Literature DB >> 21765051

Predictive role of multilocus genetic polymorphisms in cardiovascular disease and inflammation-related genes on chronic kidney disease in Type 2 diabetes--an 8-year prospective cohort analysis of 1163 patients.

Ying Wang1, Andrea O Y Luk, Ronald C W Ma, Wing-Yee So, Claudia H T Tam, Maggie C Y Ng, Xilin Yang, Vincent Lam, Peter C Y Tong, Juliana C N Chan.   

Abstract

BACKGROUND: Chinese diabetic patients are at greater risk of developing chronic kidney disease (CKD) than Caucasian counterparts. In this hypothesis-generating study, we examined the independent and joint effects of multiple genetic variants on CKD in a prospective Chinese cohort of Type 2 diabetic patients.
METHODS: Seventy-seven single-nucleotide polymorphisms (SNPs) of 54 candidate genes for cardiorenal diseases and inflammation were genotyped in 1163 patients with no past history of CKD at baseline. CKD was defined as the first estimated glomerular filtration rate <60 mL/min/1.73 m(2) or the first hospitalization with a diagnosis of renal disease.
RESULTS: In Cox-regression analysis, 15 SNPs of 13 genes were associated with incident CKD. After correction for multiple comparisons, 6 SNPs including PON1 55Met, PON2 311Cys CETP-629C, ITGA2 873A, LTA 26Asn and LTA 252Gly remained independently associated with CKD, with respective hazard ratios (95% confidence interval):2.6 (1.4-4.8, P = 0.002), 1.5 (1.2-1.9, P = 0.003), 1.4 (1.1-1.7, P = 0.001), 2.2 (1.3-3.7, P = 0.002), 1.6 (1.1-2.2, P = 0.008) and 1.5 (1.1-2.1, P = 0.019). Analysis of joint effect of the six SNPs showed stepwise increase in risk of CKD with the accumulation of risk alleles and weighted genetic risk score (P(trend) = 8.9 × 10(-7) and 4.0 × 10(-5), respectively).
CONCLUSIONS: In Type 2 diabetes, there are independent and joint effects of multiple genetic variants on risk of CKD. Risk associations with PON1, PON2, CETP, ITGA2 and LTA genetic polymorphisms underline the importance of lipid metabolism, haemostasis and inflammation in the development of CKD in patients with Type 2 diabetes.

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Year:  2011        PMID: 21765051     DOI: 10.1093/ndt/gfr343

Source DB:  PubMed          Journal:  Nephrol Dial Transplant        ISSN: 0931-0509            Impact factor:   5.992


  5 in total

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3.  Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case-control cohort analysis.

Authors:  Ross K K Leung; Ying Wang; Ronald C W Ma; Andrea O Y Luk; Vincent Lam; Maggie Ng; Wing Yee So; Stephen K W Tsui; Juliana C N Chan
Journal:  BMC Nephrol       Date:  2013-07-23       Impact factor: 2.388

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  5 in total

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