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. 2024 Oct 28;21(14):2851–2861. doi: 10.7150/ijms.103186

Table 2.

Association between SDF-1/CXCR4 genotypes and diabetic kidney disease.

Variable Non-diabetic kidney disease (N=335) diabetic kidney disease (N=388) AOR (95% CI) p value
SDF-1
rs1801157
Additive model
GG 175 (52.2%) 176 (45.4%) 1.000 (reference)
GA 139 (41.5%) 180 (46.4%) 2.242 (1.060-4.741) p=0.035
AA 21 (6.3%) 32 (8.2%) 1.741 (0.441-6.875) p=0.429
Dominant model
GG 175 (52.2%) 176 (45.4%) 1.000 (reference)
GA+AA 160 (47.8%) 212 (54.6%) 2.156 (1.051-4.424) p=0.036
SDF-1
rs2297630
Additive model
GG 267 (79.7%) 303 (78.1%) 1.000 (reference)
GA 60 (17.9%) 75 (19.3%) 0.583 (0.211-1.613) p=0.299
AA 8 (2.4%) 10 (2.6%) 0.571 (0.052-6.274) p=0.647
Dominant model
GG 267 (79.7%) 303 (78.1%) 1.000 (reference)
GA+AA 68 (20.3%) 85 (21.9%) 0.581 (0.224-1.509) p=0.265
SDF-1
rs2839693
Additive model
CC 263 (78.5%) 296 (76.3%) 1.000 (reference)
CT 68 (20.3%) 87 (22.4%) 1.832 (0.769-4.367) p=0.172
TT 4 (1.2%) 5 (1.3%) 0.597 (0.029-12.144) p=0.737
Dominant model
CC 263 (78.5%) 296 (76.3%) 1.000 (reference)
CT+TT 72 (21.5%) 92 (23.7%) 1.712 (0.726-4.036) p=0.219
SDF-1
rs266085
Additive model
TT 119 (35.5%) 123 (31.7%) 1.000 (reference)
TC 166 (49.6%) 193 (49.7%) 0.689 (0.319-1.488) p=0.343
CC 50 (14.9%) 72 (18.6%) 1.150 (0.402-3.285) p=0.794
Dominant model
TT 119 (35.5%) 123 (31.7%) 1.000 (reference)
TC+CC 216 (64.5%) 265 (68.3%) 0.786 (0.384-1.608) p=0.509
CXCR4
rs2228014
Additive model
CC 257 (76.7%) 295 (76.0%) 1.000 (reference)
CT 70 (20.9%) 84 (21.6%) 1.581 (0.667-3.745) p=0.298
TT 8 (2.4%) 9 (2.4%) 1.845 (0.186-18.342) p=0.601
Dominant model
CC 257 (76.7%) 295 (76.0%) 1.000 (reference)
CT+TT 78 (23.3%) 93 (24.0%) 1.606 (0.703-3.671) p=0.261
CXCR4
rs6430612
Additive model
CC 310 (92.5%) 353 (91.0%) 1.000 (reference)
CT 25 (7.5%) 35 (9.0%) 1.233 (0.370-4.110) p=0.734
TT 0 (0.0%) 0 (0.0%) --- ---
Dominant model
CC 310 (92.5%) 353 (91.0%) 1.000 (reference)
CT+TT 25 (7.5%) 35 (9.0%) 1.233 (0.370-4.110) p=0.734

The adjusted odds ratio (AOR) with their 95% confidence intervals were estimated by multiple logistic regression models.