Abstract
BACKGROUND
Biomarkers of iron status are affected by inflammation. In order to interpret them in individuals with inflammation, the use of correction factors (CFs) has been proposed.
OBJECTIVE
The objective of this study was to investigate the use of regression models as an alternative to the CF approach.
METHODS
Morbidity data were collected during clinical examinations with morbidity recalls in a cross-sectional study in children aged 6-23 mo with moderate acute malnutrition. C-reactive protein (CRP), α1-acid glycoprotein (AGP), serum ferritin (SF), and soluble transferrin receptor (sTfR) were measured in serum. Generalized additive, quadratic, and linear models were used to model the relation between SF and sTfR as outcomes and CRP and AGP as categorical variables (model 1; equivalent to the CF approach), CRP and AGP as continuous variables (model 2), or CRP and AGP as continuous variables and morbidity covariates (model 3) as predictors. The predictive performance of the models was compared with the use of 10-fold crossvalidation and quantified with the use of root mean square errors (RMSEs). SF and sTfR were adjusted with the use of regression coefficients from linear models.
RESULTS
Crossvalidation revealed no advantage to using generalized additive or quadratic models over linear models in terms of the RMSE. Linear model 3 performed better than models 2 and 1. Furthermore, we found no difference in CFs for adjusting SF and those from a previous meta-analysis. Adjustment of SF and sTfR with the use of the best-performing model led to a 17% point increase and <1% point decrease, respectively, in estimated prevalence of iron deficiency.
CONCLUSION
Regression analysis is an alternative to adjust SF and may be preferable in research settings, because it can take morbidity and severity of inflammation into account. In clinical settings, the CF approach may be more practical. There is no benefit from adjusting sTfR. This trial was registered at www.controlled-trials.com as ISRCTN42569496.
