-4,6,8(14),22-tetraen-3-one. Chem Biol Interact 2013, 301:31-38.
118. Zhang Z.H., Zhao Y.Y., Cheng X.L., Lin R.C., Dai Z., Zhou C. Metabonomic study of biochemical changes in the rat urine induced by Pinellia ternata (Thunb.) Berit. J Pharm Biomed Anal 2013, 85:186-193.
119. E. J Proteome Res 2013, 12:692-703.
120. E. J Pharm Biomed Anal 2013, 81-82:202-209.