Bernardette Cichon1,2, Christian Ritz3, Christian Fabiansen3, Vibeke Brix Christensen3,4, Suzanne Filteau5, Henrik Friis3, Pernille Kæstel3. 1. Department of Nutrition, Exercise, and Sports, University of Copenhagen, Frederiksberg, Denmark; cichon_b@yahoo.com. 2. Doctors Without Borders-Denmark, Copenhagen, Denmark. 3. Department of Nutrition, Exercise, and Sports, University of Copenhagen, Frederiksberg, Denmark. 4. Department of Pediatrics, Righospitalet, Copenhagen, Denmark; and. 5. London School of Hygiene and Tropical Medicine, Faculty of Epidemiology and Population Health, London, United Kingdom.
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.
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.
Authors: Maren J H Rytter; Bernardette Cichon; Christian Fabiansen; Charles W Yameogo; Sylvain Z Windinmi; Kim F Michaelsen; Suzanne Filteau; Dorthe L Jeppesen; Henrik Friis; André Briend; Vibeke B Christensen Journal: Pediatr Res Date: 2020-07-20 Impact factor: 3.756
Authors: Fabian Rohner; Sorrel Ml Namaste; Leila M Larson; O Yaw Addo; Zuguo Mei; Parminder S Suchdev; Anne M Williams; Fayrouz A Sakr Ashour; Rahul Rawat; Daniel J Raiten; Christine A Northrop-Clewes Journal: Am J Clin Nutr Date: 2017-06-14 Impact factor: 7.045
Authors: Blessings H Likoswe; Felix P Phiri; Martin R Broadley; Edward J M Joy; Noel Patson; Kenneth M Maleta; John C Phuka Journal: Nutrients Date: 2020-05-27 Impact factor: 5.717
Authors: Mette F Olsen; Ann-Sophie Iuel-Brockdorff; Charles W Yaméogo; Bernardette Cichon; Christian Fabiansen; Suzanne Filteau; Kevin Phelan; Albertine Ouédraogo; Kim F Michaelsen; Melissa Gladstone; Per Ashorn; André Briend; Christian Ritz; Henrik Friis; Vibeke B Christensen Journal: PLoS Med Date: 2020-12-23 Impact factor: 11.069
Authors: Mette F Olsen; Ann-Sophie Iuel-Brockdorff; Charles W Yaméogo; Bernardette Cichon; Christian Fabiansen; Suzanne Filteau; Kevin Phelan; Albertine Ouédraogo; Jonathan C Wells; André Briend; Kim F Michaelsen; Lotte Lauritzen; Christian Ritz; Per Ashorn; Vibeke B Christensen; Melissa Gladstone; Henrik Friis Journal: Matern Child Nutr Date: 2019-12-11 Impact factor: 3.092