Biomarkers for the prediction of type 2 diabetes and cardiovascular disease

Clinical Pharmacology and Therapeutics

Volumn 90, Issue 1, 2011, Pages 52-66

  Herder, C ^a   Karakas, M ^b   Koenig, W ^b  

^a HEINRICH HEINE UNIVERSITY   (Germany)

^b UNIVERSITY HOSPITAL ULM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADIPONECTIN; ALANINE AMINOTRANSFERASE; APOLIPOPROTEIN B; BIOLOGICAL MARKER; BLOOD CLOTTING FACTOR 8; C REACTIVE PROTEIN; CYSTATIN C; ENDOTHELIAL LEUKOCYTE ADHESION MOLECULE 1; FERRITIN; FIBRINOGEN; FOLIC ACID; GAMMA GLUTAMYLTRANSFERASE; GAMMA INTERFERON INDUCIBLE PROTEIN 10; HIGH DENSITY LIPOPROTEIN CHOLESTEROL; HOMOCYSTEINE; INSULIN; INTERCELLULAR ADHESION MOLECULE 1; INTERLEUKIN 18; INTERLEUKIN 6; INTERLEUKIN 8; LEPTIN; MACROPHAGE MIGRATION INHIBITION FACTOR; MONOCYTE CHEMOTACTIC PROTEIN 1; RANTES; SERUM AMYLOID A; TRANSFORMING GROWTH FACTOR BETA1; TRIACYLGLYCEROL; TUMOR NECROSIS FACTOR RECEPTOR 2; UNINDEXED DRUG; VASCULAR CELL ADHESION MOLECULE 1;

ARTICLE; ATHEROSCLEROSIS; CARDIOVASCULAR DISEASE; CLINICAL ASSESSMENT TOOL; CORONARY ARTERY DISEASE; DIAGNOSTIC ACCURACY; DISEASE MARKER; GENETIC MARKER; GENOMICS; GENOTYPE; HUMAN; INSULIN RESISTANCE; LEUKOCYTE COUNT; METABOLOMICS; NON INSULIN DEPENDENT DIABETES MELLITUS; OUTCOME ASSESSMENT; PATHOPHYSIOLOGY; PRIORITY JOURNAL; PROTEOMICS; RISK ASSESSMENT; SCREENING TEST; TRANSCRIPTOMICS;

BIOLOGICAL MARKERS; BLOOD PROTEINS; CARDIOVASCULAR DISEASES; CORONARY ARTERY DISEASE; DIABETES MELLITUS, TYPE 2; GENE EXPRESSION PROFILING; GENETIC MARKERS; GENOMICS; HUMANS; METABOLOMICS; PREDICTIVE VALUE OF TESTS; PROTEOMICS; RISK FACTORS;

EID: 79959378842     PISSN: 00099236     EISSN: 15326535     Source Type: Journal    
DOI: 10.1038/clpt.2011.93     Document Type: Article

References (76)

1. Koenig, W. & Khuseyinova, N. Biomarkers of atherosclerotic plaque instability and rupture. Arterioscler. Thromb. Vasc. Biol. 27, 15-26 (2007). (Pubitemid 46360377)
2. Khot, U.N. et al. Prevalence of conventional risk factors in patients with coronary heart disease. JAMA 290, 898-904 (2003). (Pubitemid 37430461)
3. Hu, F.B. et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N. Engl. J. Med. 345, 790-797 (2001).
4. Morrow, D.A. & de Lemos, J.A. Benchmarks for the assessment of novel cardiovascular biomarkers. Circulation 115, 949-952 (2007). (Pubitemid 46328287)
5. Hlatky, M.A. et al.; American Heart Association Expert Panel on Subclinical Atherosclerotic Diseases and Emerging Risk Factors and the Stroke Council. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation 119, 2408-2416 (2009).
6. Fibrinogen Studies Collaboration. Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: an individual participant meta-analysis. JAMA. 294, 1799-1809 (2005).
7. Castelli, W.P., Garrison, R.J., Wilson, P.W., Abbott, R.D., Kalousdian, S. & Kannel, W.B. Incidence of coronary heart disease and lipoprotein cholesterol levels. The Framingham Study. JAMA 256, 2835-2838 (1986). (Pubitemid 17178361)
8. Khuseyinova, N. & Koenig, W. Predicting the risk of cardiovascular disease: where does lipoprotein-associated phospholipase A(2) fit in? Mol. Diagn. Ther. 11, 203-217 (2007). (Pubitemid 47359073)
9. Gillies, C.L. et al. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ 334, 299 (2007).
10. The 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature. 467, 1061-1073 (2010).
11. The ENCODE Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature. 447, 799-816 (2007).
12. Anderson, N.L. & Anderson, N.G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell Proteomics 1, 845-867 (2002).
13. Apweiler, R. et al. Approaching clinical proteomics: current state and future fields of application in fluid proteomics. Clin. Chem. Lab. Med. 47, 724-744 (2009).
14. Voight, B.F. et al.; MAGIC investigators; GIANT Consortium. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579-589 (2010).
15. Herder, C. & Roden, M. Genetics of type 2 diabetes: pathophysiologic and clinical relevance. Eur. J. Clin. Invest. (2010); 41, 679-692 (2011).
16. Y amauchi, T. et al. A genome-wide association study in the Japanese population identifies susceptibility loci for type 2 diabetes at UBE2E2 and C2CD4A-C2CD4B. Nat. Genet. 42, 864-868 (2010).
17. Qi, L. et al.; Meta-Analysis of Glucose and Insulin-Related Traits Consortium (MAGIC); Diabetes Genetics Replication and Meta-Analysis (DIAGRAM) Consortium. Genetic variants at 2q24 are associated with susceptibility to type 2 diabetes. Hum. Mol. Genet. 19, 2706-2715 (2010).
18. Dupuis, J. et al.; DIAGRAM Consortium; GIANT Consortium; Global BPgen Consortium; Anders Hamsten on behalf of Procardis Consortium; MAGIC investigators. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105-116 (2010).
19. Soranzo, N. et al.; WTCCC. Common variants at 10 genomic loci influence hemoglobin A1(c) levels via glycemic and nonglycemic pathways. Diabetes 59, 3229-3239 (2010).
20. van Hoek, M. et al. Predicting type 2 diabetes based on polymorphisms from genome-wide association studies: a population-based study. Diabetes 57, 3122-3128 (2008).
21. Lango, H. et al.; UK Type 2 Diabetes Genetics Consortium. Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk. Diabetes 57, 3129-3135 (2008).
22. Lyssenko, V. et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N. Engl. J. Med. 359, 2220-2232 (2008).
23. Meigs, J.B. et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N. Engl. J. Med. 359, 2208-2219 (2008).
24. Lin, X. et al. Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score-the CoLaus Study. Diabetologia 52, 600-608 (2009).
25. Sparso, T. et al. Combined analysis of 19 common validated type 2 diabetes susceptibility gene variants shows moderate discriminative value and no evidence of gene-gene interaction. Diabetologia 52, 1308-1314 (2009).
26. Schulze, M.B. et al. Use of multiple metabolic and genetic markers to improve the prediction of type 2 diabetes: the EPIC-Potsdam Study. Diabetes Care 32, 2116-2119 (2009).
27. Cornelis, M.C. et al. Joint effects of common genetic variants on the risk for type 2 diabetes in U.S. men and women of European ancestry. Ann. Intern. Med. 150, 541-550 (2009).
28. Talmud, P.J. et al. Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ 340, b4838 (2010).
29. Wang, J., Stancakova, A., Kuusisto, J. & Laakso, M. Identification of undiagnosed type 2 diabetic individuals by the Finnish diabetes risk score and biochemical and genetic markers: a population-based study of 7232 Finnish men. J. Clin. Endocrinol. Metab. 95, 3858-3862 (2010).
30. Qi, Q. et al. Combined effects of 17 common genetic variants on type 2 diabetes risk in a Han Chinese population. Diabetologia 53, 2163-2166 (2010).
31. de Miguel-Yanes, J.M. et al.; MAGIC Investigators; DIAGRAM+ Investigators. Genetic risk reclassification for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide polymorphisms. Diabetes Care 34, 121-125 (2011).
32. Schunkert, H. et al.; Cardiogenics; CARDIoGRAM Consortium. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333-338 (2011).
33. The Coronary Artery Disease Genetics Consortium. A genome-wide association study in Europeans and South Asians identifies five novel loci for coronary artery disease. Nat. Genet. 43, 339-344 (2011).
34. Myocardial Infarction Genetics Consortium. Genome-wide association of early onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat. Genet. e-pub ahead of print 8 February 2009.
35. Reilly, M.R. et al.; Identification of ADAMTS7 as a novel locus for coronary atherosclerosis and associations of ABO with myocardial infarction in the presence of coronary atherosclerosis: two genome-wide association studies. Lancet. 377, 383-392 (2011).
36. Clarke, R. et al.; PROCARDIS Consortium. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. N. Engl. J. Med. 361, 2518-2528 (2009).
37. Morrison, A.C. et al. Prediction of coronary heart disease risk using a genetic risk score: the Atherosclerosis Risk in Communities Study. Am. J. Epidemiol. 166, 28-35 (2007). (Pubitemid 47234300)
38. Humphries, S.E., Cooper, J.A., Talmud, P.J. & Miller, G.J. Candidate gene genotypes, along with conventional risk factor assessment, improve estimation of coronary heart disease risk in healthy UK men. Clin. Chem. 53, 8-16 (2007). (Pubitemid 46047758)
39. Paynter, N.P. et al. Association between a literature-based genetic risk score and cardiovascular events in women. JAMA 303, 631-637 (2010).
40. Kathiresan, S. et al. Polymorphisms associated with cholesterol and risk of cardiovascular events. N. Engl. J. Med. 358, 1240-1249 (2008). (Pubitemid 351439225)
41. Ripatti, S. et al. A multilocus genetic risk score for coronary heart disease: casecontrol and prospective cohort analyses. Lancet 376, 1393-1400 (2010).
42. Qi, L., Ma, J., Qi, Q., Hartiala, J., Allayee, H. & Campos, H. Genetic risk score and risk of myocardial infarction in Hispanics. Circulation 123, 374-380 (2011).
43. Sattar, N., Wannamethee, S.G. & Forouhi, N.G. Novel biochemical risk factors for type 2 diabetes: pathogenic insights or prediction possibilities? Diabetologia 51, 926-940 (2008).
44. Duncan, B.B. et al.; Atherosclerosis Risk in Communities Study. Low-grade systemic inflammation and the development of type 2 diabetes: the atherosclerosis risk in communities study. Diabetes 52, 1799-1805 (2003). (Pubitemid 36792473)
45. Herder, C. et al. Chemokines and incident coronary heart disease: results from the MONICA/KORA Augsburg case-cohort study, 1984-2002. Arterioscler. Thromb. Vasc. Biol. 26, 2147-2152 (2006). (Pubitemid 44253801)
46. Ley, S.H. et al. Adipokines and incident type 2 diabetes in an Aboriginal Canadian [corrected] population: the Sandy Lake Health and Diabetes Project. Diabetes Care 31, 1410-1415 (2008).
47. Salomaa, V. et al. Thirty-one novel biomarkers as predictors for clinically incident diabetes. PLoS ONE 5, e10100 (2010).
48. Rathmann, W. et al. Prediction models for incident type 2 diabetes mellitus in the older population: KORA S4/F4 cohort study. Diabet. Med. 27, 1116-1123 (2010).
49. Chao, C. et al. The lack of utility of circulating biomarkers of inflammation and endothelial dysfunction for type 2 diabetes risk prediction among postmenopausal women: the Women's Health Initiative Observational Study. Arch. Intern. Med. 170, 1557-1565 (2010).
50. Herder, C. et al. Immunological and cardio metabolic risk factors in the prediction of type 2 diabetes and coronary events: MONICA/KORA Augsburg case-cohort study. PLoS ONE, in press.
51. Kolberg, J.A. et al. Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort. Diabetes Care 32, 1207-1212 (2009).
52. Goldfine, A.B. et al. Biomarkers in fasting serum to estimate glucose tolerance, insulin sensitivity, and insulin secretion. Clin. Chem. 57, 326-337 (2011).
53. Shlipak, M.G. et al. Cardiovascular mortality risk in chronic kidney disease: comparison of traditional and novel risk factors. JAMA 293, 1737-1745 (2005).
54. Wang, T.J. et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N. Engl. J. Med. 355, 2631-2639 (2006). (Pubitemid 44969122)
55. Folsom, A.R. et al. An assessment of incremental coronary risk prediction using C-reactive protein and other novel risk markers: the atherosclerosis risk in communities study. Arch. Intern. Med. 166, 1368-1373 (2006). (Pubitemid 44036348)
56. Zethelius, B. et al. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. N. Engl. J. Med. 358, 2107-2116 (2008). (Pubitemid 351679597)
57. Melander, O. et al. Novel and conventional biomarkers for prediction of incident cardiovascular events in the community. JAMA 302, 49-57 (2009).
58. Blankenberg, S. et al.; MORGAM Project. Contribution of 30 biomarkers to 10-year cardiovascular risk estimation in 2 population cohorts: the MONICA, risk, genetics, archiving, and monograph (MORGAM) biomarker project. Circulation 121, 2388-2397 (2010).
59. Sattar, N. et al.; Prospective Study of Pravastatin in the Elderly at Risk (PROSPER) Study Group. Are markers of inflammation more strongly associated with risk for fatal than for nonfatal vascular events? PLoS Med. 6, e1000099 (2009).
60. de Ruijter, W. et al. Use of Framingham risk score and new biomarkers to predict cardiovascular mortality in older people: population based observational cohort study. BMJ 338, a3083 (2009).
61. Schnabel, R.B. et al. Multiple marker approach to risk stratification in patients with stable coronary artery disease. Eur. Heart J. 31, 3024-3031 (2010).
62. Takamura, T. et al. Gene expression profiles in peripheral blood mononuclear cells reflect the pathophysiology of type 2 diabetes. Biochem. Biophys. Res. Commun. 361, 379-384 (2007). (Pubitemid 47187270)
63. Zampetaki, A. et al. Plasma microRNA profiling reveals loss of endothelial miR- 126 and other microRNAs in type 2 diabetes. Circ. Res. 107, 810-817 (2010).
64. Wingrove, J.A. et al. Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis. Circ. Cardiovasc. Genet. 1, 31-38 (2008).
65. Sinnaeve, P.R. et al. Gene expression patterns in peripheral blood correlate with the extent of coronary artery disease. PLoS ONE 4, e7037 (2009).
66. Rosenberg, S. et al.; PREDICT (Personalized Risk Evaluation and Diagnosis in the Coronary Tree) Investigators. Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients. Ann. Intern. Med. 153, 425-434 (2010).
67. Taurino, C. et al. Gene expression profiling in whole blood of patients with coronary artery disease. Clin. Sci. 119, 335-343 (2010).
68. Hoekstra, M. et al. The peripheral blood mononuclear cell microRNA signature of coronary artery disease. Biochem. Biophys. Res. Commun. 394, 792-797 (2010).
69. Fichtlscherer, S. et al. Circulating microRNAs in patients with coronary artery disease. Circ. Res. 107, 677-684 (2010).
70. Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423-428 (2008). (Pubitemid 351450845)
71. Suhre, K. et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS ONE 5, e13953 (2010).
72. Wang, T.J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448-453 (2011).
73. J affer, F.A., Libby, P. & Weissleder, R. Molecular and cellular imaging of atherosclerosis: emerging applications. J. Am. Coll. Cardiol. 47, 1328-1338 (2006).
74. Danesh, J. et al. Long-term interleukin-6 levels and subsequent risk of coronary heart disease: two new prospective studies and a systematic review. PLoS Med. 5, e78 (2008).
75. Tabak, A.G., Jokela, M., Akbaraly, T.N., Brunner, E.J., Kivimaki, M. & Witte, D.R. Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Lancet 373, 2215-2221 (2009).
76. Carstensen, M. et al. Accelerated increase in serum interleukin-1 receptor antagonist starts 6 years before diagnosis of type 2 diabetes: Whitehall II prospective cohort study. Diabetes 59, 1222-1227 (2010).