Table 3.

Independent predictors of iron deficiency on univariate^* and multivariate^† logistic regression modeling

	OR	95% Conf. Int.	P value
Univariate analysis
Female sex	2.99	2.08–4.31	<0.001
Obesity	1.99	1.37–2.90	<0.001
Diabetes present	1.97	1.39–2.80	<0.001
BMI (kg/m2)	1.07	1.04–1.09	<0.001
Hepcidin (ng/ml)	0.99	0.99–1.00	<0.001
ALT (U/L)	0.99	0.99–1.00	<0.001
Black race	6.10	2.19–17.00	0.001
Waist circumference	1.02	1.01–1.03	0.001
Alcohol consumption	0.79	0.66–0.93	0.005
Metabolic Syndrome	1.59	1.11–2.27	0.011
American Indian or Alaska Native	2.12	1.14–3.94	0.018
Model 1 (all subjects)
Female sex	2.23	1.43–3.48	<0.001
BMI (kg/m2)	1.07	1.03–1.10	<0.001
American Indian or Alaska Native	3.44	1.60–7.41	0.002
Hepcidin (ng/ml)	0.99	0.99–1.00	0.003
ALT (U/L)	0.99	0.99–1.00	0.005
Black race	3.54	1.12–10.81	0.027
Model 2 (female subjects)
BMI mean (kg/m2)	1.06	1.02–1.10	0.002
Hepcidin (ng/ml)	0.99	0.99–1.00	0.006
ALT (U/L)	0.99	0.99–1.00	0.009
American Indian or Alaska Native	3.45	1.26–9.42	0.016
Model 3 (male subjects)
Waist circumference	1.04	1.01–1.07	0.005

^*Univariate logistic was performed using presence of ID as the dependent variable.

^†Stepwise multivariate logistic regression modeling was performed with ID as the dependent variable and each of the variables significantly associated with ID in univariate analysis; a p value of <0.20 was used as a cutoff for incorporation into the model.