GlucoMatchMaker · A new tool to guide consumers on how to mix and match foods to improve their blood glucose profiles

A new tool to guide consumers on how to mix and match foods to improve their blood glucose profiles

Starchy foods are one of the main components of our diet. They supply up to half of our energy needs and, because starch is exclusively made of glucose, these foods play a major role in our blood glucose levels. High blood glucose is linked to a number of chronic diseases and the rate at which starch in a given food is digested is directly correlated with blood glucose levels (i.e. the glycaemic response). Simply put, quickly digested starchy foods lead to high blood glucose levels. However, this effect can be counteracted by an adequate combination with other foods. Low pH foods can attenuate the glycemic response to starch-rich foods. It has been demonstrated, for example, that lemon juice, due to its low pH (pH≈2.3), inhibited key digestive enzymes, thereby interrupting gastric digestion of starch in vitro . This effect can significantly reduce the glycemic response in humans. In particular, adding lemon juice to a starch rich meal reduced the mean blood glucose concentration peak by 30% . Considering the panoply of food options available to consumers, it is likely that other combinations have similar effects but no work had ever been conducted to develop a consolidated knowledge base to exploit this strategy. GlucoMatchMaker went beyond the state-of-the art with a work plan designed to address this knowledge gap. The main goal was to test, in a real-life context, the effectiveness of guiding individuals on how to mix and match foods and beverages to improve glucose responses. Four specific objectives have been defined: Specific objective 1: To select and characterize starch-rich foods, low-pH foods and beverages and to investigate how their combination influences the rate of starch digestion in vitro (WP1). Specific objective 2: To determine the conditions of effectiveness of these combinations (in silico models) (WP2). Specific objective 3: To develop the first mobile app that will integrate this knowledge to guide the user on how to mix and match starch-rich foods to lower their glycemic impact (WP3). Specific objective 4: To test the effectiveness of the developed strategy in a real-life context (Secondment, WP5).

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз