Association between major dietary patterns and mental health problems among college students

Abstract

BACKGROUND:

Mental health problems, specifically, depression, anxiety, and stress are among the major public health issues worldwide. Diet modification can be a helpful strategy for the prevention and management of psychological disorders. Therefore, the present study aims to explore the association between major dietary patterns and mental health problems among Iranian college students.

MATERIALS AND METHODS:

This cross-sectional study was conducted on 412 college students. Dietary intakes were assessed using a 168-item semi-quantitative Food Frequency Questionnaire (FFQ). The 42-item Depression, Anxiety, and Stress Scale was applied to evaluate subjects’ mental health. Major dietary patterns were identified using principal component analysis. Logistic regression was applied to assess the association between major dietary patterns and mental health problems.

RESULTS:

Participants in the third tertile of the “plant-based” dietary pattern had lower odds of depression compared with the first tertile, after adjustment for the potential confounders [odds ratio (OR) = 0.44, 95% confidence interval (CI): 0.17–0.65, P trend <0.01 for model I and OR = 0.42, 95% CI: 0.17–0.67, P trend <0.01 for model II]. The “plant-based” dietary pattern showed no significant association with the risk of stress and anxiety. However, this association for anxiety became marginally significant in model II (OR = 0.53, 95% CI: 0.36–0.98, P trend = 0.07). The “Western” dietary pattern also was not associated with the likelihood of depression, stress, and anxiety.

CONCLUSION:

A strong inverse association was observed between the “plant-based” dietary pattern and depression. While the “Western” dietary pattern was not associated with mental health problems among college students, further prospective studies are warranted.

Introduction

Mental health problems, specifically, depression, anxiety, and stress are among the major public health issues worldwide due to increasing prevalence, disease progression, difficulties in therapeutic management, and negative outcomes.[1] Depression affects how a person feels, thinks, and acts. It is marked by lack of pleasure or interest, weight loss or gain, disturbed sleep pattern, low energy, poor concentration, and feelings of low self-worth or guilt.[1,2] According to the American Psychological Association, anxiety also manifests emotional responses including changes in sleep patterns, fatigue, irritability, and muscle tension. While stress is usually triggered by an external stressor and lasts for a short period of time, anxiety is persistent, even after a concern has passed.[1,3]

Mental health problems are common experiences among young adults particularly university students[1,4] and it is reported that 12–50% of university students meet the criteria for one or more mental problems, worldwide.[1] This remarkable rate of psychological distress has been shown to be associated with inappropriate life outcomes including lower academic performance, employment rate and income.[4] The huge social and economic burden of impaired mental health for subjects, families, and society necessitates public health actions, for both treatment and prevention of it.[4] Diet modification can be a helpful strategy for prevention and management of most of the chronic illnesses including mental disorders. However, studying the effects of individual nutrients or foods on mental health may provide an imperfect picture of the relationship between diet and psychological distress, given the complicated synergistic effects or interactions among nutrients and foods in our overall diets.[5] Therefore, the dietary pattern approach is a comprehensive and multifactorial method highly recommended for identifying the links between diet and diseases.[4,6] This is a more practical approach in both public health and clinical interventions in comparison with the single nutrient or food approach.[6]

According to an accumulating body of evidence, consumption of “comfort foods,” sweet foods, and a Western-type diet had an effect on psychological well-being and was related to higher depression scores and obesity in the general population.[7] Lazarevich et al.[7] also reported that in female Mexican college students, a higher depression score was associated with a higher frequency of fast food, fried food, and sugary food intake. However, no association was observed in male students. The findings by Faghih et al.[4] have indicated that higher adherence to the DASH (dietary approaches to stop hypertension) dietary pattern as a healthy eating pattern was related to better mental health in university students. However, Nasir et al. reported no significant association between unhealthy dietary patterns and depression, anxiety, and stress scores in apparently healthy adults.[8] Due to these inconsistent findings with respect to mental health problems and dietary patterns, and given the importance of psychological distress in college students, we decided to investigate the association between major dietary patterns and mental health problems among Iranian college students. In addition to the facility of recruitment, appropriate cooperation, and assumed lower response bias, using a sample of college students may reduce the effects of some cofounders on the results of the study due to their relatively similar lifestyle and socio-demographic and cultural backgrounds that may influence both dietary patterns and mental health.

Materials and Methods

Study design and setting

This cross-sectional study was conducted in Khalkhal University of Medical Sciences and Faculty of Medical Sciences of Islamic Azad University.

Study participants and sampling

All undergraduate students of Khalkhal University of Medical Sciences and Faculty of Medical Sciences of Islamic Azad University were invited to contribute to the study, and 435 subjects agreed to take part in the study between September and November 2022. After excluding those who were taking dietary supplements and psychiatric medications (N = 5), following specific diets (N = 5), had a history of mental diseases (N = 2), had experienced psychological trauma during the past year (N = 3), had left blank more than 40% of the questionnaires (N = 5), and with total energy intake outside the range of 1000–4500 kcal (N = 3), 412 subjects remained for the final analysis.

Data collection tool and technique

Information on age, gender, marital status, undergraduate year, family size, medical history, drugs and supplement intake, family history of mental disorders, smoking, and sleep duration were collected from all participants by general questionnaire and trained interviewers. Anthropometric measurements including weight and height were performed, and body mass index (BMI) was calculated as body weight in kilograms divided by height in meters squared.

The validated Iranian version of the International Physical Activity Questionnaire (IPAQ)-long form was used to evaluate physical activity levels.[9] Subjects were asked to report the number of days and the time spent on walking and moderate intensity and vigorous intensity activities during the last week, and results were represented as metabolic equivalents hours per day (MET-h/day). A more comprehensive guide on Met-time computation and scoring the IPAQ is reported in the literature.[10]

The usual dietary intake of the subjects over a year was evaluated through a valid and reliable semi-quantitative FFQ.[11] The FFQ consisted of 168 food items, including the most common Iranian meal recipes and a well-trained nutritionist filled in all of the FFQs during a face-to-face interview. A validated food photograph album[12] and a set of household measurements (tablespoon, teaspoon, cup, glass, plate, bowl, spatula, etc.) were applied to help participants to estimate the portion sizes of food items. They were asked to report the frequency of intake of a given serving of each food item on a daily, weekly, monthly, or yearly basis, and then the daily intake of each food item was calculated in grams. To minimize the within-individual variations in consumption of each food, the 168 food items were classified into 26 food groups based on their nutritional composition and culinary usage. Some food items were left as unique food groups because it was improper to incorporate them into a certain food group (e.g., salt, eggs, and mayonnaise). The total energy intakes of the subjects, as a covariate, were calculated using the Nutritionist IV software, version 3.5.2 (The Hearst Corp., San Bruno, CA).

Subjects’ mental health was assessed by a validated Persian version of the 42-item Depression, Anxiety, and Stress Scale (DASS-42).[13] The DASS-42 is an easy-to-fill-in self-report scale designed to measure the major symptoms of three prevalent mental health disorders in both clinical and nonclinical samples.[13] It contains 42 items comprising three subscales including depression, anxiety, and stress of 14 items. Each item is rated on a 4-point Likert-type scale (0 = “did not apply to me at all,” 1 = “applied to me to some degree or some of the time,” 2 = “applied to me to a considerable degree or a good part of the time,” and 3 = “applied to me very much or most of the time”) and refers to the past week.[14] Scores for each subscale were computed by summing the scores for the relevant items. The lower the score on this questionnaire, the better the mental health. Depression, anxiety, and stress scores are classified and interpreted as shown in Table 1.[14]

Table 1.

Interpretation of DASS-42 subscales scores

Interpretation		Subscales
		Depression		Anxiety		Stress
Normal		0–9		0–7		0–14
Mild		10–13		8–9		15–18
Moderate		14–20		10–14		19–25
Severe		21–27		15–19		26–33
Extremely severe		28–42		20–42		34–42

Data were analyzed with SPSS version 24 (SPSS Inc., Chicago, IL, USA) and a two-sided P value of < 0.05 was considered statistically significant. For a check of data normality, Kolmogorov-Smirnov test was used. Factor analysis by extraction method of principal component analysis (PCA) was applied to detect major dietary patterns on the basis of 26 food groups. Sampling adequacy was supported by Kaiser–Meyer–Olkin test (KMO = 0.75), and Bartlett’s test of sphericity indicated that factor analysis was appropriate for the data (P < 0.01). The number of factors with an Eigenvalue of >2 was determined by scree plot, and two interpretable factors were retained. The orthogonal varimax rotation method was used to simplify the factors and to improve their interpretability. Each food group received a factor loading in relation to each dietary pattern which indicates the correlation coefficient between the food group and the dietary pattern [Table 2]. In the present study, food groups with factor loadings ≥2 were considered to contribute to the factor significantly and were thought as the most informative variable for explaining the patterns. The extracted factors were named based on the loaded items in each factor. Then for each pattern, the factor scores were calculated by summing up intakes of food groups weighted by factor loading. Each person received a factor score for each dietary pattern, and then subjects were divided into three categories based on dietary pattern scores. Subjects in the lowest tertile had the minimum adherence to the dietary pattern and those in the highest tertile had the maximum adherence to it.

Table 2.

Factor loading matrix for major dietary patterns identified among study population^a

Food groups		Food items		Dietary pattern
				Plant-based		Western
Fruits		All kinds of fresh fruits		0.506
Dried fruits		All kinds of dried fruits		0.494		-0.290
Olive		Olive and olive oil		0.471
Liquid oils		All kinds of liquid vegetable oils except olive oil		0.433
Vegetables		Green leafy, yellow, and all other types of raw or cooked vegetables, vegetable juices		0.417
Nuts and seeds		Peanuts, pistachios, walnuts, hazelnuts, almonds, seeds		0.414
Pickles		All kinds of vegetable pickles		0.369
Natural fruit juice		All kinds of fresh fruit juices		0.360
Legumes		All kinds of beans, peas, and lentil		0.332		0.236
Sweets and deserts		Confectionary products, jelly, canned fruits, jam, honey, biscuit, chocolate, candy		0.321
Poultry		All kinds of poultry meats		0.218
Solid fats		Animal fat, hydrogenated vegetable oil, margarine, butter				0.671
Sugar		Sugar				0.539
Snack and fast foods		Pizza, French fries, cheese snacks, potato chips		0.213		0.529
Egg		Eggs		0.232		0.453
Tea and coffee		Tea and coffee				0.450
Salt		Salt				0.338
Processed meat		Sausage, hot dogs, salami, hamburger, bacon				0.274
Soft drinks		Nonalcoholic sweetened beverages				0.229
% of variance explained				8.96%		7.59%

^aFactor loading lower than 0.2 are not shown for simplicity

The independent samples t-test (for quantitative variables) and Chi-squared test (for qualitative variables) were used to compare the subjects with or without mental health problems. Logistic regression was applied to assess the association between major dietary patterns and mental health problems by adjusting for confounders in different models (age, gender, and energy intake for model I and age, gender, BMI, energy intake, physical activity, smoking, sleep duration, family size, family history of mental disorders, marital status, and undergraduate year for model II) and the odds ratio (OR) with a 95% confidence interval (CI) was estimated. The first tertile of each dietary pattern score was determined as the reference in the analyses. In order to obtain the overall trend of ORs across increasing tertiles of dietary pattern scores (P for trend), we treated the tertiles categories as an ordinal variable.

Ethical consideration

The study was approved by the Ethics Committee of Khalkhal University of Medical Sciences (Approval ID: IR.KHALUMS.REC.1399.005). All participants signed a written informed consent before taking part in the study. All questionnaires were nameless, and participants were assured about the confidentiality of information.

Results

General characteristics of the study population are presented in Table 3. Of 412 participants, 158 (38.3%) had symptoms of mental health problems including depression (N = 55), anxiety (N = 31), and stress (N = 72). In participants with symptoms of mental health problems, the mean (SD) scores of depression, anxiety, and stress were 16.31 (5.93), 10.22 (3.35) and 20.74 (9.35), respectively (data not shown).

Table 3.

Comparison of the general characteristics for the participants with and without mental health problems (depression, anxiety, and stress)

Variables		With mental health problems (n=158)		Without mental health problems (n=254)		P
Type of mental health problem
Depression     Anxiety     Stress		55 (34.81) 31 (19.62) 72 (45.56)		- - -		-
Gender
Female     Male		114 (72.1) 44 (27.9)		202 (79.5) 52 (20.5)		0.24
Marital status
Single     Married		146 (92.4) 12 (7.3)		238 (93.7) 16 (6.3)		0.96
Undergraduate years
First     Second     Third     Fourth		49 (31.1) 40 (25.3) 42 (26.5) 27 (17.1)		71 (27.9) 74 (29.1) 64 (25.2) 45 (17.8)		0.72
Smoking
Yes     No		13 (8.3) 145 (91.7)		8 (3.1) 246 (96.9)		0.02
Family history of mental disorders
Yes     No		12 (7.6) 146 (92.4)		15 (5.9) 239 (94.1)		0.66
Age (year)		20.82±1.24		20.79±1.29		0.84
BMI (kg/m2)		23.61±4.21		22.96±3.11		0.08
Total energy intake (kcal/day)		2747.37±798.61		2629.49±755.29		0.15
Physical activity (Met-h/day)		39.42±6.74		40.72±7.14		0.08
Family size (person)		4.51±1.01		4.30±0.89		0.04
Sleep duration (hours)		7.58±1.85		7.18±1.59		0.02

Data presented as mean±standard deviation or number (percentage). Quantitative variables were tested by independent samples t-test and qualitative variables were tested by Chi-squared test. BMI: body mass index; MET-h/day: metabolic equivalents hours per day

There were significant differences between subjects with and without symptoms of mental health problems in terms of smoking, sleep duration, and family size (P < 0.05). These two groups of subjects were similar in other characteristics including gender, marital status, undergraduate years, family history of mental disorders, age, BMI, total energy intake, and physical activity.

Using PCA, two major dietary patterns were explored from 26 food groups of the FFQ [Table 3]: the “plant-based” dietary pattern that was high in fruits, dried fruits, olive, liquid oils, vegetables, nuts and seeds, pickles, natural fruit juice, legumes, sweets and deserts, poultry, and the “Western” dietary pattern which was characterized by high intakes of solid fats, sugar, snack and fast foods, egg, tea and coffee, salt, processed meat and soft drinks, and low intake of dried fruits. These two patterns explained 16.55% of the whole variance in dietary intake. The loading factors of all food groups for the explored dietary patterns are presented in Table 3. The positive loadings indicate a positive relation between food groups and dietary patterns, while the negative loadings demonstrate a negative association. The higher loading of a food group in a certain dietary pattern indicates a greater proportion of that food group in that dietary pattern.

The average intake of food groups across the tertiles of the two identified major dietary patterns is shown in Table 4. The mean consumption of fruits, dried fruits, olives, liquid oils, vegetables, nuts and seeds, pickles, natural fruit juice, legumes, poultry, snack and fast foods, and eggs was significantly higher in the third tertile of the “plant-based” dietary pattern rather than the first tertile (all P < 0.05). While compared with those in the lowest tertile of the “Western” dietary pattern, subjects in the third tertile had a significantly higher intake of legumes, solid fats, sugars, snacks and fast foods, eggs, tea and coffee, salt, processed meat, and soft drinks and a lower intake of fruits, dried fruits, olives, and vegetables (all P < 0.05).

Table 4.

Average intake of food groups across the tertiles of major dietary patterns identified among study population

Food groups		Dietary pattern
		Plant-based						P		Western						P
		Tertile 1 (n=137)		Tertile 2 (n=137)		Tertile 3 (n=138)				Tertile 1 (n=137)		Tertile 2 (n=137)		Tertile 3 (n=138)
Fruits (g/day)		255.57±95.29		313.41±135.92		381.79±134.45		<0.001		303.62±151.20		284.94±136.81		293.15±138.45		0.07
Dried fruits (g/day)		1.74±1.91		1.92±1.35		3.25±5.36		<0.001		2.12±4.45		1.62±3.60		1.01±2.28		0.04
Olive (g/day)		0.90±1.39		1.57±2.31		4.18±5.97		<0.001		3.46±5.71		1.50±3.12		1.41±2.27		<0.001
Liquid oils (g/day)		6.58±6.20		10.84±10.42		17.59±15.12		<0.001		12.14±10.30		12.62±13.70		10.68±12.12		0.41
Vegetables (g/day)		191.01±73.39		244.69±89.71		272.14±113.25		<0.001		226.91±114.45		203.53±85.28		194.92±98.32		0.04
Nuts and seeds (g/day)		6.74±7.79		11.05±11.52		20.42±25.49		<0.01		9.86±15.24		11.33±19.05		14.06±19.76		0.17
Pickles (g/day)		5.71±6.57		9.94±10.66		15.88±17.48		<0.001		9.83±13.65		11.05±17.78		12.70±18.93		0.40
Natural fruit juice (g/day)		7.82±12.01		17.52±24.03		24.50±28.92		<0.001		13.53±20.77		16.67±23.66		19.68±26.29		0.12
Legumes (g/day)		17.74±10.98		24.39±13.60		29.72±16.52		<0.01		19.97±12.32		23.09±13.29		28.82±16.81		<0.001
Sweets and deserts (g/day)		11.81±7.69		13.96±10.16		15.04±12.70		0.07		14.45±12.86		15.60±14.54		15.79±11.62		0.67
Poultry (g/day)		15.43±11.61		19.63±13.39		21.57±15.79		<0.01		18.58±14.80		18.79±12.66		19.28±14.30		0.92
Solid fats (g/day)		8.60±9.10		8.44±10.91		7.66±11.61		0.75		5.92±4.18		9.65±6.30		16.09±13.50		<0.001
Sugar (g/day)		17.19±13.89		16.59±11.34		17.07±13.55		0.92		11.17±7.26		17.42±10.76		24.19±14.91		<0.001
Snack and fast foods (g/day)		22.46±23.62		29.14±27.15		36.18±32.55		0.01		24.72±18.22		36.95±22.93		56.03±33.00		<0.001
Egg (g/day)		16.31±12.31		24.29±18.59		27.77±17.57		<0.001		13.66±11.98		22.91±16.32		31.79±18.33		<0.001
Tea and coffee (g/day)		534.20±374.88		520.59±347.07		467.27±338.55		0.28		348.31±267.17		464.11±279.11		708.17±400.57		<0.001
Salt (g/day)		4.41±2.54		4.01±2.37		4.64±2.76		0.14		4.27±1.73		4.94±2.63		5.44±2.86		<0.001
Processed meat (g/day)		1.44±2.72		1.93±3.13		2.14±3.27		0.06		1.12±2.15		1.90±3.51		3.18±4.04		<0.001
Soft drinks (g/day)		20.71±17.62		20.23±14.38		20.52±14.41		0.97		18.12±12.01		21.70±14.8		24.62±19.62		<0.01

Values of food groups are energy adjusted. P-value obtained from one-way analysis of variance (ANOVA)

Table 5 shows the crude and adjusted OR and 95% CI for the association between two major dietary patterns and mental health problems in the study population. According to the crude model, participants in the third tertile of the “plant-based” dietary pattern had lower odds of depression compared with the first tertile, (OR = 0.44, 95% CI: 0.18–0.67, P trend <0.01). This association remained significant after adjustment for the potential confounding effects of age, gender, BMI, energy intake, physical activity, smoking, sleep duration, family size, family history of mental disorders, marital status, and undergraduate year (OR = 0.44, 95% CI: 0.17–0.65, P trend <0.01 for model I and OR = 0.42, 95% CI: 0.17–0.67, P trend <0.01 for model II). The “plant-based” dietary pattern had no significant association with the risk of stress and anxiety. However, this association for anxiety became marginally significant in model II (OR = 0.53, 95% CI: 0.36–0.98, P trend = 0.07). The “Western” dietary pattern also was not associated with the likelihood of depression, stress, and anxiety neither in crude nor in adjusted models.

Table 5.

Crude and adjusted OR and 95% CI for the association between major dietary patterns and mental health problems (depression, stress, and anxiety) among study population

	Plant-based dietary pattern				Western dietary pattern
	Tertile 1	Tertile 2	Tertile 3	P trend	Tertile 1	Tertile 2	Tertile 3	P trend
Depression
Crude	Ref	0.68 (0.38–1.12)	0.44 (0.18-0.67)*	<0.01	Ref	1.20 (0.63–2.29)	1.66 (0.89–3.10)	0.58
Model I	Ref	0.69 (0.38–1.07)	0.44 (0.17–0.65)*	<0.01	Ref	1.18 (0.62–2.27)	1.73 (0.92–3.25)	0.61
Model II	Ref	0.62 (0.33–1.05)	0.42 (0.17–0.67)*	<0.01	Ref	1.32 (0.67–2.60)	1.91 (0.97–3.73)	0.43
Stress
Crude	Ref	0.80 (0.51–1.12)	0.58 (0.38–1.05)	0.11	Ref	1.17 (0.66–2.06)	1.23 (0.73–1.71)	0.89
Model I	Ref	0.80 (0.51–1.15)	0.56 (0.37–1.03)	0.08	Ref	1.19 (0.67–2.12)	1.25 (0.76–1.71)	0.87
Model II	Ref	0.82 (0.51–1.06)	0.53 (0.36–0.98)	0.07	Ref	1.08 (0.57–1.96)	1.18 (0.62–1.74)	0.87
Anxiety
Crude	Ref	0.60 (0.27–1.26)	0.67 (0.36–0.95)	0.23	Ref	1.15 (0.55–2.37)	0.9 (0.59–1.72)	0.31
Model I	Ref	0.55 (0.24–1.17)	0.69 (0.36–0.93)	0.23	Ref	1.23 (0.58–2.57)	1.08 (0.64–2.19)	0.34
Model II	Ref	0.48 (0.20–1.11)	0.61 (0.35–0.80)	0.18	Ref	1.31 (0.60–2.87)	1.13 (0.0.53–2.13)	0.31

Data are presented as OR (95% CI). Crude: Unadjusted. Model I: Adjusted for age, gender, and energy intake. Model II: Adjusted for age, gender, BMI, energy intake, physical activity, smoking, sleep duration, family size, family history of mental disorders, marital status, and undergraduate year. *P<0.01

Discussion

In the present cross-sectional study examining the interdependence between major dietary patterns and mental health problems among Iranian college students, we discovered that the highest tertile of “plant-based” dietary pattern (which was high in fruits, dried fruits, olives, liquid oils, vegetables, nuts and seeds, pickles, natural fruit juice, etc.) was associated by 56% and 58% reduction of depression risk in adjusted models I and II, respectively, relative to the lowest tertile. The negative association between the “plant-based” dietary pattern and anxiety was near-significant and it was not significant between “plant-based” dietary pattern and stress. Our study highlights the importance of improving the college student’s food choices through nutrition education, and increasing their access to healthy plant-based foods via justification of the college officials and policymakers may improve their mental health and academic performance.

Our observation is further supported by the findings from a number of previously conducted studies.[15,16,17,18] Mousavi et al.[16] found a negative association between “healthy plant-based” dietary patterns and odds of depression, anxiety, and psychological distress in Iranian adults. Liu et al.[18] also have reported that adherence to a “whole-plant” dietary pattern reduced the risk of depression by 26% in Chinese women. A “plant-based” dietary pattern probably reduces the risk of mental health problems by several possible mechanisms. Such a dietary pattern is replete with antioxidants (e.g., vitamin C, vitamin E, carotenoids, and phenolic compounds) and may reduce the neuronal dysfunction induced by oxidative stress which is associated with the deterioration of depression and anxiety.[19] Moreover, folic acid level is high in “plant-based” dietary pattern, and prior evidence has shown that folate deficiency can lead to homocysteine elevation and S-adenosyl methionine (SAM) depletion.[20,21] SAM has a fundamental role in the synthesis of monoamines such as serotonin, noradrenaline, and dopamine, and SAM insufficiency in cerebrospinal fluid has been implicated in mood disorders, particularly depression.[21] Additionally, high fiber intake from fruits, vegetables, and whole grains could have a beneficial effect on mood and depression symptoms by promoting gastrointestinal microbiome diversity and neurotransmitter metabolism.[22,23] Saghafian et al.[23] reported that higher consumption of dietary fiber was linked with reduced odds of depression and anxiety in women. Xu et al.[24] also obtained similar findings that total fiber intake from fruits and vegetables was associated with a decreased risk of depression in adults ages 20 years or older.

A “Western” dietary pattern is typically composed of processed foods, red meat, refined grains, salt, and high-sugar and high-fat products.[25] There have been contradictory results in identifying a positive association between adherence to “Western” dietary patterns and mental health problems.[25,26] In our study, adherence to the “Western” dietary pattern had no significant relationship with the risk of depression, anxiety, and stress. Our findings are consistent with some previous studies.[26,27,28,29] No significant association was observed between “Western” dietary patterns and mental disorders including depression, anxiety, and stress in a sample of Iranian migraine patients.[27] Rasouli et al.[30] also have reported that there was no significant relationship between “Western” dietary pattern and anxiety scores. However, adherence to this dietary pattern led to an increase in depression and stress scores.

In contrast, several other investigations reported that individuals with greater scores in “Western” dietary patterns were at increased risk of depressive symptoms, anxiety, or stress as compared with lower scores.[31,32,33,34] In a study carried out by Weng et al.,[35] snack and animal food patterns were associated with an increased risk of depression and anxiety in Chinese adolescents. The sources of these discrepancies in the results of previous studies are not obvious. They might be partly related to the differences in study design, method of assessing mental health problems, definition, and number of the food groups and study population (age, one sex or both, healthy individuals versus patients, etc.). Moreover, the constituent food groups of the dietary pattern that was labeled “Western” were somewhat different in various studies which can affect the results.

Some authors have claimed that “Western” dietary pattern, often considered an unhealthy dietary pattern rich in sugar, saturated fats, and energy and low in essential micronutrients, increases the risk of mood disorders through biological mechanisms including insulin resistance, gut dysbiosis, and an altered microbiota-gut-brain axis, increased levels of inflammatory markers, and free radical cell damage.[15,36,37,38] Chronic subclinical inflammation and subsequent oxidative stress are associated with apoptosis and cerebral atrophy, especially in the hippocampus which increases the risk of mental health problems particularly depression.[37]

Limitation and recommendation

We collected extensive information on potential confounders, including personal and lifestyle characteristics, anthropometric measures, energy intake, and physical activity and adjustment was made for all of them in the regression models, which was the main strength of the present study. Our research also has some limitations. First, because of the cross-sectional design of the study, relationship between dietary patterns and the risk of mental health problems does not necessarily imply causation. Second, several arbitrary but important decisions such as the number of components to retain, the dietary pattern labeling, the rotation procedure, and their interpretation might affect the PCA results. Third, although, the FFQ which was used in our study has acceptable relative validity and good reproducibility, and we also excluded the subjects with extremely high or low energy intakes, measurement errors are unavoidable which can lead to underestimation of the true associations. Future studies are required to confirm the causal relationship exists between dietary patterns and mental health problems in college students.

Conclusion

Overall, a strong inverse and a near-significant inverse association were observed between “plant-based” dietary patterns and depression and anxiety, respectively. The findings of this study are important to emphasize that providing access to healthy plant-based foods may improve the college student’s mental health and well-being, which needs dietary interventions to confirm the causal relationship.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

This study was financially supported by a grant from the Vice Chancellor for Research Affairs of Khalkhal University of Medical Sciences (IR-KHS-1398-12-06).