Investigating associations between diet and depression at the population level over time: results from the National Health and Nutrition Examination Survey.

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Deborah N Ashtree, Emma Todd, Sarah Gauci, Rebecca Orr, Melissa M Lane, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7588630/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 Depression contributes substantially to disease burden in the United States. Diet is a modifiable risk factor, yet surprisingly the population-level diet-depression association has never been comprehensively evaluated over time. We aimed to assess prevalence of suboptimal dietary intakes and depression, and the association between diet and depression over time using National Health and Nutrition Examination Survey (2005–2018) data. Complete-case logistic regression assessed associations between diet (following Global Burden of Disease definitions, plus ultra-processed food [UPF]; measured using 24-hour recalls) and odds of depression (defined as PHQ-9 ≥ 10). Depression prevalence (7.69%) was consistent from 2007–2018. Prevalence of suboptimal milk intake increased over time (p = 0.003), but suboptimal processed meat (p = 0.038), red meat (p = 0.022), nuts/seeds (p = 0.001), and polyunsaturated fat intake decreased (p < 0.001). Higher fiber intake was consistently associated with lower odds of depression from 2005–2018, while higher UPF intakes were associated with higher odds from 2007–2018. All other dietary risks were associated at various years but not consistently at each wave. Given previously observed causal relationships between diet and depression, improving population-level fiber intake and reducing UPF consumption may plausibly reduce depression burden in the United States. These results support the Global Burden of Disease Lifestyle And mental Disorders project (DERR2- 10.2196/65576 ). Epidemiology Nutrition & Dietetics Psychology Diet nutrition depression ultra processed foods Global Burden of Diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Mental disorders including depression are among the leading contributors to disability adjusted life years (DALYs) globally, affecting millions (IHME, 2019 ). Within the United States of America, the economic burden of depression has substantially increased over time, with a 37.9% increase between 2010 and 2018 (Greenberg et al., 2021 ). In 2019, the cost of major depressive disorder to the US was US $ 333.7 billion, with the primary drivers being healthcare costs, household costs, and workplace presenteeism/absenteeism (Greenberg et al., 2023 ). Depression is a complex disorder, driven by various environmental, genetic, biological, and behavioral risk factors (Marx et al., 2023 ). One behavioral risk factor receiving increased attention is diet (Sarris et al., 2015 ), with multiple diet quality metrics and dietary components found to be cross-sectionally and prospectively associated with depressive symptoms and disorders (Lassale et al., 2019 ; Li et al., 2017 ; Quirk et al., 2013 ; Wang et al., 2019 ). There is also growing evidence that dietary intervention is an effective adjunctive treatment strategy for depression, suggesting that the relationship is causal (Bayes et al., 2022 ; Francis et al., 2019 ; Jacka et al., 2017 ; Parletta et al., 2019 ). Despite its relevance to depression and a host of other health conditions, diet quality and intake in many Western countries remains particularly poor compared to the rest of the world (Imamura et al., 2015 ). There are multiple, complex reasons for this, including the industrialized food system (Nadathur et al., 2017 ), financial barriers (Fanzo et al., 2022 ), and prevalence of ‘food deserts’ (Fanzo et al., 2022 ). Given that as much as 14% of depression cases could be offset by improving lifestyle behaviours including dietary intake (Adjibade et al., 2018 ), improving diet at the population level could be a promising public health target to alleviate depression burden in the US. To do this effectively, we require comprehensive data on population-level diet and depression prevalence, and their associations, over time in the US. The Global Burden of Disease and Risk Factors Study (GBD) provides a useful classification framework by which to do this. The GBD quantifies the burden of diseases attributable to various risk factors (Brauer et al., 2024 ), which is commonly used by governments and policymakers globally to prioritize public health policy decisions. Although the GBD does not currently estimate dietary risks for mental disorders (Ashtree DN, 2025), the GBD definitions provide a useful framework to quantify the contribution of 15 dietary risk factors to depression. This study therefore aims to assess 1) prevalence of not meeting GBD theoretical minimum risk dietary intakes, 2) prevalence of depression, and 3) the association between diet and depression over time in a large, nationally representative, survey from the US (2005–2018). Methods This manuscript has been prepared in accordance with the Global burden of disease Lifestyle And mental Disorders Taskforce (GLAD) (Ashtree DN, 2025), as part of a global collaborative project to inform the GBD. GLAD was designed to generate the required level of evidence to accurately estimate the population attributable fractions of diet to depression burden at regional and global levels, and ultimately embed these estimates in subsequent rounds of GBD data collection (Ashtree DN, 2025). As such, this paper uses the following methods, which were prospectively registered on Open Science Framework (Ashtree et al., 2023 ). Sample This study used data from the National Health and Nutrition Examination Survey (NHANES) (CDC, 1999–2018, 2013, 2018). Full details of the NHANES methods are available on the NHANES website (CDC, 1999–2018, 2013, 2018). Briefly, NHANES is a biennial cross-sectional survey examining a discrete, nationally representative sample of adults and children from across the US generated at each wave; it was established to assess the health and nutritional status of adults and children from 1999-present. NHANES field operations were suspended in March 2020 due to COVID-19, rendering the 2019–2020 data non-representative. Subsequent years have not yet been added to the food patterns database, making them unavailable for our analyses. Due to differences in methods (the number of dietary recalls and outcome ascertainment), we also excluded data from 1999–2004. Thus our analyses used data from 2005–2018 and were further restricted to individuals ≥ 18 years old with available day 1 and day 2 energy intake data (Fig. 1 ). Outcome Depression was measured via a nine-item depression self-report instrument derived from the Diagnostic Statistical Manual IV (the Patient Health Questionnaire; PHQ-9). A cut-off score of ≥ 10 was used to indicate ‘likely depression diagnosis’ (henceforth, ‘depression’) on the PHQ-9, due to its high concordance with structured mental health professional interview (sensitivity and specificity 88%) (Kroenke et al., 2001 ). Exposure Dietary intake was measured at each wave using two 24-hour dietary recalls, and the average intake of these was used in this analysis. We calculated the intake of each GBD-defined dietary risk based on previously published methods (Fulgoni et al., 2018 ). Briefly, we used data from the 2005–2018 Food Pattern Equivalents Database (Bowman et al., 2020 ) to convert ‘cup equivalents’ to ‘grams’ of of each food item. Food items were then grouped to align with GBD definitions based on the formulas in Table 2 of Fulgoni et al. ( 2018 ). These gram intakes were treated in two ways to answer our research questions, to: 1) determine the prevalence of suboptimal dietary intake, based on GBD definitions; and 2) assess the association between dietary intake (continuous) and odds of depression at each wave. In addition to the GBD dietary variables, ultra-processed food (UPF) exposure was examined due to its relevance to non-communicable diseases, including depression (Lane, Gamage, et al., 2024). Classification of food items as UPF or non-UPF in the NHANES dietary data has been completed previously (Martínez Steele et al., 2023 ), and the classifications and data generated during this process were used in these current analyses. Briefly, each food code was classified according to the Nova food classification system (Monteiro et al., 2019 ), where food items are categorized as “unprocessed/minimally processed”, “processed culinary ingredients”, “processed foods” or “ultra-processed”. For potential homemade recipes, the Nova classification was applied to underlying constituent ingredients (Martínez Steele et al., 2023 ). Grams per day of Nova category four (UPFs) were obtained for each participant at each wave and used in these analyses. Prevalence of Suboptimal Dietary Intake To determine the prevalence of suboptimal dietary intake, daily intakes of each dietary variable were dichotomized based on the GBD’s theoretical minimum risk exposure level (TMREL) – the hypothetical level of exposure associated with the lowest health risk (Brauer et al., 2024 ). Suboptimal intake was defined as intake of fruit < 345g; vegetables < 339g; legumes < 105g; wholegrains < 185g; nuts and seeds < 21.5g; milk < 310g for males, < 555g for females; fiber < 23.5g; calcium < 0.79g for males, < 1.15g for females; omega-3 < 565mg; polyunsaturated fat 100g; processed meat > 0g; SSB > 0g; and sodium > 3g. UPF was excluded from this analysis, as the GBD do not currently estimate TMRELs for UPF. Using these thresholds, we calculated the number and percent of participants with suboptimal dietary intake. Dietary Exposures for Regression Models Although the GBD framework defines dietary exposures in grams per day, we opted to scale dietary intake for each food group to recommended serving sizes, where available, to improve interpretability. Where possible, we based recommended serving sizes on data from the US (Agriculture, 2020 ; Clapp et al., 2018 ), and where serving sizes in grams were unavailable we used recommendations from the UK or Australia or as presented in peer-reviewed literature (Micha et al., 2012 ; MR, 2021) (Supplementary Information [SI] Table 1 ). The serving sizes were: fruit − 150g, vegetables − 75g, legumes − 150g, wholegrains − 28g, nuts and seeds − 30g, milk − 250g, red meat − 65g, processed meat − 50g, SSB − 355g, ultra-processed food − 90g. Nutrient-based exposures were included as per-gram only, due to lack of information regrading what constitutes a serving size. Per gram models were also generated for all dietary exposures (SI). Covariates At each wave, participants self-reported demographic and general health information, including sex, age, BMI, and highest educational qualification. Age, sex, education, and total energy intake were used as covariates, with education acting as a proxy for socio-economic status, in accordance with the GLAD protocol (Ashtree DN, 2025). We adjusted for energy intake (as measured in the nutrient intake dataset) using Willett’s residual method (Willett et al., 1997 ). We included BMI only as a subgroup model, as we consider BMI as a mediator rather than a confounder in the diet-depression relationship (Ma et al., 2021 ). BMI was categorized into < 18.5kg/m 2 , 18.5 to < 25 kg/m 2 , 25 to 30 kg/m 2 , and ≥ 30 kg/m 2 . Statistical Analyses We conducted descriptive analyses to examine the prevalence of both suboptimal dietary intake and depression over time (from 2005–2018). These estimates were calculated both overall and stratified by sex. To assess trends in prevalence over time, we used linear regression to estimate P-values for trend across survey waves. We used a complete-case logistic regression to assess the association between each dietary exposure and the likelihood of depression at each time point. Models were fitted using day two dietary survey sample weights (CDC) using the svy commands in Stata 18.0 (StataCorp, 2025 ). We fitted two models: 1) unadjusted model; 2) adjusted for age, sex, education, and total energy intake using Willett’s residual method (Willett et al., 1997 ). We conducted additional subgroup analyses based on sex and BMI. All model assumptions were assessed prior to fitting the final models. We conducted a sensitivity analysis excluding participants with extreme energy intakes (energy intake 99th percentile). We used the Simes method of p-value adjustment, termed q-values, to account for multiple testing in the logistic regressions (Simes, 1986 ). Q-values adjust the p-value and not the inference level and so are interpreted in the same way as p-values. All p- and q-values were two tailed at the 5% significance level. Ethics NHANES was conducted with approval from the NHCS ethics review board (full details available from the CDC website (CDC, 2024 )). The current project was approved in March 2024 by the Deakin University Human Research Ethics Committee for exemption from ethical review in accordance with the National Statement on Ethical Conduct in Human Research (2007, updated 2018) Section 5.1.22 (project number: 2024-085). Results Demographics over Time Each wave of the study surveyed distinct samples, ranging from n = 8,704 (2017–2018) to n = 10,253 (2009–2010), resulting in sample sizes ranging between 4,983 (2017–2018) and 6,052 (2009–2010) included in our analyses (Fig. 1 ). After applying survey weights, sample sizes ranged from 218,065,271 to 247,079,471 equivalent participants (Table 1 ). Based on the weighted sample, slightly more females than males completed the NHANES survey at each wave (ranging from 51.4% in 2011–2012 and 2015–2016 to 53.9% in 2007–2008). Across all years, most participants were at least high-school or equivalent educated, with an average age between 45.7 in 2007–2008 to 47.4 in 2015–2016 and 2017–2018. Dietary intake over time Most participants had suboptimal dietary intake for each food and nutrient group, with intakes lower than the GBD TMREL for calcium, fiber, fruits, legumes, milk, nuts and seeds, polyunsaturated fat, vegetables, and wholegrains, and intakes higher than the TMREL for processed meat and sodium. The prevalence of suboptimal processed meat and red meat intake decreased overall and for male respondents between 2005–2006 to 2017–2018 (processed meat: 68.2% (74.3% male) in 2005–2006 to 65.3% (69.2% male) in 2017–2018; red meat: 17.0% (25.6% male) in 2005–2006 to 13.6% (20.7% male) in 2017–2018). Dietary intake of polyunsaturated fat and nuts and seeds increased across all sexes, corresponding to a decrease in the prevalence of suboptimal intake for these dietary exposures (Fig. 2 ). Conversely, the prevalence of suboptimal milk intake increased from 2005–2018, from 84.7% (74.5% male, 93.9% female) in 2005–2006 to 90.8% (83.9% male, 97.1% female) in 2017–2018. Prevalence of suboptimal calcium, fiber, legume, milk, nuts and seeds, omega-3 and wholegrain appeared to be higher among female respondents compared to male. Conversely, male respondents tended to have a higher prevalence of suboptimal processed meat, red meat, sodium, and sugar-sweetened beverage and polyunsaturated fat intake. Depression over time More females than males had self-reported depression at each time point (Fig. 3 ). Whilst no overall trend over time was observed, changes in prevalence were observed between all individual timepoints, except for females between 2009/10 and 2011/12. Association between dietary risk factors and depression After adjusting for age, sex, education, and energy intake, higher intake of fiber was consistently associated with lower risk of depression at each wave between 2005–2006 and 2017–2018 (Fig. 4 ; all years combined OR = 0.96 per gram, 95%CI = 0.95–0.97). Moreover, there was a consistent association between higher intakes of UPF and higher odds of depression at all waves between 2007–2008 and 2017–2018 (Fig. 4 ; all years combined OR = 1.04 per 90 gram serve, 95%CI = 1.03–1.04; SI Tables 2–4 show the per gram results and conversion to per serve). Whilst we found evidence for an association for all other dietary exposures at various time points, these were not consistently observed at each wave. Despite this, all years with an observed association for a particular dietary risk were consistent in magnitude and direction, except for processed meat, which was associated with a higher odds of depression in 2009–2010 and 2015–2016 but a lower odds of depression in 2013–2014. When combining data across all years and participants, higher intakes of several dietary factors were associated with lower odds of depression (Fig. 4 a, SI Table 2). These included calcium (OR = 0.74 per gram, 95% CI: 0.58–0.95), fruit (OR = 0.72 per 150 g serve, 95% CI: 0.65–0.80), legumes (OR = 0.78 per 150 g serve, 95% CI: 0.67–0.91), nuts and seeds (OR = 0.93 per 30 g serve, 95% CI: 0.87–0.98), omega-3 (OR = 0.88 per gram, 95% CI: 0.80–0.97), polyunsaturated fats (OR = 0.97 per 1% energy, 95% CI: 0.95–0.9997), vegetables (OR = 0.88 per 75 g serve, 95% CI: 0.83–0.92), wholegrains (OR = 0.86 per 28 g serve, 95% CI: 0.81–0.91), red meat (OR = 0.92 per 65 g serve, 95% CI: 0.86–0.99), and sodium (OR = 0.82 per gram, 95% CI: 0.76–0.88). In contrast, higher intake of sugar-sweetened beverages (SSBs) was associated with increased odds of depression (OR = 1.14 per 355 g serve, 95% CI: 1.08–1.20). However, not all associations observed in the full sample were consistent across individual waves (Fig. 4 a, SI Table 2). When analyses were stratified by sex, associations between fibre, fruit, sodium, SSBs, UPFs, vegetables, and wholegrains with depression were observed in both males and females (Fig. 4 b/c, SI Table 3). Additionally in females, higher intakes of nuts and seeds (OR = 0.88, 95% CI: 0.79–0.98), omega-3 (OR = 0.80, 95% CI: 0.70–0.92), and polyunsaturated fats (OR = 0.96, 95% CI: 0.93–0.993) were also linked to lower odds of depression (Fig. 4 b, SI Table 3). Results were consistent in magnitude and direction between main and sensitivity models, after excluding participants with extreme energy intake (SI Table 5) and BMI subgroups (SI Table 6). Discussion This study sought to understand whether population-level dietary intake, depression prevalence, and their association changed over time in the US, using data from the nationally-representative NHANES. We focused on dietary exposures defined by the GBD to assess population-level patterns of suboptimal intake. Most respondents had suboptimal dietary intakes at each wave, with prevalence of suboptimal intake reducing for processed and red meat, nuts and seeds, and polyunsaturated fat between 2005 and 2018. There was no overall change in self-reported (PHQ-9) depression prevalence from 2007 to 2018, despite fluctuations in individual years. Depression prevalence was higher in females compared to males. Higher fiber intake was consistently associated with lower odds of depression at each wave, while higher UPF intake was consistently associated with higher odds. When combining data from all years, several dietary exposures were associated with odds of depression: fruit, legumes, vegetables, wholegrains, fiber, polyunsaturated fat, omega-3, and nuts and seeds were associated with lower odds; and SSBs and UPFs were associated with higher odds. Contrary to expectations, sodium and red meat intake were associated with lower odds of depression. Together, our findings propose several key dietary exposures associated with depression that warrant further causal investigation in large prospective cohorts to determine whether they could be effective preventative targets. The high prevalence of suboptimal dietary intakes observed in this study is consistent with the broader literature (Imamura et al., 2015 ). A greater proportion of female respondents had suboptimal intake of calcium and fiber, which is concordant with existing literature showing women tend to have lower intakes (Balk et al., 2017 ; Fayet-Moore et al., 2018 ) compared to men. Similarly, higher meat (Hopwood et al., 2024 ), SSB (Lara-Castor et al., 2023 ), and sodium (Chen et al., 2021 ) intake in males compared to females has been previously observed. For dietary trends, the prevalence of suboptimal processed meat, red meat, nuts and seeds, and polyunsaturated fat intake decreased. However, the prevalence of suboptiomal intakes for the remaining food groups remained stable or increased – such as suboptimal intake of milk increasing over time. Some of these changes could be attributed to changes in dietary guidelines and patterns at the population-level. For example, dietary guidelines surrounding nuts and seeds have changed from 1) being included in a ‘meat and beans’ group for a total intake of up to 5 ounces per week in 2000, 2) being separated from meat but amalgamated with legumes for a total recommendation of 4–5 servings (approx. 2.5oz) in 2005, 3) separated from all legumes except soy for a recommended 4 ounces per week in 2010, and 4) increased to 5 ounces per week in 2015 (Agriculture, 2000 , 2005 , 2010 , 2015 ). For polyunsaturated fats, there was no specific recommendation on their consumption in the 2005 USDA guidelines, other than to use ‘moderate’ amounts whilst ‘taking care to avoid excess calories’. Between 2005 and 2010, oils were given specific gram recommendations because of their high polyunsaturated fat content, and in 2015 two specific polyunsaturated fats – linoleic and linolenic acid – were given specific gram recommendations (Agriculture, 2000 , 2005 , 2010 , 2015 ). Additionally, some changes may be driven at the consumer level rather than through government intervention. For example, in recent years there has been a rise in the popularity of alternative milks in the US, and a corresponding decrease in dairy-based milk consumption (Emmerich et al., 2024 ). Similarly, there is increased interest in plant-based proteins and a rise in casual vegetarianism among consumers (Niva et al., 2017 ). With this reduction in animal protein consumption, individuals are becoming more familiar and comfortable with using foods like pulses, nuts, and seeds in their cooking – potentially contributing to the increased consumption of nuts in our data as well as the decreased consumption of dairy-based milks (Niva et al., 2017 ). These all indicate the potential value of both government and community-based drives to shift dietary habits within the community, and may explain, at least in part, the improving patterns of intake we see in the NHANES study. With targeted changes to government guidance or popularity of certain foods, the prevalence of suboptimal intake could be reduced, potentially mitigating depression burden at the same time. However, future research should seek to evaluate whether changes in guidelines lead to changes in dietary intake across the population and within subgroups. The potential is also there for natural experiments evaluating whether dietary changes at the population or subpopulation level causally associate with changes in health outcomes, including mental health. Whilst there was an increase in the economic burden of depression between 2010 and 2018 in the US (Greenberg et al., 2021 ), our study found no overall trend in depression prevalence over time in this cohort. Globally, other literature supports stable depression burden, with the GBD study finding age-standardized DALYs from mental disorders remaining constant from 1990 to 2019 (Ferrari et al., 2022 ), and a review of evidence from the United Kingdom, Australia, Canada, and the US reporting stagnant depression prevalence from 1990 to 2015 (Jorm et al., 2017 ). A lack of change in depression prevalence indicates that current treatment and prevention strategies are not reducing depression burden, pointing to the need for different approaches, particularly preventative (Jorm et al., 2017 ). With that in mind, these cross-sectional data identified several potentially protective dietary factors in the form of higher intakes of fruit, legumes, vegetables, wholegrains, fiber, polyunsaturated fat, omega-3, and nuts and seeds. These results are concordant with the many longitudinal studies linking dietary patterns and quality with depression risk (Lassale et al., 2019 ; Lee et al., 2025 ). The potential protective value of these foods is further supported by these foods being included in diet quality scores (Oliván-Blázquez et al., 2021 ; Sanchez-Villegas et al., 2015 ), as well as in key components of the highly-studied Mediterranean diet, which has been shown to be efficacious in treating depression in randomized controlled trials (Bayes et al., 2022 ; Bizzozero-Peroni et al., 2024 ; Francis et al., 2019 ; Jacka et al., 2017 ; Parletta et al., 2019 ). Contrary to previous research (Teasdale et al., 2019 ; Yun et al., 2021 ), higher sodium intake was associated with lower odds of probable depression. Whilst unexpected, there are animal models demonstrating that low sodium intake can result in depression-like symptoms, which can be reversed with increased sodium intake (Grippo et al., 2006 ; Leshem, 2011 ; Morris et al., 2006 ; Morris et al., 2010 ). It is also possible that our results are an artefact of the unreliability of sodium intake from dietary questionnaires, due to underreporting from social desirability bias (de Mestral et al., 2017 ; McLean et al., 2017 ) – it would be valuable to confirm these associations in a cohort that has urinary sodium measured. Although NHANES collected urinary data from 2014 onwards (Jackson et al., 2018 ), due to limited overlap with our included sample we were unable to validate dietary sodium against urinary sodium. We also identified that both SSBs and UPFs were associated with higher odds of depression. This is consistent with the prevailing literature, as SSBs and UPFs have been associated with higher odds of depression in multiple studies and a very large umbrella review (Lane, Gamage, et al., 2024; Lane et al., 2023 ; Lane, Travica, et al., 2024), and observed to have a dose-response relationship with higher depression risk in a meta-analysis (Wang et al., 2022 ). UPFs may influence depression risk through specific non-nutritive components such as artificial sweeteners, emulsifiers, or colourants impacting the hypothalamic-pituitary-adrenal axis, gut microbiota, and inflammatory systems (Lane et al., 2022 ). These foods may also act as indicators of overall diet quality, as high UPF intake has been associated with poorer results on various dietary indices (Liu et al., 2022 ; Moubarac et al., 2017 ), as well as with reduced consumption of healthful foods such as fruits, vegetables, legumes, and wholegrains (Marchese et al., 2022 ). Similarly, SSB consumption correlates with poorer diet quality and markers of food insecurity (Fontes et al., 2019 ; Leung et al., 2014 ). Interestingly, the two peak timepoints of depression prevalence were in 2007/08 and 2013/14, during which times the global financial crisis and major cuts to the Supplemental Nutrition Assistance Program occurred, respectively, reflected in increased rates of financial stress and food insecurity (Habibov et al., 2019 ; Katare & Kim, 2017 ). Hence, whilst these foods could be targets for interventions and may act independently on depression risk (Dicken & Batterham, 2021 ), they may also be markers of other environmental drivers. Given that dietary exposures are also associated with physical health outcomes (Afshin et al., 2019 ), such as cardiovascular diseases (Dong et al., 2022 ), monitoring population dietary risks can inform cost-effective prevention and/or prevalence reduction strategies for depression and other conditions of public health importance. Our GLAD project (Ashtree DN, 2025), which aims to calculate the population burden of depression attributable to these dietary risks, will play a critical role in informing policy and public health interventions. Strengths and Limitations Our study is the first to comprehensively assess the relationship between diet and odds of depression in a large, representative survey of adults in the US at multiple time-points. However, some aspects of this study that could be improved. Firstly, the associations presented here are based on cross-sectional data, and are vulnerable to limitations such as potentially missing the etiologically relevant window of the exposure, prevalence instead of incidence as the outcome potentially diluting exposure-outcome associations, and a limited ability to address reverse causality (Savitz & Wellenius, 2022 ). As such, it is important that these analyses are repeated in longitudinal cohorts and continue to be causally tested via randomized controlled trials. For the UPF results, whilst NHANES collects some information indicative of food processing, these data are not consistently determined for all food items, which could lead to imprecise estimation of the consumption of UPF. Furthermore, social desirability bias may lead to underreporting (e.g. for UPF) or overreporting (e.g. for fruit and vegetables) of certain food items. Additionally, data from the 2019–2020 wave of NHANES is incomplete and non-representative due to the COVID-19 pandemic and could not be included in this study and the most recent 2021–2023 wave has yet to be incorporated in the food patterns database, precluding use of these waves in our analyses. Once newer waves are complete and uploaded, future research should revisit these analyses to see whether prevalence of suboptimal dietary intake and associations between GBD-defined dietary exposures and depression remain. As the importance of disaggregating by both sex and gender in health-studies is increasingly emphasized (Tannenbaum et al., 2019 ), a further limitation of this study is that only sex data are available. Examining the intersection between sex, gender, and diet-depression dynamics would be an interesting avenue for future studies. Conclusion We have shown that dietary habits in the US between 2005 and 2018 were generally suboptimal. Whilst dietary intake improved over time for some food groups, for most it remained stagnant. Encouragingly, higher intakes of fruit, legumes, vegetables, wholegrains, fiber, polyunsaturated fat, omega-3, and nuts and seeds corresponded to lower odds of depression, whereas higher SSBs and UPF intakes were associated with higher odds, concordant with the wider epidemiological literature and clinical trials. Our results suggest that a greater focus on improving population dietary inake may represent a potent policy target for reducing depression prevalence and burden in the US. Declarations Funding This work, AON and DNA are supported by a National Health and Medical Research Council (NHMRC) Emerging Leader 2 Fellowship (grant #2009295 to AON). FNJ is supported by a NHMRC Leader 1 Fellowship (grant #1194982). MML is supported by a Deakin University Postdoctoral Fellowship. RO and ET are supported by a Deakin University Postgraduate Research Scholarship. SG is supported by a postdoctoral fellowship from the SOLVE-CHD NHMRC Synergy Grant (APP1182301). SLD is supported by a Medical Research Future Fund Grant (#2025947). EMS is supported by a Fundação de Amparo à Pesquisa do Estado de São Paulo grant (2023/16144-3). Disclosure of potential conflicts of interest Financial interests: DNA, ET, SG, RO, MML, WM, FNJ, and AON 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 has received travel funding support from the International Society for Nutritional Psychiatry Research (ISNPR), the Nutrition Society of Australia (NSA), the Australasian Society of Lifestyle Medicine, and the Gut Brain Congress. WM has received fellowship funding support from the NHMRC (#2008971) and Multiple Sclerosis Research Australia, consultation and remuneration funding support from Nutrition Research Australia and ParachuteBH, and travel funding support from the NSA, Mind Body Interface Symposium, and VitaFoods. FNJ has received fellowship payment or honorariums for lectures, presentations, speakers bureaus, manuscript writing, or educational events from the Malaysian Society of Gastroenterology and Hepatology, JNPN Congress, American Nutrition Association, Personalised Nutrition Summit, and American Academy of Craniofacial Pain, and has written two books for commercial publication on the topic of nutritional psychiatry and gut health. AON has received grant funding support from the Medical Research Future Fund, Dasman Diabetes Institute, MTP Connect—Targeted Translation Research Accelerator Program, the NHMRC, Barwon Health, and the Waterloo Foundation, and has received funding support for academic editing and as a grant reviewer from SLACK Incorporated (Psychiatric Annals) and the NHMRC and travel funding support from the ISNPR. Non-financial interests: MML is a committee member for the Melbourne Branch Committee of the NSA(unpaid); WM is president of the ISNPR (unpaid); FNJ is a Scientific Advisory Board member of Dauten Family Centre for Bipolar Treatment Innovation and Zoe Nutrition (unpaid) Ethical approval NHANES survey data are available for public use via the National Center for Health Statistics and Centers for Disease Control and Prevention, under the authority granted by the Public Health Service Act (42 U.S.C. § 242k). Data are protected by US Federal confidentiality laws (Section 308(d) Public Health Service Act 42 U.S.C. 242m(d)) and the Confidential Information Protection and Statistical Efficiency Act (Pub. L. No. 115-435, 132 Stat. 5529 § 302). An ethics exemption was granted by Deakin University’s Human Research Ethics committee in March 2024 (2024-085). Informed Consent Participants provided written informed consent prior to the first interview. Clinical Trial Registration Not applicable. Author Contributions Conceptualization: AON, FNJ, DNA, MML, RO; Methodology: DNA, MML, RO; Validation: DNA, ET; Formal Analysis: DNA, ET; Resources: EMS; Data Curation: DNA, EMS; Visualization: DNA, ET, SG; Supervision: DNA, FNJ, AON; Project Administration: DNA; Funding Acquisition: AON; Writing – Original Draft: DNA, ET, SG; Writing – Reviewing & Editing: All Authors Acknowledgements The opinions, methods, and conclusions reported in this paper are those of the authors and are independent from the funding sources. This manuscript has been prepared in accordance with the requirements of the GLAD Taskforce, as part of a global collaborative project to inform the Global Burden of Diseases, Injuries, and Risk Factors Study. References Adjibade, M., Lemogne, C., Julia, C., Hercberg, S., Galan, P., Assmann, K. E., & Kesse-Guyot, E. (2018). Prospective association between combined healthy lifestyles and risk of depressive symptoms in the French NutriNet-Santé cohort. 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MML has received travel funding support from the International Society for Nutritional Psychiatry Research (ISNPR), the Nutrition Society of Australia (NSA), the Australasian Society of Lifestyle Medicine, and the Gut Brain Congress. WM has received fellowship funding support from the NHMRC (#2008971) and Multiple Sclerosis Research Australia, consultation and remuneration funding support from Nutrition Research Australia and ParachuteBH, and travel funding support from the NSA, Mind Body Interface Symposium, and VitaFoods. FNJ has received fellowship payment or honorariums for lectures, presentations, speakers bureaus, manuscript writing, or educational events from the Malaysian Society of Gastroenterology and Hepatology, JNPN Congress, American Nutrition Association, Personalised Nutrition Summit, and American Academy of Craniofacial Pain, and has written two books for commercial publication on the topic of nutritional psychiatry and gut health. AON has received grant funding support from the Medical Research Future Fund, Dasman Diabetes Institute, MTP Connect—Targeted Translation Research Accelerator Program, the NHMRC, Barwon Health, and the Waterloo Foundation, and has received funding support for academic editing and as a grant reviewer from SLACK Incorporated (Psychiatric Annals) and the NHMRC and travel funding support from the ISNPR. Non-financial interests: MML is a committee member for the Melbourne Branch Committee of the NSA(unpaid); WM is president of the ISNPR (unpaid); FNJ is a Scientific Advisory Board member of Dauten Family Centre for Bipolar Treatment Innovation and Zoe Nutrition (unpaid) Supplementary Files NHANESGLADSupplementaryMaterialTitlePage.docx NHANESGLADSupplementaryTablesforsubmission.xlsx SupplementaryFigures.zip Table1.docx 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. We do this by developing innovative software and high quality services for the global research community. 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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-7588630","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513477596,"identity":"0bb769be-e118-4338-affe-5aeb40d747f3","order_by":0,"name":"Deborah N Ashtree","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-3680-3860","institution":"Food \u0026 Mood Centre, Deakin University, Institute for Mental and Physical Health and Clinical Translation (IMPACT), Geelong, Australia","correspondingAuthor":true,"prefix":"","firstName":"Deborah","middleName":"N","lastName":"Ashtree","suffix":""},{"id":513477597,"identity":"74dc535e-562c-4ee2-a0c5-1c74b746380d","order_by":1,"name":"Emma Todd","email":"","orcid":"https://orcid.org/0000-0003-1752-9069","institution":"Food \u0026 Mood Centre, Deakin University, Institute for Mental and Physical Health and Clinical Translation (IMPACT), Geelong, Australia","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Todd","suffix":""},{"id":513477598,"identity":"e2774e37-a90c-457e-acd6-ec3489d0fe00","order_by":2,"name":"Sarah Gauci","email":"","orcid":"https://orcid.org/0000-0002-1907-1066","institution":"Food \u0026 Mood Centre, Deakin University, Institute for Mental and Physical Health and Clinical Translation (IMPACT), Geelong, Australia","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Gauci","suffix":""},{"id":513477599,"identity":"0d98bbd3-f360-4091-b303-31531c623ec1","order_by":3,"name":"Rebecca Orr","email":"","orcid":"https://orcid.org/0000-0002-2422-5533","institution":"Food \u0026 Mood Centre, Deakin University, Institute for Mental and Physical Health and Clinical Translation (IMPACT), Geelong, Australia","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"","lastName":"Orr","suffix":""},{"id":513477600,"identity":"8d7e178b-e677-4948-a05b-ae7fa27fe610","order_by":4,"name":"Melissa M Lane","email":"","orcid":"https://orcid.org/0000-0002-5739-4560","institution":"Food \u0026 Mood Centre, Deakin University, Institute for Mental and Physical Health and Clinical Translation (IMPACT), Geelong, Australia","correspondingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"M","lastName":"Lane","suffix":""},{"id":513477601,"identity":"89ba2f49-e148-4591-99c9-14042f807e94","order_by":5,"name":"Euridice Martinez Steele","email":"","orcid":"https://orcid.org/0000-0002-2907-3153","institution":"Department of Nutrition, University of São Paulo, São Paulo, Brazil","correspondingAuthor":false,"prefix":"","firstName":"Euridice","middleName":"Martinez","lastName":"Steele","suffix":""},{"id":513477602,"identity":"ebba1029-ea11-481a-aa34-77937b7faa07","order_by":6,"name":"Samantha L Dawson","email":"","orcid":"https://orcid.org/0000-0002-4701-1220","institution":"Food \u0026 Mood Centre, Deakin University, Institute for Mental and Physical Health and Clinical Translation (IMPACT), Geelong, Australia","correspondingAuthor":false,"prefix":"","firstName":"Samantha","middleName":"L","lastName":"Dawson","suffix":""},{"id":513477603,"identity":"82cf1004-2591-4075-872c-f3770466dd6c","order_by":7,"name":"Wolfgang Marx","email":"","orcid":"https://orcid.org/0000-0002-8556-8230","institution":"Food \u0026 Mood Centre, Deakin University, Institute for Mental and Physical Health and Clinical Translation (IMPACT), Geelong, Australia","correspondingAuthor":false,"prefix":"","firstName":"Wolfgang","middleName":"","lastName":"Marx","suffix":""},{"id":513477604,"identity":"6e410b4e-257d-4774-aefe-0e9397be1510","order_by":8,"name":"Felice N Jacka","email":"","orcid":"https://orcid.org/0000-0002-9825-0328","institution":"Food \u0026 Mood Centre, Deakin University, Institute for Mental and Physical Health and Clinical Translation (IMPACT), Geelong, Australia","correspondingAuthor":false,"prefix":"","firstName":"Felice","middleName":"N","lastName":"Jacka","suffix":""},{"id":513477605,"identity":"66cc7ec7-3b60-4a80-9009-4a7b8c6e40e3","order_by":9,"name":"Adrienne O’Neil","email":"","orcid":"https://orcid.org/0000-0002-4811-5830","institution":"Food \u0026 Mood Centre, Deakin University, Institute for Mental and Physical Health and Clinical Translation (IMPACT), Geelong, Australia","correspondingAuthor":false,"prefix":"","firstName":"Adrienne","middleName":"","lastName":"O’Neil","suffix":""}],"badges":[],"createdAt":"2025-09-11 06:57:11","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7588630/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7588630/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91199193,"identity":"10244e11-5cf4-47ef-a8ef-8a7df1f349a4","added_by":"auto","created_at":"2025-09-12 15:19:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33262,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eParticipant flowchart, showing total discrete sample in each wave, derived from NHANES response rates (CDC) and participants excluded for these analyses\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7588630/v1/c08823beff8cdd617c3fdbdd.png"},{"id":91199196,"identity":"b3bb11e3-243c-480c-b706-f61bb33942de","added_by":"auto","created_at":"2025-09-12 15:19:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":133447,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePrevalence of suboptimal dietary intake among NHANES survey respondents at each wave, based on the GBD TMREL, broken down by sex. Suboptimal intake was defined as: intake of fruit \u0026lt;345g, vegetables \u0026lt;339g, legumes \u0026lt;105g, wholegrains \u0026lt;185g, nuts and seeds \u0026lt;21.5g, milk \u0026lt;310g (for males) and \u0026lt;555g (for females), fiber \u0026lt;23.5g, calcium \u0026lt;0.79g (for males) and \u0026lt;1.15 (for females), omega-3 \u0026lt;565mg, polyunsaturated fat \u0026lt;9.5% energy, red meat \u0026gt;100g, processed meat \u0026gt;0g, SSB \u0026gt;0g, and sodium \u0026gt;3g. GBD TMRELs for UPF intake are not currently available. Text in the bottom left of each plot shows the beta coefficient and p-value for the trend over time in persons at risk. For an interactive version of this plot showing mean, standard deviation, median and inter-quartile range of intake for these dietary exposures, see SI Fig 1\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7588630/v1/a5cac97b384edfd6e626aa09.png"},{"id":91200506,"identity":"fc6b0907-fbd4-4357-9b17-bc8a9a46a325","added_by":"auto","created_at":"2025-09-12 15:27:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eProportion of survey respondents with self-reported depression (as defined as PHQ-9 score ≥ 10) at each wave of the NHANES study. For an interactive version of this plot with raw participant numbers and percentages, and individual difference testing between years, see SI Fig 2. For all years and subgroups, there were statistically significant differences between each pair of years, except for the female data between 2009-10 and 2011-12\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7588630/v1/076a67123cfae29b0c29fdac.png"},{"id":91199202,"identity":"d92a1bc5-2aed-4c46-aa30-04d2ce872ded","added_by":"auto","created_at":"2025-09-12 15:19:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":163929,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eOdds ratios for depression for each dietary exposure at each wave of the NHANES study, overall (a), and for female (b) and male (c) participants. The units presented for each food group represent the approximate recommended serving sizes, and nutrients are left unscaled at 1g or 1% energy. A dotted line has been provided at one for ease of interpretation, and a translucent ribbon indicates the confidence intervals. Written in text within each facet are the results from the ‘all years’ models. See the interactive version in SI Fig 3 for overlayed results\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7588630/v1/410f2c243f8ba6193a0d0688.png"},{"id":91201976,"identity":"6681727a-2b89-41df-b97f-4969a3b60a7f","added_by":"auto","created_at":"2025-09-12 15:51:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1027994,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7588630/v1/f8c79b81-0c2a-4498-854e-2feac3dcbf9e.pdf"},{"id":91199194,"identity":"e3b956e7-6bfc-4308-a1e6-e53fcbc58de6","added_by":"auto","created_at":"2025-09-12 15:19:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16372,"visible":true,"origin":"","legend":"","description":"","filename":"NHANESGLADSupplementaryMaterialTitlePage.docx","url":"https://assets-eu.researchsquare.com/files/rs-7588630/v1/26cc2c7a5d3d984d43a12113.docx"},{"id":91199197,"identity":"0a004e80-2635-4e69-ba97-85b650035644","added_by":"auto","created_at":"2025-09-12 15:19:02","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":155189,"visible":true,"origin":"","legend":"","description":"","filename":"NHANESGLADSupplementaryTablesforsubmission.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7588630/v1/63ab4403d525e7e5a0441f9c.xlsx"},{"id":91199206,"identity":"b9be7b22-d3b7-4648-98f1-2b2724bc9f1e","added_by":"auto","created_at":"2025-09-12 15:19:02","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1230278,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.zip","url":"https://assets-eu.researchsquare.com/files/rs-7588630/v1/689f9d71f69aef19be634d7e.zip"},{"id":91200507,"identity":"42ed2f95-86ff-4db0-9bb8-41c8b619ab65","added_by":"auto","created_at":"2025-09-12 15:27:02","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20960,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7588630/v1/cba0765fdf58cd49ac3d1115.docx"}],"financialInterests":"The authors declare potential competing interests as follows: Financial interests: DNA, ET, SG, RO, MML, WM, FNJ, and AON 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 has received travel funding support from the International Society for Nutritional Psychiatry Research (ISNPR), the Nutrition Society of Australia (NSA), the Australasian Society of Lifestyle Medicine, and the Gut Brain Congress. WM has received fellowship funding support from the NHMRC (#2008971) and Multiple Sclerosis Research Australia, consultation and remuneration funding support from Nutrition Research Australia and ParachuteBH, and travel funding support from the NSA, Mind Body Interface Symposium, and VitaFoods. FNJ has received fellowship payment or honorariums for lectures, presentations, speakers bureaus, manuscript writing, or educational events from the Malaysian Society of Gastroenterology and Hepatology, JNPN Congress, American Nutrition Association, Personalised Nutrition Summit, and American Academy of Craniofacial Pain, and has written two books for commercial publication on the topic of nutritional psychiatry and gut health. AON has received grant funding support from the Medical Research Future Fund, Dasman Diabetes Institute, MTP Connect—Targeted Translation Research Accelerator Program, the NHMRC, Barwon Health, and the Waterloo Foundation, and has received funding support for academic editing and as a grant reviewer from SLACK Incorporated (Psychiatric Annals) and the NHMRC and travel funding support from the ISNPR.\nNon-financial interests: MML is a committee member for the Melbourne Branch Committee of the NSA(unpaid); WM is president of the ISNPR (unpaid); FNJ is a Scientific Advisory Board member of Dauten Family Centre for Bipolar Treatment Innovation and Zoe Nutrition (unpaid)","formattedTitle":"\u003cp\u003eInvestigating associations between diet and depression at the population level over time: results from the National Health and Nutrition Examination Survey.\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMental disorders including depression are among the leading contributors to disability adjusted life years (DALYs) globally, affecting millions (IHME, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Within the United States of America, the economic burden of depression has substantially increased over time, with a 37.9% increase between 2010 and 2018 (Greenberg et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In 2019, the cost of major depressive disorder to the US was US\u003cspan\u003e$\u003c/span\u003e333.7\u0026nbsp;billion, with the primary drivers being healthcare costs, household costs, and workplace presenteeism/absenteeism (Greenberg et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDepression is a complex disorder, driven by various environmental, genetic, biological, and behavioral risk factors (Marx et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). One behavioral risk factor receiving increased attention is diet (Sarris et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with multiple diet quality metrics and dietary components found to be cross-sectionally and prospectively associated with depressive symptoms and disorders (Lassale et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Quirk et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). There is also growing evidence that dietary intervention is an effective adjunctive treatment strategy for depression, suggesting that the relationship is causal (Bayes et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Francis et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jacka et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Parletta et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite its relevance to depression and a host of other health conditions, diet quality and intake in many Western countries remains particularly poor compared to the rest of the world (Imamura et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). There are multiple, complex reasons for this, including the industrialized food system (Nadathur et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), financial barriers (Fanzo et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and prevalence of \u0026lsquo;food deserts\u0026rsquo; (Fanzo et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given that as much as 14% of depression cases could be offset by improving lifestyle behaviours including dietary intake (Adjibade et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), improving diet at the population level could be a promising public health target to alleviate depression burden in the US. To do this effectively, we require comprehensive data on population-level diet and depression prevalence, and their associations, over time in the US.\u003c/p\u003e\u003cp\u003eThe Global Burden of Disease and Risk Factors Study (GBD) provides a useful classification framework by which to do this. The GBD quantifies the burden of diseases attributable to various risk factors (Brauer et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which is commonly used by governments and policymakers globally to prioritize public health policy decisions. Although the GBD does not currently estimate dietary risks for mental disorders (Ashtree DN, 2025), the GBD definitions provide a useful framework to quantify the contribution of 15 dietary risk factors to depression. This study therefore aims to assess 1) prevalence of not meeting GBD theoretical minimum risk dietary intakes, 2) prevalence of depression, and 3) the association between diet and depression over time in a large, nationally representative, survey from the US (2005\u0026ndash;2018).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e This manuscript has been prepared in accordance with the Global burden of disease Lifestyle And mental Disorders Taskforce (GLAD) (Ashtree DN, 2025), as part of a global collaborative project to inform the GBD. GLAD was designed to generate the required level of evidence to accurately estimate the population attributable fractions of diet to depression burden at regional and global levels, and ultimately embed these estimates in subsequent rounds of GBD data collection (Ashtree DN, 2025). As such, this paper uses the following methods, which were prospectively registered on Open Science Framework (Ashtree et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSample\u003c/h2\u003e\u003cp\u003eThis study used data from the National Health and Nutrition Examination Survey (NHANES) (CDC, 1999\u0026ndash;2018, 2013, 2018). Full details of the NHANES methods are available on the NHANES website (CDC, 1999\u0026ndash;2018, 2013, 2018). Briefly, NHANES is a biennial cross-sectional survey examining a discrete, nationally representative sample of adults and children from across the US generated at each wave; it was established to assess the health and nutritional status of adults and children from 1999-present. NHANES field operations were suspended in March 2020 due to COVID-19, rendering the 2019\u0026ndash;2020 data non-representative. Subsequent years have not yet been added to the food patterns database, making them unavailable for our analyses. Due to differences in methods (the number of dietary recalls and outcome ascertainment), we also excluded data from 1999\u0026ndash;2004. Thus our analyses used data from 2005\u0026ndash;2018 and were further restricted to individuals\u0026thinsp;\u0026ge;\u0026thinsp;18 years old with available day 1 and day 2 energy intake data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOutcome\u003c/h3\u003e\n\u003cp\u003eDepression was measured via a nine-item depression self-report instrument derived from the Diagnostic Statistical Manual IV (the Patient Health Questionnaire; PHQ-9). A cut-off score of \u0026ge;\u0026thinsp;10 was used to indicate \u0026lsquo;likely depression diagnosis\u0026rsquo; (henceforth, \u0026lsquo;depression\u0026rsquo;) on the PHQ-9, due to its high concordance with structured mental health professional interview (sensitivity and specificity 88%) (Kroenke et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eExposure\u003c/h3\u003e\n\u003cp\u003eDietary intake was measured at each wave using two 24-hour dietary recalls, and the average intake of these was used in this analysis. We calculated the intake of each GBD-defined dietary risk based on previously published methods (Fulgoni et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Briefly, we used data from the 2005\u0026ndash;2018 Food Pattern Equivalents Database (Bowman et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to convert \u0026lsquo;cup equivalents\u0026rsquo; to \u0026lsquo;grams\u0026rsquo; of of each food item. Food items were then grouped to align with GBD definitions based on the formulas in Table\u0026nbsp;2 of Fulgoni et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These gram intakes were treated in two ways to answer our research questions, to: 1) determine the prevalence of suboptimal dietary intake, based on GBD definitions; and 2) assess the association between dietary intake (continuous) and odds of depression at each wave.\u003c/p\u003e\u003cp\u003eIn addition to the GBD dietary variables, ultra-processed food (UPF) exposure was examined due to its relevance to non-communicable diseases, including depression (Lane, Gamage, et al., 2024). Classification of food items as UPF or non-UPF in the NHANES dietary data has been completed previously (Mart\u0026iacute;nez Steele et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the classifications and data generated during this process were used in these current analyses. Briefly, each food code was classified according to the Nova food classification system (Monteiro et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), where food items are categorized as \u0026ldquo;unprocessed/minimally processed\u0026rdquo;, \u0026ldquo;processed culinary ingredients\u0026rdquo;, \u0026ldquo;processed foods\u0026rdquo; or \u0026ldquo;ultra-processed\u0026rdquo;. For potential homemade recipes, the Nova classification was applied to underlying constituent ingredients (Mart\u0026iacute;nez Steele et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Grams per day of Nova category four (UPFs) were obtained for each participant at each wave and used in these analyses.\u003c/p\u003e\n\u003ch3\u003ePrevalence of Suboptimal Dietary Intake\u003c/h3\u003e\n\u003cp\u003eTo determine the prevalence of suboptimal dietary intake, daily intakes of each dietary variable were dichotomized based on the GBD\u0026rsquo;s theoretical minimum risk exposure level (TMREL) \u0026ndash; the hypothetical level of exposure associated with the lowest health risk (Brauer et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Suboptimal intake was defined as intake of fruit\u0026thinsp;\u0026lt;\u0026thinsp;345g; vegetables\u0026thinsp;\u0026lt;\u0026thinsp;339g; legumes\u0026thinsp;\u0026lt;\u0026thinsp;105g; wholegrains\u0026thinsp;\u0026lt;\u0026thinsp;185g; nuts and seeds\u0026thinsp;\u0026lt;\u0026thinsp;21.5g; milk\u0026thinsp;\u0026lt;\u0026thinsp;310g for males, \u0026lt;\u0026thinsp;555g for females; fiber\u0026thinsp;\u0026lt;\u0026thinsp;23.5g; calcium\u0026thinsp;\u0026lt;\u0026thinsp;0.79g for males, \u0026lt;\u0026thinsp;1.15g for females; omega-3\u0026thinsp;\u0026lt;\u0026thinsp;565mg; polyunsaturated fat\u0026thinsp;\u0026lt;\u0026thinsp;9.5% energy; red meat\u0026thinsp;\u0026gt;\u0026thinsp;100g; processed meat\u0026thinsp;\u0026gt;\u0026thinsp;0g; SSB\u0026thinsp;\u0026gt;\u0026thinsp;0g; and sodium\u0026thinsp;\u0026gt;\u0026thinsp;3g. UPF was excluded from this analysis, as the GBD do not currently estimate TMRELs for UPF. Using these thresholds, we calculated the number and percent of participants with suboptimal dietary intake.\u003c/p\u003e\n\u003ch3\u003eDietary Exposures for Regression Models\u003c/h3\u003e\n\u003cp\u003eAlthough the GBD framework defines dietary exposures in grams per day, we opted to scale dietary intake for each food group to recommended serving sizes, where available, to improve interpretability. Where possible, we based recommended serving sizes on data from the US (Agriculture, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Clapp et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and where serving sizes in grams were unavailable we used recommendations from the UK or Australia or as presented in peer-reviewed literature (Micha et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; MR, 2021) (Supplementary Information [SI] Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The serving sizes were: fruit \u0026minus;\u0026thinsp;150g, vegetables \u0026minus;\u0026thinsp;75g, legumes \u0026minus;\u0026thinsp;150g, wholegrains \u0026minus;\u0026thinsp;28g, nuts and seeds \u0026minus;\u0026thinsp;30g, milk \u0026minus;\u0026thinsp;250g, red meat \u0026minus;\u0026thinsp;65g, processed meat \u0026minus;\u0026thinsp;50g, SSB \u0026minus;\u0026thinsp;355g, ultra-processed food \u0026minus;\u0026thinsp;90g. Nutrient-based exposures were included as per-gram only, due to lack of information regrading what constitutes a serving size. Per gram models were also generated for all dietary exposures (SI).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCovariates\u003c/h2\u003e\u003cp\u003eAt each wave, participants self-reported demographic and general health information, including sex, age, BMI, and highest educational qualification. Age, sex, education, and total energy intake were used as covariates, with education acting as a proxy for socio-economic status, in accordance with the GLAD protocol (Ashtree DN, 2025). We adjusted for energy intake (as measured in the nutrient intake dataset) using Willett\u0026rsquo;s residual method (Willett et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). We included BMI only as a subgroup model, as we consider BMI as a mediator rather than a confounder in the diet-depression relationship (Ma et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). BMI was categorized into \u0026lt;\u0026thinsp;18.5kg/m\u003csup\u003e2\u003c/sup\u003e, 18.5 to \u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e, 25 to 30 kg/m\u003csup\u003e2\u003c/sup\u003e, and \u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eWe conducted descriptive analyses to examine the prevalence of both suboptimal dietary intake and depression over time (from 2005\u0026ndash;2018). These estimates were calculated both overall and stratified by sex. To assess trends in prevalence over time, we used linear regression to estimate P-values for trend across survey waves.\u003c/p\u003e\u003cp\u003eWe used a complete-case logistic regression to assess the association between each dietary exposure and the likelihood of depression at each time point. Models were fitted using day two dietary survey sample weights (CDC) using the svy commands in Stata 18.0 (StataCorp, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). We fitted two models: 1) unadjusted model; 2) adjusted for age, sex, education, and total energy intake using Willett\u0026rsquo;s residual method (Willett et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). We conducted additional subgroup analyses based on sex and BMI. All model assumptions were assessed prior to fitting the final models. We conducted a sensitivity analysis excluding participants with extreme energy intakes (energy intake\u0026thinsp;\u0026lt;\u0026thinsp;1st or \u0026gt;\u0026thinsp;99th percentile). We used the Simes method of p-value adjustment, termed q-values, to account for multiple testing in the logistic regressions (Simes, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Q-values adjust the p-value and not the inference level and so are interpreted in the same way as p-values. All p- and q-values were two tailed at the 5% significance level.\u003c/p\u003e\n\u003ch3\u003eEthics\u003c/h3\u003e\n\u003cp\u003eNHANES was conducted with approval from the NHCS ethics review board (full details available from the CDC website (CDC, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)). The current project was approved in March 2024 by the Deakin University Human Research Ethics Committee for exemption from ethical review in accordance with the National Statement on Ethical Conduct in Human Research (2007, updated 2018) Section 5.1.22 (project number: 2024-085).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDemographics over Time\u003c/h2\u003e\u003cp\u003eEach wave of the study surveyed distinct samples, ranging from n\u0026thinsp;=\u0026thinsp;8,704 (2017\u0026ndash;2018) to n\u0026thinsp;=\u0026thinsp;10,253 (2009\u0026ndash;2010), resulting in sample sizes ranging between 4,983 (2017\u0026ndash;2018) and 6,052 (2009\u0026ndash;2010) included in our analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After applying survey weights, sample sizes ranged from 218,065,271 to 247,079,471 equivalent participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on the weighted sample, slightly more females than males completed the NHANES survey at each wave (ranging from 51.4% in 2011\u0026ndash;2012 and 2015\u0026ndash;2016 to 53.9% in 2007\u0026ndash;2008). Across all years, most participants were at least high-school or equivalent educated, with an average age between 45.7 in 2007\u0026ndash;2008 to 47.4 in 2015\u0026ndash;2016 and 2017\u0026ndash;2018.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDietary intake over time\u003c/h2\u003e\u003cp\u003e Most participants had suboptimal dietary intake for each food and nutrient group, with intakes lower than the GBD TMREL for calcium, fiber, fruits, legumes, milk, nuts and seeds, polyunsaturated fat, vegetables, and wholegrains, and intakes higher than the TMREL for processed meat and sodium.\u003c/p\u003e\u003cp\u003eThe prevalence of suboptimal processed meat and red meat intake decreased overall and for male respondents between 2005\u0026ndash;2006 to 2017\u0026ndash;2018 (processed meat: 68.2% (74.3% male) in 2005\u0026ndash;2006 to 65.3% (69.2% male) in 2017\u0026ndash;2018; red meat: 17.0% (25.6% male) in 2005\u0026ndash;2006 to 13.6% (20.7% male) in 2017\u0026ndash;2018). Dietary intake of polyunsaturated fat and nuts and seeds increased across all sexes, corresponding to a decrease in the prevalence of suboptimal intake for these dietary exposures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Conversely, the prevalence of suboptimal milk intake increased from 2005\u0026ndash;2018, from 84.7% (74.5% male, 93.9% female) in 2005\u0026ndash;2006 to 90.8% (83.9% male, 97.1% female) in 2017\u0026ndash;2018.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrevalence of suboptimal calcium, fiber, legume, milk, nuts and seeds, omega-3 and wholegrain appeared to be higher among female respondents compared to male. Conversely, male respondents tended to have a higher prevalence of suboptimal processed meat, red meat, sodium, and sugar-sweetened beverage and polyunsaturated fat intake.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDepression over time\u003c/h2\u003e\u003cp\u003eMore females than males had self-reported depression at each time point (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Whilst no overall trend over time was observed, changes in prevalence were observed between all individual timepoints, except for females between 2009/10 and 2011/12.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAssociation between dietary risk factors and depression\u003c/h2\u003e\u003cp\u003eAfter adjusting for age, sex, education, and energy intake, higher intake of fiber was consistently associated with lower risk of depression at each wave between 2005\u0026ndash;2006 and 2017\u0026ndash;2018 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; all years combined OR\u0026thinsp;=\u0026thinsp;0.96 per gram, 95%CI\u0026thinsp;=\u0026thinsp;0.95\u0026ndash;0.97). Moreover, there was a consistent association between higher intakes of UPF and higher odds of depression at all waves between 2007\u0026ndash;2008 and 2017\u0026ndash;2018 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; all years combined OR\u0026thinsp;=\u0026thinsp;1.04 per 90 gram serve, 95%CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.04; SI Tables\u0026nbsp;2\u0026ndash;4 show the per gram results and conversion to per serve). Whilst we found evidence for an association for all other dietary exposures at various time points, these were not consistently observed at each wave. Despite this, all years with an observed association for a particular dietary risk were consistent in magnitude and direction, except for processed meat, which was associated with a higher odds of depression in 2009\u0026ndash;2010 and 2015\u0026ndash;2016 but a lower odds of depression in 2013\u0026ndash;2014.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen combining data across all years and participants, higher intakes of several dietary factors were associated with lower odds of depression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, SI Table\u0026nbsp;2). These included calcium (OR\u0026thinsp;=\u0026thinsp;0.74 per gram, 95% CI: 0.58\u0026ndash;0.95), fruit (OR\u0026thinsp;=\u0026thinsp;0.72 per 150 g serve, 95% CI: 0.65\u0026ndash;0.80), legumes (OR\u0026thinsp;=\u0026thinsp;0.78 per 150 g serve, 95% CI: 0.67\u0026ndash;0.91), nuts and seeds (OR\u0026thinsp;=\u0026thinsp;0.93 per 30 g serve, 95% CI: 0.87\u0026ndash;0.98), omega-3 (OR\u0026thinsp;=\u0026thinsp;0.88 per gram, 95% CI: 0.80\u0026ndash;0.97), polyunsaturated fats (OR\u0026thinsp;=\u0026thinsp;0.97 per 1% energy, 95% CI: 0.95\u0026ndash;0.9997), vegetables (OR\u0026thinsp;=\u0026thinsp;0.88 per 75 g serve, 95% CI: 0.83\u0026ndash;0.92), wholegrains (OR\u0026thinsp;=\u0026thinsp;0.86 per 28 g serve, 95% CI: 0.81\u0026ndash;0.91), red meat (OR\u0026thinsp;=\u0026thinsp;0.92 per 65 g serve, 95% CI: 0.86\u0026ndash;0.99), and sodium (OR\u0026thinsp;=\u0026thinsp;0.82 per gram, 95% CI: 0.76\u0026ndash;0.88). In contrast, higher intake of sugar-sweetened beverages (SSBs) was associated with increased odds of depression (OR\u0026thinsp;=\u0026thinsp;1.14 per 355 g serve, 95% CI: 1.08\u0026ndash;1.20).\u003c/p\u003e\u003cp\u003eHowever, not all associations observed in the full sample were consistent across individual waves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, SI Table\u0026nbsp;2). When analyses were stratified by sex, associations between fibre, fruit, sodium, SSBs, UPFs, vegetables, and wholegrains with depression were observed in both males and females (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb/c, SI Table\u0026nbsp;3). Additionally in females, higher intakes of nuts and seeds (OR\u0026thinsp;=\u0026thinsp;0.88, 95% CI: 0.79\u0026ndash;0.98), omega-3 (OR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.70\u0026ndash;0.92), and polyunsaturated fats (OR\u0026thinsp;=\u0026thinsp;0.96, 95% CI: 0.93\u0026ndash;0.993) were also linked to lower odds of depression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, SI Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eResults were consistent in magnitude and direction between main and sensitivity models, after excluding participants with extreme energy intake (SI Table\u0026nbsp;5) and BMI subgroups (SI Table\u0026nbsp;6).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study sought to understand whether population-level dietary intake, depression prevalence, and their association changed over time in the US, using data from the nationally-representative NHANES. We focused on dietary exposures defined by the GBD to assess population-level patterns of suboptimal intake. Most respondents had suboptimal dietary intakes at each wave, with prevalence of suboptimal intake reducing for processed and red meat, nuts and seeds, and polyunsaturated fat between 2005 and 2018. There was no overall change in self-reported (PHQ-9) depression prevalence from 2007 to 2018, despite fluctuations in individual years. Depression prevalence was higher in females compared to males. Higher fiber intake was consistently associated with lower odds of depression at each wave, while higher UPF intake was consistently associated with higher odds. When combining data from all years, several dietary exposures were associated with odds of depression: fruit, legumes, vegetables, wholegrains, fiber, polyunsaturated fat, omega-3, and nuts and seeds were associated with lower odds; and SSBs and UPFs were associated with higher odds. Contrary to expectations, sodium and red meat intake were associated with lower odds of depression. Together, our findings propose several key dietary exposures associated with depression that warrant further causal investigation in large prospective cohorts to determine whether they could be effective preventative targets.\u003c/p\u003e\u003cp\u003eThe high prevalence of suboptimal dietary intakes observed in this study is consistent with the broader literature (Imamura et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A greater proportion of female respondents had suboptimal intake of calcium and fiber, which is concordant with existing literature showing women tend to have lower intakes (Balk et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fayet-Moore et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) compared to men. Similarly, higher meat (Hopwood et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), SSB (Lara-Castor et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and sodium (Chen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) intake in males compared to females has been previously observed.\u003c/p\u003e\u003cp\u003eFor dietary trends, the prevalence of suboptimal processed meat, red meat, nuts and seeds, and polyunsaturated fat intake decreased. However, the prevalence of suboptiomal intakes for the remaining food groups remained stable or increased \u0026ndash; such as suboptimal intake of milk increasing over time. Some of these changes could be attributed to changes in dietary guidelines and patterns at the population-level. For example, dietary guidelines surrounding nuts and seeds have changed from 1) being included in a \u0026lsquo;meat and beans\u0026rsquo; group for a total intake of up to 5 ounces per week in 2000, 2) being separated from meat but amalgamated with legumes for a total recommendation of 4\u0026ndash;5 servings (approx. 2.5oz) in 2005, 3) separated from all legumes except soy for a recommended 4 ounces per week in 2010, and 4) increased to 5 ounces per week in 2015 (Agriculture, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For polyunsaturated fats, there was no specific recommendation on their consumption in the 2005 USDA guidelines, other than to use \u0026lsquo;moderate\u0026rsquo; amounts whilst \u0026lsquo;taking care to avoid excess calories\u0026rsquo;. Between 2005 and 2010, oils were given specific gram recommendations because of their high polyunsaturated fat content, and in 2015 two specific polyunsaturated fats \u0026ndash; linoleic and linolenic acid \u0026ndash; were given specific gram recommendations (Agriculture, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, some changes may be driven at the consumer level rather than through government intervention. For example, in recent years there has been a rise in the popularity of alternative milks in the US, and a corresponding decrease in dairy-based milk consumption (Emmerich et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, there is increased interest in plant-based proteins and a rise in casual vegetarianism among consumers (Niva et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). With this reduction in animal protein consumption, individuals are becoming more familiar and comfortable with using foods like pulses, nuts, and seeds in their cooking \u0026ndash; potentially contributing to the increased consumption of nuts in our data as well as the decreased consumption of dairy-based milks (Niva et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These all indicate the potential value of both government and community-based drives to shift dietary habits within the community, and may explain, at least in part, the improving patterns of intake we see in the NHANES study. With targeted changes to government guidance or popularity of certain foods, the prevalence of suboptimal intake could be reduced, potentially mitigating depression burden at the same time. However, future research should seek to evaluate whether changes in guidelines lead to changes in dietary intake across the population and within subgroups. The potential is also there for natural experiments evaluating whether dietary changes at the population or subpopulation level causally associate with changes in health outcomes, including mental health.\u003c/p\u003e\u003cp\u003eWhilst there was an increase in the economic burden of depression between 2010 and 2018 in the US (Greenberg et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), our study found no overall trend in depression prevalence over time in this cohort. Globally, other literature supports stable depression burden, with the GBD study finding age-standardized DALYs from mental disorders remaining constant from 1990 to 2019 (Ferrari et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and a review of evidence from the United Kingdom, Australia, Canada, and the US reporting stagnant depression prevalence from 1990 to 2015 (Jorm et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A lack of change in depression prevalence indicates that current treatment and prevention strategies are not reducing depression burden, pointing to the need for different approaches, particularly preventative (Jorm et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith that in mind, these cross-sectional data identified several potentially protective dietary factors in the form of higher intakes of fruit, legumes, vegetables, wholegrains, fiber, polyunsaturated fat, omega-3, and nuts and seeds. These results are concordant with the many longitudinal studies linking dietary patterns and quality with depression risk (Lassale et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The potential protective value of these foods is further supported by these foods being included in diet quality scores (Oliv\u0026aacute;n-Bl\u0026aacute;zquez et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sanchez-Villegas et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), as well as in key components of the highly-studied Mediterranean diet, which has been shown to be efficacious in treating depression in randomized controlled trials (Bayes et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bizzozero-Peroni et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Francis et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jacka et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Parletta et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eContrary to previous research (Teasdale et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yun et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), higher sodium intake was associated with lower odds of probable depression. Whilst unexpected, there are animal models demonstrating that low sodium intake can result in depression-like symptoms, which can be reversed with increased sodium intake (Grippo et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Leshem, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Morris et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Morris et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It is also possible that our results are an artefact of the unreliability of sodium intake from dietary questionnaires, due to underreporting from social desirability bias (de Mestral et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; McLean et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) \u0026ndash; it would be valuable to confirm these associations in a cohort that has urinary sodium measured. Although NHANES collected urinary data from 2014 onwards (Jackson et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), due to limited overlap with our included sample we were unable to validate dietary sodium against urinary sodium.\u003c/p\u003e\u003cp\u003eWe also identified that both SSBs and UPFs were associated with higher odds of depression. This is consistent with the prevailing literature, as SSBs and UPFs have been associated with higher odds of depression in multiple studies and a very large umbrella review (Lane, Gamage, et al., 2024; Lane et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lane, Travica, et al., 2024), and observed to have a dose-response relationship with higher depression risk in a meta-analysis (Wang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). UPFs may influence depression risk through specific non-nutritive components such as artificial sweeteners, emulsifiers, or colourants impacting the hypothalamic-pituitary-adrenal axis, gut microbiota, and inflammatory systems (Lane et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These foods may also act as indicators of overall diet quality, as high UPF intake has been associated with poorer results on various dietary indices (Liu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Moubarac et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), as well as with reduced consumption of healthful foods such as fruits, vegetables, legumes, and wholegrains (Marchese et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, SSB consumption correlates with poorer diet quality and markers of food insecurity (Fontes et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Leung et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Interestingly, the two peak timepoints of depression prevalence were in 2007/08 and 2013/14, during which times the global financial crisis and major cuts to the Supplemental Nutrition Assistance Program occurred, respectively, reflected in increased rates of financial stress and food insecurity (Habibov et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Katare \u0026amp; Kim, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Hence, whilst these foods could be targets for interventions and may act independently on depression risk (Dicken \u0026amp; Batterham, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), they may also be markers of other environmental drivers.\u003c/p\u003e\u003cp\u003eGiven that dietary exposures are also associated with physical health outcomes (Afshin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), such as cardiovascular diseases (Dong et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), monitoring population dietary risks can inform cost-effective prevention and/or prevalence reduction strategies for depression and other conditions of public health importance. Our GLAD project (Ashtree DN, 2025), which aims to calculate the population burden of depression attributable to these dietary risks, will play a critical role in informing policy and public health interventions.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eOur study is the first to comprehensively assess the relationship between diet and odds of depression in a large, representative survey of adults in the US at multiple time-points. However, some aspects of this study that could be improved. Firstly, the associations presented here are based on cross-sectional data, and are vulnerable to limitations such as potentially missing the etiologically relevant window of the exposure, prevalence instead of incidence as the outcome potentially diluting exposure-outcome associations, and a limited ability to address reverse causality (Savitz \u0026amp; Wellenius, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As such, it is important that these analyses are repeated in longitudinal cohorts and continue to be causally tested via randomized controlled trials. For the UPF results, whilst NHANES collects some information indicative of food processing, these data are not consistently determined for all food items, which could lead to imprecise estimation of the consumption of UPF. Furthermore, social desirability bias may lead to underreporting (e.g. for UPF) or overreporting (e.g. for fruit and vegetables) of certain food items. Additionally, data from the 2019\u0026ndash;2020 wave of NHANES is incomplete and non-representative due to the COVID-19 pandemic and could not be included in this study and the most recent 2021\u0026ndash;2023 wave has yet to be incorporated in the food patterns database, precluding use of these waves in our analyses. Once newer waves are complete and uploaded, future research should revisit these analyses to see whether prevalence of suboptimal dietary intake and associations between GBD-defined dietary exposures and depression remain. As the importance of disaggregating by both sex and gender in health-studies is increasingly emphasized (Tannenbaum et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a further limitation of this study is that only sex data are available. Examining the intersection between sex, gender, and diet-depression dynamics would be an interesting avenue for future studies.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe have shown that dietary habits in the US between 2005 and 2018 were generally suboptimal. Whilst dietary intake improved over time for some food groups, for most it remained stagnant. Encouragingly, higher intakes of fruit, legumes, vegetables, wholegrains, fiber, polyunsaturated fat, omega-3, and nuts and seeds corresponded to lower odds of depression, whereas higher SSBs and UPF intakes were associated with higher odds, concordant with the wider epidemiological literature and clinical trials. Our results suggest that a greater focus on improving population dietary inake may represent a potent policy target for reducing depression prevalence and burden in the US.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work, AON and DNA are supported by a National Health and Medical Research Council (NHMRC) Emerging Leader 2 Fellowship (grant #2009295 to AON). \u0026nbsp;FNJ is supported by a NHMRC Leader 1 Fellowship (grant #1194982). MML is supported by a Deakin University Postdoctoral Fellowship. RO and ET are supported by a Deakin University Postgraduate Research Scholarship. SG is supported by a postdoctoral fellowship from the SOLVE-CHD NHMRC Synergy Grant (APP1182301). SLD is supported by a Medical Research Future Fund Grant (#2025947). EMS is supported by a Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado de S\u0026atilde;o Paulo grant (2023/16144-3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of potential conflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial interests:\u0026nbsp;\u003c/strong\u003eDNA, ET, SG, RO, MML, WM, FNJ, and AON 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, the Serp Hills Foundation, the Roberts Family Foundation, and the Fernwood Foundation. MML has received travel funding support from the International Society for Nutritional Psychiatry Research (ISNPR), the Nutrition Society of Australia (NSA), the Australasian Society of Lifestyle Medicine, and the Gut Brain Congress. WM has received fellowship funding support from the NHMRC (#2008971) and Multiple Sclerosis Research Australia, consultation and remuneration funding support from Nutrition Research Australia and ParachuteBH, and travel funding support from the NSA, Mind Body Interface Symposium, and VitaFoods. FNJ has received fellowship payment or honorariums for lectures, presentations, speakers bureaus, manuscript writing, or educational events from the Malaysian Society of Gastroenterology and Hepatology, JNPN Congress, American Nutrition Association, Personalised Nutrition Summit, and American Academy of Craniofacial Pain, and has written two books for commercial publication on the topic of nutritional psychiatry and gut health. AON has received grant funding support from the Medical Research Future Fund, Dasman Diabetes Institute, MTP Connect\u0026mdash;Targeted Translation Research Accelerator Program, the NHMRC, Barwon Health, and the Waterloo Foundation, and has received funding support for academic editing and as a grant reviewer from SLACK Incorporated (Psychiatric Annals) and the NHMRC and travel funding support from the ISNPR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNon-financial interests:\u0026nbsp;\u003c/strong\u003eMML is a committee member for the Melbourne Branch Committee of the NSA(unpaid); WM is president of the ISNPR (unpaid); FNJ is a Scientific Advisory Board member of Dauten Family Centre for Bipolar Treatment Innovation and Zoe Nutrition (unpaid)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNHANES survey data are available for public use via the National Center for Health Statistics and Centers for Disease Control and Prevention, under the authority granted by the Public Health Service Act (42 U.S.C. \u0026sect; 242k). Data are protected by US Federal confidentiality laws (Section 308(d) Public Health Service Act 42 U.S.C. 242m(d)) and the Confidential Information Protection and Statistical Efficiency Act (Pub. L. No. 115-435, 132 Stat. 5529 \u0026sect; 302). An ethics exemption was granted by Deakin University\u0026rsquo;s Human Research Ethics committee in March 2024 (2024-085).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants provided written informed consent prior to the first interview.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: AON, FNJ, DNA, MML, RO; Methodology: DNA, MML, RO; Validation: DNA, ET; Formal Analysis: DNA, ET; Resources: EMS; Data Curation: DNA, EMS; Visualization: DNA, ET, SG; Supervision: DNA, FNJ, AON; Project Administration: DNA; Funding Acquisition: AON; Writing \u0026ndash; Original Draft: DNA, ET, SG; Writing \u0026ndash; Reviewing \u0026amp; Editing: All Authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe opinions, methods, and conclusions reported in this paper are those of the authors and are independent from the funding sources. This manuscript has been prepared in accordance with the requirements of the GLAD Taskforce, as part of a global collaborative project to inform the Global Burden of Diseases, Injuries, and Risk Factors Study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdjibade, M., Lemogne, C., Julia, C., Hercberg, S., Galan, P., Assmann, K. E., \u0026amp; Kesse-Guyot, E. (2018). Prospective association between combined healthy lifestyles and risk of depressive symptoms in the French NutriNet-Sant\u0026eacute; cohort. Journal of Affective Disorders, 238, 554-562. https://doi.org/https://doi.org/10.1016/j.jad.2018.05.038 \u003c/li\u003e\n\u003cli\u003eAfshin, A., Sur, P. J., Fay, K. A., et al. (2019). Health effects of dietary risks in 195 countries, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 393(10184), 1958-1972. https://doi.org/10.1016/s0140-6736(19)30041-8 \u003c/li\u003e\n\u003cli\u003eAgriculture, U. 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Nutrients, 13(4), 1360. https://doi.org/10.3390/nu13041360\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Deakin University","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":"Diet, nutrition, depression, ultra processed foods, Global Burden of Diseases","lastPublishedDoi":"10.21203/rs.3.rs-7588630/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7588630/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDepression contributes substantially to disease burden in the United States. Diet is a modifiable risk factor, yet surprisingly the population-level diet-depression association has never been comprehensively evaluated over time. We aimed to assess prevalence of suboptimal dietary intakes and depression, and the association between diet and depression over time using National Health and Nutrition Examination Survey (2005\u0026ndash;2018) data. Complete-case logistic regression assessed associations between diet (following Global Burden of Disease definitions, plus ultra-processed food [UPF]; measured using 24-hour recalls) and odds of depression (defined as PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10). Depression prevalence (7.69%) was consistent from 2007\u0026ndash;2018. Prevalence of suboptimal milk intake increased over time (p\u0026thinsp;=\u0026thinsp;0.003), but suboptimal processed meat (p\u0026thinsp;=\u0026thinsp;0.038), red meat (p\u0026thinsp;=\u0026thinsp;0.022), nuts/seeds (p\u0026thinsp;=\u0026thinsp;0.001), and polyunsaturated fat intake decreased (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher fiber intake was consistently associated with lower odds of depression from 2005\u0026ndash;2018, while higher UPF intakes were associated with higher odds from 2007\u0026ndash;2018. All other dietary risks were associated at various years but not consistently at each wave. Given previously observed causal relationships between diet and depression, improving population-level fiber intake and reducing UPF consumption may plausibly reduce depression burden in the United States. These results support the Global Burden of Disease Lifestyle And mental Disorders project (DERR2-\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/65576\u003c/span\u003e\u003cspan address=\"10.2196/65576\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e","manuscriptTitle":"Investigating associations between diet and depression at the population level over time: results from the National Health and Nutrition Examination Survey.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 15:18:57","doi":"10.21203/rs.3.rs-7588630/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":"8d942ae0-63ca-4041-af7b-f87e4340156a","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54546468,"name":"Epidemiology"},{"id":54546469,"name":"Nutrition \u0026 Dietetics"},{"id":54546470,"name":"Psychology"}],"tags":[],"updatedAt":"2025-09-12T15:18:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 15:18:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7588630","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7588630","identity":"rs-7588630","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}