Table 2

Simple and multivariate logistic regression for testing the association between Trp64Arg polymorphism and hyperuricemia.

ModelNormouricemic (%)Hyperuricemic (%)OR (unadjusted)95% CIpOR (adjusted)95% CIp*
Codominant
  T/T557 (88.2)46 (76.5)1.00.0021.0< 0.0001
  T/A70 (11.2)10 (15.7)1.60.7–3.62.070.8–5.2
  A/A3 (0.6)5 (7.8)16.13.4–74.529.54.9–177.2
Dominant
  T/T557 (88.2)46 (76.5)1.00.021.00.0084
  T/A, A/A73 (11.8)15 (23.5)2.31.1–4.63.151.39–7.15
Recessive
  T/T-T/A627 (99.4)56 (92.2)1.00.0011.00.0005
  A/A3 (0.6)5 (7.8)15.13.2–69.325.904.4–152.3
Overdominant
  T/T-A/A560 (88.8)51 (84.3)1.0NS1.0NS
  T/A70 (11.2)10 (15.7)1.40.66–3.271.780.72–4.44
Log-additive
  0, 1,2630 (91.3)61 (8.7)2.51.4–4.40.0023.351.73–6.500.0006
  • The codominant model compared heterozygous T/A and homozygous A/A genotypes to the homozygous for the most frequent allele T/T. The dominant model compared a combination of T/A-A/A genotypes to the homozygous T/T. The recessive model compared a combination of T/T-T/A genotypes to the homozygous A/A. The log-additive model is equivalent to calculating the odds ratio for the risk A allele. Data are number of subjects, percentage of subjects with each genotype for each group (normouricemic and hyperuricemic).

  • *p values adjusted for age, weight gain, triglyceride concentrations, uric acid, and insulin resistance.