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

Table 4.

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

Variable Non-diabetic kidney disease (N=335) Early CKD (N=308) AOR (95% CI) p value
SDF-1
rs1801157
Additive model
GG 175 (52.2%) 137 (44.5%) 1.000 (reference)
GA 139 (41.5%) 143 (46.4%) 2.198 (1.036-4.663) p=0.040
AA 21 (6.3%) 28 (9.1%) 1.721 (0.438-6.758) p=0.436
Dominant model
GG 175 (52.2%) 137 (44.5%) 1.000 (reference)
GA+AA 160 (47.8%) 171 (55.5%) 2.116 (1.029-4.353) p=0.042
SDF-1
rs2297630
Additive model
GG 267 (79.7%) 238 (77.3%) 1.000 (reference)
GA 60 (17.9%) 62 (20.1%) 0.593 (0.215-1.633) p=0.312
AA 8 (2.4%) 8 (2.6%) 0.614 (0.056-6.715) p=0.689
Dominant model
GG 267 (79.7%) 238 (77.3%) 1.000 (reference)
GA+AA 68 (20.3%) 70 (22.7%) 0.595 (0.230-1.544) p=0.286
SDF-1
rs2839693
Additive model
CC 263 (78.5%) 238 (77.3%) 1.000 (reference)
CT 68 (20.3%) 65 (21.1%) 1.846 (0.774-4.402) p=0.167
TT 4 (1.2%) 5 (1.6%) 0.613 (0.032-11.899) p=0.746
Dominant model
CC 263 (78.5%) 238 (77.3%) 1.000 (reference)
CT+TT 72 (21.5%) 70 (22.7%) 1.724 (0.731-4.066) p=0.213
SDF-1
rs266085
Additive model
TT 119 (35.5%) 105 (34.1%) 1.000 (reference)
TC 166 (49.6%) 146 (47.4%) 0.693 (0.321-1.497) p=0.350
CC 50 (14.9%) 57 (18.5%) 1.174 (0.410-3.362) p=0.764
Dominant model
TT 119 (35.5%) 105 (34.1%) 1.000 (reference)
TC+CC 216 (64.5%) 203 (65.9%) 0.793 (0.387-1.622) p=0.525
CXCR4
rs2228014
Additive model
CC 257 (76.7%) 232 (75.3%) 1.000 (reference)
CT 70 (20.9%) 68 (22.1%) 1.591 (0.674-3.758) p=0.289
TT 8 (2.4%) 8 (2.6%) 1.858 (0.196-17.595) p=0.589
Dominant model
CC 257 (76.7%) 232 (75.3%) 1.000 (reference)
CT+TT 78 (23.3%) 76 (24.7%) 1.618 (0.711-3.682) p=0.252
CXCR4
rs6430612
Additive model
CC 310 (92.5%) 276 (89.6%) 1.000 (reference)
CT 25 (7.5%) 32 (10.4%) 1.173 (0.344-4.001) p=0.799
TT 0 (0.0%) 0 (0.0%) --- ---
Dominant model
CC 310 (92.5%) 276 (89.6%) 1.000 (reference)
CT+TT 25 (7.5%) 32 (10.4%) 1.173 (0.344-4.001) p=0.799

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