Simon Ballance1, Svein Halvor Knutsen2, Øivind Winther Fosvold3, Aida Sainz Fernandez4, John Monro5. 1. Nofima AS, Norwegian Institute of Food, Fisheries and Aquaculture Research, Osloveien 1, 1433, Ås, Norway. simon.ballance@nofima.no. 2. Nofima AS, Norwegian Institute of Food, Fisheries and Aquaculture Research, Osloveien 1, 1433, Ås, Norway. 3. Fjordland AS, Brynsengveien, Oslo, Norway. 4. Leatherhead Food Research, Epsom, UK. 5. The New Zealand Institute for Plant and Food Research Limited, Palmerston North, New Zealand.
Abstract
PURPOSE: To determine the influence of meal composition on the glycaemic impact of different carbohydrate staples, and the accuracy of "adjusted calculated meal GI" compared with "measured mixed-meal GI". METHODS: In a non-blind randomized crossover trial fasted healthy subjectsconsumed four dinner-type mixed meals of realistic serving size comprising a carbohydrate staple of either mashed potato, pasta, rice or a glucose drink, combined with fixed portions of boiled carrots, poached salmon and herb sauce. Blood samples collected between 0 and 180 min were analysed for glucose and insulin concentrations. Adjusted calculated meal GI values were determined against a 50 g reference glucose drink, and compared to corresponding measured mixed-meal GIs, supplemented with data from four previous mixed-meal postprandial glycaemic response studies. RESULTS: The common carbohydrate staples, and the glucose drink, ingested as part of the salmon mixed meal induced a significantly lower post-prandial relative glycaemic response (RGR) and concurrent higher relative insulin response than the same amount of staple eaten alone. Adjusted calculated mixed-meal GI closely predicted measured mixed-meal GI in healthy subjects for 15 out of 17 mixed meals examined, showing the need to account for effects of fat and protein when predicting measured mixed-meal GI. Further, we showed the validity of using customarily consumed food amounts in mixed-meal postprandial RGR study design. CONCLUSIONS: Adjusted calculated mixed-meal GI appears a useful model to predict measured mixed-meal GI in healthy subjects and with further development and validation could aid nutrition research and rational design of healthy meals for personalized nutrition and particular consumer groups.
RCT Entities:
PURPOSE: To determine the influence of meal composition on the glycaemic impact of different carbohydrate staples, and the accuracy of "adjusted calculated meal GI" compared with "measured mixed-meal GI". METHODS: In a non-blind randomized crossover trial fasted healthy subjects consumed four dinner-type mixed meals of realistic serving size comprising a carbohydrate staple of either mashed potato, pasta, rice or a glucose drink, combined with fixed portions of boiled carrots, poached salmon and herb sauce. Blood samples collected between 0 and 180 min were analysed for glucose and insulin concentrations. Adjusted calculated meal GI values were determined against a 50 g reference glucose drink, and compared to corresponding measured mixed-meal GIs, supplemented with data from four previous mixed-meal postprandial glycaemic response studies. RESULTS: The common carbohydrate staples, and the glucose drink, ingested as part of the salmon mixed meal induced a significantly lower post-prandial relative glycaemic response (RGR) and concurrent higher relative insulin response than the same amount of staple eaten alone. Adjusted calculated mixed-meal GI closely predicted measured mixed-meal GI in healthy subjects for 15 out of 17 mixed meals examined, showing the need to account for effects of fat and protein when predicting measured mixed-meal GI. Further, we showed the validity of using customarily consumed food amounts in mixed-meal postprandial RGR study design. CONCLUSIONS: Adjusted calculated mixed-meal GI appears a useful model to predict measured mixed-meal GI in healthy subjects and with further development and validation could aid nutrition research and rational design of healthy meals for personalized nutrition and particular consumer groups.
Authors: Ruixin Zhu; Thomas M Larsen; Mikael Fogelholm; Sally D Poppitt; Pia S Vestentoft; Marta P Silvestre; Elli Jalo; Santiago Navas-Carretero; Maija Huttunen-Lenz; Moira A Taylor; Gareth Stratton; Nils Swindell; Mathijs Drummen; Tanja C Adam; Christian Ritz; Jouko Sundvall; Liisa M Valsta; Roslyn Muirhead; Shannon Brodie; Teodora Handjieva-Darlenska; Svetoslav Handjiev; J Alfredo Martinez; Ian A Macdonald; Margriet S Westerterp-Plantenga; Jennie Brand-Miller; Anne Raben Journal: Diabetes Care Date: 2021-05-27 Impact factor: 17.152