Skip to main content

Table 2 Multiple regression analyses of hyperuricemia and associated factors in all subjects

From: The prevalence of hyperuricemia and its correlates in Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China

Variables

OR

95%CI

Female

0.903

(0.629–1.297)

Age(years)

 18–30

1

(reference)

 31–40

0.618

(0.326–1.17)

 41–50

0.818

(0.442–1.515)

 51–60

0.836

(0.431–1.622)

 61–70

0.893

(0.459–1.735)

 ≥ 71

0.601

(0.289–1.251)

Ethnicity

 Han

1

(reference)

 Tibetan

0.71

(0.501–1.006)

 Yi

1.054

(0.653–1.701)

Occupation

 Morks

1

(reference)

 Farmers-herdsmen

1.749

(1.022–2.992)

 Residents

1.697

(0.905–3.183)

Education

 1

1

(reference)

 2

1.57

(1.102–2.237)

 3

1.86

(1.167–2.963)

 4

1.195

(0.691–2.066)

 Drinking buttered tea

0.945

(0.669–1.335)

 Eating meat

1.364

(0.835–2.227)

Physical activity

 Level 1

1

(reference)

 Level 2

0.929

(0.589–1.464)

 Level 3

0.895

(0.554–1.448)

 Level 4

0.872

(0.578–1.317)

 Current smoking

0.99

(0.647–1.513)

 Current drinking

1.795

(1.193–2.702)

 Hypertension

1.48

(1.091–2.006)

 BMI

1.116

(1.077–1.156)

 WHR

1.336

(0.299–5.959)

 CREA

1.046

(1.034–1.059)

  1. BMI body mass index, WHR Waist-to-hip ratio, CREA creatinine Education level was assessed as illiteracy, primary school, middle school, or high school and higher for1,2,3,4; Physical activity was classified by exercise time into 4 levels, less than 30 min level 1, 30 min-1 h level 2, 1–1.5 h level 3, more than 1.5 h level 4. Education level was assessed as illiteracy, primary school, middle school, or high school and higher; OR odds ratio, 95% CI: 95% confidence interval. *P < 0.05 for the independent association between hyperuricemia and each factor after adjusting for the remaining factors