Longitudinal Associations between Dietary Intake and Common Mental Disorders among Adolescents in Australia

Longitudinal Associations between Dietary Intake and Common Mental Disorders among Adolescents in Australia | 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 Longitudinal Associations between Dietary Intake and Common Mental Disorders among Adolescents in Australia Dr Lily Davidson, Dr Debbie Ashtree, Dr Bridie Osman, Dr Scarlett Smout, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7634151/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: There is need for more longitudinal research exploring temporal associations between diet quality and common mental disorders (CMDs) during adolescence. This study examined prospective associations between indicators of adolescents’ diet quality (fruit, vegetable and sugar-sweetened beverage [SSB] intakes) and risk of depression and anxiety onset, over three years. We hypothesised higher fruit and vegetable intake, and lower SSB intake, would be associated with lower risk of CMDs. Method: This study included 6537 Australian adolescents (mean age 12.65 years at baseline). Students completed surveys at baseline, 1-, 2-, and 3-year follow-ups. We fitted Generalised Estimating Equations Poisson regression models to assess longitudinal associations between daily serves of fruit, vegetables, and SSBs, and risk of depression and anxiety onset (binary cut-points on validated scales) over 3 years. Models were re-run for males and females. Analyses excluded participants who met thresholds for depression or anxiety at baseline, to increase confidence that diet predicted onset of CMDs. Results: Adolescents with heathier diet indicators (higher fruit and vegetable, lower SSB intakes) had lower risk of developing depression. The same was true for associations between both fruit and SSB (but not vegetable) intake and anxiety. Subgroup analyses revealed sex differences for associations between healthy indicators (fruit, vegetables) and CMDs, but higher SSB intake was associated with higher CMD risk for both sexes. Results inform the Global burden of disease Lifestyle And mental Disorders project (DERR2-10.2196/65576). Conclusions: Longitudinal research exposing temporal associations between diet and CMDs may inform prevention/intervention efforts targeting modifiable risk factors for CMDs. Nutrition & Dietetics Psychology adolescent diet depression anxiety mental disorder prospective studies Figures Figure 1 Key Practitioner Message What is known: There is growing evidence for the diet to mental health association among children and adolescents, but prior adolescent studies are majority cross-sectional, and the few prospective adolescent studies of diet to mental health outcomes show mixed findings. What is shown: This study addresses gaps in literature by assessing prospective (3-year) associations between specific indicators of diet quality and risk of depression and anxiety onset (determined by binary cut-points on validated scales), among a large adolescent sample, and for subgroups by sex. Heathier diet indicators (especially higher fruit and lower SSB intakes) were associated with reduced likelihood of meeting thresholds for depression and anxiety over three years during high school. Clinical Significance: Findings have implications for identifying modifiable targets for prevention/intervention of depression and anxiety during adolescence, when independence in nutritional intake increases and first CMD experience is most common. Introduction Many people’s first experience of common mental disorders (CMDs) occurs during adolescence (~ 50% before 18 years old; Solmi et al., 2022 ), when changes in the developing brain make people vulnerable to negative environmental influences (Blakemore, 2019 ). Efforts to address modifiable risk factors for CMDs during adolescence are critical, given high and rising rates of mental disorders among young people in Australia (e.g., 38.8% aged 16–24 years reported a past-12-month mental disorder in 2020-21; Australian Bureau of Statistics, 2023 ), and other developed countries (e.g., United Kingdom, United States; Wright et al., 2020 , Tkacz and Brady, 2021 ). Increasingly, research supports the association between diet quality and mental health during childhood and adolescence (see systematic reviews; O’neil et al., 2014 , Orlando et al., 2022 ). Adolescence is a critical period for establishing optimal health behaviours, like maintaining a nutritious diet, which can have long-term implications for physical and mental well-being (Winpenny et al., 2017 ). Therefore, understanding temporal associations between specific diet quality indicators and CMDs (e.g., depression, anxiety) among adolescents is important for identifying modifiable targets for prevention/early intervention of CMDs. A systematic review by O’neil et al. ( 2014 ) identified 10 cross-sectional and 3 prospective cohort studies that explored associations between diet and mental health among children and adolescents. This review revealed consistent cross-sectional associations between unhealthy diet patterns (e.g., more ultra-processed foods [UPFs], sugars, saturated fats) and poorer mental health outcomes (e.g., frequency of depression, internalising and emotional disfunction symptoms). However, for associations between healthy diet patterns (i.e., greater intake of nutrient-dense foods like fruit and vegetables) and better mental health outcomes the review found less consistent results, indicating need for further investigation. More recently, Orlando et al. ( 2022 ) published a meta-analysis on the association between dietary patterns and internalising symptoms (e.g., for depression, anxiety, emotional problems) among children and adolescents, which included 12 longitudinal and 26 cross-sectional studies. There were five adolescent specific longitudinal studies in this meta-analysis that explored prospective associations between diet quality indicators and later internalising symptoms, and only three (60%) found significant prospective associations between diet and mental health. These three studies found prospective associations between: 1) reduced fruit and vegetable intake (combined) and increased depressive symptoms from age 15–18 years (N = 3681; Andersen et al., 2013 ); 2) higher generalised ‘healthy diet’ scores (ordinal) and better overall mental health scores from baseline to 2-year follow-up (N = 2915 aged 11–18 years at baseline; Jacka et al., 2011 ); and 3) being in the highest quintiles of generalised ‘healthy diet’ (inverse) and ‘unhealthy diet’ (positive) and risk of meeting the cut-off for high psychological distress at 2-year follow-up (N = 2790 aged 11–12 years at baseline; although associations were attenuated by adjustments for confounders; Jacka et al., 2013 ). Mixed findings for prospective associations between diet quality and internalising symptoms among adolescents could be explained by validity and heterogeneity of measures used in prior studies. For example, only three of the five aforementioned studies used empirically supported cut-off or diagnosis scores on a validated measure of CMDs, including depression (Mood and Feelings Questionnaire [MFQ] score ≥ 20 and short-MFQ score ≥ 8; Winpenny et al., 2018 , Jacka et al., 2013 ) and other internalising disorders including general anxiety disorder or emotional disorders (physician-diagnosed using the International Classification of Diseases version 9 or 10; Wu et al., 2018 ). Using empirically supported cut-off scores on validated measures of CMDs is important for identifying adolescents who are likely to have a CMD, therefore guiding decisions about interventions. Other studies used continuous scores on various measures of internalising symptoms, including: depressive symptoms (Andersen et al., 2013 ) and emotional dysfunction symptoms (Jacka et al., 2011 ). None of the aforementioned five prospective studies used a validated measure of anxiety as a stand-alone outcome; the paucity of research on the association between diet and anxiety among adolescents is a limitation noted previously (O’neil et al., 2014 ). Additionally, there was heterogeneity in measures of diet quality used across the aforementioned adolescent studies. Several studies used ‘whole of diet’ predictors (wholistic measures of ‘healthy’ or ‘unhealthy’ diet; e.g., Jacka et al., 2011 , Jacka et al., 2013 , Wu et al., 2018 ), rather than specific markers of diet quality (e.g., fruit, vegetable intake). Using ‘whole of diet’ predictors is useful for capturing the complexity of how people eat, but limits ability to identify specific targets for prevention/intervention efforts for CMDs. Identifying specific dietary targets for intervention arguably can have greater utility when developing dietary improvement strategies for adolescents. For example, awareness of health benefits of specific foods has been associated with higher diet quality and fruit and vegetable intake, and lower unhealthy food intake among young adults (Bell and Holder, 2019 ). Mixed findings for the five adolescent-specific studies in the meta-analysis by Orlando et al. ( 2022 ) that explored prospective associations between diet quality and later mental health highlight the need for further research on this topic. Future studies should utilise measures of specific diet quality indicators (to identify modifiable targets for prevention/intervention) and use validated measures of CMDs (with empirically supported binary cut-off scores), to validate existing findings and reveal novel information about how specific dietary factors relate to onset of CMDs (especially anxiety, which is less well-researched). Finally, few studies have specifically explored whether prospective associations between diet quality indicators and CMDs differ for male and female adolescents. This is despite sex differences in adolescents’ diet patterns and mental health, such as males reporting higher average unhealthy diet scores and greater declines in healthy eating than females during adolescence (e.g., Jacka et al., 2011 ), or female adolescents reporting higher anxiety than males (Bao and Han, 2025 ). Limited number of studies have demonstrated that longitudinal associations between higher unhealthy and “Western” food intake (e.g., take-away foods, processed meats, soft drinks, sweet and fatty foods) and externalising behaviors (e.g., conduct disorder, Attention-Deficit-Hyperactivity Disorder; Trapp et al., 2016 ), and depression (Aparicio et al., 2017 ) may be more pronounced among female adolescents. Additionally, the meta-analysis by Orlando et al. ( 2022 ) found studies with a higher percentage of male participants had stronger effect sizes for the association between healthier diet patterns and lower internalising symptoms. This research adds to emerging evidence of sex differences in associations between dietary factors and mental health among adolescents, though further research is needed. To address these gaps, this study aimed to assess prospective associations between specific diet quality indicators (daily serves of fruit, vegetables and sugar-sweetened beverages [SSBs]) and risk of meeting empirically supported cut-offs for depression and anxiety over three years, among a large sample of Australian adolescents. We hypothesised that higher fruit, higher vegetable, and lower SSB intakes would be associated with lower risk of meeting thresholds for depression and anxiety over time. Given the paucity of prospective research investigating how diet to mental health associations differ by sex among adolescents, this research question was exploratory. Understanding temporal associations between diet quality indicators and CMDs is important for improving prevention and early intervention strategies for CMDs, especially during adolescence when people develop independence around dietary choices and experience heightened risk for CMD onset. Although there is growing evidence for associations between diet quality and mental health among children and adolescents, most research is cross-sectional, and existing adolescent-specific studies have not consistently demonstrated significant prospective associations between diet quality indicators and mental health outcomes (Orlando et al., 2022 ). Materials and Methods Participants and Procedures This study included 6537 students from 71 secondary schools (independent, government-funded, Catholic), across 3 Australian states (Queensland, New South Wales, Western Australia). Participants were recruited for the ‘Health4Life’ study, a 3-year cluster randomised controlled trial testing the efficacy of a school-based e-health intervention. ‘Health4Life’ aimed to improve the ‘Big 6’ lifestyle risk factors for chronic disease among adolescents, which are also risk factors for CMDs (poor diet, physical inactivity, sedentary recreational screen time, poor sleep, and cigarette and alcohol use; for study protocol and main outcome paper see Teesson et al., 2020 , Champion et al., 2023 ). Participants completed online surveys at baseline (2019, Grade 7, 12–13 years old), post-intervention (7-week follow-up), and 1-, 2-, and 3-years post baseline (finishing 2022; Grade 10, 15–16 years old). This study excludes post-intervention (7-week) data, as longitudinal associations were of interest (annual follow-ups). ‘Health4Life’ participants had better outcomes on psychological distress immediately post-intervention (7-week follow-up; Smout et al., 2024 ), but effects did not hold beyond this timepoint, and ‘Health4Life’ proved no more effective than active control condition regarding behaviour change for the ‘Big 6’ (Champion et al., 2023 , O'Dean et al., 2024 ). We included control and intervention participants to maximise sample size. Analyses adjust for the clustered nature of school data, and intervention allocation (see ‘Analysis Plan’). Consent methods differed depending on procedures approved by various ethics boards, which required active student consent and either active or passive parental consent (University of Sydney HREC2018/882, University of Queensland 2019000037, Curtin University HRE2019-0083, University of Newcastle H-2020-0232, NSW Department of Education SERAP 2019006, and several Catholic Diocese committees). The trial was pre-registered (ACTRN12619000431123). Measures Demographics and covariates. Demographics relevant to this study were age (years) and sex at birth (male/female/prefer not to say). Family affluence was included as a proxy for socio-economic status (SES), using the Family Affluence Scale III (FAS III; Torsheim et al., 2016 ). This 6-item measure assesses family assets and wealth by asking adolescents about indicators of wealth that young people know (e.g. number of computers, bathrooms at home). It has demonstrated good test-retest reliability ( r = 0.9) and strong correlation with parental report (Torsheim et al., 2016 ). For sensitivity analyses (see ‘Analysis Plan’), we included a single-item measure of typical weekly physical activity, worded: “Over a typical or usual week, on how many days are you engaged in moderate to vigorous physical activity for a total of at least 60 minutes per day?” (range 0–7). Diet Variables (exposure). Diet variables were measured in self-reported serves consumed per day, for fruit and vegetables. SSB intake was measured in serves per week (re-coded into serves per day for consistency). To measure fruit intake, participants were asked: “How many serves of fruit do you usually eat each day?” Participants responded on a 7-point scale from ‘Less than 1 serve per day’ (0) to ‘More than six serves per day’ (6), or could select, ‘I don’t eat fruit’ (recoded ‘0’) or ‘Don’t know’ (recoded ‘missing’). To standardise responses, participants were presented a reference image and instructions representing a standard portion of fruit (see Fig. 1 ), in line with Australian dietary guidelines (1 serve = 150 grams; National Health and Medical Research Council, 2025a ). To measure daily vegetable intake, participants were asked: “How many serves of vegetables do you usually eat each day?” The same response options described for fruit intake were presented. A reference image/instruction was presented (see Fig. 1 ) in line with Australian dietary guidelines (1 serve = 75 grams; National Health and Medical Research Council, 2025b ). SSB intake was measured by asking participants to “Indicate how many cups of soft drink, cordials or sports drinks (such as Coke, lemonade, Ribena or Gatorade) you usually consume”. Response options were: ‘Never/Rarely drink’, ‘1 cup or less a week’, ‘2 to 4 cups a week’, ‘5 to 6 cups a week’, ‘1 cup a day’, ‘1.5 cups or more a day’, and ‘2 or more cups a day’. A reference image/instruction was presented (see Fig. 1 ). For consistency across diet variables, SSB intake was recoded into ‘cups per day ’ (1 cup = 250mL). If a participant’s response was in ‘cups per week ’, their response was recoded into cups per day (e.g., ‘1 cup or less per week’=0.14 cups per day ; i.e., 1 cup divided by 7 days of a week). Common Mental Disorders (outcome). To measure depression, we used a modified version of the Patient Health Questionnaire - Adolescent version (PHQ-A; Johnson et al., 2002 ). Item 9 (suicidal ideation) was removed for the ‘Health4Life’ study, upon request from an ethics committee. Therefore, we used an 8-item version of the PHQ-A (PHQ-A-8; total score 24). Higher scores reflect higher depression symptoms. Confirmatory factor analysis of the PHQ-A-8 has previously demonstrated better fit than the 9-item PHQ-A (López-Torres et al., 2019 ). We recoded the PHQ-A-8 total into a binary outcome reflecting likely depression (score ≥ 10 coded ‘1’ for depression; score < 10 coded ‘0’ for no depression), based on evidence that this PHQ-8 cut-off represents clinically significant depression (Kroenke et al., 2009 ). Internal consistency was good to excellent for the PHQ-A-8 across all timepoints (α = 0.87–0.92). To measure anxiety, we used the 13-item Patient-Reported Outcomes Measurement Information System Anxiety Paediatric (PROMIS-AP) scale (Irwin et al., 2010 ). The PROMIS-AP asks about feelings of worry, fear, dread and more, over the past 7 days (total score 13–65; higher scores reflect greater anxiety). PROMIS-AP has demonstrated greater reliability among adolescent samples than other anxiety measures (Irwin et al., 2010 ). We recoded the total as a binary outcome, reflecting none/slight to mild anxiety (score < 34; coded ‘0’) or moderate to severe anxiety (score ≥ 34; coded ‘1’). This cut-off was based on evidence that the T-score corresponding to a PROMIS-AP score of 34 (T-score = 60) is one standard deviation higher than average (calibrated using normative samples in United States) and represents more severe anxiety conditions (Northwestern University, 2023 , Carle et al., 2021, American Psychiatric Association, 2013 ). Internal consistency was excellent for the PROMIS-AP across all timepoints (α = 0.94–0.96). Analysis Plan To estimate risk of depression and anxiety attributable to each dietary exposure over 3 years, we followed the Global burden of disease Lifestyle and Mental Disorder Taskforce analysis procedure (Ashtree et al., 2025 ). We fitted Generalised Estimating Equation (GEE) Poisson regression with robust standard errors to model the longitudinal association of each dietary exposure (considered as time-varying exposures) with risk of depression and anxiety. We fitted three models: 1) unadjusted, 2) adjusted for sex at birth, and 3) subgroup analysis by sex at birth. Since there were missing data on key variables (20.51%), we used multiple imputation (Multivariate Imputations by Chained Equations) to impute missing data with 20 repeats (Jakobsen et al., 2017 ). We included key demographic variables (sex, age, SES) in the imputation to improve model fit. We included a complete-case model as a sensitivity analysis to determine the impact of missing data (see Supplementary Table S1 for descriptive statistics using complete-case analysis and Table S2 for model output). We included additional sensitivity analyses, wherein we further adjusted model 2 for physical activity (Table S3), as well as age and SES (Table S4), given potential associations between these covariates and key variables (e.g., depression, anxiety and dietary variables). Model assumptions were assessed prior to model fitting. For the SSB variable, we used a log-transformation to correct for non-linearity in the logit; all other assumptions were upheld. All models included fixed variables for timepoint (1 ‘baseline’ to 4 ‘3-year follow-up’), trial group (intervention, control), exposure by timepoint, and exposure by trial group. All main analyses excluded participants who, at baseline, met the threshold for depression (in models where depression was the outcome) or anxiety (where anxiety was the outcome), to increase confidence that diet variables predicted onset of depression and anxiety over time. Main analyses were conducted in IBM SPSS Statistics (Version 28). Results Sample Descriptives Participants’ mean age at baseline was 12.65 years ( SD = 0.50), and half identified as female ( n = 3229; 49.40%; Table 1 ). Ninety-four (1.40%) participants reported sex as ‘prefer not to say’ and were excluded from models due to insufficient sample size for subgroup estimation. The mean family affluence score was 9.34 (out of 13; SD = 1.92), indicating a medium to high SES sample (e.g., Corell et al., 2021 , Torsheim et al., 2016 ). Table 1 shows other baseline descriptives. Fruit, vegetable and SSB intake appeared relatively stable over time, with females reporting slightly higher intakes of fruit and vegetables and males reporting higher SSB intake (Table 2). Table 1 Baseline Characteristics of Total Sample, Analytic Sample and Cases in Depression and Anxiety Models Total Sample ( N = 6639) Analytic Sample ( n = 6537) Depression Analysis Cases ( n = 5097) Anxiety Analysis Cases ( n = 5120) Demographic Variables Sex ( n , %) Male 3308 (49.80) 3308 (50.60) 2658 (52.10) 2726 (53.20) Female 3229 (48.70) 3229 (49.40) 2439 (47.90) 2394 (46.80) Prefer not to say or missing 102 (1.50) 0 (0) 0 (0) 0 (0) Age ( M, SD ) 12.66 (0.50) 12.65 (0.50) 12.66 (0.50) 12.66 (0.50) Family Affluence ( M, SD ) 9.37 (1.86) 9.34 (1.92) 9.37 (1.86) 9.35 (1.89) Diet Variables Fruit serves ( M, SD ) 2.46 (1.55) 2.43 (1.59) 2.46 (1.55) 2.44 (1.56) Vegetables serves ( M, SD ) 2.64 (1.66) 2.62 (1.68) 2.64 (1.66) 2.62 (1.65) SSB serves ( M, SD ) 0.23 (0.36) 0.25 (0.39) 0.23 (0.36) 0.23 (0.37) Mental Health Variables Depression Total ( M, SD ) 5.07 (5.16) 5.04 (5.12) 3.19 (2.76) 3.88 (4.09) Anxiety Total ( M, SD ) 22.99 (10.60) 22.97 (10.59) 20.42 (8.00) 19.25 (5.88) Meets Depression Cutoff ( n , %) 1039 (16.80) 1015 (16.60) 0 (0) 454 (8.90) Meets Anxiety Cutoff ( n , %) 1001 (16.20) 983 (16.10) 417 (8.20) 0 (0) Note. SSB = Sugar-sweetened beverages. Serves = per day (150 grams fruit, 75 grams vegetables, 250 grams SSBs). Table 2. Diet Variables for Analytic Sample and Sex Subsamples at each Timepoint (Multiply Imputed Dataset) Variable Baseline Year 1 Year 2 Year 3 Analytic Sample ( n = 6537 ) Fruit serves ( M , S D ) 2.43 (1.59) 2.36 (1.57) 2.14 (1.51) 2.05 (1.54) Vegetable serves ( M , SD ) 2.62 (1.68) 2.71 (1.63) 2.58 (1.60) 2.54 (1.63) SSB serves ( M , SD ) 0.25 (0.39) 0.23 (0.38) 0.23 (0.39) 0.25 (0.39) Females ( n = 3229 ) Fruit serves ( M , S D ) 2.51 (1.51) 2.40 (1.46) 2.16 (1.41) 2.03 (1.46) Vegetable serves ( M , SD ) 2.72 (1.64) 2.78 (1.55) 2.65 (1.54) 2.57 (1.56) SSB serves ( M , SD ) 0.17 (0.33) 0.15 (0.29) 0.16 (0.30) 0.15 (0.28) Males ( n = 3308 ) Fruit serves ( M , S D ) 2.35 (1.67) 2.33 (1.68) 2.13 (1.60) 2.08 (1.62) Vegetable serves ( M , SD ) 2.52 (1.71) 2.64 (1.69) 2.52 (1.65) 2.51 (1.69) SSB serves ( M , SD ) 0.31 (0.43) 0.30 (0.44) 0.31 (0.45) 0.34 (0.46) Prefer not to Say ( n =94) Fruit serves ( M , S D ) 2.22 (1.99) 2.14 (1.75) 1.92 (1.67) 1.77 (1.70) Vegetable serves ( M , SD ) 2.39 (1.99) 2.58 (1.87) 2.42 (1.75) 2.24 (1.85) SSB serves ( M , SD ) 0.36 (0.53) 0.37 (0.52) 0.37 (0.53) 0.25 (0.39) Note. SSB=Sugar-sweetened beverages. Serves=per day (150 grams fruit, 75 grams vegetables, 250 grams SSBs). M =mean; SD =standard deviation. Table 3. Longitudinal Associations Between Diet Quality Indicators and Depression and Anxiety for Analytic Sample and Sex Subsamples (Multiply Imputed Dataset) Vegetable (per serve increase) Fruit (per serve increase) SSB (per cup increase) Risk Ratio 95% Confidence Interval P-value Risk Ratio 95% Confidence Interval P-value Risk Ratio 95% Confidence Interval P-value Depression Model 1 .94 .89, 1.00 .040 .88 .82, .93 <.001 2.48 1.79, 3.44 <.001 Model 2 .93 .88, .99 .015 .86 .81, .92 <.001 3.32 2.44, 4.52 <.001 Model 3 Males .94 .86, 1.04 .236 .87 .79, .97 .011 3.46 2.15, 5.58 <.001 Females .93 .87, .99 .027 .85 .79, .93 <.001 3.13 2.07, 4.72 <.001 Anxiety Model 1 .98 .92, 1.04 .410 .93 .87, .99 .020 2.46 1.71, 3.54 <.001 Model 2 .96 .91, 1.02 .229 .91 .85, .98 .008 3.49 2.48, 4.90 <.001 Model 3 Males .87 .78, .98 .017 .89 .79, .99 .038 5.97 3.67, 9.71 <.001 Females 1.02 .95, 1.10 .508 .93 .86, 1.01 .094 2.04 1.23, 3.39 .006 Note. bold=significant at p <.05. Model 1 is unadjusted. Model 2 is adjusted for sex. Model 3 includes sex as subgroup. Associations between Dietary Intake and CMDs Higher vegetable (adjusted risk ratio [RR]: 0.93, 95%CI: 0.88–0.99) and fruit intake (adjusted RR:0.86, 95%CI: 0.81–0.92) were associated with lower risk of depression over 3 years follow-up (Table 3). When analysed by sex, higher vegetable intake was associated with lower risk of depression for females, not males, whereas higher fruit intake was associated with lower risk of depression for both sexes. For vegetables and fruit, higher intake was associated with lower risk of anxiety for males, not females. Higher SSB intake was associated with higher risk of depression (adjusted RR: 3.32, 95%CI: 2.44–4.52) and anxiety (adjusted RR: 3.49 95%CI: 2.48–4.90) for the overall sample, and males and females separately. Sensitivity Analyses Complete case analyses (Supplementary Table S2) mostly showed results consistent with those described above, except that: 1) vegetable intake was no longer significantly associated with depression in the overall sample ( p = .112) or female subsample ( p = .129), and 2) for male adolescents, fruit intake was not significantly associated with depression ( p = .060) or anxiety ( p = .062), indicating missing data may have influenced these results. In the sensitivity analyses accounting for physical activity in model 2 (Table S3), the association between vegetable intake and depression became non-significant in the overall sample ( p = .053) and female subsample ( p = .049). Finally, after accounting for physical activity, the following associations specific to males became non-significant: vegetable intake to anxiety ( p = .063), and fruit intake to anxiety ( p = .118). Discussion This study investigated longitudinal associations between diet quality indicators (daily fruit, vegetable, and SSB intake) and risk of depression and anxiety onset over 3 years, among a large adolescent sample, and whether associations differed by sex. In line with hypotheses, key findings (from adjusted models including sex as covariate) were that higher fruit intake and lower SSB intake were significantly associated with lower risk of meeting thresholds for depression and anxiety, over three years during high school (Grades 7–10). Partially in line with hypotheses, higher vegetable intake was associated with lower risk of depression over time, but not anxiety (associations in expected direction but non-significant). Furthermore, associations between vegetable intake and depression became non-significant in sensitivity analyses, indicating results may be influenced by missing data (Table S2) or engagement in healthy behaviours other than vegetable intake (like physical activity; Table S3). There was evidence that associations between fruit and vegetable intake (healthy diet indicators) and risk of depression and anxiety differed by sex, whereas higher SSB consumption was associated with higher risk of both CMDs for both sexes. Differing results regarding associations between fruit intake and CMD risk (consistent significant associations), versus vegetable intake and CMD risk (inconsistent associations), are concordant with inconsistencies in the literature regarding associations between healthy diet quality indicators and CMDs among adolescents. Our findings corroborate results of the systematic review by O’neil et al. ( 2014 ), which found less consistent results for the association between healthy (versus unhealthy) diet patterns and mental health outcomes. These findings also address the need for more research on how specific indicators of healthy diet (i.e., fruit and vegetable intake) relate to mental health during adolescence, rather than ‘whole of diet’ indicators, wherein certain foods might 'cancel each other out' and mask effects for associations between healthy diet indicators and CMDs. Alternatively, observed differences in statistical significance for associations between fruit versus vegetable intake and CMDs could be explained by less variability in average self-reported daily vegetable intake across timepoints (2.54–2.71 serves) compared to fruit intake (2.05–2.43 serves), rather than differences in the strength of associations between fruit versus vegetable intake and CMDs. Subgroup analyses by sex revealed higher fruit intake was associated with lower risk of depression onset for both sexes. Specific to females was the association between higher vegetable intake and lower depression risk. Specific to males were the associations between higher vegetable and fruit intake and lower risk of anxiety . However, each finding specific to one sex became non-significant after accounting for physical activity, which highlights the multi-faceted nature of health behaviours like nutrition and exercise in relation to onset of CMDs among adolescents. Nonetheless, finding evidence that the association between higher fruit and vegetable intake and lower anxiety likelihood was specific to males does align with prior literature. The meta-analysis by Orlando et al. ( 2022 ) revealed studies with higher percentage of male adolescents tended to have stronger effect sizes for the association between healthy dietary patterns and lower internalizing symptoms. One possible explanation is that males, who typically report poorer overall diet quality and greater declines in healthy eating during adolescence compared to females (e.g., Jacka et al., 2011 ), may have more to gain from consuming healthy dietary components in terms of protecting against CMDs, especially anxiety. Regarding unhealthy diet quality indicators and CMDs, this study was limited to SSB consumption and found this indicator was associated with higher risk of developing depression and anxiety for both sexes. These findings align with hypotheses and prior research on the association between SSB consumption and poorer mental health among adolescents (Liu et al., 2022 , Ra, 2022 ). These findings also support research showing consistent cross-sectional and longitudinal associations between unhealthy diet patterns (e.g., UPF consumption) and poorer mental health, among adolescents (e.g., internalizing and depressive symptoms; Orlando et al., 2022 , O’neil et al., 2014 ) and adults (see review by Lane et al., 2024 ). The association between higher SSB intake and higher CMD-risk may relate to biological implications of consuming high-sugar beverages, like increased circulating inflammatory markers or changes in hormone levels (e.g., cortisol), which may affect mood (Kivimäki et al., 2014 , Di Polito et al., 2023 ). However, SSB consumption is only one unhealthy behaviour, and is likely conflated with other unhealthy dietary and behavioural patterns that may moderate the association between SSB consumption and poorer mental health (Liu et al., 2022 ). Although the association between SSB consumption and CMDs remained robust after controlling for covariates like physical activity, future research should explore how other factors (e.g., consumption of other UPFs) may moderate the association between SSB consumption and CMDs. The magnitude of associations between SSB intake and both CMDs was higher for males than females (especially for anxiety), indicating male adolescents may be more susceptible to the impact of SSB-consumption on mental health (as per previous literature, e.g., Liu et al., 2022 ). Implications Findings suggest targeting modifiable dietary factors in prevention/early intervention efforts for CMDs has potential to reduce risk of developing depression and anxiety throughout adolescence, when CMDs often first emerge (Solmi et al., 2022 ), and independence in dietary choices increases. This study indicates that encouraging fruit intake (and vegetable intake, although with more limited evidence), and especially limiting SSB intake, may play a role in preventing onset of CMDs during adolescence. Additionally, male adolescents may experience greater protective benefits from improving diet quality, in terms of preventing CMD onset. Limitations and Strengths This study was limited by few dietary variables in the dataset that could be coded into serves per day. We used variables that could be coded this way to be consistent across exposures, and increase comparability of results with other studies on the impact of diet on health, particularly those that follow the rigorous ‘Global Burden of Disease study’ measurement protocols (page 170 of Supplementary appendix 1 in Apostol et al., 2024). While using few dietary exposures limited ability to understand how overall diet quality relates to CMD onset, this approach provides specific targets for action. Using self-reported dietary intake is another limitation, due to potential self-report biases and recall inaccuracy; use of standardised portion size images/descriptions aimed to mitigate this issue. This study has several strengths. It addressed a need for adolescent-specific prospective studies exploring how specific dietary components relate to meeting empirically supported thresholds for CMDs over time. The validated measures of depression and anxiety in this study (PHQ-A-8, PROMIS-A) have not been used in similar prior studies, so using these measures helps to validate prior findings for the prospective association between diet quality and depression, and especially anxiety (for which there is little research). The sample in our study (N = 6537) was larger than samples in the five most-relevant studies of diet to later CMDs in the meta-analysis by Orlando et al. ( 2022 ; N = 603 to N = 4861). This large sample provided statistical power to investigate specificity of associations by sex, which has seldom been done, and allowed us to exclude cases that met thresholds for depression or anxiety at baseline to better assess whether dietary factors related to onset of CMDs. Future Directions Future studies should replicate our findings and include other dietary exposures (e.g., whole grains, lean proteins, healthy fats) to increase knowledge of how specific dietary components may relate to CMDs among adolescents, and whether associations differ by sex. Future studies should control for additional covariates like total daily energy intake (unavailable), to account for the possible impact of differences in energy intake on mental health (Ammar et al., 2024 ). Conclusion This study suggests adolescents with heathier diet indicators (especially higher fruit and lower SSB intakes) had a reduced likelihood of meeting empirically supported thresholds for depression and anxiety over three years during high school. Associations between fruit and vegetable intakes and CMDs were less consistent and differed by sex, compared to associations between SSB intake and CMDs, which were comparatively stronger and evident for both sexes. This research can inform targeting of modifiable nutrition factors within prevention, intervention and public health messaging efforts aimed at preventing onset of CMDs, which are high and rising among adolescents in Australia and other developed countries. Declarations Acknowledgements Study funding: ‘ Health4Life’ was funded by Paul Ramsay Foundation, Australian National Health and Medical Research Council (NHMRC), and a Centre of Research Excellence in Prevention and Early Intervention of Mental Illness and Substance Use (PREMISE; APP11349009). AON and DNA are supported by a NHMRC Emerging Leader 2 Fellowship (2009295). FNJ is supported by a NHMRC Leader 1 Fellowship (GNT1194982). Contributorships: LD led analyses and manuscript writing. MT, KEC, and NCN secured funding for ‘Health4Life’; KEC and LAG were responsible for ethics, governance, trial coordination, and supervision of staff with oversight from MT and NCN. LAG, BO, and SS were responsible for recruitment and data collection.AON and FNJ conceptualised the GLAD taskforce and project. AON acquired funding for the GLAD project. DNA, RO, and MML contributed to development of methodology for GLAD. DNA, RO, MML, FNJ, and AON contributed to GLAD resources (provision of study materials). DNA contributed to GLAD administration and supervision of statistical analyses. Professor Timothy Slade, Associate Professor Matthew Sunderland, and Dr Siobhan O’Dean provided informal statistical guidance. Declaration of interests: NN and MT are Directors of Climate Schools Pty Ltd and OurFutures Institute Ltd. DNA, RO, MML, FNJ, and AON are affiliated with the Food & Mood Centre, Deakin University. Data Availability. Available upon request from corresponding author. Ethics Statement Collection of data used in this study was approved by Human Research Ethics Committees at University of Sydney (HREC2018/882; December 17, 2018), University of Queensland (2019000037; February 5, 2019), Curtin University (HRE2019-0083; February 2022, 2019), and New South Wales State Education Research and Partnerships (SERAP) process (2019006; April 15, 2019). Ethical approval was also granted by several Catholic Diocese committees, for which approval numbers are not applicable (Catholic Maitland-Newcastle, March 7, 2019; Edmund Rice Education Australia, May 13, 2019; Brisbane Catholic Education, June 12, 2019; Catholic Education Office Western Australia, June 4, 2019; Bathurst Catholic Education, May 22, 2019). Informed consent methods differed depending on procedures approved by various ethics boards, which required active student consent and either active or passive parental consent. References AMERICAN PSYCHIATRIC ASSOCIATION. 2013. Instruction Manual: LEVEL 2—Anxiety—Child Age 11–17 (PROMIS Emotional Distress— Anxiety—Pediatric Item Bank) [Online]. Available: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.psychiatry.org/File%20Library/Psychiatrists/Practice/DSM/APA_DSM5_Level-2-Anxiety-Child-Age-11-to-17.pdf [Accessed 19 February 2025]. AMMAR, A., TRABELSI, K., HAMMOUDA, O. & CLARK, C. C. 2024. 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B., JONES, P. & KIM, J. H. 2022. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Molecular psychiatry, 27 , 281-295. TEESSON, M., CHAMPION, K. E., NEWTON, N. C., KAY-LAMBKIN, F., CHAPMAN, C., THORNTON, L., SLADE, T., SUNDERLAND, M., MILLS, K. & GARDNER, L. A. 2020. Study protocol of the Health4Life initiative: a cluster randomised controlled trial of an eHealth school-based program targeting multiple lifestyle risk behaviours among young Australians. BMJ open, 10 , e035662. TKACZ, J. & BRADY, B. L. 2021. Increasing rate of diagnosed childhood mental illness in the United States: Incidence, prevalence and costs. Public Health in Practice, 2 , 100204. TORSHEIM, T., CAVALLO, F., LEVIN, K. A., SCHNOHR, C., MAZUR, J., NICLASEN, B., CURRIE, C. & GROUP, F. D. S. 2016. Psychometric validation of the revised family affluence scale: a latent variable approach. Child indicators research, 9 , 771-784. TRAPP, G. S., ALLEN, K. L., BLACK, L. J., AMBROSINI, G. L., JACOBY, P., BYRNE, S., MARTIN, K. E. & ODDY, W. H. 2016. A prospective investigation of dietary patterns and internalizing and externalizing mental health problems in adolescents. Food science & nutrition, 4 , 888-896. WINPENNY, E. M., PENNEY, T. L., CORDER, K., WHITE, M. & VAN SLUIJS, E. M. 2017. Change in diet in the period from adolescence to early adulthood: a systematic scoping review of longitudinal studies. International Journal of Behavioral Nutrition and Physical Activity, 14 , 60. WINPENNY, E. M., VAN HARMELEN, A.-L., WHITE, M., VAN SLUIJS, E. M. & GOODYER, I. M. 2018. Diet quality and depressive symptoms in adolescence: no cross-sectional or prospective associations following adjustment for covariates. Public health nutrition, 21 , 2376-2384. WRIGHT, B., GARSIDE, M., ALLGAR, V., HODKINSON, R. & THORPE, H. 2020. A large population-based study of the mental health and wellbeing of children and young people in the North of England. Clinical Child Psychology and Psychiatry, 25 , 877-890. WU, X., BASTIAN, K., OHINMAA, A. & VEUGELERS, P. 2018. Influence of physical activity, sedentary behavior, and diet quality in childhood on the incidence of internalizing and externalizing disorders during adolescence: a population-based cohort study. Annals of epidemiology, 28 , 86-94. Additional Declarations The authors declare potential competing interests as follows: NN and MT are Directors of Climate Schools Pty Ltd and OurFutures Institute Ltd. DNA, RO, MML, FNJ, and AON are affiliated with the Food & Mood Centre, Deakin University. Supplementary Files SupplementaryMaterialsGLADtaskforce20250723LiveDoc.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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08:19:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1740153,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7634151/v1/a3970c63-68a3-4050-84ea-b326f2dc9269.pdf"},{"id":91961189,"identity":"c3eeb398-c061-4938-80dd-ea44dab8b782","added_by":"auto","created_at":"2025-09-23 07:52:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32647,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsGLADtaskforce20250723LiveDoc.docx","url":"https://assets-eu.researchsquare.com/files/rs-7634151/v1/8acbfc232e4732f095f04a19.docx"}],"financialInterests":"The authors declare potential competing interests as follows: NN and MT are Directors of Climate Schools Pty Ltd and OurFutures Institute Ltd. DNA, RO, MML, FNJ, and AON are affiliated with the Food \u0026 Mood Centre, Deakin University.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eLongitudinal Associations between Dietary Intake and Common Mental Disorders among Adolescents in Australia\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Key Practitioner Message","content":"\u003cul\u003e\n \u003cli\u003eWhat is known: There is growing evidence for the diet to mental health association among children and adolescents, but prior adolescent studies are majority cross-sectional, and the few prospective adolescent studies of diet to mental health outcomes show mixed findings.\u003c/li\u003e\n \u003cli\u003eWhat is shown: This study addresses gaps in literature by assessing prospective (3-year) associations between specific indicators of diet quality and risk of depression and anxiety onset (determined by binary cut-points on validated scales), among a large adolescent sample, and for subgroups by sex. Heathier diet indicators (especially higher fruit and lower SSB intakes) were associated with reduced likelihood of meeting thresholds for depression and anxiety over three years during high school.\u003c/li\u003e\n \u003cli\u003eClinical Significance: Findings have implications for identifying modifiable targets for prevention/intervention of depression and anxiety during adolescence, when independence in nutritional intake increases and first CMD experience is most common.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eMany people\u0026rsquo;s first experience of common mental disorders (CMDs) occurs during adolescence (~\u0026thinsp;50% before 18 years old; Solmi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), when changes in the developing brain make people vulnerable to negative environmental influences (Blakemore, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Efforts to address modifiable risk factors for CMDs during adolescence are critical, given high and rising rates of mental disorders among young people in Australia (e.g., 38.8% aged 16\u0026ndash;24 years reported a past-12-month mental disorder in 2020-21; Australian Bureau of Statistics, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and other developed countries (e.g., United Kingdom, United States; Wright et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Tkacz and Brady, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Increasingly, research supports the association between diet quality and mental health during childhood and adolescence (see systematic reviews; O\u0026rsquo;neil et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Orlando et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Adolescence is a critical period for establishing optimal health behaviours, like maintaining a nutritious diet, which can have long-term implications for physical and mental well-being (Winpenny et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, understanding temporal associations between specific diet quality indicators and CMDs (e.g., depression, anxiety) among adolescents is important for identifying modifiable targets for prevention/early intervention of CMDs.\u003c/p\u003e\u003cp\u003eA systematic review by O\u0026rsquo;neil et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) identified 10 cross-sectional and 3 prospective cohort studies that explored associations between diet and mental health among children and adolescents. This review revealed consistent \u003cem\u003ecross-sectional\u003c/em\u003e associations between \u003cem\u003eunhealthy\u003c/em\u003e diet patterns (e.g., more ultra-processed foods [UPFs], sugars, saturated fats) and poorer mental health outcomes (e.g., frequency of depression, internalising and emotional disfunction symptoms). However, for associations between \u003cem\u003ehealthy\u003c/em\u003e diet patterns (i.e., greater intake of nutrient-dense foods like fruit and vegetables) and better mental health outcomes the review found less consistent results, indicating need for further investigation.\u003c/p\u003e\u003cp\u003eMore recently, Orlando et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) published a meta-analysis on the association between dietary patterns and internalising symptoms (e.g., for depression, anxiety, emotional problems) among children and adolescents, which included \u003cem\u003e12 longitudinal\u003c/em\u003e and 26 cross-sectional studies. There were five \u003cem\u003eadolescent\u003c/em\u003e specific longitudinal studies in this meta-analysis that explored prospective associations between diet quality indicators and later internalising symptoms, and only three (60%) found significant prospective associations between diet and mental health. These three studies found prospective associations between: 1) reduced fruit and vegetable intake (combined) and increased depressive symptoms from age 15\u0026ndash;18 years (N\u0026thinsp;=\u0026thinsp;3681; Andersen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); 2) higher generalised \u0026lsquo;healthy diet\u0026rsquo; scores (ordinal) and better overall mental health scores from baseline to 2-year follow-up (N\u0026thinsp;=\u0026thinsp;2915 aged 11\u0026ndash;18 years at baseline; Jacka et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e); and 3) being in the highest quintiles of generalised \u0026lsquo;healthy diet\u0026rsquo; (inverse) and \u0026lsquo;unhealthy diet\u0026rsquo; (positive) and risk of meeting the cut-off for high psychological distress at 2-year follow-up (N\u0026thinsp;=\u0026thinsp;2790 aged 11\u0026ndash;12 years at baseline; although associations were attenuated by adjustments for confounders; Jacka et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMixed findings for prospective associations between diet quality and internalising symptoms among adolescents could be explained by validity and heterogeneity of measures used in prior studies. For example, only three of the five aforementioned studies used empirically supported cut-off or diagnosis scores on a validated measure of CMDs, including depression (Mood and Feelings Questionnaire [MFQ] score\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;20 and short-MFQ score\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;8; Winpenny et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Jacka et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and other internalising disorders including general anxiety disorder or emotional disorders (physician-diagnosed using the International Classification of Diseases version 9 or 10; Wu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Using empirically supported cut-off scores on validated measures of CMDs is important for identifying adolescents who are likely to have a CMD, therefore guiding decisions about interventions. Other studies used \u003cem\u003econtinuous\u003c/em\u003e scores on various measures of internalising symptoms, including: depressive symptoms (Andersen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and emotional dysfunction symptoms (Jacka et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). None of the aforementioned five prospective studies used a validated measure of anxiety as a stand-alone outcome; the paucity of research on the association between diet and anxiety among adolescents is a limitation noted previously (O\u0026rsquo;neil et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, there was heterogeneity in measures of diet quality used across the aforementioned adolescent studies. Several studies used \u0026lsquo;whole of diet\u0026rsquo; predictors (wholistic measures of \u0026lsquo;healthy\u0026rsquo; or \u0026lsquo;unhealthy\u0026rsquo; diet; e.g., Jacka et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Jacka et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Wu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), rather than specific markers of diet quality (e.g., fruit, vegetable intake). Using \u0026lsquo;whole of diet\u0026rsquo; predictors is useful for capturing the complexity of how people eat, but limits ability to identify specific targets for prevention/intervention efforts for CMDs. Identifying specific dietary targets for intervention arguably can have greater utility when developing dietary improvement strategies for adolescents. For example, awareness of health benefits of specific foods has been associated with higher diet quality and fruit and vegetable intake, and lower unhealthy food intake among young adults (Bell and Holder, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMixed findings for the five adolescent-specific studies in the meta-analysis by Orlando et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that explored prospective associations between diet quality and later mental health highlight the need for further research on this topic. Future studies should utilise measures of specific diet quality indicators (to identify modifiable targets for prevention/intervention) and use validated measures of CMDs (with empirically supported binary cut-off scores), to validate existing findings and reveal novel information about how specific dietary factors relate to onset of CMDs (especially anxiety, which is less well-researched).\u003c/p\u003e\u003cp\u003eFinally, few studies have specifically explored whether prospective associations between diet quality indicators and CMDs differ for male and female adolescents. This is despite sex differences in adolescents\u0026rsquo; diet patterns and mental health, such as males reporting higher average unhealthy diet scores and greater declines in healthy eating than females during adolescence (e.g., Jacka et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), or female adolescents reporting higher anxiety than males (Bao and Han, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Limited number of studies have demonstrated that longitudinal associations between higher unhealthy and \u0026ldquo;Western\u0026rdquo; food intake (e.g., take-away foods, processed meats, soft drinks, sweet and fatty foods) and externalising behaviors (e.g., conduct disorder, Attention-Deficit-Hyperactivity Disorder; Trapp et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and depression (Aparicio et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) may be more pronounced among female adolescents. Additionally, the meta-analysis by Orlando et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found studies with a higher percentage of male participants had stronger effect sizes for the association between \u003cem\u003ehealthier\u003c/em\u003e diet patterns and lower internalising symptoms. This research adds to emerging evidence of sex differences in associations between dietary factors and mental health among adolescents, though further research is needed.\u003c/p\u003e\u003cp\u003eTo address these gaps, this study aimed to assess prospective associations between specific diet quality indicators (daily serves of fruit, vegetables and sugar-sweetened beverages [SSBs]) and risk of meeting empirically supported cut-offs for depression and anxiety over three years, among a large sample of Australian adolescents. We hypothesised that higher fruit, higher vegetable, and lower SSB intakes would be associated with lower risk of meeting thresholds for depression and anxiety over time. Given the paucity of prospective research investigating how diet to mental health associations differ by sex among adolescents, this research question was exploratory.\u003c/p\u003e\u003cp\u003eUnderstanding temporal associations between diet quality indicators and CMDs is important for improving prevention and early intervention strategies for CMDs, especially during adolescence when people develop independence around dietary choices and experience heightened risk for CMD onset. Although there is growing evidence for associations between diet quality and mental health among children and adolescents, most research is cross-sectional, and existing adolescent-specific studies have not consistently demonstrated significant prospective associations between diet quality indicators and mental health outcomes (Orlando et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants and Procedures\u003c/h2\u003e\u003cp\u003eThis study included 6537 students from 71 secondary schools (independent, government-funded, Catholic), across 3 Australian states (Queensland, New South Wales, Western Australia). Participants were recruited for the \u0026lsquo;Health4Life\u0026rsquo; study, a 3-year cluster randomised controlled trial testing the efficacy of a school-based e-health intervention. \u0026lsquo;Health4Life\u0026rsquo; aimed to improve the \u0026lsquo;Big 6\u0026rsquo; lifestyle risk factors for chronic disease among adolescents, which are also risk factors for CMDs (poor diet, physical inactivity, sedentary recreational screen time, poor sleep, and cigarette and alcohol use; for study protocol and main outcome paper see Teesson et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Champion et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Participants completed online surveys at baseline (2019, Grade 7, 12\u0026ndash;13 years old), post-intervention (7-week follow-up), and 1-, 2-, and 3-years post baseline (finishing 2022; Grade 10, 15\u0026ndash;16 years old). This study excludes post-intervention (7-week) data, as longitudinal associations were of interest (annual follow-ups). \u0026lsquo;Health4Life\u0026rsquo; participants had better outcomes on psychological distress immediately post-intervention (7-week follow-up; Smout et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), but effects did not hold beyond this timepoint, and \u0026lsquo;Health4Life\u0026rsquo; proved no more effective than active control condition regarding behaviour change for the \u0026lsquo;Big 6\u0026rsquo; (Champion et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, O'Dean et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We included control and intervention participants to maximise sample size. Analyses adjust for the clustered nature of school data, and intervention allocation (see \u0026lsquo;Analysis Plan\u0026rsquo;). Consent methods differed depending on procedures approved by various ethics boards, which required active student consent and either active or passive parental consent (University of Sydney HREC2018/882, University of Queensland 2019000037, Curtin University HRE2019-0083, University of Newcastle H-2020-0232, NSW Department of Education SERAP 2019006, and several Catholic Diocese committees). The trial was pre-registered (ACTRN12619000431123).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eDemographics and covariates.\u003c/b\u003e Demographics relevant to this study were age (years) and sex at birth (male/female/prefer not to say). Family affluence was included as a proxy for socio-economic status (SES), using the Family Affluence Scale III (FAS III; Torsheim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This 6-item measure assesses family assets and wealth by asking adolescents about indicators of wealth that young people know (e.g. number of computers, bathrooms at home). It has demonstrated good test-retest reliability (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9) and strong correlation with parental report (Torsheim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For sensitivity analyses (see \u0026lsquo;Analysis Plan\u0026rsquo;), we included a single-item measure of typical weekly physical activity, worded: \u0026ldquo;Over a typical or usual week, on how many days are you engaged in moderate to vigorous physical activity for a total of at least 60 minutes per day?\u0026rdquo; (range 0\u0026ndash;7).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiet Variables (exposure).\u003c/b\u003e Diet variables were measured in self-reported serves consumed per day, for fruit and vegetables. SSB intake was measured in serves per week (re-coded into serves per day for consistency). To measure fruit intake, participants were asked: \u0026ldquo;How many serves of fruit do you usually eat each day?\u0026rdquo; Participants responded on a 7-point scale from \u0026lsquo;Less than 1 serve per day\u0026rsquo; (0) to \u0026lsquo;More than six serves per day\u0026rsquo; (6), or could select, \u0026lsquo;I don\u0026rsquo;t eat fruit\u0026rsquo; (recoded \u0026lsquo;0\u0026rsquo;) or \u0026lsquo;Don\u0026rsquo;t know\u0026rsquo; (recoded \u0026lsquo;missing\u0026rsquo;). To standardise responses, participants were presented a reference image and instructions representing a standard portion of fruit (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), in line with Australian dietary guidelines (1 serve\u0026thinsp;=\u0026thinsp;150 grams; National Health and Medical Research Council, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). To measure daily vegetable intake, participants were asked: \u0026ldquo;How many serves of vegetables do you usually eat each day?\u0026rdquo; The same response options described for fruit intake were presented. A reference image/instruction was presented (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in line with Australian dietary guidelines (1 serve\u0026thinsp;=\u0026thinsp;75 grams; National Health and Medical Research Council, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). SSB intake was measured by asking participants to \u0026ldquo;Indicate how many cups of soft drink, cordials or sports drinks (such as Coke, lemonade, Ribena or Gatorade) you usually consume\u0026rdquo;. Response options were: \u0026lsquo;Never/Rarely drink\u0026rsquo;, \u0026lsquo;1 cup or less a week\u0026rsquo;, \u0026lsquo;2 to 4 cups a week\u0026rsquo;, \u0026lsquo;5 to 6 cups a week\u0026rsquo;, \u0026lsquo;1 cup a day\u0026rsquo;, \u0026lsquo;1.5 cups or more a day\u0026rsquo;, and \u0026lsquo;2 or more cups a day\u0026rsquo;. A reference image/instruction was presented (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For consistency across diet variables, SSB intake was recoded into \u0026lsquo;cups per \u003cem\u003eday\u003c/em\u003e\u0026rsquo; (1 cup\u0026thinsp;=\u0026thinsp;250mL). If a participant\u0026rsquo;s response was in \u0026lsquo;cups per \u003cem\u003eweek\u003c/em\u003e\u0026rsquo;, their response was recoded into cups per day (e.g., \u0026lsquo;1 cup or less per week\u0026rsquo;=0.14 cups per \u003cem\u003eday\u003c/em\u003e; i.e., 1 cup divided by 7 days of a week).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCommon Mental Disorders (outcome).\u003c/b\u003e To measure depression, we used a modified version of the Patient Health Questionnaire - Adolescent version (PHQ-A; Johnson et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Item 9 (suicidal ideation) was removed for the \u0026lsquo;Health4Life\u0026rsquo; study, upon request from an ethics committee. Therefore, we used an 8-item version of the PHQ-A (PHQ-A-8; total score 24). Higher scores reflect higher depression symptoms. Confirmatory factor analysis of the PHQ-A-8 has previously demonstrated better fit than the 9-item PHQ-A (L\u0026oacute;pez-Torres et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We recoded the PHQ-A-8 total into a binary outcome reflecting likely depression (score\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;10 coded \u0026lsquo;1\u0026rsquo; for depression; score\u0026thinsp;\u0026lt;\u0026thinsp;10 coded \u0026lsquo;0\u0026rsquo; for no depression), based on evidence that this PHQ-8 cut-off represents clinically significant depression (Kroenke et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Internal consistency was good to excellent for the PHQ-A-8 across all timepoints (α\u0026thinsp;=\u0026thinsp;0.87\u0026ndash;0.92). To measure anxiety, we used the 13-item Patient-Reported Outcomes Measurement Information System Anxiety Paediatric (PROMIS-AP) scale (Irwin et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The PROMIS-AP asks about feelings of worry, fear, dread and more, over the past 7 days (total score 13\u0026ndash;65; higher scores reflect greater anxiety). PROMIS-AP has demonstrated greater reliability among adolescent samples than other anxiety measures (Irwin et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). We recoded the total as a binary outcome, reflecting none/slight to mild anxiety (score\u0026thinsp;\u0026lt;\u0026thinsp;34; coded \u0026lsquo;0\u0026rsquo;) or moderate to severe anxiety (score\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;34; coded \u0026lsquo;1\u0026rsquo;). This cut-off was based on evidence that the T-score corresponding to a PROMIS-AP score of 34 (T-score\u0026thinsp;=\u0026thinsp;60) is one standard deviation higher than average (calibrated using normative samples in United States) and represents more severe anxiety conditions (Northwestern University, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Carle et al., 2021, American Psychiatric Association, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Internal consistency was excellent for the PROMIS-AP across all timepoints (α\u0026thinsp;=\u0026thinsp;0.94\u0026ndash;0.96).\u003c/p\u003e\n\u003ch3\u003eAnalysis Plan\u003c/h3\u003e\n\u003cp\u003eTo estimate risk of depression and anxiety attributable to each dietary exposure over 3 years, we followed the Global burden of disease Lifestyle and Mental Disorder Taskforce analysis procedure (Ashtree et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). We fitted Generalised Estimating Equation (GEE) Poisson regression with robust standard errors to model the longitudinal association of each dietary exposure (considered as time-varying exposures) with risk of depression and anxiety. We fitted three models: 1) unadjusted, 2) adjusted for sex at birth, and 3) subgroup analysis by sex at birth. Since there were missing data on key variables (20.51%), we used multiple imputation (Multivariate Imputations by Chained Equations) to impute missing data with 20 repeats (Jakobsen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We included key demographic variables (sex, age, SES) in the imputation to improve model fit. We included a complete-case model as a sensitivity analysis to determine the impact of missing data (see Supplementary Table S1 for descriptive statistics using complete-case analysis and Table S2 for model output). We included additional sensitivity analyses, wherein we further adjusted model 2 for physical activity (Table S3), as well as age and SES (Table S4), given potential associations between these covariates and key variables (e.g., depression, anxiety and dietary variables).\u003c/p\u003e\u003cp\u003eModel assumptions were assessed prior to model fitting. For the SSB variable, we used a log-transformation to correct for non-linearity in the logit; all other assumptions were upheld. All models included fixed variables for timepoint (1 \u0026lsquo;baseline\u0026rsquo; to 4 \u0026lsquo;3-year follow-up\u0026rsquo;), trial group (intervention, control), exposure by timepoint, and exposure by trial group. All main analyses excluded participants who, at baseline, met the threshold for depression (in models where depression was the outcome) or anxiety (where anxiety was the outcome), to increase confidence that diet variables predicted \u003cem\u003eonset\u003c/em\u003e of depression and anxiety over time. Main analyses were conducted in IBM SPSS Statistics (Version 28).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eSample Descriptives\u003c/h2\u003e\n \u003cp\u003eParticipants\u0026rsquo; mean age at baseline was 12.65 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.50), and half identified as female (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3229; 49.40%; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Ninety-four (1.40%) participants reported sex as \u0026lsquo;prefer not to say\u0026rsquo; and were excluded from models due to insufficient sample size for subgroup estimation. The mean family affluence score was 9.34 (out of 13; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.92), indicating a medium to high SES sample (e.g., Corell et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e, Torsheim et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows other baseline descriptives. Fruit, vegetable and SSB intake appeared relatively stable over time, with females reporting slightly higher intakes of fruit and vegetables and males reporting higher SSB intake (Table 2).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics of Total Sample, Analytic Sample and Cases in Depression and Anxiety Models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Sample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6639)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnalytic Sample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6537)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDepression Analysis Cases (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5097)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnxiety Analysis Cases (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5120)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3308 (49.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3308 (50.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2658 (52.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2726 (53.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3229 (48.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3229 (49.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2439 (47.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2394 (46.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrefer not to say or missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (\u003cem\u003eM, SD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.66 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.65 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.66 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.66 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily Affluence (\u003cem\u003eM, SD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.37 (1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.34 (1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.37 (1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.35 (1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiet Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFruit serves (\u003cem\u003eM, SD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.46 (1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43 (1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.46 (1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.44 (1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetables serves (\u003cem\u003eM, SD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64 (1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62 (1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64 (1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62 (1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSB serves (\u003cem\u003eM, SD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23 (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23 (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental Health Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression Total (\u003cem\u003eM, SD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07 (5.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.04 (5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.19 (2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.88 (4.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety Total (\u003cem\u003eM, SD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.99 (10.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.97 (10.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.42 (8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.25 (5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeets Depression Cutoff (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1039 (16.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1015 (16.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e454 (8.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeets Anxiety Cutoff (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1001 (16.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e983 (16.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e417 (8.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e SSB\u0026thinsp;=\u0026thinsp;Sugar-sweetened beverages. Serves\u0026thinsp;=\u0026thinsp;per day (150 grams fruit, 75 grams vegetables, 250 grams SSBs).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"627\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 627px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eDiet Variables for Analytic Sample and Sex Subsamples at each Timepoint (Multiply Imputed Dataset)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalytic Sample (\u003cem\u003en\u003c/em\u003e=\u003c/strong\u003e\u003cstrong\u003e6537\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFruit serves (\u003cem\u003eM\u003c/em\u003e, S\u003cem\u003eD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.43 (1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.36 (1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.14 (1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.05 (1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 217px;\"\u003e\n \u003cp\u003eVegetable serves (\u003cem\u003eM\u003c/em\u003e, \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.62 (1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.71 (1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.58 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.54 (1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 217px;\"\u003e\n \u003cp\u003eSSB serves\u0026nbsp;(\u003cem\u003eM\u003c/em\u003e, \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.25 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.23 (0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.23 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.25 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemales\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e=\u003c/strong\u003e\u003cstrong\u003e3229\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFruit serves (\u003cem\u003eM\u003c/em\u003e, S\u003cem\u003eD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.51 (1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.40 (1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.16 (1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.03 (1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 217px;\"\u003e\n \u003cp\u003eVegetable serves (\u003cem\u003eM\u003c/em\u003e, \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.72 (1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.78 (1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.65 (1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.57 (1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 217px;\"\u003e\n \u003cp\u003eSSB serves\u0026nbsp;(\u003cem\u003eM\u003c/em\u003e, \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.17 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.15 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.16 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.15 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMales\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e=\u003c/strong\u003e\u003cstrong\u003e3308\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFruit serves (\u003cem\u003eM\u003c/em\u003e, S\u003cem\u003eD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.35 (1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.33 (1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.13 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.08 (1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 217px;\"\u003e\n \u003cp\u003eVegetable serves (\u003cem\u003eM\u003c/em\u003e, \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.52 (1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.64 (1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.52 (1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.51 (1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 217px;\"\u003e\n \u003cp\u003eSSB serves\u0026nbsp;(\u003cem\u003eM\u003c/em\u003e, \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.31 (0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.30 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.31 (0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.34 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrefer not to Say (\u003cem\u003en\u003c/em\u003e=94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFruit serves (\u003cem\u003eM\u003c/em\u003e, S\u003cem\u003eD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.22 (1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.14 (1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.92 (1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.77 (1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 217px;\"\u003e\n \u003cp\u003eVegetable serves (\u003cem\u003eM\u003c/em\u003e, \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.39 (1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.58 (1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.42 (1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.24 (1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 217px;\"\u003e\n \u003cp\u003eSSB serves\u0026nbsp;(\u003cem\u003eM\u003c/em\u003e, \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.36 (0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.37 (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.37 (0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.25 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 627px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e SSB=Sugar-sweetened beverages. Serves=per day (150 grams fruit, 75 grams vegetables, 250 grams SSBs). \u003cem\u003eM\u003c/em\u003e=mean; \u003cem\u003eSD\u003c/em\u003e=standard deviation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"864\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 864px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Longitudinal Associations Between Diet Quality Indicators and Depression and Anxiety for Analytic Sample and Sex Subsamples (Multiply Imputed Dataset)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVegetable (per serve increase)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFruit (per serve increase)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSB (per cup increase)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eRisk Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eRisk Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eRisk Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 864px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.94\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.89, 1.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.88\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.82, .93\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.79, 3.44\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.93\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.88, .99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.81, .92\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.44, 4.52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e.86, 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.79, .97\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.46\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.15, 5.58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.93\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.87, .99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.79, .93\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.07, 4.72\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 864px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e.92, 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.93\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.87, .99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.46\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.71, 3.54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e.91, 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.91\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.85, .98\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.48, 4.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.78, .98\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.89\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.79, .99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.97\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.67, 9.71\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e.95, 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e.86, 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.23, 3.39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 864px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e bold=significant at \u003cem\u003ep\u003c/em\u003e\u0026lt;.05. Model 1 is unadjusted. Model 2 is adjusted for sex. Model 3 includes sex as subgroup.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociations between Dietary Intake and CMDs\u003c/h2\u003e\n \u003cp\u003eHigher vegetable (adjusted risk ratio [RR]: 0.93, 95%CI: 0.88\u0026ndash;0.99) and fruit intake (adjusted RR:0.86, 95%CI: 0.81\u0026ndash;0.92) were associated with lower risk of depression over 3 years follow-up (Table\u0026nbsp;3). When analysed by sex, higher vegetable intake was associated with lower risk of depression for females, not males, whereas higher fruit intake was associated with lower risk of depression for both sexes. For vegetables and fruit, higher intake was associated with lower risk of anxiety for males, not females. Higher SSB intake was associated with higher risk of depression (adjusted RR: 3.32, 95%CI: 2.44\u0026ndash;4.52) and anxiety (adjusted RR: 3.49 95%CI: 2.48\u0026ndash;4.90) for the overall sample, and males and females separately.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSensitivity Analyses\u003c/h3\u003e\n\u003cp\u003eComplete case analyses (Supplementary Table S2) mostly showed results consistent with those described above, except that: 1) vegetable intake was no longer significantly associated with depression in the overall sample (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.112) or female subsample (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.129), and 2) for male adolescents, fruit intake was not significantly associated with depression (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.060) or anxiety (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.062), indicating missing data may have influenced these results. In the sensitivity analyses accounting for physical activity in model 2 (Table S3), the association between vegetable intake and depression became non-significant in the overall sample (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.053) and female subsample (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.049). Finally, after accounting for physical activity, the following associations specific to males became non-significant: vegetable intake to anxiety (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.063), and fruit intake to anxiety (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.118).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated longitudinal associations between diet quality indicators (daily fruit, vegetable, and SSB intake) and risk of depression and anxiety onset over 3 years, among a large adolescent sample, and whether associations differed by sex. In line with hypotheses, key findings (from adjusted models including sex as covariate) were that higher fruit intake and lower SSB intake were significantly associated with lower risk of meeting thresholds for depression and anxiety, over three years during high school (Grades 7\u0026ndash;10). Partially in line with hypotheses, higher \u003cem\u003evegetable\u003c/em\u003e intake was associated with lower risk of depression over time, but not anxiety (associations in expected direction but non-significant). Furthermore, associations between \u003cem\u003evegetable\u003c/em\u003e intake and depression became non-significant in sensitivity analyses, indicating results may be influenced by missing data (Table S2) or engagement in healthy behaviours other than vegetable intake (like physical activity; Table S3). There was evidence that associations between fruit and vegetable intake (healthy diet indicators) and risk of depression and anxiety differed by sex, whereas higher SSB consumption was associated with higher risk of both CMDs for both sexes.\u003c/p\u003e\u003cp\u003eDiffering results regarding associations between fruit intake and CMD risk (consistent significant associations), versus vegetable intake and CMD risk (inconsistent associations), are concordant with inconsistencies in the literature regarding associations between \u003cem\u003ehealthy\u003c/em\u003e diet quality indicators and CMDs among adolescents. Our findings corroborate results of the systematic review by O\u0026rsquo;neil et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which found less consistent results for the association between healthy (versus unhealthy) diet patterns and mental health outcomes. These findings also address the need for more research on how \u003cem\u003especific\u003c/em\u003e indicators of healthy diet (i.e., fruit and vegetable intake) relate to mental health during adolescence, rather than \u0026lsquo;whole of diet\u0026rsquo; indicators, wherein certain foods might 'cancel each other out' and mask effects for associations between healthy diet indicators and CMDs. Alternatively, observed differences in statistical significance for associations between fruit versus vegetable intake and CMDs could be explained by less variability in average self-reported daily vegetable intake across timepoints (2.54\u0026ndash;2.71 serves) compared to fruit intake (2.05\u0026ndash;2.43 serves), rather than differences in the strength of associations between fruit versus vegetable intake and CMDs.\u003c/p\u003e\u003cp\u003eSubgroup analyses by sex revealed higher \u003cem\u003efruit\u003c/em\u003e intake was associated with lower risk of \u003cem\u003edepression\u003c/em\u003e onset for both sexes. Specific to females was the association between higher \u003cem\u003evegetable\u003c/em\u003e intake and lower \u003cem\u003edepression\u003c/em\u003e risk. Specific to males were the associations between higher \u003cem\u003evegetable and fruit\u003c/em\u003e intake and lower risk of \u003cem\u003eanxiety\u003c/em\u003e. However, each finding specific to one sex became non-significant after accounting for physical activity, which highlights the multi-faceted nature of health behaviours like nutrition and exercise in relation to onset of CMDs among adolescents. Nonetheless, finding evidence that the association between higher fruit and vegetable intake and lower \u003cem\u003eanxiety\u003c/em\u003e likelihood was specific to males does align with prior literature. The meta-analysis by Orlando et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) revealed studies with higher percentage of male adolescents tended to have stronger effect sizes for the association between \u003cem\u003ehealthy\u003c/em\u003e dietary patterns and lower internalizing symptoms. One possible explanation is that males, who typically report poorer overall diet quality and greater declines in healthy eating during adolescence compared to females (e.g., Jacka et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), may have more to gain from consuming healthy dietary components in terms of protecting against CMDs, especially anxiety.\u003c/p\u003e\u003cp\u003eRegarding \u003cem\u003eunhealthy\u003c/em\u003e diet quality indicators and CMDs, this study was limited to SSB consumption and found this indicator was associated with higher risk of developing depression and anxiety for both sexes. These findings align with hypotheses and prior research on the association between SSB consumption and poorer mental health among adolescents (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Ra, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These findings also support research showing consistent cross-sectional and longitudinal associations between \u003cem\u003eunhealthy\u003c/em\u003e diet patterns (e.g., UPF consumption) and poorer mental health, among adolescents (e.g., internalizing and depressive symptoms; Orlando et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, O\u0026rsquo;neil et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and adults (see review by Lane et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The association between higher SSB intake and higher CMD-risk may relate to biological implications of consuming high-sugar beverages, like increased circulating inflammatory markers or changes in hormone levels (e.g., cortisol), which may affect mood (Kivim\u0026auml;ki et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Di Polito et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, SSB consumption is only one unhealthy behaviour, and is likely conflated with other unhealthy dietary and behavioural patterns that may moderate the association between SSB consumption and poorer mental health (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although the association between SSB consumption and CMDs remained robust after controlling for covariates like physical activity, future research should explore how other factors (e.g., consumption of other UPFs) may moderate the association between SSB consumption and CMDs. The magnitude of associations between SSB intake and both CMDs was higher for males than females (especially for anxiety), indicating male adolescents may be more susceptible to the impact of SSB-consumption on mental health (as per previous literature, e.g., Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eImplications\u003c/h2\u003e\u003cp\u003eFindings suggest targeting modifiable dietary factors in prevention/early intervention efforts for CMDs has potential to reduce risk of developing depression and anxiety throughout adolescence, when CMDs often first emerge (Solmi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and independence in dietary choices increases. This study indicates that encouraging fruit intake (and vegetable intake, although with more limited evidence), and especially limiting SSB intake, may play a role in preventing onset of CMDs during adolescence. Additionally, male adolescents may experience greater protective benefits from improving diet quality, in terms of preventing CMD onset.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Strengths\u003c/h2\u003e\u003cp\u003eThis study was limited by few dietary variables in the dataset that could be coded into serves per day. We used variables that could be coded this way to be consistent across exposures, and increase comparability of results with other studies on the impact of diet on health, particularly those that follow the rigorous \u0026lsquo;Global Burden of Disease study\u0026rsquo; measurement protocols (page 170 of Supplementary appendix 1 in Apostol et al., 2024). While using few dietary exposures limited ability to understand how overall diet quality relates to CMD onset, this approach provides specific targets for action. Using self-reported dietary intake is another limitation, due to potential self-report biases and recall inaccuracy; use of standardised portion size images/descriptions aimed to mitigate this issue.\u003c/p\u003e\u003cp\u003eThis study has several strengths. It addressed a need for adolescent-specific prospective studies exploring how specific dietary components relate to meeting empirically supported thresholds for CMDs over time. The validated measures of depression and anxiety in this study (PHQ-A-8, PROMIS-A) have not been used in similar prior studies, so using these measures helps to validate prior findings for the prospective association between diet quality and depression, and especially anxiety (for which there is little research). The sample in our study (N\u0026thinsp;=\u0026thinsp;6537) was larger than samples in the five most-relevant studies of diet to later CMDs in the meta-analysis by Orlando et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; N\u0026thinsp;=\u0026thinsp;603 to N\u0026thinsp;=\u0026thinsp;4861). This large sample provided statistical power to investigate specificity of associations by sex, which has seldom been done, and allowed us to exclude cases that met thresholds for depression or anxiety at baseline to better assess whether dietary factors related to \u003cem\u003eonset\u003c/em\u003e of CMDs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eFuture Directions\u003c/h2\u003e\u003cp\u003eFuture studies should replicate our findings and include other dietary exposures (e.g., whole grains, lean proteins, healthy fats) to increase knowledge of how specific dietary components may relate to CMDs among adolescents, and whether associations differ by sex. Future studies should control for additional covariates like total daily energy intake (unavailable), to account for the possible impact of differences in energy intake on mental health (Ammar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study suggests adolescents with heathier diet indicators (especially higher fruit and lower SSB intakes) had a reduced likelihood of meeting empirically supported thresholds for depression and anxiety over three years during high school. Associations between fruit and vegetable intakes and CMDs were less consistent and differed by sex, compared to associations between SSB intake and CMDs, which were comparatively stronger and evident for both sexes. This research can inform targeting of modifiable nutrition factors within prevention, intervention and public health messaging efforts aimed at preventing onset of CMDs, which are high and rising among adolescents in Australia and other developed countries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy funding: ‘\u003c/strong\u003eHealth4Life’ was funded by Paul Ramsay Foundation, Australian National Health and Medical Research Council (NHMRC), and a Centre of Research Excellence in Prevention and Early Intervention of Mental Illness and Substance Use (PREMISE; APP11349009). AON and DNA are supported by a NHMRC Emerging Leader 2 Fellowship (2009295). FNJ is supported by a NHMRC Leader 1 Fellowship (GNT1194982).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributorships:\u0026nbsp;\u003c/strong\u003eLD led analyses and manuscript writing. MT, KEC, and NCN secured funding for ‘Health4Life’; KEC and LAG were responsible for ethics, governance, trial coordination, and supervision of staff with oversight from MT and NCN. LAG, BO, and SS were responsible for recruitment and data collection.AON and FNJ conceptualised the GLAD taskforce and project. AON acquired funding for the GLAD project. DNA, RO, and MML contributed to development of methodology for GLAD. DNA, RO, MML, FNJ, and AON contributed to GLAD resources (provision of study materials). DNA contributed to GLAD administration and supervision of statistical analyses. Professor Timothy Slade, Associate Professor Matthew Sunderland, and Dr Siobhan O’Dean provided informal statistical guidance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests:\u003c/strong\u003e NN and MT are Directors of Climate Schools Pty Ltd and OurFutures Institute Ltd. DNA, RO, MML, FNJ, and AON are affiliated with the Food \u0026amp; Mood Centre, Deakin University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability.\u0026nbsp;\u003c/strong\u003eAvailable upon request from corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollection of data used in this study was approved by Human Research Ethics Committees at\u0026nbsp;University of Sydney (HREC2018/882; December 17, 2018), University of Queensland (2019000037; February 5, 2019), Curtin University (HRE2019-0083; February 2022, 2019), and New South Wales State Education Research and Partnerships (SERAP) process (2019006; April 15, 2019). Ethical approval was also granted by several Catholic Diocese committees, for which approval numbers are not applicable (Catholic Maitland-Newcastle, March 7, 2019; Edmund Rice Education Australia, May 13, 2019; Brisbane Catholic Education, June 12, 2019; Catholic Education Office Western Australia, June 4, 2019; Bathurst Catholic Education, May 22, 2019). Informed consent methods differed depending on procedures approved by various ethics boards, which required active student consent and either active or passive parental consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAMERICAN PSYCHIATRIC ASSOCIATION. 2013. \u003cem\u003eInstruction Manual: LEVEL 2\u0026mdash;Anxiety\u0026mdash;Child Age 11\u0026ndash;17 (PROMIS Emotional Distress\u0026mdash; Anxiety\u0026mdash;Pediatric Item Bank)\u0026nbsp;\u003c/em\u003e[Online]. Available: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.psychiatry.org/File%20Library/Psychiatrists/Practice/DSM/APA_DSM5_Level-2-Anxiety-Child-Age-11-to-17.pdf [Accessed 19 February 2025].\u003c/li\u003e\n \u003cli\u003eAMMAR, A., TRABELSI, K., HAMMOUDA, O. \u0026amp; CLARK, C. C. 2024. Nutrition and wellbeing: how do energy intake, fasting and prudent diets affect mental health. Frontiers Media SA.\u003c/li\u003e\n \u003cli\u003eANDERSEN, J. H., LABRIOLA, M., LUND, T. \u0026amp; HANSEN, C. D. 2013. Development of health and depressive symptoms among Danish adolescents\u0026mdash;Socioeconomic differences and effects of life-style. \u003cem\u003eOpen Journal of Preventive Medicine,\u003c/em\u003e 3\u003cstrong\u003e,\u003c/strong\u003e 104-110.\u003c/li\u003e\n \u003cli\u003eAPARICIO, E., CANALS, J., VOLTAS, N., VALENZANO, A. \u0026amp; ARIJA, V. 2017. 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Influence of physical activity, sedentary behavior, and diet quality in childhood on the incidence of internalizing and externalizing disorders during adolescence: a population-based cohort study. \u003cem\u003eAnnals of epidemiology,\u003c/em\u003e 28\u003cstrong\u003e,\u003c/strong\u003e 86-94.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Sydney","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":"adolescent, diet, depression, anxiety, mental disorder, prospective studies","lastPublishedDoi":"10.21203/rs.3.rs-7634151/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7634151/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e There is need for more longitudinal research exploring temporal associations between diet quality and common mental disorders (CMDs) during adolescence. This study examined prospective associations between indicators of adolescents’ diet quality (fruit, vegetable and sugar-sweetened beverage [SSB] intakes) and risk of depression and anxiety onset, over three years. We hypothesised higher fruit and vegetable intake, and lower SSB intake, would be associated with lower risk of CMDs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e This study included 6537 Australian adolescents (mean age 12.65 years at baseline). Students completed surveys at baseline, 1-, 2-, and 3-year follow-ups. We fitted Generalised Estimating Equations Poisson regression models to assess longitudinal associations between daily serves of fruit, vegetables, and SSBs, and risk of depression and anxiety onset (binary cut-points on validated scales) over 3 years. Models were re-run for males and females. Analyses excluded participants who met thresholds for depression or anxiety at baseline, to increase confidence that diet predicted onset of CMDs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAdolescents with heathier diet indicators (higher fruit and vegetable, lower SSB intakes) had lower risk of developing depression. The same was true for associations between both fruit and SSB (but not vegetable) intake and anxiety. Subgroup analyses revealed sex differences for associations between healthy indicators (fruit, vegetables) and CMDs, but higher SSB intake was associated with higher CMD risk for both sexes. Results inform the Global burden of disease Lifestyle And mental Disorders project (DERR2-10.2196/65576).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Longitudinal research exposing temporal associations between diet and CMDs may inform prevention/intervention efforts targeting modifiable risk factors for CMDs.\u003c/p\u003e","manuscriptTitle":"Longitudinal Associations between Dietary Intake and Common Mental Disorders among Adolescents in Australia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 07:52:36","doi":"10.21203/rs.3.rs-7634151/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":"f7eb9d90-7dfa-41bf-ac74-fbdd9d38c423","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54839046,"name":"Nutrition \u0026 Dietetics"},{"id":54839047,"name":"Psychology"}],"tags":[],"updatedAt":"2025-09-23T07:52:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 07:52:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7634151","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7634151","identity":"rs-7634151","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}