Table 4 Logistic regression analysis

From: Gut microbiome-derived phenyl sulfate contributes to albuminuria in diabetic kidney disease

 

Model 1

Model 2

Model 3

 

Odds

95% CI

p

Odds

95% CI

p

Odds

95% CI

p

Log(PS + 1)

1.42

1.02

1.98

0.040

1.27

0.88

1.84

0.202

1.34

0.91

1.95

0.135

Log SuPAR

2.42

1.27

4.59

0.007

1.42

0.66

3.08

0.371

1.39

0.61

3.14

0.435

Age

    

0.99

0.95

1.02

0.447

0.98

0.94

1.02

0.294

gender (ref. male)

    

1.18

0.61

2.28

0.624

1.32

0.65

2.68

0.447

BMI

    

1.01

0.93

1.09

0.866

1.00

0.91

1.09

0.976

SBP*

    

1.01

0.99

1.04

0.262

1.03

1.00

1.06

0.026

HbA1c

    

0.93

0.65

1.33

0.704

0.99

0.68

1.43

0.950

log eGFR*

    

0.10

0.03

0.32

<0.001

0.12

0.03

0.46

0.002

duration

        

0.99

0.95

1.04

0.729

DBP*

        

0.96

0.93

1.00

0.071

ALT

        

1.00

0.97

1.03

0.970

TC*

        

0.99

0.97

1.00

0.093

TG

        

1.00

1.00

1.01

0.279

HDL*

        

1.03

1.00

1.06

0.049

UA*

        

1.22

1.02

1.45

0.026

  1. Two-year ACR deterioration shown by logistic regression analysis based on whole DKD. Model 1: only log(PS + 1) and Log suPAR (crude model), Model 2: Model 1 with known factors (age, gender, BMI, SBP, HbA1c and log eGFR)23, Model 3: Model 2 with other factors (duration, DBP, ALT, TC (total cholesterol), TG (triglyceride), HDL (high-density lipoprotein), and UA (uric acid)). The 95% confidence interval (95% CI) is listed
  2. Remaining variables after the stepwise method based on the Akaike’s information criterion (AIC) in model 3 are depicted as *
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