domain: sdv.aen sde qfin

text:
Hafsa Toujgani
email: h.toujgani@eren.smbh.univ-paris13.fr

Joséphine Brunin

Elie Perraud

Benjamin Allès

Mathilde Touvier

Denis Lairon

François Mariotti

Philippe Pointereau

Julia Baudry

Emmanuelle Kesse- Guyot

The nature of protein intake as a discriminating factor of diet sustainability: a multi-criteria approach

Keywords: Sustainable, dietary patterns, protein sources, environmental pressures, health risk, food expenditure, protein expenditure

Animal production is responsible for 56-58% of the GHG emissions and limiting meat consumption would strongly contribute to reducing human health risks in Western countries. This study aimed to investigate the nature of protein intake as a discriminating factor for diets' sustainability.

Using data from 29,210 French adults involved in the NutriNet-Santé cohort, we identified clusters according to 23 protein sources. A multicriteria (environmental, economic, nutritional and health) sustainability analysis was then conducted on the identified clusters. The economic analysis focused on both food and protein expenditure structures, using a budget coefficient approach. Relative values of clusters compared to the whole sample were calculated.

We identified five clusters: milk-based, meat-based, fast food-based, healthy-fish-based, and healthy-plant-based. We found that the healthy-plant-based and healthy-fish-based clusters were the most sustainable, conciliating the compromise between human health (0.25 and 0.53 respectively for the Health Risk Score) and the protection of the environment (-62% and -19% respectively for the pReCiPe indicator). Conversely, the highest environmental impacts (+33% for the pReCiPe indicator) and the highest health risk (0.95 for the HRS) were observed for the meat-based cluster, which was associated with the lowest nutritional scores (-61% for the PNNS-GS2 score). The economic analysis showed that the healthy-plant-based cluster was the one with the highest food budget coefficient (+46%), followed by the healthy-fish-based cluster (+8%), partly explained by a strong share of organic food in the diet. However, the meat-based cluster spent more of their food budget on their protein intake (+13%), while the healthy-plant-based cluster exhibited the lowest expenditure for this intake (-41%).

Our results demonstrate that the nature of protein intake is a discriminating factor in diet sustainability. Also, reducing animal protein consumption would generate co-benefits beyond environmental impacts, by being favorable for health, while reducing the monetary cost associated with protein intake.

Introduction

Some planetary boundaries being officially crossed (1-3), recent efforts to limit global warming to 1.5°C, as stipulated in the Paris Climate Agreement, remain insufficient according to the 2021 Intergovernmental Panel on Climate Change (IPCC) report [START_REF] Masson-Delmotte | Climate change 2021: the physical science basis : summary for policymakers : working group I contribution to the sixth Assessment report of the Intergovernmental Panel on Climate Change[END_REF]. Moreover, The 2023 Sustainable Development Goals Report not only confirms this assessment but also sounds the alarm, urgently calling for a doubling of efforts to get these objectives back on track (5). Actually, current food systems are responsible for one third of global GHG emissions [START_REF] Crippa | Food systems are responsible for a third of global anthropogenic GHG emissions[END_REF], one third to 80% of which originates from the production stage [START_REF] Crippa | Food systems are responsible for a third of global anthropogenic GHG emissions[END_REF][START_REF] Clune | Systematic review of greenhouse gas emissions for different fresh food categories[END_REF]. Besides, as populations become more urbanized and affluent, their dietary patterns are shifting towards calorie-rich diets with more animal-based and protein-dense foods [START_REF] Clark | The Diet, Health, and Environment Trilemma[END_REF]. These dietary patterns have been largely influenced by the "protein" debate, which has been evolving for decades. It was in the 1930s that particular attention was paid to this nutrient, with the widespread incidence of kwashiorkor, and its association with protein deficiency [START_REF] Nott | No one may starve in the British Empire": Kwashiorkor, Protein and the Politics of Nutrition Between Britain and Africa[END_REF]. A special Protein Advisory Group was then created by the United Nations, whose mission was to "fight to close the protein gap", while dietary guidelines encouraged the consumption of protein-rich foods, namely meat and milk [START_REF]The politics of protein: examining claims about livestock, fish, 'alternative proteins' and sustainability[END_REF]. Furthermore, the "protein" debate has been largely influenced by animal production industry, given the great market opportunities offered by this sector, whose trade balance reached 28 billion euros in 2018 in the EU [START_REF] Chatellier | Les performances économiques de l'élevage européen : de la "compétitivité coût" à la "compétitivité hors coût[END_REF]. However, the debate has gone off in another direction, and it is now beyond doubt that the reduction of meat consumption would contribute to strongly reducing human health risks in Western countries according to the Global Burden of Diseases (GBD) analysis [START_REF] Murray | Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[END_REF]. Moreover, animal food production is responsible for 56-58% of the emissions generated by food production while providing only 37% of the protein supply [START_REF] Poore | Reducing food's environmental impacts through producers and consumers[END_REF]. In that regard, the IPCC has strongly recommended to reduce meat consumption by twothirds (4), as red meat and processed meat production have been shown to have the highest impact on all dimensions (GHG emissions, land use, water use, acidification and eutrophication) [START_REF] Poore | Reducing food's environmental impacts through producers and consumers[END_REF][START_REF] Clark | Multiple health and environmental impacts of foods[END_REF].

Note that these emissions are double those generated by plant-based foods [START_REF] Xu | Global greenhouse gas emissions from animal-based foods are twice those of plant-based foods[END_REF]. Although it has been proven that there is no longer protein gap in Western countries, as protein intake exceeding 6 needs [START_REF] Mariotti | Dietary Protein and Amino Acids in Vegetarian Diets-A Review[END_REF], the protein debate persists and has shifted towards new trends of "protein transitions".

The new markets of plant-based meat and dairy substitutes are growing exponentially [START_REF]Bloomberg Intelligence. Plant-Based Foods Poised for Explosive Growth[END_REF], contributing to collapse the global dietary issues into a single nutrient. That being said, the individuals' dietary patterns seem to be strongly influenced by this debate. Indeed, it has been shown that the overall diet of meat eaters is less healthy than the one of plant-based foods eaters [START_REF] Sobiecki | High compliance with dietary recommendations in a cohort of meat eaters, fish eaters, vegetarians, and vegans: results from the European Prospective Investigation into Cancer and Nutrition-Oxford study[END_REF][START_REF] Vergnaud | Meat consumption and prospective weight change in participants of the EPIC-PANACEA study[END_REF][START_REF] Fogelholm | Association between red and processed meat consumption and chronic diseases: the confounding role of other dietary factors[END_REF].

Thus, as we assume that the nature of protein intake might be indicative of the overall dietary patterns of individuals, we hypothesized that it could be a discriminating factor for diet sustainability, as defined by FAO in 2012 [START_REF] Burlingame | Sustainable diets and biodiversity: directions and solutions for policy[END_REF]. We identified a previous study comparing diets defined by protein sources in relation to sustainability [START_REF] Perraud | Dietary protein consumption profiles show contrasting impacts on environmental and health indicators[END_REF] but the economic dimension was not assessed. However, we believe that this aspect holds considerable importance regarding individual dietary choices, particularly given the established literature linking healthier and acceptable diets to increased expenses [START_REF] Darmon | Contribution of food prices and diet cost to socioeconomic disparities in diet quality and health: a systematic review and analysis[END_REF]. Our objective was to identify clusters in the population of French adults participating in the NutriNet-Santé cohort, according to the sources of protein intake, and then to characterize, in a multi-criteria approach, the level of sustainability of these clusters according to environmental, economic, nutritional and health aspects. We specifically conducted an economic analysis to investigate the expenditure structure of both food and protein intake across the identified clusters as this dimension of diets' sustainability is often omitted.

Methods and Data

Study population

The present study used observational data from the NutriNet-Santé study. The NutriNet-Santé study is an Internet-based cohort launched in May 2009 [START_REF] Hercberg | The Nutrinet-Santé Study: a web-based prospective study on the relationship between nutrition and health and determinants of dietary patterns and nutritional status[END_REF]. Its purpose is to study the determinants of diets, nutritional status, and physical activity as well as their associations with health. The participants, recruited on a voluntary basis, are adults living in France with an access to internet.

Participants have to complete annual or biannual questionnaires on socioeconomic status, lifestyle, anthropometry, dietary intake and physical activity. Regularly, specific questionnaires are proposed.

Gender, occupational status, income, place of residence, physical activity, and smoking habits are self-reported using validated questionnaires [START_REF] Vergnaud | Agreement between web-based and paper versions of a socio-demographic questionnaire in the NutriNet-Santé study[END_REF]. The NutriNet-Santé study is in line with the principles of the Helsinki Declaration [START_REF]General Assembly of the World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects[END_REF] and validated by both the INSERM Ethical Evaluation Committee (CEEI) (no. 0000388FWA00005831) and the National Committee for Information Technology and Freedom (CNIL) (nos. 908450 and 909216). The study is registered in ClinicalTrials.gov (NCT03335644).

Assessment of food consumption and protein intake in total and by food groups

Food consumption data were collected via an Organic Food Frequency Questionnaire (Org-FFQ) developed in 2014, including 264 organic and conventional food items [START_REF] Baudry | Contribution of Organic Food to the Diet in a Large Sample of French Adults (the NutriNet-Santé Cohort Study)[END_REF]. In the present study, a total of 23 food groups were defined based on their protein content as follows: meat (including red meat and pork), processed meat, poultry, seafood, eggs, milk, dairy (including all dairy products except for milk), fast food, sweetened and fatty foods (SFF), fat (including animal fat and margarine), dressing, potatoes, legumes, whole-grain products, cereals (including all cereals products), nuts, soya-based products (including also substitutes), vegetables, fruits, fruit juice, beverages (including non-alcoholic beverages), oil (including vegetable oils) and alcohol (including all alcoholic beverages).

Nutrient values were derived from the food composition table developed for the NutriNet-Santé study [START_REF] Nutrinet-Santé | Table de Composition Des Aliments ETUDE NUTRINET SANTE -Economica[END_REF]. Detailed information on the composition of the food groups is provided in the legend of Figure 1.

Environmental data

Environmental pressures, greenhouse gas (GHG) emissions (CO2-eq), Cumulative Energy Demand (MJ), Land use (m 2 ), were assessed using the DIALECTE tool [START_REF] Pointereau | DIALECTE, a comprehensive and quick tool to assess the agro-environmental performance of farms. Producing and reproducing farming systems New modes of organisation for sustainable food systems of tomorrow[END_REF]. Developed by Solagro, this French diagnostic tool aims to evaluate the environmental performance of French farms using a comprehensive approach. The Life Cycle Assessment method was used on 60 raw agricultural products. The scope of the analysis was limited to the agricultural production stage but organic and conventional products were distinguished. Details are provided in Supplemental Material 1. In addition, the pReCiPe score, a synthetic impact indicator including the three indicators above, has been calculated [START_REF] Goedkoop | ReCiPE 2008: A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level[END_REF]. To balance conflicting environmental indicators, the ReCiPe method considers both midpoint and endpoint measures. Developed in the Netherlands, this LCA method aligns the indicators to provide a comprehensive view [START_REF] Goedkoop | ReCiPE 2008: A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level[END_REF]. It focuses on 18 indicators, three of which are oriented towards final impacts, including resource availability, human health and ecosystem diversity. In practice, some authors have found that the environmental impact of food products and diets can be assessed by measuring greenhouse gas emissions, primary energy consumption, and land occupation. These factors make up about 90% of the total environmental dimension of the ReCiPe model. To calculate the environmental impact of a food product or diet, one can use the partial ReCiPe score (pReCiPe), with normalization and weighting factors [START_REF] Kramer | Decreasing the overall environmental impact of the Dutch diet: how to find healthy and sustainable diets with limited changes[END_REF].

The pReCiPe score is computed as follows:

with GHGe, in kg of CO 2 eq/d, CED, in MJ/d and LO, in m 2 /d. The highest the pReCiPe, the highest the environmental impact.

Nutritional quality data

Three dietary indexes were computed. The PANDiet (Diet Quality Index based on the Probability of Adequate Nutrient Intake) is a nutritional adequacy score based on the nutritional references values [START_REF] De Gavelle | The Initial Dietary Pattern Should Be Considered when Changing Protein Food Portion Sizes to Increase Nutrient Adequacy in French Adults[END_REF][START_REF] Verger | Evaluation of a Diet Quality Index Based on the Probability of Adequate Nutrient Intake (PANDiet) Using National French and US Dietary Surveys[END_REF]. The PNNS-GS2 (Program National Nutrition Health Guidelines Score) measures the adherence of individuals to the French dietary guidelines established by the High Council of Public Health in 2017 [START_REF] Kesse-Guyot | Association between adherence to the French dietary guidelines and the risk of type 2 diabetes[END_REF]. The cDQI (Comprehensive Diet Quality Index) aims to assess the quality of plant and animal foods consumed [START_REF] Keaver | Plant-and animal-based diet quality and mortality among US adults: a cohort study[END_REF]. Further details are provided in Supplemental Material 2.

Health risk data

Health risk was assessed using a "Health Risk Score" (HRS) of the diet, computed using the distance to the Theoretical Minimum-Risk Exposure Level (TMREL), provided in the GBD study in 2019 [START_REF] Murray | Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[END_REF]. It reflects the overall risk of death associated with the individual dietary pattern, resulted from a suboptimal consumption of each food group. The computation of the HRS, ranging from 0 to 1, is provided in the Supplemental Material 3.

Economic data

The economic data used were participants' monthly income, and their estimated food expenditures for their whole diet and each food group.

Participants' income was collected as part of the socio-economic status questionnaire, where each participant provided the income class corresponding to his/her monthly income. Income per consumption unit (C.U) were estimated using household composition and age of family members according to the INSEE procedure (36). In the NutriNet-Santé study, the monetary cost of the diet (€/d) was calculated for each participant using prices (€/g) from several databases. Further details are provided in Supplemental Material 4.

Statistical Analysis

Among the participants in the cohort NutriNet-Santé, 29, 210 individuals were selected for this study, with Org-FFQ data, no missing data for sociodemographic aspects (except for monthly income which is a non-mandatory question) and with available information on place of purchase. Those considered as under-or over-reporters for energy intake were excluded as previously published [START_REF] Baudry | Contribution of Organic Food to the Diet in a Large Sample of French Adults (the NutriNet-Santé Cohort Study)[END_REF]. A flowchart is provided in Supplemental Figure 1.

Construction of the protein-source-typology and description of clusters

The contribution (in %) to total protein intake of the 23 food groups was calculated for each individual, to focus on the sources independently of the total intake. The typology aiming to identify groups of individuals with similar protein sources was built using a two-step procedure. First, a Principal Component Analysis [START_REF] O'rourke | A step-by-step approach to using SAS for univariate & multivariate statistics[END_REF] was applied on the 23 protein contributing food groups (the list is available on Figure 1). This multivariate data analysis method allowed to reduce the initial range of information by maximizing the variance. Nine dimensions were retained according to Kaiser criterion (eigenvalues >1). Then, on the basis of the retained dimensions, an Ascending Hierarchical Classification (AHC) was performed with data preprocessing using the K-means algorithm reiterated 10 100 times. As this study used a large database, the complementary use of the k-means and AHC methods allowed to stabilize the solution. Further details are provided in Supplemental Material 5.

Description and comparison of clusters

The clusters were named according to the food groups contributing the most to the protein intake of each cluster compared to the whole sample. First, means (SD) of protein contribution of each food group were computed (%/day) for the whole sample. Then, as cluster potentially exhibits a different energy intake than the whole sample, energy-adjusted means of protein contributions of the 23 food groups (SEM) were calculated for each cluster, using ANCOVA models. The identified clusters were described according to the socio-demographic characteristics reporting mean (SD) or % for continuous and categorical variables respectively. Means comparison across clusters was performed using Pearson's Chi-square test for categorical variables and ANOVA test for continuous variables. For food groups consumption, mean (SD) were presented for the whole sample, and for each cluster, energy-adjusted mean of food group intakes (g/day) and standard error of the mean (SEM) were calculated, using ANCOVA models.

Percentages of total energy intake were calculated for macronutrients. For vitamins, minerals and fiber, each nutrient energy-adjusted intake was calculated based on the "residual method" [START_REF] Willett | TOTAL ENERGY INTAKE: IMPLICATIONS FOR EPIDEMIOLOGIC ANALYSES[END_REF].

Prevalence of adequate protein intake is computed as defined in the PANDiet score [START_REF] Verger | Evaluation of a Diet Quality Index Based on the Probability of Adequate Nutrient Intake (PANDiet) Using National French and US Dietary Surveys[END_REF].

To allow comparison of clusters to the whole sample in relative values for all indicators, standardized means were computed for the whole sample, corresponding to the mean that the whole sample would have if its energy intake was that of the cluster ( . Relative values as regards energyadjusted indicator, were then calculated with the following formula:

Where i denotes clusters.

Multicriteria analysis

For each sustainability indicator considered, we calculated the mean (SD), and for each cluster, energy-adjusted means (SEM) were calculated via ANCOVA models. Comparison between clusters was based on relative values computed as defined above. A comparison of means across clusters was performed using ANCOVA models.

Economic analysis

The objective was to analyze both food and protein expenditure structure across clusters. The economic analysis included 27,244 of the 29,210 participants, for whom there were no missing income data (since the question was optional). The monthly income variable, modelled, as category was transformed into a numeric variable by considering the class center of the daily income category for each individual as previously done [START_REF] Seconda | Assessment of the Sustainability of the Mediterranean Diet Combined with Organic Food Consumption: An Individual Behaviour Approach[END_REF] and converted as euros per day.

The expenditure structure analysis across clusters was conducted using a budget coefficient approach [START_REF] Ferrant | Structure des budgets alimentaires dans l'enquête Budget de famille 2011[END_REF]. This approach makes comparable the share of food expenditure between individuals with different incomes and different diets [START_REF] Ferrant | Structure des budgets alimentaires dans l'enquête Budget de famille 2011[END_REF].

To do so, we first computed for each participant, the budget coefficients of both the overall diet and the food groups, using the following formulas:

Where i denotes individuals and j denotes food groups.

Insofar as we assume that the production mode (organic/conventional) affects food expenditure, the analysis was detailed by distinguishing expenditures allocated to organic products from those allocated to conventional products. To do so, budget coefficients of organic and conventional foods, for the overall diet and for each food group were computed for each individual. For the overall diet, budget coefficients of the overall diet by production mode were computed with respect to the overall diet budget. The budget coefficients of the food groups by production mode were calculated in relation to the overall diet budget allocated to foods from the corresponding production mode.

We defined the protein expenditure as the share of the food group expenditure allocated to the daily protein intake. It was calculated, for each food group and for each participant, using the following formula:

Where i denotes individuals and j denotes food groups.

Then, the "Total protein expenditure" was calculated for each participant by summing the protein expenditures for all food groups.

The budget coefficients of protein intake were then computed, using the following formulas:

Where i denotes individuals and j denotes food groups.

Afterwards, non-adjusted means (SD) were computed for all the calculated budget coefficients for the whole sample, and for each cluster, means and standard error of the mean (SEM) adjusted for energy intake were estimated using ANCOVA models. Comparison between clusters was based on relative percentage values computed using standardized means as defined above. Comparison of means across clusters was performed using the ANCOVA test.

Data management and statistical analyses were performed using RStudio software (RStudio, Version

Results

The study population was predominantly female (75%), with a mean age of 54 (SD=14) years (Supplemental Table 1).

Description of the protein-source typology

Based on food group contribution to protein intake, we identified 5 clusters (Figure 1

Multi-criteria analysis of clusters

Results of the multicriteria analysis of clusters are presented in Table 2 and figure 2.

Nutritional quality

Table 2 shows that the healthy-plant-based had the highest PNNS-GS2 score (+144% compared to the whole sample), reflecting a better adherence to the PNNS guidelines and the highest PANDiet score (+12% compared to the whole sample) based on nutritional reference values, followed by the healthy-fish-based cluster. This latter cluster, though, showed the highest cDQI score (+15% compared to the whole sample), indicating the highest quality of both animal and plant foods consumed. Inversely, the meat-based cluster had the lowest nutritional scores for all computed indicators (-61%, -5% and -8% respectively for the PNNS-GS2, the PANDiet, and the cDQI indicators).

Health risk score

The health risk analysis (Table 2) showed that the structure of the healthy-plant-based cluster was the most beneficial compared to the other clusters (0.25 for the HRS score), followed by the healthyfish-based cluster (0.53 for the HRS score). Yet, the health risk score associated with the meat-based cluster was the highest among the five clusters (0.95 for the HRS score). Furthermore, the analysis of the contribution of food groups to the health risk score shows that for all clusters, a low consumption of wholegrain products and legumes and a high consumption of red meat contribute the most to the value of the HRS.

Environmental impacts

Table 2 shows that for all observed indicators, the healthy-plant-based cluster had the least impact on the environment (pReCIPe: -62% compared to the whole sample), while the meat-based cluster showed the highest impact (pReCIPe: + 33% compared to the whole sample) among the five identified diets. The environmental impacts of healthy-fish-based and fast-food-based clusters were lower than the whole sample means for all indicators. The milk-based diet showed similar environmental impacts to the whole sample means.

Organic food consumption across clusters

Table 2 shows that participants of the healthy-plant-based cluster had the highest share of organic consumption (+127% compared to the whole sample), followed by the participants of the healthyfish-based cluster showing a 40% higher share than the average of the population. However, the participants of the meat-based and milk-based clusters showed the lowest share of organic food (-30% compared to the whole sample) among the five identified clusters.

Economic analysis

The healthy-plant-based cluster had the largest share of income allocated to food (+46% compared to the whole sample) (Figure 3). On the opposite, consumers of milk-based and fast-food-based clusters spent the smallest share of their income for food (-10% and -6% compared to the whole sample), while the food budget coefficient of the meat-based cluster is similar to the mean of the population studied. Comparison of clusters by food groups revealed that budget coefficients followed the same trend as food intake across the clusters. Furthermore, analysis of food expenditure structure by cluster showed that vegetables are the food group for which all clusters spent the largest share of their overall diet expenditure, with the exception of the meat-based cluster, who spent the most for meat.

After vegetables, the milk-based cluster spent more for meat and non-alcoholic beverages, the fastfood-based cluster for dairy products, the healthy-fish-based cluster for fruit and seafood, and the healthy-plant-based cluster for fruits and soy-based foods. The detailed values are presented in

Supplemental table 3.

Analysis of the overall diet budget coefficients according to the production mode (Supplemental Table 4) showed that 70% of the food expenditure of the healthy-plant-based cluster was allocated to organic products, which contributes to the higher diet expenditure. Conversely, the meat-based cluster had the lowest share of the budget allocated to organic food (20%).

In terms of budget coefficients of protein intake (Figure 3), meat-based cluster participants were those spending the most of their overall diet expenditure for their protein intake (+13% compared to the whole sample). The healthy-plant-based cluster exhibited the lowest protein expenditure (-41% compared to the whole sample), followed by the fast-foodbased and milk-based clusters (-5% and -3% respectively, compared to the whole sample). Analysis of protein expenditure structure by cluster showed that meat-based, milk-based and fast-food-based clusters spent the largest share of their overall diet expenditure on meat proteins. The healthy-fishbased cluster allocated the largest share of their overall diet expenditure to seafood proteins, while the healthy-plant-based cluster spent more on nuts proteins.

The detailed values are presented in Supplemental Table 5.

Discussion

We extracted five clusters based according to food-group contribution to protein intake (all analyses are adjusted for energy intake). The healthy-plant-based cluster (3%) and the healthy-fish-based cluster (25%) were the most sustainable for the environmental, nutritional and health dimensions.

Conversely, the meat-based cluster (26%) exhibited the highest environmental pressures, the lowest nutritional scores and a high health risk. Furthermore, based on an economic analysis, we observed that although the healthy-plant-based cluster had the highest food budget coefficient, its expenditure for protein intake was the lowest. Conversely, expenditure on protein intake was high 16 for the meat-based cluster eaters. This study therefore argues that the protein sources of a diet are a good factor in the sustainability of diets.

Nutritional quality and health risk across diets

The healthy-plant-based cluster exhibited the highest PANDiet score, and a better adherence to the PNNS guidelines. In fact, while the choice of protein sources in individuals' diets often raises the question of protein adequacy (including protein and amino acid intakes) [START_REF] Mariotti | Dietary Protein and Amino Acids in Vegetarian Diets-A Review[END_REF], previous studies documented that balanced diets in accordance with public health goals and with low meat intake, provide an adequate intake for most nutrients [START_REF] Springmann | Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail[END_REF]. Indeed, the amounts and quality of plant-based proteins are frequently underestimated or misunderstood [START_REF] Katz | Perspective: The Public Health Case for Modernizing the Definition of Protein Quality[END_REF]. Moreover, maintaining protein adequacy in spite of the decrease in the consumed quantity of animal protein could be explained by the great excess above the needs in terms of protein intake in Western countries [START_REF] Mariotti | Dietary Protein and Amino Acids in Vegetarian Diets-A Review[END_REF]. But this issue is still being debated and for example, a Canadian cohort study stated that the transition to plantbased protein might be nutritionally challenging since animal protein contributes overwhelmingly to total protein intake, particularly for the elderly [START_REF] Auclair | Protein consumption in Canadian habitual diets: usual intake, inadequacy, and the contribution of animal-and plant-based foods to nutrient intakes[END_REF].

The health risk analysis showed that the dietary structure of the healthy-plant-based cluster eaters is the most protective in terms of health benefits, as reflected using the HRS, while the health risk associated with the meat-based cluster is much higher. Similar results were found using another health risk estimator, the HiDiet score, aiming to evaluate the effect of diet on long-term morbidity and mortality [START_REF] Perraud | Dietary protein consumption profiles show contrasting impacts on environmental and health indicators[END_REF]. Thus, plant-based protein consumption was proven to be associated with nutrient intakes and dietary profiles that are supportive of cardiometabolic health [START_REF] Mariotti | Protéines animales et végétales : quels effets sur la santé cardio-métabolique ?[END_REF]. Moreover, the reduction in premature mortality associated with the adoption of balanced low-meat diets, was estimated at 19% for the flexitarian diet and 22% for the vegan diet [START_REF] Springmann | Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail[END_REF]. Furthermore, the association between animal sourced food consumption and the risk of chronic diseases has been established by literature [START_REF] Giromini | Benefits and Risks Associated with Meat Consumption during Key Life Processes and in Relation to the Risk of Chronic Diseases[END_REF][START_REF] Daniel | Trends in meat consumption in the USA[END_REF][START_REF] Salter | The effects of meat consumption on global health: -EN--FR-Les effets de la consommation de viande sur la santé dans le monde -ES-Efectos del consumo de carne en la salud mundial[END_REF]. For instance, it has been demonstrated that a 5% substitution of animal protein with plant protein would reduce the risk of incidence of type 2 diabetes by 23% [START_REF] Malik | Dietary Protein Intake and Risk of Type 2 Diabetes in US Men and Women[END_REF].

Indeed, red and processed meat were proved to be risk factors for type 2 diabetes, while soy and dairy products provide protection [START_REF] Tian | Dietary Protein Consumption and the Risk of Type 2 Diabetes: A Systematic Review and Meta-Analysis of Cohort Studies[END_REF].

Environmental impacts across the diets

As the literature on the environmental impacts of protein consumption has not been sufficiently developed, we situated our results in relation to existing studies comparing predominantly animalbased diets to more plant-based diets. In our study, the healthy plant-based cluster had less environmental impact than the meat-based cluster, which exhibited the highest environmental pressures. This is in accordance with the available literature regarding the lowest impact of plantbased dietary patterns [START_REF] Clark | The Diet, Health, and Environment Trilemma[END_REF][START_REF] Perraud | Dietary protein consumption profiles show contrasting impacts on environmental and health indicators[END_REF][START_REF] Weindl | Sustainable food protein supply reconciling human and ecosystem health: A Leibniz Position[END_REF][START_REF] Auestad | What Current Literature Tells Us about Sustainable Diets: Emerging Research Linking Dietary Patterns, Environmental Sustainability, and Economics[END_REF]. Moreover, it has been demonstrated, in a comprehensive review [START_REF] Aleksandrowicz | The Impacts of Dietary Change on Greenhouse Gas Emissions, Land Use, Water Use, and Health: A Systematic Review[END_REF], that the decrease of the environmental footprint is generally proportional to the extent to which animal-based food consumption is restricted [START_REF] Springmann | Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail[END_REF]. Furthermore, we reported that belonging to the healthy-plant-based cluster was concomitant with a higher consumption of organic products, as shown before [START_REF] Baudry | Typology of eaters based on conventional and organic food consumption: results from the NutriNet-Santé cohort study[END_REF], which could contribute to the lower environmental impacts, especially for energy demand. A previous study also based on the NutriNet-Santé cohort [START_REF] Lacour | Environmental Impacts of Plant-Based Diets: How Does Organic Food Consumption Contribute to Environmental Sustainability?[END_REF], showed that organic food consumption could partly explain the inverse link between plant-based diet and some environmental impacts (GHG emissions and energy demand), specifying that this link is only established for individuals with diets rich in plant-based foods.

Economic analysis

We showed that, at constant energy intake, the overall diet expenditure of the healthy-plant-based cluster was the highest among the five clusters, followed by the healthy-fish-based cluster, and to a lesser extent the meat-based cluster. In that regard, a previous study based on a representative sample of 1,719 French adults (INCA2), showed that meeting with nutritional reference values systematically increased the cost of food [START_REF] Maillot | Dietary changes needed to reach nutritional adequacy without increasing diet cost according to income: An analysis among French adults[END_REF], which is consistent with our previous findings regarding the superiority of the nutritional quality of healthy-plant-based and healthy-fish-based clusters. Nevertheless, improving diet quality by optimization on nutritional constraints without increasing food expenditure, regardless of the initially observed cost, has been shown to be possible [START_REF] Maillot | Dietary changes needed to reach nutritional adequacy without increasing diet cost according to income: An analysis among French adults[END_REF]. However, for food budgets below 3.85€/day, significant diet changes were needed.

Furthermore, the high food budget coefficients associated with the healthy-plant-based and healthyfish-based clusters might also be explained by the much higher share of organic food consumption of these two clusters. Similarly, another study also based on the NutriNet-Santé cohort [START_REF] Baudry | Improvement of diet sustainability with increased level of organic food in the diet: findings from the BioNutriNet cohort[END_REF] demonstrated that high consumers of organic food displayed a high consumption of plant-based foods and a healthier diet. Thus, the monetary cost of their diet was higher (+26%) compared to that of low consumers due to the higher prices of organic products as shown by a decomposition model of the effects. This dual choice seems a best option by resulting in a markedly reduced exposure to pesticides from diet [START_REF] Baudry | Improvement of diet sustainability with increased level of organic food in the diet: findings from the BioNutriNet cohort[END_REF].

Interestingly, the protein budget coefficient of the meat-based cluster was higher than that of other clusters, which could be explained by both the higher prices of protein foods characteristic of the diet adopted by this cluster's participants (meat, poultry and processed meat) and their higher protein intake (+6% compared to the whole sample). Inversely, both the lower protein intake of the healthyplant-based cluster eaters (-27% compared to the whole sample) and the lower prices of their diet's proteins sources (soya-based foods, legumes and nuts), might explain the lower protein budget coefficient. Indeed, it has been previously demonstrated in a meta-analysis assessing the nutritional status of meat-based diets compared to plant-based diets, that the protein intake of meat eaters was higher than that of people adopting a plant-based diet, although it was still within the recommended levels [START_REF] Neufingerl | Nutrient Intake and Status in Adults Consuming Plant-Based Diets Compared to Meat-Eaters: A Systematic Review[END_REF].

Multicriteria analysis of diets' sustainability according to protein intake

The scarcity of studies on sustainability in its entirety is inherent to its multidimensionality, which makes it complex to conduct research in this sense. Indeed, while a multi-criteria analysis of protein profiles [START_REF] Perraud | Dietary protein consumption profiles show contrasting impacts on environmental and health indicators[END_REF] close to our study only addressed 2 of the 4 dimensions of diets sustainability (environment, nutrition and health) according to the FAO definition [START_REF] Burlingame | Sustainable diets and biodiversity: directions and solutions for policy[END_REF], we also focused on the economic aspect since this dimension has rarely been accounted for in multi-criteria studies on sustainability. The economic analysis we carried out aimed to provide an initial overview of the aspect of economic affordability. However, cultural acceptability, which is a significant obstacle to achieving changes in dietary behaviour, haven't been sufficiently addressed neither in our work nor in that of Perraud et al. [START_REF] Perraud | Dietary protein consumption profiles show contrasting impacts on environmental and health indicators[END_REF]. On the other hand, the multiplicity of aspects making up these dimensions prevent from covering them entirely. Indeed, although we assessed the environmental pressures and impacts on 3 aspects, the above-mentioned study [START_REF] Perraud | Dietary protein consumption profiles show contrasting impacts on environmental and health indicators[END_REF] evaluated other impacts by mobilizing more indicators, but without distinguishing organic and conventional foods as we did. The results of this study [START_REF] Perraud | Dietary protein consumption profiles show contrasting impacts on environmental and health indicators[END_REF] remain consistent with ours, showing that protein profiles associated with low meat consumption tend to be more sustainable on the two dimensions analysed, namely environment, nutrition and health. Moreover, due to the difference in dietary behaviours of the different populations considered in these two studies, discrepancies with our results are identified, notably in the identification of clusters (distinction between ruminant and monogastric meat in the protein profiles [START_REF] Perraud | Dietary protein consumption profiles show contrasting impacts on environmental and health indicators[END_REF], identification of more plant-based protein profiles in our work).

Action levers for greater sustainability

Insofar as we concluded that the nature of protein intake is a discriminating factor in the sustainability of diets, it is relevant to consider this factor for the development of action plans for changing dietary patterns towards greater sustainability. As previously mentioned, meat consumption is associated with socio-cultural values, making transitions to plant-based diets more complex to manage in practice [START_REF] Niva | Barriers to Increasing Plant Protein Consumption in Western Populations[END_REF][START_REF] Graça | Reducing meat consumption and following plant-based diets: Current evidence and future directions to inform integrated transitions[END_REF]. To this end, some suggestions have been developed in the literature. First, at the production stage, some environmental impacts could be reduced by integrating crops and livestock [START_REF] Smith | How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals?[END_REF] and promoting grazing systems. This could improve efficiency of animal feeding and nutrient cycling, besides crop rotation through temporary grasslands. Also, nonfood biomass would serve as feed for animals, which provide organic fertilizer [START_REF] Smith | How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals?[END_REF]. Then, at the consumption stage, an intrinsic change in diet at the food group level could be operated. As we demonstrated that beef, pork and poultry consumption are correlated for the meat-based cluster, replacing ruminant meat by poultry [START_REF] Poore | Reducing food's environmental impacts through producers and consumers[END_REF][START_REF] Clark | Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice[END_REF] could contribute to reduce some environmental pressures, especially GHG emissions [START_REF] Poore | Reducing food's environmental impacts through producers and consumers[END_REF] and to lower the health risk compared to red meat [START_REF] Poore | Reducing food's environmental impacts through producers and consumers[END_REF].

However, this raises the question of the individualized nature of dietary pattern, which conditions the feasibility of the effective transition from potential pathways to action plans for sustainable dietary changes. Acceptability is thus enhanced by considering personalized and targeted recommendations.

Strengths and limitation

First of all, it is important to note that the participants in the NutriNet-Santé cohort are volunteers, who may have a greater interest in nutrition and health compared to the general population. As a result, this sample exhibits certain characteristics such as a higher proportion of women, older individuals, those with higher education and healthier dietary habits [START_REF] Kesse-Guyot | Lessons Learned From Methodological Validation Research in E-Epidemiology[END_REF]. However, the large sample size allows access to a wide variety of dietary profiles and probably a higher representation of diets rich in plant-based foods. However, our sample is not representative of the French population and these results cannot be directly generalized. Secondly, the environmental data mobilized are limited to the production stage. However, this stage represents the major part of the environmental impacts of the food system. And, as mentioned above, the socio-cultural dimension associated with the choice of protein sources in diets was not considered in this study. However, our study is the first to provide a multi-criteria analysis of sustainability according to protein profiles, while including an economic analysis. It covers a large French population, with various dietary patterns, including plantbased diets. Moreover, our study considers production modes (organic, conventional), and the data on food expenditures are quite accurate by considering consumers' places of purchase.

Conclusion

In conclusion, the nature of protein intake is a good discriminating factor of diets sustainability. The healthy-plant-based and healthy-fish-based clusters were the most sustainable, allowing to conciliate the trade-off between individual and environmental health. Conversely, the meat-based cluster exhibited the highest environmental pressures, the lowest nutritional scores and a higher health risk score. Additionally, although the healthy-plant-based cluster had the highest food budget coefficient, their expenditure on protein intake was the lowest. However, this same expenditure was high for the to submit the paper. JBr was supported by a Doctoral Funding from National Research Institute for Agriculture, Food and the Environment (INRAE) and ADEME -Galilée Doctoral School.

29 1 Values are energy-adjusted means of budget coefficients computed using ANCOVA model.

Panel A refers to food budget coefficients across clusters according to production mode (organic/conventional). Panel B refers to protein budget coefficients across clusters. Abbreviations: SFF, Sweetened and Fatty Foods. 1 Values are mean (SD) contribution (%) for the whole sample, and energy-adjusted means of protein contributions (SEM) across clusters (ANCOVA model).

A B

2 P values were calculated using ANCOVA. 3 Other food groups with minor contribution to protein intake are not represented 33 33

1. 4 .

 4 1717, © 2009-2021 RStudio, PBC).

Figure 1 : 1 1

 11 Figure 1: Protein contributions per food group across clusters 1

Figure 2 : 31 Figure 3 :

 2313 Figure 2: Sustainability indicators across clusters 1

and Table 1):

  The characteristics of the participants in the 5 clusters are shown in Supplemental

	Milk-based cluster (17% of the population), characterized by high contributions of milk (+336%
	higher than the whole sample) and beverages (coffee, tea (including with milk), all sweetened
	beverages except fruit juice 100% ; Meat-based cluster (26% of the population) with high

contributions of red meat to proteins (+54%) , poultry and processed meat; Fast-food-based cluster (29% of the population) showing a higher protein intake derived from fast food to proteins (+37%) , refined or wholegrain cereals, fatty and sweet products; Healthy-fish-based cluster (25% of the population), characterized by high protein intake from seafood (+49%), wholegrain cereal products, fruit and vegetables; and finally the Healthy-plant-based cluster (3% of the population) for which the most of protein intake is derived from the consumption of soy (+909%), legumes, nuts, fruit and vegetables. The detailed values are presented in Table

1

, and figure 1.

Table 1 .

 1 Food group consumptions across clusters are presented in Supplemental figure 2, and computed nutrient intakes across clusters are presented in Supplemental

Table 2 .

 2 Total Protein intakes range from

67g/d in the healthy-plant-based cluster to 99g/d in the meat-based cluster, while plant-based protein intakes vary from 25g/d in the meat-based cluster to 53g/d in the healthy-plant-based cluster.

Table 1 : Protein contributions of food groups across clusters 1,2,3

 1

	Food groups	Whole sample	Milk-based	Meat-based Fast-food-based Healthy-fish-based	Healthy-plant-based	p
		n = 29,210	n = 4,966 (17%)	n = 7,569 (26%) n = 8,469 (29%)	n = 7,189 (25%)	n = 1,017 (3%)
	Legumes	1.41 (2.49)	0.86 (0.03)	0.95 (0.02)	1.06 (0.02)	1.71 (0.02)	8.49 (0.07)	< 0.0001
	Soya-based foods	1.62 (4.72)	0.43 (0.05)	0.42 (0.04)	0.83 (0.04)	2.56 (0.04)	16.33 (0.12)	< 0.0001
	Cereals	8.9 (6.38)	8.83 (0.09)	9.39 (0.07)	10.84 (0.07)	5.91 (0.07)	10.68 (0.19)	< 0.0001
	Nuts	2.39 (4.37)	1.23 (0.05)	1.00 (0.04)	1.60 (0.04)	4.37 (0.04)	11.00 (0.12)	< 0.0001
	Wholegrain products	5.59 (7.06)	4.26 (0.09)	2.74 (0.07)	4.91 (0.07)	9.73 (0.08)	9.93 (0.20)	< 0.0001
	Fruits	2.41 (2.69)	1.83 (0.04)	1.98 (0.03)	2.07 (0.03)	3.35 (0.03)	4.81 (0.08)	< 0.0001
	Fruit juice	0.61 (0.91)	0.59 (0.01)	0.44 (0.01)	0.89 (0.01)	0.42 (0.01)	1.07 (0.03)	< 0.0001
	Vegetables	5.21 (3.63)	4.00 (0.05)	4.45 (0.04)	4.61 (0.04)	6.69 (0.04)	11.55 (0.10)	< 0.0001
	Potatoes	0.46 (0.49)	0.39 (0.01)	0.59 (0.01)	0.42 (0.01)	0.41 (0.01)	0.60 (0.01)	< 0.0001
	Alcohol	0.22 (0.53)	0.16 (0.01)	0.29 (0.01)	0.22 (0.01)	0.21 (0.01)	0.20 (0.01)	< 0.0001
	Dressing	0.23 (0.28)	0.18 (0.00)	0.21 (0.00)	0.31 (0.00)	0.21 (0.00)	0.31 (0.01)	< 0.0001
	Beverages	3.19 (4.24)	8.46 (0.05)	1.99 (0.04)	2.38 (0.04)	1.95 (0.04)	2.13 (0.11)	< 0.0001
	Oil	0 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	< 0.0001
	Meat	15.26 (10.16)	14.86 (0.12)	23.94 (0.10)	12.36 (0.10)	11.8 (0.10)	1.35 (0.26)	< 0.0001
	Processed meat	4.55 (3.88)	4.22 (0.05)	5.99 (0.04)	4.84 (0.04)	3.50 (0.04)	0.55 (0.12)	< 0.0001
	Eggs	1.6 (1.66)	1.34 (0.02)	1.36 (0.02)	1.47 (0.02)	2.25 (0.02)	1.23 (0.05)	< 0.0001
	Fast food	3.64 (3.24)	3.22 (0.04)	3.31 (0.04)	4.94 (0.03)	2.73 (0.04)	3.81 (0.10)	< 0.0001
	Fat	0.08 (0.13)	0.07 (0.00)	0.07 (0.00)	0.12 (0.00)	0.08 (0.00)	0.12 (0.00)	< 0.0001
	Poultry	6.03 (5.14)	5.38 (0.07)	8.76 (0.05)	5.03 (0.05)	5.52 (0.06)	0.83 (0.15)	< 0.0001
	Dairy	17.28 (10.06)	15.14 (0.13)	15.05 (0.11)	23.16 (0.10)	15.73 (0.11)	6.29 (0.29)	< 0.0001
	Seafood	10.55 (7.84)	8.51 (0.10)	9.90 (0.08)	9.09 (0.08)	15.42 (0.08)	3.14 (0.23)	< 0.0001
	Milk	2.19 (5.06)	9.68 (0.05)	0.57 (0.04)	0.87 (0.04)	0.57 (0.04)	0.31 (0.12)	< 0.0001
	SFF	4.24 (3.42)	4.20 (0.05)	3.81 (0.04)	5.52 (0.04)	3.21 (0.04)	4.53 (0.10)	< 0.0001

Table 2 : Sustainability indicators across clusters 1,2

 2 Adequation sub-score of PANDiet; MS, Moderation sub-score of PANDiet; HRS: Health Risk Score; RR: relative risk1 Values are mean (SD) for the whole sample, and adjusted means of indicators on total energy intake (SEM) across clusters (ANCOVA model).

	Indicators	Whole sample	Milk-based	Meat-based	Fast-food-based	Healthy-fish-based Healthy-plant-based	p
	Nutritional quality
	PANDiet	64.97 (7.86)	65.17 (0.09)	63.01 (0.07)	62.65 (0.07)	68.53 (0.07)	72.68 (0.21)	< 0.0001
	AS	78.86 (12.74)	78.43 (0.13)	78.11 (0.1)	75.73 (0.1)	83.79 (0.1)	77.64 (0.29)	< 0.0001
	MS	51.08 (18.49)	51.9 (0.15)	47.9 (0.12)	49.57 (0.12)	53.27 (0.13)	67.72 (0.35)	< 0.0001
	cDQI	51.48 (9.18)	49.57 (0.11)	48.56 (0.09)	49.33 (0.08)	58.21 (0.09)	52.97 (0.25)	< 0.0001
	aDQI	15.86 (3.9)	16.84 (0.05)	14.59 (0.04)	15.73 (0.04)	17.05 (0.04)	13.16 (0.11)	< 0.0001
	pDQI	35.62 (7.47)	32.72 (0.09)	33.96 (0.07)	33.6 (0.07)	41.16 (0.07)	39.81 (0.2)	< 0.0001
	PNNS-GS2	2.51 (3.56)	1.96 (0.03)	1.01 (0.03)	2.34 (0.02)	4.15 (0.03)	6.13 (0.08)	< 0.0001
	Environmental impacts
	GHG emissions (kg CO2 eq/d)	4.05 (2.48)	4.13 (0.02)	5.47 (0.02)	3.61 (0.01)	3.4 (0.02)	1.31 (0.05)	< 0.0001
	Cumulative Energy Demand (MJ/d)	17.62 (7.56)	17.08 (0.06)	21.14 (0.05)	16.18 (0.04)	17.09 (0.05)	9.85 (0.14)	< 0.0001
	Land Use (m²/d)	10.6 (6.75)	10.57 (0.07)	14.24 (0.05)	9.33 (0.05)	9.15 (0.05)	4.52 (0.15)	< 0.0001
	pRecipe	0.28 (0.16)	0.28 (0.00)	0.39 (0.00)	0.26 (0.00)	0.23 (0.00)	0.11 (0.00)	< 0.0001
	Organic food share	0.29 (0.27)	0.21 (0.00)	0.21 (0.00)	0.26 (0.00)	0.41 (0.00)	0.67 (0.01)	< 0.0001
	Health risk
	RR (%) Whole grain	19.51 (12.35)	20.59 (0.16)	17.45 (0.13)	21.17 (0.12)	17.1 (0.14)	32.9 (0.37)	< 0.0001
	RR (%) Fruits	6.98 (7.59)	7.8 (0.10)	5.17 (0.08)	8.26 (0.08)	5.8 (0.08)	14.07 (0.23)	< 0.0001
	RR (%) Vegetables	2.19 (2.58)	2.87 (0.03)	1.54 (0.02)	2.77 (0.02)	1.69 (0.02)	2.4 (0.07)	< 0.0001
	RR (%) Nuts/Seeds	3.82 (3.17)	3.84 (0.04)	2.95 (0.03)	3.88 (0.03)	4.1 (0.03)	7.56 (0.09)	< 0.0001
	RR (%) Legumes	10.24 (6.47)	10.29 (0.08)	7.63 (0.07)	10.63 (0.06)	12.21 (0.07)	12.14 (0.19)	< 0.0001
	RR (%) Milk	6.65 (5.86)	3.03 (0.06)	4.41 (0.05)	6.95 (0.05)	9.15 (0.05)	20.65 (0.14)	< 0.0001
	RR (%) Processed meat	16.87 (10.22)	16.58 (0.14)	18.26 (0.11)	17.73 (0.1)	16.47 (0.11)	3.75 (0.3)	< 0.0001
	RR (%) Red meat	32.97 (16.75)	34.08 (0.21)	41.97 (0.17)	27.67 (0.16)	32.89 (0.17)	5.19 (0.46)	< 0.0001
	RR (%) Sweetened beverages	0.73 (1.96)	0.86 (0.02)	0.58 (0.02)	0.88 (0.02)	0.53 (0.02)	1.29 (0.06)	< 0.0001
	HRS	0.69 (0.29)	0.7 (0.00)	0.95 (0.00)	0.65 (0.00)	0.53 (0.00)	0.25 (0.00)	< 0.0001
	Abbreviations: AS,

2

P values were calculated using ANCOVA.

Acknowledgements

We thank Cédric Agaësse, Alexandre De-Sa and Rebecca Lutchia (dietitians) (dietitian); Thi Hong Van Duong, Younes Esseddik (IT manager), Régis Gatibelza, Jagatjit Mohinder and Aladi Timera (computer scientists); Julien Allegre, Nathalie Arnault, Laurent Bourhis, Nicolas Dechamp and Fabien Szabo de Edelenyi, PhD (supervisor) (data-manager/statisticians) for their technical contribution to the NutriNet-Santé study, ); Merveille Kouam (health event validator), Maria Gomes (Nutrinaute support) and Nathalie Druesne-Pecollo, PhD (operational coordination). We thank all the volunteers of the NutriNet-Santé cohort. Nicolas Dechamp (data-manager/statisticians); Merveille Kouam (health event validator); Maria Gomes (participant support)

Programme National Nutrition Santé PNNS-GS2: Program National Nutrition Health Guidelines Score cDQI: Comprehensive Diet Quality Index AS: Adequation sub-score of PANDiet MS: Moderation sub-score of PANDiet TMREL: Theorical Minimum-

Funding

The NutriNet-Santé study is funded by French Ministry of Health and Social Affairs, Santé Publique France, Institut National de la Santé et de la Recherche Médicale, National Research Institute for Agriculture, Food and the Environment, Conservatoire National des Arts et Métiers, and Sorbonne Paris Nord University. The BioNutriNet project (PI EKG) was supported by the French National Research Agency (Agence Nationale de la Recherche) in the context of the 2013 Programme de Recherche Systèmes Alimentaires Durables (ANR-13-ALID-0001). The funders had no role in the study design, data collection, analysis, interpretation of data, preparation of the manuscript, and decision

Data Availability

Analytic code will be made available upon request pending. Researchers from public institutions can submit a collaboration request including information on the institution and a brief description of the project to collaboration@etude-nutrinet-sante.fr. All requests will be reviewed by the steering committee of the NutriNet-Santé study. If the collaboration is accepted, a data access agreement will be necessary and appropriate authorizations from the competent administrative authorities may be needed. In accordance with existing regulations, no personal data will be accessible

Alexandre De Sa (dietitian), Rebecca Lutchia (dietitian)

The authors' contributions are as follows:

EKG, BA, DL, PP and JB, led the BioNutriNet project on which the data used.

HT conducted the analysis supervised by EKG.

HT wrote the statistical script, conducted analyses and drafted the manuscript.

All authors critically helped in the interpretation of results, revised the manuscript and provided relevant intellectual input. They all read and approved the final manuscript.

EKG had primary responsibility for the final content, she is the guarantor.

Additional Information

No author declared conflict of interest.