In:
Clinical Journal of the American Society of Nephrology, Ovid Technologies (Wolters Kluwer Health), Vol. 13, No. 9 ( 2018-9), p. 1330-1338
Abstract:
At least 20 susceptibility loci of IgA nephropathy have been identified by genome-wide association studies to date. Whether these loci were associated with disease progression is unclear. Design, setting, participants, & measurements We enrolled 613 adult patients with IgA nephropathy for a follow-up of ≥12 months. All 20 IgA nephropathy susceptibility loci were selected and their tag single nucleotide polymorphisms (SNPs) were genotyped. After strict quality control, 16 SNPs and 517 patients with IgA nephropathy were eligible for subsequent analysis. Progression was defined as ESKD or 50% decrease in eGFR. A stepwise Cox regression analysis of all SNPs on Akaike information criterion was performed to select the best model. Results A four-SNP model, rs11150612 ( ITGAM-ITGAX ), rs7634389 ( ST6GAL1 ), rs2412971 ( HORMAD2 ), and rs2856717 ( HLA-DQ/DR ), was selected as the best predictive model. The genetic risk score calculated on the basis of the four SNPs was independently associated with disease progression before (hazard ratio [HR], 1.65; 95% confidence interval [95% CI] , 1.29 to 2.12) and after adjustment by a recently reported clinical model (HR, 1.29; 95% CI, 1.03 to 1.62) or clinical–pathologic model (HR, 1.35; 95% CI, 1.03 to 1.77). Compared with low genetic risk, patients with middle genetic risk had a 2.12-fold (95% CI, 1.33 to 3.40) increase of progression risk, whereas patients with high genetic risk had 3.61-fold (95% CI, 2.00 to 6.52) progression risk increase. In addition, incorporation of genetic risk score could potentially increase discrimination of the clinical model (c-statistic increase from 0.83 to 0.86) or the clinical–pathologic model (c-statistic increase from 0.82 to 0.85) in predicting 5-year progression risk. Conclusions The four-SNP genetic risk score was independently associated with IgA nephropathy progression and could enhance the performance of clinical and clinical–pathologic risk models.
Type of Medium:
Online Resource
ISSN:
1555-9041
,
1555-905X
DOI:
10.2215/CJN.13701217
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2018
detail.hit.zdb_id:
2216582-4
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