A food-group based algorithm to predict non-heme iron absorption

Abstract

Objective To develop an algorithm to predict the percentage non-heme iron absorption based on the foods contained in a meal (wholemeal cereal, tea, cheese, etc.). Existing algorithms use food constituents (phytate, polyphenols, calcium, etc.), which can be difficult to obtain.Design A meta-analysis of published studies using erythrocyte incorporation of radio-isotopic iron to measure non-heme iron absorption.Methods A database was compiled and foods were categorized into food groups likely to modify non-heme iron absorption. Absorption data were then adjusted to a common iron status and a weighted multiple regression was performed.Results Data from 53 research papers (3,942 individual meals) were used to produce an algorithm to predict non-heme iron absorption (R2=0.22, P<0.0001).Conclusions The percentage non-heme iron absorption can be predicted from information on the types of foods contained in a meal with similar efficacy to that of food-constituent-based algorithms (R2=0.16, P=0.0001).