Are European Diets Healthy and Sustainable? Evidence from Nine Countries Using the Planetary Health Diet Framework

Are European Diets Healthy and Sustainable? 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Evidence from Nine Countries Using the Planetary Health Diet Framework Agustin Ramiro Miranda, Joseph Meunier, Sofia Romagosa Vilarnau, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7751935/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Mar, 2026 Read the published version in European Journal of Nutrition → Version 1 posted You are reading this latest preprint version Abstract Purpose Contemporary food systems pose challenges for both human and planetary health. This study aimed to assess and compare adherence to the Planetary Health Diet (PHD) in nine European countries. Methods Nationally representative dietary surveys (post-2013) from Estonia, Finland, France, Hungary, the Netherlands, Portugal, Spain, Switzerland, and the United Kingdom, with ≥ 2 non-consecutive 24-hour recalls, were used (n = 16,083 adults). Adherence to the PHD was assessed at two levels: 1) compliance for each food group, calculated as the intake relative to the corresponding PHD targets, and 2) overall adherence, captured by three valid dietary indices. Multivariate regression analyses were conducted to identify associations with demographic factors. Results Dietary patterns across Europe were characterized by insufficient intake of plant-based foods (whole grains, legumes, nuts, vegetables, and unsaturated oils) relative to PHD targets, alongside excessive consumption of foods to limit (red meat, saturated fats, and added sugars). Spain, Portugal, and the Netherlands showed comparatively better alignment with the PHD, whereas Hungary, the United Kingdom, and Estonia had the lowest scores. Red meat, particularly pork, and added sugars were the primary drivers of low scores across PHD indices. Being female, older, and having a higher level of education were positively associated with PHD adherence. Conclusion European diets show systematic deviations from the PHD. Targeted and multilevel policies are needed to promote healthy and sustainable diets. Nutrition Surveys Dietary Patterns EAT-Lancet diet Sustainable Nutrition Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The impact of current food systems on human and planetary health is a growing concern [ 1 ], prompting international and local calls for healthier and more sustainable food systems [ 2 , 3 ]. Agriculture is a key driver of global change, responsible for up to 40% of land use, 85% of freshwater consumption, 90% of nitrogen and phosphorus use, and approximately 30% of greenhouse gas emissions, largely from intensive animal production [ 4 , 5 ]. Diets high in added sugars, meat, and saturated fats additionally increase the risk of non-communicable diseases (such as cardiovascular diseases, cancer, and mental health issues) [ 6 , 7 ]. Multiple mechanisms are involved, including poor nutritional quality, microbiota disruption, pro-inflammatory metabolites, impaired metabolism, and harmful preservatives [ 8 , 9 ]. In 2019, the EAT-Lancet Commission introduced the Planetary Health Diet (PHD) as a global reference model for a healthy and sustainable diet [ 10 ]. Designed for an average daily intake of 2500 kcal, the diet emphasizes plant-based foods (including vegetables, fruits, legumes, whole grains, and nuts) as well as seafood, while recommending moderate consumption of eggs, poultry, and dairy, and limiting red meat, tubers, added sugars, and saturated fats [ 10 ]. Although the diet has faced critiques for its consumer-centred approach and limited applicability in low-resource contexts, it remains as a major contributor to global efforts toward food system transformation amid climate change [ 11 , 12 ]. Evidence supports its benefits for environmental sustainability and human health. For example, high adherence to the PHD is linked to up to 50% lower GHGE, 62% less land use, and potential prevention of 19–63% of deaths and 10–39% of cancers [ 13 ]. Translating global dietary guidelines such as the PHD into actionable policies requires understanding regional dietary patterns, as food systems are highly heterogeneous, reflecting diverse environmental, cultural, economic and health contexts across regions [ 14 ]. In this scenario, the European Union (EU) has committed to reducing GHG emissions by at least 55% by 2030 and 90% by 2050 compared to 1990 levels, identifying agriculture as a key emitting sector [ 15 ]. Non-EU countries, including Switzerland and the UK, have adopted similar goals [ 16 , 17 ]. At the same time, Europe faces rising rates of overnutrition and diet-related non-communicable diseases [ 18 ]. Achieving healthier and more sustainable diets therefore require not only individual behavioural change but also coordinated policy action across multiple governance levels [ 19 ]. Understanding the variability in dietary patterns across European populations is thus critical for informing targeted policies and interventions for sustainable diet promotion [ 20 ]. However, many existing studies are limited in scope, focusing on a narrow set of countries or neglecting the diversity of food groups [ 20 ]. To address this gap, this study utilized recent and harmonized population-based dietary survey data to assess and compare adherence to healthy and sustainable dietary patterns across nine European countries. Methods Food consumption data This study used the most recent nationally representative food consumption surveys from nine European countries: Estonia [ 21 ], Finland [ 22 ], France [ 23 ], Hungary [ 24 ], the Netherlands [ 25 ], Portugal [ 26 ], Spain [ 27 ], Switzerland [ 28 ], and the United Kingdom (UK) [ 29 ] (Table 1 ). Countries were selected based on the availability of national dietary survey data collected after 2013 using at least two non-consecutive 24-hour dietary recalls. For each country, data on food consumption (grams per day, g/d), were obtained for the adult population (≥ 18 years old). Data access was obtained from the data owners, European Food Safety Authority, and public repositories. All surveys complied with the Declaration of Helsinki and received ethics committee approval. Sampling flow chart is available in Supplementary Materials (Fig. S1 ). Table 1 Characteristics of the national dietary surveys Country Survey name Year N a Women Men Days b Estonia RTU 2013–2015 2649 1764 885 2 Finland FINDIET 2017 1488 780 708 2 France INCA3 2014–2015 2121 1234 887 3 Hungary HU-EU-Menu 2018–2020 1056 528 528 2 The Netherlands DNFCS 2019–2021 1747 867 880 2 Portugal IAN-AF 2015–2016 3764 1994 1770 2 Spain ENALIA 2 2014–2015 933 532 401 2 Switzerland menuCH 2014–2015 2057 1128 929 2 United Kingdom NDNS 2020 524 303 221 4 a Only adults. b Maximum number of 24-hour dietary recall days. Food consumption was categorized into key groups based on the PHD framework [ 10 ]. Whole grains included rice, wheat, and products containing whole grain components. Vegetables and fruits included fresh, frozen, cooked, canned, and dried forms, excluding juices. Dairy comprised milk (whole or skimmed), cheese, and yoghurt, excluding butter and cream. Red meat included unprocessed and processed meats (e.g., beef, pork, lamb). Fish included all fish and shellfish. Eggs and poultry included chickens, ducks, and geese. Legumes included beans, lentils, and soybeans. Nuts included tree nuts and groundnuts (e.g., peanuts). Unsaturated oils comprised plant oils (e.g., olive, rapeseed, sunflower), and saturated fats included dairy fats, tallow, and palm oil. Added sugars, including those from foods and sugar-sweetened beverages, were also quantified. Detailed definitions are available elsewhere [ 30 ]. To estimate the composition of mixed dishes with multiple ingredients, a standardized recipe calculation method was consistently applied across all the national surveys [ 31 ]. A harmonized recipe database was developed to ensure cross-country comparison. Rather than disaggregating dishes into individual ingredients, standardized formulations were created at the food group level for commonly consumed foods (e.g., stews, pizzas, sandwiches). Where multiple versions of a dish existed (e.g., fish-, red meat-, poultry-, or vegetable-based), each was represented by a separate, standardized recipe reflecting typical preparation. Recipe information was compiled from recipe databases, food labels, and culinary websites [ 30 ]. Using these sources, we estimated the proportion of each food group within the dish; for example, a ham and vegetable sandwich with black bread may contained 20% red meat, 10% vegetables, and 50% whole grains. Each recipe was broken down into food group components according to the PHD categories. To quantify food group intake, the total reported weight of each dish consumed was distributed among the food groups based on standardized proportions, converting dish weight into grams per food group. For instance, if an individual reported consuming 200 g of the ham and vegetable sandwich with black bread, the intake was calculated as 40 g red meat (20%), 20 g vegetables (10%), and 100 g whole grains (50%). A total of 141 standardized recipes were included in the final database. Importantly, the allocation of ingredients to food groups was guided by both their presence and nutritional value. Foods considered healthy, such as whole grains, were not included in their corresponding group if consumed as part of an unhealthy preparation. For example, a whole grain breakfast cereal with added sugars was considered only for “added sugars” and not as a source of “whole grains”, to remain consistent with the PHD framework [ 10 ]. Data harmonisation A standardized harmonisation protocol was implemented to ensure consistency across datasets and enable comparative analysis [ 32 , 33 ]. Participant and dietary data at the individual level were extracted using a pre-defined codebook and a uniform data collection template. Variables from each dataset were recoded and described according to harmonised definitions to ensure alignment across countries. Each data entry captured the food group intake (g/d), nutrient intake, and participant data. Individual-level microdata were aggregated into subgroups stratified jointly by age (18–44, 45–64, or ≥ 65 years), sex (women or men), and education level (lower or higher, defined by post-secondary education) [ 33 ]. Data quality was systematically monitored throughout the process, and any irregularities (i.e., implausible values or structural inconsistencies) were reviewed and resolved collaboratively by the research team [ 32 ]. To account for age- and sex-related differences, food intake was standardized to grams per 2,000 kcal [ 33 ]. Participants reporting extreme energy intakes ( 4200 kcal/day for men; 3500 kcal/day for women) on the dietary recalls were excluded (n = 256; Supplementary Materials: Table S1 ) [ 34 ]. Survey weights were calculated separately for each national dataset. For this, the age- and sex-distribution of the survey sample was compared with the corresponding national population distribution obtained from Eurostat. Each participant was assigned a weight proportional to the under- or over-representation of their age-sex stratum in the survey. These weights were then applied in the pooled analysis, so that each country contributed to the results in proportion to its national population. PHD adherence assessment First, to evaluate compliance with the PHD, we calculated for each food group the percentage of the recommended target intake achieved, defined as % = daily intake/PHD target × 100 [ 35 ]. A value of 100% indicates full adherence, > 100% indicates consumption above the target, and < 100% indicates consumption below the target. This allowed the identification of over- and under-consumed food groups in each country relative to the PHD benchmarks (Supplementary Table S4). In addition, three composite indices were employed to provide a more integrative evaluation of dietary patterns. These indices differ in terms of scoring systems, energy adjustment, treatment of food categories, and thresholds, offering complementary perspectives [ 30 ]: The World Index for Sustainability and Health (WISH) assesses diet across 13 food groups classified as neutral, protective, or harmful to human and planetary health [ 36 ]. Food groups are scored from 0 (noncompliance) to 10 (full compliance) based on reference intakes (g) reflecting adherence to the PHD. The total score ranges from 0 to 130. Further details on WISH are described elsewhere [ 36 ]. The EAT-Lancet Diet Index (ELD-I) measures how closely a diet aligns with the PHD across 14 food groups using proportional scoring adjusted for individual energy intake (2,500 kcal reference) [ 37 ]. Recommended foods score positively when intake exceeds targets; foods to limit score positively when consumption is below limits. Underconsumption of recommended foods or excess intake of limited foods yields negative scores. The resulting unbounded continuous score (positive or negative) reflects the overall adherence. Further details on ELD-I are provided elsewhere [ 37 ]. The EAT-Lancet Index (ELI) is composed of 14 food groups divided into two categories: seven positive components or “emphasized foods” and seven negative components or “limited foods” [ 38 ]. Each component is scored on a graded scale from 0 (noncompliance) to 3 points (high compliance), based on how closely intake aligns with the targets. Total ELI score ranges from 0 to 42. More details on ELI are available elsewhere [ 38 ]. For each index, the contribution of individual food groups to the total score was calculated, enabling the identification of components driving higher or lower adherence within countries. The reliability and validity of the WISH, ELD-I, and ELI in capturing the nutritional health and environmental impacts of diets have been previously established [ 30 ]. Statistical analysis Descriptive statistics were used to summarize the sample characteristics and dietary intake. Continuous variables were presented as mean ± standard deviation, and categorical variables as frequencies. Food consumption and adherence to the PHD were described and compared across countries and demographic subgroups. Within-country differences in WISH, ELD-I and ELI according to demographics were evaluated using multivariate regressions, with results expressed as standardized beta coefficients (β) and 95% confidence intervals. To account for variation in total energy intake, food group intakes and indices were standardized to 2000 kcal. This adjustment was applied consistently across descriptive and regression analyses. Analyses were performed using RStudio (vR4.5.0, RStudio Team) and Stata (v18, StataCorp, College Station, TX, USA), accounting for sampling weights and considering p < 0.05 (two-sided) as statistically significant. Results Baseline characteristics The study included 16,083 adults from nine European countries, with sample sizes ranging from 519 in the UK to 3,703 in Portugal. Sociodemographic characteristics varied by country (Table 2 ). Women represented 56.1% of the sample, with the largest gender imbalance in Estonia. Overall, 39.8% were aged 18–45 years, 35.7% were 45–65 years, and 24.6% were 65–80 years. Young and middle-aged adults predominated across most countries, except in Hungary, where over half of the participants were aged 65–80 years. In total, 59.4% had lower education and 40.7% had higher education, ranging from a predominance of lower education in Portugal and Hungary to higher education in Estonia. Sampling weights were applied to adjust for cross-country differences, with the weighted proportions shown in Supplementary Materials (Tables S2 and S3). Table 2 Baseline characteristics of participants Sex Age Education Country Total Women Men 18–44 45–64 65–80 Lower Higher Europe 16083 9025 (56.12) 7058 (43.88) 6397 (39.77) 5738 (35.68) 3948 (24.55) 8155 (59.35) 5586 (40.65) United Kingdom 519 301 (50.58) 218 (42.00) 193 (37.19) 195 (37.57) 131 (25.24) n/a n/a France 2074 1217 (58.68) 857 (41.32) 761 (36.69) 809 (39.01) 504 (24.30) 1213 (58.51) 860 (41.49) Spain 929 531 (57.16) 398 (42.84) 449 (48.33) 217 (23.36) 263 (28.31) 547 (58.88) 382 (41.12) The Netherlands 1733 860 (49.62) 873 (50.38) 433 (24.99) 695 (40.10) 605 (34.91) 949 (55.11) 773 (44.89) Portugal 3703 1970 (53.20) 1733 (46.80) 1730 (46.72) 1322 (35.70) 651 (17.58) 2832 (76.52) 869 (23.48) Hungary 1032 521 (50.48) 511 (49.52) 236 (22.87) 272 (26.36) 524 (50.78) 489 (68.97) 220 (31.03) Switzerland 2013 1115 (55.39) 898 (44.61) 903 (44.86) 776 (38.55) 334 (16.59) 1033 (51.39) 977 (48.61) Finland 1488 778 (52.53) 703 (47.47) 508 (34.30) 553 (37.34) 420 (28.36) n/a n/a Estonia 2599 1732 (66.64) 867 (33.36) 1184 (45.56) 899 (34.59) 516 (19.85) 1092 (42.05) 1505 (57.95) n/a = Data not available in the survey. Exploring food consumption patterns In the pooled European sample, the mean daily intakes were highest for dairy (263.8 ± 85.6 g/d), vegetables (189.6 ± 24.6 g/d), and fruits (177.1 ± 47.7 g/d), followed by tubers (66.2 ± 13.6) and whole grains (31.5 ± 19.5 g/d) (Supplementary Fig. 1). For animal-based foods, mean intakes were 82.5 ± 10.5 g/d for red meat (40.4 g/d for beef and lamb; 42.0 g/d for pork), 49.9 ± 17.8 g/d for poultry, 22.8 ± 7.0 g/d for eggs, and 38.6 ± 17.4 g/d for fish and seafood. Other components included legumes (27.5 ± 12.8 g/d), nuts (5.2 ± 3.2 g/d), unsaturated oils (14.0 ± 7.4 g/d), saturated fats (28.2 ± 9.3 g/d), and added sugars (54.6 ± 9.5 g/d). The absolute food consumption by country is shown in Supplementary Materials (Fig. S2). Figure 1 shows country-specific deviations from the European mean for food groups, highlighting notable differences (≥ 30%). The UK exhibited higher consumption of poultry (+ 41%), eggs (+ 43%), and legumes (+ 36%), along with a lower intake of unsaturated oils (− 62%) and fruits (− 32%). France showed a higher intake of saturated fats (+ 30%) and lower intakes of whole grains (− 52%), legumes (− 43%), and nuts (− 51%). Spain had higher consumption of unsaturated oils (+ 71%), fish (+ 65%), legumes (+ 53%), and dairy (+ 52%), whereas saturated fat intake was lower (− 50%). The Netherlands showed higher consumption of nuts (+ 163%) and whole grains (+ 121%), alongside lower consumption of fish (− 62%) and poultry (− 48%). Portugal exhibited higher intakes of fish (+ 72%), tubers (+ 69%), and poultry (+ 34%), with legumes (− 63%), whole grains (− 54%), and added sugars (− 35%) consumed less. Hungary showed higher intakes of whole grains (+ 77%), unsaturated oils (+ 57%), and pork (+ 98%) and decreased intakes of fish (− 74%), dairy (− 34%), and legumes (− 32%). Switzerland reported lower poultry (− 48%), legume (− 45%), and egg (− 39%) consumption. Finland showed high whole grain (+ 215%), unsaturated oil (+ 68%), and dairy (+ 45%) intakes, with reduced legume (− 58%) intake. Estonia had higher tuber (+ 100%), fruit (+ 55%), and pork (+ 86%) consumption, and markedly lower legume (− 93%) and nut (− 48%) consumption. PHD Adherence Figure 2 illustrates the alignment of mean dietary intakes with the PHD reference values. No country achieved the recommended intake levels for whole grains (6–42%), nuts (5–27%), legumes (4–84%), unsaturated oils (14–60%), or vegetables (56–75%). Fruit consumption was also below optimal levels in the UK, the Netherlands, Hungary, Switzerland, and Finland (60–85%), while fish intake was insufficient in the Netherlands, Hungary, and Switzerland (35–70%). Conversely, poultry consumption exceeded the PHD target in all countries except the Netherlands and Switzerland, ranging from 114% in France to 242% in the UK, and eggs were overconsumed in all regions, with figures ranging from 107% in Switzerland to 252% in the UK. Red meat consumption exhibited the most significant excess, ranging from 465% in the Netherlands to 821% in Estonia. In Hungary (1,188%), Estonia (1,118%), Spain (748%), Finland (867%), and Portugal (676%), pork was the primary contributor, whereas beef and lamb were more prominent in France (695% vs. 596% for pork) and the UK (637% vs. 410% for pork); Switzerland and the Netherlands reported nearly equal proportions. Saturated fat intake ranged from 120% in Spain to 323% in Finland of the reference values, while added sugars ranged from 114% in Portugal to 224% in Switzerland. Tuber consumption was moderately above the target (< 140%) in most countries but was significantly higher in Estonia (264%), Portugal (223%), and Finland (162%). Figure 3 shows the mean score of the dietary indices measuring adherence to PHD, and the mean values of their components. The European mean WISH score was 42.69 ± 5.01, highest in Spain (50.19 ± 16.15), the Netherlands (46.82 ± 16.93), and Portugal (46.22 ± 16.31), and lowest in Hungary (37.47 ± 14.19), the UK (37.67 ± 17.23), and Estonia (38.98 ± 14.27). ELI followed a similar pattern (European mean 18.90 ± 1.31), with Spain (20.94 ± 4.39), the Netherlands (19.80 ± 4.51), and Portugal (19.64 ± 4.37) having the highest score, and Estonia (16.78 ± 4.19), the UK (17.57 ± 5.25), and Hungary (18.39 ± 3.83) having the lowest. ELD-I averaged − 14.62 ± 10.62 across Europe; the Netherlands scored positively (3.52 ± 35.70), followed by Spain (− 4.62 ± 37.69) and Portugal (− 6.22 ± 42.15), while the UK (− 27.06 ± 40.91), Finland (− 26.38 ± 42.81), and Hungary (− 23.07 ± 32.34) deviated most. The indices were positively correlated across countries (Supplementary Material: Fig. S3). Across Europe, adherence was highest for eggs, dairy, and poultry, particularly in WISH and ELI. Eggs approached the WISH maximum (10 points) in Switzerland (7.56), Portugal (7.18), and France (7.16; mean 6.23), whilst dairy and poultry averaged 5.68 and 5.94, exceeding 7.5 in Switzerland and the Netherlands. In ELI, eggs and poultry scored 2.09 and 2.05 out of 3. In ELD-I, fruit contributed most positively (0.51 UK to 2.43 Estonia), along with whole grains, tubers, vegetables, legumes, fish, dairy, and unsaturated oils. Negative ELD-I contributions were obtained for poultry (UK, Spain, Portugal, and Hungary), tubers (Estonia and Portugal), and eggs (UK, Spain, Hungary and Estonia). The lowest WISH and ELI scores were observed for whole grains (respectively 0.66 and 0.27 in Europe, from 0.23 and 0.1 in France to 3.14 and 1.03 in Finland), legumes (respectively 2.79 and 0.73 in Europe, from 0.23 and 0.06 in Estonia to 4.31 and 1.21 in Spain), and nuts (respectively 0.82 and 0.21 in Europe, from 0.41 and 0.1 in Portugal to 2.26 and 0.64 in the Netherlands). Red meat was the main negative contributor to all indices (WISH: 1.01 in Hungary–2.80 in Switzerland; ELD-I: −1.91 in the Netherlands and − 4.13 in Estonia). In ELI, beef and lamb (1.40) ranged from 1.03 (France) to 2.00 (Hungary), pork (1.11) from 0.68 (Estonia) to 1.48 (the Netherlands). Saturated fats and added sugars also strongly lowered ELD-I. Demographic determinants of PHD adherence Within each country, associations between sociodemographic factors and PHD adherence were generally consistent across the three indices (WISH, ELD-I, and ELI), indicating robust patterns regardless of the specific adherence measure used. Figure 4 presents the associations between PHD indices and sociodemographic factors, with France, Portugal, and Estonia showing significant associations for age, sex, and education. Age was the strongest predictor of adherence across countries and indices, with individuals aged 45–64 and 65–80 scoring consistently higher than those 18–44, especially in France, Spain, and Portugal (p < 0.0001). Sex differences varied, with women scoring higher than men, particularly in Switzerland, Finland, and Estonia (p < 0.0001). Higher education level was associated with better scores, particularly in France, Portugal, the Netherlands, and Estonia (p < 0.0001). In Spain, Switzerland, and Hungary, the educational differences were non-significant. Additional information is available in the Supplementary Material (Fig. S4-S6). Discussion This study provides a cross-national assessment of dietary patterns in relation to the PHD across nine European countries. Our findings show large differences between countries and significant sociodemographic variations, but also point to a convergence toward overall low adherence to PHD targets. While some countries had relatively high intakes of vegetables, dairy, and fish, these were counterbalanced by excessive consumption of red meat, saturated fats, and added sugars, as well as insufficient intake of whole grains, legumes, and nuts. Notably, none of the countries met the PHD targets for food groups requiring increased intake, which is consistent with prior evidence from high-income countries [ 39 , 40 ]. Marked heterogeneity in European dietary patterns was observed, reflecting both cultural traditions and established nutritional trends. Northern European countries, such as Finland, reported high whole grain consumption, consistent with traditional rye- and oat-based foods linked to improved health outcomes [ 41 , 42 ], whereas intake was low in France, highlighting persistent gaps despite national efforts [ 43 – 45 ]. Legumes and nuts were underconsumed across all countries, in line with previous studies showing that no European country meets recommended targets [ 46 – 49 ]. Mediterranean countries, including Spain and Portugal, demonstrated higher intakes of fish and unsaturated oils, consistent with traditional dietary patterns [ 50 , 51 ]. Northern and Central European countries relied more on animal fats and red meat, which remain difficult to reduce due to entrenched dietary habits and socioeconomic factors [ 50 , 52 – 55 ]. Although fruit and vegetable intake exceeded global averages [ 40 ], most countries still fell short of PHD targets [ 56 , 57 ], suggesting that public health efforts should continue to prioritize these food groups. Similarly, added sugars and saturated fats were above recommended levels across Europe [ 58 – 60 ], underscoring the need for integrated policy approaches that combine regulatory measures, education, and agricultural strategies [ 61 – 63 ]. Overall, our results corroborate previous European evidence and emphasize persistent challenges in achieving adherence to healthy and sustainable dietary patterns [ 64 – 67 ]. A detailed country- and food-group-specific discussion is provided in Supplementary Material. Composite indices enhanced the dietary assessment by integrating the PHD thresholds and encompassing the multidimensional structure of the diet. Furthermore, prior research indicates that these indices are differentially associated with nutritional and environmental indicators, underscoring their utility in evaluating complementary aspects of PHD [ 30 ]. In this study, composite indices confirmed that overall adherence to the PHD was low. Spain, the Netherlands, and Portugal exhibited the highest scores, while the UK, Hungary, and Estonia had the lowest scores, aligning with the poor PHD adherence reported in high-income countries [ 65 ]. Low alignment with the PHD was primarily driven by excess red meat, saturated fats, and added sugars, alongside insufficient whole grains, legumes, nuts, and vegetables [ 39 , 65 ]. Eggs, poultry, and dairy intakes were relatively consistent across countries. The Netherlands stood out in terms of whole grain and nut consumption, while Spain led in fish, legume, and unsaturated fat consumption, reflecting the closer alignment of the Mediterranean diet with the PHD [ 65 , 68 ]. Using a modified PHDI, Li et al. found that modest scores in high-income countries were largely due to excess red meat and added sugars [ 39 ]. In Central and Eastern European countries, such as Hungary and Estonia, tuber overconsumption was an additional contributing factor. Furthermore, temporal trends showed the greatest PHDI improvements in high-income countries (+ 6.5 points from 1990–2018), mainly from tuber and nut intake, with the Netherlands showing the largest gains [ 39 ]. Across the nine European countries studied, age was the most consistent predictor of higher adherence to the PHD, with older adults scoring significantly higher than younger adults, particularly in France, Spain, and Portugal, with the largest differences observed in France, Spain, and Portugal. These findings are consistent with previous research showing healthier dietary patterns and lower consumption of ultra-processed and meat products among older adults in Europe, including Spain [ 69 , 70 ], Switzerland [ 71 , 72 ], the UK [ 73 ], Portugal [ 74 ], and Finland [ 75 , 76 ]. Older age has also been linked to greater vegetable and fruit intakes [ 70 , 77 ] and lower fast food and processed meat consumption [ 69 , 78 ]. Factors contributing to these differences include greater cooking skills, more time for food preparation, adherence to traditional diets, generational norms, and increased health consciousness [ 70 ]. Sex differences were more variable across countries, although women tended to score higher on the PHD indices. This aligns with the literature indicating that women report healthier and more plant-forward diets, including lower red and processed meat intake and higher fruit and vegetable consumption [ 70 , 79 – 81 ]. Similar sex-related differences have been observed for sustainable dietary indices [ 79 – 83 ] and for specific components, such as vegetable and seafood [ 76 , 84 ]. This pattern is especially pronounced in Baltic and Nordic regions, such as Estonia, where men’s higher red and processed meat intakes drive the gap [ 75 , 85 , 86 ]. Health consciousness, environmental awareness, and willingness to change dietary habits may explain women’s higher scores, whereas cultural norms and men’s preference for energy-dense foods may contribute to lower scores among men [ 70 , 75 , 77 , 79 – 81 ]. Higher education was associated with better dietary scores, particularly in France, Portugal, the Netherlands, and Estonia, consistent with previous findings linking education to healthier and more sustainable diets, including higher intakes of fruit, vegetable, whole grain, and fish, and lower intakes of red and processed meat [ 79 – 81 , 87 , 88 ]. In France, higher education correlates with plant-forward diets and higher Sustainable Diet Index scores [ 79 ]. Across Europe, lower education levels are often linked to higher processed meat consumption, lower fruit and vegetable intake, and higher ultra-processed food consumption [ 70 , 71 , 79 , 89 ]. However, in Spain, Switzerland, and Hungary, educational differences were smaller, reflecting patterns similar to those in previous research [ 72 , 90 ]. These differences likely arise from a combination of better nutritional awareness, food literacy, health- and environment-focused attitudes, and enhanced access to quality foods among individuals with higher education levels [ 70 , 89 ]. This study drew on harmonized data from a large and diverse sample of adults across nine European countries, offering a multidimensional perspective on diet quality through three complementary indices. However, some limitations should be acknowledged. The cross-sectional design limits causal inference, and self-reported intake may introduce recall and social desirability bias, especially for foods such as red meat and added sugars. National surveys varied in terms of demographic composition and collection periods (e.g., Estonia 2013–2015 vs. UK 2020), affecting comparability. Some datasets lacked key variables, such as education in the UK and Finland, limiting the analyses. Future research should consider additional factors such as income, employment, and urbanisation [ 91 ]. Standardized recipes ensured comparability but may not capture regional differences in preparation, portion size, and ingredient quality, potentially misestimating foods that are difficult to quantify, such as added sugars or fats [ 92 ]. Shifting toward healthier and more sustainable diets requires coordinated cultural, structural, and behavioural changes, with policy playing a central role. Effective action depends on multi-level and multi-sectoral governance across EU, national, and regional levels, as well as throughout food value chains. The EU’s 2020 Farm-to-Fork strategy and the UK’s 2021 National Food Strategy exemplified such approaches, proposing measures to reduce pesticide use, promote agroecology, and harmonize front-of-pack nutrition labels [ 93 , 94 ]. Despite strong evidence and alignment with policy priorities, both strategies were undermined by corporate and regulatory capture from powerful multinational food corporations and lobby groups, reflecting a global challenge in which commercial interests override the right to food [ 95 , 96 ]. Given the diversity of Europe’s food systems, no blanket one-size-fits-all solution can address entrenched power imbalances. Action is therefore needed across levels and sectors [ 19 ], with initiatives such as the 2028–2034 Multiannual Financial Framework and post-2027 CAP reforms offering promising steps. Nevertheless, EU-level measures alone are insufficient; coordinated action across all levels and sectors of Europe’s food systems is essential to achieve meaningful transformation. Example multi-level and -sectoral actions that can promote healthier and more sustainable food choice architecture are provided in Supplementary Materials (Table S5). Our findings provide a comprehensive cross-national perspective on adherence to the PHD. The use of PHD indices enabled a multidimensional evaluation of dietary patterns and underscored the influence of sociodemographic factors. Overall, European diets remain misaligned with PHD targets, with legumes, nuts, whole grains and vegetables underconsumed, while red meat, saturated fats and added sugars are overconsumed. Although certain countries exhibit favourable trends in specific food categories, entrenched cultural, economic and environmental drivers continue to shape these patterns, and disparities across sociodemographic groups highlight the need for equity-focused and culturally tailored strategies. Importantly, meaningful progress towards healthier and more sustainable diets will require not only individual change but also coordinated structural and policy action across governance levels and food system sectors. Declarations Funding This study is part of the FEAST project funded by the European Union's Horizon Europe research and innovation program (101060536) and by Innovate UK (10041509). Swiss participant in FEAST is supported by the Swiss State Secretariat for Education, Research and Innovation (22.00156). More details in https://www.feast2030.eu/. Competing interests The authors declare that they have no competing interests. Author’s contributions Research conception and design: ARM and EOV; Data curation, cleaning and harmonisation: JMMM; formal analysis: JMMM; interpretation: ARM and EOV; writing - original draft preparation: ARM, SRV, and AJ; writing - review and editing: ARM, JMMM, SRV, AJ, EOV; funding acquisition: AJ and EOV; project administration: AJ; supervision: EOV. Data availability The INCA3, IAN-AF, and RTU datasets are publicly available at: https://www.data.gouv.fr; https://www.ian-af.up.pt, https://globaldietarydatabase.org. Access to the FINDIET, HU-EU-Menu, and ENALIA 2 datasets was requested through the European Food Safety Authority portal (https://www.efsa.europa.eu). For the DNFCS (https://www.rivm.nl/en/dutch-national-food-consumption-survey), menuCH (https://www.studydata.blv.admin.ch/home, and NDNS (https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8956) datasets, access was obtained by directly contacting the respective national data owners. 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Développement","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Verger","suffix":""}],"badges":[],"createdAt":"2025-09-30 13:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7751935/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7751935/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00394-026-03929-5","type":"published","date":"2026-03-07T15:59:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94191606,"identity":"c7e21dd3-ecf4-45fa-bcac-c67b14988d29","added_by":"auto","created_at":"2025-10-23 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12:04:49","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":54515,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7751935/v1/304ab0af189b81bfa15bcc2b.png"},{"id":94191610,"identity":"411ba8f7-3917-4ca0-ab56-4afeeb402e64","added_by":"auto","created_at":"2025-10-23 12:12:49","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68485,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7751935/v1/2817c908470f4c79a2ac1968.png"},{"id":94190839,"identity":"415ae6a3-bc5a-4638-a520-9348f99a8f02","added_by":"auto","created_at":"2025-10-23 12:04:49","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187264,"visible":true,"origin":"","legend":"","description":"","filename":"b733e8fd69be490893b3a4768d204bec1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7751935/v1/b1ac8c5cb3a1b56474982de0.xml"},{"id":94190840,"identity":"03b65dab-bbbd-42df-9ca5-afafbe0f1849","added_by":"auto","created_at":"2025-10-23 12:04:49","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":201956,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7751935/v1/6517defcc31229e68ef57280.html"},{"id":94190823,"identity":"c60a6de9-2da9-4f48-8e7b-049bec493e2d","added_by":"auto","created_at":"2025-10-23 12:04:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":344193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDeviation (%) of the daily individual food consumption from the overall aggregated mean in Europe.\u003c/strong\u003e The reference circle corresponds to the European mean.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7751935/v1/90998d669e74440b9a48f7ae.png"},{"id":94191604,"identity":"2356afd8-7b8c-4493-86ea-8b7fd5cac447","added_by":"auto","created_at":"2025-10-23 12:12:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":486638,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage of compliance with the Planetary Health Diet targets across nine European countries.\u003c/strong\u003e Bars show the average national intake per food group as a percentage of the PHD target (dashed line = 100 %). Values above dashed line indicate intake exceeding the target, and values below it indicates under-consumption.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7751935/v1/dc0f375cdaea4a7a3c0a383a.png"},{"id":94191605,"identity":"55119d7c-5a8a-47dd-98c3-b3ebe6905572","added_by":"auto","created_at":"2025-10-23 12:12:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":721912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlanetary Health Diet indices in European countries: A) World Index for Sustainability and Health (WISH), B) EAT–Lancet Index (ELI), C) EAT–Lancet Diet Index (ELD-I).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7751935/v1/f380b60d691c3573568063f1.png"},{"id":94190822,"identity":"b670c5eb-1741-4169-8b2c-97f307135725","added_by":"auto","created_at":"2025-10-23 12:04:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":358052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between sociodemographic factors and Planetary Health Diet indices in nine European countries: World Index for Sustainability and Health (WISH), EAT–Lancet Index (ELI), EAT–Lancet Diet Index (ELD-I).\u003c/strong\u003e Coefficients (β) and 95% confidence intervals are shown. The reference groups were adults aged 18–44 years, women, and individuals with lower education. No educational data were available for the United Kingdom or Finland. Indices were energy-standardized to account for differences in total intake. Interactions between sociodemographic factors were included in the regression models. *p\u0026lt;0.05; **p\u0026lt;0.001; ***p\u0026lt;0.0001\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7751935/v1/f6340c05c16399aec4a781c8.png"},{"id":104250878,"identity":"446cc734-6c22-41ca-baf3-9cb2cff8991d","added_by":"auto","created_at":"2026-03-09 16:11:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2919948,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7751935/v1/5fdddd00-0281-4bd9-b39b-d71ba978a432.pdf"},{"id":94190829,"identity":"29ca359a-e46b-4616-b031-6d043d2d038e","added_by":"auto","created_at":"2025-10-23 12:04:49","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1462867,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7751935/v1/14d0477d8f2e54b2caf5c5e3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Are European Diets Healthy and Sustainable? Evidence from Nine Countries Using the Planetary Health Diet Framework","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe impact of current food systems on human and planetary health is a growing concern [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], prompting international and local calls for healthier and more sustainable food systems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Agriculture is a key driver of global change, responsible for up to 40% of land use, 85% of freshwater consumption, 90% of nitrogen and phosphorus use, and approximately 30% of greenhouse gas emissions, largely from intensive animal production [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Diets high in added sugars, meat, and saturated fats additionally increase the risk of non-communicable diseases (such as cardiovascular diseases, cancer, and mental health issues) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Multiple mechanisms are involved, including poor nutritional quality, microbiota disruption, pro-inflammatory metabolites, impaired metabolism, and harmful preservatives [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn 2019, the EAT-Lancet Commission introduced the Planetary Health Diet (PHD) as a global reference model for a healthy and sustainable diet [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Designed for an average daily intake of 2500 kcal, the diet emphasizes plant-based foods (including vegetables, fruits, legumes, whole grains, and nuts) as well as seafood, while recommending moderate consumption of eggs, poultry, and dairy, and limiting red meat, tubers, added sugars, and saturated fats [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although the diet has faced critiques for its consumer-centred approach and limited applicability in low-resource contexts, it remains as a major contributor to global efforts toward food system transformation amid climate change [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Evidence supports its benefits for environmental sustainability and human health. For example, high adherence to the PHD is linked to up to 50% lower GHGE, 62% less land use, and potential prevention of 19\u0026ndash;63% of deaths and 10\u0026ndash;39% of cancers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTranslating global dietary guidelines such as the PHD into actionable policies requires understanding regional dietary patterns, as food systems are highly heterogeneous, reflecting diverse environmental, cultural, economic and health contexts across regions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this scenario, the European Union (EU) has committed to reducing GHG emissions by at least 55% by 2030 and 90% by 2050 compared to 1990 levels, identifying agriculture as a key emitting sector [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Non-EU countries, including Switzerland and the UK, have adopted similar goals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. At the same time, Europe faces rising rates of overnutrition and diet-related non-communicable diseases [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Achieving healthier and more sustainable diets therefore require not only individual behavioural change but also coordinated policy action across multiple governance levels [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Understanding the variability in dietary patterns across European populations is thus critical for informing targeted policies and interventions for sustainable diet promotion [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, many existing studies are limited in scope, focusing on a narrow set of countries or neglecting the diversity of food groups [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To address this gap, this study utilized recent and harmonized population-based dietary survey data to assess and compare adherence to healthy and sustainable dietary patterns across nine European countries.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eFood consumption data\u003c/h2\u003e\u003cp\u003eThis study used the most recent nationally representative food consumption surveys from nine European countries: Estonia [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], Finland [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], France [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Hungary [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the Netherlands [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Portugal [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], Spain [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], Switzerland [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and the United Kingdom (UK) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Countries were selected based on the availability of national dietary survey data collected after 2013 using at least two non-consecutive 24-hour dietary recalls. For each country, data on food consumption (grams per day, g/d), were obtained for the adult population (\u0026ge;\u0026thinsp;18 years old). Data access was obtained from the data owners, European Food Safety Authority, and public repositories. All surveys complied with the Declaration of Helsinki and received ethics committee approval. Sampling flow chart is available in Supplementary Materials (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the national dietary surveys\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurvey name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDays\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRTU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2013\u0026ndash;2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFINDIET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINCA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2014\u0026ndash;2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHungary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHU-EU-Menu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026ndash;2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe Netherlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDNFCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2019\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePortugal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIAN-AF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2015\u0026ndash;2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eENALIA 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2014\u0026ndash;2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSwitzerland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emenuCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2014\u0026ndash;2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnited Kingdom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNDNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eOnly adults.\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eMaximum number of 24-hour dietary recall days.\u003c/p\u003e\u003cp\u003eFood consumption was categorized into key groups based on the PHD framework [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Whole grains included rice, wheat, and products containing whole grain components. Vegetables and fruits included fresh, frozen, cooked, canned, and dried forms, excluding juices. Dairy comprised milk (whole or skimmed), cheese, and yoghurt, excluding butter and cream. Red meat included unprocessed and processed meats (e.g., beef, pork, lamb). Fish included all fish and shellfish. Eggs and poultry included chickens, ducks, and geese. Legumes included beans, lentils, and soybeans. Nuts included tree nuts and groundnuts (e.g., peanuts). Unsaturated oils comprised plant oils (e.g., olive, rapeseed, sunflower), and saturated fats included dairy fats, tallow, and palm oil. Added sugars, including those from foods and sugar-sweetened beverages, were also quantified. Detailed definitions are available elsewhere [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo estimate the composition of mixed dishes with multiple ingredients, a standardized recipe calculation method was consistently applied across all the national surveys [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A harmonized recipe database was developed to ensure cross-country comparison. Rather than disaggregating dishes into individual ingredients, standardized formulations were created at the food group level for commonly consumed foods (e.g., stews, pizzas, sandwiches). Where multiple versions of a dish existed (e.g., fish-, red meat-, poultry-, or vegetable-based), each was represented by a separate, standardized recipe reflecting typical preparation. Recipe information was compiled from recipe databases, food labels, and culinary websites [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Using these sources, we estimated the proportion of each food group within the dish; for example, a ham and vegetable sandwich with black bread may contained 20% red meat, 10% vegetables, and 50% whole grains. Each recipe was broken down into food group components according to the PHD categories. To quantify food group intake, the total reported weight of each dish consumed was distributed among the food groups based on standardized proportions, converting dish weight into grams per food group. For instance, if an individual reported consuming 200 g of the ham and vegetable sandwich with black bread, the intake was calculated as 40 g red meat (20%), 20 g vegetables (10%), and 100 g whole grains (50%). A total of 141 standardized recipes were included in the final database.\u003c/p\u003e\u003cp\u003e Importantly, the allocation of ingredients to food groups was guided by both their presence and nutritional value. Foods considered healthy, such as whole grains, were not included in their corresponding group if consumed as part of an unhealthy preparation. For example, a whole grain breakfast cereal with added sugars was considered only for \u0026ldquo;added sugars\u0026rdquo; and not as a source of \u0026ldquo;whole grains\u0026rdquo;, to remain consistent with the PHD framework [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData harmonisation\u003c/h3\u003e\n\u003cp\u003eA standardized harmonisation protocol was implemented to ensure consistency across datasets and enable comparative analysis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Participant and dietary data at the individual level were extracted using a pre-defined codebook and a uniform data collection template. Variables from each dataset were recoded and described according to harmonised definitions to ensure alignment across countries. Each data entry captured the food group intake (g/d), nutrient intake, and participant data. Individual-level microdata were aggregated into subgroups stratified jointly by age (18\u0026ndash;44, 45\u0026ndash;64, or \u0026ge;\u0026thinsp;65 years), sex (women or men), and education level (lower or higher, defined by post-secondary education) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eData quality was systematically monitored throughout the process, and any irregularities (i.e., implausible values or structural inconsistencies) were reviewed and resolved collaboratively by the research team [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. To account for age- and sex-related differences, food intake was standardized to grams per 2,000 kcal [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Participants reporting extreme energy intakes (\u0026lt;\u0026thinsp;800 or \u0026gt;\u0026thinsp;4200 kcal/day for men; \u0026lt;600 or \u0026gt;\u0026thinsp;3500 kcal/day for women) on the dietary recalls were excluded (n\u0026thinsp;=\u0026thinsp;256; Supplementary Materials: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Survey weights were calculated separately for each national dataset. For this, the age- and sex-distribution of the survey sample was compared with the corresponding national population distribution obtained from Eurostat. Each participant was assigned a weight proportional to the under- or over-representation of their age-sex stratum in the survey. These weights were then applied in the pooled analysis, so that each country contributed to the results in proportion to its national population.\u003c/p\u003e\n\u003ch3\u003ePHD adherence assessment\u003c/h3\u003e\n\u003cp\u003eFirst, to evaluate compliance with the PHD, we calculated for each food group the percentage of the recommended target intake achieved, defined as % = daily intake/PHD target \u0026times; 100 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. A value of 100% indicates full adherence, \u0026gt;\u0026thinsp;100% indicates consumption above the target, and \u0026lt;\u0026thinsp;100% indicates consumption below the target. This allowed the identification of over- and under-consumed food groups in each country relative to the PHD benchmarks (Supplementary Table S4).\u003c/p\u003e\u003cp\u003eIn addition, three composite indices were employed to provide a more integrative evaluation of dietary patterns. These indices differ in terms of scoring systems, energy adjustment, treatment of food categories, and thresholds, offering complementary perspectives [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe World Index for Sustainability and Health (WISH) assesses diet across 13 food groups classified as neutral, protective, or harmful to human and planetary health [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Food groups are scored from 0 (noncompliance) to 10 (full compliance) based on reference intakes (g) reflecting adherence to the PHD. The total score ranges from 0 to 130. Further details on WISH are described elsewhere [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe EAT-Lancet Diet Index (ELD-I) measures how closely a diet aligns with the PHD across 14 food groups using proportional scoring adjusted for individual energy intake (2,500 kcal reference) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Recommended foods score positively when intake exceeds targets; foods to limit score positively when consumption is below limits. Underconsumption of recommended foods or excess intake of limited foods yields negative scores. The resulting unbounded continuous score (positive or negative) reflects the overall adherence. Further details on ELD-I are provided elsewhere [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe EAT-Lancet Index (ELI) is composed of 14 food groups divided into two categories: seven positive components or \u0026ldquo;emphasized foods\u0026rdquo; and seven negative components or \u0026ldquo;limited foods\u0026rdquo; [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Each component is scored on a graded scale from 0 (noncompliance) to 3 points (high compliance), based on how closely intake aligns with the targets. Total ELI score ranges from 0 to 42. More details on ELI are available elsewhere [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eFor each index, the contribution of individual food groups to the total score was calculated, enabling the identification of components driving higher or lower adherence within countries. The reliability and validity of the WISH, ELD-I, and ELI in capturing the nutritional health and environmental impacts of diets have been previously established [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were used to summarize the sample characteristics and dietary intake. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables as frequencies. Food consumption and adherence to the PHD were described and compared across countries and demographic subgroups. Within-country differences in WISH, ELD-I and ELI according to demographics were evaluated using multivariate regressions, with results expressed as standardized beta coefficients (β) and 95% confidence intervals. To account for variation in total energy intake, food group intakes and indices were standardized to 2000 kcal. This adjustment was applied consistently across descriptive and regression analyses. Analyses were performed using RStudio (vR4.5.0, RStudio Team) and Stata (v18, StataCorp, College Station, TX, USA), accounting for sampling weights and considering p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided) as statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics\u003c/h2\u003e\u003cp\u003eThe study included 16,083 adults from nine European countries, with sample sizes ranging from 519 in the UK to 3,703 in Portugal. Sociodemographic characteristics varied by country (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Women represented 56.1% of the sample, with the largest gender imbalance in Estonia. Overall, 39.8% were aged 18\u0026ndash;45 years, 35.7% were 45\u0026ndash;65 years, and 24.6% were 65\u0026ndash;80 years. Young and middle-aged adults predominated across most countries, except in Hungary, where over half of the participants were aged 65\u0026ndash;80 years. In total, 59.4% had lower education and 40.7% had higher education, ranging from a predominance of lower education in Portugal and Hungary to higher education in Estonia. Sampling weights were applied to adjust for cross-country differences, with the weighted proportions shown in Supplementary Materials (Tables S2 and S3).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18\u0026ndash;44\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45\u0026ndash;64\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65\u0026ndash;80\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEurope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9025\u003c/p\u003e\u003cp\u003e(56.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7058\u003c/p\u003e\u003cp\u003e(43.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6397\u003c/p\u003e\u003cp\u003e(39.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5738\u003c/p\u003e\u003cp\u003e(35.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3948\u003c/p\u003e\u003cp\u003e(24.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8155\u003c/p\u003e\u003cp\u003e(59.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5586\u003c/p\u003e\u003cp\u003e(40.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnited Kingdom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e301\u003c/p\u003e\u003cp\u003e(50.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e218\u003c/p\u003e\u003cp\u003e(42.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e193\u003c/p\u003e\u003cp\u003e(37.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e195\u003c/p\u003e\u003cp\u003e(37.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e131\u003c/p\u003e\u003cp\u003e(25.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1217\u003c/p\u003e\u003cp\u003e(58.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e857\u003c/p\u003e\u003cp\u003e(41.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e761\u003c/p\u003e\u003cp\u003e(36.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e809\u003c/p\u003e\u003cp\u003e(39.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e504\u003c/p\u003e\u003cp\u003e(24.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1213\u003c/p\u003e\u003cp\u003e(58.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e860\u003c/p\u003e\u003cp\u003e(41.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e531\u003c/p\u003e\u003cp\u003e(57.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e398\u003c/p\u003e\u003cp\u003e(42.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e449\u003c/p\u003e\u003cp\u003e(48.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e217\u003c/p\u003e\u003cp\u003e(23.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e263\u003c/p\u003e\u003cp\u003e(28.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e547\u003c/p\u003e\u003cp\u003e(58.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e382\u003c/p\u003e\u003cp\u003e(41.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe Netherlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e860\u003c/p\u003e\u003cp\u003e(49.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e873\u003c/p\u003e\u003cp\u003e(50.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e433\u003c/p\u003e\u003cp\u003e(24.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e695\u003c/p\u003e\u003cp\u003e(40.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e605\u003c/p\u003e\u003cp\u003e(34.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e949\u003c/p\u003e\u003cp\u003e(55.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e773\u003c/p\u003e\u003cp\u003e(44.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePortugal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1970\u003c/p\u003e\u003cp\u003e(53.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1733\u003c/p\u003e\u003cp\u003e(46.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1730\u003c/p\u003e\u003cp\u003e(46.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1322\u003c/p\u003e\u003cp\u003e(35.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e651\u003c/p\u003e\u003cp\u003e(17.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2832\u003c/p\u003e\u003cp\u003e(76.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e869\u003c/p\u003e\u003cp\u003e(23.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHungary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e521\u003c/p\u003e\u003cp\u003e(50.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e511\u003c/p\u003e\u003cp\u003e(49.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e236\u003c/p\u003e\u003cp\u003e(22.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e272\u003c/p\u003e\u003cp\u003e(26.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e524\u003c/p\u003e\u003cp\u003e(50.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e489\u003c/p\u003e\u003cp\u003e(68.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e220\u003c/p\u003e\u003cp\u003e(31.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSwitzerland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1115\u003c/p\u003e\u003cp\u003e(55.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e898\u003c/p\u003e\u003cp\u003e(44.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e903\u003c/p\u003e\u003cp\u003e(44.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e776\u003c/p\u003e\u003cp\u003e(38.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e334\u003c/p\u003e\u003cp\u003e(16.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1033\u003c/p\u003e\u003cp\u003e(51.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e977\u003c/p\u003e\u003cp\u003e(48.61)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e778\u003c/p\u003e\u003cp\u003e(52.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e703\u003c/p\u003e\u003cp\u003e(47.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e508\u003c/p\u003e\u003cp\u003e(34.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e553\u003c/p\u003e\u003cp\u003e(37.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e420\u003c/p\u003e\u003cp\u003e(28.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1732\u003c/p\u003e\u003cp\u003e(66.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e867\u003c/p\u003e\u003cp\u003e(33.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1184\u003c/p\u003e\u003cp\u003e(45.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e899\u003c/p\u003e\u003cp\u003e(34.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e516\u003c/p\u003e\u003cp\u003e(19.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1092\u003c/p\u003e\u003cp\u003e(42.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1505\u003c/p\u003e\u003cp\u003e(57.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003en/a\u0026thinsp;=\u0026thinsp;Data not available in the survey.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExploring food consumption patterns\u003c/h3\u003e\n\u003cp\u003eIn the pooled European sample, the mean daily intakes were highest for dairy (263.8\u0026thinsp;\u0026plusmn;\u0026thinsp;85.6 g/d), vegetables (189.6\u0026thinsp;\u0026plusmn;\u0026thinsp;24.6 g/d), and fruits (177.1\u0026thinsp;\u0026plusmn;\u0026thinsp;47.7 g/d), followed by tubers (66.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6) and whole grains (31.5\u0026thinsp;\u0026plusmn;\u0026thinsp;19.5 g/d) (Supplementary Fig.\u0026nbsp;1). For animal-based foods, mean intakes were 82.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5 g/d for red meat (40.4 g/d for beef and lamb; 42.0 g/d for pork), 49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.8 g/d for poultry, 22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0 g/d for eggs, and 38.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4 g/d for fish and seafood. Other components included legumes (27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8 g/d), nuts (5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 g/d), unsaturated oils (14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4 g/d), saturated fats (28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3 g/d), and added sugars (54.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5 g/d). The absolute food consumption by country is shown in Supplementary Materials (Fig. S2).\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows country-specific deviations from the European mean for food groups, highlighting notable differences (\u0026ge;\u0026thinsp;30%). The UK exhibited higher consumption of poultry (+\u0026thinsp;41%), eggs (+\u0026thinsp;43%), and legumes (+\u0026thinsp;36%), along with a lower intake of unsaturated oils (\u0026minus;\u0026thinsp;62%) and fruits (\u0026minus;\u0026thinsp;32%). France showed a higher intake of saturated fats (+\u0026thinsp;30%) and lower intakes of whole grains (\u0026minus;\u0026thinsp;52%), legumes (\u0026minus;\u0026thinsp;43%), and nuts (\u0026minus;\u0026thinsp;51%). Spain had higher consumption of unsaturated oils (+\u0026thinsp;71%), fish (+\u0026thinsp;65%), legumes (+\u0026thinsp;53%), and dairy (+\u0026thinsp;52%), whereas saturated fat intake was lower (\u0026minus;\u0026thinsp;50%). The Netherlands showed higher consumption of nuts (+\u0026thinsp;163%) and whole grains (+\u0026thinsp;121%), alongside lower consumption of fish (\u0026minus;\u0026thinsp;62%) and poultry (\u0026minus;\u0026thinsp;48%). Portugal exhibited higher intakes of fish (+\u0026thinsp;72%), tubers (+\u0026thinsp;69%), and poultry (+\u0026thinsp;34%), with legumes (\u0026minus;\u0026thinsp;63%), whole grains (\u0026minus;\u0026thinsp;54%), and added sugars (\u0026minus;\u0026thinsp;35%) consumed less. Hungary showed higher intakes of whole grains (+\u0026thinsp;77%), unsaturated oils (+\u0026thinsp;57%), and pork (+\u0026thinsp;98%) and decreased intakes of fish (\u0026minus;\u0026thinsp;74%), dairy (\u0026minus;\u0026thinsp;34%), and legumes (\u0026minus;\u0026thinsp;32%). Switzerland reported lower poultry (\u0026minus;\u0026thinsp;48%), legume (\u0026minus;\u0026thinsp;45%), and egg (\u0026minus;\u0026thinsp;39%) consumption. Finland showed high whole grain (+\u0026thinsp;215%), unsaturated oil (+\u0026thinsp;68%), and dairy (+\u0026thinsp;45%) intakes, with reduced legume (\u0026minus;\u0026thinsp;58%) intake. Estonia had higher tuber (+\u0026thinsp;100%), fruit (+\u0026thinsp;55%), and pork (+\u0026thinsp;86%) consumption, and markedly lower legume (\u0026minus;\u0026thinsp;93%) and nut (\u0026minus;\u0026thinsp;48%) consumption.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003ePHD Adherence\u003c/h3\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the alignment of mean dietary intakes with the PHD reference values. No country achieved the recommended intake levels for whole grains (6\u0026ndash;42%), nuts (5\u0026ndash;27%), legumes (4\u0026ndash;84%), unsaturated oils (14\u0026ndash;60%), or vegetables (56\u0026ndash;75%). Fruit consumption was also below optimal levels in the UK, the Netherlands, Hungary, Switzerland, and Finland (60\u0026ndash;85%), while fish intake was insufficient in the Netherlands, Hungary, and Switzerland (35\u0026ndash;70%). Conversely, poultry consumption exceeded the PHD target in all countries except the Netherlands and Switzerland, ranging from 114% in France to 242% in the UK, and eggs were overconsumed in all regions, with figures ranging from 107% in Switzerland to 252% in the UK. Red meat consumption exhibited the most significant excess, ranging from 465% in the Netherlands to 821% in Estonia. In Hungary (1,188%), Estonia (1,118%), Spain (748%), Finland (867%), and Portugal (676%), pork was the primary contributor, whereas beef and lamb were more prominent in France (695% vs. 596% for pork) and the UK (637% vs. 410% for pork); Switzerland and the Netherlands reported nearly equal proportions. Saturated fat intake ranged from 120% in Spain to 323% in Finland of the reference values, while added sugars ranged from 114% in Portugal to 224% in Switzerland. Tuber consumption was moderately above the target (\u0026lt;\u0026thinsp;140%) in most countries but was significantly higher in Estonia (264%), Portugal (223%), and Finland (162%).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the mean score of the dietary indices measuring adherence to PHD, and the mean values of their components. The European mean WISH score was 42.69\u0026thinsp;\u0026plusmn;\u0026thinsp;5.01, highest in Spain (50.19\u0026thinsp;\u0026plusmn;\u0026thinsp;16.15), the Netherlands (46.82\u0026thinsp;\u0026plusmn;\u0026thinsp;16.93), and Portugal (46.22\u0026thinsp;\u0026plusmn;\u0026thinsp;16.31), and lowest in Hungary (37.47\u0026thinsp;\u0026plusmn;\u0026thinsp;14.19), the UK (37.67\u0026thinsp;\u0026plusmn;\u0026thinsp;17.23), and Estonia (38.98\u0026thinsp;\u0026plusmn;\u0026thinsp;14.27). ELI followed a similar pattern (European mean 18.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31), with Spain (20.94\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39), the Netherlands (19.80\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51), and Portugal (19.64\u0026thinsp;\u0026plusmn;\u0026thinsp;4.37) having the highest score, and Estonia (16.78\u0026thinsp;\u0026plusmn;\u0026thinsp;4.19), the UK (17.57\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25), and Hungary (18.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83) having the lowest. ELD-I averaged \u0026minus;\u0026thinsp;14.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.62 across Europe; the Netherlands scored positively (3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;35.70), followed by Spain (\u0026minus;\u0026thinsp;4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;37.69) and Portugal (\u0026minus;\u0026thinsp;6.22\u0026thinsp;\u0026plusmn;\u0026thinsp;42.15), while the UK (\u0026minus;\u0026thinsp;27.06\u0026thinsp;\u0026plusmn;\u0026thinsp;40.91), Finland (\u0026minus;\u0026thinsp;26.38\u0026thinsp;\u0026plusmn;\u0026thinsp;42.81), and Hungary (\u0026minus;\u0026thinsp;23.07\u0026thinsp;\u0026plusmn;\u0026thinsp;32.34) deviated most. The indices were positively correlated across countries (Supplementary Material: Fig. S3).\u003c/p\u003e\u003cp\u003eAcross Europe, adherence was highest for eggs, dairy, and poultry, particularly in WISH and ELI. Eggs approached the WISH maximum (10 points) in Switzerland (7.56), Portugal (7.18), and France (7.16; mean 6.23), whilst dairy and poultry averaged 5.68 and 5.94, exceeding 7.5 in Switzerland and the Netherlands. In ELI, eggs and poultry scored 2.09 and 2.05 out of 3. In ELD-I, fruit contributed most positively (0.51 UK to 2.43 Estonia), along with whole grains, tubers, vegetables, legumes, fish, dairy, and unsaturated oils. Negative ELD-I contributions were obtained for poultry (UK, Spain, Portugal, and Hungary), tubers (Estonia and Portugal), and eggs (UK, Spain, Hungary and Estonia).\u003c/p\u003e\u003cp\u003eThe lowest WISH and ELI scores were observed for whole grains (respectively 0.66 and 0.27 in Europe, from 0.23 and 0.1 in France to 3.14 and 1.03 in Finland), legumes (respectively 2.79 and 0.73 in Europe, from 0.23 and 0.06 in Estonia to 4.31 and 1.21 in Spain), and nuts (respectively 0.82 and 0.21 in Europe, from 0.41 and 0.1 in Portugal to 2.26 and 0.64 in the Netherlands). Red meat was the main negative contributor to all indices (WISH: 1.01 in Hungary\u0026ndash;2.80 in Switzerland; ELD-I: \u0026minus;1.91 in the Netherlands and \u0026minus;\u0026thinsp;4.13 in Estonia). In ELI, beef and lamb (1.40) ranged from 1.03 (France) to 2.00 (Hungary), pork (1.11) from 0.68 (Estonia) to 1.48 (the Netherlands). Saturated fats and added sugars also strongly lowered ELD-I.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDemographic determinants of PHD adherence\u003c/h2\u003e\u003cp\u003eWithin each country, associations between sociodemographic factors and PHD adherence were generally consistent across the three indices (WISH, ELD-I, and ELI), indicating robust patterns regardless of the specific adherence measure used. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the associations between PHD indices and sociodemographic factors, with France, Portugal, and Estonia showing significant associations for age, sex, and education. Age was the strongest predictor of adherence across countries and indices, with individuals aged 45\u0026ndash;64 and 65\u0026ndash;80 scoring consistently higher than those 18\u0026ndash;44, especially in France, Spain, and Portugal (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Sex differences varied, with women scoring higher than men, particularly in Switzerland, Finland, and Estonia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Higher education level was associated with better scores, particularly in France, Portugal, the Netherlands, and Estonia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In Spain, Switzerland, and Hungary, the educational differences were non-significant. Additional information is available in the Supplementary Material (Fig. S4-S6).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a cross-national assessment of dietary patterns in relation to the PHD across nine European countries. Our findings show large differences between countries and significant sociodemographic variations, but also point to a convergence toward overall low adherence to PHD targets. While some countries had relatively high intakes of vegetables, dairy, and fish, these were counterbalanced by excessive consumption of red meat, saturated fats, and added sugars, as well as insufficient intake of whole grains, legumes, and nuts. Notably, none of the countries met the PHD targets for food groups requiring increased intake, which is consistent with prior evidence from high-income countries [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMarked heterogeneity in European dietary patterns was observed, reflecting both cultural traditions and established nutritional trends. Northern European countries, such as Finland, reported high whole grain consumption, consistent with traditional rye- and oat-based foods linked to improved health outcomes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], whereas intake was low in France, highlighting persistent gaps despite national efforts [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Legumes and nuts were underconsumed across all countries, in line with previous studies showing that no European country meets recommended targets [\u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Mediterranean countries, including Spain and Portugal, demonstrated higher intakes of fish and unsaturated oils, consistent with traditional dietary patterns [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Northern and Central European countries relied more on animal fats and red meat, which remain difficult to reduce due to entrenched dietary habits and socioeconomic factors [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan additionalcitationids=\"CR53 CR54\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Although fruit and vegetable intake exceeded global averages [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], most countries still fell short of PHD targets [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], suggesting that public health efforts should continue to prioritize these food groups. Similarly, added sugars and saturated fats were above recommended levels across Europe [\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], underscoring the need for integrated policy approaches that combine regulatory measures, education, and agricultural strategies [\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Overall, our results corroborate previous European evidence and emphasize persistent challenges in achieving adherence to healthy and sustainable dietary patterns [\u003cspan additionalcitationids=\"CR65 CR66\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. A detailed country- and food-group-specific discussion is provided in Supplementary Material. Composite indices enhanced the dietary assessment by integrating the PHD thresholds and encompassing the multidimensional structure of the diet. Furthermore, prior research indicates that these indices are differentially associated with nutritional and environmental indicators, underscoring their utility in evaluating complementary aspects of PHD [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, composite indices confirmed that overall adherence to the PHD was low. Spain, the Netherlands, and Portugal exhibited the highest scores, while the UK, Hungary, and Estonia had the lowest scores, aligning with the poor PHD adherence reported in high-income countries [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLow alignment with the PHD was primarily driven by excess red meat, saturated fats, and added sugars, alongside insufficient whole grains, legumes, nuts, and vegetables [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Eggs, poultry, and dairy intakes were relatively consistent across countries. The Netherlands stood out in terms of whole grain and nut consumption, while Spain led in fish, legume, and unsaturated fat consumption, reflecting the closer alignment of the Mediterranean diet with the PHD [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Using a modified PHDI, Li et al. found that modest scores in high-income countries were largely due to excess red meat and added sugars [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In Central and Eastern European countries, such as Hungary and Estonia, tuber overconsumption was an additional contributing factor. Furthermore, temporal trends showed the greatest PHDI improvements in high-income countries (+\u0026thinsp;6.5 points from 1990\u0026ndash;2018), mainly from tuber and nut intake, with the Netherlands showing the largest gains [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAcross the nine European countries studied, age was the most consistent predictor of higher adherence to the PHD, with older adults scoring significantly higher than younger adults, particularly in France, Spain, and Portugal, with the largest differences observed in France, Spain, and Portugal. These findings are consistent with previous research showing healthier dietary patterns and lower consumption of ultra-processed and meat products among older adults in Europe, including Spain [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], Switzerland [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], the UK [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], Portugal [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], and Finland [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Older age has also been linked to greater vegetable and fruit intakes [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] and lower fast food and processed meat consumption [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Factors contributing to these differences include greater cooking skills, more time for food preparation, adherence to traditional diets, generational norms, and increased health consciousness [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSex differences were more variable across countries, although women tended to score higher on the PHD indices. This aligns with the literature indicating that women report healthier and more plant-forward diets, including lower red and processed meat intake and higher fruit and vegetable consumption [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Similar sex-related differences have been observed for sustainable dietary indices [\u003cspan additionalcitationids=\"CR80 CR81 CR82\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e] and for specific components, such as vegetable and seafood [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. This pattern is especially pronounced in Baltic and Nordic regions, such as Estonia, where men\u0026rsquo;s higher red and processed meat intakes drive the gap [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Health consciousness, environmental awareness, and willingness to change dietary habits may explain women\u0026rsquo;s higher scores, whereas cultural norms and men\u0026rsquo;s preference for energy-dense foods may contribute to lower scores among men [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHigher education was associated with better dietary scores, particularly in France, Portugal, the Netherlands, and Estonia, consistent with previous findings linking education to healthier and more sustainable diets, including higher intakes of fruit, vegetable, whole grain, and fish, and lower intakes of red and processed meat [\u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. In France, higher education correlates with plant-forward diets and higher Sustainable Diet Index scores [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Across Europe, lower education levels are often linked to higher processed meat consumption, lower fruit and vegetable intake, and higher ultra-processed food consumption [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. However, in Spain, Switzerland, and Hungary, educational differences were smaller, reflecting patterns similar to those in previous research [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. These differences likely arise from a combination of better nutritional awareness, food literacy, health- and environment-focused attitudes, and enhanced access to quality foods among individuals with higher education levels [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study drew on harmonized data from a large and diverse sample of adults across nine European countries, offering a multidimensional perspective on diet quality through three complementary indices. However, some limitations should be acknowledged. The cross-sectional design limits causal inference, and self-reported intake may introduce recall and social desirability bias, especially for foods such as red meat and added sugars. National surveys varied in terms of demographic composition and collection periods (e.g., Estonia 2013\u0026ndash;2015 vs. UK 2020), affecting comparability. Some datasets lacked key variables, such as education in the UK and Finland, limiting the analyses. Future research should consider additional factors such as income, employment, and urbanisation [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. Standardized recipes ensured comparability but may not capture regional differences in preparation, portion size, and ingredient quality, potentially misestimating foods that are difficult to quantify, such as added sugars or fats [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eShifting toward healthier and more sustainable diets requires coordinated cultural, structural, and behavioural changes, with policy playing a central role. Effective action depends on multi-level and multi-sectoral governance across EU, national, and regional levels, as well as throughout food value chains. The EU\u0026rsquo;s 2020 Farm-to-Fork strategy and the UK\u0026rsquo;s 2021 National Food Strategy exemplified such approaches, proposing measures to reduce pesticide use, promote agroecology, and harmonize front-of-pack nutrition labels [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. Despite strong evidence and alignment with policy priorities, both strategies were undermined by corporate and regulatory capture from powerful multinational food corporations and lobby groups, reflecting a global challenge in which commercial interests override the right to food [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. Given the diversity of Europe\u0026rsquo;s food systems, no blanket one-size-fits-all solution can address entrenched power imbalances. Action is therefore needed across levels and sectors [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], with initiatives such as the 2028\u0026ndash;2034 Multiannual Financial Framework and post-2027 CAP reforms offering promising steps. Nevertheless, EU-level measures alone are insufficient; coordinated action across all levels and sectors of Europe\u0026rsquo;s food systems is essential to achieve meaningful transformation. Example multi-level and -sectoral actions that can promote healthier and more sustainable food choice architecture are provided in Supplementary Materials (Table S5).\u003c/p\u003e\u003cp\u003eOur findings provide a comprehensive cross-national perspective on adherence to the PHD. The use of PHD indices enabled a multidimensional evaluation of dietary patterns and underscored the influence of sociodemographic factors. Overall, European diets remain misaligned with PHD targets, with legumes, nuts, whole grains and vegetables underconsumed, while red meat, saturated fats and added sugars are overconsumed. Although certain countries exhibit favourable trends in specific food categories, entrenched cultural, economic and environmental drivers continue to shape these patterns, and disparities across sociodemographic groups highlight the need for equity-focused and culturally tailored strategies. Importantly, meaningful progress towards healthier and more sustainable diets will require not only individual change but also coordinated structural and policy action across governance levels and food system sectors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is part of the FEAST project funded by the European Union's Horizon Europe research and innovation program (101060536) and by Innovate UK (10041509). Swiss participant in FEAST is supported by the Swiss State Secretariat for Education, Research and Innovation (22.00156). More details in https://www.feast2030.eu/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch conception and design: ARM and EOV; Data curation, cleaning and harmonisation: JMMM; formal analysis: JMMM; interpretation: ARM and EOV; writing - original draft preparation: ARM, SRV, and AJ; writing - review and editing: ARM, JMMM, SRV, AJ, EOV; funding acquisition: AJ and EOV; project administration: AJ; supervision: EOV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe INCA3, IAN-AF, and RTU datasets are publicly available at: https://www.data.gouv.fr; https://www.ian-af.up.pt, https://globaldietarydatabase.org. Access to the FINDIET, HU-EU-Menu, and ENALIA 2 datasets was requested through the European Food Safety Authority portal (https://www.efsa.europa.eu). For the DNFCS (https://www.rivm.nl/en/dutch-national-food-consumption-survey), menuCH (https://www.studydata.blv.admin.ch/home, and NDNS (https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8956) datasets, access was obtained by directly contacting the respective national data owners.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMago A, Dhali A, Kumar H, Maity R, Kumar B (2024) Planetary health and its relevance in the modern era: A topical review. 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Accessed 24 September 2025\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Nutrition Surveys, Dietary Patterns, EAT-Lancet diet, Sustainable Nutrition","lastPublishedDoi":"10.21203/rs.3.rs-7751935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7751935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eContemporary food systems pose challenges for both human and planetary health. This study aimed to assess and compare adherence to the Planetary Health Diet (PHD) in nine European countries.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eNationally representative dietary surveys (post-2013) from Estonia, Finland, France, Hungary, the Netherlands, Portugal, Spain, Switzerland, and the United Kingdom, with \u0026ge;\u0026thinsp;2 non-consecutive 24-hour recalls, were used (n\u0026thinsp;=\u0026thinsp;16,083 adults). Adherence to the PHD was assessed at two levels: 1) compliance for each food group, calculated as the intake relative to the corresponding PHD targets, and 2) overall adherence, captured by three valid dietary indices. Multivariate regression analyses were conducted to identify associations with demographic factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e Dietary patterns across Europe were characterized by insufficient intake of plant-based foods (whole grains, legumes, nuts, vegetables, and unsaturated oils) relative to PHD targets, alongside excessive consumption of foods to limit (red meat, saturated fats, and added sugars). Spain, Portugal, and the Netherlands showed comparatively better alignment with the PHD, whereas Hungary, the United Kingdom, and Estonia had the lowest scores. Red meat, particularly pork, and added sugars were the primary drivers of low scores across PHD indices. Being female, older, and having a higher level of education were positively associated with PHD adherence.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eEuropean diets show systematic deviations from the PHD. Targeted and multilevel policies are needed to promote healthy and sustainable diets.\u003c/p\u003e","manuscriptTitle":"Are European Diets Healthy and Sustainable? Evidence from Nine Countries Using the Planetary Health Diet Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 12:04:44","doi":"10.21203/rs.3.rs-7751935/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee8c2904-09d0-482b-b5b3-62cf8b44fc0d","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:06:29+00:00","versionOfRecord":{"articleIdentity":"rs-7751935","link":"https://doi.org/10.1007/s00394-026-03929-5","journal":{"identity":"european-journal-of-nutrition","isVorOnly":false,"title":"European Journal of Nutrition"},"publishedOn":"2026-03-07 15:59:22","publishedOnDateReadable":"March 7th, 2026"},"versionCreatedAt":"2025-10-23 12:04:44","video":"","vorDoi":"10.1007/s00394-026-03929-5","vorDoiUrl":"https://doi.org/10.1007/s00394-026-03929-5","workflowStages":[]},"version":"v1","identity":"rs-7751935","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7751935","identity":"rs-7751935","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}