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Iron deficiency in infancy: applying a physiologic framework for prediction.

Abstract

BACKGROUND: Infants aged 6-24 mo are at high risk of iron deficiency. Numerous studies worldwide have sought to identify predictors of iron deficiency in this age group. OBJECTIVE: The objectives of the study were to apply a physiologic model to identify risk factors for iron deficiency and to consider those risk factors under different conditions of iron supplementation. We predicted that factors related to iron status at birth (lower gestational age and lower birth weight), postnatal needs for iron (more rapid growth), and bioavailable iron (more cow milk) would be major risk factors. DESIGN: The physiologic framework was assessed in 1657 Chilean infants (aged 12 mo) with birth weights >or=3 kg who were randomly assigned at age 6 mo to high or low iron supplementation or no added iron. Based on venous blood, the analysis used mean corpuscular volume and concentrations of hemoglobin, free erythrocyte protoporphyrin, and ferritin. Logistic regression models were used to identify predictors of iron deficiency anemia and iron deficiency without anemia. RESULTS: The prevalence of iron deficiency (>or=2 abnormal iron measures) was 34.9% at age 12 mo. Of 186 infants with hemoglobin concentrations <110 g/L, 158 (84.9%) were iron deficient. The only consistent (and the strongest) predictor of iron deficiency or iron deficiency anemia was lower 6-mo hemoglobin. Factors related to poorer iron status at birth (lower birth weight, shorter gestation though full-term, or both) were predictors in the no-added-iron and high-iron groups. Otherwise, predictors varied by iron supplementation. CONCLUSION: Variations in predictors of iron deficiency or iron deficiency anemia according to iron supplementation suggest that direct comparisons across studies are tenuous at best without data on early iron status and certainty that specific conditions are comparable.

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BibTeXRIS

Betsy Lozoff, Niko Kaciroti, Tomás Walter. 2006. Iron deficiency in infancy: applying a physiologic framework for prediction.. https://doi.org/10.1093/ajcn%2F84.6.1412

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