Next Article in Journal
Flavonoids in Inflammatory Bowel Disease: A Review
Previous Article in Journal
The Potential of an in Vitro Digestion Method for Predicting Glycemic Response of Foods and Meals
Article Menu

Export Article

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

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

Charles Perkins Centre, and the School of Life and Environmental Sciences, the University of Sydney, Sydney 2006, Australia
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
View Full-Text   |   Download PDF [1783 KB, uploaded 8 April 2016]   |  


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

Figure 1

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).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Nutrients EISSN 2072-6643 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top