Interplay between Superoxide Dismutase, Glutathione Peroxidase, and Peroxisome Proliferator Activated Receptor Gamma Polymorphisms on the Risk of End-Stage Renal Disease among Han Chinese Patients

Table 6

Multivariate analysis of the predictors of dialysis by fitting multiple logistic regression model with the stepwise variable selection method, including SOD2, GPX1, and PPAR- polymorphisms^#.

Covariates	Regression coefficient	Standard error	value	value	Odds ratio	95% confidence interval
Intercept		0.162		<0.001	—	—	—
Age (between 28 and 65)		0.125		<0.001	4.879	3.820	6.232
Male		0.123		0.051	0.786	0.618	1.001
DM		0.186		<0.001	5.957	4.141	8.569
Non-DM × SOD2 exon 2 TT		0.151		0.014	0.691	0.514	0.928
PPAR-γ exon 6 CC × PPAR-γ exon B CC		0.123		0.040	0.778	0.609	0.988

Multiple logistic regression model: = 1451, adjusted generalized = 0.304, estimated area under the receiver operating characteristic (ROC) curve = 0.769, and the Hosmer and Lemeshow goodness-of-fit chi-square test = 0.0047 (df = 8).
^#Variables with odds ratio (OR) of extreme values were listed below: DM × SOD2 exon 2 CC, OR 2.7 × 10⁶ ( < 0.001); non-DM × PPAR-γ exon B GG, OR 3.8 × 10¹³ ( < 0.001); PPAR-γ exon 6 TT × PPAR-γ exon B GG, OR 8.2 × 10¹¹ ( < 0.001); GPX1 exon 2 CC × PPAR-γ exon B GG, OR 1.9 × 10⁻⁷ ( < 0.001).