Do Swiss Diets Align with the Planetary Health Diet Recommendations?

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Nele Endner, Philipp Schuetz, Selina Randegger, Carla Wunderle, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8500417/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background: In 2019, the global EAT-Lancet Planetary Health Diet (PHD) introduced specific recommendations to promote sustainable, health-focused dietary changes in response to the increasing prevalence of diet-related diseases and escalating environmental challenges. We assessed the alignment of Swiss dietary habits with these recommendations. Methods: We analysed data from 2057 adults (18–75 years) from the 2014–2015 national nutrition survey menuCH . We matched different food categories with the PHD classification and calculated mean intakes (g and kcal) per food category, standardised to 2500 kcal, expressed as a percentage of the PHD recommendations (PHD = 100%). We also calculated the Planetary Health Diet Index (PHDI), with higher scores indicating higher adherence. Results: Of the 14 food categories, adherence was highest for fish and seafood ( menuCh vs. PHD; mean intake in kcal/day: 41 vs. 40; 103% of the PHD recommendations), followed by grains (775 vs. 811; 96%). The highest level of overconsumption was observed for meat (all types excluding poultry; 255 vs. 30; 850%) and dairy foods (556 vs. 153; 363%). The largest underconsumption was found for legumes (11 vs. 284; 4%) and unsaturated oils (138 vs. 354; 39%). Males consumed significantly more meat than females (298 vs. 214). The overall PHDI was 82.2 out of 140 points, with women showing higher scores than men (84.7 vs. 79.3). Conclusions: Swiss dietary intakes exceeded PHD recommendations, particularly for meat and dairy foods, while legumes remain underconsumed. A shift towards more plant-based foods could improve alignment with health and environmental sustainability goals. Swiss food consumption national nutrition survey menuCH planetary health diet sustainability Figures Figure 1 Figure 2 Background The rising prevalence of diet-related diseases and growing environmental concerns highlight the urgent need for sustainable and health-conscious dietary shifts. It is evident that nutrition affects not only the health of the population, but also the health of the planet (1, 2). It has been estimated that diets high in processed foods, refined sugars, fats and high amount of meat consumption significantly contributes to the important increase in overweight and obesity, which now affects 2.5 billion people worldwide (1). This increase in body mass index (BMI) is directly linked to a growing global incidence of chronic non-communicable diseases (NCDs), including type 2 diabetes, cardiovascular diseases, and certain cancers (3, 4). In 2017, unhealthy diets contributed significantly to 1 in 5 deaths (11 million deaths worldwide), with cardiovascular diseases the leading cause (5). This global trend is also reflected in Switzerland, where in 2022, 43% of the population were overweight or obese, 52% of men and 34% of women, highlighting the need for improvements in dietary habits (6). At the same time, global food production is the largest driver of global environmental change, threatening not only local ecosystems but also the stability of the Earth system (1). Agriculture and food production account for approximately 30% of global anthropogenic greenhouse gas emissions, 40% of the global land use and 70% of freshwater use (7, 8). In Switzerland, 35% of the national territory are agricultural land, which is responsible for at least 12% of greenhouse gas emissions (9, 10). Food production, including transport, processing and trade, accounts for around one quarter of Switzerland's total ecological footprint - with animal products having the highest impact, accounting for around 85% of greenhouse gas emissions from Swiss agriculture (9). In 2019, the EAT-Lancet Commission introduced the Planetary Health Dietary Recommendations – a global reference diet designed to promote human health while respecting planetary boundaries. It advocates a predominantly plant-based diet by reducing consumption of unhealthy foods by more than 50% and increasing consumption of healthy foods (nuts, fruits, vegetables and legumes) by more than 100% (1). In 2024, Switzerland updated its national dietary guidelines to include sustainability considerations for the first time (11). While Swiss diets are known to deviate from the national health recommendations (12), little is known about their alignment with PHD recommendations. The report “ Planetary Health Diet in Switzerland 2024 – trends and developments” provides insights based on questionnaires and purchase data, but lacks detailed information on actual food intake (13). Switzerland’s first national nutrition survey menuCH provides valuable data to fill this gap. The aim of this analysis was therefore to assess the adherence of Swiss dietary habits to the EAT-Lancet Planetary Health Diet recommendations. Methods Study design and participants We analysed data from 2057 adults (18–75 years) from menuCh , the 2014–2015 national nutrition survey of Switzerland (see Supplementary Fig. 1 ). The population-based cross-sectional survey used a stratified random sample of the Swiss adult population. Participants were recruited by mail or telephone and invited to the study centre for a personal interview and nutritional assessment. Dietary intake was assessed through two non-consecutive 24-hour dietary recalls (24HDRs) – the first face-to-face and the second by telephone. Trained dietitians collected detailed information on all foods and beverages consumed and grouped them into specific food categories. Additional data were collected on sociodemographic, socioeconomic, and lifestyle factors. Anthropometric measurements (body weight, height, waist circumference, hip circumference) were also measured and documented. Methodological details of the menuCH survey have been published previously (14). Sociodemographic and Socioeconomic factors Participants reported age, sex and nationality, as well as education and household income. Age was calculated from the reported date of birth and categorized as < 47 years and ≥ 47 years. Nationality was categorised as Swiss or non-Swiss. Education level was classified as primary, secondary or tertiary, based on the highest degree completed. Household income was grouped into three categories: 9000 CHF per month. Sex was self-reported as male or female; no information on gender identity or sex at birth were collected. Food category matching with the Planetary Health Diet Three nutrition experts (dietitian, nutrition physician, nutrition scientist) independently matched food groups from the menuCH survey to the 14 food categories of the PHD. Food categories not addressed by the PHD (e.g. beverages, spices) were excluded from the comparison. Details of the matched food categories can be found in Supplementary Table 1 . The PHD recommendations provide a target based on an average amount, along with lower and upper limits for grams (g) and target values for kilocalories (kcal) based on a daily total energy intake of 2500 kcal. To enable an energy-standardised comparison, we converted the gram ranges into kilocalorie ranges by dividing the PHD target kilocalories by the corresponding target grams. We then multiplied this value by the lower and upper gram limits to calculate the kilocalorie reference range. Whole grain ranges were estimated proportionally based on the PHD fruit category due to absent reference values. We used kilocalories as the primary unit of comparison, as they more accurately represent a food’s energy and nutrient density than weight alone. For the categories of unsaturated oils, saturated oils, and added sugars, energy intake was calculated from the respective nutrient component within foods (e.g. unsaturated and saturated fats from oils, sugar in cakes) rather than the total food energy, avoiding overestimating intakes of composite foods. Remaining kilocalories (e.g. starch in cakes) were allocated to the appropriate food group, like grains (see Supplementary Table 1 ). Planetary Health Diet Index The Planetary Health Diet Index (PHDI) is a validated tool to quantify adherence to the EAT-Lancet recommendations (15). It consists of 15 food groups, each contributing up to 10 points to the total score (legumes and soy: 5 points each), resulting in a total possible score of 0-140 points. Healthy food groups score highest if intake meets or exceeds the recommended range, whereas unhealthy food groups (e.g. red meat, added sugars) score highest score if intake remains within or below the target range. Scoring is based on gram-specific intake thresholds from the EAT-Lancet reference diet, with sex-specific adjustments for whole grains to account for differences in dietary requirements. Higher scores indicate better adherence with the PHD. Outcomes Primary outcome: adherence of the Swiss population to the EAT-Lancet dietary recommendations, overall and stratified by sex and age, expressed as a percentage (PHD = 100%), identifying well-aligned food groups or over- and underconsumption. Secondary outcome: total PHDI score, assessed overall and stratified by sex, age, income, and education level. Statistical analysis Descriptive statistics are presented as means and standard deviation (SD) for continuous variables and counts and percentages for categorical variables. Baseline characteristics were compared overall and by gender using χ² tests for categorical variables and weighted linear regression for continuous variables. Analyses were weighted for sex, marital status, major area of Switzerland, nationality, household size, season, and weekday. Mean daily food intakes per group were calculated from both 24HDRs. Individual energy intakes were XXXtandardized to 2500 kcal/day to match the PHD reference diet. Mean intakes were reported in kcal and grams (g), with corresponding standard errors (SE). Differences between mean intakes and PHD targets, percentages of individuals within PHD reference ranges, and average intakes as a percentage of PHD targets were calculated. The PHDI was calculated overall and stratified by sex, age, income, and education level using the validated openly available dietaryindex R package (PHDI_V2.R) (15). Univariable (weighted) and multivariable linear regression models identified sociodemographic predictors of PHDI adherence. Univariable models assessed each predictor separately, applying survey weights and, where indicated, adjusted for potential confounders. Multivariable models included all predictors simultaneously to examine their independent associations with the PHDI scores. Logistic regression was used to estimate odds ratios (OR) with 95% Cis (CI) for meeting PHD intake ranges per food group. Models were adjusted for sex, age, income and education, with all results weighted to reflect the Swiss adult population aged 18–75. All analyses were conducted using R version 4.4.2. Statistical significance was set at p < 0.05 (two-sided). Figures were generated using R 4.4.2, BioRender, and PowerPoint version 16.96. Results Participant characteristics A total of 2057 individuals were included in the analysis, with 1124 (54.6%) female and 933 (45.4%) male participants ( Table 1 ) . The mean age was 46.5 (SD 15.8) years, with women being slightly younger than men (45.6 vs 47.4 years, p = 0.009). The mean BMI was 25.0 kg/m² (SD 4.4), with women having a significantly lower BMI than men (24.1 vs. 26.0 kg/m², p = 25 kg/m 2 ), and 2.4% as underweight. Regarding sociodemographic characteristics, most participants had a tertiary (48.5%) or secondary (47.1%) education, with a higher proportion of men having a tertiary education compared to women (p = < 0.001). One-third of participants reported a household income of more than 9000 CHF per month, which was more frequently reported by men than women (p = 0.010). The majority were of Swiss nationality (84.2%), with no significant sex difference (p = 0.69). Baseline characteristics stratified by age are shown in SupplementaryTable 2 . Table 1 Baseline characteristics stratified by gender Overall Female Male p-value n (%) 2057 (100) 1124 (54.6) 933 (45.4) General characteristics Age years, mean (SD) 46.5 (15.8) 45.6 (15.8) 47.4 (15.8) 0.009 Age (n, %) <47 years 1027 (49.9) 588 ( 52.3) 439 ( 47.1) 0.020 ≥47 years 1030 (50.1) 536 (47.7) 494 (52.9) Anthropometrics Weight kg, mean (SD) 73.5 (15.7) 66.1 (13.3) 82.2 (13.6) < 0.001 Height cm, mean (SD) 169.9 (9.2) 164.4 (6.6) 176.5 (7.3) < 0.001 BMI kg/m 2 , mean (SD) 25.0 (4.4) 24.1 (4.7) 26.0 (3.9) < 0.001 BMI (n, %) Underweight (< 18.5 kg/m 2 ) 50 (2.4) 42 (3.8) 8 (0.9) 25 kg/m 2 ) 880 (42.8) 354 (32.4) 526 (56.6) Unknown 34 (1.7) 30 (2.7) 4 (0.4) Sociodemographics Education level (n, %) Primary 89 (4.3) 51 (4.5) 38 (4.1) < 0.001 Secondary 968 (47.1) 587 (52.3) 381 (40.9) Tertiary 997 (48.5) 485 (43.2) 512 (55.0) Unknown 3 (0.1) 1 (0.1) 2 (0.2) Income (n, %) 9000 CHF/month 700 (34.0) 352 (31.3) 348 (37.3) Unknown 585 (28.4) 338 (30.1) 247 (26.5) Nationality (n, %) Swiss 1731 (84.2) 950 (84.5) 781 (83.7) 0.69 Non-Swiss 323 (15.7) 173 (15.4) 150 (16.1) Unknown 3 (0.1) 1 (0.1) 2 (0.2) Table 1 Baseline characteristics of the study population overall and by gender. Values are presented as weighted percentages for categorical variables and as weighted means (SD) for continuous variables. Comparisons between gender (male or female) were performed using χ² tests for categorical variables and weighted linear regression for continuous variables. P-values indicate statistical significance of differences between the two age groups. All results were weighted for sex, marital status, major area of Switzerland, nationality, household size, season, and weekday. Overall dietary intake in comparison to the PHD Certain food groups closely matched the reference intakes, others displayed clear trends of overconsumption or underconsumption (Table 2 , Fig. 1 ). Grains were consumed at an average of 775 kcal/day, which is close to the PHD recommendation of 811 kcal (96% of the PHD). Fish and seafood intake was also close to the recommendation (41 vs. 40 kcal, 103% of the PHD), with 92% of the participants within the reference range. Meat (excluding poultry) showed the highest level of overconsumption, with an average intake of 255 kcal per day, which is equivalent to 850% of the PHD recommendation. Only 27% of participants were within the recommended range. Dairy foods were also overconsumed (556 vs 153 kcal, 363% of the PHD), with only 36% of the participants within the reference range. The highest level of underconsumption relative to the PHD target was observed for legumes and unsaturated oils, with intakes reaching only 4% and 39% of the respective PHD recommendations. However, for legumes, with a reference range of 0-284 kcal, 100% of participants were within the reference range. For nuts, 21% of participants were within the reference range. When stratified by sex, women consumed 121 kcal from fruits compared to 85 kcal in men (96% vs 67% of the PHD), and 177 kcal from nuts, compared to 118 kcal in men (61% vs 41%). In contrast, men had a higher intake of meat (excluding poultry), with 298 kcal, compared to 214 kcal for women (993% vs 713%). Participants younger than 47 years consumed more kcal from potatoes and starchy vegetables (82 vs 48; 209% vs 123% of the recommendation) and poultry (46 vs 32; 74% vs 51%) than older adults. In contrast, those aged 47 years and older had higher intakes of dairy foods (596 vs 520; 389% vs 340%) and fruits (120 vs 88; 95% vs 70%). Supplementary Table 3 shows the mean daily intakes in grams for each food group. Notably, dairy foods showed a discrepancy: while the mean intake in grams (254 g/day) was close to the PHD reference of 250 g (102% of the PHD), the corresponding intake in kcal was much higher (363%; Table 2 ). A similar pattern was observed for nuts and seeds, with a low mean intake in grams (6 g/day; 12%), but a comparatively higher contribution to energy intake (51%). Results stratified by sex and age in grams showed similar patterns to those observed for energy intake. Table 2 Mean daily intakes in kilocalories for the 14 food groups, shown overall and stratified by sex and age group. Values are presented as mean intake in kcal/day (SE), Difference in mean, % within PHD range and % of PHD recommendation. All results were weighted for sex, age, marital status, major area of Switzerland, nationality, household size, season, and weekday. Table 2 : Daily intakes in kilocalories for each food group Grains Potatoes & starchy vegetables Vegetables Fruits Meat (excluding poultry) Poultry Eggs Fish and seafood Legumes Nuts and Seeds Dairy foods Unsaturated oils Saturated oils Sugar EAT-Lancet Planetary Health reference diet Reference intake (kcal/day) 811 39 78 126 30 62 19 40 284 291 153 354 96 120 Reference range (kcal/day) 461–1161 0–78 52–156 63–189 0–60 0-124 0–37 0-143 0-379 0-437 0-306 177–708 0–96 0-120 Overall Swiss diet Mean intake (kcal/day), (SE) 775 (7) 66 (3) 113 (2) 103 (3) 255 (6) 40 (2) 52 (2) 41 (2) 11 (1) 148 (7) 556 (8) 138 (3) 88 (2) 114 (3) Difference in mean (menuCH - PHD) -36 + 27 + 35 -23 + 225 -22 + 33 + 1 -273 -143 + 403 -216 -8 -6 % within PHD range 49 77 44 32 27 90 64 92 100 90 36 21 71 70 % of PHD recommendation (mean; PHD = 100%) 96 169 145 82 850 65 274 103 4 51 363 39 92 95 Women Mean intake (kcal/day), (SE) 783 (10) 60 (3) 120 (3) 121 (5) 214 (8) 39 (3) 51 (3) 42 (3) 13 (2) 177 (10) 561 (10) 130 (3) 84 (3) 104 (4) Difference in mean (menuCH - PHD) -28 + 21 + 42 -5 + 184 -23 + 32 + 2 -271 -114 + 408 -224 -12 -16 % within PHD range 46 80 46 34 35 91 68 93 100 89 37 17 75 76 % of PHD recommendation (mean; PHD = 100%) 97 154 154 96 713 63 268 105 5 61 367 37 88 87 Men Mean intake (kcal/day), (SE) 768 (11) 72 (5) 105 (3) 85 (4) 298 (10) 40 (3) 53 (3) 40 (3) 8 (1) 118 (9) 550 (12) 147 (4) 91 (2) 124 (4) Difference in mean (menuCH - PHD) -43 + 33 + 27 -41 + 268 -22 + 34 0 -276 -173 + 397 -207 -5 + 4 % within PHD range 52 74 41 29 17 88 60 91 100 91 34 25 67 62 % of PHD recommendation (mean; PHD = 100%) 95 185 135 67 993 65 279 100 3 41 359 42 95 103 Age < 47 years Mean intake (kcal/day), (SE) 790 (10) 82 (5) 119 (4) 88 (4) 258 (9) 46 (3) 52 (3) 44 (3) 13 (2) 136 (9) 520 (11) 145 (4) 89 (3) 117 (4) Difference in mean (menuCH - PHD) -21 + 43 + 41 -38 + 228 -16 + 33 + 4 -271 -155 + 367 -209 -7 -3 % within PHD range 48 72 44 28 26 87 66 91 99 91 37 23 70 68 % of PHD recommendation (mean; PHD = 100%) 97 209 152 70 859 74 273 109 5 47 340 41 92 98 Age ≥ 47 years Mean intake (kcal/day), (SE) 759 (11) 48 (3) 106 (3) 120 (4) 252 (9) 32 (3) 51 (3) 37 (3) 8 (1) 161 (9) 596 (11) 131 (4) 86 (2) 111 (4) Difference in mean (menuCH - PHD) -52 + 9 + 28 -6 + 222 -30 + 32 -3 -276 -130 + 443 -223 -10 -9 % within PHD range 49 83 43 36 28 93 63 93 100 89 34 18 73 72 % of PHD recommendation (mean; PHD = 100%) 94 123 135 95 841 51 271 94 3 55 389 37 90 92 SE = standard error, PHD = Planetary Health Diet Created in https://BioRender.com Comparison of PHDI Scores by Sociodemographic Characteristics The mean PHDI score was 82.1 (SD 12.5), with a median of 81.8, on a scale from 0 to 140 (Supplementary Table 4, Supplementary Fig. 2) . Women had a higher mean score than men (84.5 vs. 79.6) ( Fig. 2 ) . Participants aged ≥ 47 years had slightly higher scores than those aged < 47 years (82.9 vs. 81.3). With regard to educational level, mean scores increased slightly from 80.5 for participants with primary education to 82.9 for those with tertiary education. PHDI scores were similar across income groups, ranging from 81.8 to 82.3, with no clear gradient. Sociodemographic predictors of PHDI scores In the multivariable analysis, female gender was significantly associated with a higher PHDI score compared to male gender (difference of 5.24 points [95% CI, 3.80 to 6.68], p < 0.001) ( Table 3 ). Participants aged 47 years and older had a significantly higher PHDI score compared to those younger than 47 years (difference of 1.60 points [95% CI, 0.18 to 3.02], p = 0.027). We found no significant differences of the association of education with PHDI (secondary: -0.61, 95% CI, -4.48 to 3.25, p = 0.76; tertiary: 1.48, 95% CI, -2.43 to 5.38, p = 0.46). Income levels were also not significantly associated with higher PHDI scores. Results from the weighted univariable analysis, with additional adjustments where indicated, are presented in Supplementary Table 5. Table 3 Multivariable linear regression Coefficient (95%CI) p-value Sex* Female vs. male 5.24 (3.80 to 6.68) p = < 0.001 Age* ≥ 47 years vs. <47 years 1.60 (0.18 to 3.02) p = 0.027 Education** Primary reference Secondary -0.61 (-4.48 to 3.25) p = 0.76 Tertiary 1.48 (-2.43 to 5.38) p = 0.46 Income*** 9000 CHF/month 0.31 (-1.62 to 2.25) p = 0.75 Unknown -0.51 (-3.00 to 1.98) p = 0.69 Table 3 Multivariable linear regression analyses of sociodemographic factors associated with the Planetary Health Diet Index (PHDI). Regression coefficients (β) with 95% confidence intervals (CI) are presented for sex, age group, education level, and household income. Age was dichotomised at the sample median (< 47 / ≥47 years). Education categories: low, middle, high, unknown. Income categories: low, middle, high, unknown. All analyses were weighted for sex, age, marital status, major area of Switzerland, nationality, household size, season, and weekday. *adjusted for education and income ** adjusted for income *** adjusted for education Sociodemographic predictors of being within the PHD range In the adjusted logistic regression models, women were significantly more likely than men to be within the recommended range for potatoes and starchy vegetables (OR 1.41 [95% CI, 1.08 to 1.84], p = 0.011), fruits (OR 1.38 [95% CI, 1.08 to 1.76], p = 0.011), and meat (excluding poultry; OR 2.12 [95% CI, 1.55 to 2.91], p = < 0.001). Women were also more likely to meet recommendations for saturated oils (OR 1.33) and sugar (OR 1.59), but less likely for nuts and seeds (OR 0.52) and unsaturated oils (OR 0.72) (Supplementary Table 6). Participants aged 47 years and older were more likely to be within the range for potatoes and starchy vegetables (OR 1.99 [95% CI, 1.52 to 2.60], p = < 0.001), poultry (OR 1.88 [95% CI, 1.24 to 2.84], p = 0.003), but less likely for dairy foods (OR 0.54 [95% CI, 0.39 to 0.75], p = < 0.001) and unsaturated oils (OR 0.73 [95% CI, 0.55 to 0.97], p = 0.028). There were no statistically significant associations between education or income and being within the PHDI range for most food groups. Discussion This analysis, which compares the food intake of the Swiss population from a 2014 national survey with the 2019 Planetary Health Dietary Recommendations from the EAT-Lancet Commission, yields three key findings: Firstly, the highest level of adherence was observed for grains and fish/seafood. Secondly, substantial overconsumption was evident for animal-based products, particularly for meat (excluding poultry) and dairy foods. Third, pronounced underconsumption was found for legumes and unsaturated oils. These findings need further comment. Grain consumption showed the second-highest adherence to the PHD recommendations. However, since the PHD includes only whole grains, this likely overestimate actual adherence. A recent menuCH analysis reported that whole grains accounted for just 17.5% of total grain intake in Switzerland, highlighting a substantial discrepancy between current consumption patterns and PHD recommendations (16). A shift towards whole grains would improve nutrient density and support plant-based diets. The overconsumption of animal based foods is consistent with existing literature describing Western dietary patterns as being high in meat and dairy consumption – often exceeding both sustainable and health-oriented thresholds (17). The most pronounced overconsumption was found for meat (excluding poultry), with an average intake eight times higher than the PHD recommendations, while 90% of the population was in the reference range for poultry. Beef production accounts for the largest proportion of agricultural greenhouse gas emissions in Switzerland and contributes to global land and water use challenges (9). Dairy products were also overconsumed in kcal, although consumption in grams was more close to the recommended level. This indicates that dairy consumption is largely driven by processed products with higher caloric density (e.g. cheese), which may negatively impact health. Although milk consumption has declined over the past two decades, intake of processed dairy products, such as cheese, has increased in Switzerland and other European countries (18). In our analysis, older participants (≥ 47 years) were more likely to meet dairy target, possibly due to age-related dietary preferences or greater levels of health awareness. A Swiss study attributed reductions in dairy consumption among older adults to concerns about fat and cholesterol intake, as well as difficulty digesting certain dairy products (19). Reducing consumption of high-fat dairy products offers considerable health and environmental benefits. Plant-based alternatives are becoming increasingly popular and can provide as a sustainable replacement, although their nutritional quality varies and not all products are fortified with essential micronutrients such as calcium, which should be taken into account (18, 20). Legumes were the most under-consumed food group in Switzerland. A review of 33 countries found that all European countries consumed less than 50 g of legumes per day, with 36% of countries falling below 10 g/day. The highest intakes were found in Asian countries, with Vietnam reporting intakes above 100 g/day (21). The low consumption of legumes in Western countries is largely due to a lack of familiarity with them and their limited use as an ingredient in main meals. Despite being perceived as healthier and more environmentally friendly, legumes and legume-based meat substitutes were seen as less tasty, enjoyable and harder to prepare than meat (22). While plant protein were historically inferior, recent evidence confirms that diets incorporating legumes can provide all the essential amino acids necessary for supporting muscle and bone health, while offering additional cardiovascular and metabolic benefits (23). In order to increase legume intake, multifaceted strategies are required, such as improving taste and availability, offering culinary education to boost cooking confidence, and implementing public health campaigns that highlight the health and environmental advantages of legumes. Interestingly, of the food groups assessed, fish and seafood showed the highest adherence to the PHD recommendations. This is a positive finding, as fish is not only a more environmentally sustainable source of animal protein than red meat, but also a valuable source of unsaturated fatty acids - especially omega-3 fatty acids (24). Nevertheless, overall intake of unsaturated fats remain low in the Swiss diet, indicating an inadequate intake of plant-based fat sources such as vegetable oils. Given the well-established cardiovascular benefits of mono- and polyunsaturated fats, increasing these - particularly as a replacement for saturated fats - should be a public health priority (25). We also found differences among males and females in regard to over- and underconsumption: women were more likely than men to meet meat consumption recommendations, consistent with existing evidence showing higher meat intak amond men, particularly in countries with higher levels of human development and gender equality (26). We also assessed adherence to the PHD using the PHDI, a composite score reflecting alignment with the EAT-Lancet reference diet in terms of both health and environmental sustainability. Previous studies have shown that higher PHDI scores are associated with improved cardiovascular outcomes (27). In our analysis, the mean score was 82.8 out of possible 140 points (59.1%) with women scoring higher than men (84.7 vs. 79.3). These results are consistent with European studies reporting similar scores depending on sample characteristics and surey year (28). The observed sex difference in PHDI is consistent with evidence indicating that women, particularly in high-income countries, tend to adopt more health-conscious and environmentally sustainable diets than men (29). Similarly, older individuals (≥ 47 years) had higher PHDI scores than younger, supporting findings from previous studies, including earlier analysis of the menuCH data, that suggest that diet quality improves with age, particularly in terms of meat and sweets intake (30). Key strengths of this study include its large, nationally represantive sample and robust methodology of the swiss menuCH survey. The results contribute valuable evidence to the limited data on adherence to the PHD in high-income European countries. While recent studies from Sweden and Portugal have examined similar outcomes, this is the first to systematically evaluate nationally representative dietary data from Switzerland in this context (28). Notably, our results broadly align with those of previous research. Still, we are aware of limitations to this analysis. Although the menuCH data remain the most recent nationally representative dietary dataset for Switzerland, it was collected several years ago, and no comparable survey has since been conducted. The 24-hour recall method, although widely accepted, is prone to recall bias, social desirability bias, and underreporting. In addition, certain food categories - such as beverages - were excluded in the analysis as they are not explicitly addressed in the PHD framework. Conclusion In conclusion, while dietary patterns in Switzerland show partial alignment with the PHD, significant changes are needed to fully support both public health and environmental sustainability. This stuy identifies key areas for intervention, notably the reduction of meat and processed animal products and the promotion of plant-based protein sources such as legumes. In light of accelarating environmental degradation and the rising burden of diet-related diseases, incorporating sustainability considerations into nutrition policy is both urgent and essential. Moving forward, effective strategies to faciliate this transition could include targeted public health policies, such as fiscal measures to incentivise healthier and more sustainable food choices, comprehensive educational initiatives to increase awareness of the health and environmental impacts of dietary patterns, and continued innovation in developing affordable, appealing and nutritionally adequate plant-based alternatives. Together, these approaches could promote dietary shifts that benefit both population health and the planetary boundaries. Still, further research is required to better understand and address the sociocultural, structural and individual barriers to adopting sustainable and health promoting diets. Abbreviations BMI, body mass index CHF, Swiss francs CI, confidence interval kcal, kilocalories OR, odds ratio PHD, planetary health diet PHDI, planetary health diet index SD, standard deviation SE, standard error 24HDR, 24-hour dietary recall Declarations Ethics approval Ethical approval was not required for this study, because it used only publicly available data and did not involve human participants or animals. Data Availability: Data described in the manuscript, code book, and analytic code are available on request via the official side of the Federal Food Safety and Veterinary Office ("Bundensamt für Lebensmittelsicherheit und Veterinärwesen"; (https://www.blv.admin.ch/blv/de/home/lebensmittel-und-ernaehrung/ernaehrung/menuCH/menuch-publikationen-daten-forschung.html). Funding: This study received no funding. Author Disclosures Unrelated to this study, Nestlé Health Science and Abbott Nutrition previously provided unre-stricted grant money to the institution of P.S. All other authors report no conflicts of interest. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the authors used OpenAI. (2024). ChatGPT (Version 4) [large language model] to improve the writing and English language quality of their manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. Author Contributions Selina Randegger: literature search, conceptualization, data analysis, data interpretation, figures,writing: original draft; Nele Endner: literature search, conceptualization, data analysis, data interpretation, figures, writing: original draft; Carla Wunderle: supervision, writing: review and editing; Nina Kaegi-Braun: methodology, writing: review and editing Philipp Schuetz: conceptualization, writing: review and editing References 1. Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, et al. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet. 2019;393(10170):447 − 92. 2. Springmann M, Wiebe K, Mason-D'Croz D, Sulser TB, Rayner M, Scarborough P. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet Health. 2018;2(10):e451-e61. 3. Budreviciute A, Damiati S, Sabir DK, Onder K, Schuller-Goetzburg P, Plakys G, et al. Management and Prevention Strategies for Non-communicable Diseases (NCDs) and Their Risk Factors. Front Public Health. 2020;8:574111. 4. Scott P. Global panel on agriculture and food systems for nutrition: food systems and diets: facing the challenges of the 21st century. Food Security. 2017;9(3):653-4. 5. Collaborators GD. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;393(10184):1958-72. 6. Statistik Bf. Übergewicht und Adipositas: Bundesamt für Statistik (BFS); 2024. 7. Crippa M, Solazzo E, Guizzardi D, Monforti-Ferrario F, Tubiello FN, Leip A. Food systems are responsible for a third of global anthropogenic GHG emissions. Nat Food. 2021;2(3):198–209. 8. Foley JA, Defries R, Asner GP, Barford C, Bonan G, Carpenter SR, et al. Global consequences of land use. Science. 2005;309(5734):570-4. 9. Bretscher D AC, Wüst C, Nyfeler A, Felder D. Reduktionspotenziale von Treibhausgasemissionen aus der Schweizer Nutztierhaltung Switzerland: Agroscope; 2016 [Available from: https://www.agroscope.admin.ch/agroscope/de/home/themen/umwelt-ressourcen/klima-lufthygiene/treibhausgas-emissionen/landwirtschaftliches-treibhausgasinventar-schweiz.html. 10. Agricultural areas Switzerland: Federal Statistical Office; 2024 [cited 2025 09.04.2025]. Available from: https://www.bfs.admin.ch/bfs/en/home/statistics/territory-environment/land-use-cover/agricultural-areas.html. 11. BLV. Schweizer Ernährungsempfehlungen Bern, Switzerland: Schweizerische Gesellschaft für Ernährung SGE; 2024 [cited 2025 09.04.2025]. Available from: https://www.blv.admin.ch/blv/de/home/lebensmittel-und-ernaehrung/ernaehrung/empfehlungen-informationen/schweizer-ernaehrungsempfehlungen.html. 12. Chatelan A, Beer-Borst S, Randriamiharisoa A, Pasquier J, Blanco JM, Siegenthaler S, et al. Major Differences in Diet across Three Linguistic Regions of Switzerland: Results from the First National Nutrition Survey menuCH. Nutrients. 2017;9(11). 13. Eggenschwiler M, Linzmajer M, Stoll M, Bally L. Planetary Health Diet in der Schweiz 2024 - Trends und Entwicklungen2025. 14. Chatelan A, Marques-Vidal P, Bucher S, Siegenthaler S, Metzger N, Zuberbühler CA, et al. Lessons Learnt About Conducting a Multilingual Nutrition Survey in Switzerland: Results from menuCH Pilot Survey. Int J Vitam Nutr Res. 2017;87(1–2):25–36. 15. Zhan JJ, Hodge RA, Dunlop AL, Lee MM, Bui L, Liang D, et al. Dietaryindex: A User-Friendly and Versatile R Package for Standardizing Dietary Pattern Analysis in Epidemiological and Clinical Studies. bioRxiv. 2023. 16. von Blumenthal F, Schönenberger KA, Huwiler VV, Stanga Z, Pestoni G, Faeh D. Dietary fibre intake in the adult Swiss population: a comprehensive analysis of timing and sources. J Nutr Sci. 2025;14:e27. 17. Hemler EC, Hu FB. Plant-Based Diets for Personal, Population, and Planetary Health. Adv Nutr. 2019;10(Suppl_4):S275-s83. 18. Moore SS, Costa A, Pozza M, Vamerali T, Niero G, Censi S, et al. How animal milk and plant-based alternatives diverge in terms of fatty acid, amino acid, and mineral composition. NPJ Sci Food. 2023;7(1):50. 19. Chollet M, Gille D, Piccinali P, Bütikofer U, Schmid A, Stoffers H, et al. Short communication: dairy consumption among middle-aged and elderly adults in Switzerland. J Dairy Sci. 2014;97(9):5387-92. 20. Sterup Moore S, Costa A, Pozza M, Weaver CM, De Marchi M. Nutritional scores of milk and plant-based alternatives and their difference in contribution to human nutrition. LWT. 2024;191:115688. 21. Hughes J, Pearson E, Grafenauer S. Legumes-A Comprehensive Exploration of Global Food-Based Dietary Guidelines and Consumption. Nutrients. 2022;14(15). 22. Röös E, de Groote A, Stephan A. Meat tastes good, legumes are healthy and meat substitutes are still strange - The practice of protein consumption among Swedish consumers. Appetite. 2022;174:106002. 23. Ferrari L, Panaite SA, Bertazzo A, Visioli F. Animal- and Plant-Based Protein Sources: A Scoping Review of Human Health Outcomes and Environmental Impact. Nutrients. 2022;14(23). 24. Xia S, Takakura J, Tsuchiya K, Park C, Heneghan RF, Takahashi K. Unlocking the potential of forage fish to reduce the global burden of disease. BMJ Glob Health. 2024;9(3). 25. Petersen KS, Maki KC, Calder PC, Belury MA, Messina M, Kirkpatrick CF, et al. Perspective on the health effects of unsaturated fatty acids and commonly consumed plant oils high in unsaturated fat. Br J Nutr. 2024;132(8):1039-50. 26. Hopwood CJ, Zizer JN, Nissen AT, Dillard C, Thompkins AM, Graça J, et al. Paradoxical gender effects in meat consumption across cultures. Scientific Reports. 2024;14(1):13033. 27. Sawicki CM, Ramesh G, Bui L, Nair NK, Hu FB, Rimm EB, et al. Planetary health diet and cardiovascular disease: results from three large prospective cohort studies in the USA. Lancet Planet Health. 2024;8(9):e666-e74. 28. Stubbendorff A, Sonestedt E, Ramne S, Drake I, Hallström E, Ericson U. Development of an EAT-Lancet index and its relation to mortality in a Swedish population. Am J Clin Nutr. 2022;115(3):705 − 16. 29. Miller V, Webb P, Cudhea F, Shi P, Zhang J, Reedy J, et al. Global dietary quality in 185 countries from 1990 to 2018 show wide differences by nation, age, education, and urbanicity. Nat Food. 2022;3(9):694–702. 30. Pestoni G, Krieger JP, Sych JM, Faeh D, Rohrmann S. Cultural Differences in Diet and Determinants of Diet Quality in Switzerland: Results from the National Nutrition Survey menuCH. Nutrients. 2019;11(1). Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":158877,"visible":true,"origin":"","legend":"\u003cp\u003eSwiss diet compared with the Planetary Health Diet recommendations by food categories.\u003c/p\u003e\n\u003cp\u003eCreated in \u003ca href=\"https://biorender.com/\"\u003ehttps://BioRender.com\u003c/a\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8500417/v1/ac6dc28e1c60569892f7c411.jpeg"},{"id":100059457,"identity":"fc1bb3d2-a49b-4c71-a2c1-eddc36fdb735","added_by":"auto","created_at":"2026-01-12 14:26:58","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":198001,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted density ppt of the Planetary Health Diet Index by Sex. The density plot is a smoothed representation of a histogram and shows the distribution of a variable. In the graph, the total area under the curve is 1 (i.e., the integral of the variables is scaled to 1). The density plot allows for a direct comparison of the two sexes, although each female and male have a different number of participants. The density plot was weighted for age, marital status, major area of Switzerland, nationality, household size, season and weekday.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8500417/v1/7b617eaaf5a3e568449155e2.jpeg"},{"id":100382016,"identity":"a71c268e-c719-4178-9b25-468b3c11f30c","added_by":"auto","created_at":"2026-01-16 10:40:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1488238,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8500417/v1/1b811076-4c40-4908-b1af-5cdbbf73a901.pdf"},{"id":100059465,"identity":"ebf04832-7409-4519-beca-ca72b77466a5","added_by":"auto","created_at":"2026-01-12 14:26:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":137717,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryswissplanetaryhealthdietBMC.docx","url":"https://assets-eu.researchsquare.com/files/rs-8500417/v1/ab79be0840949461697e6b8d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Do Swiss Diets Align with the Planetary Health Diet Recommendations?","fulltext":[{"header":"Background","content":"\u003cp\u003eThe rising prevalence of diet-related diseases and growing environmental concerns highlight the urgent need for sustainable and health-conscious dietary shifts. It is evident that nutrition affects not only the health of the population, but also the health of the planet (1, 2). It has been estimated that diets high in processed foods, refined sugars, fats and high amount of meat consumption significantly contributes to the important increase in overweight and obesity, which now affects 2.5\u0026nbsp;billion people worldwide (1). This increase in body mass index (BMI) is directly linked to a growing global incidence of chronic non-communicable diseases (NCDs), including type 2 diabetes, cardiovascular diseases, and certain cancers (3, 4). In 2017, unhealthy diets contributed significantly to 1 in 5 deaths (11\u0026nbsp;million deaths worldwide), with cardiovascular diseases the leading cause (5). This global trend is also reflected in Switzerland, where in 2022, 43% of the population were overweight or obese, 52% of men and 34% of women, highlighting the need for improvements in dietary habits (6).\u003c/p\u003e \u003cp\u003eAt the same time, global food production is the largest driver of global environmental change, threatening not only local ecosystems but also the stability of the Earth system (1). Agriculture and food production account for approximately 30% of global anthropogenic greenhouse gas emissions, 40% of the global land use and 70% of freshwater use (7, 8). In Switzerland, 35% of the national territory are agricultural land, which is responsible for at least 12% of greenhouse gas emissions (9, 10). Food production, including transport, processing and trade, accounts for around one quarter of Switzerland's total ecological footprint - with animal products having the highest impact, accounting for around 85% of greenhouse gas emissions from Swiss agriculture (9).\u003c/p\u003e \u003cp\u003eIn 2019, the EAT-Lancet Commission introduced the Planetary Health Dietary Recommendations \u0026ndash; a global reference diet designed to promote human health while respecting planetary boundaries. It advocates a predominantly plant-based diet by reducing consumption of unhealthy foods by more than 50% and increasing consumption of healthy foods (nuts, fruits, vegetables and legumes) by more than 100% (1). In 2024, Switzerland updated its national dietary guidelines to include sustainability considerations for the first time (11). While Swiss diets are known to deviate from the national health recommendations (12), little is known about their alignment with PHD recommendations. The report \u0026ldquo;\u003cem\u003ePlanetary Health Diet in Switzerland 2024 \u0026ndash; trends and developments\u0026rdquo;\u003c/em\u003e provides insights based on questionnaires and purchase data, but lacks detailed information on actual food intake (13). Switzerland\u0026rsquo;s first national nutrition survey \u003cem\u003emenuCH\u003c/em\u003e provides valuable data to fill this gap. The aim of this analysis was therefore to assess the adherence of Swiss dietary habits to the EAT-Lancet Planetary Health Diet recommendations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eWe analysed data from 2057 adults (18\u0026ndash;75 years) from \u003cem\u003emenuCh\u003c/em\u003e, the 2014\u0026ndash;2015 national nutrition survey of Switzerland (see \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). The population-based cross-sectional survey used a stratified random sample of the Swiss adult population. Participants were recruited by mail or telephone and invited to the study centre for a personal interview and nutritional assessment. Dietary intake was assessed through two non-consecutive 24-hour dietary recalls (24HDRs) \u0026ndash; the first face-to-face and the second by telephone. Trained dietitians collected detailed information on all foods and beverages consumed and grouped them into specific food categories. Additional data were collected on sociodemographic, socioeconomic, and lifestyle factors. Anthropometric measurements (body weight, height, waist circumference, hip circumference) were also measured and documented. Methodological details of the \u003cem\u003emenuCH\u003c/em\u003e survey have been published previously (14).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSociodemographic and Socioeconomic factors\u003c/h3\u003e\n\u003cp\u003eParticipants reported age, sex and nationality, as well as education and household income. Age was calculated from the reported date of birth and categorized as \u0026lt;\u0026thinsp;47 years and \u0026ge;\u0026thinsp;47 years. Nationality was categorised as Swiss or non-Swiss. Education level was classified as primary, secondary or tertiary, based on the highest degree completed. Household income was grouped into three categories: \u0026lt;6000 CHF per month, 6000\u0026ndash;9000 CHF per month, and \u0026gt;\u0026thinsp;9000 CHF per month. Sex was self-reported as male or female; no information on gender identity or sex at birth were collected.\u003c/p\u003e\n\u003ch3\u003eFood category matching with the Planetary Health Diet\u003c/h3\u003e\n\u003cp\u003eThree nutrition experts (dietitian, nutrition physician, nutrition scientist) independently matched food groups from the \u003cem\u003emenuCH\u003c/em\u003e survey to the 14 food categories of the PHD. Food categories not addressed by the PHD (e.g. beverages, spices) were excluded from the comparison. Details of the matched food categories can be found in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe PHD recommendations provide a target based on an average amount, along with lower and upper limits for grams (g) and target values for kilocalories (kcal) based on a daily total energy intake of 2500 kcal. To enable an energy-standardised comparison, we converted the gram ranges into kilocalorie ranges by dividing the PHD target kilocalories by the corresponding target grams. We then multiplied this value by the lower and upper gram limits to calculate the kilocalorie reference range. Whole grain ranges were estimated proportionally based on the PHD fruit category due to absent reference values. We used kilocalories as the primary unit of comparison, as they more accurately represent a food\u0026rsquo;s energy and nutrient density than weight alone.\u003c/p\u003e \u003cp\u003eFor the categories of unsaturated oils, saturated oils, and added sugars, energy intake was calculated from the respective nutrient component within foods (e.g. unsaturated and saturated fats from oils, sugar in cakes) rather than the total food energy, avoiding overestimating intakes of composite foods. Remaining kilocalories (e.g. starch in cakes) were allocated to the appropriate food group, like grains (see \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003ePlanetary Health Diet Index\u003c/h3\u003e\n\u003cp\u003eThe Planetary Health Diet Index (PHDI) is a validated tool to quantify adherence to the EAT-Lancet recommendations (15). It consists of 15 food groups, each contributing up to 10 points to the total score (legumes and soy: 5 points each), resulting in a total possible score of 0-140 points. Healthy food groups score highest if intake meets or exceeds the recommended range, whereas unhealthy food groups (e.g. red meat, added sugars) score highest score if intake remains within or below the target range. Scoring is based on gram-specific intake thresholds from the EAT-Lancet reference diet, with sex-specific adjustments for whole grains to account for differences in dietary requirements. Higher scores indicate better adherence with the PHD.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003ePrimary outcome: adherence of the Swiss population to the EAT-Lancet dietary recommendations, overall and stratified by sex and age, expressed as a percentage (PHD\u0026thinsp;=\u0026thinsp;100%), identifying well-aligned food groups or over- and underconsumption.\u003c/p\u003e \u003cp\u003eSecondary outcome: total PHDI score, assessed overall and stratified by sex, age, income, and education level.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics are presented as means and standard deviation (SD) for continuous variables and counts and percentages for categorical variables. Baseline characteristics were compared overall and by gender using χ\u0026sup2; tests for categorical variables and weighted linear regression for continuous variables. Analyses were weighted for sex, marital status, major area of Switzerland, nationality, household size, season, and weekday.\u003c/p\u003e \u003cp\u003eMean daily food intakes per group were calculated from both 24HDRs. Individual energy intakes were XXXtandardized to 2500 kcal/day to match the PHD reference diet. Mean intakes were reported in kcal and grams (g), with corresponding standard errors (SE). Differences between mean intakes and PHD targets, percentages of individuals within PHD reference ranges, and average intakes as a percentage of PHD targets were calculated. The PHDI was calculated overall and stratified by sex, age, income, and education level using the validated openly available \u003cem\u003edietaryindex\u003c/em\u003e R package (PHDI_V2.R) (15). Univariable (weighted) and multivariable linear regression models identified sociodemographic predictors of PHDI adherence. Univariable models assessed each predictor separately, applying survey weights and, where indicated, adjusted for potential confounders. Multivariable models included all predictors simultaneously to examine their independent associations with the PHDI scores. Logistic regression was used to estimate odds ratios (OR) with 95% Cis (CI) for meeting PHD intake ranges per food group. Models were adjusted for sex, age, income and education, with all results weighted to reflect the Swiss adult population aged 18\u0026ndash;75.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using R version 4.4.2. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided). Figures were generated using R 4.4.2, BioRender, and PowerPoint version 16.96.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eA total of 2057 individuals were included in the analysis, with 1124 (54.6%) female and 933 (45.4%) male participants \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The mean age was 46.5 (SD 15.8) years, with women being slightly younger than men (45.6 vs 47.4 years, p\u0026thinsp;=\u0026thinsp;0.009). The mean BMI was 25.0 kg/m\u0026sup2; (SD 4.4), with women having a significantly lower BMI than men (24.1 vs. 26.0 kg/m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The majority had a normal BMI (53.1%), while 42.8% were classified as overweight or obese (BMI\u0026thinsp;\u0026gt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e), and 2.4% as underweight. Regarding sociodemographic characteristics, most participants had a tertiary (48.5%) or secondary (47.1%) education, with a higher proportion of men having a tertiary education compared to women (p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001). One-third of participants reported a household income of more than 9000 CHF per month, which was more frequently reported by men than women (p\u0026thinsp;=\u0026thinsp;0.010). The majority were of Swiss nationality (84.2%), with no significant sex difference (p\u0026thinsp;=\u0026thinsp;0.69). Baseline characteristics stratified by age are shown in \u003cb\u003eSupplementaryTable 2\u003c/b\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\u003eBaseline characteristics stratified by gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2057 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1124 (54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e933 (45.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge years, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.5 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.6 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.4 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;47 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1027 (49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e588 ( 52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e439 ( 47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;47 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1030 (50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e536 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e494 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnthropometrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight kg, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.5 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.1 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.2 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight cm, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169.9 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e164.4 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e176.5 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI kg/m\u003csup\u003e2\u003c/sup\u003e, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.0 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.1 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.0 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (18.5\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1093 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e698 (63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e395 (42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight or Obese (\u0026gt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e880 (42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e354 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e526 (56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e968 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e587 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e381 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e997 (48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e485 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e512 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;6000 CHF/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e346 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e206 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6000\u0026ndash;9000 CHF/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e228 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;9000 CHF/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e700 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e352 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e348 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e585 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e338 (30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e247 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNationality (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwiss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1731 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e950 (84.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e781 (83.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Swiss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e323 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/strong\u003e \u003cp\u003e \u003cem\u003eBaseline characteristics of the study population overall and by gender.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eValues are presented as weighted percentages for categorical variables and as weighted means (SD) for continuous variables. Comparisons between gender (male or female) were performed using χ\u0026sup2; tests for categorical variables and weighted linear regression for continuous variables. P-values indicate statistical significance of differences between the two age groups. All results were weighted for sex, marital status, major area of Switzerland, nationality, household size, season, and weekday.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOverall dietary intake in comparison to the PHD\u003c/h2\u003e \u003cp\u003eCertain food groups closely matched the reference intakes, others displayed clear trends of overconsumption or underconsumption (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Grains were consumed at an average of 775 kcal/day, which is close to the PHD recommendation of 811 kcal (96% of the PHD). Fish and seafood intake was also close to the recommendation (41 vs. 40 kcal, 103% of the PHD), with 92% of the participants within the reference range. Meat (excluding poultry) showed the highest level of overconsumption, with an average intake of 255 kcal per day, which is equivalent to 850% of the PHD recommendation. Only 27% of participants were within the recommended range. Dairy foods were also overconsumed (556 vs 153 kcal, 363% of the PHD), with only 36% of the participants within the reference range. The highest level of underconsumption relative to the PHD target was observed for legumes and unsaturated oils, with intakes reaching only 4% and 39% of the respective PHD recommendations. However, for legumes, with a reference range of 0-284 kcal, 100% of participants were within the reference range. For nuts, 21% of participants were within the reference range.\u003c/p\u003e \u003cp\u003eWhen stratified by sex, women consumed 121 kcal from fruits compared to 85 kcal in men (96% vs 67% of the PHD), and 177 kcal from nuts, compared to 118 kcal in men (61% vs 41%). In contrast, men had a higher intake of meat (excluding poultry), with 298 kcal, compared to 214 kcal for women (993% vs 713%). Participants younger than 47 years consumed more kcal from potatoes and starchy vegetables (82 vs 48; 209% vs 123% of the recommendation) and poultry (46 vs 32; 74% vs 51%) than older adults. In contrast, those aged 47 years and older had higher intakes of dairy foods (596 vs 520; 389% vs 340%) and fruits (120 vs 88; 95% vs 70%).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e shows the mean daily intakes in grams for each food group. Notably, dairy foods showed a discrepancy: while the mean intake in grams (254 g/day) was close to the PHD reference of 250 g (102% of the PHD), the corresponding intake in kcal was much higher (363%; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A similar pattern was observed for nuts and seeds, with a low mean intake in grams (6 g/day; 12%), but a comparatively higher contribution to energy intake (51%). Results stratified by sex and age in grams showed similar patterns to those observed for energy intake.\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\u003eMean daily intakes in kilocalories for the 14 food groups, shown overall and stratified by sex and age group. Values are presented as mean intake in kcal/day (SE), Difference in mean, % within PHD range and % of PHD recommendation. All results were weighted for sex, age, marital status, major area of Switzerland, nationality, household size, season, and weekday.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Daily intakes in kilocalories for each food group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePotatoes \u0026amp; starchy vegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFruits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMeat (excluding poultry)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePoultry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEggs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFish and seafood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLegumes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNuts and Seeds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDairy foods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eUnsaturated oils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eSaturated oils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003eSugar\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAT-Lancet Planetary Health reference diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference intake (kcal/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference range (kcal/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e461\u0026ndash;1161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52\u0026ndash;156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63\u0026ndash;189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0-124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026ndash;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0-143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0-379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0-437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0-306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e177\u0026ndash;708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u0026ndash;96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e0-120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Swiss diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean intake (kcal/day), (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e775 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e255 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e148 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e556 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e138 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e88 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e114 (3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference in mean (menuCH - PHD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u0026thinsp;403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e-6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% within PHD range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of PHD recommendation (mean; PHD\u0026thinsp;=\u0026thinsp;100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean intake (kcal/day), (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e783 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e214 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e177 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e561 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e130 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e84 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e104 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference in mean (menuCH - PHD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u0026thinsp;408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% within PHD range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of PHD recommendation (mean; PHD\u0026thinsp;=\u0026thinsp;100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean intake (kcal/day), (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e768 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e298 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e118 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e550 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e147 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e91 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e124 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference in mean (menuCH - PHD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u0026thinsp;397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e+\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% within PHD range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of PHD recommendation (mean; PHD\u0026thinsp;=\u0026thinsp;100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026lt;\u0026thinsp;47 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean intake (kcal/day), (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e790 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e258 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e136 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e520 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e145 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e89 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e117 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference in mean (menuCH - PHD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u0026thinsp;367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% within PHD range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of PHD recommendation (mean; PHD\u0026thinsp;=\u0026thinsp;100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;47 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean intake (kcal/day), (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e759 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e252 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e161 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e596 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e131 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e86 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e111 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference in mean (menuCH - PHD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u0026thinsp;443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e-9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% within PHD range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of PHD recommendation (mean; PHD\u0026thinsp;=\u0026thinsp;100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSE\u0026thinsp;=\u0026thinsp;standard error, PHD\u0026thinsp;=\u0026thinsp;Planetary Health Diet\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCreated in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://BioRender.com\u003c/span\u003e\u003cspan address=\"https://BioRender.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of PHDI Scores by Sociodemographic Characteristics\u003c/h2\u003e \u003cp\u003eThe mean PHDI score was 82.1 (SD 12.5), with a median of 81.8, on a scale from 0 to 140 \u003cb\u003e(Supplementary Table\u0026nbsp;4, Supplementary Fig.\u0026nbsp;2)\u003c/b\u003e. Women had a higher mean score than men (84.5 vs. 79.6) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Participants aged\u0026thinsp;\u0026ge;\u0026thinsp;47 years had slightly higher scores than those aged\u0026thinsp;\u0026lt;\u0026thinsp;47 years (82.9 vs. 81.3). With regard to educational level, mean scores increased slightly from 80.5 for participants with primary education to 82.9 for those with tertiary education. PHDI scores were similar across income groups, ranging from 81.8 to 82.3, with no clear gradient.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic predictors of PHDI scores\u003c/h2\u003e \u003cp\u003eIn the multivariable analysis, female gender was significantly associated with a higher PHDI score compared to male gender (difference of 5.24 points [95% CI, 3.80 to 6.68], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Participants aged 47 years and older had a significantly higher PHDI score compared to those younger than 47 years (difference of 1.60 points [95% CI, 0.18 to 3.02], p\u0026thinsp;=\u0026thinsp;0.027). We found no significant differences of the association of education with PHDI (secondary: -0.61, 95% CI, -4.48 to 3.25, p\u0026thinsp;=\u0026thinsp;0.76; tertiary: 1.48, 95% CI, -2.43 to 5.38, p\u0026thinsp;=\u0026thinsp;0.46). Income levels were also not significantly associated with higher PHDI scores. Results from the weighted univariable analysis, with additional adjustments where indicated, are presented in \u003cb\u003eSupplementary Table\u0026nbsp;5.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable linear regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient (95%CI) p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale vs. male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.24 (3.80 to 6.68) p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;47 years vs. \u0026lt;47 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 (0.18 to 3.02) p\u0026thinsp;=\u0026thinsp;0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.61 (-4.48 to 3.25) p\u0026thinsp;=\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48 (-2.43 to 5.38) p\u0026thinsp;=\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6000 CHF/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6000\u0026ndash;9000 CHF/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22 (-2.41 to 1.98) p\u0026thinsp;=\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;9000 CHF/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31 (-1.62 to 2.25) p\u0026thinsp;=\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.51 (-3.00 to 1.98) p\u0026thinsp;=\u0026thinsp;0.69\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 \u003cstrong\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/strong\u003e \u003cp\u003e \u003cem\u003eMultivariable linear regression analyses of sociodemographic factors associated with the Planetary Health Diet Index (PHDI).\u003c/em\u003e Regression coefficients (β) with 95% confidence intervals (CI) are presented for sex, age group, education level, and household income. Age was dichotomised at the sample median (\u0026lt;\u0026thinsp;47 / \u0026ge;47 years).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eEducation categories: low, middle, high, unknown. Income categories: low, middle, high, unknown. All analyses were weighted for sex, age, marital status, major area of Switzerland, nationality, household size, season, and weekday.\u003c/p\u003e \u003cp\u003e*adjusted for education and income ** adjusted for income *** adjusted for education\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic predictors of being within the PHD range\u003c/h2\u003e \u003cp\u003eIn the adjusted logistic regression models, women were significantly more likely than men to be within the recommended range for potatoes and starchy vegetables (OR 1.41 [95% CI, 1.08 to 1.84], p\u0026thinsp;=\u0026thinsp;0.011), fruits (OR 1.38 [95% CI, 1.08 to 1.76], p\u0026thinsp;=\u0026thinsp;0.011), and meat (excluding poultry; OR 2.12 [95% CI, 1.55 to 2.91], p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women were also more likely to meet recommendations for saturated oils (OR 1.33) and sugar (OR 1.59), but less likely for nuts and seeds (OR 0.52) and unsaturated oils (OR 0.72) \u003cb\u003e(Supplementary Table\u0026nbsp;6).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eParticipants aged 47 years and older were more likely to be within the range for potatoes and starchy vegetables (OR 1.99 [95% CI, 1.52 to 2.60], p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001), poultry (OR 1.88 [95% CI, 1.24 to 2.84], p\u0026thinsp;=\u0026thinsp;0.003), but less likely for dairy foods (OR 0.54 [95% CI, 0.39 to 0.75], p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and unsaturated oils (OR 0.73 [95% CI, 0.55 to 0.97], p\u0026thinsp;=\u0026thinsp;0.028). There were no statistically significant associations between education or income and being within the PHDI range for most food groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis analysis, which compares the food intake of the Swiss population from a 2014 national survey with the 2019 Planetary Health Dietary Recommendations from the EAT-Lancet Commission, yields three key findings: Firstly, the highest level of adherence was observed for grains and fish/seafood. Secondly, substantial overconsumption was evident for animal-based products, particularly for meat (excluding poultry) and dairy foods. Third, pronounced underconsumption was found for legumes and unsaturated oils. These findings need further comment. Grain consumption showed the second-highest adherence to the PHD recommendations. However, since the PHD includes only whole grains, this likely overestimate actual adherence. A recent \u003cem\u003emenuCH\u003c/em\u003e analysis reported that whole grains accounted for just 17.5% of total grain intake in Switzerland, highlighting a substantial discrepancy between current consumption patterns and PHD recommendations (16). A shift towards whole grains would improve nutrient density and support plant-based diets. The overconsumption of animal based foods is consistent with existing literature describing Western dietary patterns as being high in meat and dairy consumption \u0026ndash; often exceeding both sustainable and health-oriented thresholds (17). The most pronounced overconsumption was found for meat (excluding poultry), with an average intake eight times higher than the PHD recommendations, while 90% of the population was in the reference range for poultry. Beef production accounts for the largest proportion of agricultural greenhouse gas emissions in Switzerland and contributes to global land and water use challenges (9). Dairy products were also overconsumed in kcal, although consumption in grams was more close to the recommended level. This indicates that dairy consumption is largely driven by processed products with higher caloric density (e.g. cheese), which may negatively impact health. Although milk consumption has declined over the past two decades, intake of processed dairy products, such as cheese, has increased in Switzerland and other European countries (18). In our analysis, older participants (\u0026ge;\u0026thinsp;47 years) were more likely to meet dairy target, possibly due to age-related dietary preferences or greater levels of health awareness. A Swiss study attributed reductions in dairy consumption among older adults to concerns about fat and cholesterol intake, as well as difficulty digesting certain dairy products (19). Reducing consumption of high-fat dairy products offers considerable health and environmental benefits. Plant-based alternatives are becoming increasingly popular and can provide as a sustainable replacement, although their nutritional quality varies and not all products are fortified with essential micronutrients such as calcium, which should be taken into account (18, 20).\u003c/p\u003e \u003cp\u003eLegumes were the most under-consumed food group in Switzerland. A review of 33 countries found that all European countries consumed less than 50 g of legumes per day, with 36% of countries falling below 10 g/day. The highest intakes were found in Asian countries, with Vietnam reporting intakes above 100 g/day (21). The low consumption of legumes in Western countries is largely due to a lack of familiarity with them and their limited use as an ingredient in main meals. Despite being perceived as healthier and more environmentally friendly, legumes and legume-based meat substitutes were seen as less tasty, enjoyable and harder to prepare than meat (22). While plant protein were historically inferior, recent evidence confirms that diets incorporating legumes can provide all the essential amino acids necessary for supporting muscle and bone health, while offering additional cardiovascular and metabolic benefits (23). In order to increase legume intake, multifaceted strategies are required, such as improving taste and availability, offering culinary education to boost cooking confidence, and implementing public health campaigns that highlight the health and environmental advantages of legumes.\u003c/p\u003e \u003cp\u003eInterestingly, of the food groups assessed, fish and seafood showed the highest adherence to the PHD recommendations. This is a positive finding, as fish is not only a more environmentally sustainable source of animal protein than red meat, but also a valuable source of unsaturated fatty acids - especially omega-3 fatty acids (24). Nevertheless, overall intake of unsaturated fats remain low in the Swiss diet, indicating an inadequate intake of plant-based fat sources such as vegetable oils. Given the well-established cardiovascular benefits of mono- and polyunsaturated fats, increasing these - particularly as a replacement for saturated fats - should be a public health priority (25).\u003c/p\u003e \u003cp\u003eWe also found differences among males and females in regard to over- and underconsumption: women were more likely than men to meet meat consumption recommendations, consistent with existing evidence showing higher meat intak amond men, particularly in countries with higher levels of human development and gender equality (26).\u003c/p\u003e \u003cp\u003eWe also assessed adherence to the PHD using the PHDI, a composite score reflecting alignment with the EAT-Lancet reference diet in terms of both health and environmental sustainability. Previous studies have shown that higher PHDI scores are associated with improved cardiovascular outcomes (27). In our analysis, the mean score was 82.8 out of possible 140 points (59.1%) with women scoring higher than men (84.7 vs. 79.3). These results are consistent with European studies reporting similar scores depending on sample characteristics and surey year (28). The observed sex difference in PHDI is consistent with evidence indicating that women, particularly in high-income countries, tend to adopt more health-conscious and environmentally sustainable diets than men (29). Similarly, older individuals (\u0026ge;\u0026thinsp;47 years) had higher PHDI scores than younger, supporting findings from previous studies, including earlier analysis of the \u003cem\u003emenuCH\u003c/em\u003e data, that suggest that diet quality improves with age, particularly in terms of meat and sweets intake (30).\u003c/p\u003e \u003cp\u003eKey strengths of this study include its large, nationally represantive sample and robust methodology of the swiss \u003cem\u003emenuCH\u003c/em\u003e survey. The results contribute valuable evidence to the limited data on adherence to the PHD in high-income European countries. While recent studies from Sweden and Portugal have examined similar outcomes, this is the first to systematically evaluate nationally representative dietary data from Switzerland in this context (28). Notably, our results broadly align with those of previous research.\u003c/p\u003e \u003cp\u003eStill, we are aware of limitations to this analysis. Although the \u003cem\u003emenuCH\u003c/em\u003e data remain the most recent nationally representative dietary dataset for Switzerland, it was collected several years ago, and no comparable survey has since been conducted. The 24-hour recall method, although widely accepted, is prone to recall bias, social desirability bias, and underreporting. In addition, certain food categories - such as beverages - were excluded in the analysis as they are not explicitly addressed in the PHD framework.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, while dietary patterns in Switzerland show partial alignment with the PHD, significant changes are needed to fully support both public health and environmental sustainability. This stuy identifies key areas for intervention, notably the reduction of meat and processed animal products and the promotion of plant-based protein sources such as legumes. In light of accelarating environmental degradation and the rising burden of diet-related diseases, incorporating sustainability considerations into nutrition policy is both urgent and essential.\u003c/p\u003e \u003cp\u003eMoving forward, effective strategies to faciliate this transition could include targeted public health policies, such as fiscal measures to incentivise healthier and more sustainable food choices, comprehensive educational initiatives to increase awareness of the health and environmental impacts of dietary patterns, and continued innovation in developing affordable, appealing and nutritionally adequate plant-based alternatives. Together, these approaches could promote dietary shifts that benefit both population health and the planetary boundaries. Still, further research is required to better understand and address the sociocultural, structural and individual barriers to adopting sustainable and health promoting diets.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eBMI, body mass index\u003c/p\u003e\n\u003cp\u003eCHF, Swiss francs\u003c/p\u003e\n\u003cp\u003eCI, confidence interval\u003c/p\u003e\n\u003cp\u003ekcal, kilocalories\u003c/p\u003e\n\u003cp\u003eOR, odds ratio\u003c/p\u003e\n\u003cp\u003ePHD, planetary health diet\u003c/p\u003e\n\u003cp\u003ePHDI, planetary health diet index\u003c/p\u003e\n\u003cp\u003eSD, standard deviation\u003c/p\u003e\n\u003cp\u003eSE, standard error\u003c/p\u003e\n\u003cp\u003e24HDR, 24-hour dietary recall\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required for this study, because it used only publicly available data and did not involve human participants or animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData described in the manuscript, code book, and analytic code are available on request via the official side of the Federal Food Safety and Veterinary Office (\"Bundensamt für Lebensmittelsicherheit und Veterinärwesen\"; (https://www.blv.admin.ch/blv/de/home/lebensmittel-und-ernaehrung/ernaehrung/menuCH/menuch-publikationen-daten-forschung.html).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Disclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnrelated to this study, Nestlé Health Science and Abbott Nutrition previously provided unre-stricted grant money to the institution of P.S. All other authors report no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the authors used OpenAI. (2024). ChatGPT (Version 4) [large language model] to improve the writing and English language quality of their manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSelina Randegger: literature search, conceptualization, data analysis, data interpretation, figures,writing: original draft; Nele Endner: literature search, conceptualization, data analysis, data interpretation, figures, writing: original draft; Carla Wunderle: supervision, writing: review and editing; Nina Kaegi-Braun: methodology, writing: review and editing\u003c/p\u003e\n\u003cp\u003ePhilipp Schuetz: conceptualization, writing: review and editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1. Willett W, Rockstr\u0026ouml;m J, Loken B, Springmann M, Lang T, Vermeulen S, et al. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet. 2019;393(10170):447\u0026thinsp;\u0026minus;\u0026thinsp;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2. Springmann M, Wiebe K, Mason-D'Croz D, Sulser TB, Rayner M, Scarborough P. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet Health. 2018;2(10):e451-e61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e3. Budreviciute A, Damiati S, Sabir DK, Onder K, Schuller-Goetzburg P, Plakys G, et al. Management and Prevention Strategies for Non-communicable Diseases (NCDs) and Their Risk Factors. Front Public Health. 2020;8:574111.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e4. Scott P. 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Nutrients. 2019;11(1).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-global-and-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Global and Public Health](https://bmcglobalpublichealth.biomedcentral.com/)","snPcode":"44263","submissionUrl":"https://submission.springernature.com/new-submission/44263/3","title":"BMC Global and Public Health","twitterHandle":"@BMC_GPH","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Swiss food consumption, national nutrition survey, menuCH, planetary health diet, sustainability","lastPublishedDoi":"10.21203/rs.3.rs-8500417/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8500417/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 2019, the global EAT-Lancet Planetary Health Diet (PHD) introduced specific recommendations to promote sustainable, health-focused dietary changes in response to the increasing prevalence of diet-related diseases and escalating environmental challenges. We assessed the alignment of Swiss dietary habits with these recommendations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analysed data from 2057 adults (18–75 years) from the 2014–2015 national nutrition survey \u003cem\u003emenuCH\u003c/em\u003e. We matched different food categories with the PHD classification and calculated mean intakes (g and kcal) per food category, standardised to 2500 kcal, expressed as a percentage of the PHD recommendations (PHD = 100%). We also calculated the Planetary Health Diet Index (PHDI), with higher scores indicating higher adherence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 14 food categories, adherence was highest for fish and seafood (\u003cem\u003emenuCh\u003c/em\u003e vs. PHD; mean intake in kcal/day: 41 vs. 40; 103% of the PHD recommendations), followed by grains (775 vs. 811; 96%). The highest level of overconsumption was observed for meat (all types excluding poultry; 255 vs. 30; 850%) and dairy foods (556 vs. 153; 363%). The largest underconsumption was found for legumes (11 vs. 284; 4%) and unsaturated oils (138 vs. 354; 39%). Males consumed significantly more meat than females (298 vs. 214). The overall PHDI was 82.2 out of 140 points, with women showing higher scores than men (84.7 vs. 79.3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSwiss dietary intakes exceeded PHD recommendations, particularly for meat and dairy foods, while legumes remain underconsumed. A shift towards more plant-based foods could improve alignment with health and environmental sustainability goals.\u003c/p\u003e","manuscriptTitle":"Do Swiss Diets Align with the Planetary Health Diet Recommendations?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 14:26:49","doi":"10.21203/rs.3.rs-8500417/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-26T03:45:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T13:36:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T14:48:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314170674933069533945099431677797270153","date":"2026-03-06T07:34:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1438784072431181602789582310970660843","date":"2026-02-23T13:29:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87446861466768518784001345233597748902","date":"2026-02-02T10:12:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T07:25:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T05:02:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-05T04:46:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Global and Public Health","date":"2026-01-02T11:01:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-global-and-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Global and Public Health](https://bmcglobalpublichealth.biomedcentral.com/)","snPcode":"44263","submissionUrl":"https://submission.springernature.com/new-submission/44263/3","title":"BMC Global and Public Health","twitterHandle":"@BMC_GPH","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a31edc4d-cd34-404f-b7ac-76e6e75f4007","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T03:55:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 14:26:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8500417","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8500417","identity":"rs-8500417","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}