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

• Published in: International journal of food sciences and nutrition Vol. 58; no. 1; pp. 29 - 41
• Main Authors: Conway, Rana E., Powell, Jonathan J., Geissler, Catherine A.
• Format: Journal Article
• Language: English
• Published: England Informa UK Ltd 2007; Taylor & Francis; Taylor & Francis Ltd
• Subjects: Absorption; algorithm; Algorithms; Biological Availability; calcium; cheeses; Dietary iron; Dietary minerals; erythrocytes; Food; food groups; Food science; Humans; Intestinal Absorption - physiology; Iron; iron absorption; iron bioavailability; iron status; Iron, Dietary - metabolism; meta-analysis; Models, Biological; Models, Statistical; non-heme iron absorption; Nonheme Iron Proteins - metabolism; Nutrition; Nutritive Value; phytic acid; polyphenols; regression analysis; tea
• ISSN: 0963-7486; 1465-3478; 1465-3478
• DOI: 10.1080/09637480601121250

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).