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. 2023 Aug 10;23:1525. doi: 10.1186/s12889-023-16469-y

Table 3.

The association between hyperuricemia and nephrolithiasis based on the multivariate binary logistic regression

Covariate OR (95%CI)
Model 1 Model 2 Model 3 Model 4
Hyperuricemia
 No Ref Ref Ref Ref
 Yes 1.592(1.436,1.764)*** 1.555(1.398,1.730)*** 1.559(1.401,1.735)*** 1.464(1.312,1.633)***
Sex
 Male Ref Ref Ref
 Female 0.648(0.595,0.705)*** 0.611(0.554,0.674)*** 0.614(0.557,0.678)***
Ethnic Groups
 Han nationality Ref Ref Ref
 Yi nationality 1.790(1.623,1.975)*** 1.775(1.609,1.959)*** 1.725(1.562,1.905)***
 Bai nationality 1.583(1.431,1.750)*** 1.562(1.411,1.729)*** 1.540(1.391,1.705)***
Drinking frequency
 Never Ref Ref
 Occasionally 1.006(0.894,1.132) 1.018(0.904,1.146)
 1–2 days/week 0.723(0.498,1.050) 0.721(0.496,1.047)
 3–5 days/week 1.020(0.717,1.449) 1.008(0.708,1.433)
 Everyday 0.766(0.651,0.903)** 0.760(0.645,0.896)**
Hypertension
 No Ref
 Yes 1.207(1.107,1.316)***
Hyperlipidemia
 No Ref
 Yes 1.110(1.017,1.212)*

Bolden numbers indicate statistical significance (*p < 0.05, **p < 0.01, ***p < 0.001)

Model 1: crude model (without adjustment)

Model 2: Model 1 adjusted for demographic features (i.e., age, sex, ethnic group, educational level)

Model 3: Model 2 adjusted for life behavior factors (i.e., smoking status, drinking frequency)

Model 4: Model 3 adjusted for metabolic-related indicators and diseases (i.e., BMI, fasting blood glucose, Scr, Urea, hypertension, diabetes, fatty liver, hyperlipidemia)