Task 3.1 Development of a method to determine food quality scores based on pattern of nutrient release taking process and composition into account (PIs: Le Feunteun (GMPA), C. Michon (GENIAL), G. Fromentin (PNCA))
Aims. Numerous food quality scores have been proposed. However these indicators take into account only the nutrient contents but not the food processing. To fill this gap, an international classification of food products based on their level of processing has been proposed (Moubarac et al. 2014) but only three categories have been suggested to discriminate the level of processing. In this task, we propose i) to develop a quantitative method to characterize food products based on their level of processing and their nutrient content (month 12) and ii) to validate the pertinence of the method through an animal study by studying the impact of food on their metabolisms and biological markers of risks (month 36).
Methods and data. Main nutrients, identified as responsible for modulation of T2D risk, are carbohydrates, saturated fat and dietary fibres. We propose to develop a set of different food models based on the same composition but with different structures (bread and/or breakfast cereal type models). The design of these food models will draw upon expertise in food sciences and food process engineering. The design of different structures allowing different enzymatic accessibilities for substrates during digestion will be performed (for instance, protein network around starch, or fiber network etc.). For each model, we will characterize in vitro conditions the kinetics of nutrient release (DIDGI® system, Menard et al. 2015) and we will study in vivo conditions the modulation of biomarkers when animals eat these model foods. Each model food will be characterized in terms of composition, structure, processing level, energy and exergy balances. We will establish correlations and mathematical models to predict the link between process, structure, pattern of nutrient releases and biomarkers in the case of complex foods containing all the main nutrients and fibers.
Expected results. The basis for a first model to calculate food quality scores based on nutrient composition and level of processing as an indicator of the structure that could modulate biomarkers (will be used in task 3.2); Data on modulation of biomarkers in animal eating model foods; Knowledge for reformulation of foods.
Risks. The choice of food models have to be as complex as possible but have to remain well controlled systems leading to a risk of insufficiently generic method.
Collaborations. This work will be based on collaboration between GENIAL (food structuration and characterization of the food models), GMPA (in vitro digestion and development of the method to predict the “food quality score”) and PNCA (links between nutrient delivery and biomarkers)
Task 3.2. Variety of product characteristics within food categories and consumption patterns of T2D at-risk individuals (PIs: I. Souchon (GMPA), L.G. Soler (Aliss))
Aims. Current diet recommendations rely on an average nutrient composition of the different food categories. However, each food category actually covers a wide variety of compositions and processes. Only part of this variety is known, but it seems necessary to take the very coarse food supply into account in regards to the diet for at-risk consumers. The aims of this task will thus be 1) to map the variety of food supply, based on fine composition and quality scores (including process level and comparable sensory characteristics), for the main food categories identified as major components of the diet in at-risk consumer groups 2) to study statistical associations between the nutritional profile of food categories and subcategories and at-risk consumption patterns identified in WP2.
Methods and data. Only a limited number of at-risk food categories and subcategories will be investigated among starchy foods (traditional bread, processed bread, biscuits, pizzas, breakfast cereals…). Other categories could also be included in the study should they have a strong impact and leeway for reformulation (e.g. desserts, drinks, sodas…). For each category, characteristics of products available on the market (salt, fibre contents, etc.) will be extracted from the Oqali data set (www.oqali.fr) and completed when necessary for some ingredients with data from the Ciqual database (https://pro.anses.fr/tableciqual/). Quality scores will be determined on all products of the different studied categories and maps will be performed.
Expected results. Data from this task will help determine a multi-criteria cartography. Results will be used for customized expectations used in WP3.3. Results from the task could be used for subsequent reformulation of food products.
Collaborations. The work will be a collaboration between GMPA (determination of different scores, statistical analysis), GENIAL (Statistical analysis, exergetic balance) and ALISS (accessibility to Oqali data bases).
Task 3.3 Identification of sensory drivers of liking for targeted groups of subjects (PIs: A. Saint-Eve (GMPA), J. Delarue (GENIAL))
Aims. Consumers’ choice and eating habits within a given category of foods may thus be an important risk (or protective) factor for T2D prevention. The aims of the task will thus be to evaluate compliance with dietary recommendations when an alternative food offer exists considering targeted at-risk consumer groups and their eating behavior as a starting point.
Methods and data. In a first step, well characterized selected products that are representative of the diversity of each map (task 3.2) will be evaluated for liking by 250 subjects carefully selected for their T2D at-risk consumption pattern and individual factors, as identified in WP2. Each subject’s structure of food preferences will be modelled thanks to external preference mapping and individual sensory drivers of liking will be determined within each studied food category. These results will be used to set up experimental, yet realistic, conditions where subjects will have to choose between products they are used to consume, and healthier alternatives with varying degrees of sensory appeal. Consumer behavior will be studied in naturalistic conditions and by video observation during consumption of selected products in meal context. Individual strategies and trade-offs between healthy and sensory-improved alternatives will be evaluated following a joint analysis approach in order to estimate best chances for each consumer to comply with dietary recommendations. For a selected number of products, choices will be further studied by including retail price as a variable and examining willingness to pay for best alternatives (all criteria included).
Expected results. Data from this task will help determine the leeway for efficient recommendations and sustainable changes of behaviors. Individual trade-offs will be weighed and analyzed with regards to the subjects’ motivations. This will allow better understanding chances of success for food substitution strategies when compared with other possible behavioral strategies (e.g. attempts to eat the same products in smaller amounts or less frequently). Results will be used for customized recommendations taking into account the greatest chances of success.
Collaborations. Sensory expertise will be provided by GMPA, and consumer sciences by GENIAL in close collaboration with partners of WP2
Task 3.4. Estimated impact of substituting products within food categories to prevent T2D (PIs: L.G. Soler (Aliss), N. Darcel (PNCA), I. Souchon (GMPA))
Aims. Considering the T2D prevention issue, the aim is to assess to what extent the choice of healthier items in each food category may contribute to reduce T2D risk for at-risk individuals. This will help to take into account the product variability within food categories in the design of tailored dietary recommendations.
Methods and data. Using T2D at-risk consumption patterns identified in WP2, we will simulate different scenarios of product substitutions within and between food categories. Different methods will be used to generate product substitutions: they will be based on pre-defined rules, or driven by preferences estimated in Task 3.3 and in WP2. Using Relative Risks (RR) available in the literature we will estimate the potential health gains of the simulated scenarios.
Expected results. Simulation results will inform about the magnitude of health benefits obtained if at-risk consumers would implement different strategies of product substitutions within and between food categories. This will provide insights about: (i) how to combine product substitutions within and between food categories in order to facilitate the consumers’ compliance with nutritional guidelines, (ii) the potential contribution of food reformulation by the food industry to the reduction of T2D incidence and prevalence.
Collaborations. Aliss will contribute to the design of the simulation model. PNCA will propose to build the methodology on choice behavior. GMPA and GENIAL will identify the impact of food recommendations or possible reformulations on diets.