Algorithms to improve the prediction of postprandial insulinaemia in response to common foods

Kirstine J. Bell, Peter Petocz, Stephen Colagiuri, Jennie C. Brand-Miller*

*Corresponding author for this work

    Research output: Contribution to journalArticle

    5 Citations (Scopus)
    13 Downloads (Pure)

    Abstract

    Dietary patterns that induce excessive insulin secretion may contribute to worsening insulin resistance and beta-cell dysfunction. Our aim was to generate mathematical algorithms to improve the prediction of postprandial glycaemia and insulinaemia for foods of known nutrient composition, glycemic index (GI) and glycemic load (GL).We used an expanded database of food insulin index (FII) values generated by testing 1000 kJ portions of 147 common foods relative to a reference food in lean, young, healthy volunteers. Simple and multiple linear regression analyses were applied to validate previously generated equations for predicting insulinaemia, and develop improved predictive models. Large differences in insulinaemic responses within and between food groups were evident. GL, GI and available carbohydrate content were the strongest predictors of the FII, explaining 55%, 51% and 47% of variation respectively. Fat, protein and sugar were significant but relatively weak predictors, accounting for only 31%, 7% and 13% of the variation respectively. Nutritional composition alone explained only 50% of variability. The best algorithm included a measure of glycemic response, sugar and protein content and explained 78% of variation. Knowledge of the GI or glycaemic response to 1000 kJ portions together with nutrient composition therefore provides a good approximation for ranking of foods according to their “insulin demand”.

    Original languageEnglish
    Article number210
    Pages (from-to)1-11
    Number of pages11
    JournalNutrients
    Volume8
    Issue number4
    DOIs
    Publication statusPublished - 8 Apr 2016

    Bibliographical note

    Copyright the Author(s) 2016. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • glycaemia
    • insulin
    • carbohydrate
    • protein
    • fat
    • glycemic index
    • food insulin index

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