Table 3.

The association between VAI and IR in a multiple logistics regression model.

Variable	n(%)	Model 1		Model 2		Model 3		Model 4
		OR (95%CI)	P-value	OR (95%CI)	P-value	OR (95%CI)	P-value	OR (95%CI)	P-value
VAI	27309	1.6 (1.5~1.71)	<0.001	1.62 (1.51~1.73)	<0.001	1.29 (1.2~1.38)	<0.001	1.28 (1.2~1.37)	<0.001
VAI group
Q1	6827	1(Ref)		1(Ref)		1(Ref)		1(Ref)
Q2	6827	2.55 (1.91~3.4)	<0.001	2.69 (2.01~3.61)	<0.001	1.87 (1.34~2.61)	<0.001	1.79 (1.28~2.5)	0.001
Q3	6827	5.52 (4.18~7.29)	<0.001	6.01 (4.51~8.03)	<0.001	3.06 (2.2~4.27)	<0.001	2.79 (2~3.91)	<0.001
Q4	6828	12.27 (9.25~16.28)	<0.001	13.73 (10.22~18.45)	<0.001	5.73 (4.07~8.06)	<0.001	5.03 (3.56~7.12)	<0.001
P for trend	27309	2.27 (2.09~2.47)	<0.001	2.35 (2.15~2.57)	<0.001	1.77 (1.6~1.97)	<0.001	1.69 (1.52~1.88)	<0.001

Model 1: non-adjusted.

Model 2: adjutesd age, gender, race.

Model 3: adjutesd age, gender, race, education, smoke, Alcohol use, diabetes, hypertension.

Model 3: adjutesd age, gender, race, education, smoke, Alcohol use, diabetes, hypertension, ALT, AST, GGT, BUN.