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A food-group based algorithm to predict non-heme iron absorption.
Int J Food Sci Nutr 2007; 58(1):29-41IJ

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 (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).

Authors+Show Affiliations

The Iron Metabolism Interdisciplinary Research Group, King's College London, UK.No affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

17415954

Citation

Conway, Rana E., et al. "A Food-group Based Algorithm to Predict Non-heme Iron Absorption." International Journal of Food Sciences and Nutrition, vol. 58, no. 1, 2007, pp. 29-41.
Conway RE, Powell JJ, Geissler CA. A food-group based algorithm to predict non-heme iron absorption. Int J Food Sci Nutr. 2007;58(1):29-41.
Conway, R. E., Powell, J. J., & Geissler, C. A. (2007). A food-group based algorithm to predict non-heme iron absorption. International Journal of Food Sciences and Nutrition, 58(1), pp. 29-41.
Conway RE, Powell JJ, Geissler CA. A Food-group Based Algorithm to Predict Non-heme Iron Absorption. Int J Food Sci Nutr. 2007;58(1):29-41. PubMed PMID: 17415954.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A food-group based algorithm to predict non-heme iron absorption. AU - Conway,Rana E, AU - Powell,Jonathan J, AU - Geissler,Catherine A, PY - 2007/4/10/pubmed PY - 2007/9/19/medline PY - 2007/4/10/entrez SP - 29 EP - 41 JF - International journal of food sciences and nutrition JO - Int J Food Sci Nutr VL - 58 IS - 1 N2 - 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 (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). SN - 0963-7486 UR - https://www.unboundmedicine.com/medline/citation/17415954/A_food_group_based_algorithm_to_predict_non_heme_iron_absorption_ DB - PRIME DP - Unbound Medicine ER -