Adjusting Iron Deficiency for Inflammation in Cuban Children Aged Under Five Years: New Approaches Using Quadratic and Quantile Regression

• Published in: MEDICC review Vol. 24; no. 3-4; pp. 36 - 45
• Main Authors: Montero-Díaz, Minerva, Chávez-Chong, Cristina, Rodríguez-Martínez, Ernesto, Pita-Rodríguez, Gisela María, Lambert-Lamazares, Brenda, Basabe-Tuero, Beatriz, Alfonso-Sagué, Karen
• Format: Journal Article
• Language: English
• Published: 31.10.2022
• ISSN: 1527-3172; 1555-7960; 1527-3172
• DOI: 10.37757/MR2022.V24.N3-4.1

INTRODUCTIONFerritin is the best biomarker for assessing iron deficiency, but ferritin concentrations increase with inflammation. Several adjustment methods have been proposed to account for inflammation's effect on iron biomarker interpretation. The most recent and highly recommended method uses linear regression models, but more research is needed on other models that may better define iron status in children, particularly when distributions are heterogenous and in contexts where the effect of inflammation on ferritin is not linear. OBJECTIVEAssess the utility and relevance of quadratic regression models and quantile quadratic regression models in adjusting ferritin concentration in the presence of inflammation. METHODSWe used data from children aged under five years, taken from Cuba's national anemia and iron deficiency survey, which was carried out from 2015-2018 by the National Hygiene, Epidemiology and Microbiology Institute. We included data from 1375 children aged 6 to 59 months and collected ferritin concentrations and two biomarkers for inflammation: C-reactive protein and α-1 acid glycoprotein. Quadratic regression and quantile regression models were used to adjust for changes in ferritin concentration in the presence of inflammation. RESULTSUnadjusted iron deficiency prevalence was 23% (316/1375). Inflammation-adjusted ferritin values increased iron-deficiency prevalence by 2.6-4.5 percentage points when quadratic regression correction model was used, and by 2.8-6.2 when quantile regression was used. The increase when using the quantile regression correction model was more pronounced and statistically significant when both inflammation biomarkers were considered, but adjusted prevalence was similar between the two correction methods when only one biomarker was analyzed. CONCLUSIONSThe use of quadratic regression and quantile quadratic regression models is a complementary strategy in adjusting ferritin for inflammation, and is preferable to standard regression analysis when the linear model's basic assumptions are not met, or when it can be assumed that ferritin-inflammation relationships within a subpopulation may deviate from average trends.