Dietary Factors and Risk of Late-Life Depression: Findings from a Swedish Population-Based Cohort Study

Dietary Factors and Risk of Late-Life Depression: Findings from a Swedish Population-Based 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 Dietary Factors and Risk of Late-Life Depression: Findings from a Swedish Population-Based Cohort Study Bruno Bizzozero-Peroni, Adrián Carballo-Casla, Federico Triolo, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8020122/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 Although diet is increasingly implicated in depression prevention, prospective evidence remains scarce with respect to which food groups and nutrients influence depression onset in later life. We sought to examine associations between 15 dietary factors and incident depression among older adults. Data were obtained from the Swedish National study on Aging and Care in Kungsholmen (SNAC-K), with up to 15 years of follow-up among community-dwelling adults aged ≥ 60 years and free of depression at baseline. Dietary exposures (food groups and nutrients), harmonized with Global Burden of Disease (GBD) study definitions, were assessed three times over the first 6 years using a validated 98-item food frequency questionnaire. We also examined an overall dietary risk score reflecting the number of GBD recommendations not met. Onset of major or minor depression were identified through physician-administered interviews based on DSM-IV-TR criteria, and depressive symptom severity was assessed with the Montgomery–Åsberg Depression Rating Scale. Associations between dietary exposures and incident forms of depression were estimated using Cox proportional hazards models adjusted for sociodemographic, lifestyle, and health-related covariates. Among 2148 participants with available data at follow-up (mean age 71.3 ± 9.3 years, 61.4% females), higher whole-grain consumption (per 30 g/day; HR = 0.63, 95% CI: 0.43–0.91) and total dietary fiber intake (per 5 g/day; HR = 0.69, 95% CI: 0.52–0.91) were associated with lower risk of major depression. Fruit consumption was inversely associated with minor depression (per 100 g/day; HR = 0.81, 95% CI: 0.67–0.97) and mild-to-moderate depressive symptoms (HR = 0.87, 95% CI: 0.76–0.99). Further, higher consumption of vegetables (per 100 g/day; HR = 0.86, 95% CI: 0.74–1.00) was linked with reduced risk of mild-to-moderate depressive symptoms. When considering overall dietary risk, higher scores were associated with increased incidence of major depression and mild-to-moderate symptoms. Some associations differed by sex, age, and body mass index, although most interactions were not statistically significant. Specific dietary components, alongside overall diet quality, may represent preventive targets for late-life depression at the population level and inform more tailored dietary strategies. Epidemiology Nutrition & Dietetics Preventive Medicine dietary patterns nutritional psychiatry healthy aging lifestyle factors mental disorder preventive psychiatry longitudinal study Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Depression is the 11th leading cause of disability worldwide ( 1 ). According to the Global Burden of Disease (GBD) study, an estimated 322 million people (4.1% of the global population) were living with depressive disorder in 2023, including 63 million adults over 60 years (5.4% of that age group) ( 2 ). These estimates are likely conservative, as depression tends to be underdiagnosed and undertreated ( 3 ). This concern is especially relevant for late-life major depression ( 4 ), typically affecting adults aged ≥ 60 years ( 5 ) and associated with poorer health-related quality of life ( 4 ), increased mortality risk ( 6 ), and accelerated somatic multimorbidity ( 7 ). With the rapid global aging of the population ( 8 ), the absolute number of individuals affected by late-life depression is expected to rise substantially over the coming decades ( 9 ), amplifying its social and economic burden. Even subsyndromal depression may substantially contribute to this burden, being associated with greater disability, morbidity, and healthcare costs ( 10 , 11 ). Furthermore, although psychotherapies and pharmacotherapies remain important for treatment, they have limited response rates and overall effectiveness ( 12 ). Beyond biological, psychosocial, and environmental factors, emerging evidence identifies lifestyle behaviors as major contributors to depression risk, presenting promising targets for prevention and treatment ( 13 – 16 ). Among these, diet quality has garnered substantial attention in older adults as a practical and impactful avenue to increase life expectancy ( 17 ), promote healthy aging ( 18 ), reduce the risk( 19 ) and accumulation( 20 ) of neuropsychiatric diseases, and, more specifically, as a modifiable lifestyle factor associated with late-life depression ( 21 ). Importantly, beyond affective symptoms, somatic manifestations of depression such as reduced appetite and cognitive difficulties are common in older adults and closely linked to the burden of somatic diseases ( 22 ). In late life, vulnerability to poor nutritional status may further increase the risk of subsequent depression ( 23 , 24 ), underscoring the importance of dietary preventive strategies. Healthy dietary patterns, rich in fruit, vegetables, dietary fiber, polyunsaturated fatty acids, and other beneficial components, provide bioactive compounds, vitamins, and minerals with anti-inflammatory and antioxidant properties that may reduce cytokine-induced depression and oxidative stress markers ( 25 ). Yet, diet remains suboptimal worldwide (e.g., characterized by low consumption of fruit, vegetables, nuts and seeds, and whole grains) and ranks among the top five risk factors for attributable mortality and disability burden in older populations ( 2 ). The GBD study provides robust estimates of 15 dietary exposures (fruit, vegetables, legumes, whole grains, nuts, milk, red meat, processed meat, sugar-sweetened beverages, dietary fiber, calcium, omega-3 and omega-6 fatty acids, trans fats, and sodium) in relation to the burden of major physical health outcomes such as cancer and cardiovascular diseases ( 26 ). However, comparable estimates for common mental disorders remain absent, limiting efforts to integrate dietary prevention strategies into mental health policy. This evidence gap reflects several challenges, including the consistent operationalization of dietary exposures and major depression ( 26 , 27 ), as well as the need to establish robust longitudinal associations ( 28 ). To address these limitations, the Global burden of disease Lifestyle And mental Disorder (GLAD) initiative conducts pooled analyses across worldwide cohorts to estimate population attributable fractions for 15 dietary factors in relation to incident major depression, with the goal of facilitating their integration into future GBD estimates ( 29 ). Recent nutritional epidemiology on late-life depression has increasingly focused on overall dietary patterns and depressive symptom severity, whereas the role of specific foods and nutrients in distinct forms of depression remains less well understood ( 30 ). Evidence from longitudinal studies indicates that higher consumption of certain foods, particularly fruit and vegetables, is associated with a lower burden of depressive symptoms in this population ( 31 ). However, evidence for other food groups and nutrients, as well as for subgroup differences, remains limited across different manifestations of depression, underscoring a critical gap in developing both population-level and precision dietary strategies for the prevention of late-life depression. Accordingly, this study aimed to examine the associations of 15 dietary factors with the risk of three complementary forms of depression—major depression, minor depression, and mild-to-moderate depressive symptoms—among older adults, thereby allowing comparison across clinical and symptom-based definitions. Methods Study design and participants The Swedish National study on Aging and Care in Kungsholmen (SNAC-K) is an ongoing population-based cohort of randomly sampled older adults residing in the urban district of Kungsholmen, Stockholm, Sweden. The study was approved by the Regional Ethical Review Board in Stockholm in accordance with the Declaration of Helsinki. At baseline (2001–2004, wave 1), 3363 individuals aged ≥60 years were recruited (participation rate: 73%). Standardized health assessments are repeated every three years in participants aged ≥78 years and every six years for those aged 60–72 years. Written informed consent was obtained from all participants or, if not possible, from next of kin. Detailed descriptions of the cohort design and methodology are available elsewhere (32). The present study was reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (33) ( Table S1 ) and complied with the requirements of the GLAD Taskforce (29), as part of a global collaborative initiative to inform the GBD study. Analytical samples Of the 3363 participants enrolled at baseline, 877 were excluded due to insufficient dietary information (≥50% missing items in the food frequency questionnaire). Depending on the outcome, baseline exclusions comprised prevalent depression (major depression [ n =8], minor depression [ n =105], or mild-to-severe depressive symptoms [ n =251]), missing data on depression status ( n =9–70), implausible energy intakes ( n =15–19; males: 5000 kcal/day and females: 4000 kcal/day across the first three SNAC-K waves), prevalent dementia ( n =12–29), and institutionalization ( n =6–7). Follow-up extended from baseline (2001–2004) to wave 6 (2016–2019), with mortality ascertained through national death registers (34). The final analytical samples of new depression cases were n=2411 for major depression and n=2327 for minor depression, and n=2130 for the analysis of new-onset mild-to-moderate depressive symptoms. Participant attrition during follow-up, reasons for censoring, and exclusions due to missing covariate data are illustrated in the flow chart ( Figure S1 ). Study variables Dietary exposures (waves 1–3, 2001–2004 to 2007–2010) Dietary assessment was conducted using a validated 98-item food frequency questionnaire (35). Participants reported their habitual consumption of foods and beverages over the past year using a nine-point Likert scale (ranging from ‘never or less than once per year’ to ‘more than four times per day’) in the first wave and a five-point scale in the second and third waves (ranging from ‘never or a few times per year’ to ‘two or more times a day’). Portion sizes were estimated with the aid of color photographs, and nutrient intakes were estimated using food composition tables from the Swedish National Food Agency (36). Dietary exposures were grouped according to the definitions established in the GBD study (37) and expressed in their original units as grams per day (fruit, vegetables, legumes, whole grains, nuts and seeds, milk, red meat, processed meat, sugar-sweetened beverages, total dietary fiber [hereafter fiber], calcium, and sodium), milligrams per day (omega-3 fatty acids), and percentage of total energy intake (omega-6 and trans fatty acids). A detailed description of the dietary factors is provided in Table S2 . To facilitate interpretation, selected variables were rescaled to meaningful increments prior to regression analyses: fruit, vegetables and milk (per 100 g), legumes and processed meat (per 10 g), whole grains and red meat (per 30 g), fiber and nuts/seeds (per 5 g), sugar-sweetened beverages (per 50 g), and omega-3 fatty acids (per 100 mg). Scaling was based on dietary guidelines and standard portion sized compiled by the European Commission (38) and the European Food Safety Authority (39). In addition, each dietary exposure was dichotomized using GBD recommendations based on the theoretical minimum risk exposure level (TMREL) (37). Participants were coded as “at risk” if consumption or intake fell below the midpoint of the TMREL range for protective foods (e.g., fruit <345 g/day, vegetables <339 g/day, whole grains <185 g/day) or nutrients (fiber 0 g/day) ( Table S2 ). Finally, an overall dietary risk score was derived by summing the number of GBD-aligned recommendations not met (range 0–15), with a higher score indicating a greater dietary risk. Late-life depression (waves 1 – 6, 2001–2004 to 2016–2019) At each SNAC-K wave, depressive symptoms were measured with the Comprehensive Psychopathological Rating Scale (CPRS), a validated semi-structured interview for psychiatric evaluation. Trained physicians rate each symptom on a 0–6 scale (absent to severe) based on frequency, duration, and intensity (40). This instrument has demonstrated good applicability and reliability in older adults (41). As part of the CPRS assessment, major and minor depression were ascertained according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria, using an algorithm previously validated in population-based studies (42). Major depression was defined as the presence of at least five symptoms, including one core symptom (depressed mood and/or loss of interest). Minor depression was defined as the presence of two to four symptoms, with at least one core symptom. In addition, the Montgomery–Åsberg Depression Rating Scale (MADRS), a 10-item subscale of the CPRS, was used to assess depressive symptom severity (range 0–60; higher scores indicate greater severity) (43). Participants without major or minor depression who scored 7–19 or 20–34 on the MADRS were classified as having mild or moderate depressive symptoms, respectively (44). MADRS scores <7 defined absence of depressive symptoms, whereas a separate category for severe depressive symptoms was not defined because participants with MADRS scores ≥35 met diagnostic criteria for major or minor depression. Covariates (wave 1–3, 2001–2004 to 2007–2010) Potential confounders related to both dietary exposures and depression were identified a priori based on the GLAD and GBD frameworks (26,29) and previous evidence (13,21,45). These included age (years), sex (female, male), previous occupation (manual, nonmanual; based on longest held occupation), educational level (primary school, high school, university), social network index (based on multiple items on social connections and support) (46), body mass index (BMI), tobacco smoking status (never, former, current), physical activity level (inadequate, health-enhancing, fitness-enhancing; based on intensity and frequency) (47), diet quality (Alternate Healthy Eating Index [AHEI]), and overall health status (Health Assessment Tool [HAT]) (48). HAT is a validated multidimensional instrument for older adults, ranging from 0 (poor health) to 10 (good health), and integrates cognitive status, gait speed, daily living activities, and morbidity burden (46). Potential confounders were evaluated at each visit through physician examinations, structured interviews, and medical records. Participant data were additionally linked to the Swedish National Patient Register to integrated clinical data from secondary and tertiary care settings (49). Statistical analysis Descriptive analyses Baseline characteristics were summarized separately for each analytical sample (major depression, minor depression, and mild-to-moderate depressive symptoms) and by depression status (no vs. incident cases). Categorical variables are reported as absolute ( n ) and relative (%) frequencies, and continuous variables as means ± standard deviations. Dietary risk factors were described according to GBD criteria, including prevalence (not meeting the theoretical minimum risk exposure level) (37), overall risk score, and mean consumption or intake across the three analytical follow-up samples. Dietary exposures were further presented by sex and age group. Main survival analyses Cox proportional hazards regression models with Huber–White robust standard errors (Breslow method for ties) were used, with time-on-study (person-years) as the time scale. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated separately for each risk-outcome pair across 15 dietary exposures, examining incidence of major depression, minor depression, and mild-to-moderate depressive symptoms. Participants were followed from baseline until the date of their first depression event, death, loss to follow-up (withdrawal, moving out of the areas, or inability to contact), or the end of the follow-up period, whichever occurred first. All analyses were performed using complete-case models. Average dietary exposures and continuous covariates were calculated using all available data from waves 1 – 3 collected before the event or censoring, while categorical covariates were defined at baseline (wave 1). Participants were censored at the first occurrence of major depression when analyzing minor depression, and at the first occurrence of either major or minor depression when analyzing symptom severity, to preserve temporal sequence and minimize misclassification. Analyses were performed both unadjusted and with basic covariate adjustment aligned with the GBD comparative risk-assessment framework (37). As a complementary step, a third Cox model was fitted to further adjust for additional relevant covariates in late life, to ensure robustness and minimize residual confounding. Model 0 was unadjusted (crude model). Model 1 was adjusted for sociodemographic characteristics, including age, sex, and educational level. Model 2 additionally accounted for each dietary factor corrected for total energy intake using Willett’s residual method (50), except for omega-6 and trans fatty acids, which were modelled as a percentage of total energy intake. In this model, total energy intake was used to derive energy-adjusted dietary exposures rather than entered as a covariate. Model 3 further adjusted for previous occupation, social network index, BMI, tobacco smoking status, physical activity level, diet quality (AHEI score excluding each specific dietary factor; for milk, fiber, and calcium—components not included in the AHEI—the full score was used), and overall health status (HAT score). Multicollinearity among covariates was assessed using variance inflation factors (VIFs) derived from auxiliary ordinary least squares regressions including all predictors in Cox models. The proportional hazards assumption was evaluated using scaled Schoenfeld residuals and the Grambsch–Therneau test. For dietary exposures showing evidence of nonproportionality ( p < 0.05, i.e., indicating that effect sizes were not constant over time), extended Cox models with time-varying coefficients were fitted to estimate HRs at fixed follow-up times (3, 5, and 10 years). To examine potential nonlinear associations between dietary factors and depression risk, restricted cubic spline models were applied. Nonlinearity was tested using the Wald test for the added spline terms. Spline models were retained regardless of p -values to allow flexible interpretation and presentation of study associations. Models with three knots (51) were specified at fixed percentiles (5 th , 50 th , and 95 th ) of the exposure distribution (52), with the median consumption or intake (50 th percentile) as the reference value. All spline analyses were conducted in fully adjusted models. In addition to the individual analyses of the 15 dietary exposures, the risk of developing different forms of depression was examined in relation to the overall number of dietary risk factors, operationalized as the count of GBD-aligned recommendations not met (range 0–15). For this analysis, Model 3 did not apply Willett’s residual method, as the cutoffs were based on raw consumption or intake values; therefore, total energy intake was included as an additional covariate. To avoid collinearity, the overall AHEI score (a proxy for diet quality) was excluded, and alcohol consumption (g/day)—originally included as a component of the AHEI—was entered as a separate covariate. Interactions, subgroup and sensitivity analyses Formal tests for statistical interaction by sex and age were conducted using likelihood ratio tests comparing models with and without the corresponding interaction terms, with p <0.05 indicating evidence of interaction. Results from subgroup analyses are presented irrespective of statistical evidence for interaction, to enable descriptive comparison by sex (males, females) and age group (60–69, 70–79, ≥80 years). In addition, the same approach was used to test multiplicative first-order interactions between the number of dietary risk factors and each covariate from the fully adjusted model, with robust Wald χ² statistics reported on the HR scale. Sensitivity analyses were performed to assess the robustness of the findings. To minimize potential reverse causality, Cox regression models were repeated: (i) applying lag times of 3, 5, and 7 years whereby follow-up was left-truncated at each lag and events and person-time occurring before that point were excluded; (ii) excluding participants with self-reported past depression or other mental disorders at baseline (i.e., sleep disorders, schizophrenia, delusional disorders, neurotic or stress-related, somatoform disorders, or other psychiatric and behavioral disorders); (iii) excluding participants with probable cognitive impairment at baseline, defined as Mini-Mental State Examination score <24; (iv) for the major depression analytical sample, additionally excluding individuals with minor depression or mild-to-severe depressive symptoms at baseline; and (v) for the minor depression analytical sample, additionally excluding individuals with mild-to-moderate depressive symptoms at baseline. Two-sided p -values were reported, with p <0.05 considered suggestive of an association. All analyses were performed using Stata version 17.0 (StataCorp LLC, College Station, TX, USA). Results Table 1 summarizes baseline characteristics of participants across the different analytical samples. At baseline, the mean age of participants in the largest study sample (n = 2411) was 71.3 years and 61.4% were female. Nearly four in five reported a nonmanual occupation, and 87% had completed high school or university education. The mean BMI was 25.8 kg/m², approximately 11% were current smokers, and over 50% reported a health-enhancing level of physical activity. These characteristics are also presented separately by incident depression status in the analytical samples ( Table S3 ). As for the dietary exposures, participants had broadly suboptimal diets, averaging 11.7 of 15 GBD dietary risk factors, with 84% accumulating 11 – 13 ( Figure S2 ). Several components (e.g., legumes, nuts and seeds, omega-6 fatty acids) were commonly under consumed relative to GBD recommendations ( Tables S4–S6 ). Sex- and age-related differences in dietary exposures are presented in Figure S3 . Table 1 Baseline characteristics of participants in the three analytic samples used to examine the risk of late-life depression. a Characteristic Major depression Minor depression Mild-to-moderate depressive symptoms Total ( n = 2411) Total ( n = 2327) Total ( n = 2130) Sociodemographic Age, years 71.3 ± 9.3 71.3 ± 9.3 71.1 ± 9.2 Female 1480 (61.4) 1424 (61.2) 1287 (60.4) Manual worker 452 (18.8) 437 (18.8) 401 (18.8) Educational level Primary school 306 (12.7) 297 (12.8) 275 (12.9) High school 1185 (49.1) 1140 (49.0) 1027 (48.2) University 920 (38.2) 890 (38.2) 828 (38.9) Social network index 0.10 ± 0.46 0.11 ± 0.46 0.13 ± 0.45 BMI, kg/m 2 25.8 ± 3.9 25.8 ± 3.9 25.9 ± 3.9 Lifestyle Alcohol consumption, g/day 12.5 ± 12.7 12.5 ± 12.6 12.7 ± 12.7 Tobacco smoking status Never 1077 (44.8) 1049 (45.2) 961 (45.2) Former 1055 (43.8) 1017 (43.8) 934 (44.0) Current 275 (11.4) 257 (11.0) 230 (10.8) Physical activity level Inadequate 460 (19.6) 432 (19.1) 377 (18.2) Health-enhancing 1264 (54.0) 1222 (54.0) 1126 (54.4) Fitness-enhancing 617 (26.4) 608 (26.9) 566 (27.4) Diet quality (AHEI) 61.6 ± 8.8 61.6 ± 8.8 61.8 ± 8.8 GBD dietary risk factors 11.7 ± 1.2 11.7 ± 1.2 11.7 ± 1.2 Overall health status Health Assessment Tool 7.4 ± 1.6 7.4 ± 1.6 7.5 ± 1.5 Notes: Data are expressed as means ± standard deviations or n (%). a Baseline analytical samples were defined by inclusion criteria and availability of basic covariates for risk models according to the standardized GBD framework (age, sex, education, energy intake). Missing data on additional covariates is detailed in the flow chart (Figure S1). Analytical samples represent overlapping subsets of the same cohort (i.e., counts are not additive). Abbreviations: AHEI, Alternative Healthy Eating Index; BMI, body mass index; GBD, Global Burden of Disease study; SD, standard deviation. Model diagnostics and overall associations VIFs indicated no problematic multicollinearity ( Table S7 ). Scaled Schoenfeld residuals provided no evidence of departure from the proportional hazard’s assumption for most dietary parameters ( Table S8 ). For dietary exposures showing evidence of nonproportionality, results from extended Cox models with time-varying coefficients are reported in the following sections for each of the three depression analytical samples. Wald tests identified nonlinear associations for some energy-adjusted dietary components ( Table S9 ). Basic-adjusted Cox regression results for the 15 dietary factors across depression measures are presented in Tables S10–S15 . Figure 1 illustrates the multivariable-adjusted associations of 15 individual dietary factors and the overall dietary risk score with the three different forms of late-life depression, stratified by sex and age groups. Individual dietary factors and major depression Among 2148 participants with available data at follow-up, 27 (1.3%) developed major depression over a mean follow-up of 10.2 years. In fully adjusted models, higher whole-grain consumption (per 30 g/day; HR = 0.63; 95% CI: 0.43–0.91) and fiber intake (per 5 g/day; HR = 0.69; 95% CI: 0.52–0.91) were associated with lower risk of major depression (Table 2 ). Spline analyses provided evidence of nonlinearity for milk ( p = 0.019) and revealed a J-shaped pattern, with consumption above ~ 160 g/day associated with lower risk (Fig. 2 ). The associations of whole grains and processed meat with major depression showed evidence of nonproportional hazards. Analyses with time-varying coefficients confirmed the robustness of the inverse association for whole grains at 3 and 5 years, while processed meat was linked to increased risk at later follow-up ( Table S16 ). Table 2 Fully adjusted Cox regression results for the associations between dietary factors and risk of late-life depression. Dietary exposure (unit per day) a Major depression ( n = 2067; 25 incident cases) Minor depression ( n = 2004; 152 incident cases) Mild-to-moderate symptoms ( n = 1830; 228 incident cases) HR LL UL p -value HR LL UL p -value HR LL UL p -value Fruit (per 100 g) 0.92 0.67 1.27 0.628 0.81 0.67 0.97 0.026 0.87 0.76 0.99 0.042 Vegetables (per 100 g) 0.94 0.58 1.53 0.798 0.92 0.77 1.10 0.376 0.86 0.74 1.00 0.045 Legumes (per 10 g) 0.77 0.44 1.36 0.372 0.96 0.76 1.22 0.741 1.03 0.85 1.25 0.730 Whole grains (per 30 g) 0.63 0.43 0.91 0.014 1.04 0.86 1.26 0.703 0.98 0.83 1.15 0.820 Nuts and seeds (per 5 g) 0.55 0.04 6.74 0.638 0.92 0.67 1.27 0.612 0.68 0.44 1.07 0.095 Milk (per 100 g) 0.90 0.72 1.14 0.406 0.89 0.77 1.04 0.146 0.89 0.78 1.02 0.095 Red meat (per 30 g) 0.72 0.41 1.26 0.250 1.09 0.90 1.31 0.385 0.98 0.80 1.21 0.875 Processed meat (per 10 g) 0.94 0.75 1.19 0.628 0.98 0.88 1.08 0.650 1.01 0.92 1.10 0.898 SSBs (per 50 g) 0.80 0.62 1.02 0.070 1.02 0.93 1.11 0.695 1.05 0.96 1.15 0.249 Fiber (per 5 g) 0.69 0.52 0.91 0.009 0.90 0.74 1.08 0.252 0.90 0.77 1.05 0.183 Calcium (per 1 g) 0.50 0.12 2.05 0.340 0.99 0.51 1.94 0.986 0.75 0.41 1.40 0.376 Omega-3 fatty acids (per 100 mg) 0.99 0.87 1.13 0.930 0.99 0.92 1.06 0.717 0.97 0.92 1.02 0.207 Omega-6 fatty acids (per 1% TEI) 0.88 0.62 1.23 0.446 0.94 0.81 1.08 0.393 0.98 0.87 1.09 0.679 Trans fatty acids (per 1% TEI) 1.10 0.31 3.87 0.883 0.78 0.45 1.35 0.369 0.88 0.57 1.36 0.560 Sodium (per 1 g) 0.71 0.30 1.68 0.435 1.01 0.74 1.39 0.924 1.10 0.79 1.53 0.582 Notes: Values are hazard ratios and 95% confidence intervals in fully adjusted Cox regression model (Model 3), reflecting the risk associated with a 1-unit increase in daily dietary exposure. Bold values indicate p < 0.05. Model 3: adjusted for age (years), sex (male or female), educational level (primary, high school, or university), dietary factors corrected for total energy intake using Willett’s residual method, previous occupation (manual or nonmanual worker), social network index (z-score), body mass index (kg/m 2 ), tobacco smoking status (never, former, or current), physical activity level (inadequate, health-enhancing, or fitness-enhancing), diet quality (AHEI score excluding each specific dietary factor; for milk, fiber, and calcium—components not included in the AHEI—the full score was used), and overall health status (HAT score). Omega-6 fatty acids and trans fatty acids were not adjusted for total energy intake, as they are harmonized as percentage of total energy intake. a Dietary exposures were modelled as continuous variables in their original units for calcium, omega-6 fatty acids, trans fatty acids, and sodium. The other dietary factors were scaled to different unit increments to enhance interpretability of effect size variation. Abbreviations: d, day; g, grams; HR, hazard ratio; LL, lower limit of the 95% confidence interval; mg, milligrams; SSBs, sugar-sweetened beverages; TEI, total energy intake; UL, upper limit of the 95% confidence interval. Subgroup analyses suggested potential associations for whole grains and fiber among males, although p -for-interaction > 0.05 ( Table S17 ). Analyses by age were not feasible given that most incident cases (n = 20) clustered in the 70–79 age group ( Table S18 ). Sensitivity analyses yielded consistent findings ( Tables S19–S22 ). In landmark analyses, the inverse associations of whole grains and fiber persisted at 3-year lag-time, and fiber remained associated with a 5-year lag-time. At 5- and 7-year lag-times, inverse associations emerged for calcium and milk, respectively. After excluding participants with past self-reported depression or other mental disorders at baseline, milk consumption was linked to lower risk. Individual dietary factors and minor depression Among 2079 participants with available data at follow-up, 156 (7.5%) developed minor depression over a mean follow-up of 10.0 years. In fully adjusted models (Table 2 ), higher fruit consumption was associated with lower risk of minor depression (per 100 g/day; HR = 0.81; 95% CI: 0.67–0.97). Spline analyses provided evidence of nonlinearity for fruit ( p = 0.014) and revealed an inverse L-shaped pattern, with consumption above ~ 180 g/day associated with lower risk (Fig. 3 ). Whole grains showed evidence of nonproportional hazards, with time-varying models indicating that HRs attenuated with longer follow-up ( Table S16 ). In subgroup analyses, no statistical evidence of interaction was observed ( Tables S17–S18 ). However, the inverse association for fruit appeared more evident among females. By age, milk and omega-6 fatty acids tended to be associated with lower risk in participants aged 60–69 years, whereas fruit and fiber showed inverse trends among those aged 70–79 years. Sensitivity analyses confirmed the results for fruit consumption ( Tables S19–S22 ). Individual dietary factors and mild-to-moderate depressive symptoms Among 1903 participants with available data at follow-up, 244 (12.8%) developed mild-to-moderate depressive symptoms over a mean follow-up of 9.9 years. In fully adjusted models, higher consumption of fruit (per 100 g/day; HR = 0.87; 95% CI: 0.76–0.99) and vegetables (per 100 g/day; HR = 0.86; 95% CI: 0.75–1.00) were associated with lower risk of mild-to-moderate depressive symptoms (Table 2 ). Spline analyses revealed nonlinear associations for fruit ( p = 0.027) and sodium ( p = 0.027). Fruit consumption showed an inverse L-shaped pattern, with risk decreasing up to ~ 180 g/day and reaching a plateau beyond ~ 330 g/day, whereas sodium displayed a U-shaped curve, with intakes above ~ 2.7 g/day linked to higher risk (Fig. 4 ). Fruit and trans fatty acids showed indications of nonproportional hazards. Analyses with time-varying coefficients identified inverse associations for fruit at 5 years and a progressive attenuation of risk estimates for trans fatty acids ( Table S16 ). In subgroup analyses, no statistical evidence of interaction was observed ( Tables S17–S18 ). However, vegetables tended to show inverse associations among females, and for nuts and seeds among males. By age group, vegetables, milk, and fiber showed inverse trends with mild-to-moderate depressive symptoms in participants aged 70–79 years, whereas fruit and omega-3 fatty acids appeared inversely associated in those ≥ 80 years. Findings for fruit and vegetables were consistent in sensitivity analyses but attenuated in landmark analyses ( Tables S19–S22 ). Using a 5-year lag-time, an additional potential association emerged for milk. After excluding participants with past self-reported depression or other mental disorders, fiber intake was linked to lower risk. Dietary risk factors and late-life depression A greater number of dietary risk factors (i.e., unmet GBD-defined dietary recommendations) was associated with increased risk of major depression (HR = 1.53; 95% CI: 1.05–2.22) and mild-to-moderate depressive symptoms (HR = 1.21; 95% CI: 1.06–1.38; Table 3 ). For major and minor depression, results were consistent across sex strata ( Figure S4 ). For mild-to-moderate depressive symptoms, subgroup analyses suggested more pronounced associations in females and in participants aged ≥ 70 years, although no evidence for interaction was found ( p > 0.05). Results suggested possible effect modification by BMI for minor depression ( p = 0.020) and mild-to-moderate depressive symptoms ( p = 0.041; Table S23 ). In participants with BMI ≥ 25 kg/m², a higher number of dietary risk factors was associated with increased risk of minor depression and mild-to-moderate symptoms, with no evidence of associations in those with BMI < 25 kg/m² ( Figure S4 ). Sensitivity analyses yielded consistent results ( Table S24 ). Table 3 Cox regression results for the association between the number of dietary risk factors and incident late-life depression. Cox proportional hazard Major depression ( n = 2148; cases: 27) Minor depression ( n = 2079; cases: 156) Mild-to-moderate symptoms ( n = 1903; cases: 244) HR LL UL p -value HR LL UL p -value HR LL UL p -value Unadjusted Model 1.29 0.92 1.80 0.142 1.15 1.01 1.32 0.040 1.22 1.09 1.37 < 0.001 Adjusted Model 1 1.46 1.00 2.14 0.050 1.12 0.97 1.29 0.111 1.19 1.05 1.33 0.004 Adjusted Model 2 1.50 1.05 2.13 0.025 1.09 0.93 1.27 0.294 1.19 1.05 1.36 0.007 Adjusted Model 3 a 1.53 1.05 2.22 0.027 1.13 0.96 1.33 0.142 1.21 1.06 1.38 0.006 Notes: Values are hazard ratios and 95% confidence intervals, representing the association between each additional dietary risk factor (cumulative count, 0–15) and the risk of depression phenotypes. Bold values indicate p < 0.05. Model 1: adjusted for age (years), sex (male or female), and educational level (primary, high school, or university); Model 2: adjusted for Model 1 plus total energy intake (kcal/d). Model 3: adjusted for Model 2 plus previous occupation (manual or nonmanual worker), social network index (z-score), body mass index (kg/m 2 ), alcohol consumption (g/day), tobacco smoking status (never, former, or current), physical activity level (inadequate, health-enhancing, or fitness-enhancing), and overall health status (HAT score). a The adjusted model 3 included covariates with missing data: for major depression, n = 2067 (cases: 25); for minor depression, n = 2004 (cases: 152); and for mild-to-moderate depressive symptoms, n = 1830 (cases: 228). Abbreviations: HR, hazard ratio; LL, lower limit of the 95% confidence interval; UL, upper limit of the 95% confidence interval. Discussion In this population-based cohort of Swedish older adults, higher whole-grain consumption and fiber intake were associated with lower risk of major depression, fruit consumption was linked to reduced risk of minor depression, and both fruit and vegetables were inversely associated with risk of mild-to-moderate depressive symptoms, which appeared to differ by sex and age, although no statistical evidence of interaction was observed. Additional potential associations emerged for nuts and seeds, milk, calcium, omega-3 and omega-6 fatty acids, and sodium, but only in specific subgroups. In general, most associations indicated lower depression risk with healthier dietary exposures, whereas few potentially unhealthy components showed clear adverse relationships. Furthermore, a higher overall dietary risk score was associated with increased incidence of major depression, and with minor depression and mild-to-moderate depressive symptoms, particularly in the context of metabolic dysregulation. Taken together, these findings support the potential relevance of dietary factors in the etiology of distinct forms of late-life depression and may help inform more tailored prevention strategies. In our study, whole grains and fiber appeared most relevant for preventing clinically defined major depression in late life, aligning with prior studies implicating fiber-rich foods in improved metabolic regulation, reduced systemic inflammation, short-chain fatty acid production, and modulation of the gut–brain axis ( 53 – 55 ). In contrast, fruit and vegetables were more strongly associated with minor depression and broader subclinical symptomatology, consistent with previous evidence on their potential micronutrient contributions, antioxidant capacity, and potential neuroprotective effects ( 56 – 58 ). Suggestive associations were also observed for nuts and seeds, milk, calcium, omega-3 and omega-6 fatty acids, and sodium, with patterns differing by age and depression measure. Previous prospective cohort studies have reported similar associations ( 59 – 62 ), although evidence specific to late life remains limited. Importantly, the associations of key plant-based dietary factors may differ by sex, aligning with prior evidence across adult populations suggesting that the mental health benefits of total dietary fiber may be more pronounced in males ( 63 ), while both fruit and vegetables consumption appears particularly relevant for females ( 64 ). In our cohort, males reported higher whole-grain consumption and fiber intake, while females reported higher fruit consumption, consistent with previous studies in Swedish adults ( 65 ). Biological mechanisms may contribute to the observed associations with depression, including variation in gut microbiota, metabolic processing of fiber-rich foods, and hormonal influences on inflammation and neuroplasticity ( 54 ). Social and behavioral factors could be also relevant, as gender-related social roles and dietary preferences often lead to different perceptions ( 66 ) and consumption ( 67 ) of foods. Although interactions were not statistically significant, these patterns suggest that dietary strategies for mental health could consider individual characteristics such as sex, rather than applying a one-size-fits-all approach. The cumulative number of dietary risk factors was consistently associated with major depression and mild-to-moderate depressive symptoms, but not with minor depression in the overall sample, suggesting that diet may be strongly implicated in severe and extended forms of depressive symptomatology. These associations for minor depression and mild-to-moderate symptoms were evident only among participants with overweight or obesity, indicating potential effect modification by BMI. This pattern may reflect interactions between diet, adiposity, and metabolic health. Individuals with overweight or obesity may be more vulnerable to the adverse impact of diets characterized by high intakes of refined grains, added sugars, and saturated fats through chronic inflammation, insulin resistance, dysregulated hypothalamic–pituitary–adrenal axis activity, and impaired neuroplasticity, while such diets may also promote adiposity, further reinforcing the link with depression ( 25 ). In contrast, among those without overweight or obesity, the contribution of diet to minor depression and subclinical symptoms may be determined more by psychosocial, behavioral, or other biological determinants (ref). These findings suggest that adiposity may play a central role in the diet-depression relationship, potentially outweighing the specific dietary pathways through which this association operates. From a public health perspective, interventions that integrate nutritional counseling with metabolic risk management, such as weight control, may yield synergistic benefits for mental health in late life (ref). In addition to mental health benefits, promoting healthy dietary patterns in older populations could also help reduce multimorbidity and healthcare costs, given the shared prevention framework across chronic and mental diseases (ref). Over the past decade, nutritional psychiatry has rapidly evolved into a major area of research, highlighting the potential of diet-based prevention strategies with potential public health implications ( 68 – 72 ). Yet, diet–disease associations remain inherently complex ( 73 ), particularly for late-life depression, a heterogeneous and dynamic condition shaped by bidirectional relationships with diet ( 74 ). Our findings suggest that specific dietary components may contribute to a lower risk of late-life depression, but they should not be interpreted as prescriptive thresholds or exact estimates of risk reduction, as they could likely be affected by cumulative bias, including measurement error in dietary assessment and residual confounding ( 74 ). Moreover, it is unlikely that any single dietary factor alone would yield meaningful population-level benefits, given the multifactorial nature of brain aging ( 75 ). Instead, our results point to overall diet quality and specific dietary components—particularly fruit, vegetables, whole grains, and fiber—as key contributors to a lower risk of late-life depression, underscoring the importance of adequate consumption of healthy foods and nutrients for mental health. Accordingly, public health strategies for older adults should prioritize the inclusion of nutrient-dense, plant-based foods as part of comprehensive approaches to depression prevention. Comparative efforts, particularly pooled analyses and Burden of Proof methodology ( 76 ) within the GLAD ( 29 ) and GBD ( 37 ) frameworks, will be essential to refine risk estimates and establish evidence-based dietary recommendations. Importantly, the GBD-derived cut-offs applied in this study to construct the overall dietary risk score were originally defined for major physical outcomes ( 37 ) and may not directly translate to late-life depression. Nutritional needs and vulnerabilities change across the aging spectrum ( 77 ), and some cut-offs, such as ≥ 345 g/day of fruit or ≥ 339 g/day of vegetables, may be overly ambitious for older adults with reduced energy intake ( 78 ). Declines in appetite, food variety, basal metabolic rate, and physical activity, together with sarcopenia ( 79 , 80 ), can reshape dietary requirements and modify the biological impact of nutrition on mental health. Furthermore, dietary exposures may follow nonlinear, U-, L-, or J-shaped associations with depression, as suggested for milk, fruit, and sodium. Overall, these complexities highlight the need to refine old-age nutritional epidemiology toward more nuanced modeling of dose-response relationships, ideally complemented by biological and omics data to enable personalized approaches ( 77 ). Accordingly, future studies should aim to develop age-specific adaptations to dietary risk models—including revised exposure distributions, theoretical minimum risk exposure levels, and age-stratified relative risks—to more accurately estimate the preventable burden of late-life depression. Future research should also examine how dietary exposures influence dynamic trajectories and variability across symptom profiles. The strengths of this study include its large population-based sample with long-term follow-up, repeated assessments of diet and depression, and the use of time-varying, spline, subgroup, and multiple sensitivity analyses to test robustness. To our knowledge, this is the first prospective cohort of older adults to comprehensively evaluate 15 GBD-aligned dietary factors in relation to incident depression, distinguishing major, minor, and mild-to-moderate symptoms. This approach provides a nuanced characterization of diet–depression associations in late life and reinforces the role of dietary patterns as a modifiable determinant of depression in aging populations. Some limitations of the study should also be noted. First, the findings may have limited generalizability beyond Swedish older adults. SNAC-K comprises an urban, community-dwelling population with relatively high educational and socioeconomic position, affluence, and limited ethnic diversity. Second, dietary exposures were self-reported, introducing potential measurement error and recall bias. Third, while cumulative average dietary exposures were used to reduce within-person variability and better reflect habitual intake, this approach may obscure time-varying associations, and regression to the mean could attenuate observed associations, potentially underestimating true effects. Fourth, the relatively small number of incident cases of major depression constrained statistical power. Moreover, associations identified in subgroup and sensitivity analyses warrant cautious interpretation given their exploratory nature. Fifth, although landmark analyses excluding early incident cases reduced concerns about reverse causation, it cannot be ruled out. Finally, despite adjustment for multiple covariates, residual confounding cannot be excluded. Conclusions In this population-based study, higher consumption of fruit, vegetables, whole grains, and dietary fiber were associated with a lower risk of distinct forms of late-life depression. Associations for whole grains and fiber tended to be stronger in males, whereas those for fruit and vegetables appeared more pronounced in females and in adults over 70 years. Overall, these healthier dietary factors—as well as nuts and seeds, milk, calcium, and omega-3 and omega-6 fatty acids in specific subpopulations—tended to show inverse associations with depression risk, whereas limited evidence suggested that potentially unhealthy components (i.e., red and processed meat, sugar-sweetened beverages, trans fatty acids, and sodium) were related to higher risk. Cumulative dietary risk was associated with higher incidence of major depression and mild-to-moderate depressive symptoms in the overall sample, and with minor depression and mild-to-moderate symptoms among participants with overweight or obesity. These findings highlight key dietary factors as potential targets for population-level prevention strategies. Despite nonsignificant interactions, differences in risk estimates across sex and age suggest patterns that may inform future precision nutrition approaches. Replication in diverse populations is essential to confirm these associations and to strengthen the evidence base for dietary guidelines aimed at preventing late-life depression. Declarations Acknowledgements We thank all SNAC-K participants and the SNAC-K organization for their collaboration in data collection and management. Author contributions BBP co-conceived the original idea, prepared the dataset, conducted data analyses, interpreted the results, and drafted the manuscript. ACC and FT assisted in preparation of the dataset and data analyses, interpreted the results, and gave critical input on revisions of the paper. DNA, RO, MML, FNJ, and AO co-conceived the original idea and gave critical input on revisions of the paper. CQ provided academic supervision to BBP, assisted in data analyses, interpreted the results, and gave critical input on revisions of the paper. ACL co-conceived the original idea, granted access to SNAC-K data, assisted in data analyses, provided academic supervision to BBP, interpreted the results, and gave critical input on revisions of the paper. All authors approved the final version of this manuscript. Funding Data collection of the SNAC-K was supported by the Swedish Research Council (ongoing/current grant: 2021-00178); the Swedish Ministry of Health and Social Affairs; and the participating County Councils and Municipalities. BBP received funding from the Swedish Research Council for Health, Working Life and Welfare (project number 2023-01125, program PI, Mia Kivipelto) and the Loo and Hans Osterman Foundation for Medical Research (project number 2025-01945). ACC received funding from the Foundation for Geriatric Diseases at Karolinska Institutet (project number 2024:0011), the Karolinska Institutet Research Foundation Grants (project number 2024:0017), the David and Astrid Hagelén foundation (project number 2024:0005), and the Swedish Research Council for Health, Working Life and Welfare (project number STY-2024/0005). FT received funding from the from the Svenska Sällskapet för Medicinsk Forskning (SSMF; PG-24-0326-H-01). CQ received grants from the Swedish Research Council (grant# 2017-05819 and 2020-01574) and the Swedish Foundation for International Cooperation in Research and Higher Education (CH2019-8320), Stockholm, Sweden. ACL receives funding from the Swedish Research Council (project number 2021-06398), the Swedish Research Council for Health, Working Life and Welfare (project numbers 2024-01830 and 2021-00256), Karolinska Institutet’s Strategic Research Area in Epidemiology and Biostatistics SFOepi (consolidator bridging grant, 2023), and Alzheimerfonden (AF-1010573, 2024). CQ and ACL are work-package leaders of a Program Grant from the Swedish Research Council for Health, Working Life and Welfare (project number 2023-01125, program PI, Mia Kivipelto). DNA and AON are supported by a National Health and Medical Research Council Fellowship (Leader 2 Fellowship #2009295 to AON). RO is supported by a Deakin University Postgraduate Research Scholarship. MML is supported by an Alfred Deakin Postgraduate Fellowship. FNJ is supported by a National Health and Medical Research Council Leader 1 Fellowship (grant #1194982). The opinions, methods, and conclusions reported in this paper are those of the authors and are independent from the funding sources. This manuscript was conducted within the GLAD Taskforce framework, as part of a global collaborative project to inform the Global Burden of Diseases, Injuries, and Risk Factors Study. Conflict of interests DNA, RO, MML, FNJ, and AO are affiliated with the Food & Mood Centre, Deakin University, which has received research funding support from Be Fit Food, Bega Dairy and Drinks, and the a2 Milk Company and philanthropic research funding support from the Waterloo Foundation, Wilson Foundation, the JTM Foundation, the Serp Hills Foundation, the Roberts Family Foundation, and the Fernwood Foundation. MML is a member (and former Secretary) of the Melbourne Branch Committee of the Nutrition Society of Australia (unpaid). She has received travel funding support from the International Society for Nutritional Psychiatry Research, the Nutrition Society of Australia, the Australasian Society of Lifestyle Medicine, and the Gut Brain Congress. MML is also an Associate Investigator for the MicroFit Study, an investigator-led randomized controlled trial examining the effects of diets with varying levels of industrial processing on gut microbiome composition, partially funded by Be Fit Food (payment received by the Food & Mood Centre, Deakin University). Ethical approval SNAC-K was approved by the Regional Ethical Review Board in Stockholm (Dnrs: 2001–114, 2004–929/3, 2007/279–31, 2009/595–32, 2010/447–31/2, 2013/828–31/3, and 2016/730–31/1). Data availability Data are derived from the SNAC-K project, a population-based study design to investigate the aging process, improve health, and identify potential preventive strategies. 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Bidirectional associations between food groups and depressive symptoms: longitudinal findings from the Invecchiare in Chianti (InCHIANTI) study. British Journal of Nutrition . 2019;121(4):439–50. Booth SL, English LK, Reigh NA, Jacques PF, Forester BP, Kyla Shea M. Dietary Patterns and Brain Aging: Enthusiasm Before Evidence? Annu Rev Nutr . 2025;45(1):251–68. Haile D, Harding KL, McLaughlin SA, Ashbaugh C, Garcia V, Gilbertson NM, et al. Health effects associated with consumption of processed meat, sugar-sweetened beverages and trans fatty acids: a Burden of Proof study. Nature Medicine 2025 31:7 . 2025;31(7):2244–54. Ordovas JM, Berciano S. Personalized nutrition and healthy aging. Nutr Rev . 2020;78(Supplement_3):58–65. Manini TM. Energy expenditure and aging. Ageing Res Rev . 2010;9(1):1–11. Roberts SB, Rosenberg I. Nutrition and aging: Changes in the regulation of energy metabolism with aging. Physiol Rev . 2006;86(2):651–67. Roberts SB, Silver RE, Das SK, Fielding RA, Gilhooly CH, Jacques PF, et al. Healthy Aging—Nutrition Matters: Start Early and Screen Often. Advances in Nutrition . 2021;12(4):1438–48. Additional Declarations The authors declare potential competing interests as follows: DNA, RO, MML, FNJ, and AO are affiliated with the Food & Mood Centre, Deakin University, which has received research funding support from Be Fit Food, Bega Dairy and Drinks, and the a2 Milk Company and philanthropic research funding support from the Waterloo Foundation, Wilson Foundation, the JTM Foundation, the Serp Hills Foundation, the Roberts Family Foundation, and the Fernwood Foundation. MML is a member (and former Secretary) of the Melbourne Branch Committee of the Nutrition Society of Australia (unpaid). She has received travel funding support from the International Society for Nutritional Psychiatry Research, the Nutrition Society of Australia, the Australasian Society of Lifestyle Medicine, and the Gut Brain Congress. MML is also an Associate Investigator for the MicroFit Study, an investigator-led randomized controlled trial examining the effects of diets with varying levels of industrial processing on gut microbiome composition, partially funded by Be Fit Food (payment received by the Food & Mood Centre, Deakin University). Supplementary Files SMSNACKGLADpreprint.docx Supplementary Material Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8020122","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540658902,"identity":"117468d1-0aff-41e5-be31-f7aaa99bd893","order_by":0,"name":"Bruno Bizzozero-Peroni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie2QMUvDQBiGv3BwWT6cIxH9C184yBEI7V8RAjc5dBJHwaHL2a7Z/BdODgcH7VLqKrRgQyGucevQwSQdFE0qbg73LHfc3cP73gfgcPxD6LAY/H6eEv+ron5VflxY6ismw0W8qWB9djG1tnp/SkH698V2NHoWJ+AXmw4lmVzJKIcSvdvZZZSXChI9FyKnVcwBRVcULTAOESwyT1OGxgK9KB4irdJa4cExhTMku2+U17JRlrXiv+2OKcgxuoM2hTeKqYtB3PX9RPPr05xKDJBnnjYKE62YQMpEnSu6iklkj0F1sx62E9uZ9Fz6M2+L+0H0MB4XVd+k4ctg8HPPet/36w6Hw+Fo+QBWz1I4QkVyhAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0614-5561","institution":"Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden; Higher Institute of Physical Education, Universidad de la República, Rivera, Uruguay","correspondingAuthor":true,"prefix":"","firstName":"Bruno","middleName":"","lastName":"Bizzozero-Peroni","suffix":""},{"id":540658903,"identity":"3ff9cae3-2cc3-423e-a9f3-0803d5e92932","order_by":1,"name":"Adrián Carballo-Casla","email":"","orcid":"","institution":"Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden; Center for Networked Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain","correspondingAuthor":false,"prefix":"","firstName":"Adrián","middleName":"","lastName":"Carballo-Casla","suffix":""},{"id":540658904,"identity":"dfad5a62-1c96-46d1-85fc-3a9c20273a0b","order_by":2,"name":"Federico Triolo","email":"","orcid":"","institution":"Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden","correspondingAuthor":false,"prefix":"","firstName":"Federico","middleName":"","lastName":"Triolo","suffix":""},{"id":540658905,"identity":"b7c21c41-374b-449e-91eb-ed0a65fb08bf","order_by":3,"name":"Deborah N. 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05:43:16","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":229647,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8020122/v1/c21f0f6dbdaab663ddc951e8.html"},{"id":95504369,"identity":"f61166c7-fc42-48d0-8618-34735b7509b3","added_by":"auto","created_at":"2025-11-10 05:43:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":277329,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of multivariable-adjusted hazard ratios for associations between dietary factors and risk of depression in older Swedish adults.\u003c/p\u003e\n\u003cp\u003eNotes: Heatmap cells display hazard ratios (HRs) per 1-unit daily increase in each dietary factor and per each additional dietary risk factor (cumulative count, 0–15) for the risk of major depression (\u003cem\u003en\u003c/em\u003e=2067; cases: 25), minor depression (\u003cem\u003en\u003c/em\u003e=2004; cases: 152), and mild-to-moderate depressive symptoms (\u003cem\u003en\u003c/em\u003e=1830; cases: 228). Results are presented for the overall sample and stratified by sex and age group. For major depression, age-specific models are not reported because most incident cases occurred at ages 70–79 years (n = 20), yielding sparse data in other strata. Color coding indicates direction and magnitude of associations: green tones represent HRs \u0026lt; 1.0 (suggesting lower risk); red tones indicate HRs \u0026gt; 1.0 (suggesting higher risk); light gray reflects HRs ≈ 1.0. Asterisks (*) indicate HRs at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. For individual dietary factors, Cox regression models were adjusted for age (years), sex (female or male), educational level (primary, high school, or university), previous occupation (manual or nonmanual worker), social network index (z-score), body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e), tobacco smoking status (never, former, or current), physical activity level (inadequate, health-enhancing, or fitness-enhancing), diet quality (AHEI score), and overall health status (HAT score). Each dietary factor was energy-adjusted using Willett’s residual method. The AHEI covariate was computed excluding the specific dietary component under study; for milk, fiber, and calcium (not included in AHEI), the full AHEI score was used. For the cumulative number of dietary risk factors, the same covariates were included except the AHEI diet-quality covariate (omitted to avoid overadjustment); alcohol consumption (g/day) was included as a separate covariate. Abbreviations: g, grams; HR, hazard ratio; mg, milligrams; SSBs, sugar-sweetened beverages; TEI, total energy intake.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8020122/v1/5df1c281f350a836f0d431d3.png"},{"id":95504368,"identity":"d133ac8b-4d7b-483c-9cd7-9e49b5611e13","added_by":"auto","created_at":"2025-11-10 05:43:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":436660,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analyses of energy-adjusted dietary factors in relation to risk of major depression in fully adjusted Cox models (\u003cem\u003en\u003c/em\u003e=2067; cases: 25).\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotes: Restricted cubic spline curves (with three knots at the 5th, 50th, and 95th percentiles of the dietary exposure distribution) showing hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between energy-adjusted intakes of individual dietary factors and risk of major depression in older Swedish adults (\u003cem\u003en\u003c/em\u003e=2067; cases: 25). Solid lines represent HRs, shaded areas represent 95% CIs, and the dashed line indicates HR=1 (reference). The reference point for each spline was set at the 50th percentile of dietary intake, based on the observed distribution. Upper limit was truncated at HR=3. Dietary intake values were rounded to the nearest 0.1 unit in most cases, or to 0.05 for calcium, omega-6 fatty acids, and trans fatty acids. Cox regression models were adjusted for age (years), sex (female or male), educational level (primary, high school, or university), previous occupation (manual or nonmanual worker), social network index (z-score), body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e), tobacco smoking status (never, former, or current), physical activity level (inadequate, health-enhancing, or fitness-enhancing), diet quality (AHEI score), and overall health status (HAT score). Each dietary factor was energy-adjusted using Willett’s residual method. The AHEI covariate was computed excluding the specific dietary component under study; for milk, fiber, and calcium (not included in AHEI), the full AHEI score was used. Abbreviations: CI, confidence interval; g, grams; HR, hazard ratio; mg, milligrams.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8020122/v1/aae097b954b137dac720c8f0.png"},{"id":95504364,"identity":"f5e77eaa-7f62-4e7a-82d6-722f0b653d0d","added_by":"auto","created_at":"2025-11-10 05:43:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":401002,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analyses of energy-adjusted dietary factors in relation to risk of minor depression in fully adjusted Cox models (\u003cem\u003en\u003c/em\u003e=2004; cases: 152).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotes: Restricted cubic spline curves (with three knots at the 5th, 50th, and 95th percentiles of the dietary exposure distribution) showing hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between energy-adjusted intakes of individual dietary factors and risk of minor depression in older Swedish adults (\u003cem\u003en\u003c/em\u003e=2004; cases: 152). Solid lines represent HRs, shaded areas represent 95% CIs, and the dashed line indicates HR=1 (reference). The reference point for each spline was set at the 50th percentile of dietary intake, based on the observed distribution. Upper limit was truncated at HR=2. Dietary intake values were rounded to the nearest 0.1 unit in most cases, or to 0.05 for calcium, omega-6 fatty acids, and trans fatty acids. Cox regression models were adjusted for age (years), sex (female or male), educational level (primary, high school, or university), previous occupation (manual or nonmanual worker), social network index (z-score), body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e), alcohol intake (g/day), tobacco smoking status (never, former, or current), physical activity level (inadequate, health-enhancing, or fitness-enhancing), diet quality (AHEI score), and overall health status (HAT score). Each dietary factor was energy-adjusted using Willett’s residual method. The AHEI covariate was computed excluding the specific dietary component under study; for milk, fiber, and calcium (not included in AHEI), the full AHEI score was used. Abbreviations: CI, confidence interval; g, grams; HR, hazard ratio; mg, milligrams.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8020122/v1/da55b96bd0191d958727ac8c.png"},{"id":95504375,"identity":"9d35e97f-d26c-4b84-9e50-7378c6bb5d47","added_by":"auto","created_at":"2025-11-10 05:43:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":399470,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analyses of energy-adjusted dietary factors in relation to risk of mild-to-moderate depressive symptoms in fully adjusted Cox models (\u003cem\u003en\u003c/em\u003e=1830; cases: 228).\u003c/p\u003e\n\u003cp\u003eNotes: Restricted cubic spline curves (with three knots at the 5th, 50th, and 95th percentiles of the dietary exposure distribution) showing hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between energy-adjusted intakes of individual dietary factors and risk of mild-to-moderate depressive symptoms in older Swedish adults (\u003cem\u003en\u003c/em\u003e=1830; cases: 228). Solid lines represent HRs, shaded areas represent 95% CIs, and the dashed line indicates HR=1 (reference). The reference point for each spline was set at the 50th percentile of dietary intake, based on the observed distribution. Upper limit was truncated at HR=2. Dietary intake values were rounded to the nearest 0.1 unit in most cases, or to 0.05 for calcium, omega-6 fatty acids, and trans fatty acids. Cox regression models were adjusted for age (years), sex (female or male), educational level (primary, high school, or university), previous occupation (manual or nonmanual worker), social network index (z-score), body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e), alcohol intake (g/day), tobacco smoking status (never, former, or current), physical activity level (inadequate, health-enhancing, or fitness-enhancing), diet quality (AHEI score), and overall health status (HAT score). Each dietary factor was energy-adjusted using Willett’s residual method. The AHEI covariate was computed excluding the specific dietary component under study; for milk, fiber, and calcium (not included in AHEI), the full AHEI score was used. Abbreviations: CI, confidence interval; g, grams; HR, hazard ratio; mg, milligrams.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8020122/v1/2ad505c3b12ca332bbdf8373.png"},{"id":95654142,"identity":"fe521db0-ce14-4456-8c09-7edef36643fe","added_by":"auto","created_at":"2025-11-11 16:09:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2824321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8020122/v1/e8b9abec-e46e-43bc-a041-26d9f88b1d30.pdf"},{"id":95504362,"identity":"44ff7da6-888d-4d57-ba1e-ff4bb781ffd3","added_by":"auto","created_at":"2025-11-10 05:43:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":619403,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material\u003c/p\u003e","description":"","filename":"SMSNACKGLADpreprint.docx","url":"https://assets-eu.researchsquare.com/files/rs-8020122/v1/1f063c481cf8314741ec937a.docx"}],"financialInterests":"The authors declare potential competing interests as follows: DNA, RO, MML, FNJ, and AO are affiliated with the Food \u0026 Mood Centre, Deakin University, which has received research funding support from Be Fit Food, Bega Dairy and Drinks, and the a2 Milk Company and philanthropic research funding support from the Waterloo Foundation, Wilson Foundation, the JTM Foundation, the Serp Hills Foundation, the Roberts Family Foundation, and the Fernwood Foundation. MML is a member (and former Secretary) of the Melbourne Branch Committee of the Nutrition Society of Australia (unpaid). She has received travel funding support from the International Society for Nutritional Psychiatry Research, the Nutrition Society of Australia, the Australasian Society of Lifestyle Medicine, and the Gut Brain Congress. MML is also an Associate Investigator for the MicroFit Study, an investigator-led randomized controlled trial examining the effects of diets with varying levels of industrial processing on gut microbiome composition, partially funded by Be Fit Food (payment received by the Food \u0026 Mood Centre, Deakin University).","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDietary Factors and Risk of Late-Life Depression: Findings from a Swedish Population-Based Cohort Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDepression is the 11th leading cause of disability worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). According to the Global Burden of Disease (GBD) study, an estimated 322\u0026nbsp;million people (4.1% of the global population) were living with depressive disorder in 2023, including 63\u0026nbsp;million adults over 60 years (5.4% of that age group) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These estimates are likely conservative, as depression tends to be underdiagnosed and undertreated (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This concern is especially relevant for late-life major depression (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), typically affecting adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and associated with poorer health-related quality of life (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), increased mortality risk (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and accelerated somatic multimorbidity (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). With the rapid global aging of the population (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), the absolute number of individuals affected by late-life depression is expected to rise substantially over the coming decades (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), amplifying its social and economic burden. Even subsyndromal depression may substantially contribute to this burden, being associated with greater disability, morbidity, and healthcare costs (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Furthermore, although psychotherapies and pharmacotherapies remain important for treatment, they have limited response rates and overall effectiveness (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Beyond biological, psychosocial, and environmental factors, emerging evidence identifies lifestyle behaviors as major contributors to depression risk, presenting promising targets for prevention and treatment (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong these, diet quality has garnered substantial attention in older adults as a practical and impactful avenue to increase life expectancy (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), promote healthy aging (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), reduce the risk(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and accumulation(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) of neuropsychiatric diseases, and, more specifically, as a modifiable lifestyle factor associated with late-life depression (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Importantly, beyond affective symptoms, somatic manifestations of depression such as reduced appetite and cognitive difficulties are common in older adults and closely linked to the burden of somatic diseases (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In late life, vulnerability to poor nutritional status may further increase the risk of subsequent depression (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), underscoring the importance of dietary preventive strategies. Healthy dietary patterns, rich in fruit, vegetables, dietary fiber, polyunsaturated fatty acids, and other beneficial components, provide bioactive compounds, vitamins, and minerals with anti-inflammatory and antioxidant properties that may reduce cytokine-induced depression and oxidative stress markers (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Yet, diet remains suboptimal worldwide (e.g., characterized by low consumption of fruit, vegetables, nuts and seeds, and whole grains) and ranks among the top five risk factors for attributable mortality and disability burden in older populations (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe GBD study provides robust estimates of 15 dietary exposures (fruit, vegetables, legumes, whole grains, nuts, milk, red meat, processed meat, sugar-sweetened beverages, dietary fiber, calcium, omega-3 and omega-6 fatty acids, trans fats, and sodium) in relation to the burden of major physical health outcomes such as cancer and cardiovascular diseases (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, comparable estimates for common mental disorders remain absent, limiting efforts to integrate dietary prevention strategies into mental health policy. This evidence gap reflects several challenges, including the consistent operationalization of dietary exposures and major depression (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), as well as the need to establish robust longitudinal associations (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). To address these limitations, the Global burden of disease Lifestyle And mental Disorder (GLAD) initiative conducts pooled analyses across worldwide cohorts to estimate population attributable fractions for 15 dietary factors in relation to incident major depression, with the goal of facilitating their integration into future GBD estimates (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecent nutritional epidemiology on late-life depression has increasingly focused on overall dietary patterns and depressive symptom severity, whereas the role of specific foods and nutrients in distinct forms of depression remains less well understood (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Evidence from longitudinal studies indicates that higher consumption of certain foods, particularly fruit and vegetables, is associated with a lower burden of depressive symptoms in this population (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). However, evidence for other food groups and nutrients, as well as for subgroup differences, remains limited across different manifestations of depression, underscoring a critical gap in developing both population-level and precision dietary strategies for the prevention of late-life depression. Accordingly, this study aimed to examine the associations of 15 dietary factors with the risk of three complementary forms of depression\u0026mdash;major depression, minor depression, and mild-to-moderate depressive symptoms\u0026mdash;among older adults, thereby allowing comparison across clinical and symptom-based definitions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Swedish National study on Aging and Care in Kungsholmen (SNAC-K) is an ongoing population-based cohort of randomly sampled older adults residing in the urban district of Kungsholmen, Stockholm, Sweden.\u0026nbsp;The study was approved by the Regional Ethical Review Board in Stockholm in accordance with the Declaration of Helsinki. At baseline (2001\u0026ndash;2004, wave 1), 3363 individuals aged \u0026ge;60 years were recruited (participation rate: 73%). \u0026nbsp;Standardized health assessments are repeated every three years in participants aged \u0026ge;78 years and every six years for those aged 60\u0026ndash;72 years.\u0026nbsp;Written informed consent was obtained from all participants or, if not possible, from next of kin. Detailed descriptions of the cohort design and methodology are available elsewhere (32).\u0026nbsp;The present study was reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (33) (\u003cstrong\u003eTable S1\u003c/strong\u003e) and complied with the requirements of the GLAD Taskforce (29), as part of a global collaborative initiative to inform the GBD study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 3363 participants enrolled at baseline, 877 were excluded due to insufficient dietary information (\u0026ge;50% missing items in the food frequency questionnaire). Depending on the outcome, baseline exclusions comprised prevalent depression (major depression [\u003cem\u003en\u003c/em\u003e=8], minor depression [\u003cem\u003en\u003c/em\u003e=105], or mild-to-severe depressive symptoms [\u003cem\u003en\u003c/em\u003e=251]), missing data on depression status (\u003cem\u003en\u003c/em\u003e=9\u0026ndash;70), implausible energy intakes (\u003cem\u003en\u003c/em\u003e=15\u0026ndash;19; males: \u0026lt;800 or \u0026gt;5000 kcal/day and females: \u0026lt;500 or \u0026gt;4000 kcal/day across the first three SNAC-K waves),\u0026nbsp;prevalent dementia (\u003cem\u003en\u003c/em\u003e=12\u0026ndash;29), and institutionalization (\u003cem\u003en\u003c/em\u003e=6\u0026ndash;7). Follow-up extended from baseline (2001\u0026ndash;2004) to wave 6 (2016\u0026ndash;2019), with mortality ascertained through national death registers\u0026nbsp;(34). The\u0026nbsp;final analytical samples of new depression cases were n=2411 for major depression and n=2327 for minor depression, and n=2130 for the analysis of new-onset mild-to-moderate depressive symptoms. Participant attrition during follow-up, reasons for censoring, and exclusions due to missing covariate data\u0026nbsp;are illustrated in the flow chart (\u003cstrong\u003eFigure S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDietary exposures (waves 1\u0026ndash;3, 2001\u0026ndash;2004 to 2007\u0026ndash;2010)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDietary assessment was conducted using a validated 98-item food frequency questionnaire (35). Participants reported their habitual consumption of foods and beverages over the past year using a nine-point Likert scale (ranging from \u0026lsquo;never or less than once per year\u0026rsquo; to \u0026lsquo;more than four times per day\u0026rsquo;) in the first wave and a five-point scale in the second and third waves (ranging from \u0026lsquo;never or a few times per year\u0026rsquo; to \u0026lsquo;two or more times a day\u0026rsquo;). Portion sizes were estimated with the aid of color photographs, and nutrient intakes were estimated using food composition tables from the Swedish National Food Agency (36).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDietary exposures were grouped according to the definitions established in the GBD study (37) and expressed in their original units as grams per day (fruit, vegetables, legumes, whole grains, nuts and seeds, milk, red meat, processed meat, sugar-sweetened beverages, total dietary fiber [hereafter fiber], calcium, and sodium), milligrams per day (omega-3 fatty acids), and percentage of total energy intake (omega-6 and trans fatty acids). A detailed description of the dietary factors is provided in \u003cstrong\u003eTable S2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTo facilitate interpretation, selected variables were rescaled to meaningful increments prior to regression analyses: fruit, vegetables and milk (per 100 g), legumes and processed meat (per 10 g), whole grains and red meat (per 30 g), fiber and nuts/seeds (per 5 g), sugar-sweetened beverages (per 50 g), and omega-3 fatty acids (per 100 mg). Scaling was based on dietary guidelines and standard portion sized compiled by the European Commission (38) and the European Food Safety Authority (39). In addition, each dietary exposure was dichotomized using GBD recommendations based on the theoretical minimum risk exposure level (TMREL) (37). Participants were coded as \u0026ldquo;at risk\u0026rdquo; if consumption or intake fell below the midpoint of the TMREL range for protective foods (e.g., fruit \u0026lt;345 g/day, vegetables \u0026lt;339 g/day, whole grains \u0026lt;185 g/day) or nutrients (fiber \u0026lt;21.5 g/day), and analogously for harmful exposures (e.g., processed meat \u0026gt;0 g/day)\u0026nbsp;(\u003cstrong\u003eTable S2\u003c/strong\u003e). Finally, an overall dietary risk score was derived by summing the number of GBD-aligned recommendations not met (range 0\u0026ndash;15), with a higher score indicating a greater dietary risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLate-life depression (waves 1\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026ndash;\u003cem\u003e6, 2001\u0026ndash;2004 to 2016\u0026ndash;2019)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt each SNAC-K wave, depressive symptoms were measured with the Comprehensive Psychopathological Rating Scale (CPRS), a validated semi-structured interview for psychiatric evaluation. Trained physicians rate each symptom on a 0\u0026ndash;6 scale (absent to severe) based on frequency, duration, and intensity (40). This instrument has demonstrated good applicability and reliability in older adults (41). As part of the CPRS assessment, major and minor depression were ascertained according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria, using an algorithm previously validated in population-based studies (42). Major depression was defined as the presence of at least five symptoms, including one core symptom (depressed mood and/or loss of interest). Minor depression was defined as the presence of two to four symptoms, with at least one core symptom. In addition, the Montgomery\u0026ndash;\u0026Aring;sberg Depression Rating Scale (MADRS), a 10-item subscale of the CPRS, was used to assess depressive symptom severity (range 0\u0026ndash;60; higher scores indicate greater severity) (43). Participants without major or minor depression who scored 7\u0026ndash;19 or 20\u0026ndash;34 on the MADRS were classified as having mild or moderate depressive symptoms, respectively\u0026nbsp;(44). MADRS scores \u0026lt;7 defined absence of depressive symptoms, whereas a separate category for severe depressive symptoms was not defined because participants with MADRS scores \u0026ge;35 met diagnostic criteria for major or minor depression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCovariates (wave 1\u0026ndash;3, 2001\u0026ndash;2004 to 2007\u0026ndash;2010)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePotential confounders related to both dietary exposures and depression were identified a priori based on the GLAD and GBD frameworks (26,29) and previous evidence (13,21,45). These included age (years), sex (female, male), previous occupation (manual, nonmanual; based on longest held occupation), educational level (primary school, high school, university), social network index (based on multiple items on social connections and support) (46), body mass index (BMI), tobacco smoking status (never, former, current), physical activity level (inadequate, health-enhancing, fitness-enhancing; based on intensity and frequency) (47), diet quality (Alternate Healthy Eating Index [AHEI]), and overall health status (Health Assessment Tool [HAT]) (48). HAT is a validated multidimensional instrument for older adults, ranging from 0 (poor health) to 10 (good health), and integrates cognitive status, gait speed, daily living activities, and morbidity burden (46).\u0026nbsp;Potential confounders were evaluated at each visit through physician examinations, structured interviews, and medical records. Participant data were additionally linked to the Swedish National Patient Register to integrated clinical data from secondary and tertiary care settings (49).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDescriptive analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics were summarized separately for each analytical sample (major depression, minor depression, and mild-to-moderate depressive symptoms) and by depression status (no vs. incident cases). Categorical variables are reported as absolute (\u003cem\u003en\u003c/em\u003e) and relative (%) frequencies, and continuous variables as means \u0026plusmn; standard deviations.\u0026nbsp;Dietary risk factors were described according to GBD criteria, including prevalence (not meeting the theoretical minimum risk exposure level) (37), overall risk score, and mean consumption or intake across the three analytical follow-up samples. Dietary exposures were further presented by sex and age group. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMain survival analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCox proportional hazards regression models with Huber\u0026ndash;White robust standard errors (Breslow method for ties) were used, with time-on-study (person-years) as the time scale. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated separately for each risk-outcome pair across 15 dietary exposures, examining incidence of major depression, minor depression, and mild-to-moderate depressive symptoms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants were followed from baseline until the date of their first depression event, death, loss to follow-up (withdrawal, moving out of the areas, or inability to contact), or the end of the follow-up period, whichever occurred first. All analyses were performed using complete-case models. Average dietary exposures and continuous covariates were calculated using all available data from waves 1\u003cstrong\u003e\u003cem\u003e\u0026ndash;\u003c/em\u003e\u003c/strong\u003e3 collected before the event or censoring, while categorical covariates were defined at baseline (wave 1). Participants were censored at the first occurrence of major depression when analyzing minor depression, and at the\u0026nbsp;first occurrence\u0026nbsp;of either major or minor depression when analyzing symptom severity, to\u0026nbsp;preserve temporal sequence and minimize misclassification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalyses were performed both unadjusted and with basic covariate adjustment aligned with the GBD comparative risk-assessment framework (37). As a complementary step, a third Cox model was fitted to further adjust for additional relevant covariates in late life, to ensure robustness and minimize residual confounding. Model 0 was unadjusted (crude model). Model 1 was adjusted for sociodemographic characteristics, including age, sex, and educational level. Model 2 additionally accounted for each dietary factor corrected for total energy intake using Willett\u0026rsquo;s residual method (50), except for omega-6 and trans fatty acids, which were modelled as a percentage of total energy intake. In this model, total energy intake was used to derive energy-adjusted dietary exposures rather than entered as a covariate. Model 3 further adjusted for previous occupation, social network index, BMI, tobacco smoking status, physical activity level, diet quality (AHEI score excluding each specific dietary factor; for milk, fiber, and calcium\u0026mdash;components not included in the AHEI\u0026mdash;the full score was used), and overall health status (HAT score). Multicollinearity among covariates was assessed using variance inflation factors (VIFs) derived from auxiliary ordinary least squares regressions including all predictors in Cox models.\u003c/p\u003e\n\u003cp\u003eThe proportional hazards assumption was evaluated using scaled Schoenfeld residuals and the Grambsch\u0026ndash;Therneau test. For dietary exposures showing evidence of nonproportionality (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, i.e., indicating that effect sizes were not constant over time), extended Cox models with time-varying coefficients were fitted to estimate HRs at fixed follow-up times (3, 5, and 10 years). To examine potential nonlinear associations between dietary factors and depression risk, restricted cubic spline models were applied. Nonlinearity was tested using the Wald test for the added spline terms. Spline models were retained regardless of \u003cem\u003ep\u003c/em\u003e-values to allow flexible interpretation and presentation of study associations. Models with three knots (51) were specified at fixed percentiles (5\u003csup\u003eth\u003c/sup\u003e, 50\u003csup\u003eth\u003c/sup\u003e, and 95\u003csup\u003eth\u003c/sup\u003e) of the exposure distribution (52), with the median consumption or intake (50\u003csup\u003eth\u003c/sup\u003e percentile) as the reference value. All spline analyses were conducted in fully adjusted models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to the individual analyses of the 15 dietary exposures, the risk of developing different forms of depression was examined in relation to the overall number of dietary risk factors, operationalized as the count of GBD-aligned recommendations not met (range 0\u0026ndash;15). For this analysis, Model 3 did not apply Willett\u0026rsquo;s residual method, as the cutoffs were based on raw consumption or intake values; therefore, total energy intake was included as an additional covariate. To avoid collinearity, the overall AHEI score (a proxy for diet quality) was excluded, and alcohol consumption (g/day)\u0026mdash;originally included as a component of the AHEI\u0026mdash;was entered as a separate covariate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInteractions, subgroup and sensitivity analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFormal tests for statistical interaction by sex and age were conducted using likelihood ratio tests comparing models with and without the corresponding interaction terms, with \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 indicating evidence of interaction. Results from subgroup analyses are presented irrespective of statistical evidence for interaction, to enable descriptive comparison by sex (males, females) and age group (60\u0026ndash;69, 70\u0026ndash;79, \u0026ge;80 years). In addition, the same approach was used to test multiplicative first-order interactions between the number of dietary risk factors and each covariate from the fully adjusted model, with robust Wald \u0026chi;\u0026sup2; statistics reported on the HR scale. Sensitivity analyses were performed to assess the robustness of the findings. To minimize potential reverse causality, Cox regression models were repeated: (i) applying lag times of 3, 5, and 7 years whereby follow-up was left-truncated at each lag and events and person-time occurring before that point were excluded; (ii) excluding participants with self-reported past depression or other mental disorders at baseline (i.e., \u0026nbsp;sleep disorders, schizophrenia, delusional disorders, neurotic or stress-related, somatoform disorders, or other psychiatric and behavioral disorders); (iii) excluding participants with probable cognitive impairment at baseline, defined as Mini-Mental State Examination score \u0026lt;24; (iv) for the major depression analytical sample, additionally excluding individuals with minor depression or mild-to-severe depressive symptoms at baseline; and (v) for the minor depression analytical sample, additionally excluding individuals with mild-to-moderate depressive symptoms at baseline.\u003c/p\u003e\n\u003cp\u003eTwo-sided\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-values were reported, with\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 considered suggestive of an association. All analyses were performed using Stata version 17.0 (StataCorp LLC, College Station, TX, USA).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes baseline characteristics of participants across the different analytical samples. At baseline, the mean age of participants in the largest study sample (n\u0026thinsp;=\u0026thinsp;2411) was 71.3 years and 61.4% were female. Nearly four in five reported a nonmanual occupation, and 87% had completed high school or university education. The mean BMI was 25.8 kg/m\u0026sup2;, approximately 11% were current smokers, and over 50% reported a health-enhancing level of physical activity. These characteristics are also presented separately by incident depression status in the analytical samples (\u003cb\u003eTable S3\u003c/b\u003e). As for the dietary exposures, participants had broadly suboptimal diets, averaging 11.7 of 15 GBD dietary risk factors, with 84% accumulating 11\u003cb\u003e\u0026ndash;\u003c/b\u003e13 (\u003cb\u003eFigure S2\u003c/b\u003e). Several components (e.g., legumes, nuts and seeds, omega-6 fatty acids) were commonly under consumed relative to GBD recommendations (\u003cb\u003eTables S4\u0026ndash;S6\u003c/b\u003e). Sex- and age-related differences in dietary exposures are presented in \u003cb\u003eFigure S3\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of participants in the three analytic samples used to examine the risk of late-life depression. \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMajor depression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMinor depression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMild-to-moderate depressive symptoms\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2411)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2327)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2130)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSociodemographic\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1480 (61.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1424 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1287 (60.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManual worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e452 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e437 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e401 (18.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e306 (12.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e297 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e275 (12.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1185 (49.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1140 (49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1027 (48.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e920 (38.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e890 (38.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e828 (38.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial network index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI, kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLifestyle\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol consumption, g/day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTobacco smoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1077 (44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1049 (45.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e961 (45.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1055 (43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1017 (43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e934 (44.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e275 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e257 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e230 (10.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical activity level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInadequate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e460 (19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e432 (19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e377 (18.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth-enhancing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1264 (54.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1222 (54.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1126 (54.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitness-enhancing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e617 (26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e608 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e566 (27.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiet quality (AHEI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGBD dietary risk factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverall health status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Assessment Tool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: Data are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations or \u003cem\u003en\u003c/em\u003e (%). \u003csup\u003ea\u003c/sup\u003e Baseline analytical samples were defined by inclusion criteria and availability of basic covariates for risk models according to the standardized GBD framework (age, sex, education, energy intake). Missing data on additional covariates is detailed in the flow chart (Figure S1). Analytical samples represent overlapping subsets of the same cohort (i.e., counts are not additive). Abbreviations: AHEI, Alternative Healthy Eating Index; BMI, body mass index; GBD, Global Burden of Disease study; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eModel diagnostics and overall associations\u003c/h2\u003e\u003cp\u003eVIFs indicated no problematic multicollinearity (\u003cb\u003eTable S7\u003c/b\u003e). Scaled Schoenfeld residuals provided no evidence of departure from the proportional hazard\u0026rsquo;s assumption for most dietary parameters (\u003cb\u003eTable S8\u003c/b\u003e). For dietary exposures showing evidence of nonproportionality, results from extended Cox models with time-varying coefficients are reported in the following sections for each of the three depression analytical samples. Wald tests identified nonlinear associations for some energy-adjusted dietary components (\u003cb\u003eTable S9\u003c/b\u003e). Basic-adjusted Cox regression results for the 15 dietary factors across depression measures are presented in \u003cb\u003eTables S10\u0026ndash;S15\u003c/b\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the multivariable-adjusted associations of 15 individual dietary factors and the overall dietary risk score with the three different forms of late-life depression, stratified by sex and age groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eIndividual dietary factors and major depression\u003c/h2\u003e\u003cp\u003eAmong 2148 participants with available data at follow-up, 27 (1.3%) developed major depression over a mean follow-up of 10.2 years. In fully adjusted models, higher whole-grain consumption (per 30 g/day; HR\u0026thinsp;=\u0026thinsp;0.63; 95% CI: 0.43\u0026ndash;0.91) and fiber intake (per 5 g/day; HR\u0026thinsp;=\u0026thinsp;0.69; 95% CI: 0.52\u0026ndash;0.91) were associated with lower risk of major depression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Spline analyses provided evidence of nonlinearity for milk (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) and revealed a J-shaped pattern, with consumption above ~\u0026thinsp;160 g/day associated with lower risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The associations of whole grains and processed meat with major depression showed evidence of nonproportional hazards. Analyses with time-varying coefficients confirmed the robustness of the inverse association for whole grains at 3 and 5 years, while processed meat was linked to increased risk at later follow-up (\u003cb\u003eTable S16\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFully adjusted Cox regression results for the associations between dietary factors and risk of late-life depression.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDietary exposure (unit per day) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eMajor depression\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2067; 25 incident cases)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eMinor depression\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2004; 152 incident cases)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\u003cp\u003eMild-to-moderate symptoms\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1830; 228 incident cases)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eLL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eUL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFruit (per 100 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetables (per 100 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLegumes (per 10 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.730\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhole grains (per 30 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNuts and seeds (per 5 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMilk (per 100 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed meat (per 30 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProcessed meat (per 10 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSBs (per 50 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFiber (per 5 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium (per 1 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmega-3 fatty acids (per 100 mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmega-6 fatty acids (per 1% TEI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrans fatty acids (per 1% TEI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.560\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium (per 1 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eNotes: Values are hazard ratios and 95% confidence intervals in fully adjusted Cox regression model (Model 3), reflecting the risk associated with a 1-unit increase in daily dietary exposure. Bold values indicate \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Model 3: adjusted for age (years), sex (male or female), educational level (primary, high school, or university), dietary factors corrected for total energy intake using Willett\u0026rsquo;s residual method, previous occupation (manual or nonmanual worker), social network index (z-score), body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e), tobacco smoking status (never, former, or current), physical activity level (inadequate, health-enhancing, or fitness-enhancing), diet quality (AHEI score excluding each specific dietary factor; for milk, fiber, and calcium\u0026mdash;components not included in the AHEI\u0026mdash;the full score was used), and overall health status (HAT score). Omega-6 fatty acids and trans fatty acids were not adjusted for total energy intake, as they are harmonized as percentage of total energy intake. \u003csup\u003ea\u003c/sup\u003e Dietary exposures were modelled as continuous variables in their original units for calcium, omega-6 fatty acids, trans fatty acids, and sodium. The other dietary factors were scaled to different unit increments to enhance interpretability of effect size variation. Abbreviations: d, day; g, grams; HR, hazard ratio; LL, lower limit of the 95% confidence interval; mg, milligrams; SSBs, sugar-sweetened beverages; TEI, total energy intake; UL, upper limit of the 95% confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubgroup analyses suggested potential associations for whole grains and fiber among males, although \u003cem\u003ep\u003c/em\u003e-for-interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (\u003cb\u003eTable S17\u003c/b\u003e). Analyses by age were not feasible given that most incident cases (n\u0026thinsp;=\u0026thinsp;20) clustered in the 70\u0026ndash;79 age group (\u003cb\u003eTable S18\u003c/b\u003e). Sensitivity analyses yielded consistent findings (\u003cb\u003eTables S19\u0026ndash;S22\u003c/b\u003e). In landmark analyses, the inverse associations of whole grains and fiber persisted at 3-year lag-time, and fiber remained associated with a 5-year lag-time. At 5- and 7-year lag-times, inverse associations emerged for calcium and milk, respectively. After excluding participants with past self-reported depression or other mental disorders at baseline, milk consumption was linked to lower risk.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eIndividual dietary factors and minor depression\u003c/h2\u003e\u003cp\u003eAmong 2079 participants with available data at follow-up, 156 (7.5%) developed minor depression over a mean follow-up of 10.0 years. In fully adjusted models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), higher fruit consumption was associated with lower risk of minor depression (per 100 g/day; HR\u0026thinsp;=\u0026thinsp;0.81; 95% CI: 0.67\u0026ndash;0.97). Spline analyses provided evidence of nonlinearity for fruit (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and revealed an inverse L-shaped pattern, with consumption above ~\u0026thinsp;180 g/day associated with lower risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Whole grains showed evidence of nonproportional hazards, with time-varying models indicating that HRs attenuated with longer follow-up (\u003cb\u003eTable S16\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn subgroup analyses, no statistical evidence of interaction was observed (\u003cb\u003eTables S17\u0026ndash;S18\u003c/b\u003e). However, the inverse association for fruit appeared more evident among females. By age, milk and omega-6 fatty acids tended to be associated with lower risk in participants aged 60\u0026ndash;69 years, whereas fruit and fiber showed inverse trends among those aged 70\u0026ndash;79 years. Sensitivity analyses confirmed the results for fruit consumption (\u003cb\u003eTables S19\u0026ndash;S22\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eIndividual dietary factors and mild-to-moderate depressive symptoms\u003c/h2\u003e\u003cp\u003eAmong 1903 participants with available data at follow-up, 244 (12.8%) developed mild-to-moderate depressive symptoms over a mean follow-up of 9.9 years. In fully adjusted models, higher consumption of fruit (per 100 g/day; HR\u0026thinsp;=\u0026thinsp;0.87; 95% CI: 0.76\u0026ndash;0.99) and vegetables (per 100 g/day; HR\u0026thinsp;=\u0026thinsp;0.86; 95% CI: 0.75\u0026ndash;1.00) were associated with lower risk of mild-to-moderate depressive symptoms (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Spline analyses revealed nonlinear associations for fruit (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) and sodium (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027). Fruit consumption showed an inverse L-shaped pattern, with risk decreasing up to ~\u0026thinsp;180 g/day and reaching a plateau beyond ~\u0026thinsp;330 g/day, whereas sodium displayed a U-shaped curve, with intakes above ~\u0026thinsp;2.7 g/day linked to higher risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Fruit and trans fatty acids showed indications of nonproportional hazards. Analyses with time-varying coefficients identified inverse associations for fruit at 5 years and a progressive attenuation of risk estimates for trans fatty acids (\u003cb\u003eTable S16\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn subgroup analyses, no statistical evidence of interaction was observed (\u003cb\u003eTables S17\u0026ndash;S18\u003c/b\u003e). However, vegetables tended to show inverse associations among females, and for nuts and seeds among males. By age group, vegetables, milk, and fiber showed inverse trends with mild-to-moderate depressive symptoms in participants aged 70\u0026ndash;79 years, whereas fruit and omega-3 fatty acids appeared inversely associated in those\u0026thinsp;\u0026ge;\u0026thinsp;80 years. Findings for fruit and vegetables were consistent in sensitivity analyses but attenuated in landmark analyses (\u003cb\u003eTables S19\u0026ndash;S22\u003c/b\u003e). Using a 5-year lag-time, an additional potential association emerged for milk. After excluding participants with past self-reported depression or other mental disorders, fiber intake was linked to lower risk.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eDietary risk factors and late-life depression\u003c/h2\u003e\u003cp\u003eA greater number of dietary risk factors (i.e., unmet GBD-defined dietary recommendations) was associated with increased risk of major depression (HR\u0026thinsp;=\u0026thinsp;1.53; 95% CI: 1.05\u0026ndash;2.22) and mild-to-moderate depressive symptoms (HR\u0026thinsp;=\u0026thinsp;1.21; 95% CI: 1.06\u0026ndash;1.38; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For major and minor depression, results were consistent across sex strata (\u003cb\u003eFigure S4\u003c/b\u003e). For mild-to-moderate depressive symptoms, subgroup analyses suggested more pronounced associations in females and in participants aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years, although no evidence for interaction was found (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Results suggested possible effect modification by BMI for minor depression (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020) and mild-to-moderate depressive symptoms (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041; \u003cb\u003eTable S23\u003c/b\u003e). In participants with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;, a higher number of dietary risk factors was associated with increased risk of minor depression and mild-to-moderate symptoms, with no evidence of associations in those with BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u0026sup2; (\u003cb\u003eFigure S4\u003c/b\u003e). Sensitivity analyses yielded consistent results (\u003cb\u003eTable S24\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCox regression results for the association between the number of dietary risk factors and incident late-life depression.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCox proportional hazard\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eMajor depression\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2148; cases: 27)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eMinor depression\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2079; cases: 156)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\u003cp\u003eMild-to-moderate symptoms\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1903; cases: 244)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eLL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eUL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnadjusted Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted Model 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.050\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted Model 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted Model 3 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eNotes: Values are hazard ratios and 95% confidence intervals, representing the association between each additional dietary risk factor (cumulative count, 0\u0026ndash;15) and the risk of depression phenotypes. Bold values indicate \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Model 1: adjusted for age (years), sex (male or female), and educational level (primary, high school, or university); Model 2: adjusted for Model 1 plus total energy intake (kcal/d). Model 3: adjusted for Model 2 plus previous occupation (manual or nonmanual worker), social network index (z-score), body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e), alcohol consumption (g/day), tobacco smoking status (never, former, or current), physical activity level (inadequate, health-enhancing, or fitness-enhancing), and overall health status (HAT score). \u003csup\u003ea\u003c/sup\u003e The adjusted model 3 included covariates with missing data: for major depression, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2067 (cases: 25); for minor depression, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2004 (cases: 152); and for mild-to-moderate depressive symptoms, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1830 (cases: 228). Abbreviations: HR, hazard ratio; LL, lower limit of the 95% confidence interval; UL, upper limit of the 95% confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this population-based cohort of Swedish older adults, higher whole-grain consumption and fiber intake were associated with lower risk of major depression, fruit consumption was linked to reduced risk of minor depression, and both fruit and vegetables were inversely associated with risk of mild-to-moderate depressive symptoms, which appeared to differ by sex and age, although no statistical evidence of interaction was observed. Additional potential associations emerged for nuts and seeds, milk, calcium, omega-3 and omega-6 fatty acids, and sodium, but only in specific subgroups. In general, most associations indicated lower depression risk with healthier dietary exposures, whereas few potentially unhealthy components showed clear adverse relationships. Furthermore, a higher overall dietary risk score was associated with increased incidence of major depression, and with minor depression and mild-to-moderate depressive symptoms, particularly in the context of metabolic dysregulation. Taken together, these findings support the potential relevance of dietary factors in the etiology of distinct forms of late-life depression and may help inform more tailored prevention strategies.\u003c/p\u003e\u003cp\u003eIn our study, whole grains and fiber appeared most relevant for preventing clinically defined major depression in late life, aligning with prior studies implicating fiber-rich foods in improved metabolic regulation, reduced systemic inflammation, short-chain fatty acid production, and modulation of the gut\u0026ndash;brain axis (\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). In contrast, fruit and vegetables were more strongly associated with minor depression and broader subclinical symptomatology, consistent with previous evidence on their potential micronutrient contributions, antioxidant capacity, and potential neuroprotective effects (\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Suggestive associations were also observed for nuts and seeds, milk, calcium, omega-3 and omega-6 fatty acids, and sodium, with patterns differing by age and depression measure. Previous prospective cohort studies have reported similar associations (\u003cspan additionalcitationids=\"CR60 CR61\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), although evidence specific to late life remains limited.\u003c/p\u003e\u003cp\u003eImportantly, the associations of key plant-based dietary factors may differ by sex, aligning with prior evidence across adult populations suggesting that the mental health benefits of total dietary fiber may be more pronounced in males (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e), while both fruit and vegetables consumption appears particularly relevant for females (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). In our cohort, males reported higher whole-grain consumption and fiber intake, while females reported higher fruit consumption, consistent with previous studies in Swedish adults (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Biological mechanisms may contribute to the observed associations with depression, including variation in gut microbiota, metabolic processing of fiber-rich foods, and hormonal influences on inflammation and neuroplasticity (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Social and behavioral factors could be also relevant, as gender-related social roles and dietary preferences often lead to different perceptions (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e) and consumption (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e) of foods. Although interactions were not statistically significant, these patterns suggest that dietary strategies for mental health could consider individual characteristics such as sex, rather than applying a one-size-fits-all approach.\u003c/p\u003e\u003cp\u003eThe cumulative number of dietary risk factors was consistently associated with major depression and mild-to-moderate depressive symptoms, but not with minor depression in the overall sample, suggesting that diet may be strongly implicated in severe and extended forms of depressive symptomatology. These associations for minor depression and mild-to-moderate symptoms were evident only among participants with overweight or obesity, indicating potential effect modification by BMI. This pattern may reflect interactions between diet, adiposity, and metabolic health. Individuals with overweight or obesity may be more vulnerable to the adverse impact of diets characterized by high intakes of refined grains, added sugars, and saturated fats through chronic inflammation, insulin resistance, dysregulated hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis activity, and impaired neuroplasticity, while such diets may also promote adiposity, further reinforcing the link with depression (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In contrast, among those without overweight or obesity, the contribution of diet to minor depression and subclinical symptoms may be determined more by psychosocial, behavioral, or other biological determinants (ref). These findings suggest that adiposity may play a central role in the diet-depression relationship, potentially outweighing the specific dietary pathways through which this association operates. From a public health perspective, interventions that integrate nutritional counseling with metabolic risk management, such as weight control, may yield synergistic benefits for mental health in late life (ref). In addition to mental health benefits, promoting healthy dietary patterns in older populations could also help reduce multimorbidity and healthcare costs, given the shared prevention framework across chronic and mental diseases (ref).\u003c/p\u003e\u003cp\u003eOver the past decade, nutritional psychiatry has rapidly evolved into a major area of research, highlighting the potential of diet-based prevention strategies with potential public health implications (\u003cspan additionalcitationids=\"CR69 CR70 CR71\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Yet, diet\u0026ndash;disease associations remain inherently complex (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e), particularly for late-life depression, a heterogeneous and dynamic condition shaped by bidirectional relationships with diet (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). Our findings suggest that specific dietary components may contribute to a lower risk of late-life depression, but they should not be interpreted as prescriptive thresholds or exact estimates of risk reduction, as they could likely be affected by cumulative bias, including measurement error in dietary assessment and residual confounding (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). Moreover, it is unlikely that any single dietary factor alone would yield meaningful population-level benefits, given the multifactorial nature of brain aging (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Instead, our results point to overall diet quality and specific dietary components\u0026mdash;particularly fruit, vegetables, whole grains, and fiber\u0026mdash;as key contributors to a lower risk of late-life depression, underscoring the importance of adequate consumption of healthy foods and nutrients for mental health. Accordingly, public health strategies for older adults should prioritize the inclusion of nutrient-dense, plant-based foods as part of comprehensive approaches to depression prevention. Comparative efforts, particularly pooled analyses and Burden of Proof methodology (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e) within the GLAD (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and GBD (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) frameworks, will be essential to refine risk estimates and establish evidence-based dietary recommendations.\u003c/p\u003e\u003cp\u003eImportantly, the GBD-derived cut-offs applied in this study to construct the overall dietary risk score were originally defined for major physical outcomes (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and may not directly translate to late-life depression. Nutritional needs and vulnerabilities change across the aging spectrum (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e), and some cut-offs, such as \u0026ge;\u0026thinsp;345 g/day of fruit or \u0026ge;\u0026thinsp;339 g/day of vegetables, may be overly ambitious for older adults with reduced energy intake (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). Declines in appetite, food variety, basal metabolic rate, and physical activity, together with sarcopenia (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e), can reshape dietary requirements and modify the biological impact of nutrition on mental health. Furthermore, dietary exposures may follow nonlinear, U-, L-, or J-shaped associations with depression, as suggested for milk, fruit, and sodium. Overall, these complexities highlight the need to refine old-age nutritional epidemiology toward more nuanced modeling of dose-response relationships, ideally complemented by biological and omics data to enable personalized approaches (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). Accordingly, future studies should aim to develop age-specific adaptations to dietary risk models\u0026mdash;including revised exposure distributions, theoretical minimum risk exposure levels, and age-stratified relative risks\u0026mdash;to more accurately estimate the preventable burden of late-life depression. Future research should also examine how dietary exposures influence dynamic trajectories and variability across symptom profiles.\u003c/p\u003e\u003cp\u003eThe strengths of this study include its large population-based sample with long-term follow-up, repeated assessments of diet and depression, and the use of time-varying, spline, subgroup, and multiple sensitivity analyses to test robustness. To our knowledge, this is the first prospective cohort of older adults to comprehensively evaluate 15 GBD-aligned dietary factors in relation to incident depression, distinguishing major, minor, and mild-to-moderate symptoms. This approach provides a nuanced characterization of diet\u0026ndash;depression associations in late life and reinforces the role of dietary patterns as a modifiable determinant of depression in aging populations. Some limitations of the study should also be noted. First, the findings may have limited generalizability beyond Swedish older adults. SNAC-K comprises an urban, community-dwelling population with relatively high educational and socioeconomic position, affluence, and limited ethnic diversity. Second, dietary exposures were self-reported, introducing potential measurement error and recall bias. Third, while cumulative average dietary exposures were used to reduce within-person variability and better reflect habitual intake, this approach may obscure time-varying associations, and regression to the mean could attenuate observed associations, potentially underestimating true effects. Fourth, the relatively small number of incident cases of major depression constrained statistical power. Moreover, associations identified in subgroup and sensitivity analyses warrant cautious interpretation given their exploratory nature. Fifth, although landmark analyses excluding early incident cases reduced concerns about reverse causation, it cannot be ruled out. Finally, despite adjustment for multiple covariates, residual confounding cannot be excluded.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this population-based study, higher consumption of fruit, vegetables, whole grains, and dietary fiber were associated with a lower risk of distinct forms of late-life depression. Associations for whole grains and fiber tended to be stronger in males, whereas those for fruit and vegetables appeared more pronounced in females and in adults over 70 years. Overall, these healthier dietary factors\u0026mdash;as well as nuts and seeds, milk, calcium, and omega-3 and omega-6 fatty acids in specific subpopulations\u0026mdash;tended to show inverse associations with depression risk, whereas limited evidence suggested that potentially unhealthy components (i.e., red and processed meat, sugar-sweetened beverages, trans fatty acids, and sodium) were related to higher risk. Cumulative dietary risk was associated with higher incidence of major depression and mild-to-moderate depressive symptoms in the overall sample, and with minor depression and mild-to-moderate symptoms among participants with overweight or obesity. These findings highlight key dietary factors as potential targets for population-level prevention strategies. Despite nonsignificant interactions, differences in risk estimates across sex and age suggest patterns that may inform future precision nutrition approaches. Replication in diverse populations is essential to confirm these associations and to strengthen the evidence base for dietary guidelines aimed at preventing late-life depression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all SNAC-K participants and the SNAC-K organization for their collaboration in data collection and management.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBBP co-conceived the original idea, prepared the dataset, conducted data analyses, interpreted the results, and drafted the manuscript. ACC and FT assisted in preparation of the dataset and data analyses, interpreted the results, and gave critical input on revisions of the paper. DNA, RO, MML, FNJ, and AO co-conceived the original idea and gave critical input on revisions of the paper. CQ provided academic supervision to BBP, assisted in data analyses, interpreted the results, and gave critical input on revisions of the paper. ACL co-conceived the original idea, granted access to SNAC-K data, assisted in data analyses, provided academic supervision to BBP, interpreted the results, and gave critical input on revisions of the paper.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAll authors approved the final version of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection of the SNAC-K was supported by the Swedish Research Council (ongoing/current grant: 2021-00178); the Swedish Ministry of Health and Social Affairs; and the participating County Councils and Municipalities. BBP received funding from the Swedish Research Council for Health, Working Life and Welfare (project number 2023-01125, program PI, Mia Kivipelto) and the\u0026nbsp;Loo and Hans Osterman Foundation for Medical Research\u0026nbsp;(project number 2025-01945). ACC received funding from the Foundation for Geriatric Diseases at Karolinska Institutet (project number 2024:0011), the Karolinska Institutet Research Foundation Grants (project number 2024:0017), the David and Astrid Hagel\u0026eacute;n foundation (project number 2024:0005), and the Swedish Research Council for Health, Working Life and Welfare (project number STY-2024/0005). FT received funding from the from the Svenska S\u0026auml;llskapet f\u0026ouml;r Medicinsk Forskning (SSMF; PG-24-0326-H-01). CQ received grants from the Swedish Research Council (grant# 2017-05819 and 2020-01574) and the Swedish Foundation for International Cooperation in Research and Higher Education (CH2019-8320), Stockholm, Sweden. ACL receives funding from the Swedish Research Council (project number 2021-06398), the Swedish Research Council for Health, Working Life and Welfare (project numbers 2024-01830 and 2021-00256), Karolinska Institutet\u0026rsquo;s Strategic Research Area in Epidemiology and Biostatistics SFOepi (consolidator bridging grant, 2023), and Alzheimerfonden (AF-1010573, 2024). CQ and\u0026nbsp;ACL are work-package leaders of a Program Grant from the Swedish Research Council for Health, Working Life and Welfare (project number 2023-01125, program PI, Mia Kivipelto). DNA and AON are supported by a National Health and Medical Research Council Fellowship (Leader 2 Fellowship #2009295 to AON). RO is supported by a Deakin University Postgraduate Research Scholarship. MML is supported by an Alfred Deakin Postgraduate Fellowship. FNJ is supported by a National Health and Medical Research Council Leader 1 Fellowship (grant #1194982). The opinions, methods, and conclusions reported in this paper are those of the authors and are independent from the funding sources. This manuscript was conducted within the GLAD Taskforce framework, as part of a global collaborative project to inform the Global Burden of Diseases, Injuries, and Risk Factors Study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA, RO, MML, FNJ, and AO are affiliated with the Food \u0026amp; Mood Centre, Deakin University, which has received research funding support from Be Fit Food, Bega Dairy and Drinks, and the a2 Milk Company and philanthropic research funding support from the Waterloo Foundation, Wilson Foundation, the JTM Foundation,\u0026nbsp;the Serp Hills Foundation, the Roberts Family Foundation, and the Fernwood Foundation. MML is a member (and former Secretary) of the Melbourne Branch Committee of the Nutrition Society of Australia (unpaid). She has received travel funding support from the International Society for Nutritional Psychiatry Research, the Nutrition Society of Australia, the Australasian Society of Lifestyle Medicine, and the Gut Brain Congress. MML is also an Associate Investigator for the MicroFit Study, an investigator-led randomized controlled trial examining the effects of diets with varying levels of industrial processing on gut microbiome composition, partially funded by Be Fit Food (payment received by the Food \u0026amp; Mood Centre, Deakin University).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSNAC-K was approved by the Regional Ethical Review Board in Stockholm (Dnrs: 2001\u0026ndash;114, 2004\u0026ndash;929/3, 2007/279\u0026ndash;31, 2009/595\u0026ndash;32, 2010/447\u0026ndash;31/2, 2013/828\u0026ndash;31/3, and 2016/730\u0026ndash;31/1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are derived from the SNAC-K project, a population-based study design to investigate the aging process, improve health, and identify potential preventive strategies. Details of the study design, method, variables, and ethics framework are available on the SNAC-K website (https://www.snac-k.se/). Access to the original data is available to the research community upon approval by the SNAC-K organization. Applications to access the data can be submitted through the SNAC-K website.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2023 Disease and Injury and Risk Factor Collaborators. Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023. \u003cem\u003eLancet\u003c/em\u003e. 2025;406(10513):1873\u0026ndash;922. \u003c/li\u003e\n\u003cli\u003eInstitute of Health Metrics and Evaluation (IHME) - University of Washington. Global Health Data Exchange (GHDx). Global Burden of Disease study 2023. 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Healthy Aging\u0026mdash;Nutrition Matters: Start Early and Screen Often. \u003cem\u003eAdvances in Nutrition\u003c/em\u003e. 2021;12(4):1438\u0026ndash;48. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"b3d93b48-1235-4011-9aa8-00b7c13d63e0","identifier":"10.13039/501100006636","name":"Forskningsrådet om Hälsa, Arbetsliv och Välfärd","awardNumber":"2023-01125","order_by":0},{"identity":"15d46631-f7bc-4cd5-902c-d777a2f977b7","identifier":"10.13039/100010771","name":"Loo och Hans Ostermans Stiftelse för Medicinsk Forskning","awardNumber":"2025-01945","order_by":1},{"identity":"b5043bbc-dede-45a4-a084-29359ae33a46","identifier":"10.13039/501100004047","name":"Karolinska Institutet","awardNumber":"Foundation for Geriatric Diseases (2024:0011)","order_by":2},{"identity":"48a9c81d-08c2-4894-854b-c2435aec0835","identifier":"10.13039/501100004047","name":"Karolinska Institutet","awardNumber":"Research Foundation Grants (2024:0017)","order_by":3},{"identity":"f4149d2c-be0b-43d6-8e6f-8712c3335bd9","identifier":"10.13039/501100009741","name":"David och Astrid Hageléns Stiftelse","awardNumber":"2024:0005","order_by":4},{"identity":"fcbb5b3e-620d-4b52-86f9-d18cb0fa3184","identifier":"10.13039/501100006636","name":"Forskningsrådet om Hälsa, Arbetsliv och Välfärd","awardNumber":"STY-2024/0005","order_by":5},{"identity":"eff03304-eddd-46cc-92c4-f25da097849b","identifier":"10.13039/501100003748","name":"Svenska Sällskapet för Medicinsk Forskning","awardNumber":"PG-24-0326-H-01","order_by":6},{"identity":"04713efe-dabf-41fc-b212-7d7ed1c68a88","identifier":"10.13039/501100004359","name":"Vetenskapsrådet","awardNumber":"2017-05819 and 2020-01574","order_by":7},{"identity":"d5f58435-7f7a-478d-954b-711b91368bbf","identifier":"10.13039/501100001728","name":"Swedish Foundation for International Cooperation in Research and Higher Education","awardNumber":"CH2019-8320","order_by":8},{"identity":"a9999f45-d4af-4d15-8262-44602f68980d","identifier":"10.13039/501100004359","name":"Vetenskapsrådet","awardNumber":"2021-06398","order_by":9},{"identity":"7cef6c95-532c-417f-b27b-ed9648be307b","identifier":"10.13039/501100006636","name":"Forskningsrådet om Hälsa, Arbetsliv och Välfärd","awardNumber":"2024-01830 and 2021-00256","order_by":10},{"identity":"a93d0727-4a71-4b98-a741-be2a9078ae03","identifier":"10.13039/501100004047","name":"Karolinska Institutet","awardNumber":"Strategic Research Area in Epidemiology and Biostatistics SFOepi (consolidator bridging grant, 2023)","order_by":11},{"identity":"78dc4316-1db5-4c18-927b-d119d7e5186a","identifier":"10.13039/501100008599","name":"Alzheimerfonden","awardNumber":"AF-1010573, 2024","order_by":12},{"identity":"865801f5-230e-4e02-9194-f9dafd3e611f","identifier":"10.13039/501100000925","name":"National Health and Medical Research Council","awardNumber":"#2009295","order_by":13},{"identity":"1c7b18e9-5847-42dd-ad1f-d7f2d88a6ca1","identifier":"10.13039/501100000925","name":"National Health and Medical Research Council","awardNumber":"Leader 1 Fellowship (grant #1194982)","order_by":14}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Karolinska Institute","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":"dietary patterns, nutritional psychiatry, healthy aging, lifestyle factors, mental disorder, preventive psychiatry, longitudinal study","lastPublishedDoi":"10.21203/rs.3.rs-8020122/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8020122/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough diet is increasingly implicated in depression prevention, prospective evidence remains scarce with respect to which food groups and nutrients influence depression onset in later life. We sought to examine associations between 15 dietary factors and incident depression among older adults. Data were obtained from the Swedish National study on Aging and Care in Kungsholmen (SNAC-K), with up to 15 years of follow-up among community-dwelling adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years and free of depression at baseline. Dietary exposures (food groups and nutrients), harmonized with Global Burden of Disease (GBD) study definitions, were assessed three times over the first 6 years using a validated 98-item food frequency questionnaire. We also examined an overall dietary risk score reflecting the number of GBD recommendations not met. Onset of major or minor depression were identified through physician-administered interviews based on DSM-IV-TR criteria, and depressive symptom severity was assessed with the Montgomery\u0026ndash;\u0026Aring;sberg Depression Rating Scale. Associations between dietary exposures and incident forms of depression were estimated using Cox proportional hazards models adjusted for sociodemographic, lifestyle, and health-related covariates. Among 2148 participants with available data at follow-up (mean age 71.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3 years, 61.4% females), higher whole-grain consumption (per 30 g/day; HR\u0026thinsp;=\u0026thinsp;0.63, 95% CI: 0.43\u0026ndash;0.91) and total dietary fiber intake (per 5 g/day; HR\u0026thinsp;=\u0026thinsp;0.69, 95% CI: 0.52\u0026ndash;0.91) were associated with lower risk of major depression. Fruit consumption was inversely associated with minor depression (per 100 g/day; HR\u0026thinsp;=\u0026thinsp;0.81, 95% CI: 0.67\u0026ndash;0.97) and mild-to-moderate depressive symptoms (HR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.76\u0026ndash;0.99). Further, higher consumption of vegetables (per 100 g/day; HR\u0026thinsp;=\u0026thinsp;0.86, 95% CI: 0.74\u0026ndash;1.00) was linked with reduced risk of mild-to-moderate depressive symptoms. When considering overall dietary risk, higher scores were associated with increased incidence of major depression and mild-to-moderate symptoms. Some associations differed by sex, age, and body mass index, although most interactions were not statistically significant. Specific dietary components, alongside overall diet quality, may represent preventive targets for late-life depression at the population level and inform more tailored dietary strategies.\u003c/p\u003e","manuscriptTitle":"Dietary Factors and Risk of Late-Life Depression: Findings from a Swedish Population-Based Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 05:43:07","doi":"10.21203/rs.3.rs-8020122/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":"935a322a-9813-4ea3-9e62-c37e142ced19","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57515523,"name":"Epidemiology"},{"id":57515524,"name":"Nutrition \u0026 Dietetics"},{"id":57515525,"name":"Preventive Medicine"}],"tags":[],"updatedAt":"2025-11-10T05:43:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 05:43:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8020122","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8020122","identity":"rs-8020122","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}