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Multiplex Biomarker Approaches in Type 2 Diabetes Mellitus Research

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Multiplex Biomarker Techniques

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1546))

Abstract

Type 2 diabetes mellitus is a multifactorial condition resulting in high fasting blood glucose levels. Although its diagnosis is straightforward, there is not one set of biomarkers or drug targets that can be used for classification or personalized treatment of individuals who suffer from this condition. Instead, the application of multiplex methods incorporating a systems biology approach is essential in order to increase our understanding of this disease. This chapter reviews the state of the art in biomarker studies of human type 2 diabetes from a proteomic and metabolomic perspective. Our main focus was on biomarkers for disease prediction as these could lead to early intervention strategies for the best possible patient outcomes.

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References

  1. http://www.diabetesatlas.org/

  2. Steyn NP, Mann J, Bennett PH, Temple N, Zimmet P, Tuomilehto J et al (2004) Diet, nutrition and the prevention of type 2 diabetes. Public Health Nutr 7:147–165

    CAS  PubMed  Google Scholar 

  3. Mattei J, Malik V, Wedick NM, Hu FB, Spiegelman D, Willett WC et al (2015) Reducing the global burden of type 2 diabetes by improving the quality of staple foods: the global nutrition and epidemiologic transition initiative. Global Health 11:23

    PubMed  PubMed Central  Google Scholar 

  4. Perseghin G, Ghosh S, Gerow K, Shulman GI (1997) Metabolic defects in lean non Diabetic offspring of NIDDM parents: a cross-sectional study. Diabetes 46:1001–1009

    CAS  PubMed  Google Scholar 

  5. Henriksen JE, Levin K, Thye-Rønn P, Alford F, Hother-Nielsen O, Holst JJ et al. (2000) Glucose-mediated glucose disposal in insulin-resistant normoglycemic relatives of type 2 diabetic patients. Diabetes 49:1209–1218

    Google Scholar 

  6. Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H, Ilanne-Parikka P (2001) Prevention of type 2 diabetes mellitus by changes in life style among subjects with impaired glucose tolerance. N Engl J Med 344:1343–1350

    CAS  PubMed  Google Scholar 

  7. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA et al (2002) Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346:393–403

    CAS  PubMed  Google Scholar 

  8. Diabetes Prevention Program Research Group, Knowler WC, Fowler SE, Hamman RF, Christophi CA, Hoffman HJ et al (2009) 10-year follow-up of diabetes incidence and weight loss in the diabetes prevention program outcomes study. Lancet 374:1677–1686

    PubMed Central  Google Scholar 

  9. Hales CN, Barker DJ (1992) Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis. Diabetologia 35:595–601

    CAS  PubMed  Google Scholar 

  10. Nielsen JH, Haase TN, Jaksch C, Nalla A, Søstrup B, Nalla AA et al (2014) Impact of fetal and neonatal environment on beta cell function and development of diabetes. Acta Obstet Gynecol Scand 93:1109–1122

    PubMed  Google Scholar 

  11. Vaag A, Brons C, Gillberg L, Hansen NS, HjortL AGP et al (2014) Genetic, nongenetic and epigenetic risk determinants indevelopmental programming of type 2 diabetes. Acta Obstet Gynecol Scand 93:1099–1108

    Google Scholar 

  12. Coope A, Torsoni AS, Velloso L (2016) Mechanisms in endocrinology: metabolic and inflammatory pathways on the pathogenesis of type 2 diabetes. Eur J Endocrinol 174:R175–R187

    Google Scholar 

  13. DeFronzo RA (1992) Pathogenesis of type2 (non-insulin dependent) diabetes mellitus: a balanced overview. Diabetologia 35:389–397

    CAS  PubMed  Google Scholar 

  14. Dimas AS, Lagou V, Barker A, Knowles JW, Mägi R, Hivert MF et al (2014) Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes 63:2158–2171

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Domingueti CP, Dusse LM, Carvalho MD, de Sousa LP, Gomes KB, Fernandes AP (2016) Diabetes mellitus: the linkage between oxidative stress, inflammation, hypercoagulability and vascular complications. J Diabetes Complications 30:738–745

    Google Scholar 

  16. Ling C, Poulsen P, Simonsson S, Ronn T, Holmkvist J, Almgren P et al (2007) Genetic and epigenetic factors are associated with expression of respiratory chain component NDUFB6 in human skeletal muscle. J Clin Invest 117:3427–3435

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Ronn T, Poulsen P, Hansson O, Holmkvist J, Almgren P, Nilsson P et al (2008) Age influences DNA methylation and gene expression of COX7A1 in human skeletal muscle. Diabetologia 51:1159–1168

    CAS  PubMed  Google Scholar 

  18. Heyn H, Li N, Ferreira HJ, Moran S, Pisano DG, Gomez A et al (2012) Distinct DNA methylomes of newborns and centenarians. Proc Natl Acad Sci USA 109:10522–10527

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MP, van Eijk K et al (2012) Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol 13:R97.10

    Google Scholar 

  20. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S et al (2013) Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell 49:359–367

    CAS  PubMed  Google Scholar 

  21. Dayeh T, Volkov P, Salo S, Hall E, Nilsson E, Olsson AH et al (2014) Genome-wide DNA methylation analysis of human pancreatic islets from type 2 diabetic and non-diabetic donors identifies candidate genes that influence insulin secretion. PLoS Genet 10:e1004160

    PubMed  PubMed Central  Google Scholar 

  22. Small KS, Hedman AK, Grundberg E, Nica AC, Thorleifsson G, Kong A et al (2007) Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes. Nat Genet 43:561–564

    Google Scholar 

  23. Johansson A, Enroth S, Gyllensten U (2013) Continuous aging of the human DNA methylome throughout the human lifespan. PLoS One 8:e67378

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Steegenga WT, Boekschoten MV, Lute C, Hooiveld GJ, de Groot PJ, Morris TJ et al (2014) Genome-wide age-related changes in DNA methylation and gene expression in human PBMCs. Age (Dordr) 36:9648

    Google Scholar 

  25. Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14:R115

    PubMed  PubMed Central  Google Scholar 

  26. Voight BF et al, MAGIC investigators, GIANT Consortium (2010) Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet 42: 579–589

    Google Scholar 

  27. Ozanne SE (2015) Epigenetics and metabolism in 2014: Metabolic programming--knowns, unknowns and possibilities. Nat Rev Endocrinol 11:67–68

    PubMed  Google Scholar 

  28. Hidalgo B, Irvin MR, Sha J, Zhi D, Aslibekyan S, Absher D et al (2014) Epigenome-wide association study of fasting measures of glucose, insulin, and HOMA-IR in the genetics of lipid lowering drugs and diet network study. Diabetes 63:801–807

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Yuan W, Xia Y, Bell CG, Yet I, Ferreira T, Ward KJ et al (2014) An integrated epigenomic analysis for type 2 diabetes susceptibility loci in monozygotic twins. Nat Commun 5:5719

    CAS  PubMed  Google Scholar 

  30. Toperoff G, Aran D, Kark JD, Rosenberg M, Dubnikov T, Nissan B et al (2012) Genome-wide survey reveals predisposing diabetes type 2-related DNA methylation variations in human peripheral blood. Hum Mol Genet 21:371–383

    CAS  PubMed  Google Scholar 

  31. Ferland-McCollough D, Ozanne SE, Siddle K, Willis AE, Bushell M (2010) The involvement of microRNAs in Type 2 diabetes. Biochem Soc Trans 38:1565–1567

    CAS  PubMed  Google Scholar 

  32. Esguerra JL, Mollet IG, Salunkhe VA, Wendt A, Eliasson L (2014) Regulation of pancreatic beta cell stimulus-secretion coupling by microRNAs. Genes (Basel) 5:1018–1031

    Google Scholar 

  33. Esguerra JL, Eliasson L (2014) Functional implications of long non-coding RNAs in the pancreatic islets of Langerhans. Front Genet 5:209

    PubMed  PubMed Central  Google Scholar 

  34. Guay C, Regazzi R (2013) Circulating microRNAs as novel biomarkers for diabetes mellitus. Nat Rev Endocrinol 9:513–521

    CAS  PubMed  Google Scholar 

  35. Fulton RJ, McDade RL, Smith PL, Kienker LJ, Kettman JR Jr (1997) Advanced multiplexed analysis with the FlowMetrix system. Clin Chem 43:1749–1756

    CAS  PubMed  Google Scholar 

  36. Knowles MR, Cervino S, Skynner HA, Hunt SP, de Felipe C, Salim K et al (2003) Multiplex proteomic analysis by two-dimensional differential in-gel electrophoresis. Proteomics 3:1162–1171

    CAS  PubMed  Google Scholar 

  37. Oliveira BM, Coorssen JR, Martins-de-Souza D (2014) 2DE: the phoenix of proteomics. J Proteomics 104:140–150

    Google Scholar 

  38. Larsen MR, Roepstorff P (2000) Mass spectrometric identification of proteins and characterization of their post-translational modifications in proteome analysis. Fresenius J Anal Chem 366:677–690

    CAS  PubMed  Google Scholar 

  39. Link AJ, Eng J, Schieltz DM, Carmack E, Mize GJ, Morris DR et al (1999) Direct analysis of protein complexes using mass spectrometry. Nat Biotechnol 17:676–682

    CAS  PubMed  Google Scholar 

  40. Griffin JL (2003) Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis. Curr Opin Chem Biol 7:648–654

    CAS  PubMed  Google Scholar 

  41. Beckonert O, Keun HC, Ebbels TM, Bundy J, Holmes E, Lindon JC et al (2007) Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc 2:2692–2703

    CAS  PubMed  Google Scholar 

  42. Sundsten T, Eberhardson M, Göransson M, Bergsten P (2006) The use of proteomics in identifying differentially expressed serum proteins in humans with type 2 diabetes. Proteome Sci 4:22

    PubMed  PubMed Central  Google Scholar 

  43. Riaz S, Alam SS, Akhtar MW (2010) Proteomic identification of human serum biomarkers in diabetes mellitus type 2. J Pharm Biomed Anal 51:1103–1107

    CAS  PubMed  Google Scholar 

  44. Bhonsle HS, Korwar AM, Chougale AD, Kote SS, Dhande NL, Shelgikar KM et al (2013) Proteomic study reveals downregulation of apolipoprotein A1 in plasma of poorly controlled diabetes: a pilot study. Mol Med Rep 7:495–498

    CAS  PubMed  Google Scholar 

  45. Mao P, Wang D (2014) Top-down proteomics of a drop of blood for diabetes monitoring. J Proteome Res 13:1560–1569

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Erion DM, Park HJ, Lee HY (2016) The role of lipids in the pathogenesis and treatment of type 2 diabetes and associated co-morbidities. BMB Rep 49(3):139–148

    Google Scholar 

  47. Coccheri S (2007) Approaches to prevention of cardiovascular complications and events in diabetes mellitus. Drugs 67:997–1026

    CAS  PubMed  Google Scholar 

  48. Tuomilehto J, Lindstrom J, Qiao Q (2005) Strategies for the prevention of type 2 diabetes and cardiovascular disease. Eur Heart J Suppl 7:D18–D22

    CAS  Google Scholar 

  49. Brunner EJ, Shipley MJ, Witte DR, Fuller JH, Marmot MG (2006) Relation between blood glucose and coronary mortality over 33 years in the Whitehall Study. Diabetes Care 29:26–31

    PubMed  Google Scholar 

  50. Nissen SE, Wolski K (2007) Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. N Engl J Med 356:2457–2471

    CAS  PubMed  Google Scholar 

  51. Action to Control Cardiovascular Risk in Diabetes Study Group, Gerstein HC, Miller ME, Byington RP, Goff DC Jr, Bigger JT et al. (2008) Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med 358:2545–2559

    Google Scholar 

  52. Duckworth W, Abraira C, Moritz T, Reda D, Emanuele N, Reaven PD et al (2009) Glucose control and vascular complications in veterans with type 2 diabetes. N Engl J Med 360:129–139

    CAS  PubMed  Google Scholar 

  53. FDA Guidance for Industry: Diabetes Mellitus — Evaluating Cardiovascular Risk in New Antidiabetic Therapies to Treat Type 2 Diabetes. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER); 2008

    Google Scholar 

  54. Donahue MP, Rose K, Hochstrasser D, Vonderscher J, Grass P, Chibout SD et al (2006) Discovery of proteins related to coronary artery disease using industrial-scale proteomics analysis of pooled plasma. Am Heart J 152:478–485

    CAS  PubMed  Google Scholar 

  55. Gordois A, Scuffham P, Shearer A, Oglesby A (2004) The health care costs of diabetic nephropathy in the United States and the United Kingdom. J Diabetes Complications 18:18–26

    PubMed  Google Scholar 

  56. Mogensen CE, Neldam S, Tikkanen I, Oren S, Viskoper R, Watts RW et al (2000) Randomised controlled trial of dual blockade of renin–angiotensin system in patients with hypertension, microalbuminuria, and non-insulin dependent diabetes: the candesartan and lisinoprilmicroalbuminuria (CALM) study. Br Med J 321:1440–1444

    CAS  Google Scholar 

  57. Ninomiya T, Perkovic V, de Galan BE, Zoungas S, Pillai A, Jardine M et al (2009) Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes. J Am Soc Nephrol 20:1813–1821

    PubMed  PubMed Central  Google Scholar 

  58. van der Velde M, Halbesma N, de Charro FT, Bakker SJ, de Zeeuw D, de Jong PE et al (2009) Screening for albuminuria identifies individuals at increased renal risk. J Am Soc Nephrol 20:852–862

    PubMed  PubMed Central  Google Scholar 

  59. Kazumi T, Hozumi T, Ishida Y, Ikeda Y, Kishi K, Hayakawa M et al (1999) Increased urinary transferrin excretion predicts microalbuminuria in patients with type 2 diabetes. Diabetes Care 22:1176–1180

    CAS  PubMed  Google Scholar 

  60. Narita T, Hosoba M, Kakei M, Ito S (2006) Increased urinary excretions of immunoglobulin G, ceruloplasmin, and transferrin predict development of microalbuminuria in patients with type 2 diabetes. Diabetes Care 29:142–144

    CAS  PubMed  Google Scholar 

  61. Araki S, Haneda M, Koya D, Sugimoto T, Isshiki K, Chin-Kanasaki M et al (2007) Predictive impact of elevated serum level of IL-18 for early renal dysfunction in type 2 diabetes: an observational follow-up study. Diabetologia 50:867–873

    CAS  PubMed  Google Scholar 

  62. Hanai K, Babazono T, Nyumura I, Toya K, Tanaka N, Tanaka M et al (2009) Asymmetric dimethylarginine is closely associated with the development and progression of nephropathy in patients with type 2 diabetes. Nephrol Dial Transplant 24:1884–1888

    CAS  PubMed  Google Scholar 

  63. Persson F, Rossing P, Hovind P, Stehouwer CD, Schalkwijk CG, Tarnow L et al (2008) Endothelial dysfunction and inflammation predict development of diabetic nephropathy in the Irbesartan in patients with type 2 diabetes and microalbuminuria (IRMA 2) study. Scand J Clin Lab Invest 68:731–738

    CAS  PubMed  Google Scholar 

  64. Stehouwer CD, Gall MA, Twisk JW, Knudsen E, Emeis JJ, Parving HH (2002) Increased urinary albumin excretion, endothelial dysfunction, and chronic low-grade inflammation in type 2 diabetes: progressive, interrelated, and independently associated with risk of death. Diabetes 51:1157–1165

    CAS  PubMed  Google Scholar 

  65. Liu J, Zhao Z, Willcox MD, Xu B, Shi B (2010) Multiplex bead analysis of urinary cytokines of type 2 diabetic patients with normo- and microalbuminuria. J Immunoassay Immunochem 31:279–289

    PubMed  Google Scholar 

  66. Lu CH, Lin ST, Chou HC, Lee YR, Chan HL (2012) Proteomic analysis of retinopathy-related plasma biomarkers in diabetic patients. Arch Biochem Biophys 529:146–156

    PubMed  Google Scholar 

  67. Hang H, Yuan S, Yang Q, Yuan D, Liu Q (2014) Multiplex bead array assay of plasma cytokines in type 2 diabetes mellitus with diabetic retinopathy. Mol Vis 20:1137–1145

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Jin J, Min H, Kim SJ, Oh S, Kim K, Yu HG et al (2016) Development of diagnostic biomarkers for detecting diabetic retinopathy at early stages using quantitative proteomics. J Diabetes Res 2016:6571976. doi:10.1155/2016/6571976, Epub 2015 Nov 9

    Article  CAS  PubMed  Google Scholar 

  69. Walford GA, Porneala BC, Dauriz M, Vassy JL, Cheng S, Rhee EP et al. (2014) Metabolite traits and genetic risk provide complementary information for the prediction of future type 2 diabetes. Diabetes Care 37:2508–2514

    Google Scholar 

  70. Floegel A, Stefan N, Yu Z, Mühlenbruch K, Drogan D, Joost HG et al (2013) Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 62:639–648

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E et al (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17:448–453

    PubMed  PubMed Central  Google Scholar 

  72. Batch BC, Hyland K, Svetkey LP (2014) Branch chain amino acids: biomarkers of health and disease. Curr Opin Clin Nutr Metab Care 17:86–89

    CAS  PubMed  Google Scholar 

  73. Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y et al (2012) Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol 8:615

    PubMed  PubMed Central  Google Scholar 

  74. Padberg I, Peter E, González-Maldonado S, Witt H, Mueller M, Weis T et al (2014) A new metabolomic signature in type-2 diabetes mellitus and its pathophysiology. PLoS One 9:e85082

    PubMed  PubMed Central  Google Scholar 

  75. Ferrannini E, Natali A, Camastra S, Nannipieri M, Mari A, Adam KP et al (2013) Early metabolic markers of the development of dysglycemia and type 2 diabetes and their physiological significance. Diabetes 62:1730–1737

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Gall WE, Beebe K, Lawton KA, Adam KP, Mitchell MW, Nakhle PJ et al (2010) Alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS One 5:e10883

    PubMed  PubMed Central  Google Scholar 

  77. Schulze MB, Hoffmann K, Boeing H, Linseisen J, Rohrmann S, Möhlig M et al (2007) An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care 30:510–515

    PubMed  Google Scholar 

  78. Wu T, Xie G, Ni Y, Liu T, Yang M, Wei H et al (2015) Serum metabolite signatures of type 2 diabetes mellitus complications. J Proteome Res 14:447–456

    CAS  PubMed  Google Scholar 

  79. Shah SH, Sun JL, Stevens RD, Bain JR, Muehlbauer MJ, Pieper KS et al (2012) Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease. Am Heart J 163:844–850

    CAS  PubMed  Google Scholar 

  80. Würtz P, Havulinna AS, Soininen P, Tynkkynen T, Prieto-Merino D, Tillin T et al (2015) Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation 131:774–785

    PubMed  PubMed Central  Google Scholar 

  81. Sharma K, Karl B, Mathew AV, Gangoiti JA, Wassel CL, Saito R et al (2013) Metabolomics reveals signature of mitochondrial dysfunction in diabetic kidney disease. J Am Soc Nephrol 24:1901–1912

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Niewczas MA, Sirich TL, Mathew AV, Skupien J, Mohney RP, Warram JH et al (2014) Uremic solutes and risk of end-stage renal disease in type 2 diabetes: metabolomic study. Kidney Int 85:1214–1224

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Huang M, Liang Q, Li P, Xia J, Wang Y, Hu P et al (2013) Biomarkers for early diagnosis of type 2 diabetic nephropathy: a study based on an integrated biomarker system. Mol Biosyst 9:2134–2141

    CAS  PubMed  Google Scholar 

  84. Barba I, Garcia-Ramírez M, Hernández C, Alonso MA, Masmiquel L, García-Dorado D et al (2010) Metabolic fingerprints of proliferative diabetic retinopathy: an 1H-NMR-based metabonomic approach using vitreous humor. Invest Ophthalmol Vis Sci 51:4416–4421

    PubMed  Google Scholar 

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Acknowledgments

SEO is funded the UK Medical Research Council (MC_UU_12012/4).

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Authors and Affiliations

  1. University of Cambridge Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK

    Susan E. Ozanne

  2. Department of Clinical Biochemistry, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 2QR, UK

    Susan E. Ozanne

  3. Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge, UK

    Hassan Rahmoune

  4. Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil

    Paul C. Guest

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  1. Susan E. Ozanne
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  2. Hassan Rahmoune
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Correspondence to Susan E. Ozanne .

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  1. Laboratory of Neuroproteomics, University of Campinas (UNICAMP), Campinas, Brazil

    Paul C. Guest

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Ozanne, S.E., Rahmoune, H., Guest, P.C. (2017). Multiplex Biomarker Approaches in Type 2 Diabetes Mellitus Research. In: Guest, P.C. (eds) Multiplex Biomarker Techniques. Methods in Molecular Biology, vol 1546. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-6730-8_3

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  • DOI: https://doi.org/10.1007/978-1-4939-6730-8_3

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