Statistical results of baseline characteristics of the subjects in the four groups were expressed as mean ± SD for continuous variables with normal distribution, median (25th, 75th percentile) for continuous variables with skewed distribution, and percentages for categorical data.

The significance of differences for normally-distributed continuous variables among groups was tested using one-way ANOVA analysis with post hoc analysis by Fisher’s least significant difference test. And the difference for non-normally-distributed continuous variables was tested using Kruskall–Wallis analysis with post hoc analysis by Dunn–Bonferroni method. The difference in the distribution of categorical variables among groups was tested using the Chi-square test with post hoc analysis by Bonferroni method. Multivariable logistic regression models were used to identify the factors associated with anemia or low TIBC. The adjusted hierarchical covariates were as follows: model (1) demographic factors; model (2) plus comorbidities factors; and model (3) plus other covariates such as albumin, CRP log, ferritin log, and phosphorus. Statistical analyses were conducted using SPSS 21.0 for Windows (SPSS Inc., Chicago, Illinois). Statistical significance was set at a two-sided p-value of < 0.05.

To assess the robustness of our findings, we performed a series of sensitivity analyses, including of: (1) redefining the result of low iron as < 65 μg/dL in men and 50 μg/dL in women, according to the cutoff in our hospital; (2) redefining TSAT < 30%; (3) separating the two “low iron” groups into four subgroups according to serum ferritin levels (“low ferritin” as ferritin < 200 ng/mL, and “high ferritin” as ferritin ≧ 200 ng/mL).

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