Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.605384
Title: Prediction of the glycaemic index of simple and composite dishes
Author: Al Hamli, Sarah
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2013
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Abstract:
Dietary carbohydrates play a crucial role in human nutrition. They are considered one of the major sources of energy and provide between 55-75 % energy of the human diet (FAO/WHO, 1998). In 1980, the glycaemic index (GI) concept was developed as a tool to compare foods for their ability to provide glucose to the blood circulation after ingestion and absorption in individuals. Epidemiological studies have shown a relationship between GI and non-communicable diseases such as type 2 diabetes using published GI values (Barclay et al., 2008b). However, measuring GI in vivo for every food used in the epidemiological field, for example, is time-consuming, expensive and requires the participation of human volunteers (Jenkins, 2007). The aim of the study is to develop methodology to estimate GI from the macronutrient composition of mixed foods, and the hypothesis is that GI can be predicted from composition data without the need for human volunteers. Available carbohydrate (av.CHO) analysis of 16 foods from the cereal and legume groups were undertaken and values were used to generate the prediction models. The relationship between GI and macronutrient composition was investigated in the 16 foods using multiple regression analysis methods. The results indicate that starch and fat are the only macronutrients that correlate significantly with published GI values. Three foods were used to validate the prediction models using in vitro and in vivo measurements and these correlated significantly with the statistically predicted GI values. In conclusion, statistically predicted GI might be a useful approach to eliminate the need for human subject or blood analysis to measure GI in multi-component foods.
Supervisor: Orfila, Caroline ; Holmes, Melvin Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.605384  DOI: Not available
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