Assessment of regression models for adjustment of iron status biomarkers for inflammation in children with moderate acute malnutrition in Burkina Faso
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Assessment of regression models for adjustment of iron status biomarkers for inflammation in children with moderate acute malnutrition in Burkina Faso. / Cichon, Bernardette; Ritz, Christian; Fabiansen, Christian; Christensen, Vibeke Brix; Filteau, Suzanne; Friis, Henrik; Kæstel, Pernille.
In: The Journal of Nutrition, Vol. 147, No. 1, 2017, p. 125-132.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Assessment of regression models for adjustment of iron status biomarkers for inflammation in children with moderate acute malnutrition in Burkina Faso
AU - Cichon, Bernardette
AU - Ritz, Christian
AU - Fabiansen, Christian
AU - Christensen, Vibeke Brix
AU - Filteau, Suzanne
AU - Friis, Henrik
AU - Kæstel, Pernille
N1 - CURIS 2017 NEXS 028
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Faculty of Science
KW - Inflammation
KW - Correction factors
KW - C-reactive protein
KW - Iron deficiency
KW - Regression analysis
KW - Serum ferritin
KW - Soluble transferrin receptor
KW - Young children
U2 - 10.3945/jn.116.240028
DO - 10.3945/jn.116.240028
M3 - Journal article
C2 - 27881597
VL - 147
SP - 125
EP - 132
JO - Journal of Nutrition
JF - Journal of Nutrition
SN - 0022-3166
IS - 1
ER -
ID: 169376675