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Nutrients 2016, 8(4), 210; doi:10.3390/nu8040210

Algorithms to Improve the Prediction of Postprandial Insulinaemia in Response to Common Foods

1
Charles Perkins Centre, and the School of Life and Environmental Sciences, the University of Sydney, Sydney 2006, Australia
2
Department of Statistics, Macquarie University, Sydney 2109, Australia
*
Author to whom correspondence should be addressed.
Received: 19 January 2016 / Revised: 29 March 2016 / Accepted: 1 April 2016 / Published: 8 April 2016
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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”. View Full-Text
Keywords: glycaemia; insulin; carbohydrate; protein; fat; glycemic index; food insulin index glycaemia; insulin; carbohydrate; protein; fat; glycemic index; food insulin index
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bell, K.J.; Petocz, P.; Colagiuri, S.; Brand-Miller, J.C. Algorithms to Improve the Prediction of Postprandial Insulinaemia in Response to Common Foods. Nutrients 2016, 8, 210.

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