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


METHODS
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 (R(2) =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 (R(2) = 0.16, P= 0.0001).
Conway
Rana E
RE
The Iron Metabolism Interdisciplinary Research Group, King's College London, UK.
Powell
Jonathan J
JJ
Geissler
Catherine A
CA
D000465:Algorithms; D001682:Biological Availability; D005502:Food; D006801:Humans; D007408:Intestinal Absorption / Q000502:physiology; D019266:Iron, Dietary / Q000378:metabolism*; D008954:Models, Biological; D015233:Models, Statistical; D019159:Nonheme Iron Proteins / Q000378:metabolism; D009753:Nutritive Value
D019266:Iron, Dietary; D019159:Nonheme Iron Proteins