Temporal Changes between Adherence to a Mediterranean-Style Diet and Consumption of Ultra-Processed Foods with 12-Year Depression Risk in the Melbourne Collaborative Cohort Study

Temporal Changes between Adherence to a Mediterranean-Style Diet and Consumption of Ultra-Processed Foods with 12-Year Depression Risk in the Melbourne Collaborative Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Temporal Changes between Adherence to a Mediterranean-Style Diet and Consumption of Ultra-Processed Foods with 12-Year Depression Risk in the Melbourne Collaborative Cohort Study Sarah Gauci, Deborah N Ashtree, Allison M Hodge, Felice N Jacka, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7993550/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Consuming a Mediterranean-style diet is associated with lower depression risk. Concurrently, exposure to a dietary pattern high in ultra-processed foods (UPFs) is associated with increased depression risk. As dietary patterns shift over time towards greater UPF exposure, it is important to understand how changes in these dietary patterns relate to depression. Methods We used a subset of data from the Melbourne Collaborative Cohort Study (n = 21,718). Dietary intake was assessed at baseline and follow-up using food frequency questionnaires, from which Mediterranean diet scores and UPF intake were derived. Depression risk at follow-up was measured using the Kessler Psychological Distress Scale (K10). We assessed change in diet from baseline to follow-up 12 years later using generalised estimating equations to account for repeated measures, and associations of change in diet with depression using Poisson regression. Analyses were conducted in the overall sample and by birthplace (Australian/New Zealand/Northern European; Southern European). Results Participants increased both their adherence to the Mediterranean-style diet (β = 0.28, 95%CI = 0.25–0.31) and exposure to UPFs (β = 55.20 g/day, 95%CI = 51.71–58.69). Stratification by birthplace showed similar patterns, except for Southern European participants where no clear change in UPF exposure was observed (β=-2.38 g/day, 95%CI=-12.35–7.59). One-point increases in Mediterranean diet score between baseline and follow-up were associated with a 5% lower risk of depression (RR = 0.95, 95%CI = 0.93–0.98), while increasing UPF by 90 g/day was associated with a 5% higher risk (RR = 1.05, 95%CI = 1.03–1.07). Conclusions Adherence to Mediterranean-style diet and UPF exposure increased over time across the overall sample, although patterns varied by region, and these changes were associated with opposing risks of depression. Nutrition & Dietetics Psychology Psychiatry Depression psychological distress Mediterranean diet ultra-processed foods nutritional psychiatry Figures Figure 1 Introduction Mental disorders are among the leading contributors to the global burden of disease, with depressive disorders most prevalent( 1 ). In Australia, approximately 43% of people aged 16 to 85 years will experience a mental disorder during their lifetime, and around 16% will experience an affective disorder such as depression( 2 ). These conditions place a significant burden on individuals, families, and the health system. Over the past decade, habitual diet has emerged as an important target for the prevention and treatment of depression. Adherence to dietary patterns characterised by whole, minimally processed, and plant-based foods, such as the Mediterranean-style diet, is associated with a reduced risk of depression( 3 ) and a Mediterranean-style diet may serve as an effective treatment strategy for depression( 4 – 6 ). However, in Australia, the predominant dietary pattern is characterised by a high intake of ultra-processed foods (UPFs)( 7 ). This is concerning, given the association between UPF exposure and increased risk of adverse health outcomes, including depression( 8 – 10 ). Concurrently, even modest dietary changes are associated with reduced depression risk. For example, in a study of Swedish women, each unit increase in adherence to the Mediterranean-style diet was associated with an approximately 5% reduction in depression risk( 11 ). Therefore, even small shifts toward a Mediterranean-style diet, accompanied by lower exposure to UPFs, could yield meaningful mental health benefits. Yet an important limitation of prior research is that most studies have investigated habitual diet at a single time point( 3 , 11 , 12 ), rather than examining how changes in diet over time influence depression outcomes. Much of the research investigating the role of the Mediterranean-style diet in health outcomes comes from Mediterranean countries, where it represents a traditional dietary pattern( 3 ). Studies conducted in non-Mediterranean countries, such as Australia, have had more variable findings ( 13 , 14 ). These inconsistencies likely reflect cultural differences, traditional eating habits, and the broader food environment. Migration, too, provides a natural context in which to study a wider range of dietary patterns and how dietary patterns change when moving from a country where a traditional diet is common to one where a Western diet dominates. The Melbourne Collaborative Cohort Study (MCCS) offers a unique opportunity to explore dietary change and a greater range of dietary patterns as a result of migration, as approximately one-quarter of participants were migrants from Southern Europe, including Greece, Italy, and Malta, countries where the Mediterranean-style diet is traditionally followed( 15 , 16 ). This culturally diverse cohort provides an opportunity to explore how adherence to Mediterranean-style diets and exposure to UPFs evolve over time, particularly comparing participants of Southern European origin with those from non-Mediterranean backgrounds. We aimed to investigate how changes in adherence to a Mediterranean-style diet and changes in exposure to UPFs over time are associated with the risk of depression in the MCCS cohort (primary aim). We also investigated whether these associations differed by birthplace group (Southern European vs Australian/New Zealand/Northern European) and explored which specific dietary and nutrient components were associated with risk of depression (secondary aim). Methods Ethics approval and pre-registration 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. The GLAD data analysis plan was prospectively registered with Open Science Framework (OSF; osf.io/67qfs), and the data analysis plan and GLAD project protocol have been published elsewhere( 17 ). The MCCS was originally approved by the Cancer Council Victoria's Human Research Ethics Committee, and participants provided written informed consent to participate and for researchers' access to their medical records( 15 ). The current study was approved for exemption from ethical review in accordance with the National Statement on Ethical Conduct in Human Research (Section 5.1.22) by the Deakin University Human Research Ethics Committee (project number: 2023 − 385). Cohort profile A detailed description of the MCCS cohort has been previously published( 15 ). In brief, 41,513 participants between the ages of 27 and 76 years were recruited from Melbourne and surrounding areas between 1990 and 1994. Of these, 59% were women, and 99% were aged between 40 and 69 years. Approximately 24% of the sample were migrants from Southern Europe (comprised of the following ethnic groups: Greek, Italian, Macedonian, Maltese, and Spanish). These participants have distinct dietary and lifestyle patterns from those born in Australia or New Zealand (69%) and Northern Europe (6%; Northern European group was comprised of the following ethnic groups: English, Irish, Latvian, Scottish, Dutch and Welsh)( 18 ). Baseline data collection included questionnaires on lifestyle, medical history, medication use, and a food frequency questionnaire. The first wave of follow-up was completed between 1995 and 1998 (follow-up 1), with a second follow-up (follow-up 2) completed between 2003 and 2007. We used baseline data and data from the second follow-up in these analyses. Full details of measured data at each wave are available in the MCCS Cohort Profile (see Table 1 in the reference( 15 )). Participants were eligible for this study if they completed the baseline dietary questionnaire and both dietary and depression measures (derived from the psychological distress questionnaire) at the second follow-up (Fig. 1 ). We excluded participants taking antidepressant medication at baseline, as a proxy for pre-existing depression, and additionally excluded participants reporting dietary intake 99th percentile at baseline or follow-up, leaving n = 21,718 eligible for inclusion in these analyses. Exposure: Dietary assessment A self-administered 121-item Food Frequency Questionnaire (FFQ), designed and validated for use specifically in the MCCS( 19 – 21 ), was used to assess dietary intake at baseline (1990–1994) and a modified version including some new foods and portion size photos at follow-up 2 (2003–2007). Using this dietary data, scores were derived for adherence to the Mediterranean-style diet( 22 ) and exposure to UPFs( 23 ). Mediterranean-style diet adherence was assessed using the method described by Trichopoulou et al. (2003), and in line with other studies that have used this dataset( 13 , 22 ). Participants received a score of 1 point if their intake was above the MCCS sex-specific median for vegetables, fruit, cereals, legumes, and fish, and scored 0 if their intake was below this median for each dietary component. Additionally, participants were assigned a score of 1 if their intake for dairy and red meat was below the MCCS sex-specific median intake, and a score of 0 if their intake was above this value. For alcohol, 1 point was assigned for daily intakes of 10–50 g in men and 5–25 g in women; lower or higher intakes scored 0( 22 ). In this study, olive oil intake was used instead of the ratio of monounsaturated fats to saturated fats as used in the original Trichopoulou scoring system; this is consistent with other studies using this cohort( 13 ). Mediterranean-style diet Scores ranged from 0–9, with a higher score reflecting greater adherence( 22 ). Ultra-processed food (UPF) intake was determined using the Nova classification system, as described in detail elsewhere( 24 ). FFQ food items were previously classified according to the Nova food classification system by two researchers with expertise in the Australian food environment, following the methods of Machado et al. (2019)( 24 ) and applied in earlier MCCS studies ( 8 , 25 ). Food items were grouped into four Nova categories: Groups 1–3 represent non-UPF, while Group 4 consists of UPF. Only Group 4 intake was considered as an exposure of interest in this study. Examples of Group 4 products include regular and diet soft drinks, sweet or savoury packaged snacks, confectionery, margarine, reconstituted meat products, and many pre-prepared frozen or shelf-stable dishes when these products are made up of food substances of no culinary use and/or contain classes of additives with cosmetic function. The mean daily intake of UPFs was determined by converting frequencies into grams. This was based on sex-specific portion sizes of each food and multiplied by the daily equivalent frequency as per previous research( 19 , 26 , 27 ). Energy was estimated based on the Nutrient Data Table for Use in Australia 1995 (NUTTAB 95), a food composition database containing information for 1800 foods and beverages available in Australia( 28 ) or AUStralian Food and NUTrient Database (AUSNUT) 2007 for FUP 2. To comprehensively evaluate the relationship between the Mediterranean-style diet and UPF with depression, we further disaggregated dietary intake into food groups and nutrients. These groups included 1) Mediterranean-style diet components: grams per day of fruit, vegetables, legumes, cereals (wholegrains, pasta, rice, bread, etc), fish/seafood and olive oil; 2) broader dietary exposures as defined by the Global Burden of Disease study (GBD exposures): grams per day of nuts and seeds, milk, red meat, processed meat and sugar-sweetened beverages fibre, calcium, omega-3 and sodium, and percentage energy per day of omega-6, polyunsaturated fat, monounsaturated fat and saturated fat. We scaled the units for each dietary exposure to be equivalent to serving sizes (for food groups) or recommended daily intakes (for nutrients) to assist with interpretation (see supplementary table 3). Both scaled and unscaled units are presented in the supplementary material. Outcome: Depression The risk of depression was derived from the ten-item Kessler Psychological Distress Scale (K10)( 29 ). Although the K10 assesses non-specific psychological distress, higher K10 scores correlate with the diagnosis of common mental disorders. A cut-off score of 20 ( 30 ) has sound sensitivity (0.66) and high specificity (0.92) for the diagnosis of depressive disorder( 30 ). As a measure of depression could not be derived at baseline because it was not assessed, antidepressant medication use was considered as a proxy of baseline depression. Participants who reported taking antidepressants at baseline were excluded from the analysis to avoid confounding, which is consistent with previous analyses in this cohort( 8 ). Medication use has previously been found to correlate with depression (r = 0.60 to 0.73), with a sensitivity of 0.80 and specificity of 0.67 for depression diagnosis( 31 , 32 ). Assessment of covariates Covariates were identified a priori in the GLAD protocol paper( 17 ) in order to ensure a consistent minimal adjustment set for all studies participating in the GLAD Taskforce. These covariates included age, sex and education, and we included an adjustment for energy intake via Willett’s residual method for all dietary exposures reported in grams per day, as defined above( 33 , 34 ). We additionally included body mass index (BMI), alcohol intake (lifetime abstainers, ex-drinkers, and current drinkers), smoking (never smoked, current smoker, and former smoker), and physical activity in a sensitivity model. Physical activity was scored from 0 to 16, depending on the frequency and intensity of activities( 35 ). Statistical analyses Participant characteristics were summarised using means and standard deviations (SD) for continuous variables and frequencies and percentages for categorical variables. Descriptive statistics for dietary exposures were additionally summarised using means (SD) and medians (Q1 to Q3). To assess whether dietary intake changed over time, we fitted a generalised estimating equations linear regression with robust standard errors between each dietary exposure and time to account for the repeated measurements within participants. This model was fitted for the overall sample, and for subgroups corresponding to birthplace 1) Australian, New Zealand and Northern European-born participants, and 2) Southern European-born participants. To determine the associations between the specified dietary exposures and depression, we fitted three models: a Poisson regression with robust standard errors to estimate the risk ratios for depression associated with change in each dietary exposure. Change in diet was calculated as follow-up intake minus baseline intake (main model). To test whether changes in dietary exposures over time differed by birthplace, we included an interaction term between time and birthplace in the GEE models. To determine whether simultaneous changes to UPF and Mediterranean diet were associated with depression, we additionally created a four-level joint-change categorical variable representing 1) participants who decreased adherence to the Mediterranean-style diet and increased UPF intake (reference category); 2) participants who reduced adherence to the Mediterranean-style diet but decreased UPF intake; 3) participants who increased adherence to the Mediterranean-style diet but increased UPF intake; and 4) participants who increased adherence Mediterranean-style diet and reduced UPF intake. This categorical variable was then included in a supplementary analysis. a Poisson regression with robust standard errors to estimate depression risk associated with diet at baseline (longitudinal model) a logistic regression to determine the cross-sectional association between diet at follow-up and odds of depression at follow-up. All models were fitted for the overall sample, and separately for the subgroups, as described above. We also fitted a subgroup analysis model, disaggregating the results by sex. Three levels of adjustment were included: 1) dietary exposures (all dietary measured reported in grams) were first re-modelled for energy intake using Willett’s residual method; 2) as in 1 but additionally adjusted for age, sex, education, and ethnicity (except for place of birth subgroups), and 3) as in 2 but additionally adjusted for BMI, physical activity, smoking and alcohol. Given the number of statistical models fitted, we adjusted for multiple testing using Simes Q values( 36 ), which are interpreted in the same way as p-values with a q < 0.05 threshold. Adjustment was done separately for each level of covariate adjustment, with overall and birthplace subgroups combined, sex subgroups were done in a separate adjustment again separately for each level of covariate adjustment. Models were fitted in Stata 18.0, and all assumptions were assessed prior to analysis. Results Demographic Characteristics This study included 21,718 participants (60.8% female) at baseline. Table 1 describes the participants’ sociodemographic and lifestyle characteristics at baseline for the total sample and according to place of birth. Participants born in Southern Europe reported lower education, lower physical activity, higher BMI, and were less likely to be current drinkers. However, both groups had similar baseline characteristics. Table 1 Baseline descriptive characteristics of the study population according to place of birth Australian and Northern European Born (n = 18,174) Southern European Born (n = 3,544) Total Sample (n = 21,718) Age (years) 54.2 (8.7) 54.0 (7.6) 54.2 (8.5) Female 11122 (61.2%) 2006 (56.6%) 13128 (60.4%) Highest level of education achieved Did not complete high school 7956 (43.8%) 2854 (80.5%) 10810 (49.8%) Completed high school 2020 (11.1%) 265 (7.5%) 2285 (10.5%) Some tertiary education 8198 (45.1%) 425 (12.0%) 8623 (39.7%) BMI (kg/m2) 26.0 (4.1) 28.5 (4.2) 26.4 (4.2) Level of physical activity 4.6 (3.7) 2.7 (2.9) 4.3 (3.6) Drinking status Never drinker 4102 (22.6%) 1301 (36.7%) 5403 (24.9%) Former drinker 1816 (10.0%) 413 (11.7%) 2229 (10.3%) Current drinker 12123 (66.7%) 1787 (50.4%) 13910 (64.0%) Not reported 133 (0.7%) 43 (1.2%) 176 (0.8%) Smoking status Never smoker 10984 (60.4%) 2179 (61.5%) 13163 (60.6%) Former smoker 5738 (31.6%) 952 (26.9%) 6690 (30.8%) Current smoker 1452 (8.0%) 412 (11.6%) 1864 (8.6%) Not reported 0 (0.0%) 1 (0.0%) 1 (0.0%) Mediterranean-style diet score* 4.5 (1.8) 4.3 (1.8) 4.4 (1.8) Ultra-processed food intake (g/day) 403.4 (214.1) 341.9 (229.6) 393.4 (217.9) Note. Level of physical activity: a score was calculated ranging from 0 to 16 based on the frequency of walking, less vigorous and vigorous activity multiplied by two. Change in diet over time On average, over a follow-up period of 12 years, participants increased both adherence to the Mediterranean-style diet (β = 0.28, 95%CI = 0.25–0.31) and intake of UPF (β = 55.20 g/day, 95%CI = 51.71–58.69) (Table 2 ). Both Southern European-born and those born in Australia, New Zealand, or Northern Europe increased adherence to the Mediterranean-style diet, but those born in Southern Europe showed a larger increase (p for interaction < 0.001). In contrast, though participants born in Australia, New Zealand and Northern Europe increased their UPF intake (β = 66.23 g/day, 95%CI = 62.55–69.90), we did not observe any change for those born in Southern Europe (β=-2.38 g/day, 95%CI=-12.35-7.59). For GBD exposure food groups, participants increased consumption of vegetables, legumes, wholegrains, nuts and seeds, milk, processed meats, sugary sweetened beverages, seafood, and olive oil, but decreased consumption of fruit and red meat (Supplementary Table 1). We observed differences between participants born in Australia, New Zealand and Northern Europe compared to participants born in Southern Europe, whereby Southern European-born participants decreased vegetable intake. Changes in GBD exposure nutrients were also observed (Supplementary Table 1) Table 2 Change in diet over time Adjusted for energy* (Willett's method) β P>|z| Q L95%CI U95%CI P for interaction^ Mediterranean-style Diet Score Total Sample 0.28 < 0.001 0.000 0.25 0.31 ANZNE 0.16 < 0.001 0.000 0.13 0.19 0.000 SE 0.92 < 0.001 0.000 0.85 0.99 Ultra-processed Food (g/day) Total Sample 55.20 < 0.001 0.000 51.71 58.69 ANZNE 66.23 < 0.001 0.000 62.55 69.90 0.000 SE -2.38 0.640 0.640 -12.35 7.59 ANZNE = Australian, New Zealand or Northern European; SE = Southern European. *Energy adjustment was performed for dietary UPF, but not for the Mediterranean-style diet score. Association of change in diet with depression For every unit increase in Mediterranean-style diet score between baseline and follow-up, risk of depression decreased by 5% (RR = 0.95, 95%CI = 0.93–0.98) in the overall sample, and for birthplace subgroups (Table 3 ). This association was consistent in supplementary models where we examined diet at baseline only (longitudinal) and diet at follow-up only (cross-sectional) (Supplementary Table 3), and for changes to both male and female Mediterranean-style diet scores (Supplementary Table 4). Further, for every 90-gram/day increase in UPF between baseline and follow-up, the risk of depression increased by 5% (RR = 1.05, 95%CI = 1.03–1.07) in the overall sample and for Australian, New Zealand and Northern European-born participants (RR = 1.06, 95%CI = 1.03–1.08), but not for Southern European-born participants. Again, these results were consistent when considering associations at singular time points (Supplementary Table 3), and for males and females separately (Supplementary Table 3). For GBD exposure food groups and nutrients, we observed the following results. Across the whole sample, each additional serve of fruit, vegetables, legumes, wholegrains, nuts and seeds, and red meat was associated with a reduced risk of depression (Supplementary Table 3). However, the associations for legumes, wholegrains, and nuts and seeds were not observed in Southern European-born participants. We observed an association for milk intake for Southern European participants, whereby every increase in milk servings between baseline and follow-up was associated with a lower risk of depression. We also observed an association between Omega-3, Omega-6, polyunsaturated fat, sodium and monounsaturated fat and increased risk for depression across the whole sample. Whereas fibre and calcium were associated with reduced risk of depression across the whole sample (Supplementary Table 3). Some sex differences were observed. For example, increases in sugar-sweetened beverage intake were only associated with a higher risk of depression for males, whereas increasing consumption of wholegrains was associated with a lower risk of depression for females (Supplementary Table 4). In a supplementary analysis, we examined simultaneous changes in UPF intake and Mediterranean-style diet adherence together (Supplementary Table 4). Among the overall sample, as well as participants born in Australia, New Zealand, and Northern Europe, those who reduced their UPF intake, regardless of whether they increased the Mediterranean-style diet, had a lower risk of depression compared with those who simultaneously decreased their adherence to the Mediterranean-style diet and increased their UPF intake. Among participants born in Southern Europe, any combination of dietary improvement, either reducing UPF intake or increasing adherence to the Mediterranean-style diet, was associated with a lower risk of depression compared to those who simultaneously reduced Mediterranean-style diet and increased UPF. There was no evidence for a additive relationship whereby simultaneously increasing Mediterranean-style diet and reducing UPF intake led to a greater reduction in depression risk. Sensitivity analyses using UPF intake as a percentage of total intake (grams/day) were consistent with the primary findings (Supplementary Tables 2 and 3). Table 3 Association of change in diet with depression at follow-up Model 1 Model 2 Dietary Variable Subgroup P-value, Q-value RR L95%CI U95%CI P>|z| Q RR L95%CI U95%CI Mediterranean-style Diet Score Total Sample 0.084 0.143 0.98 0.96 1.00 0.000 0.000 0.95 0.93 0.98 ANZNE Born 0.001 0.004 0.95 0.93 0.98 0.001 0.004 0.95 0.93 0.98 SE Born 0.000 0.000 0.91 0.86 0.96 0.017 0.045 0.94 0.89 0.99 Ultra-processed Food (90g/day) Total Sample 0.339 0.454 1.01 0.99 1.03 0.000 0.000 1.05 1.03 1.07 ANZNE Born 0.000 0.000 1.06 1.04 1.08 0.000 0.000 1.06 1.03 1.08 SE Born 0.494 0.587 1.01 0.98 1.05 0.063 0.124 1.04 1.00 1.08 ANZNE = Australian, New Zealand or Northern European; SE = Southern European; RR = Risk Ratio; L95%CI = lower 95% confidence interval; Model 1. Adjusted for energy* (Willett's method) Model 2, as in 1, but additionally adjusted for age, sex, education, and ethnicity (except for place of birth subgroups) Discussion In this prospective cohort study of 21,718 participants, we examined how changes in diet composition were associated with risk of depression. Overall, both Mediterranean-style diet adherence and UPF exposure increased over time, while overall energy intake decreased over time. However, contrary to expectations, participants born in Southern Europe did not increase their UPF consumption over the follow-up period. Increasing adherence to the Mediterranean-style diet over time was associated with a lower risk of depression across all birthplace groups. In contrast, increasing exposure to UPF over the follow-up period was linked to an increased risk of depression in the whole sample and among participants born in Australia, New Zealand, and Northern Europe, but not in those born in Southern Europe, likely reflecting the absence of a meaningful change in UPF exposure within this group. Across the full sample, we observed increased intake of vegetables, legumes, wholegrains, nuts and seeds, milk, processed meats, sugar-sweetened beverages, seafood, and olive oil, alongside decreases in fruit and red meat consumption. Increasing intake of fruit, vegetables, legumes, wholegrains, nuts and seeds, and red meat over the follow-up period was associated with a reduced risk of depression. The protective association observed for red meat consumption is interesting, while high intake of red meat can increase the risk of depression, moderate intakes have been found to be protective of depression( 37 ). Consistent with previous studies, we found that exposure to UPFs was associated with increased risk of depression( 8 , 10 ), and adherence to a Mediterranean-style diet is associated with a reduced risk( 3 ). Importantly, we expanded on these past studies by demonstrating that temporal changes in dietary habits were also associated with the risk of developing depression. We also observed novel differences across birthplace in this sample, which comprised both migrants and non-migrants; participants born in Southern Europe decreased their vegetable intake, which contrasted with participants born in other regions. However, the protective associations for legumes, wholegrains, and nuts and seeds were not observed in Southern European-born participants. Instead, for this group, increased milk consumption was associated with a lower risk of depression. Future research is needed to investigate these findings, as the Southern European-born subsample was smaller and may therefore have had low power to detect these associations. Further, these inconsistencies may point to limitations of assessing individual food groups/nutrients versus whole dietary patterns. While adherence to the Mediterranean-style diet increased across all groups, exposure to UPFs did not increase for people born in southern European countries. These findings support what has been described as the Mediterranean diet paradox , whereby adherence to the Mediterranean-style diet has declined in Mediterranean countries but remained stable or even increased in non-Mediterranean countries( 38 , 39 ). It has also been suggested that a Mediterranean heritage may be protective against higher UPF consumption, consistent with evidence that UPF intake remains comparatively lower in the Mediterranean countries ( 40 ). These findings may reflect cultural preservation, as immigrant communities often maintain traditional food practices as a means of preserving cultural identity. We also explored how simultaneous changes in Mediterranean-style diet adherence and UPF exposure related to depression risk. Among the overall sample, as well as participants born in Australia, New Zealand, and Northern Europe, reducing UPF exposure, regardless of whether adherence to the Mediterranean-style diet increased, was associated with a lower risk of depression. Among those born in Southern Europe, any form of dietary improvement, either reducing UPF exposure or increasing adherence to the Mediterranean-style diet, was linked to lower depression risk. Notably, there was no evidence that making both changes simultaneously conferred additional benefit. This finding differs from a secondary analysis of the SMILES trial , which found that the therapeutic benefit of the Mediterranean-style diet was partly explained by reductions in UPF exposure( 41 ). The current findings highlight the influence of cultural heritage and acculturation on dietary behaviours and their downstream effects on mental health. Supporting traditional, health-promoting dietary practices within culturally diverse communities may therefore be a valuable strategy for preventing depression. These findings suggest that traditional dietary habits can be maintained over time even after migrating to a country where the predominant dietary pattern has a high proportion of UPFs. Interestingly, our results also demonstrate that people may simultaneously increase adherence to a Mediterranean-style dietary pattern whilst also increasing exposure to UPFs; this underscores that these are separate but complementary dimensions of diet, each of which may influence health through related yet potentially different mechanisms( 42 ). Although we did not explore mechanisms of action in this study, the Mediterranean-style diet and UPFs likely influence depression through distinct yet partially overlapping biological pathways( 43 ). One pathway is inflammation. Adherence to a Mediterranean-style diet has been consistently linked to reduced systemic inflammation, including lower levels of pro-inflammatory markers such as C-reactive protein and interleukin-6( 44 ), whereas high consumption of UPFs is associated with elevated inflammatory markers( 45 ). Chronic inflammation is a key contributor to depression, potentially altering neurotransmitter metabolism, impairing neuroplasticity, and dysregulating the hypothalamic–pituitary–adrenal axis( 46 ). Beyond inflammation, a Mediterranean-style diet may improve insulin sensitivity and vascular function( 47 , 48 ), thereby reducing the risk of metabolic and cardiovascular dysfunction, which are themselves linked to depression( 49 – 51 )). It may also support gut barrier integrity and a healthy gut microbiome, indirectly influencing brain health through the gut–brain axis( 43 ). Conversely, UPFs are often nutrient-poor and contain additives (e.g. emulsifiers), these UPF constituents can disrupt gut microbiota and compromise gut barrier integrity, insulin sensitivity and poor vascular function( 52 , 53 ). Future research is needed to further explore the potential mechanisms of action. In addition, in sub-group analysis, we observed sex differences in which dietary exposures were associated with risk of depression; for example, the Mediterranean-style diet score was only associated with a protective effect in women. Further research is needed to explore these potential sex differences and to confirm whether these are due to a loss of statistical power or a real sex difference. Our findings further suggest that specific GBD-defined dietary exposures, particularly fruit and vegetables, may be driving the protective associations observed, while other elements, such as whole grains, legumes, or olive oil, showed less consistent relationships. Similarly, sugar-sweetened beverages may be driving the negative association observed. Together, these findings suggest that interventions emphasising greater fruit and vegetable intake and fewer sugar-sweetened beverages could be particularly beneficial. The finding of this study also has implications for public health policy and initiatives such as regulations promoting the reduction of UPF. Potential regulations include warning labels on food packaging and taxes on UPF ( 54 ). This study has several strengths. Firstly, the large cohort of Australian adults and the diverse population result in a wide range of dietary patterns. The associations observed were supported by supplementary and sensitivity analyses and adjustment for important confounders. In addition to exploring diet at singular time points, we also explored change in diet over time, rather than diet measured at a single time point, a limitation of many previous studies( 3 , 11 , 12 ). Given the changing dietary landscape and the increasing prevalence of mental disorders, understanding how dietary patterns change over time and how these changes relate to mental disorders is critical to understanding whether diet could represent an appropriate population-level target to reduce the burden of mental disorders. In addition to these strengths, this study also has several limitations. Although many possible confounders were adjusted for, due to its observational design, we cannot rule out residual confounding. The dietary data were self-reported and subsequently subject to recall error and bias to over- or under-reporting. While FFQs have been used to adequately categorise foods for the Mediterranean-style diet or UPFs( 22 , 55 ), the FFQ used was not specifically designed to assess this dietary pattern, nor food processing, which may have resulted in some misclassification. Additionally, there was a change in the dietary questionnaire between baseline and follow-up and so changes in dietary patterns observed in this study may reflect changes to the questionnaire rather than the underlying diet. However, if the observed dietary changes were due solely to the change in questionnaire, we would expect the changes to occur consistently for both the Southern European-born and the Australian, New Zealand and Northern European-born participants, which is not consistent with our findings. Further, the Mediterranean-style diet score used is dependent on the dietary intake of the population studied and is not necessarily representative of a traditional Mediterranean-style diet, which limits the comparability and generalisability of the results( 22 ). Finally, depression was not assessed at baseline; therefore, antidepressant use was used as a proxy for depression at baseline( 31 , 32 ). Medication use is an imperfect proxy for depression, as antidepressants may be prescribed for other conditions in addition to depression and many people with depression may not take anti-depressant medication. This limitation may have resulted in misclassification. Conclusion The current study demonstrated that changes in dietary habits over time are associated with depression risk, highlighting the potential mental health benefits of maintaining or improving adherence to a Mediterranean-style diet. Despite an overall increase in UPF exposure, findings among participants of Mediterranean origin suggest partial preservation of traditional dietary practices even within a non-Mediterranean food environment. This dietary resilience may help buffer against adverse mental health outcomes and offer important insights for future policy and research. Public health strategies could focus on supporting culturally anchored dietary guidance and identifying factors that promote the retention of healthful, traditional eating patterns following migration. Declarations Funding The GLAD Project and D.A. are supported by a National Health and Medical Research Council Emerging Leader 2 Fellowship (grant #2009295 to A.O.).S.G. is funded by NHMRC Synergy Grant SOLVE CHD (#GNT1182301). A.O., W.M. and P.M. are funded by an NHMCRC Investigator Grant (#2009295, #2008971, and #2034008, respectively). F.N.J. is supported by a National Health and Medical Research Council Leader 1 Fellowship (grant #1194982). RO is supported by a Deakin University Postgraduate Research Scholarship. Acknowledgements MCCS cohort recruitment was funded by Cancer Council Victoria ( https://www.cancervic.org.au/ ) and VicHealth ( https://www.vichealth.vic.gov.au/ ). The MCCS was further supported by Australian National Health and Medical Research Council (NHMRC) ( https://www.nhmrc.gov.au/ ) grants 209057, 396414 and 1074383, and ongoing follow-up and data management has been funded by Cancer Council Victoria since 1995. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process. During the preparation of this work the author(s) used ChatGPT to edit the manuscript to improve readability. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article. Conflict of interest S.G., D.A., A.O., W.M., and M.M.L. 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 SerpHills Foundation, the Roberts Family Foundation, and the Fernwood Foundation. References GBD (2019) Mental Disorders Collaborators, Lancet Psychiatry . 9, 137–150 (2022) Australian Institute of Health and Welfare (2025) Prevalence and impact of mental illness (available at https://www.aihw.gov.au/mental-health/overview/prevalence-and-impact-of-mental-illness) Altun A, Brown H, Szoeke C, Goodwill AM (2019) The Mediterranean dietary pattern and depression risk: A systematic review. Neurol Psychiatry Brain Res 33:1–10 O’Neil A et al (2024) Lancet Reg Health West Pac 49:101142 Jacka FN et al (2017) BMC Med 15:1–13 Firth J et al (2020) World Psychiatry . 19, 360–380 Machado PP et al (2020) Eur J Nutr 59:2783–2792 Lane MM et al (2023) J Affect Disord 335:57–66 Jacka N (ed) (2010) Felice American Journal of Psychiatry . 167, 305–311 Mengist B et al (2025) BMC Med 23. 10.1186/s12916-025-04002-4 Yin W et al (2021) Int J Behav Nutr Phys Activity 18. 10.1186/s12966-021-01227-3 Lane MM et al (2022) Nutrients . 14, 1–22 Hodge A, Almeida OP, English DR, Giles GG, Flicker L (2013) Int Psychogeriatr 25:456–466 Allcock L, Mantzioris E, Villani A (2024) Nutrients 16. 10.3390/nu16030366 Milne RL et al (2017) Int J Epidemiol 46:1757–1757i Obeid CA, Gubbels JS, Jaalouk D, Kremers SPJ, Oenema A (2022) Adherence to the Mediterranean diet among adults in Mediterranean countries: a systematic literature review. Eur J Nutr 61:3327–3344 Ashtree DN et al (2025) JMIR Res Protoc 14:e65576 Hodge AM, English DR, O’Dea K, Giles GG (2007) Am J Epidemiol 165:603–610 Ireland P et al (1994) Asia Pac J Clin Nutr 3:19–31 Hodge AM et al (2009) Public Health Nutr 12:2438–2447 Hodge AM et al (2007) Nutrition, Metabolism and Cardiovascular Diseases . 17, 415–426 Trichopoulou A, Costacou T, Bamia C, Trichopoulou C (2003) New Engl J 348:2599–2608 Monteiro CA et al (2019) Public Health Nutr 22:936–941 Machado PP et al (2019) BMJ Open 9:e029544 Gauci S et al (2025) Eur J Prev Cardiol. 10.1093/eurjpc/zwaf378 Bassett JK et al (2016) Public Health Nutr 19:2357–2368 The Cancer Council Victoria Epidemiology Centre, Melbourne Collaborative Cohort Study Databook Vol. 3: Diet & Alcohol Online (2008) (available at https://www.cancervic.org.au/research/epidemiology/health_2020/health2020-databook3-diet ) Lewis J, Milligan G, Hunt A (1995) NUTTAB95: Nutrient data Data Table for Use in Australia. Australian Government Publishing Service, Canberra, Australia Kessler RC et al (2002) Psychol Med 32:959–976 Andrews G, Slade T (2001) Aust N Z J Public Health 25:494–497 Skelton M et al (2021) Am J Med Genet Part B: Neuropsychiatric Genet 186:389–398 Gardarsdottir H, Egberts ACG, van Dijk L, Sturkenboom MCJM, Heerdink ER (2009) Pharmacoepidemiol Drug Saf 18:7–15 Willett W, Howe G, Kushi L (1997) Am J Clin Nutr 65:1220S–1228S Brauer M et al (2024) Lancet 403:2162–2203 Hodge AM et al (2016) Cancer Causes Control 27:907–917 Simes RJ (1986) Biometrika 73:751–754 Jacka FN et al (2012) Red meat consumption and mood and anxiety disorders. Psychother Psychosom 81:196–198 Damigou E et al (2023) Adherence to a Mediterranean type of diet in the world: a geographical analysis based on a systematic review of 57 studies with 1,125,560 participants. Int J Food Sci Nutr 74:799–813 Vilarnau C et al (2019) Eur J Clin Nutr 72:83–91 Marino M et al (2021) A systematic review of worldwide consumption of ultra-processed foods: Findings and criticisms. Nutrients 13. 10.3390/nu13082778 Lane MM et al Change in Ultra-Processed Food Consumption Moderates Clinical Trial Outcomes in Depression: A Secondary Analysis of the SMILES Randomised Controlled Trial (2023), 10.20944/preprints202308.1110.v1 Tapsell LC, Neale EP, Satija A, Hu FB (2016) Foods, nutrients, and dietary patterns: Interconnections and implications for dietary guidelines. Adv Nutr 7:445–454 Marx W et al (2021) Mol Psychiatry 26:134–150 Wu PY, Chen KM, Tsai WC (2021) The Mediterranean Dietary Pattern and Inflammation in Older Adults: A Systematic Review and Meta-Analysis. Adv Nutr 12:363–373 Lane MM et al (2022) Nutrients . 14, 3309 Forbes MP et al (2021) Drugs Aging 38:451–467 Tuttolomondo A et al (2019) Metabolic and vascular effect of the mediterranean diet. Int J Mol Sci 20. 10.3390/ijms20194716 Bruna-Mejias A et al (2025) Comparison of the Mediterranean Diet and Other Therapeutic Strategies in Metabolic Syndrome: A Systematic Review and Meta-Analysis. Int J Mol Sci 26. 10.3390/ijms26125887 Kim HB, Wolf BJ, Kim JH (2023) Association of metabolic syndrome and its components with the risk of depressive symptoms: A systematic review and meta-analysis of cohort studies. J Affect Disord 323:46–54 Yang W et al (2024) European Psychiatry . 67 10.1192/j.eurpsy.2024.1775 Berk M et al (2023) Comorbidity between major depressive disorder and physical diseases: a comprehensive review of epidemiology, mechanisms and management Whelan K, Bancil AS, Lindsay JO, Chassaing B (2024) Ultra-processed foods and food additives in gut health and disease. Nat Rev Gastroenterol Hepatol 21:406–427 Juul F, Vaidean G, Parekh N (2021) Ultra-processed Foods and Cardiovascular Diseases: Potential Mechanisms of Action. Adv Nutr 12:1673–1680 Popkin BM et al (2021) Towards unified and impactful policies to reduce ultra-processed food consumption and promote healthier eating. Lancet Diabetes Endocrinol 9:462–470 Khandpur N et al (2021) J Nutr Sci 10:e77 Additional Declarations The authors declare potential competing interests as follows: S.G., D.A., A.O., W.M., and M.M.L. 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 SerpHills Foundation, the Roberts Family Foundation, and the Fernwood Foundation. Supplementary Files MCCSGLADSupplementaryTables28.10.2025.xlsx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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","formattedTitle":"\u003cp\u003eTemporal Changes between Adherence to a Mediterranean-Style Diet and Consumption of Ultra-Processed Foods with 12-Year Depression Risk in the Melbourne Collaborative Cohort Study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMental disorders are among the leading contributors to the global burden of disease, with depressive disorders most prevalent(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In Australia, approximately 43% of people aged 16 to 85 years will experience a mental disorder during their lifetime, and around 16% will experience an affective disorder such as depression(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These conditions place a significant burden on individuals, families, and the health system.\u003c/p\u003e\u003cp\u003eOver the past decade, habitual diet has emerged as an important target for the prevention and treatment of depression. Adherence to dietary patterns characterised by whole, minimally processed, and plant-based foods, such as the Mediterranean-style diet, is associated with a reduced risk of depression(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and a Mediterranean-style diet may serve as an effective treatment strategy for depression(\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, in Australia, the predominant dietary pattern is characterised by a high intake of ultra-processed foods (UPFs)(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This is concerning, given the association between UPF exposure and increased risk of adverse health outcomes, including depression(\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Concurrently, even modest dietary changes are associated with reduced depression risk. For example, in a study of Swedish women, each unit increase in adherence to the Mediterranean-style diet was associated with an approximately 5% reduction in depression risk(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Therefore, even small shifts toward a Mediterranean-style diet, accompanied by lower exposure to UPFs, could yield meaningful mental health benefits. Yet an important limitation of prior research is that most studies have investigated habitual diet at a single time point(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), rather than examining how changes in diet over time influence depression outcomes.\u003c/p\u003e\u003cp\u003eMuch of the research investigating the role of the Mediterranean-style diet in health outcomes comes from Mediterranean countries, where it represents a traditional dietary pattern(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Studies conducted in non-Mediterranean countries, such as Australia, have had more variable findings (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These inconsistencies likely reflect cultural differences, traditional eating habits, and the broader food environment. Migration, too, provides a natural context in which to study a wider range of dietary patterns and how dietary patterns change when moving from a country where a traditional diet is common to one where a Western diet dominates.\u003c/p\u003e\u003cp\u003eThe Melbourne Collaborative Cohort Study (MCCS) offers a unique opportunity to explore dietary change and a greater range of dietary patterns as a result of migration, as approximately one-quarter of participants were migrants from Southern Europe, including Greece, Italy, and Malta, countries where the Mediterranean-style diet is traditionally followed(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This culturally diverse cohort provides an opportunity to explore how adherence to Mediterranean-style diets and exposure to UPFs evolve over time, particularly comparing participants of Southern European origin with those from non-Mediterranean backgrounds. We aimed to investigate how changes in adherence to a Mediterranean-style diet and changes in exposure to UPFs over time are associated with the risk of depression in the MCCS cohort (primary aim). We also investigated whether these associations differed by birthplace group (Southern European vs Australian/New Zealand/Northern European) and explored which specific dietary and nutrient components were associated with risk of depression (secondary aim).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and pre-registration\u003c/p\u003e\u003cp\u003eThis 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. The GLAD data analysis plan was prospectively registered with Open Science Framework (OSF; osf.io/67qfs), and the data analysis plan and GLAD project protocol have been published elsewhere(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe MCCS was originally approved by the Cancer Council Victoria's Human Research Ethics Committee, and participants provided written informed consent to participate and for researchers' access to their medical records(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The current study was approved for exemption from ethical review in accordance with the National Statement on Ethical Conduct in Human Research (Section 5.1.22) by the Deakin University Human Research Ethics Committee (project number: 2023 − 385).\u003c/p\u003e\u003cp\u003eCohort profile\u003c/p\u003e\u003cp\u003eA detailed description of the MCCS cohort has been previously published(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In brief, 41,513 participants between the ages of 27 and 76 years were recruited from Melbourne and surrounding areas between 1990 and 1994. Of these, 59% were women, and 99% were aged between 40 and 69 years. Approximately 24% of the sample were migrants from Southern Europe (comprised of the following ethnic groups: Greek, Italian, Macedonian, Maltese, and Spanish). These participants have distinct dietary and lifestyle patterns from those born in Australia or New Zealand (69%) and Northern Europe (6%; Northern European group was comprised of the following ethnic groups: English, Irish, Latvian, Scottish, Dutch and Welsh)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Baseline data collection included questionnaires on lifestyle, medical history, medication use, and a food frequency questionnaire. The first wave of follow-up was completed between 1995 and 1998 (follow-up 1), with a second follow-up (follow-up 2) completed between 2003 and 2007. We used baseline data and data from the second follow-up in these analyses. Full details of measured data at each wave are available in the MCCS Cohort Profile (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in the reference(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)).\u003c/p\u003e\u003cp\u003eParticipants were eligible for this study if they completed the baseline dietary questionnaire and both dietary and depression measures (derived from the psychological distress questionnaire) at the second follow-up (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We excluded participants taking antidepressant medication at baseline, as a proxy for pre-existing depression, and additionally excluded participants reporting dietary intake \u0026lt; 1st or \u0026gt; 99th percentile at baseline or follow-up, leaving n = 21,718 eligible for inclusion in these analyses.\u003c/p\u003e\u003cp\u003eExposure: Dietary assessment\u003c/p\u003e\u003cp\u003eA self-administered 121-item Food Frequency Questionnaire (FFQ), designed and validated for use specifically in the MCCS(\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e–\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), was used to assess dietary intake at baseline (1990–1994) and a modified version including some new foods and portion size photos at follow-up 2 (2003–2007). Using this dietary data, scores were derived for adherence to the Mediterranean-style diet(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and exposure to UPFs(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Mediterranean-style diet adherence was assessed using the method described by Trichopoulou et al. (2003), and in line with other studies that have used this dataset(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Participants received a score of 1 point if their intake was above the MCCS sex-specific median for vegetables, fruit, cereals, legumes, and fish, and scored 0 if their intake was below this median for each dietary component. Additionally, participants were assigned a score of 1 if their intake for dairy and red meat was below the MCCS sex-specific median intake, and a score of 0 if their intake was above this value. For alcohol, 1 point was assigned for daily intakes of 10–50 g in men and 5–25 g in women; lower or higher intakes scored 0(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In this study, olive oil intake was used instead of the ratio of monounsaturated fats to saturated fats as used in the original Trichopoulou scoring system; this is consistent with other studies using this cohort(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Mediterranean-style diet Scores ranged from 0–9, with a higher score reflecting greater adherence(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUltra-processed food (UPF) intake was determined using the Nova classification system, as described in detail elsewhere(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). FFQ food items were previously classified according to the Nova food classification system by two researchers with expertise in the Australian food environment, following the methods of Machado et al. (2019)(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and applied in earlier MCCS studies (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Food items were grouped into four Nova categories: Groups 1–3 represent non-UPF, while Group 4 consists of UPF. Only Group 4 intake was considered as an exposure of interest in this study. Examples of Group 4 products include regular and diet soft drinks, sweet or savoury packaged snacks, confectionery, margarine, reconstituted meat products, and many pre-prepared frozen or shelf-stable dishes when these products are made up of food substances of no culinary use and/or contain classes of additives with cosmetic function. The mean daily intake of UPFs was determined by converting frequencies into grams. This was based on sex-specific portion sizes of each food and multiplied by the daily equivalent frequency as per previous research(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Energy was estimated based on the Nutrient Data Table for Use in Australia 1995 (NUTTAB 95), a food composition database containing information for 1800 foods and beverages available in Australia(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) or AUStralian Food and NUTrient Database (AUSNUT) 2007 for FUP 2.\u003c/p\u003e\u003cp\u003eTo comprehensively evaluate the relationship between the Mediterranean-style diet and UPF with depression, we further disaggregated dietary intake into food groups and nutrients. These groups included 1) Mediterranean-style diet components: grams per day of fruit, vegetables, legumes, cereals (wholegrains, pasta, rice, bread, etc), fish/seafood and olive oil; 2) broader dietary exposures as defined by the Global Burden of Disease study (GBD exposures): grams per day of nuts and seeds, milk, red meat, processed meat and sugar-sweetened beverages fibre, calcium, omega-3 and sodium, and percentage energy per day of omega-6, polyunsaturated fat, monounsaturated fat and saturated fat. We scaled the units for each dietary exposure to be equivalent to serving sizes (for food groups) or recommended daily intakes (for nutrients) to assist with interpretation (see supplementary table 3). Both scaled and unscaled units are presented in the supplementary material.\u003c/p\u003e\u003cp\u003eOutcome: Depression\u003c/p\u003e\u003cp\u003eThe risk of depression was derived from the ten-item Kessler Psychological Distress Scale (K10)(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Although the K10 assesses non-specific psychological distress, higher K10 scores correlate with the diagnosis of common mental disorders. A cut-off score of 20 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) has sound sensitivity (0.66) and high specificity (0.92) for the diagnosis of depressive disorder(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). As a measure of depression could not be derived at baseline because it was not assessed, antidepressant medication use was considered as a proxy of baseline depression. Participants who reported taking antidepressants at baseline were excluded from the analysis to avoid confounding, which is consistent with previous analyses in this cohort(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Medication use has previously been found to correlate with depression (r = 0.60 to 0.73), with a sensitivity of 0.80 and specificity of 0.67 for depression diagnosis(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAssessment of covariates\u003c/p\u003e\u003cp\u003eCovariates were identified a priori in the GLAD protocol paper(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) in order to ensure a consistent minimal adjustment set for all studies participating in the GLAD Taskforce. These covariates included age, sex and education, and we included an adjustment for energy intake via Willett’s residual method for all dietary exposures reported in grams per day, as defined above(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). We additionally included body mass index (BMI), alcohol intake (lifetime abstainers, ex-drinkers, and current drinkers), smoking (never smoked, current smoker, and former smoker), and physical activity in a sensitivity model. Physical activity was scored from 0 to 16, depending on the frequency and intensity of activities(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStatistical analyses\u003c/p\u003e\u003cp\u003eParticipant characteristics were summarised using means and standard deviations (SD) for continuous variables and frequencies and percentages for categorical variables. Descriptive statistics for dietary exposures were additionally summarised using means (SD) and medians (Q1 to Q3).\u003c/p\u003e\u003cp\u003eTo assess whether dietary intake changed over time, we fitted a generalised estimating equations linear regression with robust standard errors between each dietary exposure and time to account for the repeated measurements within participants. This model was fitted for the overall sample, and for subgroups corresponding to birthplace 1) Australian, New Zealand and Northern European-born participants, and 2) Southern European-born participants. To determine the associations between the specified dietary exposures and depression, we fitted three models:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ea Poisson regression with robust standard errors to estimate the risk ratios for depression associated with \u003cem\u003echange\u003c/em\u003e in each dietary exposure. Change in diet was calculated as follow-up intake minus baseline intake (main model). To test whether changes in dietary exposures over time differed by birthplace, we included an interaction term between time and birthplace in the GEE models. To determine whether simultaneous changes to UPF and Mediterranean diet were associated with depression, we additionally created a four-level joint-change categorical variable representing 1) participants who decreased adherence to the Mediterranean-style diet \u003cem\u003eand\u003c/em\u003e increased UPF intake (reference category); 2) participants who reduced adherence to the Mediterranean-style diet but decreased UPF intake; 3) participants who increased adherence to the Mediterranean-style diet but increased UPF intake; and 4) participants who increased adherence Mediterranean-style diet and reduced UPF intake. This categorical variable was then included in a supplementary analysis.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ea Poisson regression with robust standard errors to estimate depression risk associated with diet at baseline (longitudinal model)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ea logistic regression to determine the cross-sectional association between diet at follow-up and odds of depression at follow-up.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll models were fitted for the overall sample, and separately for the subgroups, as described above. We also fitted a subgroup analysis model, disaggregating the results by sex. Three levels of adjustment were included: 1) dietary exposures (all dietary measured reported in grams) were first re-modelled for energy intake using Willett’s residual method; 2) as in 1 but additionally adjusted for age, sex, education, and ethnicity (except for place of birth subgroups), and 3) as in 2 but additionally adjusted for BMI, physical activity, smoking and alcohol.\u003c/p\u003e\u003cp\u003eGiven the number of statistical models fitted, we adjusted for multiple testing using Simes Q values(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), which are interpreted in the same way as p-values with a q \u0026lt; 0.05 threshold. Adjustment was done separately for each level of covariate adjustment, with overall and birthplace subgroups combined, sex subgroups were done in a separate adjustment again separately for each level of covariate adjustment. Models were fitted in Stata 18.0, and all assumptions were assessed prior to analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDemographic Characteristics\u003c/p\u003e\u003cp\u003eThis study included 21,718 participants (60.8% female) at baseline. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the participants\u0026rsquo; sociodemographic and lifestyle characteristics at baseline for the total sample and according to place of birth. Participants born in Southern Europe reported lower education, lower physical activity, higher BMI, and were less likely to be current drinkers. However, both groups had similar baseline characteristics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline descriptive characteristics of the study population according to place of birth\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAustralian and Northern European Born\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;18,174)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSouthern European Born\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,544)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal Sample\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;21,718)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.2 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.0 (7.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.2 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11122 (61.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2006 (56.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13128 (60.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHighest level of education achieved\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDid not complete high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7956 (43.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2854 (80.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10810 (49.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCompleted high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2020 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e265 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2285 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSome tertiary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8198 (45.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e425 (12.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8623 (39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBMI (kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.0 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.5 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.4 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLevel of physical activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.6 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.7 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.3 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrinking status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNever drinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4102 (22.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1301 (36.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5403 (24.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFormer drinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1816 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e413 (11.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2229 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCurrent drinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12123 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1787 (50.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13910 (64.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e176 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNever smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10984 (60.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2179 (61.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13163 (60.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFormer smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5738 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e952 (26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6690 (30.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1452 (8.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e412 (11.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1864 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNot reported\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMediterranean-style diet score*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.5 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.3 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.4 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUltra-processed food intake (g/day)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e403.4 (214.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e341.9 (229.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e393.4 (217.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote. Level of physical activity: a score was calculated ranging from 0 to 16 based on the frequency of walking, less vigorous and vigorous activity multiplied by two.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eChange in diet over time\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOn average, over a follow-up period of 12 years, participants increased both adherence to the Mediterranean-style diet (β\u0026thinsp;=\u0026thinsp;0.28, 95%CI\u0026thinsp;=\u0026thinsp;0.25\u0026ndash;0.31) and intake of UPF (β\u0026thinsp;=\u0026thinsp;55.20 g/day, 95%CI\u0026thinsp;=\u0026thinsp;51.71\u0026ndash;58.69) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both Southern European-born and those born in Australia, New Zealand, or Northern Europe increased adherence to the Mediterranean-style diet, but those born in Southern Europe showed a larger increase (p for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, though participants born in Australia, New Zealand and Northern Europe increased their UPF intake (β\u0026thinsp;=\u0026thinsp;66.23 g/day, 95%CI\u0026thinsp;=\u0026thinsp;62.55\u0026ndash;69.90), we did not observe any change for those born in Southern Europe (β=-2.38 g/day, 95%CI=-12.35-7.59).\u003c/p\u003e\u003cp\u003eFor GBD exposure food groups, participants increased consumption of vegetables, legumes, wholegrains, nuts and seeds, milk, processed meats, sugary sweetened beverages, seafood, and olive oil, but decreased consumption of fruit and red meat (Supplementary Table\u0026nbsp;1). We observed differences between participants born in Australia, New Zealand and Northern Europe compared to participants born in Southern Europe, whereby Southern European-born participants decreased vegetable intake. Changes in GBD exposure nutrients were also observed (Supplementary Table\u0026nbsp;1)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChange in diet over time\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e\u003cp\u003eAdjusted for energy* (Willett's method)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026gt;|z|\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eL95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eU95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP for interaction^\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMediterranean-style Diet Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANZNE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eUltra-processed Food (g/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANZNE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-12.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eANZNE\u0026thinsp;=\u0026thinsp;Australian, New Zealand or Northern European; SE\u0026thinsp;=\u0026thinsp;Southern European. *Energy adjustment was performed for dietary UPF, but not for the Mediterranean-style diet score.\u003c/p\u003e\u003cp\u003eAssociation of change in diet with depression\u003c/p\u003e\u003cp\u003eFor every unit increase in Mediterranean-style diet score between baseline and follow-up, risk of depression decreased by 5% (RR\u0026thinsp;=\u0026thinsp;0.95, 95%CI\u0026thinsp;=\u0026thinsp;0.93\u0026ndash;0.98) in the overall sample, and for birthplace subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This association was consistent in supplementary models where we examined diet at baseline only (longitudinal) and diet at follow-up only (cross-sectional) (Supplementary Table\u0026nbsp;3), and for changes to both male and female Mediterranean-style diet scores (Supplementary Table\u0026nbsp;4). Further, for every 90-gram/day increase in UPF between baseline and follow-up, the risk of depression increased by 5% (RR\u0026thinsp;=\u0026thinsp;1.05, 95%CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.07) in the overall sample and for Australian, New Zealand and Northern European-born participants (RR\u0026thinsp;=\u0026thinsp;1.06, 95%CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.08), but not for Southern European-born participants. Again, these results were consistent when considering associations at singular time points (Supplementary Table\u0026nbsp;3), and for males and females separately (Supplementary Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eFor GBD exposure food groups and nutrients, we observed the following results. Across the whole sample, each additional serve of fruit, vegetables, legumes, wholegrains, nuts and seeds, and red meat was associated with a reduced risk of depression (Supplementary Table\u0026nbsp;3). However, the associations for legumes, wholegrains, and nuts and seeds were not observed in Southern European-born participants. We observed an association for milk intake for Southern European participants, whereby every increase in milk servings between baseline and follow-up was associated with a lower risk of depression. We also observed an association between Omega-3, Omega-6, polyunsaturated fat, sodium and monounsaturated fat and increased risk for depression across the whole sample. Whereas fibre and calcium were associated with reduced risk of depression across the whole sample (Supplementary Table\u0026nbsp;3). Some sex differences were observed. For example, increases in sugar-sweetened beverage intake were only associated with a higher risk of depression for males, whereas increasing consumption of wholegrains was associated with a lower risk of depression for females (Supplementary Table\u0026nbsp;4).\u003c/p\u003e\u003cp\u003eIn a supplementary analysis, we examined simultaneous changes in UPF intake and Mediterranean-style diet adherence together (Supplementary Table\u0026nbsp;4). Among the overall sample, as well as participants born in Australia, New Zealand, and Northern Europe, those who reduced their UPF intake, regardless of whether they increased the Mediterranean-style diet, had a lower risk of depression compared with those who simultaneously decreased their adherence to the Mediterranean-style diet and increased their UPF intake. Among participants born in Southern Europe, any combination of dietary improvement, either reducing UPF intake or increasing adherence to the Mediterranean-style diet, was associated with a lower risk of depression compared to those who simultaneously reduced Mediterranean-style diet and increased UPF. There was no evidence for a additive relationship whereby simultaneously increasing Mediterranean-style diet and reducing UPF intake led to a greater reduction in depression risk. Sensitivity analyses using UPF intake as a percentage of total intake (grams/day) were consistent with the primary findings (Supplementary Tables\u0026nbsp;2 and 3).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of change in diet with depression at follow-up\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDietary Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubgroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value,\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eL95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eU95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP\u0026gt;|z|\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eRR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eL95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eU95%CI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMediterranean-style Diet Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANZNE Born\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSE Born\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eUltra-processed Food (90g/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANZNE Born\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSE Born\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eANZNE\u0026thinsp;=\u0026thinsp;Australian, New Zealand or Northern European; SE\u0026thinsp;=\u0026thinsp;Southern European; RR\u0026thinsp;=\u0026thinsp;Risk Ratio; L95%CI\u0026thinsp;=\u0026thinsp;lower 95% confidence interval; Model 1. Adjusted for energy* (Willett's method)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 2, as in 1, but additionally adjusted for age, sex, education, and ethnicity (except for place of birth subgroups)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this prospective cohort study of 21,718 participants, we examined how changes in diet composition were associated with risk of depression. Overall, both Mediterranean-style diet adherence and UPF exposure increased over time, while overall energy intake decreased over time. However, contrary to expectations, participants born in Southern Europe did not increase their UPF consumption over the follow-up period. Increasing adherence to the Mediterranean-style diet over time was associated with a lower risk of depression across all birthplace groups. In contrast, increasing exposure to UPF over the follow-up period was linked to an increased risk of depression in the whole sample and among participants born in Australia, New Zealand, and Northern Europe, but not in those born in Southern Europe, likely reflecting the absence of a meaningful change in UPF exposure within this group.\u003c/p\u003e\u003cp\u003eAcross the full sample, we observed increased intake of vegetables, legumes, wholegrains, nuts and seeds, milk, processed meats, sugar-sweetened beverages, seafood, and olive oil, alongside decreases in fruit and red meat consumption. Increasing intake of fruit, vegetables, legumes, wholegrains, nuts and seeds, and red meat over the follow-up period was associated with a reduced risk of depression. The protective association observed for red meat consumption is interesting, while high intake of red meat can increase the risk of depression, moderate intakes have been found to be protective of depression(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Consistent with previous studies, we found that exposure to UPFs was associated with increased risk of depression(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), and adherence to a Mediterranean-style diet is associated with a reduced risk(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Importantly, we expanded on these past studies by demonstrating that temporal \u003cem\u003echanges\u003c/em\u003e in dietary habits were also associated with the risk of developing depression.\u003c/p\u003e\u003cp\u003eWe also observed novel differences across birthplace in this sample, which comprised both migrants and non-migrants; participants born in Southern Europe decreased their vegetable intake, which contrasted with participants born in other regions. However, the protective associations for legumes, wholegrains, and nuts and seeds were not observed in Southern European-born participants. Instead, for this group, increased milk consumption was associated with a lower risk of depression. Future research is needed to investigate these findings, as the Southern European-born subsample was smaller and may therefore have had low power to detect these associations. Further, these inconsistencies may point to limitations of assessing individual food groups/nutrients versus whole dietary patterns. While adherence to the Mediterranean-style diet increased across all groups, exposure to UPFs did not increase for people born in southern European countries. These findings support what has been described as the \u003cem\u003eMediterranean diet paradox\u003c/em\u003e, whereby adherence to the Mediterranean-style diet has declined in Mediterranean countries but remained stable or even increased in non-Mediterranean countries(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). It has also been suggested that a Mediterranean heritage may be protective against higher UPF consumption, consistent with evidence that UPF intake remains comparatively lower in the Mediterranean countries (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). These findings may reflect cultural preservation, as immigrant communities often maintain traditional food practices as a means of preserving cultural identity.\u003c/p\u003e\u003cp\u003eWe also explored how simultaneous changes in Mediterranean-style diet adherence and UPF exposure related to depression risk. Among the overall sample, as well as participants born in Australia, New Zealand, and Northern Europe, reducing UPF exposure, regardless of whether adherence to the Mediterranean-style diet increased, was associated with a lower risk of depression. Among those born in Southern Europe, any form of dietary improvement, either reducing UPF exposure or increasing adherence to the Mediterranean-style diet, was linked to lower depression risk. Notably, there was no evidence that making both changes simultaneously conferred additional benefit. This finding differs from a secondary analysis of the \u003cem\u003eSMILES trial\u003c/em\u003e, which found that the therapeutic benefit of the Mediterranean-style diet was partly explained by reductions in UPF exposure(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). The current findings highlight the influence of cultural heritage and acculturation on dietary behaviours and their downstream effects on mental health. Supporting traditional, health-promoting dietary practices within culturally diverse communities may therefore be a valuable strategy for preventing depression. These findings suggest that traditional dietary habits can be maintained over time even after migrating to a country where the predominant dietary pattern has a high proportion of UPFs. Interestingly, our results also demonstrate that people may simultaneously increase adherence to a Mediterranean-style dietary pattern whilst also increasing exposure to UPFs; this underscores that these are separate but complementary dimensions of diet, each of which may influence health through related yet potentially different mechanisms(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough we did not explore mechanisms of action in this study, the Mediterranean-style diet and UPFs likely influence depression through distinct yet partially overlapping biological pathways(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). One pathway is inflammation. Adherence to a Mediterranean-style diet has been consistently linked to reduced systemic inflammation, including lower levels of pro-inflammatory markers such as C-reactive protein and interleukin-6(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), whereas high consumption of UPFs is associated with elevated inflammatory markers(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Chronic inflammation is a key contributor to depression, potentially altering neurotransmitter metabolism, impairing neuroplasticity, and dysregulating the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Beyond inflammation, a Mediterranean-style diet may improve insulin sensitivity and vascular function(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), thereby reducing the risk of metabolic and cardiovascular dysfunction, which are themselves linked to depression(\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e)). It may also support gut barrier integrity and a healthy gut microbiome, indirectly influencing brain health through the gut\u0026ndash;brain axis(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Conversely, UPFs are often nutrient-poor and contain additives (e.g. emulsifiers), these UPF constituents can disrupt gut microbiota and compromise gut barrier integrity, insulin sensitivity and poor vascular function(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Future research is needed to further explore the potential mechanisms of action. In addition, in sub-group analysis, we observed sex differences in which dietary exposures were associated with risk of depression; for example, the Mediterranean-style diet score was only associated with a protective effect in women. Further research is needed to explore these potential sex differences and to confirm whether these are due to a loss of statistical power or a real sex difference.\u003c/p\u003e\u003cp\u003eOur findings further suggest that specific GBD-defined dietary exposures, particularly fruit and vegetables, may be driving the protective associations observed, while other elements, such as whole grains, legumes, or olive oil, showed less consistent relationships. Similarly, sugar-sweetened beverages may be driving the negative association observed. Together, these findings suggest that interventions emphasising greater fruit and vegetable intake and fewer sugar-sweetened beverages could be particularly beneficial. The finding of this study also has implications for public health policy and initiatives such as regulations promoting the reduction of UPF. Potential regulations include warning labels on food packaging and taxes on UPF (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study has several strengths. Firstly, the large cohort of Australian adults and the diverse population result in a wide range of dietary patterns. The associations observed were supported by supplementary and sensitivity analyses and adjustment for important confounders. In addition to exploring diet at singular time points, we also explored change in diet over time, rather than diet measured at a single time point, a limitation of many previous studies(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Given the changing dietary landscape and the increasing prevalence of mental disorders, understanding how dietary patterns change over time and how these changes relate to mental disorders is critical to understanding whether diet could represent an appropriate population-level target to reduce the burden of mental disorders.\u003c/p\u003e\u003cp\u003eIn addition to these strengths, this study also has several limitations. Although many possible confounders were adjusted for, due to its observational design, we cannot rule out residual confounding. The dietary data were self-reported and subsequently subject to recall error and bias to over- or under-reporting. While FFQs have been used to adequately categorise foods for the Mediterranean-style diet or UPFs(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), the FFQ used was not specifically designed to assess this dietary pattern, nor food processing, which may have resulted in some misclassification. Additionally, there was a change in the dietary questionnaire between baseline and follow-up and so changes in dietary patterns observed in this study may reflect changes to the questionnaire rather than the underlying diet. However, if the observed dietary changes were due solely to the change in questionnaire, we would expect the changes to occur consistently for both the Southern European-born and the Australian, New Zealand and Northern European-born participants, which is not consistent with our findings. Further, the Mediterranean-style diet score used is dependent on the dietary intake of the population studied and is not necessarily representative of a traditional Mediterranean-style diet, which limits the comparability and generalisability of the results(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Finally, depression was not assessed at baseline; therefore, antidepressant use was used as a proxy for depression at baseline(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Medication use is an imperfect proxy for depression, as antidepressants may be prescribed for other conditions in addition to depression and many people with depression may not take anti-depressant medication. This limitation may have resulted in misclassification.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study demonstrated that changes in dietary habits over time are associated with depression risk, highlighting the potential mental health benefits of maintaining or improving adherence to a Mediterranean-style diet. Despite an overall increase in UPF exposure, findings among participants of Mediterranean origin suggest partial preservation of traditional dietary practices even within a non-Mediterranean food environment. This dietary resilience may help buffer against adverse mental health outcomes and offer important insights for future policy and research. Public health strategies could focus on supporting culturally anchored dietary guidance and identifying factors that promote the retention of healthful, traditional eating patterns following migration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe GLAD Project and D.A. are supported by a National Health and Medical Research Council Emerging Leader 2 Fellowship (grant #2009295 to A.O.).S.G. is funded by NHMRC Synergy Grant SOLVE CHD (#GNT1182301). A.O., W.M. and P.M. are funded by an NHMCRC Investigator Grant (#2009295, #2008971, and #2034008, respectively). F.N.J. is supported by a National Health and Medical Research Council Leader 1 Fellowship (grant #1194982). RO is supported by a Deakin University Postgraduate Research Scholarship.\u003c/p\u003e\u003cp\u003eAcknowledgements\u003c/p\u003e\u003cp\u003eMCCS cohort recruitment was funded by Cancer Council Victoria (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancervic.org.au/\u003c/span\u003e\u003cspan address=\"https://www.cancervic.org.au/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and VicHealth (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.vichealth.vic.gov.au/\u003c/span\u003e\u003cspan address=\"https://www.vichealth.vic.gov.au/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The MCCS was further supported by Australian National Health and Medical Research Council (NHMRC) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nhmrc.gov.au/\u003c/span\u003e\u003cspan address=\"https://www.nhmrc.gov.au/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) grants 209057, 396414 and 1074383, and ongoing follow-up and data management has been funded by Cancer Council Victoria since 1995.\u003c/p\u003e\u003cp\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process.\u003c/p\u003e\u003cp\u003eDuring the preparation of this work the author(s) used ChatGPT to edit the manuscript to improve readability. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.\u003c/p\u003e\u003cp\u003eConflict of interest\u003c/p\u003e\u003cp\u003eS.G., D.A., A.O., W.M., and M.M.L. 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 SerpHills Foundation, the Roberts Family Foundation, and the Fernwood Foundation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD (2019) Mental Disorders Collaborators, \u003cem\u003eLancet Psychiatry\u003c/em\u003e. 9, 137\u0026ndash;150 (2022)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAustralian Institute of Health and Welfare (2025) Prevalence and impact of mental illness (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.aihw.gov.au/mental-health/overview/prevalence-and-impact-of-mental-illness)\u003c/span\u003e\u003cspan address=\"https://www.aihw.gov.au/mental-health/overview/prevalence-and-impact-of-mental-illness)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAltun A, Brown H, Szoeke C, Goodwill AM (2019) The Mediterranean dietary pattern and depression risk: A systematic review. 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Lancet Diabetes Endocrinol 9:462\u0026ndash;470\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhandpur N et al (2021) J Nutr Sci 10:e77\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Depression, psychological distress, Mediterranean diet, ultra-processed foods; nutritional psychiatry","lastPublishedDoi":"10.21203/rs.3.rs-7993550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7993550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eConsuming a Mediterranean-style diet is associated with lower depression risk. Concurrently, exposure to a dietary pattern high in ultra-processed foods (UPFs) is associated with increased depression risk. As dietary patterns shift over time towards greater UPF exposure, it is important to understand how changes in these dietary patterns relate to depression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe used a subset of data from the Melbourne Collaborative Cohort Study (n\u0026thinsp;=\u0026thinsp;21,718). Dietary intake was assessed at baseline and follow-up using food frequency questionnaires, from which Mediterranean diet scores and UPF intake were derived. Depression risk at follow-up was measured using the Kessler Psychological Distress Scale (K10). We assessed change in diet from baseline to follow-up 12 years later using generalised estimating equations to account for repeated measures, and associations of change in diet with depression using Poisson regression. Analyses were conducted in the overall sample and by birthplace (Australian/New Zealand/Northern European; Southern European).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eParticipants increased both their adherence to the Mediterranean-style diet (β\u0026thinsp;=\u0026thinsp;0.28, 95%CI\u0026thinsp;=\u0026thinsp;0.25\u0026ndash;0.31) and exposure to UPFs (β\u0026thinsp;=\u0026thinsp;55.20 g/day, 95%CI\u0026thinsp;=\u0026thinsp;51.71\u0026ndash;58.69). Stratification by birthplace showed similar patterns, except for Southern European participants where no clear change in UPF exposure was observed (β=-2.38 g/day, 95%CI=-12.35\u0026ndash;7.59). One-point increases in Mediterranean diet score between baseline and follow-up were associated with a 5% lower risk of depression (RR\u0026thinsp;=\u0026thinsp;0.95, 95%CI\u0026thinsp;=\u0026thinsp;0.93\u0026ndash;0.98), while increasing UPF by 90 g/day was associated with a 5% higher risk (RR\u0026thinsp;=\u0026thinsp;1.05, 95%CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.07).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eAdherence to Mediterranean-style diet and UPF exposure increased over time across the overall sample, although patterns varied by region, and these changes were associated with opposing risks of depression.\u003c/p\u003e","manuscriptTitle":"Temporal Changes between Adherence to a Mediterranean-Style Diet and Consumption of Ultra-Processed Foods with 12-Year Depression Risk in the Melbourne Collaborative Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 09:37:18","doi":"10.21203/rs.3.rs-7993550/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":"8ae09955-1550-4ccd-a5bf-cab8bcf5938e","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57195423,"name":"Nutrition \u0026 Dietetics"},{"id":57195424,"name":"Psychology"},{"id":57195425,"name":"Psychiatry"}],"tags":[],"updatedAt":"2025-11-03T09:37:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-03 09:37:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7993550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7993550","identity":"rs-7993550","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}