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Table 3 Adjusted association analysis for incident gout by risk factor using three different approaches

From: Untangling the complex relationships between incident gout risk, serum urate, and its comorbidities

 

Two-factors

 

Full

 

Stepwise regression

   
 

regressiona

 

regressionb

 

(AIC)c

 

(BIC)c

 
 

Est. (OR)

p Value

Est. (OR)

p Value

Est. (OR)

p Value

Est. (OR)

p Value

Serum urate (mg/dl)

-

-

.792 (2.21)

<.001

.805 (2.24)

<.001

.795 (2.21)

<.001

Sex: Male

-.121 (0.89)

0.424

.030 (1.03)

0.854

-

-

-

-

Ethnicity: AA

.674 (1.96)

<.001

.647 (1.91)

<.001

.622 (1.86)

<.001

.675 (1.91)

<.001

Age, years

-.001 (1.00)

0.959

.000 (1.00)

0.974

-

-

-

-

Glucose: High

.080 (1.08)

0.750

-.055 (0.95)

0.830

-

-

-

-

HDL: Low

-.148 (0.86)

0.350

-.046 (0.95)

0.796

-

-

-

-

LDL: High

-.354 (0.70)

0.015

-.381 (0.68)

0.010

-.373 (0.69)

0.011

-

-

Triglycerides: High

-.057 (0.94)

0.710

.093 (1.10)

0.579

-

-

-

-

SBP: Hypertensive

.580 (1.79)

0.002

.510 (1.66)

0.006

.508 (1.66)

0.005

-

-

BMI: Obese

.054 (1.06)

0.723

-.044 (0.96)

0.786

-

-

-

-

eGFR: Low

.364 (1.44)

0.246

.042 (1.04)

0.200

-

-

-

-

  1. aLogistic regression of gout on two predictors: serum urate plus one of the factors in rows
  2. bLogistic regression of gout all the factors listed in rows.
  3. cStepwise logistic regression. Rows with no results correspond to predictors that did not entered in the final model. p Values correspond to estimated coefficients. Bold indicates effect estimates that were statistically different from zero at 0.01 significance level
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Arthritis Research & Therapy

ISSN: 1478-6362

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