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Article

Diet Optimization for Sustainability: INDIGOO, an Innovative Multilevel Model Combining Individual and Population Objectives

MS-Nutrition, 13005 Marseille, France
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Author to whom correspondence should be addressed.
Sustainability 2022, 14(19), 12667; https://doi.org/10.3390/su141912667
Received: 20 July 2022 / Revised: 7 September 2022 / Accepted: 3 October 2022 / Published: 5 October 2022
(This article belongs to the Special Issue Quantitative and Multi-Dimensional Assessment of Sustainable Diets)

Abstract

:
Diet optimization is a powerful approach for identifying more sustainable diets that simultaneously consider nutritional, economic, cultural, and environmental dimensions. This study aimed to develop an innovative multilevel approach called Individual Diet Including Global Objectives Optimization (INDIGOO) for designing diets that fulfill nutritional requirements and minimize dietary habit shifts at the individual level while attaining environmental impact reduction targets at the population level. For each individual in a representative sample from the French adult population (INCA2 survey 2006–2007; n = 1918), isocaloric and nutritionally adequate optimized diets with minimal shifts from the observed diet were designed. Environmental targets (including a 30% greenhouse gas emissions (GHGEs) reduction) were applied either similarly for each individual (original approach) or at the population level (INDIGOO). Compared with the original approach, INDIGOO enabled smaller dietary changes while distributing the contribution to the overall 30% GHGEs reduction more fairly among individuals (contributions ranging from −69.5% to +64%). For 6.4% of individuals, INDIGOO allowed an increase in GHGEs (+11% on average). Conversely, individuals with the greatest decrease in GHGEs (−45% on average) were characterized by high energy intake and high animal-based products, water, and other beverage consumption. INDIGOO is a promising multilevel approach to support food policy development.

1. Introduction

The food system accounts for 20–30% of anthropogenic greenhouse gas emissions (GHGEs) [1]. Shifting towards more sustainable diets—defined as being “…protective and respectful of biodiversity and ecosystems, culturally acceptable, accessible, economically fair and affordable, nutritionally adequate…” [2], is one of the actions needed to reduce the environmental impact of our food systems [3]. To evaluate necessary dietary shifts towards more sustainable food consumption, diet optimization has been recognized as a powerful approach to account simultaneously for the various dimensions of a sustainable diet (namely, nutrition, economy, culture, and environment) [4].
In the literature, diet optimization has been used to find the optimal combination of decision variables (e.g., foods) that minimizes or maximizes an objective function (e.g., minimizing deviations from usual consumption patterns) while satisfying a set of constraints (e.g., nutritional requirements, environmental impact reduction) [5,6]. Relying on dietary survey databases, diet optimization models have been applied using either a population-based approach (one optimized diet based on the average diet of the population) or an individual-based approach (one optimized diet per individual) [7]. Diet optimization studies have focused on several public health issues such as ensuring nutritional adequacy while lowering diet costs or environmental impacts, identifying limiting nutrients, or testing dietary guidelines [5].
Ferrari et al. (2020) [8] and Macdiarmid et al. (2012) [9] designed population-based models to estimate the maximal possible environmental impact reduction while ensuring a nutritionally adequate diet. By minimizing the environmental impact, the shift from the average observed diet could be large and potentially unacceptable. To better account for the population’s food habits, other population-based studies on sustainable diets have focused on minimizing total deviations from the average observed diet, while achieving a nutritionally adequate diet and a stepwise reduction in dietary GHGEs [10,11,12,13]. Horgan et al. (2016) [14] applied optimization at the individual level to dietary data from the United Kingdom (UK) to simulate a maximal dietary GHGEs reduction while complying with nutritional recommendations and staying as close as possible to the individual observed diet. More recently, Gazan et al. (2021) [15] proposed an individual-based optimization approach to data from the French population to identify the role of plant-based dairy-like products in nutritionally adequate diets with a 30% dietary GHGEs reduction. Optimization at the individual level enables statistical analyses that are useful for understanding whether significant dietary changes are required; it also increases cultural acceptability by staying as close as possible to each observed diet and accounts for personal nutritional requirements. However, applying environmental impact reduction aims (e.g., −30% dietary GHGEs) equally to every individual might be seen as unfair, in particular for individuals who already have a low environmental impact, and because such aims are usually defined at a population level [14,16,17]. For instance, based on the Paris Climate Change Agreement adopted by the Conference of the Parties in December 2015, the European Union aims to reduce dietary GHGEs by at least 55% by 2030 versus the 1990 level [17]. The combination of individual and population-based approaches benefits from the strengths of individual-based diet optimization while accounting for population-based targets.
The objective of this study was to develop an innovative multilevel approach (Individual Diet Including Global Objectives Optimization (INDIGOO)) combining diet optimization at the individual and population levels for designing more sustainable diets for the French adult population. The model aimed to achieve population environmental reduction targets and fulfill individual nutritional requirements while considering dietary habits at the individual level.

2. Materials and Methods

2.1. Population Sample and Dietary Data

Dietary data were acquired from the second cross-sectional French Individual and National Food Consumption Survey (Etude Individuelle Nationale des Consommations Alimentaires (INCA2)) [18]. The INCA2 survey, which is described elsewhere [18,19], was carried out in 2006–2007 in two independent samples of 0-to 17-year-old children and 18- to 79-year-old adults, using a multi-stage cluster sampling technique. The present study focused on the adult sample (n = 2624). After excluding under-reporters (i.e., those who had under-reported their food intake, whether intentionally or not) [20,21], our analyzed sample was composed of 1918 individuals.
Individuals recorded their consumption of foods and beverages in a seven-day dietary record. Portion sizes were estimated using a photographic booklet or expressed by weight or household measures (e.g., spoon) [22]. Anthropometric and sociodemographic data were also collected via self-reported and face-to-face questionnaires. For this study, socio-occupational status was classified into four categories: ‘low’ (i.e., manual workers and unemployed people), ‘intermediate’ (i.e., office employees, technicians, and similar professions), ‘high’ (i.e., executive, top-management, and liberal professions), and ‘economically inactive’. Education level was classified into ‘high’ (i.e., university education), ‘intermediate’ (i.e., high school), and ‘low’ (i.e., mid-secondary or below). Family status was classified into ‘couples with children’, ‘couples without children’, ‘single-parent households’, and ‘single without children’. Smoking status (yes or no) was also assessed, as was the level of physical activity (low, moderate, high) based on the International Physical Activity Questionnaire [23].

2.2. Food Database

The SUStable database, compiled by Gazan et al. (2018) [24], provides metrics for 212 commonly consumed foods in France, encompassing three dimensions of diet sustainability: Economic affordability (food price, €/100 g), dietary quality (nutritional composition/100 g), and environmental impact (GHGEs: g of CO2 eq./100 g, air acidification: g of SO2 eq./100 g, and marine eutrophication: g of N eq./100 g). Alcoholic beverages were excluded because nutrient recommendations apply to non-alcoholic energy intakes. This led to the inclusion of 207 foods. The food composition data of these 207 foods were related to the individual food intakes from INCA2 to provide individual nutrient intakes, diet costs, and environmental impacts. The 207 foods were categorized into eight groups and 29 subgroups of foods (Supplementary Table S1).

2.3. Description of the Diet Optimization Models

Our innovative multilevel approach called Individual Diet Including Global Objectives Optimization (INDIGOO) was compared with the individual-based optimization approach previously used [14,15], called Individual-Based Diet Optimization (IBDO) here. With the IBDO approach, one model was run separately for each individual, leading to a set of models (one model per individual) for which all parameters (decision variable, constraints, and objective function) were set at the individual level. With the INDIGOO approach, one single model took all individuals into account with parameters at both the individual and population levels (Figure 1). Precisely, constraints on nutrient and food or food group intakes were set at the individual level and environmental impact constraints were set at the population level. With both IBDO and INDIGOO, the objective function sought to minimize dietary shifts from observed diets (Supplementary Material S3). IBDO’s objective function minimizes the sum of percentage deviations between the observed and optimized food intakes of one individual (i.e., individual deviations from dietary habits), while INDIGOO’s objective function minimizes the sum of individual deviations from dietary habits among the population (Supplementary Material S3). With both IBDO and INDIGOO, French nutritional recommendations specific to each individual’s sex, age, and energy intake were applied as constraints for macronutrients, fatty acids, fiber, vitamins, and minerals [25] (Supplementary Table S2), while the energy intakes of optimized diets were restrained to be isocaloric (within ±1% of the observed diets). This was employed because we lacked the information to correctly estimate each individual’s recommended energy intake level.
All the parameters (decision variables, objective function, constraints) of the INDIGOO and IBDO models are described in Figure 1. Further technical details can be found in Supplementary Table S2 (nutritional constraints) and Table S3 (objective function).

2.4. Feasibility

With both the IBDO and INDIGOO models, the feasibility rate was estimated as the percentage of optimized diets that were able to fulfill all constraints.

2.5. Identification of Four Population Classes Based on Dietary GHGEs Change between Observed and INDIGOO Optimized Diets

Feasible optimized diets from the INDIGOO model were categorized into four population classes based on the relative change (in %) in dietary GHGEs between the optimized and observed diets. This resulted in two basic classes: Individuals with increased dietary GHGEs and individuals with decreased dietary GHGEs, the latter being further divided into three tertiles of decreases in dietary GHGEs (%).

2.6. Comparison of IBDO and INDIGOO

Among feasible diets common to both approaches, average individual deviations from dietary habits were estimated for the whole population and by population class and were compared between the IBDO and INDIGOO models. The hypothesis was that applying environmental constraints at the population level would facilitate the fulfillment of the set of other constraints (i.e., enables a higher feasibility rate and a lower average individual dietary shifts) than applying them at the individual level.
All of the following analyses were conducted among individuals with a feasible optimized diet obtained with the INDIGOO model.

2.7. Indicators Used to Estimate Each Dimension of Diet Sustainability

The nutritional dimension was assessed through dietary quality indices estimated for each individual, such as the mean adequacy ratio (MAR), the mean excess ratio (MER), and the solid energy density (SED). The MAR (%), considered an indicator of good nutritional quality, was calculated as the mean ratio between observed intakes and the French nutrient recommendations, expressed in % [25] for 25 beneficial nutrients (capped at 100%) as already described elsewhere [30]. The MER (%), an indicator of poor nutritional quality, was calculated as the mean ratio between observed and maximum recommended values for sodium, saturated fatty acids (SFAs), and added sugars, expressed in %, as already described elsewhere [30]. SED (kcal/100 g), recognized as being negatively associated with higher nutritional quality [31], was calculated by dividing the energy supplied by solid foods (exclusion of all beverages and milk) by their weight and multiplying this by 100. The environmental dimension was assessed by estimating individual dietary GHGEs, marine eutrophication, and air acidification, expressed in g of CO2 eq./day, N eq./day and SO2 eq./day, respectively. The economic dimension was considered through the daily individual diet cost (€/day). Individual deviations from dietary habits (%), food group amounts (g/day), and dietary shifts from observed diets (g/day) were used as proxies for the cultural dimension.

2.8. Comparison of Sociodemographic Characteristics and Nutritional, Cultural, Environmental and Economic Indicators Related to Observed Diets between the Four Population Classes

Age, sex, socio-occupational category, educational level, physical activity, family status, and smoking status were compared between the four population classes. The nutritional quality of observed diets was compared between the four population classes, as were observed food group amounts (g/day), dietary GHGEs (g CO2 eq./day), marine eutrophication (N eq./day), air acidification (SO2 eq./day), and diet cost (€/day).

2.9. Impact of the INDIGOO Approach on Cultural, Environmental and Economic Indicators According to the Four Population Classes

For optimized diets, the amounts of each food group (g/day) were estimated and compared between the four population classes. Dietary shifts towards more sustainable diets were assessed by the average variation for food groups and subgroups in weight (g/day) between the observed and optimized diets in the four population classes. The trade-off between dietary GHGEs change and marine eutrophication and air acidification variations was assessed by comparing the percentages of individuals with an increase or decrease in marine eutrophication and air acidification between the four population classes. The cost of optimized diets was compared across the four population classes. Moreover, differences in diet cost between the observed and optimized diets were compared in the whole sample and in each population class.

2.10. Statistical Analyses

Pearson correlations were computed to assess the relationship between the observed dietary GHGEs and the variation in dietary GHGEs given by INDIGOO (%). Qualitative variables were compared between the four classes of individuals using Chi-squared tests. Quantitative variables were compared between the four population classes using generalized linear models, with and without adjustment for total energy intake, sex, family status, socio-occupational status, and smoking status. Linear trends were also evaluated by population class. T-tests were applied to assess whether the variation in individual dietary shifts between IBDO and INDIGOO was significantly different from zero. For the INDIGOO results, t-tests were also applied to assess whether the average variations in dietary changes and environmental and economic indicators between the observed and optimized diets were significantly different from zero. To ensure sample representativeness, all analyses accounted for the INCA2 sampling frame design and were weighted for unequal sampling probabilities and differential non-responses by region, agglomeration size, age, sex, occupation of the household head, size of the household, and season [18]. A significance level of 5% was applied. All analyses were conducted using SAS 9.4 software.(SAS Institute, Cary, North Carolina, U.S.)

3. Results

The feasibility rate was 93.5% (n = 1780) with IBDO; it was higher for INDIGOO (97.5%; n = 1863).
The following section describes the identification of the four population classes from the INDIGOO model. Then, individual deviations from dietary habits will be compared between the INDIGOO and IBDO approaches according to the four population classes. Lastly, the results will focus on INDIGOO feasible diets only: The characteristics of the four population classes and impact of INDIGOO on cultural, economic, and nutritional indicators in optimized diets.

3.1. Identification of Four Population Classes Based on Dietary GHGEs Changes among INDIGOO Feasible Diets

Among INDIGOO feasible diets, we identified four population classes based on the dietary GHGEs change (Figure 2). First, that for which dietary GHGEs increased (+11% on average) was called the “INC+11%” class (6.4% of individuals). Then, calculated tertiles among individuals with a dietary GHGEs reduction enabled three “DEC” classes to be obtained: “DEC-14%” (31.9% of individuals), “DEC-30%” (30.9% of individuals), and “DEC-45%” (30.8% of individuals), with “DEC-14%” indicating a class of individuals with an average dietary GHGEs reduction of 14%. As shown in Figure 2, the greater the observed dietary GHGEs, the greater the reduction in dietary GHGEs (%) (negative Pearson correlation of −0.68, p-value < 0.001).

3.2. Comparison of INDIGOO and IBDO

Among feasible diets common to both the INDIGOO and IBDO models (n = 1780), on average, the individual deviation from dietary habits (sum of % changes for food items) was 69% in the INDIGOO model, which was significantly lower than in the IBDO model (74%). This indicated that, on average, the INDIGOO model suggested making smaller efforts to shift from dietary habits than the IBDO model (Figure 3). Considering each population class, INDIGOO induced smaller average individual deviations from dietary habits compared to IBDO for the INC+11%, DEC-14%, and DEC-30% population classes and a larger average individual deviation from dietary habits for the DEC-45% class. In addition, the amplitude of the dietary shifts among population classes seemed more homogeneous with INDIGOO than with IBDO.
All of the following results were estimated among individuals having a feasible diet with INDIGOO (n = 1863).

3.3. Sociodemographic Characteristics by Population Class

Significant differences in socio-occupational status, sex, family status, and smoking status were observed across the population classes, which was not the case for the physical activity level or educational level (Supplementary Table S4). In short, low socio-occupational status was more frequent in the DEC-45% and DEC-30% classes than in the other population classes, and intermediate socio-occupational status was more frequent among DEC-14% and INC+11% individuals than in other population classes. The proportions of individuals in couples and smokers were higher in the DEC-45% population class than in the others. From the highest dietary GHGEs decrease (DEC-45%) population class to the increase (INC+11%) class, the proportion of men decreased and that of women increased.

3.4. Description of the Observed Diets Regarding the Nutritional, Economic, Environmental and Cultural Dimensions of a Sustainable Diet by Population Class

Energy intakes significantly decreased from the DEC-45% to INC+11% population classes, even after sociodemographic adjustments (Table 1). After energy intake and sociodemographic adjustments, the MAR decreased significantly from DEC-45% to INC+11% across population classes. SED was significantly different across population classes after energy intake and sociodemographic adjustments (Table 1). There was a significant decreasing trend in the observed environmental indicators (dietary GHGEs, marine eutrophication, and air acidification) from the DEC-45% to INC+11% population classes (Table 1). The average diet cost was €6.3/day in the whole sample and decreased linearly from DEC-45% (€7.0/day) to INC+11% (€4.7/day) (Table 1).
A decreasing trend in the total diet weight was observed from the DEC-45% (2834 g/day) to INC+11% (2079 g/day) population classes (Figure 4A). Overall, the DEC-30%, DEC-14%, and INC+11% population classes consumed less meat/eggs/fish and alternatives, dairy products and alternatives, water, and other beverages than the DEC-45% population class. Sweet-product consumption was more or less the same for all population classes (from 115 g/day to 119 g/day). Fruit and vegetable (382 g/day) and starch (from 247 g/day to 260 g/day) consumption levels were similar between the three DEC population classes, while in the INC+11% class, the average amounts of fruits and vegetables and meat/eggs/fish and alternatives consumed were particularly low (291 g/day and 102 g/day, respectively).

3.5. Impact of the INDIGOO Approach on the Cultural, Environmental and Economic Dimensions of Diet Sustainability by Population Class

Average dietary GHGEs decreased from 4041 g CO2 eq./day with observed diets to 2829 g CO2 eq./day with optimized ones, corresponding to a 30% reduction as defined by the model’s constraint.
Total dietary weight was more homogenous across population classes with optimized diets (difference of 102 g/day between the DEC-45% and INC+11% population classes, Figure 4B) than with observed diets (difference of 755 g/day between the DEC-45% and INC+11% population classes, Figure 4A). Amounts of all food groups were significantly different between population classes, except for amounts of fruits and vegetables, sweet products, and water and other beverages (Figure 4B). Amounts of meat/eggs/fish and alternatives (from 100 g/day to 114 g/day), mixed dishes and sandwiches (from 37 g/day to 58 g/day), and dairy products (from 296 g/day to 304 g/day) were similar but significantly different between population classes. Amounts of starches and fat products followed a decreasing trend from the DEC+45% population class (370 g/day and 56 g/day, respectively) to the INC+11% population class (244 g/day and 45 g/day, respectively).
In general, similar trends in dietary shifts were observed for the four population classes but differed in terms of their magnitude (Supplementary Figure S5): Increasing amounts of all fruit and vegetable subgroups, unrefined starches, eggs, milk, yogurts (except for DEC-45%), and vegetable fats, and decreasing amounts of refined grains (except for DEC-45%), ruminant meats, pork/poultry and game, deli meats, animal-based and plant-based mixed dishes, cheese, dairy desserts, cakes and pastries, biscuits and sweets, and animal fats. It should be noted that for the INC+11% population class only, no significant change was observed in the amount of meat/eggs/fish.
Air acidification with optimized diets was lower than with observed diets for 100% of DEC-45% and DEC-30% individuals and 97.5% of DEC-14% individuals; it was higher for 24.1% of the INC+11% population class (Table 2). Marine eutrophication for optimized diets was lower than for observed diets for more than three-quarters of DEC-45% and DEC-30% individuals and 63% of DEC-14% individuals. Almost all individuals had an increase in marine eutrophication in the INC+11% class.
The INDIGOO model induced a significant decrease in the average diet cost for the DEC-45% and DEC-30% population classes (−€0.9/day and −€0.2/day, respectively) and an increase for the DEC-14% and INC+11% population classes (+€0.4/day and +€1.4/day, respectively) (Figure 5). In the whole sample, the lowest diet cost for optimized diets was €4.0/day; it was much higher than the lowest diet cost for observed diets (€2.5/day).

4. Discussion

In this study, the innovative multilevel approach called Individual Diet Including Global Objectives Optimization (INDIGOO) was developed to combine individual- and population-based diet optimization approaches. INDIGOO has been applied to design more sustainable diets fulfilling population-based environmental impact reduction constraints and complying with individual-based nutritional recommendations while minimizing the shift from the observed diet for each adult from the French INCA2 dietary survey. Compared to the individual-based approach with both nutritional and environmental targets applied at the individual level (named IBDO in this study), INDIGOO may provide more acceptable dietary changes as shown by smaller deviations from dietary habits and facilitate the fulfillment of constraints as demonstrated by a higher feasibility rate. By applying environmental constraints at the population level in INDIGOO, the effort to reduce the environmental impact was distributed among all individuals; therefore, individuals had more room to meet their nutritional needs. As a result, the individual contribution to the overall 30% reduction in dietary GHGEs ranged from −69.0% to +64%. Individuals for whom INDIGOO caused dietary GHGEs to increase represented 6.4% of the population with an average 11% increase in dietary GHGEs. This subsample (INC+11%) was characterized by more women without children, from the intermediate social class, with more non-smokers than in the average population. The energy intake of the INC+11% class was lower than that of the average population. Confirming the recognized positive correlation between energy intake and both dietary environmental impact and diet cost [32,33,34,35], the observed dietary GHGEs, marine eutrophication, air acidification, and diet cost were lower for INC+11% than for other population classes. In terms of consumption by food group, the INC+11% class had lower consumption for all major food groups except for sweet products, explaining the lower nutritional quality as estimated by the MAR and the non-significant difference in the adjusted MER across population classes. Conversely, the subsample of individuals for whom INDIGOO led to the sharpest reduction in dietary GHGEs (−45% on average) was composed mostly of men with low socio-occupational status and high energy intakes, dietary GHGEs, marine eutrophication, air acidification, and diet cost. One of their specific characteristics was that they consumed more animal-based products (meat/eggs/fish and dairy products), water, and other beverages than other population classes.
Overall, with the INDIGOO approach, the dietary shifts needed to achieve environmental impact reduction at the population level while ensuring nutritionally adequate diets were consistent with previous optimization studies reported in a review identifying the most common changes required to achieve more sustainable diets [36]. In particular, amounts of all kinds of meat, cakes, pastries, and biscuits should be decreased and those of fruits, vegetables, and unrefined starches (including wholegrain cereal-based foods, legumes, and potatoes) should be increased [36]. Unlike what is generally observed, INDIGOO did not suggest increasing the consumption of fish, a source of long-chain omega-3 fatty acids, because of the imposed maximal amount (<2 servings/week) established due to toxicological concerns, as recommended by French FBDGs [37]. In our results, long-chain omega-3 fatty acids were mostly provided through an increase in vegetable oils. For some other food groups, trends in dietary changes were found to be inconsistent across studies. These differences can be attributed to differences in input data (environmental impact, nutritional composition of foods, and dietary habits) and the choice of parameters (list and level of nutritional and environmental constraints, objective function design). In the present study, INDIGOO suggested increasing intakes of eggs, milk, and yoghurts and decreasing those of refined grains and mixed dishes. The recommendation to increase milk consumption can be attributed to its good “nutrient profile/environmental impact” ratio [38] and also to its large serving size, which is fostered by the chosen design of the objective function (expressed as a percentage of the observed amount for all foods). Moreover, the results of sustainable diet optimization studies on dairy products and eggs were found to be inconsistent due to trade-offs between their nutritional quality and their environmental impact [36]. As previously emphasized, parameters need to be carefully chosen and described in detail in scientific publications in order to understand inconsistencies across results [5].
Deriving an optimized diet for each individual in a sample rather than an average diet enables the individual dietary shifts needed to improve diet sustainability to be precisely analyzed. As a result, individual dietary shifts that differ from average trends can be highlighted. For instance, in individual-based diet optimizations in the UK [14], the amount of “beef/lamb and dishes” decreased on average but these foods were added or increased for approximately 17% of individuals. In a French study, an IBDO approach showed that plant-based dairy-like products (e.g., soy-based drinks, almond-based drinks) were able to complement dairy products, especially for individuals with low energy intakes [15]. In the present study, individuals were categorized according to the relative variation in dietary GHGEs between the observed and optimized diets, and it is worth noting that even though INDIGOO suggested that the consumption of meat/eggs/fish and alternatives should be reduced at the population level, it did not require modifying the consumption of such products for individuals with an increase in dietary GHGEs (INC+11%). This was due to compensation between a small reduction in the consumption of all kinds of meat and an increase in the consumption of fish and eggs. Not surprisingly, the expected dietary shift depended on the observed consumption levels. Besides providing results at the individual level, INDIGOO enabled individual constraints to be fulfilled more easily than IBDO by distributing environmental impact reduction efforts more fairly across individuals. As a result, INDIGOO’s feasibility rate was higher and dietary shifts were smaller than with the IBDO approach. For some individuals, INDIGOO suggested making a greater effort to change their diet compared to IBDO (in particular, in the DEC-45% population class: +6.6 percent points for individual deviations), so that others had to make less of an effort (−42.3 percent points for individual deviations for the INC+11% population class). We consider this approach to be fairer than IBDO (i.e., applying a uniform environmental impact reduction) because individuals having low dietary GHGEs may not need to decrease their GHGEs, so a smaller dietary shift may improve the nutritional quality of their diet. Even though INDIGOO’s overall favorable gain in terms of the sum of individual dietary deviations compared to IBDO was found to be small (+5 percent points), imposing more stringent environmental impact reductions than in this study, as needed for planetary health reasons [39], would lead to greater dietary shifts. It is likely that INDIGOO showed better gains in terms of dietary deviation acceptability than IBDO with more ambitious environmental constraints.
Food policy development needs to consider the whole food system, which involves the “production, processing, packaging, distribution, marketing, purchasing, consumption, and waste of food” [40]. Previous simulation studies explored the connections between food consumption and food production. For instance, an exploratory scenario study on Dutch animal food products accounted for interdependencies in the animal food system to investigate future sustainable food-based dietary guidelines in the Netherlands [41]. It showed that recommending dairy and eggs inevitably included recommending a minimum level of meat consumption on a population basis when adopting a food system approach. Consuming animals “from nose to tail” improved environmental indicators but, depending on the scenario chosen, could impact the nutritional dimension (e.g., the consumption of milk and cheese, supplying calcium in particular, induced the consumption of butter supplying SFAs) [41]. Thus, when considering the food system, a near-vegetarian diet, including the consumption of dairy, eggs, and a small amount of meat, would be more sustainable than a vegetarian diet [41], which is often promoted as having the lowest environmental impact [41,42]. Similarly, in a population-based diet optimization, decreasing the amount of meat consumed was required to achieve more sustainable diets among individuals in France, but to a lower extent when considering co-produced animal food links between milk and beef and between blood sausage and pork [43]. As advocated in a recent review on the type of methodology used to explore diet sustainability [4], INDIGOO could be used to simultaneously consider interdependencies between foods at the population level while accounting for individual characteristics.
Besides the innovative multilevel approach, another strength of this study was that, in addition to the nutritional, environmental, and cultural dimensions of a sustainable diet, it also considered the economic dimension, which had seldom been explored in previous modeling studies on diet sustainability [36]. On average, achieving a more sustainable diet induced a slight decrease in the average diet cost (−€0.12/day, data not shown). In other population-based studies, a nutritionally adequate optimized diet with a 30% environmental impact reduction had a similar or lower diet cost [12,43]. In the UK, achieving nutritional adequacy with a 30% average GHGEs reduction versus the 1990 level induced a very slight increase (+1.5%) in diet cost [44]. To our knowledge, there is no individual-based optimization accounting for diet cost while reducing the environmental impact. The lowest diet cost observed with our more sustainable optimized diets was €4/day, which was close to the lowest diet cost for nutritionally adequate diets (i.e., €3.85) obtained via individual-based diet optimization in another study based on INCA2 food consumption data [27]. This means that with or without environmental constraints, at least approximately €4/day is needed to reach nutritional adequacy while minimizing deviations from dietary habits. For INC+11%, the optimized average diet cost was €1.4/day higher than for the observed diet, which may be seen as incompatible with the idea of staying close to current consumption levels. However, this result can be explained by a required slight decrease in the consumption of low-cost foods (e.g., sweet products) and a strong need to increase that of expensive foods (e.g., fruit and vegetables). For some individuals, the addition of a cost constraint to limit the increase in diet cost would lead to larger dietary deviations or possibly infeasible optimization. Even though an optimized diet does not cost more than an observed diet on average, some individuals are required to increase their diet cost in order to fulfil all of the model’s constraints.
One limitation of our study was that food consumption data were obtained from a study carried out in 2006–2007. Data from a more recent national food consumption survey conducted in 2014–2015 were ready for use in 2020, but no information on the price or environmental impact of the foods declared in this survey was available when this study was implemented. Nevertheless, no major changes in food habits between 2006 and 2015 were mentioned in a French report [45], and the results were in line with previous diet optimizations on sustainability. Moreover, our study showed several advantages of the INDIGOO approach that can be applied to a more recent database. Another limitation was the lack of environmental indicators other than dietary GHGEs, air acidification, and marine eutrophication. Capturing the broader environmental impact of foods could lead to other dietary shifts, because indicators of this impact are not necessarily correlated with one another [36]. For instance, meeting the UK recommendations for fruits and vegetables will strongly increase the water footprint but not dietary GHGEs [46]. The recently published Agribalyse V3, a database containing environmental impacts according to a dozen indicators for French food products consumed, will be implemented in INDIGOO to better control the environmental impacts of the proposed dietary shifts [47]. In addition, the use of average environmental impact values for each food could have high variability according to the food system [48], but the environmental data used in this study are representative, as much as possible, of the French consumption and national food production modes [49]. Another point worth noting is that the more sustainable diets identified are theoretical, and even though the parameters were chosen to be as acceptable as possible, the results depend on the extent to which the authors considered a variation as “acceptable” [5]. It would be interesting to test the real feasibility of adopting the more sustainable optimized diets, expressed, for instance, in the number of food serving sizes to facilitate their implementation.

5. Conclusions

INDIGOO is an innovative multilevel approach that can be used to model sustainable diets in a way that takes into account constraints at both the individual and population levels. Compared with the original Individual-Based Diet Optimization approach, INDIGOO demonstrated the possibility of facilitating the dietary shifts needed to make our diets more sustainable by distributing the efforts to be made more fairly between individuals. Moreover, by being able to model interdependencies between food production and consumption, INDIGOO is a promising tool for supporting food policy development.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su141912667/s1. Table S1. Food categorization; Table S2. Nutritional recommendations applied as constraints; Material S3. Principle of IBDO and INDIGOO objective function; Table S4. Sociodemographic characteristics (%) by population class; Figure S5. Variation for food groups (A) and Fruits and vegetables (B), Starches (C), Meat/eggs/fish and alternatives (D), Mixed dishes (E), Dairy products and alternatives (F), Sweet products (G), Water and other drinks (H), and Fat products (I) and subgroups between observed and optimized diets according to population class.

Author Contributions

Conceptualization, M.M. and F.V.; methodology, M.M. and R.G.; software, A.R. and R.G.; formal analysis, A.R., O.T. and R.G.; data curation, R.G.; writing—original draft preparation, A.R. and O.T.; writing—review and editing, M.M., F.V. and R.G.; visualization, R.G.; supervision, M.M. and F.V.; project administration, M.M. and F.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the French National Commission for Computed Data and Individual Freedom (Commission Nationale de l’Informatique et des Libertés, CNIL).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The dietary data used for this study are available; see the following reference: ANSES Données de consommations et habitudes alimentaires de l’étude INCA2 Available online: https://www.data.gouv.fr/fr/datasets/donnees-de-consommations-et-habitudes-alimentaires-de-letude-inca-2-3/ (accessed on 31 January 2022).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Joint Research Center; Institute for Prospective Technological Studies. Environmental Impact of Products (EIPRO) Analysis of the Life Cycle Environmental Impacts Related to the Final Consumption of the EU-25: Main Report: IPTS/ESTO Project; Eder, P., Delgado, L., Eds.; Scientific Publications Office: Luxembourg, 2007; ISBN 92-79-02361-6. [Google Scholar]
  2. Food and Agriculture Organization of the United Nations (FAO). Sustainable Diets and Biodiversity: Directions and Solutions for Policy, Research and Action. In Proceedings of the The International Scientific Symposium on Biodiversity and Sustainable Diets: United Against Hunger, Rome, Italy, 3–5 November 2010; Burlingame, B., Dernini, S., Eds.; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2012; p. 309. [Google Scholar]
  3. Garnett, T. Where are the best opportunities for reducing greenhouse gas emissions in the food system (including the food chain)? Food Policy 2011, 36, S23–S32. [Google Scholar] [CrossRef]
  4. Perignon, M.; Darmon, N. Advantages and limitations of the methodological approaches used to study dietary shifts towards improved nutrition and sustainability. Nutr. Rev. 2022, 80, 579–597. [Google Scholar] [CrossRef] [PubMed]
  5. Gazan, R.; Brouzes, C.M.C.; Vieux, F.; Maillot, M.; Lluch, A.; Darmon, N. Mathematical Optimization to Explore Tomorrow’s Sustainable Diets: A Narrative Review. Adv. Nutr. 2018, 9, 602–616. [Google Scholar] [CrossRef] [PubMed]
  6. Van Dooren, C. A Review of the Use of Linear Programming to Optimize Diets, Nutritiously, Economically and Environmentally. Front. Nutr. 2018, 5, 48. [Google Scholar] [CrossRef] [PubMed]
  7. Maillot, M.; Vieux, F.; Amiot, M.J.; Darmon, N. Individual diet modeling translates nutrient recommendations into realistic and individual-specific food choices. Am. J. Clin. Nutr. 2010, 91, 421–430. [Google Scholar] [CrossRef]
  8. Ferrari, M.; Benvenuti, L.; Rossi, L.; De Santis, A.; Sette, S.; Martone, D.; Piccinelli, R.; Le Donne, C.; Leclercq, C.; Turrini, A. Could Dietary Goals and Climate Change Mitigation Be Achieved Through Optimized Diet? The Experience of Modeling the National Food Consumption Data in Italy. Front. Nutr. 2020, 7, 1–13. [Google Scholar] [CrossRef]
  9. Macdiarmid, J.I.; Kyle, J.; Horgan, G.W.; Loe, J.; Fyfe, C.; Johnstone, A.; McNeill, G. Sustainable diets for the future: Can we contribute to reducing greenhouse gas emissions by eating a healthy diet? Am. J. Clin. Nutr. 2012, 96, 632–639. [Google Scholar] [CrossRef]
  10. Verly-Jr, E.; de Carvalho, A.M.; Marchioni, D.M.L.; Darmon, N. The cost of eating more sustainable diets: A nutritional and environmental diet optimisation study. Glob. Public Health 2022, 17, 1073–1086. [Google Scholar] [CrossRef]
  11. Vieux, F.; Perignon, M.; Gazan, R.; Darmon, N. Dietary changes needed to improve diet sustainability: Are they similar across Europe? Eur. J. Clin. Nutr. 2018, 72, 951–960. [Google Scholar] [CrossRef]
  12. Perignon, M.; Masset, G.; Ferrari, G.; Barré, T.; Vieux, F.; Maillot, M.; Amiot, M.-J.; Darmon, N. How low can dietary greenhouse gas emissions be reduced without impairing nutritional adequacy, affordability and acceptability of the diet? A modelling study to guide sustainable food choices. Public Health Nutr. 2016, 19, 2662–2674. [Google Scholar] [CrossRef]
  13. Chaudhary, A.; Krishna, V. Country-specific sustainable diets using optimization algorithm. Environ. Sci. Technol. 2019, 53, 7694–7703. [Google Scholar] [CrossRef]
  14. Horgan, G.W.; Perrin, A.; Whybrow, S.; Macdiarmid, J.I. Achieving dietary recommendations and reducing greenhouse gas emissions: Modelling diets to minimise the change from current intakes. Int. J. Behav. Nutr. Phys. Act. 2016, 13, 46. [Google Scholar] [CrossRef]
  15. Gazan, R.; Vieux, F.; Lluch, A.; De Vriese, S.; Trotin, B.; Darmon, N. Individual diet optimization in French adults shows that plant-based “dairy-like” products may complement dairy in sustainable diets. Sustainability 2022, 14, 2817. [Google Scholar] [CrossRef]
  16. United nations Climate Change NDC Synthesis Report. Available online: https://unfccc.int/process-and-meetings/the-paris-agreement/nationally-determined-contributions-ndcs/nationally-determined-contributions-ndcs/ndc-synthesis-report#eq-4 (accessed on 15 September 2021).
  17. European Commission Paris Agreement. Available online: https://ec.europa.eu/clima/policies/international/negotiations/paris_en (accessed on 15 September 2021).
  18. AFSSA. Etude Individuelle Nationale des Consommations Alimentaires 2 2006–2007 (INCA2); Individual National Study of Food Consumption 2006–2007; AFSSA: Maisons-Alfort, France, 2009; Volume 2, Available online: https://www.anses.fr/fr/system/files/PASER-Ra-INCA2.pdf_ (accessed on 15 September 2021).
  19. Dubuisson, C.; Lioret, S.; Touvier, M.; Dufour, A.; Calamassi-Tran, G.; Volatier, J.-L.; Lafay, L. Trends in food and nutritional intakes of French adults from 1999 to 2007: Results from the INCA surveys. Br. J. Nutr. 2010, 103, 1035–1048. [Google Scholar] [CrossRef]
  20. Black, A.E. Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. Int. J. Obes. 2000, 24, 1119–1130. [Google Scholar] [CrossRef]
  21. Goldberg, G.R.; Black, A.E.; Jebb, S.A.; Cole, T.J.; Murgatroyd, P.R.; Coward, W.A.; Prentice, A.M. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur. J. Clin. Nutr. 1991, 45, 569–581. [Google Scholar]
  22. Hercberg, S.; Deheeger, M.; Preziosi, P. Portions Alimentaires Manuel Photos Pour l’Estimation des Quantités—(Portion Sizes: Picture Booklet for the Estimation of Quantities); SU-VI-MAX; Polytechnica: Paris, France, 1994. [Google Scholar]
  23. Lee, P.H.; Macfarlane, D.J.; Lam, T.H.; Stewart, S.M. Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): A systematic review. Int. J. Behav. Nutr. Phys. Act. 2011, 8, 115. [Google Scholar] [CrossRef]
  24. Gazan, R.; Barré, T.; Perignon, M.; Maillot, M.; Darmon, N.; Vieux, F. A methodology to compile food metrics related to diet sustainability into a single food database: Application to the French case. Food Chem. 2018, 238, 125–133. [Google Scholar] [CrossRef]
  25. ANSES. Actualisation des Références Élaboration des Repères du PNNS: Élaboration des Références Nutritionnelles; ANSES: Maisons-Alfort, France, 2016.
  26. Lluch, A.; Maillot, M.; Gazan, R.; Vieux, F.; Delaere, F.; Vaudaine, S.; Darmon, N. Individual Diet Modeling Shows How to Balance the Diet of French Adults with or without Excessive Free Sugar Intakes. Nutrients 2017, 9, 162. [Google Scholar] [CrossRef]
  27. Maillot, M.; Vieux, F.; Delaere, F.; Lluch, A.; Darmon, N. Dietary changes needed to reach nutritional adequacy without increasing diet cost according to income: An analysis among French adults. PLoS ONE 2017, 12, e0174679. [Google Scholar] [CrossRef]
  28. Santé Publique France. Recommendations Concerning Diet, Physical Activity and Sedentary Behaviour for Adults; Santé Publique France: Saint-Maurice, France, 2019. [Google Scholar]
  29. ANSES. Poissons et Produits de la Pêche, Conseils de Consommation; ANSES: Maisons-Alfort, France, 2016.
  30. Vieux, F.; Soler, L.-G.; Touazi, D.; Darmon, N. High nutritional quality is not associated with low greenhouse gas emissions in self-selected diets of French adults. Am. J. Clin. Nutr. 2013, 97, 569–583. [Google Scholar] [CrossRef]
  31. Ledikwe, J.H.; Blanck, H.M.; Khan, L.K.; Serdula, M.K.; Seymour, J.D.; Tohill, B.C.; Rolls, B.J. Low-Energy-Density Diets Are Associated with High Diet Quality in Adults in the United States. J. Am. Diet. Assoc. 2006, 106, 1172–1180. [Google Scholar] [CrossRef]
  32. Walker, C.; Gibney, E.R.; Hellweg, S. Comparison of Environmental Impact and Nutritional Quality among a European Sample Population - findings from the Food4Me study. Sci. Rep. 2018, 8, 2330. [Google Scholar] [CrossRef]
  33. Vieux, F.; Darmon, N.; Touazi, D.; Soler, L.G. Greenhouse gas emissions of self-selected individual diets in France: Changing the diet structure or consuming less? Ecol. Econ. 2012, 75, 91–101. [Google Scholar] [CrossRef]
  34. Monsivais, P.; Scarborough, P.; Lloyd, T.; Mizdrak, A.; Luben, R.; Mulligan, A.A.; Wareham, N.J.; Woodcock, J. Greater accordance with the Dietary Approaches to Stop Hypertension dietary pattern is associated with lower diet-related greenhouse gas production but higher dietary costs in the United Kingdom. Am. J. Clin. Nutr. 2015, 102, 138–145. [Google Scholar] [CrossRef]
  35. Darmon, N.; Briend, A.; Drewnowski, A. Energy-dense diets are associated with lower diet costs: A community study of French adults. Public Health Nutr. 2004, 7, 21–27. [Google Scholar] [CrossRef] [PubMed]
  36. Steenson, S.; Buttriss, J.L. Healthier and more sustainable diets: What changes are needed in high-income countries? Nutr. Bull. 2021, 46, 279–309. [Google Scholar] [CrossRef]
  37. French Agency for Food Environmental and Occupational Health & Safety (ANSES). A Summary of the Agency’s Recommendations for Fish and Fishery Products. Available online: https://www.anses.fr/en/content/summary-agencys-recommendations-fish-and-fishery-products (accessed on 16 July 2019).
  38. Masset, G.; Vieux, F.; Darmon, N. Which functional unit to identify sustainable foods? Public Health Nutr. 2015, 18, 2488–2497. [Google Scholar] [CrossRef] [PubMed]
  39. Intergovernemental Panel on Climate Change. Climate change 2022. In Impacts, Adaptation and Vulnerability; Summary for Policymakers: Geneva, Switzerland, 2022. [Google Scholar]
  40. Fanzo, J.; Bellows, A.L.; Spiker, M.L.; Thorne-Lyman, A.L.; Bloem, M.W. The importance of food systems and the environment for nutrition. Am. J. Clin. Nutr. 2021, 113, 7–16. [Google Scholar] [CrossRef] [PubMed]
  41. Van Dooren, C.; Man, L.; Seves, M.; Biesbroek, S. A Food System Approach for Sustainable Food-Based Dietary Guidelines: An Exploratory Scenario Study on Dutch Animal Food Products. Front. Nutr. 2021, 8, 542. [Google Scholar] [CrossRef]
  42. Hallström, E.; Carlsson-Kanyama, A.; Börjesson, P. Environmental impact of dietary change: A systematic review. J. Clean. Prod. 2015, 91, 1–11. [Google Scholar] [CrossRef]
  43. Barré, T.; Perignon, M.; Gazan, R.; Vieux, F.; Micard, V.; Amiot, M.-J.; Darmon, N. Integrating nutrient bioavailability and co-production links when identifying sustainable diets: How low should we reduce meat consumption? PLoS ONE 2018, 13, e0191767. [Google Scholar] [CrossRef]
  44. Kramer, G.; Durlinger, B.; Kuling, L.; van Zeist, W.; Blonk, H.; Broekema, R.; Halevy, S. Eating for 2 Degrees New and Updated Livewell Plates; WWF: Surrey, UK, 2017. [Google Scholar]
  45. Equipe de Surveillance et d’Épidémiologie Nutritionnelle (Esen). Étude de Santé sur l’Environnement, la Biosurveillance, l’Activité Physique et la Nutrition (Esteban), 2014–2016. Volet Nutrition. Chapitre Consommations; Equipe de Surveillance et d’Épidémiologie Nutritionnelle (Esen): Saint-Maurice, France, 2018. [Google Scholar]
  46. Scheelbeek, P.; Green, R.; Papier, K.; Knuppel, A.; Alae-Carew, C.; Balkwill, A.; Key, T.J.; Beral, V.; Dangour, A.D. Health impacts and environmental footprints of diets that meet the Eatwell Guide recommendations: Analyses of multiple UK studies. BMJ Open 2020, 10, e037554. [Google Scholar] [CrossRef]
  47. French Agency for Ecological Transition (ADEME). Agribalyse V3. Available online: https://agribalyse.ademe.fr/ (accessed on 3 September 2021).
  48. Poore, J.; Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 2018, 360, 987–992. [Google Scholar] [CrossRef]
  49. Bertoluci, G.; Masset, G.; Gomy, C.; Mottet, J.; Darmon, N. How to Build a Standardized Country-Specific Environmental Food Database for Nutritional Epidemiology Studies. PLoS ONE 2016, 11, e0150617. [Google Scholar] [CrossRef]
Figure 1. Overview of the Individual-Based Diet Optimization (IBDO) and Individual Diet Including Global Objectives Optimization (INDIGOO) approaches. 1 maximal amount defined as the sex- and age- (18–25 years old; 26–54 years old; >55 years old) specific 95th percentile of the observed value estimated among all individuals; 2 maximal amount defined as the sex- and age-specific 95th percentile of the observed value estimated among consumers of this food exclusively; 3 maximal amount set at the individual observed intake for offal (because of the high frequency of avoidance), for fortified products (such as breakfast cereal, chocolate drinks, and plant-based products), and for bottled water, as already performed in previous papers [7,26,27]; 4 the increase for milk was limited to one serving (i.e., 150 g) per day between the observed and optimized diets. Without this limitation, the model added a high amount of milk, which was not acceptable or not compatible with the latest French food-based dietary guidelines (FBDGs) [28]; 5 a constraint was set to limit the maximal quantity of fish and seafood (including marine and freshwater fish and shellfish) to two servings per week (200 g) in order to take toxicological risks into account [29].
Figure 1. Overview of the Individual-Based Diet Optimization (IBDO) and Individual Diet Including Global Objectives Optimization (INDIGOO) approaches. 1 maximal amount defined as the sex- and age- (18–25 years old; 26–54 years old; >55 years old) specific 95th percentile of the observed value estimated among all individuals; 2 maximal amount defined as the sex- and age-specific 95th percentile of the observed value estimated among consumers of this food exclusively; 3 maximal amount set at the individual observed intake for offal (because of the high frequency of avoidance), for fortified products (such as breakfast cereal, chocolate drinks, and plant-based products), and for bottled water, as already performed in previous papers [7,26,27]; 4 the increase for milk was limited to one serving (i.e., 150 g) per day between the observed and optimized diets. Without this limitation, the model added a high amount of milk, which was not acceptable or not compatible with the latest French food-based dietary guidelines (FBDGs) [28]; 5 a constraint was set to limit the maximal quantity of fish and seafood (including marine and freshwater fish and shellfish) to two servings per week (200 g) in order to take toxicological risks into account [29].
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Figure 2. Scatter plot of observed dietary GHGEs and the change in dietary GHGEs (%) between observed diets and diets optimized with INDIGOO. Black squares represent average values by population class.
Figure 2. Scatter plot of observed dietary GHGEs and the change in dietary GHGEs (%) between observed diets and diets optimized with INDIGOO. Black squares represent average values by population class.
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Figure 3. Individual deviations from dietary habits in the IBDO and INDIGOO models by population class. An individual deviation from dietary habits was calculated as the sum of % deviations for all food items for one individual. Values indicate average individual deviations from dietary habits for IBDO and INDIGOO by population class. * indicates a difference between the IBDO and INDIGOO individual deviations from dietary habits with a p-value < 0.05; *** indicates a difference between the IBDO and INDIGOO individual deviations from dietary habits with a p-value < 0.001.
Figure 3. Individual deviations from dietary habits in the IBDO and INDIGOO models by population class. An individual deviation from dietary habits was calculated as the sum of % deviations for all food items for one individual. Values indicate average individual deviations from dietary habits for IBDO and INDIGOO by population class. * indicates a difference between the IBDO and INDIGOO individual deviations from dietary habits with a p-value < 0.05; *** indicates a difference between the IBDO and INDIGOO individual deviations from dietary habits with a p-value < 0.001.
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Figure 4. Observed (A) and optimized (B) amounts by food group (g/day) in the whole sample and by population class; values in bold showed significant (p < 0.05) differences across population classes after adjustment for energy intake, sex, family status, socio-occupational status, and smoking status.
Figure 4. Observed (A) and optimized (B) amounts by food group (g/day) in the whole sample and by population class; values in bold showed significant (p < 0.05) differences across population classes after adjustment for energy intake, sex, family status, socio-occupational status, and smoking status.
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Figure 5. Distribution of diet costs (€/day) for the observed and optimized diets by population class. Circles represent the average. In the whole population and in each population class, average diet costs were significantly different between the observed and optimized diets (p-value < 0.001); average diet cost was significantly different across population classes, both for the observed and optimized diets, with and without adjustment for energy intake, sex, family status, socio-occupational status, and smoking status. The linear trend was significant (p-value < 0.001).
Figure 5. Distribution of diet costs (€/day) for the observed and optimized diets by population class. Circles represent the average. In the whole population and in each population class, average diet costs were significantly different between the observed and optimized diets (p-value < 0.001); average diet cost was significantly different across population classes, both for the observed and optimized diets, with and without adjustment for energy intake, sex, family status, socio-occupational status, and smoking status. The linear trend was significant (p-value < 0.001).
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Table 1. Nutritional and environmental indicators and cost of observed diets by population class.
Table 1. Nutritional and environmental indicators and cost of observed diets by population class.
AllDEC-45%DEC-30%DEC-14%INC+11%p-Values
MeanStdMeanStdMeanStdMeanStdMeanStda 1b 2
Energy (kCal/day)2134.715.22267.125.62171.422.62050.624.5173743.2<0.0001<0.0001 4
SED (g/kCal)172.70.8171.81.5172.51.4173.31.3175.22.10.6678<0.0001 3
MAR (%)84.70.287.20.385.50.483.50.475.10.7<0.0001<0.0001 3
MER (%)20.80.523.61.121.80.819.50.99.30.9<0.000100.4386
Dietary GHGEs (CO2 eq./day)4041.429.8489344.14060.434.13474.932.12664.340.8<0.0001<0.0001 3
Marine eutrophication (N eq./day)16.80.119.20.316.90.215.20.211.90.2<0.0001<0.0001 3
Air acidification (SO2 eq./day)50.90.463.20.851.60.542.10.4310.7<0.0001<0.0001 3
Diet cost (€/day)6.307.00.16.40.15.90.14.70.1<0.0001<0.0001 3
SED, solid energy density; MAR, mean adequacy ratio; MER, mean excess ratio; 1 p-value of the generalized linear model to test differences in indicators across population classes; 2 p-value of the generalized linear model to test differences in indicators across population classes, adjusted for energy intake, sex, family status, socio-occupational status, and smoking status; 3 significant linear relationship between population classes adjusted for energy intake, sex, family status, socio-occupational status, and smoking status; 4 p-value of the generalized linear model to test differences in energy across population classes, adjusted for sex, family status, socio-occupational status, and smoking status.
Table 2. Proportion of individuals (%) with an increase or decrease in air acidification and marine eutrophication by population class.
Table 2. Proportion of individuals (%) with an increase or decrease in air acidification and marine eutrophication by population class.
Environmental IndicatorDirection of VariationDEC-45%
(30.8% of Individuals)
DEC-30%
(30.9% of Individuals)
DEC-14%
(31.9% of Individuals)
INC+11%
(6.4% of Individuals)
Air acidificationIncrease0%0%2.5%24.1%
Decrease100%100%97.5%75.9%
Marine eutrophicationIncrease4.2%23.1%62.7%98.4%
Decrease95.8%76.9%37.3%1.9%
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Rocabois, A.; Tompa, O.; Vieux, F.; Maillot, M.; Gazan, R. Diet Optimization for Sustainability: INDIGOO, an Innovative Multilevel Model Combining Individual and Population Objectives. Sustainability 2022, 14, 12667. https://doi.org/10.3390/su141912667

AMA Style

Rocabois A, Tompa O, Vieux F, Maillot M, Gazan R. Diet Optimization for Sustainability: INDIGOO, an Innovative Multilevel Model Combining Individual and Population Objectives. Sustainability. 2022; 14(19):12667. https://doi.org/10.3390/su141912667

Chicago/Turabian Style

Rocabois, Audrey, Orsolya Tompa, Florent Vieux, Matthieu Maillot, and Rozenn Gazan. 2022. "Diet Optimization for Sustainability: INDIGOO, an Innovative Multilevel Model Combining Individual and Population Objectives" Sustainability 14, no. 19: 12667. https://doi.org/10.3390/su141912667

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