Associations between Global Burden of Disease dietary risk factors and elevated depressive symptoms: the NutriNet-Santé prospective cohort study

Associations between Global Burden of Disease dietary risk factors and elevated depressive symptoms: the NutriNet-Santé prospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations between Global Burden of Disease dietary risk factors and elevated depressive symptoms: the NutriNet-Santé prospective cohort study Gabriela Lugon, Deborah N. Ashtree, Benjamin Allès, Serge Hercberg, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7873685/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Evidence links overall dietary patterns to mental health, but studies on specific food groups and nutrients aligned with dietary guidelines are needed to inform population-level mental health preventive strategies. Within the Global burden of disease Lifestyle And mental Disorder (GLAD) Taskforce (DERR1-10.2196/65576), we aimed to determine the associations between dietary exposures and elevated depressive symptoms, including potential sex-specific differences, in the French NutriNet-Santé cohort. Methods: Prospective observational study using NutriNet-Santé cohort data (2011-2022). Dietary intake was assessed at baseline and every six months for two years using non-consecutive 24-hour records, defining the “dietary exposure window”. Exposures included food groups (fruits, vegetables, legumes, whole grains, nuts & seeds, milk, red meat, processed meat, and sweet drinks) and nutrients (fibre, calcium, omega 3, polyunsaturated fatty acids (PUFA), and sodium) as defined by the Global Burden of Disease (GBD), as well as ultra-processed foods (UPFs) using the NOVA classification. Participants with self-reported depression at baseline or during the dietary exposure window were excluded. The Center for Epidemiologic Studies Depression (CES-D) scale was administered every two years, using validated French cutoffs (≥ 17 for men, ≥ 23 for women) to indicate elevated depressive symptoms. Associations were estimated using multivariable Generalized Estimating Equations (GEE) adjusting for confounders. Results: Among 40,658 adults (75.7% women; mean age 46.6 years), 8,739 (21.5%) met criteria for elevated depressive symptoms at least once during a median follow-up of 8.36 years. Overall, higher intakes of fruits (OR 100g/d increase =0.968; 95%CI=0.948-0.989), vegetables (OR 100g/d =0.918; 95%CI=0.890-0.946) and fibre (OR 10g/d =0.954; 95%CI=0.930-0.980) were associated with lower odds of developing elevated depressive symptoms, while higher intake of red meat (OR 10g/d =1.009; 95%CI=1.001-1.017), sweet drinks (OR 100g/d =1.046; 95%CI=1.016-1.076), and the dietary share of UPFs (OR 10%g =1.160; 95%CI=1.126-1.196) were associated with increased odds. Most associations remained only among women, in whom processed meat emerged as positively associated (OR 10g/d : 1.014; 95% CI: 1.001–1.028). Conclusions: Fruits, vegetables, and fibre were associated with reduced odds of elevated depressive symptoms, while red meat, processed meat, sweet drinks, and UPFs were associated with increased odds. These results align with official nutritional guidelines in France and in many countries, supporting their promotion to improve population-level mental health. depression diet nutritional psychiatry global burden of disease nutrient Figures Figure 1 INTRODUCTION Depressive disorders represent a significant global health burden, contributing substantially to disability and diminished quality of life across populations ( 1 ). The causes of depression are complex and differ among individuals based on age, sex, genetics, socioeconomic status, personal experiences, and environment ( 2 ). Among the environmental and lifestyle factors, dietary habits have emerged as a potential modifiable risk factor influencing the incidence and trajectory of depression ( 3, 4 ). While the underlying mechanisms remain largely unknown, several biological pathways have been proposed to explain how an inadequate diet could influence depression, including increased systemic inflammation and disruptions in the microbiota-gut-brain axis ( 5, 6 ). Over the past decade, a growing body of evidence has shown that adherence to recommended diets, considered healthy regarding the prevention of obesity, cardiovascular disease and cancer, is also associated with a reduced risk of depressive disorders, with the traditional Mediterranean diet being the most frequently studied dietary pattern (7-13) . Furthermore, several randomized controlled trials, such as PREDIMED, SMILES, HELFIMED, and AMMEND, have provided causal evidence suggesting that dietary improvements can serve as an effective strategy not only for the primary prevention of depression but also for its management by reducing symptom severity ( 14–17 ). However, most of this evidence has focused on overall dietary patterns, which may be less applicable to national guidelines, which generally provide recommendations on specific foods or nutrients. For example, the current version of the French dietary guidelines ( Programme National Nutrition Santé (PNNS) 2019, (www.mangerbouger.fr)) recommends the consumption of at least five servings of fruits and vegetables daily, legumes at least twice per week, and whole grains daily, along with reduced intake of red and processed meat, added fats, salt, and sugars ( 18 ). According to the last two national consumption surveys, the French diet still falls short of PNNS targets, with the population consuming insufficient dietary fibre, excessive salt, and a high proportion of processed foods ( 19, 20 ). In parallel, results from the 2021 Health Barometer survey in France estimated the 12-month prevalence of major depressive disorders at 12.5% among adults, with more women (15.6%) than men (9.3%) having this condition ( 21 ). These gaps between recommended and actual dietary habits, combined with the substantial burden of depressive disorders, underscores the need for evidence on how specific dietary components relate to mental health, including potential sex differences. Such evidence could help define actionable targets aligned with national guidelines and to contribute to their evolution and optimization, ultimately informing strategies to improve population mental health. Despite the growing evidence, dietary factors are still not systematically recognized as relevant to mental health. This is partly because nutritional epidemiology has faced methodological challenges when applied to mental health research ( 22 ). Variability in study designs, dietary assessment methods, approaches to measuring depression or related symptoms and treatments, differences in population characteristics, and the strong bi-directional links between nutrition and mental health make it difficult to draw definitive conclusions about the impact of specific dietary factors on mental health ( 23 ). The Global burden of disease Lifestyle And mental Disorder (GLAD) taskforce aims to address these methodological challenges ( 24 ). This international collaboration aims to integrate lifestyle factors, including diet, into future GBD studies to better estimate their role in Common Mental Disorders (CMDs), particularly depression and anxiety. Using a standardized protocol for data processing and analysis across multiple cohort studies, results will be meta-analysed to provide robust data for future GBD estimates and to calculate population attributable fractions. This study aimed to determine the associations between dietary risk factors and elevated depressive symptoms and to explore potential sex-specific differences in these associations following the GLAD protocol in the NutriNet-Santé study, a large cohort of French adults. METHODS Study population The NutriNet-Santé study is an ongoing web-based prospective cohort study launched in France in 2009 with more than 180,000 participants. The objectives of the study are to investigate the relationship between nutrition and health outcomes as well as the role of various upstream determinants of dietary patterns and nutritional status. Participants are volunteers aged 15 years or older with access to the Internet, recruited from the general population through various multimedia campaigns. After registration, participants are enrolled and monitored via a dedicated secure website (www.etude-nutrinet-sante.fr), where all questionnaires are completed. The questionnaires are self-administered and provide high-quality data, including repeated data on socio-demographics and lifestyle, anthropometrics, dietary intake, physical activity, and health status. The protocol of the NutriNet-Santé study has been published previously ( 25 ). The study is conducted in accordance with the Declaration of Helsinki guidelines and has received approval from the ethics committee of the Institutional Review Board of the French Institute for Health and Medical Research (IRB Inserm no. 0000388FWA00005831) and the National Commission on Informatics and Liberty (CNIL no. 908450/n°909216). All participants provide electronic informed consent upon enrolment, and confidentiality and security of the data are assured at all times. The NutriNet-Santé study is registered at ClinicalTrials.gov (NCT03335644). The methodology for this study was prospectively registered on the Open Science Framework ( 26 ), as part of the GLAD taskforce (DERR1-10.2196/65576) ( 24 ). A flow-chart of the study design for this analysis is provided in Figure 1 . Dietary intake assessment The “dietary exposure window” covers the first two years post-enrolment in the cohort. Consumption of various foods and beverages was assessed at baseline and every 6 months through 3 non-consecutive 24-hour dietary records. To account for intra-individual variability of intakes, these self-administered records were randomly assigned to be completed over a 2-week period (two weekdays and one weekend day). These records have been previously validated against an interview by a dietitian ( 27 ) and against blood and urinary biomarkers ( 28, 29 ). Amounts consumed were declared directly by providing exact quantity (grams or milliliters), common household measures, or using portion sizes through validated photographs ( 30 ). Daily energy (kcal/day), alcohol (g/day), and nutrients (macro, micro and fibre) intakes were computed using the NutriNet-Santé food composition table containing more than 3,500 items ( 31 ). The average consumption over the dietary exposure window was adjusted for the type of day (weekday vs. weekend) to enhance accuracy and reflect a full week. Under-reporters were identified and excluded using the Black method with Goldberg cut-offs, calculated taking into account individual physical activity level and basal metabolic rate ( 32 ). The simplified Programme National Nutrition Santé-guidelines score 2 (sPNNS-GS2) was calculated and used to measure the adherence to the 2017 French dietary guidelines ( 33 ) as a measure of overall diet quality. UPFs consumption was calculated following the NOVA classification system ( 34 ). The methodologies used for the computation of the sPNNS-GS2 and UPFs, previously described in the NutriNet-Santé studies ( 33 , 35 ), are detailed in the supplemental Text S1 and Text S2 . This study considers dietary exposures (food groups and nutrients) as defined by the GBD 2019 study (36), each with specific theoretical minimum risk exposure levels (TMRELs) thresholds indicating the intake level associated with the lowest disease risk in the population ( Table S1 ). In this study, TMRELs were used only to describe the distribution of intakes in the NutriNet-Santé cohort. Dietary exposures were analysed as continuous variables, and units reflect common portion sizes or relevant nutritional increments (e.g., 10g, 100g, % energy) rather than the 1g/day units often used in GBD ( Table S1 ). Food groups include fruits (100g/day), vegetables (100g/day), legumes (10g/day), whole grains (10g/day), nuts and seeds (10g/day), milk (100g/day), red meat (10g/day), processed meat (10g/day), and sweet drinks (beverages with ≥50 kcal per 226.8g serving) (100g/day). Nutrients include fibre (5g/day), calcium (100mg/day), seafood omega 3 fatty acids (eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)) (100mg/day), PUFA (1%kcal/day), and dietary sodium (100mg/day). Although UPFs (10%g/day) are not yet among the exposures in the GBD study, they have been considered in the present study because evidence shows their consumption is an important part of the diet in adults from the French general population (18.4% of the foods consumed in weight) ( 37 ), has been associated with the risk of incident elevated depressive symptoms in this cohort ( 38 ) and other cohorts globally ( 39 ), and they are explicitly mentioned in the French dietary guidelines ( 18 ). Depressive symptoms assessment Depressive symptoms were evaluated using the self-administered CES-D scale ( 40 ) two years after enrolment and biennially thereafter (starting in 2011). The CES-D scale consists of 20 items corresponding to depressive symptoms, designed to assess their frequency over the previous week. Participants rate each symptom on a four-point scale indicating how often they experienced it (0 = “rarely or none of the time” (less than 1 day), 1 = “some or little of the time” (1–2 days), 2 = “occasionally or a moderate amount of the time” (3–4 days), and 3 = “most or all of the time” (5–7 days)). The total score sums all item points, ranging from 0 to 60, with higher score indicating more severe depressive symptoms. In this study, elevated depressive symptoms were defined, in the main analysis, according to the established and validated cutoff values in the French population: ≥ 17 points for men and ≥ 23 points for women ( 41, 42 ). Participants with prevalent self-reported depression or treatment at inclusion or during the dietary exposure window were excluded for this study. New cases were defined as participants who were free of depressive symptoms at the end of the dietary exposure window and who met criteria for elevated depressive symptoms at least once during follow-up. Covariates Covariates were selected based on the GLAD taskforce causal framework (24) and the literature (9). Sociodemographic and lifestyle data were collected at baseline and yearly using a self-administered web-based questionnaire ( 43 ) that included data on sex (women or men), age (years), educational level (no high school diploma, high school diploma, or university level), marital status (living alone or with a partner), residence area (rural, urban, or outside France), occupational categories (never-employed/ other activity, self-employed, employee, intermediate profession, or managerial staff), monthly income per unit of consumption (the total number of unit depending on the household composition) ( 3700 euros, and a category of participants who refused to disclose their income), and smoking status (non-smoker, occasional, former or permanent smoker). Weight and height data were collected using a validated self-administered anthropometric questionnaire ( 44 ) at baseline and every 6 months, and the Body Mass Index (BMI) was calculated as the ratio of weight to squared height (kg/m 2 ). For the assessment of physical activity, a short form of the French version of the International Physical Activity Questionnaire was used yearly ( 45 ), classifying energy expenditure as low (< 30 min of physical activity; equivalent to brisk walking/day), moderate (≥ 30 and < 60 min) and high (≥ 60 min) activity levels. Due to a larger number of missing data on the physical activity variable, we created a “missing” category to avoid excluding more participants. Self-reported health events were declared every 6 months through the website, including prevalent and incident cases of cardiovascular diseases, which were validated against medical records, and depression treatment. Statistical analysis For this study, we included a subset of adult (≥ 18 years) participants from the NutriNet-Santé study who completed at least two CES-D assessments between 2011 and 2022. We excluded participants with self-reported depression or treatment for this condition at enrolment or during the first two years (dietary exposure window), and those without dietary data. Among the participants who met these criteria (n=41,809), we excluded participants with missing values in baseline covariates or the sPNNS-GS2 diet score. The final sample was of 40,658 participants, as shown in Figure S1 . Description of baseline characteristics was performed in those included in the main analysis overall and stratified by sex. Differences by sex were assessed by analysis of variance for continuous variables or chi-square tests for categorical variables. As per GLAD protocol ( 24 ), food group and nutrient intakes were energy-adjusted using Willet’s residual method ( 46 ), and the energy-adjusted continuous variables were used as the dietary exposure in all statistical models. GEE logistic regressions with repeated measures were used to assess the relationship between dietary exposures and the risk of elevated depressive symptoms (CES-D ≥ 17 for men and ≥ 23 for women) across multiple CES-D assessment waves during follow-up, estimating odds ratios (OR) and 95% confidence intervals (CI). Separate models were fitted for each of the 15 dietary exposures (not mutually adjusted), using the same adjustment strategy for all dietary exposures. All assumptions were assessed prior to model fitting. Model 1 was adjusted for sex, age, and educational level. Model 2 (main model) was additionally adjusted for smoking status, physical activity, prevalent cardiovascular disease, residence area, occupational categories, income, marital status, number of 24h records, and alcohol intake. To examine whether associations were independent of broader dietary patterns linked to higher consumption of specific foods, we ran another model (Model 3) including sPNNS-GS2 as a covariate, only as a secondary adjustment rather than as a conventional confounder. Analyses were conducted overall and separately for men and women, in which case models were not adjusted for sex. We conducted a series of sensitivity analyses (SA). First, although generally considered to be on the causal pathway, BMI can also be considered a potential confounder of the diet-depression association; thus, we adjusted main Model 2 for baseline BMI (SA-1). Second, we further adjusted main Model 2 for new onset of self-reported treatment for depression during follow-up to account for its potential effect on lowering CES-D scores (SA-2). Third, to limit the possibility of reverse causality, we excluded those participants with elevated depressive symptoms at their first CES-D assessment (close to the dietary exposure window), resulting in a study sample of 36,618 participants (SA-3). Fourth, we used an alternative CES-D cut-off value of 19 ( 42 ) to define elevated depressive symptoms (SA-4). Fifth, we conducted the analyses mutually adjusting the dietary exposures (i.e. including them all in the same model), separately for food groups together and nutrients together (SA-5). Finally, given the strong attenuation of coefficients observed in Model 3 when adjusting for the overall sPNNS-GS2 score, we aimed to address potential overadjustment due to the inclusion of some of the dietary exposures of interest within the score itself. Therefore, for fruits, vegetables, and red meat, we repeated the analyses (SA-6) using a modified version of the sPNNS-GS2 score in which the component corresponding to the exposure of interest was excluded from the total score (Model 4). Two-tailed test P values of less than 0.05 were considered statistically significant. To account for multiple testing, we applied the Benjamini-Hochberg method to adjust p-values and control the false discovery rate, maintaining the 0.05 significance threshold ( 47 ). All statistical analyses were performed using R version 4.1.2. RESULTS The final study sample comprised 40,658 participants from the NutriNet-Santé cohort, including 30,798 women (75.7%) and 9,860 men (24.3%), with a mean age of 46.6 years. After a median follow-up of 8.36 years (Q1=5.52; Q3=10.07) between baseline and last CES-D assessment, 8,739 (21.5%) participants met criteria for elevated depressive symptoms at least once during follow-up. Baseline characteristics of participants included in the study are presented in Table 1 . Women were younger than men, had a higher educational level, were more likely to live alone, and less likely to smoke. They also had a lower BMI, higher levels of physical activity, and a lower prevalence and incidence of cardiovascular disease. During the dietary exposure window, women had a lower daily energy intake, a higher sPNNS-GS2 score, and lower alcohol intake, than men. Additionally, during follow-up, women completed fewer CES-D questionnaires and endorsed fewer CES-D detected symptoms compared to men. Table 1. Characteristics of the NutriNet-Santé study participants included in the main analyses (N=40,658) Table 2 shows the differences in food group and nutrient intake between men and women. The majority (70-100%) of women and men did not reach the TMRELs thresholds established by the GBD 2019 study, except for sweet drinks, with 52% of never consumers. Women had lower dietary intake than men for most food groups and nutrients, corresponding to a greater proportion of females not meeting the TMRELs thresholds for fruits, vegetables, legumes, whole grains, nuts and seeds, milk, fibre, calcium, and omega 3. Conversely, women were less likely to exceed the sodium limits than men. Table 2. Daily intake of dietary exposures and description of theoretical minimum risk exposure levels among participants included in the main analyses (N=40,658) The associations between food groups and nutrients and odds of elevated depressive symptoms are presented in Table 3 and Table 4 . In the overall sample, after adjustment for demographic and lifestyle factors (main Model 2), higher intake of fruit (OR per 100 g/day : 0.968; 95% CI: 0.948–0.989), vegetables (OR per 100 g/day : 0.918; 95% CI: 0.890–0.946), and fibre (OR per 5 g/day : 0.954; 95% CI: 0.930–0.980) was associated with lower odds of elevated depressive symptoms. In contrast, higher consumption of red meat (OR per 10 g/day : 1.009; 95% CI: 1.001–1.017) and sweet drinks (OR per 100 g/day : 1.046; 95% CI: 1.016–1.076) was associated with higher odds. Additionally, a greater proportion of UPFs in the diet was more strongly associated with increased odds (OR per 10%g/day : 1.160; 95% CI: 1.126–1.196). No associations were found for legumes, whole grains, nuts and seeds, milk, processed meat, calcium, omega 3, PUFA, or sodium intake. After further adjustment for the sPNNS-GS2 score (Model 3), some estimates were attenuated (fruits, fibre, and red meat), which may reflect over-adjustment as explored in sensitivity analyses, while an unexpected association with sodium emerged (OR per 100 mg/day : 0.992; 95% CI: 0.987–0.997). Table 3. Associations from GEE logistic regression models (ORs and 95% CI) between food groups and elevated depressive symptoms (CES-D ≥23 for women and ≥17 for men) (N=40,658) Table 4. Associations from GEE logistic regression models (ORs and 95% CI) between nutrients and elevated depressive symptoms (CES-D ≥23 for women and ≥17 for men ) (N=40,658) In sex-specific analyses, most associations were evident among women (fruits, vegetables, fibre, sweet drinks, and UPFs). For red meat, the association was weaker, while processed meat emerged as positively associated (OR per 10 g/day : 1.014; 95% CI: 1.001–1.028). In contrast, among men, most associations were attenuated, with only vegetables, fibre, and UPFs remaining related to elevated depressive symptoms. Results using GBD units are available in Table S2 and Table S3 . Sensitivity analyses further adjusting main Model 2 for baseline BMI (SA-1) ( Table S4 )and for new onset of self-reported treatment for depression during follow-up (SA-2) ( Table S5 )did not substantially alter the associations. SA-3 excluding participants with elevated depressive symptoms at the first CES-D assessment (conducted after the two-year dietary exposure window) are presented in Tables S6–S9 . The sample (N=36,618) did not differ from the main analysis sample. In this analysis, there were 4,699 participants who showed elevated depressive symptoms at least at one of the CES-D assessment following the first one. Higher intakes of fruits, vegetables, and fibre were associated with lower odds of elevated depressive symptoms, whereas higher intakes of sweet drinks and UPFs were associated with higher odds in Model 2. These associations were weaker than in the main analysis. In SA-4 using the CES-D cut-off of 19 for both women and men ( Tables S10-S11 ), the associations observed in Model 2 were consistent with those from the main analyses. However, additional inverse associations were observed with higher legumes intake (OR per 10 g/day : 0.985; 95% CI: 0.974-0.997) and higher nuts and seeds intake (OR per 10 g/day : 0.967; 95% CI: 0.944-0.991) in the overall sample. In the mutually adjusted models for groups of exposures ( SA-5, Table S12 ), the associations of vegetables, fibre, and UPFs intake with elevated depressive symptoms were consistent with results from earlier models. Finally, when the specific dietary exposure (fruit, vegetables, or red meat) was excluded from the original sPNNS-GS2 score in Model 4, the magnitude and significance of associations were closer to that observed in Model 2, supporting the hypothesis of potential overadjustment in Model 3 ( SA-6, Table S13 ). DISCUSSION In the French NutriNet-Santé cohort, over a median of 8.36 years, higher consumption of fruits (overall and among women), vegetables, and fibre (overall and in both sexes) was associated with a lower likelihood of developing elevated depressive symptoms assessed using the CES-D. In contrast, greater intake of red meat (overall), processed meat (only among women), and sweet drinks (overall and among women) was linked to increased likelihood. Additionally, UPFs showed a strong and consistent association with higher odds of developing elevated depressive symptoms across all models and in both sexes. Beyond confirming associations already suggested in the literature, by applying the standardized GLAD protocol, this study provides results that will contribute to future GBD estimates. Our results align with previous longitudinal studies highlighting the protective role of diets rich in plant-based foods, such as fruits and vegetables and exemplified by the Mediterranean diet (7-13), and the harmful impact of Western dietary patterns, typically characterized by high consumption of red meat and sugary drinks (48, 49). Within the NutriNet-Santé cohort, earlier research found that a one standard deviation increase in three different diet quality scores—the modified French Programme National Nutrition Santé-Guideline Score, the Probability of Adequate Nutrient Intake Dietary Score, and the Diet Quality Index-International —was associated with a 5-9% reduction in the risk of elevated depressive symptoms (9). In addition,adherence to a proinflammatory dietary pattern was associated with an increased risk of developing depressive symptoms by 15% when comparing the highest to the lowest quintile of intake (95% CI: 2%, 31%) (50), and a 10% increase in UPFs intake was linked to a 21% higher risk (95% CI: 1.15–1.27) (38). Compared with earlier analyses, the present study benefited from a substantially larger number of incident cases (more than double) due to the longer follow-up period until 2022, which increased the likelihood of capturing episodes of elevated depressive symptoms. This rise in cases might have been further amplified by contextual factors such as the COVID-19 pandemic, during which a global 25% prevalence of depression (seven times higher than pre-pandemic estimates) was observed in recent analyses (51). In addition, the progressive aging of the cohort may also have contributed to higher incidence. Taken together, these elements strengthened statistical power and illustrated the stability and robustness of the associations for a same exposure (e.g., %UPFs). Looking at individual dietary factors, some components stood out in our analyses, either confirming strong evidence (sweet drinks, UPFs) or producing unexpected findings (sodium). Sweet drinks were associated with higher odds of depressive symptoms in our study, consistent with evidence from other cohorts, such as the Whitehall II study (52) and the very large UK Biobank (53), and further reinforced by a 2024 umbrella review of meta-analyses of observational studies, which concluded that there is convincing evidence (Class I) for a direct association between sugar-sweetened beverage consumption and depression risk (54). Concerning UPFs, we established a robust and consistent link with elevated depressive symptoms and results are corroborated by recent prospective cohort studies, including the NutriNet-Santé (38), the Spanish SUN cohort (55), the ASPirin in Reducing Events in the Elderly (ASPREE) in Australia (56), the Brazilian Longitudinal Study of Adult Health (57), and the Melbourne Collaborative Cohort Study (58). Consistent with this prospective evidence, a recent 2024 umbrella review of meta-analyses has documented that these products, typically high in sugar, saturated fats, salt, additives and other industrial ingredients, as well as potential contaminants from food processing and packaging, are associated with depressive outcomes (39). Finally, when further adjusting for overall diet quality using the sPNNS-GS2 score, most associations weakened, except for sodium, which emerged unexpectedly as a protective factor. This finding warrants further investigation, as dietary sodium has been suggested to adversely influence mood through its effects on blood pressure regulation and neuroinflammation (57). Sodium intake may be more prone to measurement error, which may have resulted in misclassification and could also explain the unexpected finding. In sex-stratified analyses, most associations were evident in women but attenuated in men. This likely reflects the composition of the NutriNet-Santé cohort, where women are overrepresented compared with national census data (60), therefore the statistical power is higher in women compared to men. Nonetheless, the protective role of vegetables and fibre, and the harmful association of UPFs were observed in both women and men. The direction of associations across sexes, in line with evidence from other populations, supports the robustness of these results. Low adherence to the GBD TMRELs was observed in the NutriNet-Santé cohort,with most participants not reaching the ideal intake levels for foods known to be neuroprotective such as fruits, vegetables, legumes, whole grains, and nuts and seeds. However, it is important to note that TMRELs are defined based on the 85th percentile of intake observed in epidemiological studies included in GBD meta-analyses. This implies that only about 15% of individuals in those studies typically achieve such intake levels. In this context, our findings, with 20–30% of participants meeting the TMREL for certain food groups, suggest that the NutriNet-Santé cohort shows relatively healthier dietary patterns compared to the global reference population. Dietary comparisons with the French Nutrition and Health Survey (ENNS) further suggest that, while NutriNet participants reported similar intakes of macronutrients and total energy, they tended to consume more fruits, vegetables, fibre, and several key micronutrients, and less alcohol and nonalcoholic beverages (61). Together, these characteristics indicate a relatively health-conscious profile, which may have influenced the strength of associations. Additionally, although all participants reported consuming UPFs, these accounted for an average of 15% of total daily intake by weight. While this proportion is lower than that observed in other high-income regions (62), it still reflects a notable presence of UPFs even within a health-conscious cohort. This pattern aligns with previous studies showing that higher UPFs consumption tends to be associated with lower intake of fruits and vegetables, higher energy and added sugar intake, and reduced consumption of fibre and essential micronutrients (63, 64). Similar imbalances were observed in participants with higher UPFs intake in the NutriNet-Santé cohort in a prior study, especially in young individuals with low socioeconomic profiles, highlighting existing dietary disparities (37). Several biological mechanisms can explain the observed associations between diet and elevated depressive symptoms. The observed detrimental effects of sugary drinks and of UPFs could be attributed to their high content of refined carbohydrates, trans fats, food additives (e.g. emulsifiers), and process-related contaminants, which have been associated with increased inflammation and oxidative stress, both implicated in the development of depressive disorders (65). Furthermore, neuroimaging studies suggest that higher UPFs consumption is associated with reduced volumes in brain regions involved in reward processing and conflict monitoring, potentially contributing to mood dysregulation and emotional vulnerability (66). Conversely, the protective effect of less processed plant-based foods on mental health may be partly explained by their anti-inflammatory and neuroprotective properties. Fruits and vegetables are rich sources of essential micronutrients, antioxidants, and dietary fibre, which support a healthy gut microbiota and reduce chronic inflammation, two mechanisms increasingly recognized in depression pathophysiology (5, 6, 67). Fibre intake, particularly from diverse sources, also plays a role in modulating serotonin production via gut-brain axis interactions, which could further explain its protective effects (68, 69). Our study has some limitations. Given its observational design, as well as the bidirectional relationship between diet and depression, reverse causality remains a possibility. However, to minimize this bias, we excluded all participants with a history of depression or prior treatment at baseline and throughout the two-year dietary exposure period. Additionally, in sensitivity analyses, we further excluded participants with elevated depressive symptoms at the first CES-D assessment (the closest in time to the dietary exposure window) and found that the main results remained unchanged. This consistency reinforces the robustness of our findings. Another potential limitation is selection bias, as our sample comprises a majority of women who volunteered for a nutritional cohort, likely more health-conscious than the general population, limiting the generalizability of the results and potentially generating collider bias. In addition, residual confounding cannot be ruled out, for example due to unmeasured factors such as personal experiences or family history of depression (which could act as a proxy for genetic predisposition). Despite these limitations, our study has several strengths. The high quality of the dietary data and repeated assessment of depressive symptoms (up to 7 questionnaires over a 8-year period), the prospective design and long follow-up period allowed us to include a large and well-characterized sample, enhancing statistical power and enabling more precise estimates. By focusing on specific food groups and nutrients, our analyses provide insights directly relevant to dietary guidelines, identifying actionable components that may help refine public health recommendations for mental health. In France, these findings complement the PNNS 2019 guidelines, which emphasize fruits, vegetables, legumes, and whole grains, while discouraging excess intake of red and processed meat, sugary drinks, and ultra-processed foods. Closing the gap between current dietary habits and these recommendations could therefore have benefits not only for physical health, but also for mental health at the population level. At the international level, our findings resonate with the growing policy momentum to integrate mental health into all areas of public policy. For example, in 2025, 31 European countries jointly committed to embedding mental health into all policies, recognizing that one in six people in the region live with a mental health condition (70). Nutrition is a natural component of this agenda, and our study reinforces the case for considering dietary improvements as part of comprehensive public health strategies to reduce the burden of depression alongside existing preventive and clinical approaches. In conclusion, our study aligns with previous research emphasizing the clear and consistent role of dietary exposures in influencing the chance of developing elevated depressive symptoms. Using a standardized protocol and several sensitivity analyses, we report robust findings showing that higher intake of fruits, vegetables and fibre lower the odds, while increased intake of red meat, sweet drinks and UPFs is associated with higher odds of elevated depressive symptoms. These findings suggest that promoting a healthy minimally processed plant-based diet could help improve population-level mental health. Beyond their national relevance, our findings may also have international implications by contributing to the evidence base needed to inform public health nutrition strategies worldwide. Finally, further research is warranted to explore additional dietary dimensions, such as meal timing and dietary sustainability, which may also play a role in mental health. ABBREVIATIONS GLAD: Global burden of disease Lifestyle And mental Disorder PUFA: Polyunsaturated fatty acids GBD: Global Burden of Disease UPFs: Ultra-processed foods CES-D: Center for Epidemiologic Studies Depression Scale GEE: Generalized Estimating Equations TMRELs: Theoretical minimum risk exposure levels OR: Odds ratios PNNS: Programme National Nutrition Santé INCA-3: third Individual and National Studies on Food Consumption survey CMDs: Common Mental Disorders sPNNS-GS2: The simplified Programme National Nutrition Santé-guidelines score 2 EPA: Eicosapentaenoic acid DHA: Docosahexaenoic acid BMI: Body Mass Index CI: confidence intervals SA: Sensitivity analyses ENNS: French Nutrition and Health Survey Declarations Ethics approval and consent to participate: The NutriNet-Santé study is registered at https://clinicaltrials.gov/ct2/show/NCT03335644, conducted according to the Declaration of Helsinki guidelines and was approved by the ethics committee of the Institutional Review Board of the French Institute for Health and Medical Research (IRB Inserm no. 0000388FWA00005831) and the National Commission on Informatics and Liberty (CNIL no. 908450/n°909216). All participants provided electronic informed consent prior to inclusion in the study. Consent for publication: Not applicable Availability of data and materials: Researchers from public institutions can submit a request to have access to the data for strict reproducibility analysis (systematically accepted) or for a new collaboration, including information on the institution and a brief description of the project to [email protected] . All requests will be reviewed by the steering committee of the NutriNet-Santé study. If the collaboration is accepted, a data access agreement will be necessary and appropriate authorisations from the competent administrative authorities may be needed. In accordance with existing regulations, no identifiable personal data will be accessible. Competing Interest: None of the authors have any financial interest, patents, company holdings, or stock to disclose related to this project. Funding : AON and DNA are supported by a National Health and Medical Research Council Emerging Leader 2 Fellowship (grant 2009295). FNJ is supported by a National Health and Medical Research Council Leader 1 Fellowship (GNT1194982). MML is supported by a Deakin University Postdoctoral Research Fellowship. RO is supported by a Deakin University Postgraduate Research Scholarship. CL is supported by a Ramon y Cajal Fellowship RYC2020-029599 funded by MICIU/AEI/10.13039/501100011033 and the European Social Fund “Invest in your future”. The NutriNet-Santé study was supported by the following public institutions: Ministère de la Santé, Santé Publique France, Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), Conservatoire National des Arts et Métiers (CNAM), and University Sorbonne Paris Nord. Disclaimer: The funding bodies had no role in the study design, collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication. Authors’ contributions: Design: AON and FNJ. Methodology: DNA, RO, MML, CL, EKG, GL. Responsibility for integrity of the data and accuracy of data analysis: EKG. Responsible for data collection: EKG, SH, MT, BA, BS. Data analysis: GL. Draft of the manuscript: GL, CL. All authors interpreted the results, critically revised, and approved the final manuscript. Acknowledgement: We are grateful to all the NutriNet-Santé study participants who have generously given their time and collaborated in the study. We also thank Thi Hong Van Duong, Régis Gatibelza, Amelle Aitelhadj and Aladi Timera (computer scientists) and Selim Aloui (IT manager); Julien Allegre, Nathalie Arnault, Nicolas Dechamp, Laurent Bourhis (data managers/statisticians) and Fabien Szabo de Edelenyi (Data-management coordinator); Maria Gomes and Mirette Foham (participant support); Alexandre De sa, Laure Legris and Laura Chaud (dietitians) and Cédric Agaesse (Dietitian coordinator); Paola Yvroud (health event validation and operational coordination); and Marie Ajanohun, Tassadit Haddar (administration and finance) and Nadia Khemache (administrative manager) for their technical contribution to the NutriNet-Santé study. References GBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022 Feb;9(2):137-150. 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Characteristics of the NutriNet-Santé study participants included in the main analyses (N=40,658) Total Women Men Baseline Sex (n (%)) 40,658 (100.0) 30,798 (75.7) 9,860 (24.3) Age at baseline (mean (SD)) 46.6 (14.3) 44.8 (14.0) 52.0 (13.9) Educational level (n (%)) No high school diploma 7,543 (18.6) 5,138 (16.7) 2,405 (24.4) High school diploma 6,280 (15.4) 5,082 (16.5) 1,198 (12.2) University level 26,835 (66.0) 20,578 (66.8) 6,257 (63.5) Marital status (n (%)) Living alone 10,213 (25.1) 8,353 (27.1) 1,860 (18.9) Living with a partner 30,445 (74.9) 22,445 (72.9) 8,000 (81.1) Residence area (n (%)) Rural 8,770 (21.6) 6,664 (21.6) 2,106 (21.4) Urban 31,245 (76.8) 23,622 (76.7) 7,623 (77.3) Outside France 643 (1.6) 512 (1.7) 131 (1.3) Occupational categories (n (%)) Never employed/other activity 1,490 (3.7) 1,341 (4.4) 149 (1.5) Self-employed 2,142 (5.3) 1,309 (4.3) 833 (8.4) Employee 9,984 (24.6) 8,898 (28.9) 1,086 (11.0) Intermediate profession 11,564 (28.4) 9,161 (29.7) 2,403 (24.4) Managerial staff 15,478 (38.1) 10,089 (32.8) 5,389 (54.7) Monthly income per consumption unit (n (%)) Less than 1200 euros 5,164 (12.7) 4,267 (13.9) 897 (9.1) 1200 – 2300 euros 15,531 (38.2) 11,971 (38.9) 3,560 (36.1) 2300 – 3700 euros 10,982 (27.0) 7,783 (25.3) 3,199 (32.4) More than 3700 euros 4,889 (12.0) 3,226 (10.5) 1,663 (16.9) Do not want to declare 4,092 (10.1) 3,551 (11.5) 541 (5.5) Smoking status (n (%)) Non-smoker 21,024 (51.7) 16,881 (54.8) 4,143 (42.0) Occasional smoker 1,750 (4.3) 1,319 (4.3) 431 (4.4) Former smoker 14,567 (35.8) 9,987 (32.4) 4,580 (46.5) Permanent smoker 3,317 (8.2) 2,611 (8.5) 706 (7.2) BMI (mean (SD)) 23.7 (4.2) 23.3 (4.31) 24.94 (3.7) Physical activity (IPAQ) (n (%)) Low activity levels 12,725 (31.3) 8,803 (28.6) 3,922 (39.8) Moderate activity levels 15,682 (38.6) 12,373 (40.2) 3,309 (33.6) High activity levels 7,972 (19.6) 6,211 (20.2) 1,761 (17.9) Missing 4,279 (10.5) 3,411 (11.1) 868 (8.8) Prevalent cardiovascular disease (n (%)) 9,367 (23.0) 5,987 (19.4) 3,380 (34.3) Type 2 diabetes 913 (2.2) 453 (1.5) 460 (4.7) Hypertension 5,070 (12.5) 2,987 (9.7) 2,083 (21.1) Dyslipidemia 5,769 (14.2) 3,700 (12.0) 2,069 (21.0) Other cardiovascular diseases 922 (2.3) 378 (1.2) 544 (5.5) Dietary exposure window (first 2 years) Number of 24h records during the exposure period (mean (SD)) 7.6 (2.5) 7.59 (2.55) 7.73 (2.50) Daily energy intake (kcal) (mean (SD)) 1896.0 (458.1) 1779.4 (380.1) 2260.1 (488.8) sPNNS-GS2 score (mean (SD)) 1.9 (3.4) 2.43 (3.12) 0.29 (3.64) Daily alcohol (g) (mean (SD)) 8.4 (11.5) 6.21 (8.42) 15.38 (16.00) Incident cardiovascular disease (n (%)) 1,493 (3.7) 688 (2.2) 805 (8.2) Type 2 diabetes 723 (1.8) 401 (1.3) 322 (3.3) Other cardiovascular diseases 815 (2.0) 299 (1.0) 516 (5.2) Incident self-reported treatment for depression (n (%)) 1,660 (4.1) 1,394 (4.5) 266 (2.7) Follow-up Number of completed CES-D (n (%)) 2 10,005 (24.6) 7,995 (26.0) 2,010 (20.4) 3 7,056 (17.4) 5,585 (18.1) 1,471 (14.9) 4 6,925 (17.0) 5,283 (17.2) 1,642 (16.7) 5 7,922 (19.5) 5,888 (19.1) 2,034 (20.6) 6 8,744 (21.5) 6,043 (19.6) 2,701 (27.4) 7 6 (0.0) 4 (0.0) 2 (0.0) Number of CES-D with elevated depressive symptoms (n (%)) 0 31,919 (78.5) 24,593 (79.9) 7,326 (74.3) At least one 8,739 (21.5) 6,205 (20.1) 2,534 (25.7) 1 4,944 (12.2) 3,723 (12.1) 1,221 (12.4) 2 2,060 (5.1) 1,436 (4.7) 624 (6.3) 3 877 (2.2) 559 (1.8) 318 (3.2) 4 494 (1.2) 301 (1.0) 193 (2.0) 5 253 (0.6) 132 (0.4) 121 (1.2) 6 111 (0.3) 54 (0.2) 57 (0.6) Abbreviations: BMI (Body Mass Index), IPAQ (International Physical Activity Questionnaire), sPNNS-GS2 (simplified Programme National Nutrition Santé-guidelines score 2), CES-D (Center for Epidemiologic Studies Depression) Table 2. Daily intake of dietary exposures and description of theoretical minimum risk exposure levels among participants included in the main analyses (N=40,658) Total (N=40,658) Women (N=30,798 ) Men (N=9,860) Food groups Fruits (g/day) Mean daily intake (SD) 211.3 (142.4) 203.7 (133.4) 235.0 (165.1) Less than 310g (n (%)) 32,734 (80.5) 25,406 (82.5) 7,328 (74.3) Less than 340g (n (%)) 34,583 (85.1) 26,778 (86.9) 7,805 (79.2) Vegetables (g/day) Mean daily intake (SD) 228.2 (107.4) 225.4 (105.4) 236.8 (113.0) Less than 280g (n (%)) 29,862 (73.4) 22,879 (74.3) 6,983 (70.8) Less than 320g (n (%)) 33,894 (83.4) 25,954 (84.3) 7,940 (80.5) Legumes (g/day) Mean daily intake (SD) 12.0 (20.5) 11.1 (18.8) 14.5 (24.9) Less than 90g (n (%)) 40,248 (99.0) 30,540 (99.2) 9,698 (98.4) Less than 100g (n (%)) 40,340 (99.2) 30,606 (99.4) 9,734 (98.7) Whole grains (g/day) Mean daily intake (SD) 36.2 (44.0) 34.1 (38.7) 42.8 (56.8) Less than 140g (n (%)) 39,369 (96.8) 30,153 (97.9) 9,216 (93.5) Less than 160g (n (%)) 39,802 (97.9) 30,399 (98.7) 9,403 (95.4) Nuts and seeds (g/day) Mean daily intake (SD) 5.1 (10.2) 4.8 (9.4) 6.2 (12.3) Less than 10g (n (%)) 34,434 (84.7) 26,421 (85.8) 8,013 (81.3) Less than 19g (n (%)) 37,959 (93.4) 28,948 (94.0) 9,011 (91.4) Milk (g/day) Mean daily intake (SD) 79.6 (116.7) 76.4 (113.0) 89.4 (127.4) Less than 360g (n (%)) 39,255 (96.5) 29,857 (96.9) 9,398 (95.3) Less than 500g (n (%)) 40,397 (99.4) 30,626 (99.4) 9,771 (99.1) Red meat (g/day) Mean daily intake (SD) 44.6 (34.2) 40.6 (31.1) 57.1 (39.8) Any intake (g) (n (%)) 36,819 (90.6) 27,687 (89.9) 9,132 (92.6) Processed meat (g/day) Mean daily intake (SD) 30.7 (25.3) 28.2 (22.8) 38.2 (30.7) Any intake (g) (n (%)) 37,785 (92.9) 28,564 (92.7) 9,221 (93.5) Sweet drinks (g/day) Mean daily intake (SD) 35.6 (76.7) 34.0 (71.4) 40.6 (91.1) Any intake (g) (n (%)) 19,541 (48.1) 14,820 (48.1) 4,721 (47.9) Ultra-processed food (%g/day) Mean daily intake (SD) 15.5 (8.0) 15.5 (8.1) 15.6 (7.9) Any intake (%) (n (%)) 40,653 (100.0) 30,795 (100.0) 9,858 (100.0) Nutrients Fiber (g/day) Mean daily intake (SD) 20.0 (6.8) 19.1 (6.2) 22.9 (7.8) Less than 21g (n (%)) 25,362 (62.4) 20,882 (67.8) 4,480 (45.4) Less than 22g (n (%)) 27,650 (68.0) 22,557 (73.2) 5,093 (51.7) Calcium (g/day) Mean daily intake (SD) 0.9 (0.3) 0.9 (0.3) 1.0 (0.3) Less than 1.06g (n (%)) 29,319 (72.1) 23,545 (76.4) 5,774 (58.6) Less than 1.1g (n (%)) 31,051 (76.4) 24,787 (80.5) 6,264 (63.5) Omega 3 (mg/day) Mean daily intake (SD) 324.6 (305.5) 307.3 (287.5) 378.6 (350.4) Less than 430mg (n (%)) 30,136 (74.1) 23,431 (76.1) 6,705 (68.0) Less than 470mg (n (%)) 31,604 (77.7) 24,524 (79.6) 7,080 (71.8) PUFA (%kcal/day) Mean daily intake (SD) 5.6 (1.6) 5.6 (1.6) 5.5 (1.7) Less than 7% (n (%)) 34,768 (85.5) 26,343 (85.5) 8,425 (85.4) Less than 9% (n (%)) 39,262 (96.6) 29,787 (96.7) 9,475 (96.1) Sodium (g/day) Mean daily intake (SD) 2.7 (0.8) 2.6 (0.7) 3.3 (0.9) More than 1g (n (%)) 40,526 (99.7) 30,680 (99.6) 9,846 (99.9) More than 5g (n (%)) 623 (1.5) 165 (0.5) 458 (4.6) Table 3. Associations from GEE logistic regression models (ORs and 95% CI) between food groups and elevated depressive symptoms (CES-D ≥23 for women and ≥17 for men) (N=40,658) Food groups (unit) Models All (N=40,658) Women (N=30,798) Men (N=9,860) Fruits (100g/day) Model 1 a 0.955 (0.934-0.975) **‡ 0.940 (0.914-0.968) **‡ 0.973 (0.942-1.005) Model 2 b 0.968 (0.948-0.989) *‡ 0.956 (0.929-0.984) *‡ 0.981 (0.949-1.014) Model 3 c 0.983 (0.960-1.006) 0.977 (0.947-1.007) 0.988 (0.953-1.023) Vegetables (100g/day) Model 1 a 0.906 (0.878-0.936) **‡ 0.905 (0.867-0.944) **‡ 0.911 (0.870-0.954) **‡ Model 2 b 0.918 (0.890-0.946) **‡ 0.920 (0.883-0.959) **‡ 0.911 (0.870-0.955) **‡ Model 3 c 0.927 (0.898-0.958) **‡ 0.936 (0.897-0.977) *‡ 0.913 (0.869-0.958) **‡ Legumes (10g/day) Model 1 a 0.993 (0.980-1.006) 0.993 (0.975-1.010) 0.994 (0.975-1.013) Model 2 b 0.990 (0.977-1.003) 0.990 (0.973-1.007) 0.992 (0.973-1.011) Model 3 c 0.995 (0.982-1.008) 0.997 (0.980-1.014) 0.994 (0.975-1.013) Whole grains (10g/day) Model 1 a 0.995 (0.989-1.002) 0.996 (0.987-1.004) 0.994 (0.985-1.004) Model 2 b 0.996 (0.990-1.002) 0.996 (0.988-1.005) 0.996 (0.986-1.005) Model 3 c 1.000 (0.994-1.007) 1.003 (0.994-1.012) 0.998 (0.988-1.008) Nuts and seeds (10g/day) Model 1 a 0.969 (0.943-0.996) *‡ 0.969 (0.933-1.005) 0.969 (0.932-1.008) Model 2 b 0.975 (0.950-1.001) 0.977 (0.943-1.012) 0.972 (0.935-1.011) Model 3 c 0.989 (0.963-1.015) 0.996 (0.962-1.032) 0.978 (0.939-1.018) Milk (100g/day) Model 1 a 1.004 (0.983-1.026) 1.006 (0.981-1.032) 0.999 (0.962-1.038) Model 2 b 0.995 (0.973-1.017) 0.993 (0.967-1.019) 0.996 (0.958-1.036) Model 3 c 0.991 (0.970-1.013) 0.988 (0.962-1.014) 0.995 (0.956-1.034) Red meat (10g/day) Model 1 a 1.011 (1.003-1.019) *‡ 1.011 (1.000-1.021) * 1.012 (0.999-1.025) Model 2 b 1.009 (1.001-1.017) * 1.010 (1.000-1.020) 1.008 (0.996-1.020) Model 3 c 1.006 (0.998-1.014) 1.006 (0.995-1.016) 1.006 (0.993-1.019) Processed meat (10g/day) Model 1 a 1.014 (1.003-1.024) *‡ 1.019 (1.005-1.032) *‡ 1.008 (0.991-1.025) Model 2 b 1.010 (0.999-1.020) 1.014 (1.001-1.028) * 1.005 (0.988-1.021) Model 3 c 0.998 (0.986-1.010) 0.998 (0.983-1.014) 0.999 (0.980-1.019) Sweet drinks (100g/day) Model 1 a 1.077 (1.047-1.109) **‡ 1.097 (1.059-1.138) **‡ 1.052 (1.005-1.101) * Model 2 b 1.046 (1.016-1.076) *‡ 1.057 (1.020-1.096) *‡ 1.032 (0.984-1.082) Model 3 c 1.031 (1.001-1.062) * 1.036 (0.998-1.076) 1.026 (0.977-1.076) Ultra-processed food (10%g/day) Model 1 a 1.223 (1.188-1.260) **‡ 1.228 (1.187-1.271) **‡ 1.208 (1.140-1.279) **‡ Model 2 b 1.160 (1.126-1.196) **‡ 1.161 (1.121-1.201) **‡ 1.161 (1.095-1.231) **‡ Model 3 c 1.150 (1.115-1.186) **‡ 1.143 (1.103-1.185) **‡ 1.159 (1.091-1.232) **‡ * p-value <0.05, ** p-value <0.001, ‡ Significant after Simes (Benjamini–Hochberg) correction for multiple comparisons a) Model 1: Dietary exposure adjusted by energy intake + Sex ( women, men ) + Age ( continuous ) + Education ( no high school diploma, high school, university level ) b) Model 2 : Model 1 + Smoking status ( non-smoker, occasional, former, permanent ) + Physical activity ( low, moderate, high, missing ) + Prevalent cardiovascular disease ( no, yes ) + Residence area ( rural, urban, outside France ) + Occupational category ( never employed/other activity, self-employed, employee, intermediate profession, managerial staff ) + Income per unit of consumption (3700€, do not want to declare ) + Marital status ( living alone, living with a partner ) + Number of 24h records ( continuous ) + Alcohol ( continuous ) c) Model 3: Model 2 + sPNNS-GS2 score ( continuous ) Table 4. Associations from GEE logistic regression models (ORs and 95% CI) between nutrients and elevated depressive symptoms (CES-D ≥23 for women and ≥17 for men ) (N=40,658) Nutrients (unit) Models All (N=40,658) Women (N=30,798) Men (N=9,860) Fiber (5g/day) Model 1 a 0.953 (0.929-0.978) **‡ 0.946 (0.915-0.979) *‡ 0.962 (0.926-1.000) * Model 2 b 0.954 (0.930-0.980) **‡ 0.949 (0.917-0.983) *‡ 0.959 (0.920-0.999) * Model 3 c 0.974 (0.945-1.004) 0.981 (0.943-1.020) 0.962 (0.917-1.010) Calcium (100mg/day) Model 1 a 1.000 (0.988-1.012) 1.004 (0.989-1.018) 0.995 (0.975-1.016) Model 2 b 1.001 (0.989-1.014) 1.004 (0.989-1.019) 0.998 (0.977-1.019) Model 3 c 1.002 (0.990-1.014) 1.006 (0.991-1.020) 0.998 (0.977-1.020) Omega 3 (100mg/day) Model 1 a 0.992 (0.983-1.001) 0.987 (0.975-0.999) * 0.999 (0.984-1.014) Model 2 b 0.994 (0.985-1.003) 0.991 (0.980-1.003) 0.998 (0.983-1.013) Model 3 c 0.996 (0.986-1.005) 0.994 (0.982-1.005) 0.998 (0.983-1.013) PUFA (1%kcal/day) Model 1 a 1.007 (0.991-1.023) 1.010 (0.991-1.030) 1.000 (0.973-1.028) Model 2 b 0.998 (0.982-1.014) 1.004 (0.984-1.023) 0.986 (0.959-1.014) Model 3 c 1.001 (0.985-1.018) 1.007 (0.988-1.027) 0.988 (0.961-1.017) Sodium (100mg/day) Model 1 a 0.996 (0.992-1.001) 0.996 (0.990-1.002) 0.998 (0.991-1.005) Model 2 b 0.997 (0.992-1.001) 0.997 (0.991-1.003) 0.997 (0.990-1.004) Model 3 c 0.992 (0.987-0.997) *‡ 0.991 (0.985-0.998) *‡ 0.994 (0.986-1.002) * p-value <0.05, ** p-value <0.001, ‡ Significant after Simes (Benjamini–Hochberg) correction for multiple comparisons a) Model 1: Dietary exposure adjusted by energy intake + Sex ( women, men ) + Age ( continuous ) + Education ( no high school diploma, high school, university level ) b) Model 2 : Model 1 + Smoking status ( non-smoker, occasional, former, permanent ) + Physical activity ( low, moderate, high, missing ) + Prevalent cardiovascular disease ( no, yes ) + Residence area ( rural, urban, outside France ) + Occupational category ( never employed/other activity, self-employed, employee, intermediate profession, managerial staff ) + Income per unit of consumption (3700€, do not want to declare ) + Marital status ( living alone, living with a partner ) + Number of 24h records ( continuous ) + Alcohol ( continuous ) c) Model 3: Model 2 + sPNNS-GS2 score ( continuous ) Additional Declarations No competing interests reported. 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Statistics","correspondingAuthor":false,"prefix":"","firstName":"Bernard","middleName":"","lastName":"Srour","suffix":""},{"id":535669848,"identity":"b15debb1-bf5e-47de-8c58-19504f21ec77","order_by":11,"name":"Mathilde Touvier","email":"","orcid":"","institution":"Centre of Research in Epidemiology and Statistics","correspondingAuthor":false,"prefix":"","firstName":"Mathilde","middleName":"","lastName":"Touvier","suffix":""},{"id":535669849,"identity":"e0898756-2acf-48bf-8b30-747aad8fbe7d","order_by":12,"name":"Emmanuelle Kesse-Guyot","email":"","orcid":"","institution":"Centre of Research in Epidemiology and Statistics","correspondingAuthor":false,"prefix":"","firstName":"Emmanuelle","middleName":"","lastName":"Kesse-Guyot","suffix":""},{"id":535669850,"identity":"4c25f161-cb44-438b-b3db-12504960dd2b","order_by":13,"name":"Camille Lassale","email":"","orcid":"","institution":"Barcelona Institute for Global Health","correspondingAuthor":false,"prefix":"","firstName":"Camille","middleName":"","lastName":"Lassale","suffix":""}],"badges":[],"createdAt":"2025-10-16 05:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7873685/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7873685/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95006058,"identity":"d578335d-f2cb-450a-8889-ae73aebb373b","added_by":"auto","created_at":"2025-11-03 09:37:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7873685/v1/7da8385948ad4edc24a44683.png"},{"id":95229691,"identity":"4737daeb-fa27-4c0f-9b30-e5a8685e2755","added_by":"auto","created_at":"2025-11-05 16:36:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2005714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7873685/v1/552e89eb-1ff1-42c2-b98d-60d980092440.pdf"},{"id":95220820,"identity":"8d31b18b-4f3e-4fc2-86d1-830695253bdb","added_by":"auto","created_at":"2025-11-05 16:14:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":226751,"visible":true,"origin":"","legend":"","description":"","filename":"20251015NutriNetGLADSupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7873685/v1/7ab74e76f31fcc2b11b6bfb9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between Global Burden of Disease dietary risk factors and elevated depressive symptoms: the NutriNet-Santé prospective cohort study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDepressive disorders represent a significant global health burden, contributing substantially to disability and diminished quality of life across populations (\u003cstrong\u003e1\u003c/strong\u003e). The causes of depression are complex and differ among individuals based on age, sex, genetics, socioeconomic status, personal experiences, and environment (\u003cstrong\u003e2\u003c/strong\u003e). Among the environmental and lifestyle factors, dietary habits have emerged as a potential modifiable risk factor influencing the incidence and trajectory of depression (\u003cstrong\u003e3, 4\u003c/strong\u003e). While the underlying mechanisms remain largely unknown, several biological pathways have been proposed to explain how an inadequate diet could influence depression, including increased systemic inflammation and disruptions in the microbiota-gut-brain axis (\u003cstrong\u003e5, 6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eOver the past decade, a growing body of evidence has shown that adherence to recommended diets, considered healthy regarding the prevention of obesity, cardiovascular disease and cancer, is also associated with a reduced risk of depressive disorders, with the traditional Mediterranean diet being the most frequently studied dietary pattern \u003cstrong\u003e(7-13)\u003c/strong\u003e. Furthermore, several randomized controlled trials, such as PREDIMED, SMILES, HELFIMED, and AMMEND, have provided causal evidence suggesting that dietary improvements can serve as an effective strategy not only for the primary prevention of depression but also for its management by reducing symptom severity (\u003cstrong\u003e14\u0026ndash;17\u003c/strong\u003e). However, most of this evidence has focused on overall dietary patterns, which may be less applicable to national guidelines, which generally provide recommendations on specific foods or nutrients. For example, the current version of the French dietary guidelines (\u003cem\u003eProgramme National Nutrition Sant\u0026eacute;\u003c/em\u003e (PNNS) 2019, (www.mangerbouger.fr)) recommends the consumption of at least five servings of fruits and vegetables daily, legumes at least twice per week, and whole grains daily, along with reduced intake of red and processed meat, added fats, salt, and sugars (\u003cstrong\u003e18\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the last two national consumption surveys,\u0026nbsp;the French diet still falls short of PNNS targets, with the population consuming insufficient dietary fibre, excessive salt, and a high proportion of processed foods (\u003cstrong\u003e19, 20\u003c/strong\u003e).\u0026nbsp;In parallel, results from the 2021 Health Barometer survey in France estimated the 12-month prevalence of major depressive disorders at 12.5% among adults, with more women (15.6%) than men (9.3%) having this condition (\u003cstrong\u003e21\u003c/strong\u003e). These gaps between recommended and actual dietary habits, combined with the substantial burden of depressive disorders, underscores the need for evidence on how specific dietary components relate to mental health, including potential sex differences. Such evidence could help define actionable targets aligned with national guidelines and to contribute to their evolution and optimization, ultimately informing strategies to improve population mental health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDespite the growing evidence, dietary factors are still not\u0026nbsp;\u003c/strong\u003esystematically recognized\u003cstrong\u003e\u0026nbsp;as relevant to mental health. This is partly because nutritional epidemiology has faced methodological challenges when applied to mental health research\u0026nbsp;\u003c/strong\u003e(\u003cstrong\u003e22\u003c/strong\u003e). Variability in study designs, dietary assessment methods, approaches to measuring depression or related symptoms and treatments, differences in population characteristics, and the strong bi-directional links between nutrition and mental health make it difficult to draw definitive conclusions about the impact of specific dietary factors on mental health (\u003cstrong\u003e23\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Global burden of disease Lifestyle And mental Disorder (GLAD) taskforce aims to address these methodological challenges (\u003cstrong\u003e24\u003c/strong\u003e). This international collaboration aims to integrate lifestyle factors, including diet, into future GBD studies to better estimate their role in Common Mental Disorders (CMDs), particularly depression and anxiety. Using a standardized protocol for data processing and analysis across multiple cohort studies, results will be meta-analysed to provide robust data for future GBD estimates and to calculate population attributable fractions.\u003c/p\u003e\n\u003cp\u003eThis study aimed to determine the associations between dietary risk factors and elevated depressive symptoms and to explore potential sex-specific differences in these associations following the GLAD protocol in the NutriNet-Sant\u0026eacute; study, a large cohort of French adults.\u0026nbsp;\u003c/p\u003e"},{"header":"METHODS","content":"\u003ch2\u003eStudy population\u003c/h2\u003e\n\u003cp\u003eThe NutriNet-Sant\u0026eacute; study is an ongoing web-based prospective cohort study launched in France in 2009 with more than 180,000 participants. The objectives of the study are to investigate the relationship between nutrition and health outcomes as well as the role of various upstream determinants of dietary patterns and nutritional status. Participants are volunteers aged 15 years or older with access to the Internet, recruited from the general population through various multimedia campaigns. After registration, participants are enrolled and monitored via a dedicated secure website (www.etude-nutrinet-sante.fr), where all questionnaires are completed. The questionnaires are self-administered and provide high-quality data, including repeated data on socio-demographics and lifestyle, anthropometrics, dietary intake, physical activity, and health status. The protocol of the NutriNet-Sant\u0026eacute; study has been published previously (\u003cstrong\u003e25\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study is conducted in accordance with the Declaration of Helsinki guidelines and has received approval from the ethics committee of the Institutional Review Board of the French Institute for Health and Medical Research (IRB Inserm no. 0000388FWA00005831) and the National Commission on Informatics and Liberty (CNIL no. 908450/n\u0026deg;909216). All participants provide electronic informed consent upon enrolment, and confidentiality and security of the data are assured at all times. The NutriNet-Sant\u0026eacute; study is registered at ClinicalTrials.gov (NCT03335644).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe methodology for this study was prospectively registered on the Open Science Framework (\u003cstrong\u003e26\u003c/strong\u003e), as part of the GLAD taskforce (DERR1-10.2196/65576) (\u003cstrong\u003e24\u003c/strong\u003e). A flow-chart of the study design for this analysis is provided in \u003cstrong\u003eFigure 1\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDietary intake assessment\u003c/h2\u003e\n\u003cp\u003eThe \u0026ldquo;dietary exposure window\u0026rdquo; covers the first two years post-enrolment in the cohort. Consumption of various foods and beverages was assessed at baseline and every 6 months through 3 non-consecutive 24-hour dietary records. To account for intra-individual variability of intakes, these self-administered records were randomly assigned to be completed over a 2-week period (two weekdays and one weekend day). These records have been previously validated against an interview by a dietitian (\u003cstrong\u003e27\u003c/strong\u003e) and against blood and urinary biomarkers (\u003cstrong\u003e28, 29\u003c/strong\u003e). Amounts consumed were declared directly by providing exact quantity (grams or milliliters), common household measures, or using portion sizes through validated photographs (\u003cstrong\u003e30\u003c/strong\u003e). Daily energy (kcal/day), alcohol\u0026nbsp;(g/day),\u0026nbsp;and nutrients (macro, micro and fibre) intakes were computed using the NutriNet-Sant\u0026eacute; food composition table containing more than 3,500 items (\u003cstrong\u003e31\u003c/strong\u003e). The average consumption over the dietary exposure window was adjusted for the type of day (weekday vs. weekend) to enhance accuracy and reflect a full week. Under-reporters were identified and excluded using the Black method with Goldberg cut-offs, calculated taking into account individual physical activity level and basal metabolic rate (\u003cstrong\u003e32\u003c/strong\u003e). The simplified Programme National Nutrition Sant\u0026eacute;-guidelines score 2 (sPNNS-GS2) was calculated and used to measure the adherence to the 2017 French dietary guidelines (\u003cstrong\u003e33\u003c/strong\u003e) as a measure of overall diet quality. UPFs consumption was calculated following the NOVA classification system (\u003cstrong\u003e34\u003c/strong\u003e). The methodologies used for the computation of the sPNNS-GS2 and UPFs, previously described in the NutriNet-Sant\u0026eacute; studies (\u003cstrong\u003e33\u003c/strong\u003e, \u003cstrong\u003e35\u003c/strong\u003e), are detailed in the supplemental\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Text S1\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eand\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Text S2\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThis study considers dietary exposures (food groups and nutrients) as defined by the GBD 2019 study (36), each with specific theoretical minimum risk exposure levels (TMRELs) thresholds indicating the intake level associated with the lowest disease risk in the population (\u003cstrong\u003e\u003cem\u003eTable S1\u003c/em\u003e\u003c/strong\u003e). In this study, TMRELs were used only to describe the distribution of intakes in the NutriNet-Sant\u0026eacute; cohort. Dietary exposures were analysed as continuous variables, and units reflect common portion sizes or relevant nutritional increments (e.g., 10g, 100g, % energy) rather than the 1g/day units often used in GBD (\u003cstrong\u003e\u003cem\u003eTable S1\u003c/em\u003e\u003c/strong\u003e). Food groups include fruits (100g/day), vegetables (100g/day), legumes (10g/day), whole grains (10g/day), nuts and seeds (10g/day), milk (100g/day), red meat (10g/day), processed meat (10g/day), and sweet drinks (beverages with \u0026ge;50 kcal per 226.8g serving) (100g/day). Nutrients include fibre (5g/day), calcium (100mg/day), seafood omega 3 fatty acids (eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)) (100mg/day), PUFA (1%kcal/day), and dietary sodium (100mg/day).\u0026nbsp;Although UPFs (10%g/day) are not yet among the exposures in the GBD study, they have been considered in the present study because evidence shows their consumption is an important part of the diet in adults from the French general population (18.4% of the foods consumed in weight) (\u003cstrong\u003e37\u003c/strong\u003e), has been associated with the risk of incident elevated\u0026nbsp;depressive symptoms in this cohort\u0026nbsp;(\u003cstrong\u003e38\u003c/strong\u003e) and other cohorts\u0026nbsp;globally (\u003cstrong\u003e39\u003c/strong\u003e), and they are explicitly mentioned in the French dietary guidelines (\u003cstrong\u003e18\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDepressive symptoms\u0026nbsp;assessment\u003c/h2\u003e\n\u003cp\u003eDepressive symptoms were evaluated using the self-administered CES-D scale (\u003cstrong\u003e40\u003c/strong\u003e) two years after enrolment and biennially thereafter (starting in 2011). The CES-D scale consists of 20 items corresponding to depressive symptoms, designed to assess their frequency over the previous week. Participants rate each symptom on a four-point scale indicating how often they experienced it (0 = \u0026ldquo;rarely or none of the time\u0026rdquo; (less than 1 day), 1 = \u0026ldquo;some or little of the time\u0026rdquo; (1\u0026ndash;2 days), 2 = \u0026ldquo;occasionally or a moderate amount of the time\u0026rdquo; (3\u0026ndash;4 days), and 3 = \u0026ldquo;most or all of the time\u0026rdquo; (5\u0026ndash;7 days)). The total score sums all item points, ranging from 0 to 60, with higher score indicating more severe depressive symptoms. In this study, elevated\u0026nbsp;depressive symptoms\u0026nbsp;were defined, in the main analysis, according to the established and validated cutoff values in the French population: \u0026ge; 17 points for men and \u0026ge; 23 points for women (\u003cstrong\u003e41, 42\u003c/strong\u003e). Participants with prevalent self-reported depression or treatment at inclusion or during the dietary exposure window were excluded for this study. New cases were defined as participants who were free of\u0026nbsp;depressive symptoms\u0026nbsp;at the end of the dietary exposure window and who met criteria for\u0026nbsp;elevated depressive symptoms at\u0026nbsp;least once during follow-up.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCovariates\u003c/h2\u003e\n\u003cp\u003eCovariates were selected based on the\u0026nbsp;GLAD taskforce\u0026nbsp;causal framework\u0026nbsp;(24) and the literature\u0026nbsp;(9).\u0026nbsp;Sociodemographic and lifestyle data were collected at baseline and yearly using a self-administered web-based questionnaire (\u003cstrong\u003e43\u003c/strong\u003e) that included data on sex (women or men), age (years), educational level (no high school diploma, high school diploma, or university level), marital status (living alone or with a partner), residence area (rural, urban, or outside France), occupational categories (never-employed/ other activity, self-employed, employee, intermediate profession, or managerial staff),\u0026nbsp;monthly income per unit of consumption (the total number of unit depending on the household composition)\u0026nbsp;(\u0026lt; 1200, 1200\u0026ndash;2300, 2300\u0026ndash;3700, \u0026gt; 3700 euros, and a category of participants who refused to disclose their income), and smoking status (non-smoker, occasional, former or permanent smoker).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWeight and height data were collected using a validated\u0026nbsp;self-administered anthropometric questionnaire (\u003cstrong\u003e44\u003c/strong\u003e) at baseline and every 6 months, and the Body Mass Index (BMI) was calculated as the ratio of weight to squared height (kg/m\u003csup\u003e2\u003c/sup\u003e). For the assessment of physical activity, a short form of the French version of the International Physical Activity Questionnaire was used yearly (\u003cstrong\u003e45\u003c/strong\u003e), classifying energy expenditure as low (\u0026lt; 30 min of physical activity; equivalent to brisk walking/day), moderate\u0026nbsp;(\u0026ge; 30 and \u0026lt; 60 min) and high\u0026nbsp;(\u0026ge; 60 min)\u0026nbsp;activity levels.\u0026nbsp;Due to a larger number of missing data on the physical activity variable, we created a \u0026ldquo;missing\u0026rdquo; category to avoid excluding more participants.\u0026nbsp;Self-reported health events were declared every 6 months through the website, including prevalent and incident cases of cardiovascular diseases, which were validated against medical records, and depression treatment.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eFor this study, we included a subset of adult (\u0026ge; 18 years) participants from the NutriNet-Sant\u0026eacute; study who completed at least two CES-D assessments between 2011 and 2022. We excluded participants with self-reported depression or treatment for this condition at enrolment or during the first two years (dietary exposure window), and those without dietary data. Among the participants who met these criteria (n=41,809), we excluded participants with missing values in baseline covariates or the sPNNS-GS2 diet score. The final sample was of 40,658 participants, as shown in \u003cstrong\u003e\u003cem\u003eFigure S1\u003c/em\u003e\u003c/strong\u003e. Description of baseline characteristics was performed in those included in the main analysis overall and stratified by sex. Differences by sex were assessed by analysis of variance for continuous variables or chi-square tests for categorical variables.\u003c/p\u003e\n\u003cp\u003eAs per GLAD protocol (\u003cstrong\u003e24\u003c/strong\u003e), food group and nutrient intakes were energy-adjusted using Willet\u0026rsquo;s residual method (\u003cstrong\u003e46\u003c/strong\u003e), and the energy-adjusted continuous variables were used as the dietary exposure in all statistical models. GEE logistic regressions with repeated measures were used to assess the relationship between dietary exposures and the risk of elevated depressive symptoms (CES-D \u0026ge; 17 for men and \u0026ge; 23 for women) across multiple CES-D assessment waves during follow-up, estimating odds ratios (OR) and 95% confidence intervals (CI).\u003c/p\u003e\n\u003cp\u003eSeparate models were fitted for each of the 15 dietary exposures (not mutually adjusted), using the same adjustment strategy for all dietary exposures. All assumptions were assessed prior to model fitting. Model 1 was adjusted for sex, age, and educational level. Model 2 (main model) was additionally adjusted for smoking status, physical activity, prevalent cardiovascular disease, residence area, occupational categories, income, marital status, number of 24h records, and alcohol intake. To examine whether associations were independent of broader dietary patterns linked to higher consumption of specific foods, we ran another model (Model 3) including sPNNS-GS2 as a covariate, only as a secondary adjustment rather than as a conventional confounder. Analyses were conducted overall and separately for men and women, in which case models were not adjusted for sex.\u003c/p\u003e\n\u003cp\u003eWe conducted a series of sensitivity analyses (SA). First, although generally considered to be on the causal pathway, BMI can also be considered a potential confounder of the diet-depression association; thus, we adjusted main Model 2 for baseline BMI (SA-1). Second, we further adjusted main Model 2 for new onset of self-reported treatment for depression during follow-up to account for its potential effect on lowering CES-D scores (SA-2). Third, to limit the possibility of reverse causality, we excluded those participants with elevated depressive symptoms at their first CES-D assessment (close to the dietary exposure window), resulting in a study sample of 36,618 participants (SA-3). Fourth, we used an alternative CES-D cut-off value of 19 (\u003cstrong\u003e42\u003c/strong\u003e) to define elevated depressive symptoms (SA-4). Fifth, we conducted the analyses mutually adjusting the dietary exposures (i.e. including them all in the same model), separately for food groups together and nutrients together (SA-5). Finally, given the strong attenuation of coefficients observed in Model 3 when adjusting for the overall sPNNS-GS2 score, we aimed to address potential overadjustment due to the inclusion of some of the dietary exposures of interest within the score itself. Therefore, for fruits, vegetables, and red meat, we repeated the analyses (SA-6) using a modified version of the sPNNS-GS2 score in which the component corresponding to the exposure of interest was excluded from the total score (Model 4).\u003c/p\u003e\n\u003cp\u003eTwo-tailed test P values of less than 0.05 were considered statistically significant. To account for multiple testing, we applied the Benjamini-Hochberg method to adjust p-values and control the false discovery rate, maintaining the 0.05 significance threshold (\u003cstrong\u003e47\u003c/strong\u003e). All statistical analyses were performed using R version 4.1.2.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe final study sample comprised 40,658 participants from the NutriNet-Santé cohort, including 30,798 women (75.7%) and 9,860 men (24.3%), with a mean age of 46.6 years. After a median follow-up of 8.36 years (Q1=5.52; Q3=10.07) between baseline and last CES-D assessment, 8,739 (21.5%) participants met criteria for elevated depressive symptoms at least once during follow-up. Baseline characteristics of participants included in the study are presented in \u003cstrong\u003e\u003cem\u003eTable 1\u003c/em\u003e\u003c/strong\u003e. Women were younger than men, had a higher educational level, were more likely to live alone, and less likely to smoke. They also had a lower BMI, higher levels of physical activity, and a lower prevalence and incidence of cardiovascular disease. During the dietary exposure window, women had a lower daily\u0026nbsp;energy intake, a higher sPNNS-GS2 score, and lower alcohol intake, than men.\u0026nbsp;Additionally, during follow-up, women completed fewer CES-D questionnaires and endorsed fewer CES-D detected symptoms compared to men.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1. Characteristics of the NutriNet-Santé study participants included in the main analyses (N=40,658)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2\u003c/em\u003e\u003c/strong\u003e shows the differences in food group and nutrient intake between men and women.\u0026nbsp;The majority (70-100%) of women and men did not reach the TMRELs\u0026nbsp;thresholds\u0026nbsp;established by the GBD 2019 study, except for sweet drinks, with 52% of never consumers.\u0026nbsp;Women had lower dietary intake than men for most food groups and nutrients, corresponding to a greater proportion of females not meeting the TMRELs thresholds for fruits, vegetables, legumes, whole grains, nuts and seeds, milk, fibre, calcium, and omega 3. Conversely, women were less likely to exceed the sodium limits than men.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2. Daily intake of dietary exposures and description of theoretical minimum risk exposure levels among participants included in the main analyses (N=40,658)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe associations between food groups and nutrients and odds of elevated depressive symptoms are presented in \u003cstrong\u003e\u003cem\u003eTable 3\u003c/em\u003e\u003c/strong\u003e and \u003cstrong\u003e\u003cem\u003eTable 4\u003c/em\u003e\u003c/strong\u003e. In the overall sample, after adjustment for demographic and lifestyle factors (main Model 2), higher intake of fruit (OR \u003csub\u003eper 100 g/day\u003c/sub\u003e: 0.968; 95% CI: 0.948–0.989), vegetables (OR \u003csub\u003eper 100 g/day\u003c/sub\u003e: 0.918; 95% CI: 0.890–0.946), and fibre (OR \u003csub\u003eper 5 g/day\u003c/sub\u003e: 0.954; 95% CI: 0.930–0.980) was associated with lower odds of elevated depressive symptoms. In contrast, higher consumption of red meat (OR \u003csub\u003eper 10 g/day\u003c/sub\u003e: 1.009; 95% CI: 1.001–1.017) and sweet drinks (OR \u003csub\u003eper 100 g/day\u003c/sub\u003e: 1.046; 95% CI: 1.016–1.076) was associated with higher odds. Additionally, a greater proportion of UPFs in the diet was more strongly associated with increased odds (OR \u003csub\u003eper 10%g/day\u003c/sub\u003e: 1.160; 95% CI: 1.126–1.196). No associations were found for legumes, whole grains, nuts and seeds, milk, processed meat, calcium, omega 3, PUFA, or sodium intake. After further adjustment for the sPNNS-GS2 score (Model 3), some estimates were attenuated (fruits, fibre, and red meat), which may reflect over-adjustment as explored in sensitivity analyses, while an unexpected association with sodium emerged (OR \u003csub\u003eper 100 mg/day\u003c/sub\u003e: 0.992; 95% CI: 0.987–0.997).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 3. Associations from GEE logistic regression models (ORs and 95% CI) between food groups and elevated depressive symptoms (CES-D ≥23 for women and ≥17 for men) (N=40,658)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 4. Associations from GEE logistic regression models (ORs and 95% CI) between nutrients and elevated depressive symptoms (CES-D ≥23 for women and ≥17 for men\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e) \u003cstrong\u003e(N=40,658)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn sex-specific analyses, most associations were evident among women (fruits, vegetables, fibre, sweet drinks, and UPFs). For red meat, the association was weaker, while processed meat emerged as positively associated (OR \u003csub\u003eper 10 g/day\u003c/sub\u003e: 1.014; 95% CI: 1.001–1.028). In contrast, among men, most associations were attenuated, with only vegetables, fibre, and UPFs remaining related to elevated depressive symptoms. Results using GBD units are available in \u003cstrong\u003e\u003cem\u003eTable S2\u003c/em\u003e\u003c/strong\u003e and \u003cstrong\u003e\u003cem\u003eTable S3\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eSensitivity analyses further adjusting main Model 2 for baseline BMI (SA-1) (\u003cstrong\u003e\u003cem\u003eTable S4\u003c/em\u003e\u003c/strong\u003e)and for new onset of self-reported treatment for depression during follow-up (SA-2) (\u003cstrong\u003e\u003cem\u003eTable S5\u003c/em\u003e\u003c/strong\u003e)did not substantially alter the associations. SA-3 excluding participants with elevated depressive symptoms at the first CES-D assessment (conducted after the two-year dietary exposure window) are presented in \u003cstrong\u003e\u003cem\u003eTables S6–S9\u003c/em\u003e\u003c/strong\u003e. The sample (N=36,618) did not differ from the main analysis sample. In this analysis, there were 4,699 participants who showed elevated depressive symptoms at least at one of the CES-D assessment following the first one. Higher intakes of fruits, vegetables, and fibre were associated with lower odds of elevated depressive symptoms, whereas higher intakes of sweet drinks and UPFs were associated with higher odds in Model 2. These associations were weaker than in the main analysis. In SA-4 using the CES-D cut-off of 19 for both women and men (\u003cstrong\u003eTables S10-S11\u003c/strong\u003e), the associations observed in Model 2 were consistent with those from the main analyses. However, additional inverse associations were observed with higher legumes intake (OR\u003csub\u003e\u0026nbsp;per 10 g/day\u003c/sub\u003e:\u0026nbsp;0.985; 95% CI:\u0026nbsp;0.974-0.997) and higher nuts and seeds intake (OR \u003csub\u003eper 10 g/day\u003c/sub\u003e:\u0026nbsp;0.967; 95% CI:\u0026nbsp;0.944-0.991) in the overall sample. In the mutually adjusted models for groups of exposures (\u003cstrong\u003e\u003cem\u003eSA-5, Table S12\u003c/em\u003e\u003c/strong\u003e), the associations of vegetables, fibre, and UPFs intake with elevated depressive symptoms were consistent with results from earlier models. Finally, when the specific dietary exposure (fruit, vegetables, or red meat) was excluded from the original sPNNS-GS2 score in Model 4, the magnitude and significance of associations were closer to that observed in Model 2, supporting the hypothesis of potential overadjustment in Model 3 (\u003cstrong\u003e\u003cem\u003eSA-6, Table S13\u003c/em\u003e\u003c/strong\u003e).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn the French NutriNet-Sant\u0026eacute; cohort, over a median of 8.36 years, higher consumption of fruits (overall and among women), vegetables, and fibre (overall and in both sexes) was associated with a lower likelihood of developing elevated depressive symptoms assessed using the CES-D. In contrast, greater intake of red meat (overall), processed meat (only among women), and sweet drinks (overall and among women) was linked to increased likelihood. Additionally, UPFs showed a strong and consistent association with higher odds of developing elevated depressive symptoms across all models and in both sexes. Beyond confirming associations already suggested in the literature, by applying the standardized GLAD protocol, this study provides results that will contribute to future GBD estimates.\u003c/p\u003e\n\u003cp\u003eOur results align with previous longitudinal studies highlighting the protective role of diets rich in plant-based foods, such as fruits and vegetables and exemplified by the Mediterranean diet (7-13), and the harmful impact of Western dietary patterns, typically characterized by high consumption of red meat and sugary drinks (48, 49). Within the NutriNet-Sant\u0026eacute; cohort, earlier research found that a one standard deviation increase in three different diet quality scores\u0026mdash;the modified French Programme National Nutrition Sant\u0026eacute;-Guideline Score, the Probability of Adequate Nutrient Intake Dietary Score, and the Diet Quality Index-International \u0026mdash;was associated with a 5-9% reduction in the risk of elevated depressive symptoms (9).\u0026nbsp;In addition,adherence to a proinflammatory dietary pattern was associated with an increased risk of developing depressive symptoms by 15% when comparing the highest to the lowest quintile of intake (95% CI: 2%, 31%) (50), and a 10% increase in UPFs intake was linked to a 21% higher risk (95% CI: 1.15\u0026ndash;1.27) (38). Compared with earlier analyses, the present study benefited from a substantially larger number of incident cases (more than double) due to the longer follow-up period until 2022, which increased the likelihood of capturing episodes of elevated depressive symptoms. This rise in cases might have been further amplified by contextual factors such as the COVID-19 pandemic, during which a global 25% prevalence of depression (seven times higher than pre-pandemic estimates) was observed in recent analyses (51). In addition, the progressive aging of the cohort may also have contributed to higher incidence. Taken together, these elements strengthened statistical power and illustrated the stability and robustness of the associations for a same exposure (e.g., %UPFs).\u003c/p\u003e\n\u003cp\u003eLooking at individual dietary factors, some components stood out in our analyses, either confirming strong evidence (sweet drinks, UPFs) or producing unexpected findings (sodium). Sweet drinks were associated with higher odds of depressive symptoms in our study, consistent with evidence from other cohorts, such as the Whitehall II study (52) and the very large UK Biobank (53), and further reinforced by a 2024 umbrella review of meta-analyses of observational studies, which concluded that there is convincing evidence (Class I) for a direct association between sugar-sweetened beverage consumption and depression risk\u0026nbsp;(54). Concerning UPFs, we established a robust and consistent link with elevated depressive symptoms and results are corroborated by recent prospective cohort studies, including the NutriNet-Sant\u0026eacute; (38), the Spanish SUN cohort (55), the ASPirin in Reducing Events in the Elderly (ASPREE) in Australia (56), the Brazilian Longitudinal Study of Adult Health (57), and the Melbourne Collaborative Cohort Study (58). Consistent with this prospective evidence, a recent 2024 umbrella review of meta-analyses has documented that these products, typically high in sugar, saturated fats, salt, additives and other industrial ingredients, as well as potential contaminants from food processing and packaging, are associated with depressive outcomes (39). Finally, when further adjusting for overall diet quality using the sPNNS-GS2 score,\u0026nbsp;most associations weakened, except for sodium, which emerged unexpectedly as a protective factor. This finding warrants further investigation, as dietary sodium has been suggested to adversely influence mood through its effects on blood pressure regulation and neuroinflammation (57). Sodium intake may be more prone to measurement error, which may have resulted in misclassification and could also explain the unexpected finding.\u003c/p\u003e\n\u003cp\u003eIn sex-stratified analyses, most associations were evident in women but attenuated in men. This likely reflects the composition of the NutriNet-Sant\u0026eacute; cohort, where women are overrepresented compared with national census data (60), therefore the statistical power is higher in women compared to men. \u0026nbsp; Nonetheless, the protective role of vegetables and fibre, and the harmful association of UPFs were observed in both women and men. The direction of associations across sexes, in line with evidence from other populations, supports the robustness of these results.\u003c/p\u003e\n\u003cp\u003eLow adherence to the GBD TMRELs\u0026nbsp;was observed\u0026nbsp;in the NutriNet-Sant\u0026eacute; cohort,with most participants not reaching the ideal intake levels for foods known to be neuroprotective such as fruits, vegetables, legumes, whole grains, and nuts and seeds. However, it is important to note that TMRELs are defined based on the 85th percentile of intake observed in epidemiological studies included in GBD meta-analyses. This implies that only about 15% of individuals in those studies typically achieve such intake levels. In this context, our findings, with 20\u0026ndash;30% of participants meeting the TMREL for certain food groups, suggest that the NutriNet-Sant\u0026eacute; cohort shows relatively healthier dietary patterns compared to the global reference population.\u0026nbsp;Dietary comparisons with the French Nutrition and Health Survey (ENNS) further suggest that, while NutriNet participants reported similar intakes of macronutrients and total energy, they tended to consume more fruits, vegetables, fibre, and several key micronutrients, and less alcohol and nonalcoholic beverages\u0026nbsp;(61). Together, these characteristics indicate a relatively health-conscious profile, which may have influenced the strength of associations.\u0026nbsp;Additionally, although all participants reported consuming UPFs, these accounted for an average of 15% of total daily intake by weight. While this proportion is lower than that observed in other high-income regions\u0026nbsp;(62), it still reflects a notable presence of UPFs even within a health-conscious cohort.\u0026nbsp;This pattern aligns with previous studies showing that higher UPFs consumption tends to be associated with lower intake of fruits and vegetables, higher energy and added sugar intake, and reduced consumption of fibre and essential micronutrients (63, 64). Similar imbalances were observed in participants with higher UPFs intake in the NutriNet-Sant\u0026eacute; cohort in a prior study, especially in young individuals with low socioeconomic profiles, highlighting existing dietary disparities (37).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral biological mechanisms can explain the observed associations between diet and elevated depressive symptoms. The observed detrimental effects of sugary drinks and of UPFs could be attributed to their high content of refined carbohydrates, trans fats, food additives (e.g. emulsifiers), and process-related contaminants, which have been associated with increased inflammation and oxidative stress, both implicated in the development of depressive disorders (65). Furthermore, neuroimaging studies suggest that higher UPFs consumption is associated with reduced volumes in brain regions involved in reward processing and conflict monitoring, potentially contributing to mood dysregulation and emotional vulnerability (66). Conversely, the protective effect of less processed plant-based foods on mental health may be partly explained by their anti-inflammatory and neuroprotective properties. Fruits and vegetables are rich sources of essential micronutrients, antioxidants, and dietary fibre, which support a healthy gut microbiota and reduce chronic inflammation, two mechanisms increasingly recognized in depression pathophysiology (5, 6,\u0026nbsp;67). Fibre intake, particularly from diverse sources, also plays a role in modulating serotonin production via gut-brain axis interactions, which could further explain its protective effects (68,\u0026nbsp;69).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study has some limitations. Given its observational design, as well as the bidirectional relationship between\u0026nbsp;diet and depression, reverse causality remains a possibility. However, to minimize this bias, we excluded all participants with a history of depression or prior treatment at baseline and throughout the two-year dietary exposure period. Additionally, in sensitivity analyses, we further excluded participants with elevated\u0026nbsp;depressive symptoms at the first CES-D assessment\u0026nbsp;(the closest in time to the\u0026nbsp;dietary exposure window)\u0026nbsp;and found that the main results remained unchanged. This consistency reinforces the robustness of our findings. Another potential limitation is selection bias, as our sample comprises a majority of women who volunteered for a nutritional cohort, likely more health-conscious than the general population, limiting the generalizability of the results and potentially generating collider bias.\u0026nbsp;In addition, residual confounding cannot be ruled out, for example due to unmeasured factors such as personal experiences or family history of depression (which could act as a proxy for genetic predisposition).\u0026nbsp;Despite these limitations, our study has several strengths. The high quality of the dietary data and repeated assessment of depressive symptoms (up to 7 questionnaires over a 8-year period), the prospective design and long follow-up period allowed us to include a large and well-characterized sample, enhancing statistical power and enabling more precise estimates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy focusing on specific food groups and nutrients, our analyses provide insights directly relevant to dietary guidelines, identifying actionable components that may help refine public health recommendations for mental health. In France, these findings complement the PNNS 2019 guidelines, which emphasize fruits, vegetables, legumes, and whole grains, while discouraging excess intake of red and processed meat, sugary drinks, and ultra-processed foods. Closing the gap between current dietary habits and these recommendations could therefore have benefits not only for physical health, but also for mental health at the population level. At the international level, our findings resonate with the growing policy momentum to integrate mental health into all areas of public policy. For example, in 2025, 31 European countries jointly committed to embedding mental health into all policies, recognizing that one in six people in the region live with a mental health condition (70). Nutrition is a natural component of this agenda, and our study reinforces the case for considering dietary improvements as part of comprehensive public health strategies to reduce\u0026nbsp;the burden of depression alongside existing preventive and clinical approaches.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study aligns with previous research emphasizing the clear and consistent role of dietary exposures in influencing the chance of developing elevated depressive symptoms. Using a standardized protocol and several sensitivity analyses, we report robust findings showing that higher intake of fruits, vegetables and fibre lower the odds, while increased intake of red meat, sweet drinks and UPFs is associated with higher odds of elevated depressive symptoms. These findings suggest that promoting a healthy minimally processed plant-based diet could help improve population-level mental health. Beyond their national relevance, our findings may also have international implications by contributing to the evidence base needed to inform public health nutrition strategies worldwide. Finally, further research is warranted to explore additional dietary dimensions, such as meal timing and dietary sustainability, which may also play a role in mental health.\u003c/p\u003e"},{"header":"ABBREVIATIONS","content":"\u003cp\u003eGLAD: Global burden of disease Lifestyle And mental Disorder\u003c/p\u003e\n\u003cp\u003ePUFA: Polyunsaturated fatty acids\u003c/p\u003e\n\u003cp\u003eGBD: Global Burden of Disease\u003c/p\u003e\n\u003cp\u003eUPFs: Ultra-processed foods\u003c/p\u003e\n\u003cp\u003eCES-D: Center for Epidemiologic Studies Depression Scale\u003c/p\u003e\n\u003cp\u003eGEE: Generalized Estimating Equations\u003c/p\u003e\n\u003cp\u003eTMRELs: Theoretical minimum risk exposure levels\u003c/p\u003e\n\u003cp\u003eOR: Odds ratios\u003c/p\u003e\n\u003cp\u003ePNNS: \u003cem\u003eProgramme National Nutrition Santé\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eINCA-3: third Individual and National Studies on Food Consumption survey\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCMDs: Common Mental Disorders\u003c/p\u003e\n\u003cp\u003esPNNS-GS2: The simplified Programme National Nutrition Santé-guidelines score 2\u003c/p\u003e\n\u003cp\u003eEPA: Eicosapentaenoic acid\u003c/p\u003e\n\u003cp\u003eDHA: Docosahexaenoic acid\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003eCI: confidence intervals\u003c/p\u003e\n\u003cp\u003eSA: Sensitivity analyses\u003c/p\u003e\n\u003cp\u003eENNS: French Nutrition and Health Survey\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe NutriNet-Sant\u0026eacute; study is registered at https://clinicaltrials.gov/ct2/show/NCT03335644, conducted according to the Declaration of Helsinki guidelines and was approved by the ethics committee of the Institutional Review Board of the French Institute for Health and Medical Research (IRB Inserm no. 0000388FWA00005831) and the National Commission on Informatics and Liberty (CNIL no. 908450/n\u0026deg;909216). All participants provided electronic informed consent prior to inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eResearchers from public institutions can submit a request to have access to the data for strict reproducibility analysis (systematically accepted) or for a new collaboration, including information on the institution and a brief description of the project to [email protected]. All requests will be reviewed by the steering committee of the NutriNet-Sant\u0026eacute; study. If the collaboration is accepted, a data access agreement will be necessary and appropriate authorisations from the competent administrative authorities may be needed. In accordance with existing regulations, no identifiable personal data will be accessible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest:\u0026nbsp;\u003c/strong\u003eNone of the authors have any financial interest, patents, company holdings, or stock to disclose related to this project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: AON and DNA are supported by a National Health and Medical Research Council Emerging Leader 2 Fellowship (grant 2009295). FNJ is supported by a National Health and Medical Research Council Leader 1 Fellowship (GNT1194982). MML is supported by a Deakin University Postdoctoral Research Fellowship. RO is supported by a Deakin University Postgraduate Research Scholarship. CL is supported by a Ramon y Cajal Fellowship RYC2020-029599 funded by MICIU/AEI/10.13039/501100011033 and the European Social Fund \u0026ldquo;Invest in your future\u0026rdquo;. The NutriNet-Sant\u0026eacute; study was supported by the following public institutions: Minist\u0026egrave;re de la Sant\u0026eacute;, Sant\u0026eacute; Publique France, Institut National de la Sant\u0026eacute; et de la Recherche M\u0026eacute;dicale (INSERM), Institut National de la Recherche pour l\u0026rsquo;agriculture, l\u0026rsquo;alimentation et l\u0026rsquo;environnement (INRAE), Conservatoire National des Arts et M\u0026eacute;tiers (CNAM), and University Sorbonne Paris Nord.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer:\u003c/strong\u003e The funding bodies had no role in the study design, collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e Design: AON and FNJ. Methodology: DNA, RO, MML, CL, EKG, GL. Responsibility for integrity of the data and accuracy of data analysis: EKG. Responsible for data collection: EKG, SH, MT, BA, BS. Data analysis: GL. Draft of the manuscript: GL, CL. All authors interpreted the results, critically revised, and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eWe are grateful to all the\u0026nbsp;NutriNet-Sant\u0026eacute; study participants who have generously given their time and collaborated in the study.\u0026nbsp;We also thank Thi Hong Van Duong, R\u0026eacute;gis Gatibelza, Amelle Aitelhadj and Aladi Timera (computer scientists) and Selim Aloui (IT manager); Julien Allegre, Nathalie Arnault, Nicolas Dechamp, Laurent Bourhis (data managers/statisticians) and Fabien Szabo de Edelenyi (Data-management coordinator); Maria Gomes and Mirette Foham (participant support); Alexandre De sa, Laure Legris and Laura Chaud (dietitians) and C\u0026eacute;dric Agaesse (Dietitian coordinator); Paola Yvroud (health event validation and operational coordination); and Marie Ajanohun, Tassadit Haddar (administration and finance) and Nadia Khemache (administrative manager) for their technical contribution to the NutriNet-Sant\u0026eacute; study.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":" References","content":"\u003col\u003e\n\u003cli\u003eGBD 2019 Mental Disorders Collaborators. 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Br J Nutr. 2015 Mar 28;113(6):953-62. \u003c/li\u003e\n\u003cli\u003eLassale C, Castetbon K, Laporte F, Deschamps V, Vernay M, Camilleri GM, Faure P, Hercberg S, Galan P, Kesse-Guyot E. Correlations between Fruit, Vegetables, Fish, Vitamins, and Fatty Acids Estimated by Web-Based Nonconsecutive Dietary Records and Respective Biomarkers of Nutritional Status. J Acad Nutr Diet. 2016 Mar;116(3):427-438.e5. \u003c/li\u003e\n\u003cli\u003eLe Moullec N, Deheeger M, Preziosi P, Monteiro P, Valeix P, Rolland-Cachera M, et al. Validation of the photo manual used for the collection of dietary data in the SU.VI.MAX study. Cah Nutr Di\u0026eacute;t\u0026eacute;tique. 1996;31:158-64.\u003c/li\u003e\n\u003cli\u003eNutriNet-Sant\u0026eacute; coordination. Table de composition des aliments \u0026ndash; Etude NutriNet-Sant\u0026eacute;. Paris: Economica; 2013.\u003c/li\u003e\n\u003cli\u003eBlack AE. 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Nat Food. 2023 Jun;4(6):445-448. \u003c/li\u003e\n\u003cli\u003eBeslay M, Srour B, M\u0026eacute;jean C, All\u0026egrave;s B, Fiolet T, Debras C, Chazelas E, Deschasaux M, Wendeu-Foyet MG, Hercberg S, Galan P, Monteiro CA, Deschamps V, Calixto Andrade G, Kesse-Guyot E, Julia C, Touvier M. Ultra-processed food intake in association with BMI change and risk of overweight and obesity: A prospective analysis of the French NutriNet-Sant\u0026eacute; cohort. PLoS Med. 2020 Aug 27;17(8):e1003256.\u003c/li\u003e\n\u003cli\u003eGBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2020;396(10258):1223-1249.\u003c/li\u003e\n\u003cli\u003eJulia C, Martinez L, All\u0026egrave;s B, Touvier M, Hercberg S, M\u0026eacute;jean C, Kesse-Guyot E. Contribution of ultra-processed foods in the diet of adults from the French NutriNet-Sant\u0026eacute; study. 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The French version of the Center for Epidemiologic Studies-Depression Scale. 4, 163-166. Psychiatr Psychobiol. 1989;4:163\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eMorin AJ, Moullec G, Ma\u0026iuml;ano C, Layet L, Just JL, Ninot G. Psychometric properties of the Center for Epidemiologic Studies Depression Scale (CES-D) in French clinical and nonclinical adults. Rev Epidemiol Sante Publique. 2011 Oct;59(5):327-40. \u003c/li\u003e\n\u003cli\u003eVergnaud A-C, Touvier M, M\u0026eacute;jean C, Kesse-Guyot E, Pollet C, Malon A, et al. Agreement between web-based and paper versions of a sociodemographic questionnaire in the NutriNet-Sant\u0026eacute; study. Int J Public Health. 2011;56:407\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eLassale C, P\u0026eacute;neau S, Touvier M, Julia C, Galan P, Hercberg S, Kesse-Guyot E. Validity of web-based self-reported weight and height: results of the Nutrinet-Sant\u0026eacute; study. J Med Internet Res. 2013 Aug 8;15(8):e152. \u003c/li\u003e\n\u003cli\u003eCraig CL, Marshall AL, Sj\u0026ouml;str\u0026ouml;m M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003 Aug;35(8):1381-95. \u003c/li\u003e\n\u003cli\u003eWillett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997 Apr;65(4 Suppl):1220S-1228S; discussion 1229S-1231S.\u003c/li\u003e\n\u003cli\u003eBenjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995;57(1):289\u0026ndash;300. \u003c/li\u003e\n\u003cli\u003eCherian L, Wang Y, Holland T, Agarwal P, Aggarwal N, Morris MC. DASH and Mediterranean-Dash Intervention for Neurodegenerative Delay (MIND) Diets Are Associated With Fewer Depressive Symptoms Over Time. J Gerontol A Biol Sci Med Sci. 2021 Jan 1;76(1):151-156.\u003c/li\u003e\n\u003cli\u003eShakya PR, Melaku YA, Shivappa N, H\u0026eacute;bert JR, Adams RJ, Page AJ, Gill TK. Dietary inflammatory index (DII\u0026reg;) and the risk of depression symptoms in adults. Clin Nutr. 2021 May;40(5):3631-3642. \u003c/li\u003e\n\u003cli\u003eAdjibade M, Lemogne C, Touvier M, Hercberg S, Galan P, Assmann KE, Julia C, Kesse-Guyot E. The Inflammatory Potential of the Diet is Directly Associated with Incident Depressive Symptoms Among French Adults. J Nutr. 2019 Jul 1;149(7):1198-1207. \u003c/li\u003e\n\u003cli\u003eBueno-Notivol J, Gracia-Garc\u0026iacute;a P, Olaya B, Lasheras I, L\u0026oacute;pez-Ant\u0026oacute;n R, Santab\u0026aacute;rbara J. Prevalence of depression during the COVID-19 outbreak: A meta-analysis of community-based studies. Int J Clin Health Psychol. 2021 Jan-Apr;21(1):100196. \u003c/li\u003e\n\u003cli\u003eKn\u0026uuml;ppel A, Shipley MJ, Llewellyn CH, Brunner EJ. Sugar intake from sweet food and beverages, common mental disorder and depression: prospective findings from the Whitehall II study. \u003cem\u003eSci Rep\u003c/em\u003e. 2017;7:6287.\u003c/li\u003e\n\u003cli\u003eKaiser A, Schaefer SM, Behrendt I, Eichner G, Fasshauer M. Association of sugar intake from different sources with incident depression in the prospective cohort of UK Biobank participants. Eur J Nutr. 2023 Mar;62(2):727-738. \u003c/li\u003e\n\u003cli\u003eLane MM, Travica N, Gamage E, Marshall S, Trakman GL, Young C, Teasdale SB, Dissanayaka T, Dawson SL, Orr R, Jacka FN, O\u0026apos;Neil A, Lawrence M, Baker P, Rebholz CM, Du S, Marx W. Sugar-Sweetened Beverages and Adverse Human Health Outcomes: An Umbrella Review of Meta-Analyses of Observational Studies. Annu Rev Nutr. 2024 Aug;44(1):383-404. \u003c/li\u003e\n\u003cli\u003eG\u0026oacute;mez-Donoso C, S\u0026aacute;nchez-Villegas A, Mart\u0026iacute;nez-Gonz\u0026aacute;lez MA, Gea A, Mendon\u0026ccedil;a RD, Lahortiga-Ramos F, Bes-Rastrollo M. Ultra-processed food consumption and the incidence of depression in a Mediterranean cohort: the SUN Project. Eur J Nutr. 2020 Apr;59(3):1093-1103. \u003c/li\u003e\n\u003cli\u003eMengist B, Lotfaliany M, Pasco JA, Agustini B, Berk M, Forbes M, Lane MM, Orchard SG, Ryan J, Owen AJ, Woods RL, McNeil JJ, Mohebbi M. The risk associated with ultra-processed food intake on depressive symptoms and mental health in older adults: a target trial emulation. BMC Med. 2025 Mar 24;23(1):172. \u003c/li\u003e\n\u003cli\u003eFerreira NV, Gomes Gon\u0026ccedil;alves N, Khandpur N, Steele EM, Levy RB, Monteiro C, Goulart A, Brunoni AR, Bacchi P, Lotufo P, Bense\u0026ntilde;or I, Suemoto CK. Higher Ultraprocessed Food Consumption Is Associated With Depression Persistence and Higher Risk of Depression Incidence in the Brazilian Longitudinal Study of Adult Health. J Acad Nutr Diet. 2025 May;125(5):630-640.e7. \u003c/li\u003e\n\u003cli\u003eLane MM, Lotfaliany M, Hodge AM, O\u0026apos;Neil A, Travica N, Jacka FN, Rocks T, Machado P, Forbes M, Ashtree DN, Marx W. High ultra-processed food consumption is associated with elevated psychological distress as an indicator of depression in adults from the Melbourne Collaborative Cohort Study. J Affect Disord. 2023 Aug 15;335:57-66. \u003c/li\u003e\n\u003cli\u003eRobinson AT, Edwards DG, Farquhar WB. The Influence of Dietary Salt Beyond Blood Pressure. Curr Hypertens Rep. 2019 Apr 25;21(6):42. \u003c/li\u003e\n\u003cli\u003eAndreeva VA, Salanave B, Castetbon K, Deschamps V, Vernay M, Kesse-Guyot E, Hercberg S. Comparison of the sociodemographic characteristics of the large NutriNet-Sant\u0026eacute; e-cohort with French Census data: the issue of volunteer bias revisited. J Epidemiol Community Health. 2015 Sep;69(9):893-8. \u003c/li\u003e\n\u003cli\u003eAndreeva VA, Deschamps V, Salanave B, Castetbon K, Verdot C, Kesse-Guyot E, Hercberg S. Comparison of Dietary Intakes Between a Large Online Cohort Study (Etude NutriNet-Sant\u0026eacute;) and a Nationally Representative Cross-Sectional Study (Etude Nationale Nutrition Sant\u0026eacute;) in France: Addressing the Issue of Generalizability in E-Epidemiology. Am J Epidemiol. 2016 Nov 1;184(9):660-669. \u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez Steele E, Baraldi LG, Louzada ML, Moubarac JC, Mozaffarian D, Monteiro CA. Ultra-processed foods and added sugars in the US diet: evidence from a nationally representative cross-sectional study. BMJ Open. 2016 Mar 9;6(3):e009892. \u003c/li\u003e\n\u003cli\u003eDicken SJ, Batterham RL. The Role of Diet Quality in Mediating the Association between Ultra-Processed Food Intake, Obesity and Health-Related Outcomes: A Review of Prospective Cohort Studies. Nutrients. 2021 Dec 22;14(1):23. \u003c/li\u003e\n\u003cli\u003eMartini D, Godos J, Bonaccio M, Vitaglione P, Grosso G. Ultra-Processed Foods and Nutritional Dietary Profile: A Meta-Analysis of Nationally Representative Samples. Nutrients. 2021 Sep 27;13(10):3390. \u003c/li\u003e\n\u003cli\u003eContreras-Rodriguez O, Solanas M, Escorihuela RM. Dissecting ultra-processed foods and drinks: Do they have a potential to impact the brain? Rev Endocr Metab Disord. 2022 Aug;23(4):697-717. \u003c/li\u003e\n\u003cli\u003eContreras-Rodriguez O, Reales-Moreno M, Fern\u0026aacute;ndez-Barr\u0026egrave;s S, Cimpean A, Arnoriaga-Rodr\u0026iacute;guez M, Puig J, Biarn\u0026eacute;s C, Motger-Albert\u0026iacute; A, Cano M, Fern\u0026aacute;ndez-Real JM. Consumption of ultra-processed foods is associated with depression, mesocorticolimbic volume, and inflammation. J Affect Disord. 2023 Aug 15;335:340-348. \u003c/li\u003e\n\u003cli\u003eDeledda A, Annunziata G, Tenore GC, Palmas V, Manzin A, Velluzzi F. Diet-Derived Antioxidants and Their Role in Inflammation, Obesity and Gut Microbiota Modulation. Antioxidants (Basel). 2021 Apr 29;10(5):708. \u003c/li\u003e\n\u003cli\u003eJenkins TA, Nguyen JC, Polglaze KE, Bertrand PP. Influence of Tryptophan and Serotonin on Mood and Cognition with a Possible Role of the Gut-Brain Axis. Nutrients. 2016 Jan 20;8(1):56. \u003c/li\u003e\n\u003cli\u003eSwann OG, Kilpatrick M, Breslin M, Oddy WH. Dietary fiber and its associations with depression and inflammation. Nutr Rev. 2020 May 1;78(5):394-411. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization Regional Office for Europe. With 17 % of people in the region living with a mental health condition, 31 countries commit to integrating mental health into all policies [Internet]. 2025 Jun 16 [cited 2025 Sep 24]. Available from: https://www.who.int/europe/news/item/16-06-2025-with-17--of-people-in-the-region-living-with-a-mental-health-condition--31-countries-commit-to-integrating-mental-health-into-all-policies\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of the NutriNet-Sant\u0026eacute; study participants included in the main analyses (N=40,658)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"676\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 676px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBaseline\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Sex (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;40,658 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e30,798 (75.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e9,860 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Age at baseline (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e46.6 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e44.8 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e52.0 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Educational level (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; No high school diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e7,543 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e5,138 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2,405 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; High school diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e6,280 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e5,082 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,198 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; University level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e26,835 (66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e20,578 (66.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e6,257 (63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Marital status (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Living alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e10,213 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e8,353 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,860 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Living with a partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e30,445 (74.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e22,445 (72.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e8,000 (81.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Residence area (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e8,770 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e6,664 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2,106 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e31,245 (76.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e23,622 (76.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e7,623 (77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Outside France\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e643 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e512 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e131 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Occupational categories (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Never employed/other activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1,490 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,341 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e149 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Self-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2,142 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,309 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e833 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Employee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e9,984 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e8,898 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,086 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Intermediate profession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e11,564 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e9,161 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2,403 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Managerial staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e15,478 (38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e10,089 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e5,389 (54.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Monthly income per consumption unit (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Less than 1200 euros\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e5,164 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4,267 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e897 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1200 \u0026ndash; 2300 euros\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e15,531 (38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e11,971 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3,560 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2300 \u0026ndash; 3700 euros\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e10,982 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e7,783 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3,199 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; More than 3700 euros\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4,889 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3,226 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,663 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Do not want to declare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4,092 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3,551 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e541 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Smoking status (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Non-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e21,024 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e16,881 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4,143 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Occasional smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1,750 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,319 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e431 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Former smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e14,567\u0026nbsp;(35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e9,987 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4,580 (46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Permanent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e3,317\u0026nbsp;(8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2,611 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e706 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;BMI (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e23.7 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e23.3 (4.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e24.94 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Physical activity (IPAQ) (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Low activity levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e12,725 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e8,803 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3,922 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Moderate activity levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e15,682 (38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e12,373 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3,309 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; High activity levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e7,972 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e6,211 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,761 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4,279 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3,411 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e868 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Prevalent cardiovascular disease (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e9,367 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e5,987 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3,380 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Type 2 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e913 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e453 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e460 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e5,070 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2,987 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2,083 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Dyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e5,769 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3,700 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2,069 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other cardiovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e922 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e378 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e544 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 676px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDietary exposure window (first 2 years)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Number of 24h records during the exposure period (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e7.6 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e7.59 (2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e7.73 (2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Daily energy intake (kcal) (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1896.0 (458.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1779.4 (380.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2260.1 (488.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;sPNNS-GS2 score (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.9 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2.43 (3.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.29 (3.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Daily alcohol (g) (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e8.4 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e6.21 (8.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e15.38 (16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Incident cardiovascular disease (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1,493 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e688 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e805 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Type 2 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e723 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e401 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e322 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other cardiovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e815 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e299 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e516 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Incident self-reported treatment for depression (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1,660 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,394 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e266 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 676px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFollow-up\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Number of completed CES-D (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e10,005 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e7,995 (26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2,010 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e7,056 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e5,585 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,471 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e6,925 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e5,283 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,642 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e7,922 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e5,888 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2,034 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e8,744 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e6,043 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2,701 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e6 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e4 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Number of CES-D with elevated depressive symptoms (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e31,919 (78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e24,593 (79.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e7,326 (74.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; At least one\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e8,739 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e6,205 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e2,534 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4,944 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e3,723 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,221 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e2,060 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1,436 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e624 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e877 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e559 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e318 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e494 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e301 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e193 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e253 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e132 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e121 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 374px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e111 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e54 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e57 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: BMI (Body Mass Index), IPAQ (International Physical Activity Questionnaire), sPNNS-GS2 (simplified Programme National Nutrition Sant\u0026eacute;-guidelines score 2), CES-D (Center for Epidemiologic Studies Depression)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Daily intake of dietary exposures and description of theoretical minimum risk exposure levels among participants included in the main analyses (N=40,658)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (N=40,658)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen (N=30,798\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen (N=9,860)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 680px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFood groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003eFruits (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e211.3 (142.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e203.7 (133.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e235.0 (165.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 310g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e32,734 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e25,406 (82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e7,328 (74.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 340g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e34,583 (85.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e26,778 (86.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e7,805 (79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003eVegetables (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e228.2 (107.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e225.4 (105.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e236.8 (113.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 280g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e29,862 (73.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e22,879 (74.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e6,983 (70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 320g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e33,894 (83.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e25,954 (84.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e7,940 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003eLegumes (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e12.0 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e11.1 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e14.5 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 90g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e40,248 (99.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e30,540 (99.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e9,698 (98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 100g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e40,340 (99.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e30,606 (99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e9,734 (98.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003eWhole grains (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e36.2 (44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e34.1 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e42.8 (56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 140g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e39,369 (96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e30,153 (97.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e9,216 (93.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 160g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e39,802 (97.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e30,399 (98.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e9,403 (95.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNuts and seeds (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5.1 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e4.8 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e6.2 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 10g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e34,434 (84.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e26,421 (85.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e8,013 (81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 19g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e37,959 (93.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e28,948 (94.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e9,011 (91.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003eMilk (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e79.6 (116.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e76.4 (113.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e89.4 (127.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 360g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e39,255 (96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e29,857 (96.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e9,398 (95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 500g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e40,397 (99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e30,626 (99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e9,771 (99.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003eRed meat (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e44.6 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e40.6 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e57.1 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eAny intake (g) (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e36,819 (90.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e27,687 (89.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e9,132 (92.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003eProcessed meat (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e30.7 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e28.2 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e38.2 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eAny intake (g) (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e37,785 (92.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e28,564 (92.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e9,221 (93.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSweet drinks (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e35.6 (76.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e34.0 (71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e40.6 (91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eAny intake (g) (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e19,541 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e14,820 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e4,721 (47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003eUltra-processed food (%g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e15.5 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e15.5 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e15.6 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eAny intake (%) (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e40,653 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e30,795 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e9,858 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 680px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNutrients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003eFiber (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e20.0 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e19.1 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e22.9 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 21g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e25,362 (62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e20,882 (67.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e4,480 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 22g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e27,650 (68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e22,557 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e5,093 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003eCalcium (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.9 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e0.9 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e1.0 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 1.06g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e29,319 (72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e23,545 (76.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e5,774 (58.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 1.1g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e31,051 (76.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e24,787 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e6,264 (63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003eOmega 3 (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e324.6 (305.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e307.3 (287.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e378.6 (350.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 430mg (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e30,136 (74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e23,431 (76.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e6,705 (68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 470mg (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e31,604 (77.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e24,524 (79.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e7,080 (71.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePUFA (%kcal/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5.6 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e5.6 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e5.5 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 7% (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e34,768 (85.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e26,343 (85.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e8,425 (85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eLess than 9% (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e39,262 (96.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e29,787 (96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e9,475 (96.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSodium (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMean daily intake (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2.7 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e2.6 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e3.3 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMore than 1g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e40,526 (99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e30,680 (99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e9,846 (99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMore than 5g (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e623 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e165 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e458 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Associations from GEE logistic regression models (ORs and 95% CI) between food groups and elevated depressive symptoms (CES-D \u0026ge;23 for women and \u0026ge;17 for men)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(N=40,658)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"724\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFood groups (unit)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll (N=40,658)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen (N=30,798)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen (N=9,860)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 200px;\"\u003e\n \u003cp\u003eFruits\u0026nbsp;(100g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.955 (0.934-0.975) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.940 (0.914-0.968) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.973 (0.942-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.968 (0.948-0.989) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.956 (0.929-0.984) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.981 (0.949-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.983 (0.960-1.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.977 (0.947-1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.988 (0.953-1.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 200px;\"\u003e\n \u003cp\u003eVegetables\u0026nbsp;(100g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.906 (0.878-0.936) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.905 (0.867-0.944) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.911 (0.870-0.954) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.918 (0.890-0.946) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.920 (0.883-0.959) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.911 (0.870-0.955) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.927 (0.898-0.958) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.936 (0.897-0.977) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.913 (0.869-0.958) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 200px;\"\u003e\n \u003cp\u003eLegumes\u0026nbsp;(10g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.993 (0.980-1.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.993 (0.975-1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.994 (0.975-1.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.990 (0.977-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.990 (0.973-1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.992 (0.973-1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.995 (0.982-1.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.997 (0.980-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.994 (0.975-1.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 200px;\"\u003e\n \u003cp\u003eWhole grains\u0026nbsp;(10g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.995 (0.989-1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.996 (0.987-1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.994 (0.985-1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.996 (0.990-1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.996 (0.988-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.996 (0.986-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.000 (0.994-1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.003 (0.994-1.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.998 (0.988-1.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 200px;\"\u003e\n \u003cp\u003eNuts and seeds\u0026nbsp;(10g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.969 (0.943-0.996) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.969 (0.933-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.969 (0.932-1.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.975 (0.950-1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.977 (0.943-1.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.972 (0.935-1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.989 (0.963-1.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.996 (0.962-1.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.978 (0.939-1.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 200px;\"\u003e\n \u003cp\u003eMilk\u0026nbsp;(100g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.004 (0.983-1.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.006 (0.981-1.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.999 (0.962-1.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.995 (0.973-1.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.993 (0.967-1.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.996 (0.958-1.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.991 (0.970-1.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.988 (0.962-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.995 (0.956-1.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 200px;\"\u003e\n \u003cp\u003eRed meat\u0026nbsp;(10g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.011 (1.003-1.019) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.011 (1.000-1.021) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.012 (0.999-1.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.009 (1.001-1.017) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.010 (1.000-1.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.008 (0.996-1.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.006 (0.998-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.006 (0.995-1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.006 (0.993-1.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 200px;\"\u003e\n \u003cp\u003eProcessed meat\u0026nbsp;(10g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.014 (1.003-1.024) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.019 (1.005-1.032) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.008 (0.991-1.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.010 (0.999-1.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.014 (1.001-1.028) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.005 (0.988-1.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.998 (0.986-1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.998 (0.983-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.999 (0.980-1.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 200px;\"\u003e\n \u003cp\u003eSweet drinks\u0026nbsp;(100g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.077 (1.047-1.109) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.097 (1.059-1.138) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.052 (1.005-1.101) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.046 (1.016-1.076) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.057 (1.020-1.096) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.032 (0.984-1.082)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.031 (1.001-1.062) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.036 (0.998-1.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.026 (0.977-1.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 200px;\"\u003e\n \u003cp\u003eUltra-processed food\u0026nbsp;(10%g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.223 (1.188-1.260) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.228 (1.187-1.271) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.208 (1.140-1.279) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.160 (1.126-1.196) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.161 (1.121-1.201) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.161 (1.095-1.231) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.150 (1.115-1.186) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.143 (1.103-1.185) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.159 (1.091-1.232) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* p-value \u0026lt;0.05, ** p-value \u0026lt;0.001, \u0026Dagger; Significant after Simes (Benjamini\u0026ndash;Hochberg) correction for multiple comparisons\u003c/p\u003e\n\u003cp\u003ea) Model 1: Dietary exposure adjusted by energy intake + Sex (\u003cem\u003ewomen, men\u003c/em\u003e) + Age (\u003cem\u003econtinuous\u003c/em\u003e) + Education (\u003cem\u003eno high school diploma, high school, university level\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb) Model 2\u003c/strong\u003e: Model 1 + Smoking status (\u003cem\u003enon-smoker, occasional, former, permanent\u003c/em\u003e) + Physical activity (\u003cem\u003elow, moderate, high, missing\u003c/em\u003e) + Prevalent cardiovascular disease (\u003cem\u003eno, yes\u003c/em\u003e) + Residence area (\u003cem\u003erural, urban, outside France\u003c/em\u003e) + Occupational category (\u003cem\u003enever employed/other activity, self-employed, employee, intermediate profession, managerial staff\u003c/em\u003e) + Income per unit of consumption \u003cem\u003e(\u0026lt;1200\u0026euro;, 1200\u0026ndash;2300\u0026euro;, 2300\u0026ndash;3700\u0026euro;, \u0026gt;3700\u0026euro;, do not want to declare\u003c/em\u003e) + Marital status (\u003cem\u003eliving alone, living with a partner\u003c/em\u003e) + Number of 24h records (\u003cem\u003econtinuous\u003c/em\u003e) + Alcohol (\u003cem\u003econtinuous\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003ec) Model 3: Model 2 + sPNNS-GS2 score (\u003cem\u003econtinuous\u003c/em\u003e)\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Associations from GEE logistic regression models (ORs and 95% CI) between nutrients and elevated depressive symptoms (CES-D \u0026ge;23 for women and \u0026ge;17 for men\u003c/strong\u003e) \u003cstrong\u003e(N=40,658)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"647\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNutrients (unit)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;All (N=40,658)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Women (N=30,798)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen (N=9,860)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFiber\u0026nbsp;(5g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.953 (0.929-0.978) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.946 (0.915-0.979) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.962 (0.926-1.000) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.954 (0.930-0.980) **\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.949 (0.917-0.983) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.959 (0.920-0.999) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.974 (0.945-1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.981 (0.943-1.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.962 (0.917-1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCalcium\u0026nbsp;(100mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.000 (0.988-1.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.004 (0.989-1.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.995 (0.975-1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.001 (0.989-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.004 (0.989-1.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.998 (0.977-1.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.002 (0.990-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.006 (0.991-1.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.998 (0.977-1.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 142px;\"\u003e\n \u003cp\u003eOmega 3\u0026nbsp;(100mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.992 (0.983-1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.987 (0.975-0.999) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.999 (0.984-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.994 (0.985-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.991 (0.980-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.998 (0.983-1.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.996 (0.986-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.994 (0.982-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.998 (0.983-1.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePUFA\u0026nbsp;(1%kcal/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.007 (0.991-1.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.010 (0.991-1.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.000 (0.973-1.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.998 (0.982-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.004 (0.984-1.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.986 (0.959-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1.001 (0.985-1.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.007 (0.988-1.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.988 (0.961-1.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSodium\u0026nbsp;(100mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.996 (0.992-1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.996 (0.990-1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.998 (0.991-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.997 (0.992-1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.997 (0.991-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.997 (0.990-1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.992 (0.987-0.997) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.991 (0.985-0.998) *\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.994 (0.986-1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* p-value \u0026lt;0.05, ** p-value \u0026lt;0.001, \u0026Dagger; Significant after Simes (Benjamini\u0026ndash;Hochberg) correction for multiple comparisons\u003c/p\u003e\n\u003cp\u003ea) Model 1: Dietary exposure adjusted by energy intake + Sex (\u003cem\u003ewomen, men\u003c/em\u003e) + Age (\u003cem\u003econtinuous\u003c/em\u003e) + Education (\u003cem\u003eno high school diploma, high school, university level\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb) Model 2\u003c/strong\u003e: Model 1 + Smoking status (\u003cem\u003enon-smoker, occasional, former, permanent\u003c/em\u003e) + Physical activity (\u003cem\u003elow, moderate, high, missing\u003c/em\u003e) + Prevalent cardiovascular disease (\u003cem\u003eno, yes\u003c/em\u003e) + Residence area (\u003cem\u003erural, urban, outside France\u003c/em\u003e) + Occupational category (\u003cem\u003enever employed/other activity, self-employed, employee, intermediate profession, managerial staff\u003c/em\u003e) + Income per unit of consumption \u003cem\u003e(\u0026lt;1200\u0026euro;, 1200\u0026ndash;2300\u0026euro;, 2300\u0026ndash;3700\u0026euro;, \u0026gt;3700\u0026euro;, do not want to declare\u003c/em\u003e) + Marital status (\u003cem\u003eliving alone, living with a partner\u003c/em\u003e) + Number of 24h records (\u003cem\u003econtinuous\u003c/em\u003e) + Alcohol (\u003cem\u003econtinuous\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003ec) Model 3: Model 2 + sPNNS-GS2 score (\u003cem\u003econtinuous\u003c/em\u003e)\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"depression, diet, nutritional psychiatry, global burden of disease, nutrient","lastPublishedDoi":"10.21203/rs.3.rs-7873685/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7873685/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eEvidence links overall dietary patterns to mental health, but studies on specific food groups and nutrients aligned with dietary guidelines are needed to inform population-level mental health preventive strategies. Within the Global burden of disease Lifestyle And mental Disorder (GLAD) Taskforce (DERR1-10.2196/65576), we aimed to determine the associations between dietary exposures and elevated depressive symptoms, including potential sex-specific differences, in the French NutriNet-Santé cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eProspective observational study using NutriNet-Santé cohort data (2011-2022). Dietary intake was assessed at baseline and every six months for two years using non-consecutive 24-hour records, defining the “dietary exposure window”. Exposures included food groups (fruits, vegetables, legumes, whole grains, nuts \u0026amp; seeds, milk, red meat, processed meat, and sweet drinks) and nutrients (fibre, calcium, omega 3, polyunsaturated fatty acids (PUFA), and sodium) as defined by the Global Burden of Disease (GBD), as well as ultra-processed foods (UPFs) using the NOVA classification. Participants with self-reported depression at baseline or during the dietary exposure window were excluded. The Center for Epidemiologic Studies Depression (CES-D) scale was administered every two years, using validated French cutoffs (≥ 17 for men, ≥ 23 for women) to indicate elevated depressive symptoms. Associations were estimated using multivariable Generalized Estimating Equations (GEE) adjusting for confounders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong 40,658 adults (75.7% women; mean age 46.6 years), 8,739 (21.5%) met criteria for elevated depressive symptoms at least once during a median follow-up of 8.36 years. Overall, higher intakes of fruits (OR\u003csub\u003e100g/d increase\u003c/sub\u003e=0.968; 95%CI=0.948-0.989), vegetables (OR\u003csub\u003e100g/d\u003c/sub\u003e=0.918; 95%CI=0.890-0.946) and fibre (OR\u003csub\u003e10g/d\u003c/sub\u003e=0.954; 95%CI=0.930-0.980) were associated with lower odds of developing elevated depressive symptoms, while higher intake of red meat (OR\u003csub\u003e10g/d\u003c/sub\u003e=1.009; 95%CI=1.001-1.017), sweet drinks (OR\u003csub\u003e100g/d\u003c/sub\u003e=1.046; 95%CI=1.016-1.076), and the dietary share of UPFs (OR\u003csub\u003e10%g\u003c/sub\u003e=1.160; 95%CI=1.126-1.196) were associated with increased odds. Most associations remained only among women, in whom processed meat emerged as positively associated (OR\u003csub\u003e10g/d\u003c/sub\u003e: 1.014; 95% CI: 1.001–1.028).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eFruits, vegetables, and fibre were associated with reduced odds of elevated depressive symptoms, while red meat, processed meat, sweet drinks, and UPFs were associated with increased odds. These results align with official nutritional guidelines in France and in many countries, supporting their promotion to improve population-level mental health.\u003c/p\u003e","manuscriptTitle":"Associations between Global Burden of Disease dietary risk factors and elevated depressive symptoms: the NutriNet-Santé prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 09:37:28","doi":"10.21203/rs.3.rs-7873685/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79f15926-0ad2-456c-87ba-a10c55d4cb37","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T09:37:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-03 09:37:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7873685","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7873685","identity":"rs-7873685","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}