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Acta Diabetologica
Published in: Acta Diabetologica 5/2013

01-10-2013 | Original Article

Association between protein signals and type 2 diabetes incidence

Authors: Troels Mygind Jensen, Daniel R. Witte, Damiana Pieragostino, James N. McGuire, Ellis D. Schjerning, Chiara Nardi, Andrea Urbani, Mika Kivimäki, Eric J. Brunner, Adam G. Tabàk, Dorte Vistisen

Published in: Acta Diabetologica | Issue 5/2013

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Abstract

Understanding early determinants of type 2 diabetes is essential for refining disease prevention strategies. Proteomic technology may provide a useful approach to identify novel protein patterns potentially related to pathophysiological changes that lead up to diabetes. In this study, we sought to identify protein signals that are associated with diabetes incidence in a middle-aged population. Serum samples from 519 participants in a nested case–control selection (167 cases and 352 age-, sex- and BMI-matched normoglycemic control subjects, median follow-up 14.0 years) within the Whitehall-II cohort were analyzed by linear matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Nine protein peaks were found to be associated with incident diabetes. Rate ratios for high peak intensity ranged between 0.4 (95% CI, 0.2–0.8) and 4.0 (95% CI, 1.7–9.2) and were robust to adjustment for main potential confounders, including obesity, lipids and C-reactive protein. The proteins associated with these peaks may reflect diabetes pathogenesis. Our study exemplifies the utility of an approach that combines proteomic and epidemiological data.
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Literature
1.
Stumvoll M, Goldstein BJ, van Haeften TW (2005) Type 2 diabetes: principles of pathogenesis and therapy. Lancet 365:1333–1346
2.
Duncan BB, Schmidt MI, Pankow JS, Ballantyne CM, Couper D, Vigo A, Hoogeveen R, Folsom AR, Heiss G (2003) Low-grade systemic inflammation and the development of type 2 diabetes: the atherosclerosis risk in communities study. Diabetes 52:1799–1805PubMedCrossRef
3.
Cusi K (2010) The role of adipose tissue and lipotoxicity in the pathogenesis of type 2 diabetes. Curr Diab Rep 10:306–315PubMedCrossRef
4.
Shoelson SE, Lee J, Goldfine AB (2006) Inflammation and insulin resistance. J Clin Invest 116:1793–1801PubMedCrossRef
5.
6.
Kolberg JA, Jorgensen T, Gerwien RW, Hamren S, McKenna MP, Moler E, Rowe MW, Urdea MS, Xu XM, Hansen T, Pedersen O, Borch-Johnsen K (2009) Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort. Diabetes Care 32:1207–1212PubMedCrossRef
7.
Salomaa V, Havulinna A, Saarela O, Zeller T, Jousilahti P, Jula A, Muenzel T, Aromaa A, Evans A, Kuulasmaa K, Blankenberg S (2010) Thirty-one novel biomarkers as predictors for clinically incident diabetes. PLoS One 5:e10100
8.
Albrethsen J (2011) The first decade of MALDI protein profiling: a lesson in translational biomarker research. J Proteomics 74:765–773PubMedCrossRef
9.
Albrethsen J (2007) Reproducibility in protein profiling by MALDI-TOF mass spectrometry. Clin Chem 53:852–858PubMedCrossRef
10.
Ransohoff DF (2005) Lessons from controversy: ovarian cancer screening and serum proteomics. J Natl Cancer Inst 97:315–319PubMedCrossRef
11.
12.
Herder C, Brunner EJ, Rathmann W, Strassburger K, Tabák AG, Schloot NC, Witte DR (2009) Elevated levels of the anti-inflammatory interleukin-1 receptor antagonist precede the onset of type 2 diabetes: the Whitehall II study. Diabetes Care 32:421–423PubMedCrossRef
13.
World Health Organization (1999) Definition, diagnosis and classification of diabetes mellitus and its complications. Report of a WHO Consultation, Part 1: Diagnosis and classification of diabetes mellitus. Geneva, World Health Organisation
14.
Brunner EJ, Marmot MG, Nanchahal K, Shipley MJ, Stansfeld SA, Juneja M, Alberti KG (1997) Social inequality in coronary risk: central obesity and the metabolic syndrome. Evidence from the Whitehall II study. Diabetologia 40:1341–1349PubMedCrossRef
15.
Tabák AG, Jokela M, Akbaraly TN, Brunner EJ, Kivimäki M, Witte DR (2009) Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Lancet 373:2215–2221PubMedCrossRef
16.
Hansen HG, Overgaard J, Lajer M, Hubalek F, Højrup P, Pedersen L, Tarnow L, Rossing P, Pociot F, McGuire JN (2010) Finding diabetic nephropathy biomarkers in the plasma peptidome by high-throughput magnetic bead processing and MALDI-TOF-MS analysis. Proteomics Clin Appl 4:697–705PubMedCrossRef
17.
Mantini D, Petrucci F, Pieragostino D, Del-Boccio P, Di-Nicola M, Di-Ilio C, Federici G, Sacchetta P, Comani S, Urbani A (2007) LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise. BMC Bioinformatics 8:101
18.
19.
Breiman L, Friedman JH, Olshen R, Stone CJ (1984) Classification and regression trees, Wadsworth, California
20.
Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I (2001) Controlling the false discovery rate in behavior genetics research. Behav Brain Res 125:279–284PubMedCrossRef
21.
Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C, Chang D, Milburn MV, Gall WE, Weinberger KM, Mewes HW, Hrabe de Angelis M, Wichmann HE, Kronenberg F, Adamski J, Illig T (2010) Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One 5:e13953
22.
Huffman KM, Shah SH, Stevens RD, Bain JR, Muehlbauer M, Slentz CA, Tanner CJ, Kuchibhatla M, Houmard JA, Newgard CB, Kraus WE (2009) Relationships between circulating metabolic intermediates and insulin action in overweight to obese, inactive men and women. Diabetes Care 32:1678–1683PubMedCrossRef
23.
Lewis GD, Asnani A, Gerszten RE (2008) Application of metabolomics to cardiovascular biomarker and pathway discovery. J Am Coll Cardiol 52:117–123PubMedCrossRef
24.
Sundsten T, Ostenson CG, Bergsten P (2008) Serum protein patterns in newly diagnosed type 2 diabetes mellitus–influence of diabetic environment and family history of diabetes. Diabetes Metab Res Rev 24:148–154PubMedCrossRef
25.
Fung ET (2010) A recipe for proteomics diagnostic test development: the OVA1 test, from biomarker discovery to FDA clearance. Clin Chem 56:327–329PubMedCrossRef
26.
Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, O’Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17:448–453PubMedCrossRef
27.
Hu J, Coombes KR, Morris JS, Baggerly KA (2005) The importance of experimental design in proteomic mass spectrometry experiments: some cautionary tales. Brief Funct Genomic Proteomic 3:322–331PubMedCrossRef
28.
De-Bock M, de-Seny D, Meuwis MA, Chapelle JP, Louis E, Malaise M, Merville MP, Fillet M (2010) Challenges for biomarker discovery in body fluids using SELDI-TOF-MS. J Biomed Biotechnol 2010, Art no 906082. doi:10.​1155/​2010/​906082
29.
Villar-Garea A, Griese M, Imhof A (2007) Biomarker discovery from body fluids using mass spectrometry. J Chromatogr B Anal Technol Biomed Life Sci 849:105–114CrossRef
30.
Pieragostino D, Petrucci F, Del-Boccio P, Mantini D, Lugaresi A, Tiberio S, Onofrj M, Gambi D, Sacchetta P, Di-Ilio C, Federici G, Urbani A (2010) Pre-analytical factors in clinical proteomics investigations: impact of ex vivo protein modifications for multiple sclerosis biomarker discovery. J Proteomics 73:579–592PubMedCrossRef
31.
Szajli E, Fehér T, Medzihradszky KF (2008) Investigating the quantitative nature of MALDI-TOF MS. Mol Cell Proteomics 7:2410–2418PubMedCrossRef
32.
Ekblad L, Baldetorp B, Fernö M, Olsson H, Bratt C (2007) In-source decay causes artifacts in SELDI-TOF MS spectra. J Proteome Res 6:1609–1614PubMedCrossRef
33.
Parker CE, Pearson TW, Anderson NL, Borchers CH (2010) Mass-spectrometry-based clinical proteomics–a review and prospective. Analyst 135:1830–1838PubMedCrossRef
34.
Gonzalez-Recio O, Forni S (2011) Genome-wide prediction of discrete traits using bayesian regressions and machine learning. Genet Select Evol 43:7CrossRef
35.
Wu B, Abbott T, Fishman D, McMurray W, Mor G, Stone K, Ward D, Williams K, Zhao H (2003) Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 19:1636–1643PubMedCrossRef
36.
Ziegler A, DeStefano AL, Konig IR (2007) Data mining, neural nets, trees—problems 2 and 3 of genetic analysis workshop 15. Genet Epidemiol 31:S51–S60PubMedCrossRef
37.
Díaz-Uriarte R, Alvarez de Andrés (2006) Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7:3
38.
Fan Y, Murphy TB, Byrne JC, Brennan L, Fitzpatrick JM, Watson RW (2011) Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer. J Proteome Res 10:1361–1373PubMedCrossRef
39.
Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A (2003) ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 31:3784–3788PubMedCrossRef
40.
Leighton B, Foot EA (1995) The role of the sensory peptide calcitonin-gene-related peptide(s) in skeletal muscle carbohydrate metabolism: effects of capsaicin and resiniferatoxin. Biochem J 307:707–712PubMed
41.
Muff R, Born W, Fischer JA (1995) Calcitonin, calcitonin gene-related peptide, adrenomedullin and amylin: homologous peptides, separate receptors and overlapping biological actions. Eur J Endocrinol 133:17–20PubMedCrossRef
42.
Slezak LA, Andersen DK (2001) Pancreatic resection: effects on glucose metabolism. World J Surg 25:452–460PubMedCrossRef
43.
Breland UM, Michelsen AE, Skjelland M, Folkersen L, Krohg-Sørensen K, Russell D, Ueland T, Yndestad A, Paulsson-Berne G, Damås JK, Oie E, Hansson GK, Halvorsen B, Aukrust P (2010) Raised MCP-4 levels in symptomatic carotid atherosclerosis: an inflammatory link between platelet and monocyte activation. Cardiovasc Res 86:265–273PubMedCrossRef
44.
Béliard S, Nogueira JP, Maraninchi M, Lairon D, Nicolay A, Giral P, Portugal H, Vialettes B, Valéro R (2009) Parallel increase of plasma apoproteins C-II and C-III in Type 2 diabetic patients. Diabet Med 26:736–739PubMedCrossRef
45.
Sørensen LB, Flint A, Raben A, Hartmann B, Holst JJ, Astrup A (2003) No effect of physiological concentrations of glucagon-like peptide-2 on appetite and energy intake in normal weight subjects. Int J Obes Relat Metab Disord 27:450–456PubMedCrossRef
46.
Meier JJ, Nauck MA, Pott A, Heinze K, Goetze O, Bulut K, Schmidt WE, Gallwitz B, Holst JJ (2006) Glucagon-like peptide 2 stimulates glucagon secretion, enhances lipid absorption, and inhibits gastric acid secretion in humans. Gastroenterology 130:44–54PubMedCrossRef
47.
Yu R, Kim CS, Kawada T, Kwon TW, Lim TH, Kim YW, Kwon BS (2004) Involvement of leukotactin-1, a novel CC chemokine, in human atherosclerosis. Atherosclerosis 174:35–42PubMedCrossRef
48.
49.
Onat A, Hergenç G, Ayhan E, Uğur M, Kaya H, Tuncer M, Can G (2009) Serum apolipoprotein C-III in high-density lipoprotein: a key diabetogenic risk factor in Turks. Diabet Med 26:981–988PubMedCrossRef
50.
Wang Z, Yip C, Ying Y, Wang J, Meng XY, Lomas L, Yip TT, Fung ET (2004) Mass spectrometric analysis of protein markers for ovarian cancer. Clin Chem 50:1939–1942PubMedCrossRef
Metadata
Title
Association between protein signals and type 2 diabetes incidence
Authors
Troels Mygind Jensen
Daniel R. Witte
Damiana Pieragostino
James N. McGuire
Ellis D. Schjerning
Chiara Nardi
Andrea Urbani
Mika Kivimäki
Eric J. Brunner
Adam G. Tabàk
Dorte Vistisen
Publication date
01-10-2013
Publisher
Springer Milan
Published in
Acta Diabetologica / Issue 5/2013
Print ISSN: 0940-5429
Electronic ISSN: 1432-5233
DOI
https://doi.org/10.1007/s00592-012-0376-3

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