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

Hafsa Toujgani, Joséphine Brunin, Elie Perraud, Benjamin Allès, Mathilde Touvier, Denis Lairon, François Mariotti, Philippe Pointereau, Julia Baudry, Emmanuelle Kesse-Guyot

► To cite this version:

Hafsa Toujgani, Joséphine Brunin, Elie Perraud, Benjamin Allès, Mathilde Touvier, et al.. The nature of protein intake as a discriminating factor of diet sustainability: a multi-criteria approach. Scientific Reports, 2023, 13 (1), pp.17850. 10.1038/s41598-023-44872-3. hal-04350853

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

Hafsa Toujgani*¹, Joséphine Brunin¹⁻², Elie Perraud³, Benjamin Allès¹, Mathilde Touvier¹,
Denis Lairon⁴, François Mariotti³, Philippe Pointereau⁵, Julia Baudry¹, Emmanuelle Kesse-
Guyot¹

## Affiliations

¹ Université Sorbonne Paris Nord and Université Paris Cité, Inserm, INRAE, CNAM, Center of Research in Epidemiology and StatisticS (CRESS), Nutritional Epidemiology Research Team (EREN), F-93017 Bobigny, France

² ADEME, (Agence de l'Environnement et de la Maîtrise de l'Energie), 49004 Angers, France

³ Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA, 91120, Palaiseau, France

⁴ Aix Marseille Université, Inserm, INRAE, C2VN, 13005 Marseille, France

⁵ Solagro, 75, Voie TOEC, CS 27608, F-31076 Toulouse Cedex 3, France

## Corresponding author

Hafsa Toujgani

Equipe de Recherche en Epidémiologie Nutritionnelle (EREN)

SMBH Université Sorbonne Paris Nord, 74 rue Marcel Cachin, 93017 Bobigny, France

h.toujgani@eren.smbh.univ-paris13.fr

**Running title:** Sustainability of protein profiles

**Number of tables/ figures:** 2 tables / 3 figures

**Supplemental material:** 4 methods, 5 tables and 2 figures
**Abbreviations:**

IPCC: Intergovernmental Panel on Climate Change

GBD: Global Burden of Diseases

INSEE: National Institute of Statistics and Economic Studies

Org-FFQ: Organic Food Frequency Questionnaire

GHG: greenhouse gas

CED: Cumulative energy demand

pReCiPe: partial ReCiPe

PANDiet: Diet Quality Index based on the Probability of Adequate Nutrient Intake

PNNS: 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: Theoretical Minimum-Risk Exposure Level

HRS: Health Risk Score

SD: Standard Deviation

SEM: Standard Error of the Mean

AHC: Ascending Hierarchical Classification

PCA: Principal Component Analysis

SFF: sweetened and fatty foods
# 1 Abstract

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.

# 26 Keywords
27 Sustainable, dietary patterns, protein sources, environmental pressures, health risk, food expenditure, protein expenditure.
28

29 Highlights Results

30 Five clusters were extracted based on the contributions of protein sources: milk-based, meat-based, fast-food-based, healthy-fish-based, and healthy-plant-based.
31

32 Sustainability assessment of the identified diets: the healthy-plant-based and healthy-fish-based clusters were the most sustainable with the lowest environmental impacts, the highest nutritional quality and the best adherence to the French dietary guidelines, and the lowest health risk score. In contrast, the meat-based cluster displayed the highest environmental impacts and health risk score. The fast-food-based cluster was associated with the lowest nutritional scores.
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38 **Economic analysis:** we put a specific emphasis on the economic dimensions: the healthy-plant-based cluster showed the largest share of income allocated to food, followed by the healthy-fish-based cluster. In addition, the meat-based cluster had the largest share of food budget allocated to protein intake while the healthy-plant-based cluster had the lowest.
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43 **Introduction**

44 Some planetary boundaries being officially crossed (1–3), recent efforts to limit global warming to
45 1.5°C, as stipulated in the Paris Climate Agreement, remain insufficient according to the 2021
46 Intergovernmental Panel on Climate Change (IPCC) report (4). Moreover, The 2023 Sustainable
47 Development Goals Report not only confirms this assessment but also sounds the alarm, urgently
48 calling for a doubling of efforts to get these objectives back on track (5). Actually, current food
49 systems are responsible for one third of global GHG emissions (6), one third to 80% of which
50 originates from the production stage (6,7). Besides, as populations become more urbanized and
51 affluent, their dietary patterns are shifting towards calorie-rich diets with more animal-based and
52 protein-dense foods (8). These dietary patterns have been largely influenced by the “protein”
53 debate, which has been evolving for decades. It was in the 1930s that particular attention was paid
54 to this nutrient, with the widespread incidence of kwashiorkor, and its association with protein
55 deficiency (9). A special Protein Advisory Group was then created by the United Nations, whose
56 mission was to "fight to close the protein gap", while dietary guidelines encouraged the consumption
57 of protein-rich foods, namely meat and milk (10). Furthermore, the “protein” debate has been largely
58 influenced by animal production industry, given the great market opportunities offered by this
59 sector, whose trade balance reached 28 billion euros in 2018 in the EU (11). However, the debate has
60 gone off in another direction, and it is now beyond doubt that the reduction of meat consumption
61 would contribute to strongly reducing human health risks in Western countries according to the
62 Global Burden of Diseases (GBD) analysis (12). Moreover, animal food production is responsible for
63 56-58% of the emissions generated by food production while providing only 37% of the protein
64 supply (13). In that regard, the IPCC has strongly recommended to reduce meat consumption by two-
65 thirds (4), as red meat and processed meat production have been shown to have the highest impact
66 on all dimensions (GHG emissions, land use, water use, acidification and eutrophication) (13,14).
67 Note that these emissions are double those generated by plant-based foods (15). Although it has
68 been proven that there is no longer protein gap in Western countries, as protein intake exceeding
needs (16), 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 (17), 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 (18-20).

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 (21). We identified a previous study comparing diets defined by protein sources in relation to sustainability (22) 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 (23). 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.

## 87 Methods and Data

### 88 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 (24). 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 (25). The NutriNet-Santé study is in line with the principles of the Helsinki Declaration (26) 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 (27). 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 (28). 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 (m²), were assessed using the DIALECTE tool (29). 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 (30). 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 (30). 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 (31).

The pReCiPe score is computed as follows:

$$pReCiPe = 0.0459 \times GHGe + 0.0025 \times CED + 0.0439 \times LO$$

with GHGe, in kg of CO₂eq/d, CED, in MJ/d and LO, in m²/d. The highest the pReCiPe, the highest the environmental impact.

## 134 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 (32,33). 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 (34). The cDQI (Comprehensive Diet Quality Index) aims to assess the quality of plant and animal foods consumed (35). Further details are provided in **Supplemental Material 2**.

## 141 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 (12). It reflects the overall risk of death associated with the individual dietary pattern, resulted from a
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suboptimal consumption of each food group. The computation of the HRS, ranging from 0 to 1, is
provided in the **Supplemental Material 3**.

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**Economic data**

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

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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**.

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**Statistical Analysis**

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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 (27). A flowchart is provided in **Supplemental Figure 1**.

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

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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 (37) 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

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171 100 times. As this study used a large database, the complementary use of the k-means and AHC
172 methods allowed to stabilize the solution. Further details are provided in **Supplemental Material 5**.

173 **Description and comparison of clusters**

174 The clusters were named according to the food groups contributing the most to the protein intake of
175 each cluster compared to the whole sample. First, means (SD) of protein contribution of each food
176 group were computed (%/day) for the whole sample. Then, as cluster potentially exhibits a different
177 energy intake than the whole sample, energy-adjusted means of protein contributions of the 23 food
178 groups (SEM) were calculated for each cluster, using ANCOVA models.

179 The identified clusters were described according to the socio-demographic characteristics reporting
180 mean (SD) or % for continuous and categorical variables respectively. Means comparison across
181 clusters was performed using Pearson's Chi-square test for categorical variables and ANOVA test for
182 continuous variables. For food groups consumption, mean (SD) were presented for the whole
183 sample, and for each cluster, energy-adjusted mean of food group intakes (g/day) and standard error
184 of the mean (SEM) were calculated, using ANCOVA models.

185 Percentages of total energy intake were calculated for macronutrients. For vitamins, minerals and
186 fiber, each nutrient energy-adjusted intake was calculated based on the "residual method" (38).
187 Prevalence of adequate protein intake is computed as defined in the PANDiet score (33).

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

$$ \text{Relative value of indicator } i(\%) = \frac{\text{Energy adjusted mean}_i - \text{Standardized mean}_{\text{overall}_i}}{\text{Standardized mean}_{\text{overall}_i}} \times 100 $$

192 Where i denotes clusters.

193 **Multicriteria analysis**

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

198 Economic analysis

199 The objective was to analyze both food and protein expenditure structure across clusters. The
200 economic analysis included 27,244 of the 29,210 participants, for whom there were no missing
201 income data (since the question was optional). The monthly income variable, modelled, as category
202 was transformed into a numeric variable by considering the class center of the daily income category
203 for each individual as previously done (39) and converted as euros per day.

204 The expenditure structure analysis across clusters was conducted using a budget coefficient
205 approach (40). This approach makes comparable the share of food expenditure between individuals
206 with different incomes and different diets (40).

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

$$ \textit{Budget coefficient of the overall diet}_i = \frac{\textit{Overall diet expenditure}_i}{\textit{Income}_i} \times 100 $$

$$ \textit{Budget coefficient of food group}_{i,j} = \frac{\textit{Food group expenditure}_{i,j}}{\textit{Overall diet expenditure}_i} \times 100 $$

209 Where *i* denotes individuals and *j* denotes food groups.

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

217 We defined the protein expenditure as the share of the food group expenditure allocated to the daily
218 protein intake. It was calculated, for each food group and for each participant, using the following
219 formula:
$$ \text{Protein expenditure}_{i,j}(\text{€}) = \frac{\text{Food expenditure}_{i,j}(\text{€}) \times \text{Protein intake}_{i,j}(\text{g})}{\text{Quantity consumed}_{i,j}(\text{g})} $$

220 Where i denotes individuals and j denotes food groups.

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

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

$$ \text{Budget coefficient of protein intake per food group}_{i,j} = \frac{\text{Protein expenditure}_{i,j}}{\text{Overall diet expenditure}_i} \times 100 $$

$$ \text{Budget coefficient of the total protein intake}_i = \frac{\text{Total Protein expenditure}_i}{\text{Overall diet expenditure}_i} \times 100 $$

224 Where i denotes individuals and j denotes food groups.

225 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.

230 Data management and statistical analyses were performed using RStudio software (RStudio, Version 1.4.1717, © 2009-2021 RStudio, PBC).

232 **Results**

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

235 **Description of the protein-source typology**

236 Based on food group contribution to protein intake, we identified 5 clusters (Figure 1 and Table 1): 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 (+90%), legumes, nuts, fruit and vegetables. The detailed values are presented in **Table 1**, and **figure 1**.

The characteristics of the participants in the 5 clusters are shown in **Supplemental Table 1**. Food group consumptions across clusters are presented in **Supplemental figure 2**, and computed nutrient intakes across clusters are presented in **Supplemental Table 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.

## 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 healthy-fish-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
268 the contribution of food groups to the health risk score shows that for all clusters, a low consumption
269 of wholegrain products and legumes and a high consumption of red meat contribute the most to the
270 value of the HRS.

271 **Environmental impacts**

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

278 **Organic food consumption across clusters**

279 **Table 2** shows that participants of the healthy-plant-based cluster had the highest share of organic
280 consumption (+127% compared to the whole sample), followed by the participants of the healthy-
281 fish-based cluster showing a 40% higher share than the average of the population. However, the
282 participants of the meat-based and milk-based clusters showed the lowest share of organic food (-
283 30% compared to the whole sample) among the five identified clusters.

284 **Economic analysis**

285 The healthy-plant-based cluster had the largest share of income allocated to food (+46% compared
286 to the whole sample) (Figure 3). On the opposite, consumers of milk-based and fast-food-based
287 clusters spent the smallest share of their income for food (-10% and -6% compared to the whole
288 sample), while the food budget coefficient of the meat-based cluster is similar to the mean of the
289 population studied. Comparison of clusters by food groups revealed that budget coefficients
290 followed the same trend as food intake across the clusters. Furthermore, analysis of food
291 expenditure structure by cluster showed that vegetables are the food group for which all clusters
292 spent the largest share of their overall diet expenditure, with the exception of the meat-based
293 cluster, who spent the most for meat.
After vegetables, the milk-based cluster spent more for meat and non-alcoholic beverages, the fast-food-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-food-based 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-fish-based 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
320

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.

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## Nutritional quality and health risk across diets

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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) (16), previous studies documented that balanced diets in accordance with public health goals and with low meat intake, provide an adequate intake for most nutrients (41). Indeed, the amounts and quality of plant-based proteins are frequently underestimated or misunderstood (42). 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 (16). But this issue is still being debated and for example, a Canadian cohort study stated that the transition to plant-based protein might be nutritionally challenging since animal protein contributes overwhelmingly to total protein intake, particularly for the elderly (43).

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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 (22). Thus, plant-based protein consumption was proven to be associated with nutrient intakes and dietary profiles that are supportive of cardiometabolic health (44). 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 (41). Furthermore, the association between animal sourced food consumption and the risk of chronic diseases has been established by literature (45–47). 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% (48).

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Indeed, red and processed meat were proved to be risk factors for type 2 diabetes, while soy and dairy products provide protection (49).

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## Environmental impacts across the diets

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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 animal-based 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 plant-based dietary patterns (8,22,50,51). Moreover, it has been demonstrated, in a comprehensive review (52), that the decrease of the environmental footprint is generally proportional to the extent to which animal-based food consumption is restricted (41). Furthermore, we reported that belonging to the healthy-plant-based cluster was concomitant with a higher consumption of organic products, as shown before (53), which could contribute to the lower environmental impacts, especially for energy demand. A previous study also based on the NutriNet-Santé cohort (54), 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.

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## Economic analysis

363

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 (55), 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

364

365

366

367

368

369

370
371 (55). However, for food budgets below 3.85€/day, significant diet changes were needed.
372 Furthermore, the high food budget coefficients associated with the healthy-plant-based and healthy-
373 fish-based clusters might also be explained by the much higher share of organic food consumption of
374 these two clusters. Similarly, another study also based on the NutriNet-Santé cohort (56)
375 demonstrated that high consumers of organic food displayed a high consumption of plant-based
376 foods and a healthier diet. Thus, the monetary cost of their diet was higher (+26%) compared to that
377 of low consumers due to the higher prices of organic products as shown by a decomposition model
378 of the effects. This dual choice seems a best option by resulting in a markedly reduced exposure to
379 pesticides from diet (56).

380 Interestingly, the protein budget coefficient of the meat-based cluster was higher than that of other
381 clusters, which could be explained by both the higher prices of protein foods characteristic of the diet
382 adopted by this cluster's participants (meat, poultry and processed meat) and their higher protein
383 intake (+6% compared to the whole sample). Inversely, both the lower protein intake of the healthy-
384 plant-based cluster eaters (-27% compared to the whole sample) and the lower prices of their diet's
385 proteins sources (soya-based foods, legumes and nuts), might explain the lower protein budget
386 coefficient. Indeed, it has been previously demonstrated in a meta-analysis assessing the nutritional
387 status of meat-based diets compared to plant-based diets, that the protein intake of meat eaters was
388 higher than that of people adopting a plant-based diet, although it was still within the recommended
389 levels (57).

390 Multicriteria analysis of diets' sustainability according to protein intake

391 The scarcity of studies on sustainability in its entirety is inherent to its multidimensionality, which
392 makes it complex to conduct research in this sense. Indeed, while a multi-criteria analysis of protein
393 profiles (22) close to our study only addressed 2 of the 4 dimensions of diets sustainability
394 (environment, nutrition and health) according to the FAO definition (21), we also focused on the
395 economic aspect since this dimension has rarely been accounted for in multi-criteria studies on
396 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. (22). 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 (22) evaluated other impacts by mobilizing more indicators, but without distinguishing organic and conventional foods as we did. The results of this study (22) 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 (22), identification of more plant-based protein profiles in our work).

## 409 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 (58,59). 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 (60) and promoting grazing systems. This could improve efficiency of animal feeding and nutrient cycling, besides crop rotation through temporary grasslands. Also, non-food biomass would serve as feed for animals, which provide organic fertilizer (60). 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 (13,61) could contribute to reduce some environmental pressures, especially GHG emissions (13) and to lower the health risk compared to red meat (13).
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 (62). 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 plant-based 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
449 meat-based cluster, which is explained by both higher prices of the protein sources consumed and their higher protein intake. These results could be useful for the development of food transition strategies aimed at reducing animal protein consumption.

452

453 **Data Availability**

454 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

459
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634 Acknowledgements

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

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

644 The authors' contributions are as follows:

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

646 HT conducted the analysis supervised by EKG.

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

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

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

651 Funding

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

660 Agriculture, Food and the Environment (INRAE) and ADEME - Galilée Doctoral School.

661 **Additional Information**

662 No author declared conflict of interest.
663

<figure><img src="image_2.png" /><figcaption>Figure 1: Protein contributions per food group across clusters<fnref n="1" /></figcaption></figure>

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<footnote n="1">¹ Values are energy-adjusted means of protein contributions of food groups (%/day) computed using ANCOVA model. Food groups are formed as follows: Vegetables include all vegetables and soups, fruit include fresh fruit, fruit in syrup and compote, dried fruit and seeds, beverages include fruit nectar, syrup, soda (with or without sugar), plant-based beverages (except soy-based), milk consumed with tea/coffee, Seafood, dairy products include yogurts, fresh cheese and cheese, potatoes include other tubers, cereals include breakfast cereal low in sugar, bread semolina, rice and pasta, SFF (sweet and fat foods) include croissants, pastries, chocolate, biscuits, milky dessert, ice cream, honey and marmalade, cakes, chips, salted oilseeds, salted biscuits, fast-food include sandwich, prepared foods such as pizza, hamburger, ravioli, panini, salted pancake, etc., soy includes soy-based food that are tofu, soy-based meat substitute and vegetable patties, soy-based yogurt, soy-based milk, animal fat (butter and lards) and dressing (ready-to-use salad dressing, mayonnaise or cream-based sauces) include sour cream and butter and all fat-based sauces, oil include plant-based oils, meat includes beef and pork.</footnote>
<figure><img src="image_3.png" /><figcaption>672 **Figure 2: Sustainability indicators across clusters¹**</figcaption></figure>

673
674 Abbreviations: HRS, Health Risk Score; GHG, Greenhouse Gas (kg CO2 eq/d); CED, Cumulative Energy Demand (MJ/d); Land Use (m²/d); pRecipe, partial Recipe; PANDiet, Diet Quality Index based on the Probability of Adequate Nutrient Intake; cDQI, Comprehensive Diet Quality Index; PNNS-GS2, Program National Nutrition Health Guidelines Score.

675
676 1 The energy-adjusted means of indicators, computed using ANCOVA model, were rescaled to the same scale by equalizing the maximum value to 1 and the minimum value to 0 for each indicator. For the environmental indicators and the HRS, higher values denote higher impacts/risk. For nutritional quality indicators, higher values denote higher nutritional quality.

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680
<figure><img src="image_4.png" /><figcaption>Figure 3: Food budget coefficients across clusters according to production mode (A); Protein budget coefficients across clusters (B)<fnref n="1" />.</figcaption></figure>

Panel A refers to food budget coefficients across clusters according to production mode (organic/conventional). Panel B refers to protein budget coefficients across clusters.

<footnote n="1">¹Values are energy-adjusted means of budget coefficients computed using ANCOVA model.</footnote>
<table><caption>Table 1: Protein contributions of food groups across clusters<fnref n="1" />,<fnref n="2" />,<fnref n="3" /></caption><thead><tr><th>Food groups</th><th>Whole sample<br>n = 29,210</th><th>Milk-based<br>n = 4,966 (17%)</th><th>Meat-based<br>n = 7,569 (26%)</th><th>Fast-food-based<br>n = 8,469 (29%)</th><th>Healthy-fish-based<br>n = 7,189 (25%)</th><th>Healthy-plant-based<br>n = 1,017 (3%)</th><th>p</th></tr></thead><tbody><tr><td>Legumes</td><td>1.41 (2.49)</td><td>0.86 (0.03)</td><td>0.95 (0.02)</td><td>1.06 (0.02)</td><td>1.71 (0.02)</td><td>8.49 (0.07)</td><td>&lt; 0.0001</td></tr><tr><td>Soya-based foods</td><td>1.62 (4.72)</td><td>0.43 (0.05)</td><td>0.42 (0.04)</td><td>0.83 (0.04)</td><td>2.56 (0.04)</td><td>16.33 (0.12)</td><td>&lt; 0.0001</td></tr><tr><td>Cereals</td><td>8.9 (6.38)</td><td>8.83 (0.09)</td><td>9.39 (0.07)</td><td>10.84 (0.07)</td><td>5.91 (0.07)</td><td>10.68 (0.19)</td><td>&lt; 0.0001</td></tr><tr><td>Nuts</td><td>2.39 (4.37)</td><td>1.23 (0.05)</td><td>1.00 (0.04)</td><td>1.60 (0.04)</td><td>4.37 (0.04)</td><td>11.00 (0.12)</td><td>&lt; 0.0001</td></tr><tr><td>Wholegrain products</td><td>5.59 (7.06)</td><td>4.26 (0.09)</td><td>2.74 (0.07)</td><td>4.91 (0.07)</td><td>9.73 (0.08)</td><td>9.93 (0.20)</td><td>&lt; 0.0001</td></tr><tr><td>Fruits</td><td>2.41 (2.69)</td><td>1.83 (0.04)</td><td>1.98 (0.03)</td><td>2.07 (0.03)</td><td>3.35 (0.03)</td><td>4.81 (0.08)</td><td>&lt; 0.0001</td></tr><tr><td>Fruit juice</td><td>0.61 (0.91)</td><td>0.59 (0.01)</td><td>0.44 (0.01)</td><td>0.89 (0.01)</td><td>0.42 (0.01)</td><td>1.07 (0.03)</td><td>&lt; 0.0001</td></tr><tr><td>Vegetables</td><td>5.21 (3.63)</td><td>4.00 (0.05)</td><td>4.45 (0.04)</td><td>4.61 (0.04)</td><td>6.69 (0.04)</td><td>11.55 (0.10)</td><td>&lt; 0.0001</td></tr><tr><td>Potatoes</td><td>0.46 (0.49)</td><td>0.39 (0.01)</td><td>0.59 (0.01)</td><td>0.42 (0.01)</td><td>0.41 (0.01)</td><td>0.60 (0.01)</td><td>&lt; 0.0001</td></tr><tr><td>Alcohol</td><td>0.22 (0.53)</td><td>0.16 (0.01)</td><td>0.29 (0.01)</td><td>0.22 (0.01)</td><td>0.21 (0.01)</td><td>0.20 (0.01)</td><td>&lt; 0.0001</td></tr><tr><td>Dressing</td><td>0.23 (0.28)</td><td>0.18 (0.00)</td><td>0.21 (0.00)</td><td>0.31 (0.00)</td><td>0.21 (0.00)</td><td>0.31 (0.01)</td><td>&lt; 0.0001</td></tr><tr><td>Beverages</td><td>3.19 (4.24)</td><td>8.46 (0.05)</td><td>1.99 (0.04)</td><td>2.38 (0.04)</td><td>1.95 (0.04)</td><td>2.13 (0.11)</td><td>&lt; 0.0001</td></tr><tr><td>Oil</td><td>0 (0.00)</td><td>0.00 (0.00)</td><td>0.00 (0.00)</td><td>0.00 (0.00)</td><td>0.00 (0.00)</td><td>0.00 (0.00)</td><td>&lt; 0.0001</td></tr><tr><td>Meat</td><td>15.26 (10.16)</td><td>14.86 (0.12)</td><td>23.94 (0.10)</td><td>12.36 (0.10)</td><td>11.8 (0.10)</td><td>1.35 (0.26)</td><td>&lt; 0.0001</td></tr><tr><td>Processed meat</td><td>4.55 (3.88)</td><td>4.22 (0.05)</td><td>5.99 (0.04)</td><td>4.84 (0.04)</td><td>3.50 (0.04)</td><td>0.55 (0.12)</td><td>&lt; 0.0001</td></tr><tr><td>Eggs</td><td>1.6 (1.66)</td><td>1.34 (0.02)</td><td>1.36 (0.02)</td><td>1.47 (0.02)</td><td>2.25 (0.02)</td><td>1.23 (0.05)</td><td>&lt; 0.0001</td></tr><tr><td>Fast food</td><td>3.64 (3.24)</td><td>3.22 (0.04)</td><td>3.31 (0.04)</td><td>4.94 (0.03)</td><td>2.73 (0.04)</td><td>3.81 (0.10)</td><td>&lt; 0.0001</td></tr><tr><td>Fat</td><td>0.08 (0.13)</td><td>0.07 (0.00)</td><td>0.07 (0.00)</td><td>0.12 (0.00)</td><td>0.08 (0.00)</td><td>0.12 (0.00)</td><td>&lt; 0.0001</td></tr><tr><td>Poultry</td><td>6.03 (5.14)</td><td>5.38 (0.07)</td><td>8.76 (0.05)</td><td>5.03 (0.05)</td><td>5.52 (0.06)</td><td>0.83 (0.15)</td><td>&lt; 0.0001</td></tr><tr><td>Dairy</td><td>17.28 (10.06)</td><td>15.14 (0.13)</td><td>15.05 (0.11)</td><td>23.16 (0.10)</td><td>15.73 (0.11)</td><td>6.29 (0.29)</td><td>&lt; 0.0001</td></tr><tr><td>Seafood</td><td>10.55 (7.84)</td><td>8.51 (0.10)</td><td>9.90 (0.08)</td><td>9.09 (0.08)</td><td>15.42 (0.08)</td><td>3.14 (0.23)</td><td>&lt; 0.0001</td></tr><tr><td>Milk</td><td>2.19 (5.06)</td><td>9.68 (0.05)</td><td>0.57 (0.04)</td><td>0.87 (0.04)</td><td>0.57 (0.04)</td><td>0.31 (0.12)</td><td>&lt; 0.0001</td></tr><tr><td>SFF</td><td>4.24 (3.42)</td><td>4.20 (0.05)</td><td>3.81 (0.04)</td><td>5.52 (0.04)</td><td>3.21 (0.04)</td><td>4.53 (0.10)</td><td>&lt; 0.0001</td></tr></tbody></table>

Abbreviations: SFF, Sweetened and Fatty Foods.

<footnote n="1">¹ Values are mean (SD) contribution (%) for the whole sample, and energy-adjusted means of protein contributions (SEM) across clusters (ANCOVA model).</footnote>

<footnote n="2">² P values were calculated using ANCOVA.</footnote>

<footnote n="3">³ Other food groups with minor contribution to protein intake are not represented</footnote>
<table><caption>Table 2: Sustainability indicators across clusters<fnref n="1" />,<fnref n="2" /></caption><thead><tr><th>Indicators</th><th>Whole sample</th><th>Milk-based</th><th>Meat-based</th><th>Fast-food-based</th><th>Healthy-fish-based</th><th>Healthy-plant-based</th><th>p</th></tr></thead><tbody><tr><td colspan="8"><strong>Nutritional quality</strong></td></tr><tr><td>PANDiet</td><td>64.97 (7.86)</td><td>65.17 (0.09)</td><td>63.01 (0.07)</td><td>62.65 (0.07)</td><td>68.53 (0.07)</td><td>72.68 (0.21)</td><td>&lt; 0.0001</td></tr><tr><td>AS</td><td>78.86 (12.74)</td><td>78.43 (0.13)</td><td>78.11 (0.1)</td><td>75.73 (0.1)</td><td>83.79 (0.1)</td><td>77.64 (0.29)</td><td>&lt; 0.0001</td></tr><tr><td>MS</td><td>51.08 (18.49)</td><td>51.9 (0.15)</td><td>47.9 (0.12)</td><td>49.57 (0.12)</td><td>53.27 (0.13)</td><td>67.72 (0.35)</td><td>&lt; 0.0001</td></tr><tr><td>cDQI</td><td>51.48 (9.18)</td><td>49.57 (0.11)</td><td>48.56 (0.09)</td><td>49.33 (0.08)</td><td>58.21 (0.09)</td><td>52.97 (0.25)</td><td>&lt; 0.0001</td></tr><tr><td>aDQI</td><td>15.86 (3.9)</td><td>16.84 (0.05)</td><td>14.59 (0.04)</td><td>15.73 (0.04)</td><td>17.05 (0.04)</td><td>13.16 (0.11)</td><td>&lt; 0.0001</td></tr><tr><td>pDQI</td><td>35.62 (7.47)</td><td>32.72 (0.09)</td><td>33.96 (0.07)</td><td>33.6 (0.07)</td><td>41.16 (0.07)</td><td>39.81 (0.2)</td><td>&lt; 0.0001</td></tr><tr><td>PNNS-GS2</td><td>2.51 (3.56)</td><td>1.96 (0.03)</td><td>1.01 (0.03)</td><td>2.34 (0.02)</td><td>4.15 (0.03)</td><td>6.13 (0.08)</td><td>&lt; 0.0001</td></tr><tr><td colspan="8"><strong>Environmental impacts</strong></td></tr><tr><td>GHG emissions (kg CO2 eq/d)</td><td>4.05 (2.48)</td><td>4.13 (0.02)</td><td>5.47 (0.02)</td><td>3.61 (0.01)</td><td>3.4 (0.02)</td><td>1.31 (0.05)</td><td>&lt; 0.0001</td></tr><tr><td>Cumulative Energy Demand (MJ/d)</td><td>17.62 (7.56)</td><td>17.08 (0.06)</td><td>21.14 (0.05)</td><td>16.18 (0.04)</td><td>17.09 (0.05)</td><td>9.85 (0.14)</td><td>&lt; 0.0001</td></tr><tr><td>Land Use (m<fnref n="2" />/d)</td><td>10.6 (6.75)</td><td>10.57 (0.07)</td><td>14.24 (0.05)</td><td>9.33 (0.05)</td><td>9.15 (0.05)</td><td>4.52 (0.15)</td><td>&lt; 0.0001</td></tr><tr><td>pRecipe</td><td>0.28 (0.16)</td><td>0.28 (0.00)</td><td>0.39 (0.00)</td><td>0.26 (0.00)</td><td>0.23 (0.00)</td><td>0.11 (0.00)</td><td>&lt; 0.0001</td></tr><tr><td>Organic food share</td><td>0.29 (0.27)</td><td>0.21 (0.00)</td><td>0.21 (0.00)</td><td>0.26 (0.00)</td><td>0.41 (0.00)</td><td>0.67 (0.01)</td><td>&lt; 0.0001</td></tr><tr><td colspan="8"><strong>Health risk</strong></td></tr><tr><td>RR (%) Whole grain</td><td>19.51 (12.35)</td><td>20.59 (0.16)</td><td>17.45 (0.13)</td><td>21.17 (0.12)</td><td>17.1 (0.14)</td><td>32.9 (0.37)</td><td>&lt; 0.0001</td></tr><tr><td>RR (%) Fruits</td><td>6.98 (7.59)</td><td>7.8 (0.10)</td><td>5.17 (0.08)</td><td>8.26 (0.08)</td><td>5.8 (0.08)</td><td>14.07 (0.23)</td><td>&lt; 0.0001</td></tr><tr><td>RR (%) Vegetables</td><td>2.19 (2.58)</td><td>2.87 (0.03)</td><td>1.54 (0.02)</td><td>2.77 (0.02)</td><td>1.69 (0.02)</td><td>2.4 (0.07)</td><td>&lt; 0.0001</td></tr><tr><td>RR (%) Nuts/Seeds</td><td>3.82 (3.17)</td><td>3.84 (0.04)</td><td>2.95 (0.03)</td><td>3.88 (0.03)</td><td>4.1 (0.03)</td><td>7.56 (0.09)</td><td>&lt; 0.0001</td></tr><tr><td>RR (%) Legumes</td><td>10.24 (6.47)</td><td>10.29 (0.08)</td><td>7.63 (0.07)</td><td>10.63 (0.06)</td><td>12.21 (0.07)</td><td>12.14 (0.19)</td><td>&lt; 0.0001</td></tr><tr><td>RR (%) Milk</td><td>6.65 (5.86)</td><td>3.03 (0.06)</td><td>4.41 (0.05)</td><td>6.95 (0.05)</td><td>9.15 (0.05)</td><td>20.65 (0.14)</td><td>&lt; 0.0001</td></tr><tr><td>RR (%) Processed meat</td><td>16.87 (10.22)</td><td>16.58 (0.14)</td><td>18.26 (0.11)</td><td>17.73 (0.1)</td><td>16.47 (0.11)</td><td>3.75 (0.3)</td><td>&lt; 0.0001</td></tr><tr><td>RR (%) Red meat</td><td>32.97 (16.75)</td><td>34.08 (0.21)</td><td>41.97 (0.17)</td><td>27.67 (0.16)</td><td>32.89 (0.17)</td><td>5.19 (0.46)</td><td>&lt; 0.0001</td></tr><tr><td>RR (%) Sweetened beverages</td><td>0.73 (1.96)</td><td>0.86 (0.02)</td><td>0.58 (0.02)</td><td>0.88 (0.02)</td><td>0.53 (0.02)</td><td>1.29 (0.06)</td><td>&lt; 0.0001</td></tr><tr><td>HRS</td><td>0.69 (0.29)</td><td>0.7 (0.00)</td><td>0.95 (0.00)</td><td>0.65 (0.00)</td><td>0.53 (0.00)</td><td>0.25 (0.00)</td><td>&lt; 0.0001</td></tr></tbody></table>

Abbreviations: AS, Adequation sub-score of PANDiet; MS, Moderation sub-score of PANDiet; HRS: Health Risk Score; RR: relative risk

<footnote n="1">¹ Values are mean (SD) for the whole sample, and adjusted means of indicators on total energy intake (SEM) across clusters (ANCOVA model).</footnote>

<footnote n="2">² P values were calculated using ANCOVA.</footnote>