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Journal of Cardiovascular Translational Research

Erschienen in:

01.08.2012

Does Familial Clustering of Risk Factors for Long-Term Diabetic Complications Leave Any Place for Genes That Act independently?

verfasst von: Andrew D. Paterson, Shelley B. Bull

Erschienen in: Journal of Cardiovascular Translational Research | Ausgabe 4/2012

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Abstract

Long-term complications of type 1 diabetes, including nephropathy and retinopathy, share diabetes duration and hyperglycemia as major risk factors. Cross-sectional studies of sibpairs, both with type 1 diabetes, have shown familial clustering of specific complications, leading to the hypothesis that there are genetic contributors. However, because of the cross-sectional design of these studies, they were not able to account for the long-term effect of glycemia. Glycemia, measured by HbA1c, is correlated in sibs with type 1 diabetes. Recently, specific genetic loci that are associated with differences in HbA1c between people with type 1 diabetes have been convincingly identified, and they have also been shown to be associated with diabetic complications. This raises the question: how much of the familial clustering of diabetic complications is due to genes that influence risk without acting through the conventional risk factors? Implications for the design of genetic studies of diabetic complications are discussed.

Atkinson, M. A., & Maclaren, N. K. (1994). The pathogenesis of insulin-dependent diabetes mellitus. The New England Journal of Medicine, 331(21), 1428–1436.PubMedCrossRef

Klein, R., Klein, B. E., & Moss, S. E. (1989). The Wisconsin epidemiological study of diabetic retinopathy: a review. Diabetes/Metabolism Reviews, 5(7), 559–570.PubMedCrossRef

The Diabetes Control and Complications Trial Research Group. (1993). The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The New England Journal of Medicine, 329(14), 977–986.CrossRef

WHO (2005) http://www.who.int/mediacentre/factsheets/fs282/en/. Accessed Sept 2005

Klein, R., Klein, B. E., Moss, S. E., Davis, M. D., & DeMets, D. L. (1984). The Wisconsin epidemiologic study of diabetic retinopathy. II. Prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years. Archives of Ophthalmology, 102(4), 520–526.PubMedCrossRef

Deckert, T., Simonsen, S. E., & Poulsen, J. E. (1967). Prognosis of proliferative retinopathy in juvenile diabetics. Diabetes, 16(10), 728–733.PubMed

Aiello, L. P. (2005). Angiogenic pathways in diabetic retinopathy. The New England Journal of Medicine, 353(8), 839–841.PubMedCrossRef

Moss, S. E., Klein, R., & Klein, B. E. (1994). Ten-year incidence of visual loss in a diabetic population. Ophthalmology, 101(6), 1061–1070.PubMed

The Diabetes Control and Complications Trial Research Group. (1997). Clustering of long-term complications in families with diabetes in the diabetes control and complications trial. Diabetes, 46(11), 1829–1839.CrossRef

10.

Harris, M. I., Eastman, R. C., Cowie, C. C., Flegal, K. M., & Eberhardt, M. S. (1999). Racial and ethnic differences in glycemic control of adults with type 2 diabetes. Diabetes Care, 22(3), 403–408.PubMedCrossRef

11.

Leslie, R. D., & Pyke, D. A. (1982). Diabetic retinopathy in identical twins. Diabetes, 31(1), 19–21.PubMedCrossRef

12.

Hietala, K., Forsblom, C., Summanen, P., & Groop, P. H. (2008). Heritability of proliferative diabetic retinopathy. Diabetes, 57(8), 2176–2180. doi:10.2337/db07-1495.PubMedCrossRef

13.

Hallman, D. M., Boerwinkle, E., Gonzalez, V. H., Klein, B. E., Klein, R., & Hanis, C. L. (2007). A genome-wide linkage scan for diabetic retinopathy susceptibility genes in Mexican Americans with type 2 diabetes from Starr County, Texas. Diabetes, 56(4), 1167–1173.PubMedCrossRef

14.

Looker, H. C., Nelson, R. G., Chew, E., Klein, R., Klein, B. E., Knowler, W. C., et al. (2007). Genome-wide linkage analyses to identify loci for diabetic retinopathy. Diabetes, 56(4), 1160–1166.PubMedCrossRef

15.

Grassi, M. A., Tikhomirov, A., Ramalingam, S., Below, J. E., Cox, N. J., & Nicolae, D. L. (2011). Genome-wide meta-analysis for severe diabetic retinopathy. Human Molecular Genetics. doi:10.1093/hmg/ddr121.

16.

Rossing, P., Hougaard, P., Borch-Johnsen, K., & Parving, H. H. (1996). Predictors of mortality in insulin dependent diabetes: 10 year observational follow up study. BMJ, 313(7060), 779–784.PubMedCrossRef

17.

Andersen, A. R., Christiansen, J. S., Andersen, J. K., Kreiner, S., & Deckert, T. (1983). Diabetic nephropathy in type 1 (insulin-dependent) diabetes: an epidemiological study. Diabetologia, 25(6), 496–501.PubMedCrossRef

18.

Krolewski, A. S., Warram, J. H., Christlieb, A. R., Busick, E. J., & Kahn, C. R. (1985). The changing natural history of nephropathy in type I diabetes. American Journal of Medicine, 78(5), 785–794.PubMedCrossRef

19.

Hovind, P., Tarnow, L., Rossing, K., Rossing, P., Eising, S., Larsen, N., et al. (2003). Decreasing incidence of severe diabetic microangiopathy in type 1 diabetes. Diabetes Care, 26(4), 1258–1264.PubMedCrossRef

20.

Nordwall, M., Bojestig, M., Arnqvist, H. J., & Ludvigsson, J. (2004). Declining incidence of severe retinopathy and persisting decrease of nephropathy in an unselected population of type 1 diabetes—the Linkoping Diabetes Complications Study. Diabetologia, 47(7), 1266–1272.PubMedCrossRef

21.

Pambianco, G., Zgibor, J., & Orchard, T. (2003). Temporal trends in type 1 diabetes (T1D): coronary artery disease (CAD), proliferative retinopathy (PR) and overt nephropathy (ON). Diabetes, 52(Suppl 1).

22.

Deferrari, G., Repetto, M., Calvi, C., Ciabattoni, M., Rossi, C., & Robaudo, C. (1998). Diabetic nephropathy: from micro- to macroalbuminuria. Nephrology, Dialysis, Transplantation, 13(Suppl 8), 11–15.PubMedCrossRef

23.

Rossing, P., Hougaard, P., & Parving, H. H. (2005). Progression of microalbuminuria in type 1 diabetes: ten-year prospective observational study. Kidney International, 68(4), 1446–1450.PubMedCrossRef

24.

Kalter-Leibovici, O., Van Dyk, D. J., Leibovici, L., Loya, N., Erman, A., Kremer, I., et al. (1991). Risk factors for development of diabetic nephropathy and retinopathy in Jewish IDDM patients. Diabetes, 40(2), 204–210.

25.

Karter, A. J., Ferrara, A., Liu, J. Y., Moffet, H. H., Ackerson, L. M., & Selby, J. V. (2002). Ethnic disparities in diabetic complications in an insured population. Journal of the American Medical Association, 287(19), 2519–2527.PubMedCrossRef

26.

Seaquist, E. R., Goetz, F. C., Rich, S., & Barbosa, J. (1989). Familial clustering of diabetic kidney disease. Evidence for genetic susceptibility to diabetic nephropathy [see comments]. The New England Journal of Medicine, 320(18), 1161–1165.PubMedCrossRef

27.

Borch-Johnsen, K., Norgaard, K., Hommel, E., Mathiesen, E. R., Jensen, J. S., Deckert, T., et al. (1992). Is diabetic nephropathy an inherited complication? Kidney International, 41(4), 719–722.

28.

Harjutsalo, V., Katoh, S., Sarti, C., Tajima, N., & Tuomilehto, J. (2004). Population-based assessment of familial clustering of diabetic nephropathy in type 1 diabetes. Diabetes, 53(9), 2449–2454.PubMedCrossRef

29.

Fioretto, P., Steffes, M. W., Barbosa, J., Rich, S. S., Miller, M. E., & Mauer, M. (1999). Is diabetic nephropathy inherited? Studies of glomerular structure in type 1 diabetic sibling pairs. Diabetes, 48(4), 865–869.PubMedCrossRef

30.

Rogus, J. J., Poznik, G. D., Pezzolesi, M. G., Smiles, A. M., Dunn, J., Walker, W., et al. (2008). High-density single nucleotide polymorphism genome-wide linkage scan for susceptibility genes for diabetic nephropathy in type 1 diabetes: discordant sibpair approach. Diabetes, 57(9), 2519–2526. doi:10.2337/db07-1086.

31.

Osterholm, A. M., He, B., Pitkaniemi, J., Albinsson, L., Berg, T., Sarti, C., et al. (2007). Genome-wide scan for type 1 diabetic nephropathy in the Finnish population reveals suggestive linkage to a single locus on chromosome 3q. Kidney International, 71(2), 140–145. doi:10.1038/sj.ki.5001933.

32.

Moczulski, D. K., Rogus, J. J., Antonellis, A., Warram, J. H., & Krolewski, A. S. (1998). Major susceptibility locus for nephropathy in type 1 diabetes on chromosome 3q: results of novel discordant sib-pair analysis. Diabetes, 47(7), 1164–1169.PubMedCrossRef

33.

He, B., Osterholm, A. M., Hoverfalt, A., Forsblom, C., Hjorleifsdottir, E. E., Nilsson, A. S., et al. (2009). Association of genetic variants at 3q22 with nephropathy in patients with type 1 diabetes mellitus. American Journal of Human Genetics, 84(1), 5–13. doi:10.1016/j.ajhg.2008.11.012.

34.

Zhou, J. B., & Yang, J. K. (2009). Angiotensin-converting enzyme gene polymorphism is associated with proliferative diabetic retinopathy: a meta-analysis. Acta Diabetologica. doi:10.1007/s00592-009-0160-1.

35.

Wiwanitkit, V. (2008). Angiotensin-converting enzyme gene polymorphism is correlated to diabetic retinopathy: a meta-analysis. Journal of Diabetes and its Complications, 22(2), 144–146. doi:10.1016/j.jdiacomp.2006.09.004.PubMedCrossRef

36.

Fujisawa, T., Ikegami, H., Kawaguchi, Y., Hamada, Y., Ueda, H., Shintani, M., et al. (1998). Meta-analysis of association of insertion/deletion polymorphism of angiotensin I-converting enzyme gene with diabetic nephropathy and retinopathy. Diabetologia, 41(1), 47–53.

37.

Pearson, T. A., & Manolio, T. A. (2008). How to interpret a genome-wide association study. Journal of the American Medical Association, 299(11), 1335–1344. doi:10.1001/jama.299.11.1335.PubMedCrossRef

38.

Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., Collins, F. S., et al. (2009). Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proceedings of the National Academy of Sciences of the United States of America, 106(23), 9362–9367. doi:10.1073/pnas.0903103106.

39.

Mueller, P. W., Rogus, J. J., Cleary, P. A., Zhao, Y., Smiles, A. M., Steffes, M. W., et al. (2006). Genetics of Kidneys in Diabetes (GoKinD) study: a genetics collection available for identifying genetic susceptibility factors for diabetic nephropathy in type 1 diabetes. Journal of the American Society of Nephrology, 17(7), 1782–1790.

40.

Pezzolesi, M. G., Poznik, G. D., Mychaleckyj, J. C., Paterson, A. D., Barati, M. T., Klein, J. B., et al. (2009). Genome-wide association scan for diabetic nephropathy susceptibility genes in type 1 diabetes. Diabetes, 58(6), 1403–1410. doi:10.2337/db08-1514.

41.

Pluzhnikov, A., Below, J. E., Konkashbaev, A., Tikhomirov, A., Kistner-Griffin, E., Roe, C. A., et al. (2010). Spoiling the whole bunch: quality control aimed at preserving the integrity of high-throughput genotyping. American Journal of Human Genetics, 87(1), 123–128. doi:10.1016/j.ajhg.2010.06.005.

42.

Dudbridge, F., & Gusnanto, A. (2008). Estimation of significance thresholds for genomewide association scans. Genetic Epidemiology, 32(3), 227–234.PubMedCrossRef

43.

Snieder, H., Sawtell, P. A., Ross, L., Walker, J., Spector, T. D., & Leslie, R. D. (2001). HbA(1c) levels are genetically determined even in type 1 diabetes: evidence from healthy and diabetic twins. Diabetes, 50(12), 2858–2863.PubMedCrossRef

44.

Paterson, A. D., Waggott, D., Boright, A. P., Hosseini, S. M., Shen, E., Sylvestre, M. P., et al. (2010). A genome-wide association study identifies a novel major locus for glycemic control in type 1 diabetes, as measured by both A1C and glucose. Diabetes, 59(2), 539–549.

45.

Petitti, D. B., Klingensmith, G. J., Bell, R. A., Andrews, J. S., Dabelea, D., Imperatore, G., et al. (2009). Glycemic control in youth with diabetes: the SEARCH for diabetes in Youth Study. Journal of Pediatrics, 155(5), 668–672. doi:10.1016/j.jpeds.2009.05.025. e661-663.

46.

Kilpatrick, E. S., Rigby, A. S., & Atkin, S. L. (2009). The Diabetes Control and Complications Trial: the gift that keeps giving. Nature Reviews. Endocrinology, 5(10), 537–545. doi:10.1038/nrendo.2009.179.PubMedCrossRef

47.

Chanock, S. J., Manolio, T., Boehnke, M., Boerwinkle, E., Hunter, D. J., Thomas, G., et al. (2007). Replicating genotype-phenotype associations. Nature, 447(7145), 655–660. doi:10.1038/447655a.

48.

Clee, S. M., Yandell, B. S., Schueler, K. M., Rabaglia, M. E., Richards, O. C., Raines, S. M., et al. (2006). Positional cloning of Sorcs1, a type 2 diabetes quantitative trait locus. Nature Genetics, 38(6), 688–693. doi:10.1038/ng1796.PubMedCrossRef

49.

Granhall, C., Park, H. B., Fakhrai-Rad, H., & Luthman, H. (2006). High-resolution quantitative trait locus analysis reveals multiple diabetes susceptibility loci mapped to intervals < 800 kb in the species-conserved Niddm1i of the GK rat. Genetics, 174(3), 1565–1572. doi:10.1534/genetics.106.062208.PubMedCrossRef

50.

Meigs, J. B., Panhuysen, C. I., Myers, R. H., Wilson, P. W., & Cupples, L. A. (2002). A genome-wide scan for loci linked to plasma levels of glucose and HbA(1c) in a community-based sample of Caucasian pedigrees: The Framingham Offspring Study. Diabetes, 51(3), 833–840.PubMedCrossRef

51.

Simonis-Bik, A. M., Eekhoff, E. M., Diamant, M., Boomsma, D. I., Heine, R. J., Dekker, J. M., et al. (2008). The heritability of HbA1c and fasting blood glucose in different measurement settings. Twin Research and Human Genetics, 11(6), 597–602. doi:10.1375/twin.11.6.59710.1375/twin.11.6.597.

52.

Bouatia-Naji, N., Rocheleau, G., Van Lommel, L., Lemaire, K., Schuit, F., Cavalcanti-Proenca, C., et al. (2008). A polymorphism within the G6PC2 gene is associated with fasting plasma glucose levels. Science, 320(5879), 1085–1088. doi:10.1126/science.1156849.

53.

Chen, W. M., Erdos, M. R., Jackson, A. U., Saxena, R., Sanna, S., Silver, K. D., et al. (2008). Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels. The Journal of Clinical Investigation, 118(7), 2620–2628. doi:10.1172/JCI34566.

54.

Meigs, J. B., Manning, A. K., Fox, C. S., Florez, J. C., Liu, C., Cupples, L. A., et al. (2007). Genome-wide association with diabetes-related traits in the Framingham Heart Study. BMC Medical Genetics, 8(Suppl 1), S16. doi:10.1186/1471-2350-8-S1-S16.

55.

Orho-Melander, M., Melander, O., Guiducci, C., Perez-Martinez, P., Corella, D., Roos, C., et al. (2008). Common missense variant in the glucokinase regulatory protein gene is associated with increased plasma triglyceride and C-reactive protein but lower fasting glucose concentrations. Diabetes, 57(11), 3112–3121. doi:10.2337/db08-0516.

56.

Bouatia-Naji, N., Bonnefond, A., Cavalcanti-Proenca, C., Sparso, T., Holmkvist, J., Marchand, M., et al. (2009). A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk. Nature Genetics, 41(1), 89–94. doi:10.1038/ng.277.

57.

Lyssenko, V., Nagorny, C. L., Erdos, M. R., Wierup, N., Jonsson, A., Spegel, P., et al. (2009). Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nature Genetics, 41(1), 82–88. doi:10.1038/ng.288.

58.

Prokopenko, I., Langenberg, C., Florez, J. C., Saxena, R., Soranzo, N., Thorleifsson, G., et al. (2009). Variants in MTNR1B influence fasting glucose levels. Nature Genetics, 41(1), 77–81. doi:10.1038/ng.290.

59.

Sabatti, C., Service, S. K., Hartikainen, A. L., Pouta, A., Ripatti, S., Brodsky, J., et al. (2009). Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nature Genetics, 41(1), 35–46. doi:10.1038/ng.271.

60.

Paré, G., Chasman, D. I., Parker, A. N., Nathan, D. M., Miletich, J. P., Zee, R. Y., et al. (2008). Novel association of HK1 with glycated hemoglobin in a non-diabetic population: a genome-wide evaluation of 14,618 participants in the Women's Genome Health Study. PLoS Genetics, 4(12), e1000312. doi:10.1371/journal.pgen.1000312.

61.

Dupuis, J., Langenberg, C., Prokopenko, I., Saxena, R., Soranzo, N., Jackson, A. U., et al. (2010). New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nature Genetics, 42(2), 105–116. doi:10.1038/ng.520.

62.

Saxena, R., Hivert, M. F., Langenberg, C., Tanaka, T., Pankow, J. S., Vollenweider, P., et al. (2010). Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nature Genetics, 42(2), 142–148. doi:10.1038/ng.521.

63.

Soranzo, N., Sanna, S., Wheeler, E., Gieger, C., Radke, D., Dupuis, J., et al. (2010). Common variants at 10 genomic loci influence hemoglobin A(C) levels via glycemic and nonglycemic pathways. Diabetes, 59(12), 3229–3239. doi:10.2337/db10-0502.

64.

Bonnefond, A., Vaxillaire, M., Labrune, Y., Lecoeur, C., Chevre, J. C., Bouatia-Naji, N., et al. (2009). A genetic variant in HK1 is associated with pro-anemic state and HbA1c but not other glycemic control related traits. Diabetes. doi:10.2337/db09-0652.

65.

Al-Kateb, H., Boright, A. P., Mirea, L., Xie, X., Sutradhar, R., Mowjoodi, A., et al. (2008). Multiple superoxide dismutase 1/splicing factor serine alanine 15 variants are associated with the development and progression of diabetic nephropathy: the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Genetics study. Diabetes, 57(1), 218–228.

66.

Nathan, D. M., Kuenen, J., Borg, R., Zheng, H., Schoenfeld, D., & Heine, R. J. (2008). Translating the A1C assay into estimated average glucose values. Diabetes Care, 31(8), 1473–1478. doi:10.2337/dc08-0545.PubMedCrossRef

67.

Nathan, D. M., Turgeon, H., & Regan, S. (2007). Relationship between glycated haemoglobin levels and mean glucose levels over time. Diabetologia, 50(11), 2239–2244. doi:10.1007/s00125-007-0803-0.PubMedCrossRef

68.

Kahn, R., & Fonseca, V. (2008). Translating the A1C assay. Diabetes Care, 31(8), 1704–1707. doi:10.2337/dc08-0878.PubMedCrossRef

69.

Epidemiology of Diabetes Interventions and Complications (EDIC). (1999). Design, implementation, and preliminary results of a long-term follow- up of the Diabetes Control and Complications Trial cohort. Diabetes Care, 22(1), 99–111.CrossRef

70.

The DCCT Research Group. (1988). Weight gain associated with intensive therapy in the diabetes control and complications trial. Diabetes Care, 11(7), 567–573.CrossRef

71.

The Diabetes Control and Complications Trial Research Group. (1997). Hypoglycemia in the Diabetes Control and Complications Trial. Diabetes, 46(2), 271–286.CrossRef

72.

Fava, D., Gardner, S., Pyke, D., & Leslie, R. D. (1998). Evidence that the age at diagnosis of IDDM is genetically determined. Diabetes Care, 21(6), 925–929.PubMedCrossRef

73.

Kyvik, K. O., Green, A., & Beck-Nielsen, H. (1995). Concordance rates of insulin dependent diabetes mellitus: a population based study of young Danish twins. BMJ, 311(7010), 913–917.PubMedCrossRef

74.

Hyttinen, V., Kaprio, J., Kinnunen, L., Koskenvuo, M., & Tuomilehto, J. (2003). Genetic liability of type 1 diabetes and the onset age among 22,650 young Finnish twin pairs: a nationwide follow-up study. Diabetes, 52(4), 1052–1055.PubMedCrossRef

75.

Gillespie, K. M., Gale, E. A., & Bingley, P. J. (2002). High familial risk and genetic susceptibility in early onset childhood diabetes. Diabetes, 51(1), 210–214.PubMedCrossRef

76.

Harjutsalo, V., Podar, T., & Tuomilehto, J. (2005). Cumulative incidence of type 1 diabetes in 10,168 siblings of Finnish young-onset type 1 diabetic patients. Diabetes, 54(2), 563–569.PubMedCrossRef

77.

Karjalainen, J., Salmela, P., Ilonen, J., Surcel, H. M., & Knip, M. (1989). A comparison of childhood and adult type I diabetes mellitus. The New England Journal of Medicine, 320(14), 881–886.PubMedCrossRef

78.

Valdes, A. M., Erlich, H. A., & Noble, J. A. (2005). Human leukocyte antigen class I B and C loci contribute to type 1 diabetes (T1D) susceptibility and age at T1D onset. Human Immunology, 66(3), 301–313. doi:10.1016/j.humimm.2004.12.001.PubMedCrossRef

79.

Nejentsev, S., Howson, J. M., Walker, N. M., Szeszko, J., Field, S. F., Stevens, H. E., et al. (2007). Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A. Nature, 450(7171), 887–892. doi:10.1038/nature06406.

80.

Faye, L. L., Sun, L., Dimitromanolakis, A., & Bull, S. B. (2011). A flexible genome-wide bootstrap method that accounts for ranking- and threshold- selection bias in GWAS interpretation and replication study design of high dimensional genome wide scans. Statistics in Medicine, 30, 1898–1912.PubMedCrossRef

81.

Sun, L., Dimitromanolaki, A., Faye, L., Paterson, A. D., Waggott, D., The DCCT/EDIC Research Group, et al. (2011). BR-squared: a practical solution to the winner's curse in genome-wide scans. Human Genetics, 120(5), 545–552.

82.

Hertel, J. K., Johansson, S., Raeder, H., Platou, C. G., Midthjell, K., Hveem, K., et al. (2011). Evaluation of four novel genetic variants affecting hemoglobin A1c levels in a population-based type 2 diabetes cohort (the HUNT2 study). BMC Medical Genetics, 12, 20. doi:10.1186/1471-2350-12-20.

83.

Tobin, M. D., Sheehan, N. A., Scurrah, K. J., & Burton, P. R. (2005). Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure. Statistics in Medicine, 24(19), 2911–2935. doi:10.1002/sim.2165.PubMedCrossRef

84.

Little, R. R., Rohlfing, C. L., Wiedmeyer, H. M., Myers, G. L., Sacks, D. B., & Goldstein, D. E. (2001). The national glycohemoglobin standardization program: a five-year progress report. Clinical Chemistry, 47(11), 1985–1992.PubMed

85.

Little, R. R., & Rolhfing, C. L. (2009). HbA1c standardization: background, progress and current issues. Lab Medicine, 40(6), 368–373. doi:10.1309/LM3DUSEIBXHTVZ70.CrossRef

86.

Little, R. R., & Sacks, D. B. (2009). HbA1c: how do we measure it and what does it mean? Current Opinion in Endocrinology, Diabetes, and Obesity, 16(2), 113–118. doi:10.1097/MED.0b013e328327728d01266029-200904000-00005.PubMedCrossRef

87.

The DCCT Research Group. (1992). Lipid and lipoprotein levels in patients with IDDM diabetes control and complication. Trial experience. Diabetes Care, 15(7), 886–894.CrossRef

88.

Diabetes Control and Complications Trial Research Group. (1995). Implementation of treatment protocols in the Diabetes Control and Complications Trial. Diabetes Care, 18(3), 361–376.CrossRef

89.

The DCCT Research Group. (1986). The Diabetes Control and Complications Trial (DCCT). Design and methodologic considerations for the feasibility phase. Diabetes, 35(5), 530–545.CrossRef

90.

No authors listed. (1995). Adverse events and their association with treatment regimens in the diabetes control and complications trial. Diabetes Care, 18(11), 1415–1427.CrossRef

91.

Tarnow, L., Kjeld, T., Knudsen, E., Major-Pedersen, A., & Parving, H. H. (2000). Lack of synergism between long-term poor glycaemic control and three gene polymorphisms of the renin angiotensin system on risk of developing diabetic nephropathy in type I diabetic patients. Diabetologia, 43(6), 794–799.PubMedCrossRef

92.

Gerstl, E. M., Rabl, W., Rosenbauer, J., Grobe, H., Hofer, S. E., Krause, U., et al. (2008). Metabolic control as reflected by HbA1c in children, adolescents and young adults with type-1 diabetes mellitus: combined longitudinal analysis including 27,035 patients from 207 centers in Germany and Austria during the last decade. European Journal of Pediatrics, 167(4), 447–453. doi:10.1007/s00431-007-0586-9.

93.

No authors listed. (1995). Effect of intensive diabetes management on macrovascular events and risk factors in the Diabetes Control and Complications Trial. Am J Cardiol, 75(14), 894–903.CrossRef

94.

No authors listed. (1995). The effect of intensive diabetes treatment on the progression of diabetic retinopathy in insulin-dependent diabetes mellitus. The Diabetes Control and Complications Trial. Arch Ophthalmol, 113(1), 36–51.

95.

Lachin, J. M., Genuth, S., Nathan, D. M., & Rutledge, B. N. (2007). The hemoglobin glycation index is not an independent predictor of the risk of microvascular complications in the Diabetes Control and Complications Trial. Diabetes, 56(7), 1913–1921. doi:10.2337/db07-0028.PubMedCrossRef

Titel: Does Familial Clustering of Risk Factors for Long-Term Diabetic Complications Leave Any Place for Genes That Act independently?
verfasst von: Andrew D. Paterson
Shelley B. Bull
Publikationsdatum: 01.08.2012
Verlag: Springer US
Erschienen in: Journal of Cardiovascular Translational Research / Ausgabe 4/2012
Print ISSN: 1937-5387
Elektronische ISSN: 1937-5395
DOI: https://doi.org/10.1007/s12265-012-9385-4

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Wenn unter einer medikamentösen Hochdrucktherapie der diastolische Blutdruck in den Keller geht, steigt das Risiko für schwere kardiovaskuläre Ereignisse: Darauf deutet eine Sekundäranalyse der SPRINT-Studie hin.

Therapiestart mit Blutdrucksenkern erhöht Frakturrisiko

25.04.2024 Hypertonie Nachrichten

Beginnen ältere Männer im Pflegeheim eine Antihypertensiva-Therapie, dann ist die Frakturrate in den folgenden 30 Tagen mehr als verdoppelt. Besonders häufig stürzen Demenzkranke und Männer, die erstmals Blutdrucksenker nehmen. Dafür spricht eine Analyse unter US-Veteranen.

Adipositas-Medikament auch gegen Schlafapnoe wirksam

24.04.2024 Adipositas Nachrichten

Der als Antidiabetikum sowie zum Gewichtsmanagement zugelassene Wirkstoff Tirzepatid hat in Studien bei adipösen Patienten auch schlafbezogene Atmungsstörungen deutlich reduziert, informiert der Hersteller in einer Vorab-Meldung zum Studienausgang.

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