Abstract
Background
Few studies explored the association between egg consumption and depression, anxiety, and high psychological distress, and their findings were controversial. Moreover, no investigation was carried out on the association between this dietary item and oxidative stress and inflammation, which are related to psychological disorders. We aimed to perform the current study to present a comprehensive view of the issue among adults.
Methods
In total, 533 Iranian adults (46.2% female) with a mean age of 42.59 were included in the study. Egg consumption was assessed using a semi-quantitative 168-item food frequency questionnaire (FFQ). Depression and anxiety were evaluated via the Hospital Anxiety and Depression Scale (HADS), and psychological distress was assessed using the General Health Questionnaire (GHQ). Oxidative stress and inflammatory biomarkers were evaluated using the participant’s blood sample after a 12-hour fast.
Results
Based on fully adjusted model, participants in the third tertile had lower odds of depression (ORT3 vs. T1=0.45; 95%CI: 0.24–0.82) and of high psychological distress (ORT3 vs. T1=0.44; 95%CI: 0.27–0.72). Moreover, each tertile increase was associated with reduced odds of depression (ORtrend=0.67; 95%CI: 0.49–0.91) and high psychological distress (ORtrend=0.67; 95%CI: 0.52–0.85). These associations were particularly significant among ones with normal weight regarding both depression (ORT3 vs. T1=0.04; 95%CI: 0.00-0.41) and distress (ORT3 vs. T1=0.16; 95%CI: 0.06–0.43). The same pattern was evident for continuous analyses of depression (ORtrend=0.38; 95%CI: 0.19–0.76) and distress (ORtrend=0.41; 95%CI: 0.25–0.66) in individuals with normal weight. There was a linear association between egg with malonaldehyde (B=-16.784 mU/mL; 95% CI: -30.090, -3.479) and superoxide dismutase (B = 0.084 unit; 95% CI: 0.008, 0.160) among women.
Conclusion
There was an inverse dose-related association between egg consumption and depression and high psychological distress. Moreover, egg consumption was related to lower oxidative stress among women.
Keywords: Egg, Diet, Depression, Anxiety, Psychological disorders, Inflammation, Oxidative stress
Introduction
Depression, anxiety, and high psychological distress are among the common affective disorders, along with others such as obsessive-compulsive disorder, panic, and body dysmorphia [1]. Symptoms of depression include unusual changes in appetite, inability to feel pleasure or interest loss, an unusual body weight change (as a result of appetite change because of emotional dysregulation), considerable changes in sleep duration and/or quality, feelings of self-worthlessness or unreasonable long-lasting guilt, and severe concentration impairments [2, 3]. An extensive national survey reported a prevalence of 18.4% for depression among American adults [4]. This disorder seems to be even more prevalent among Iranian adults, with a prevalence of 30% [5]. Anxiety, as another disorder, is a state of over-alertness and has different forms, such as generalized and socialized anxiety disorders and specific phobias, which had a high prevalence of 301.4 million worldwide in 2019 [6]. High psychological distress is a state in which one is not capable of coping with the common stress triggers and processing them emotionally, resulting in an overreaction toward them [7].
A state in which an imbalance occurs between oxidants and antioxidant agents is known as oxidative stress [8]. Inflammation can also be triggered by oxidative stress; as a result of the tissue damage it may cause [9]. There is an intertwined relationship between oxidative stress, inflammation, and affective disorders [10, 11]. Many factors can directly or indirectly, through inflammation and/or oxidative stress, affect affective disorders; diet is a vital factor among them [11]. Different dietary proteins and nutrients may affect mood differently [12]. Among dietary items, eggs can be associated with mood due to their nutrients.
Egg is reach of omega-3 fatty acids, vitamin E, magnesium, zinc, thiamin, riboflavin, niacin, and high-quality protein, alongside many other nutrients [13]. Vitamin E, as a fat-soluble antioxidant, neutralizes free radicals inside fatty tissue environments [14]. The brain contains huge amounts of fatty acids and also highly relies on sufficient oxygen for regular activities; not surprising that oxidative stress can easily manipulate brain processes and cause psychological disorders [15]. Other egg nutrients, including B vitamins, magnesium, protein, and omega-3 fatty acids, are also critical for pathways in the regulation of emotions, serotonin production, and brain energy metabolism [16, 17].
A cross-sectional study among Iranian adults found no association between egg consumption and depression, anxiety, or high psychological distress [18]. However, a prospective study on Chinese elderly suggested that participants with a higher frequency of egg consumption showed a lower risk of depression after a follow-up of six years [19]. On the other hand, a meta-analysis of 9 randomized controlled trials (published in 2019) found no significant effect of egg consumption on inflammation [20]. No study had evaluated the association between egg consumption and oxidative stress biomarkers.
Few studies were available regarding the association of egg intake and common affective disorders. Moreover, these limited results were inconsistent. Additionally, as stated earlier, no investigation was conducted into the potential relationship between consumption of this dietary item and oxidative stress biomarkers. We did not have pre-registered hypotheses for this cross-sectional study; however, based on previous literature, we tentatively aimed to explore whether higher egg consumption is associated with lower depression as the primary hypothesis and secondarily, with anxiety, high psychological distress, chronic inflammation, and oxidative stress.
Methods
Study design and participants
The current study was a population-based cross-sectional study conducted in 2021, on a somewhat representative sample of adults in Isfahan, a large city in the center of Iran [21]. To get an optimal sample size, a precision (d) of 4% and a 95% confidence interval (type I error of 5%) were considered. Considering that depression prevalence among Iranian adults is about 30% [5], a minimum of 505 adults would be required to reach an optimum sample size. Because of the high COVID-19 prevalence at the time of data collection, we initially invited 600 individuals to account for potential dropouts. Based on a multistage cluster-randomized sampling design, 3 or 4 schools from each of the 6 educational districts (285 private and public schools in total) were chosen. In total, 20 schools, from preschools through high schools, were selected for the study. To obtain a representative sample of the general adult population with a range of socioeconomic statuses, every adult individual working at these twenty schools (teachers, school managers, employees, assistants, and crew members) was invited to participate. Individuals who (1) were following a special weight-gain or –loss diet, (2) were pregnant or lactating at the time, or (3) had a history of neoplasms, cardiovascular diseases, stroke, or type 1 diabetes mellitus, were not included in the sampling. Of those who were invited, 543 agreed to participate. Of them, 10 were excluded before analysis with the following reasons: (1) their food frequency questionnaires were incomplete (FFQ) (n = 4), (2) their reported total energy intake fell above 4200 or below 800 kcal/d (n = 3), (3) they had not completed questionnaires of psychological disorders (n = 3). Ultimately, 533 individuals remained to be included in the analysis. The study protocol was ethically reviewed and approved by the local Ethics Committee of Isfahan University of Medical Sciences, and received an approval number. All participants signed written informed consent. The procedure of the current study was in accordance with the Helsinki and STROBE checklists.
Assessment of egg consumption
A paper-based 168-item semi-quantitative FFQ, previously validated among Iranian adults, was administered to assess egg consumption and other dietary intakes over the past year [22]. An experienced dietitian instructed the study participants on how to complete the FFQ, asking them to report their usual intakes at daily, weekly, or monthly frequencies, before handing out the questionnaires. The amounts of eggs and other dietary items were converted to g/d via standard household portion sizes [23]. Ultimately, the gram intakes were imported into “Nutritionist IV” software to obtain micro- and macro-nutrient and total energy intakes for each participant.
Assessment of depression, anxiety, and psychological distress
A paper-based validated Persian version of the Hospital Anxiety and Depression Scale (HADS) was taken into account for evaluation of depression and anxiety symptoms [24]. HADS is a two-subscale questionnaire; each subscale has seven separate items, measuring depression or anxiety. Each item is a four-option question, with a score of 0 to 3 for options 1 to 4, respectively. Hence, each participant could get a score between 0 and 21 for depression and anxiety. A score of 7 or more for depression or anxiety was considered an indicator of depression or anxiety, respectively. To evaluate psychological distress, a paper-based validated Persian translation of the General Health Questionnaire (GHQ) was used. Participants were asked to report their recent emotional discomfort using 12 four-option items from the GHQ [25]. We then used the bimodal scoring method to calculate participants’ GHQ scores. In this method, the first two options (less than usual/normal) got a score of 0, whereas the next two (more than usual/much more than usual) got 1. Thus, each participant could get an overall score from 0 to 12. A GHQ score of 4 or higher was considered an indicator of high psychological distress in the participant.
Assessment of inflammatory and oxidative stress biomarkers
Blood samples were collected from participants after 12 h of overnight fasting. The clotted blood samples were then centrifuged, and their serum was carefully separated from the whole samples. Serum samples were then stored in -80 degrees Celsius. They were defrosted at 4 degrees Celsius 24 h before biochemical tests. Serum levels of malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GPx), as oxidative stress biomarkers, were assessed using commercial enzyme-linked immunosorbent assay (ELISA) kits. High-sensitive C-reactive protein (hs-CRP) in serum, on the other hand, was measured using a turbidimetric kit and a latex-enhanced turbidimetric method to assess the subjects’ chronic inflammatory status.
Assessment of other covariates
A body composition analyzer (Tanita MC-780MA, Tokyo, Japan) with an accuracy of 0.1 kg was used to measure participants’ body weight, which was regularly calibrated before each session. Height (with a precision of 0.1 cm) was measured using a tape glued to the wall. Weight (Kg) was divided by the square of height (m2), and body mass index (BMI) was calculated with a unit of Kg/m2. Waist circumference was measured after a normal exhale using a nonelastic tape with a precision of 0.01 cm at the middle of the lower rib margin and the iliac crest, without applying any pressure. Physical activity was assessed using a paper-based Persian version of the International Physical Activity Questionnaire (IPAQ), which has been previously validated for Iranian adults [26]. Information regarding socio-demographic status (marital status, education, house ownership, and whole family’s approximate income), smoking, and antidepressant usage was gathered using a pre-tested paper-based questionnaire.
Statistical methods
Normality of variables was evaluated via the Kolmogorov-Smirnov test. Data are reported as numbers (percentages) and mean ± standard deviation (SD) or standard error (SE) for categorical and continuous variables, respectively. Daily egg intakes were adjusted for total energy intake using the residual method, and participants were ranked and classified into tertiles based on their energy-adjusted egg consumption (< 15.13, 15.13–29.78, and > 29.78 g/d for T1, T2, and T3, respectively). T1 (the lowest tertile) was considered the reference for all statistical analyses. The chi-square test and one-way analysis of variance test were applied, respectively, to assess categorical and continuous variables across egg tertiles. Additionally, analysis of covariance (ANCOVA) was used to determine total energy, macro- and micro-nutrient, and food group intakes across the study’s tertiles, adjusted for sex, age, and total energy intake.
Logistic regression test was applied to assess the association between tertiles of energy-adjusted egg with depression, anxiety, and high psychological distress, and odds ratios (ORs) along with their 95% confidence intervals (CIs) were obtained. Analyses in this regard were carried out using crude and three adjusted models. The first model included age, sex, and total energy intake as adjusted confounders. The second model further included marital status, education level, physical activity level, smoking status, antidepressant usage (nortriptyline, amitriptyline, imipramine, fluoxetine, citalopram, and fluvoxamine), house ownership, approximate income of the family per month, and daily intakes of whole grains, fruits, vegetables, nuts-soy-legumes, and low-fat dairy. In the final model, BMI was additionally adjusted. To assess the probability of a trend (for each increase in egg consumption tertiles) with odds of common affective disorders, energy-adjusted egg consumption was treated as a continuous variable rather than a categorical variable in the logistic regression.
To assess the association between each tertile increase in egg consumption and MDA (nmol/mL), SOD (unit), GPx (mU/mL) as oxidative stress biomarkers, and hs-CRP (mg/L) as an inflammatory biomarker, the linear regression analysis was performed. The test was carried out based on the crude and two adjusted models. In the first model, age and sex were adjusted. Further adjustment for BMI was made in the second model. A P-value less than 0.05 was considered significant for all statistical analyses. All analyses were carried out via SPSS version 27.
Results
In total, 533 participants were included in the current study, of whom 246 (46.2%) were female. The mean age and BMI of participants were 42.59 ± 11.15 years (± SD) and 26.90 ± 4.41 kg/m2, respectively. Mean protein proportion of total energy intake was 14.14 ± 2.67 and 14.35 ± 3.02% among men and women, respectively. Additionally, total egg consumption was respectively 27.95 and 25.68 g/d in men and women, which is approximately half an egg per day. Among participants, 173 (32.5%) had a desirable weight (BMI < 25 kg/m²), whereas 360 (67.5%) had overweight/obesity (BMI ≥ 25 kg/m²). Regarding the characteristics of study participants across egg consumption tertiles, participants in the third tertile had a significantly higher mean height than those in the second tertile. Additionally, educational levels differed significantly across tertiles. No other difference was observed in general characteristics, as illustrated in detail in Table 1.
Table 1.
General characteristics of study participants across tertiles of egg intakea
| Egg tertiles | P b | |||
|---|---|---|---|---|
| T1 (n = 177) |
T2 (n = 178) |
T3 (n = 178) |
||
| Egg consumption (g/d) | < 15.13 | 15.13–29.78 | > 29.78 | |
| Mean egg consumption (g/d) | 8.14 ± 5.83 | 21.37 ± 4.25 | 51.09 ± 20.00 | < 0.001 |
| Age (year) | 41.72 ± 11.52 | 44.01 ± 10.66 | 42.00 ± 11.18 | 0.10 |
| Sex [n, (%)] | 019 | |||
| Male | 94 (53.1) | 88 (49.4) | 105 (59.0) | |
| Female | 83 (49.6) | 90 (50.6) | 73 (41.0) | |
| Weight (kg) | 76.34 ± 16.70 | 73.87 ± 12.29 | 76.92 ± 14.10 | 0.11 |
| Height (cm) | 167.34 ± 8.71 | 166.31 ± 7.72 | 169.16 ± 8.94 | 0.01 |
| BMI (kg/m2) | 27.14 ± 4.90 | 26.70 ± 3.91 | 26.87 ± 4.39 | 0.64 |
| Waist circumference (cm) | 93.79 ± 12.79 | 91.55 ± 10.01 | 92.55 ± 11.36 | 0.19 |
| Serum MDA (nmol/mL) | 176.17 ± 96.29 | 175.84 ± 90.96 | 158.29 ± 86.70 | 0.11 |
| Serum GPx (mU/mL) | 1.80 ± 2.92 | 2.23 ± 5.09 | 1.86 ± 2.89 | 0.52 |
| Serum SOD (Unit) | 1.08 ± 0.48 | 1.12 ± 0.51 | 1.17 ± 0.49 | 0.18 |
| Serum hs-CRP (mg/L) | 3.36 ± 3.16 | 3.01 ± 2.83 | 3.19 ± 2.78 | 0.52 |
| Marital status [n, (%)] | 0.14 | |||
| Single | 36 (20.3) | 24 (13.5) | 26 (14.6) | |
| Married | 142 (79.7) | 150 (84.3) | 149 (83.7) | |
| Divorced or widowed | 0 (0.0) | 4 (2.2) | 3 (1.7) | |
| Education level [n, (%)] | 0.03 | |||
| Diploma or lower | 11 (6.2) | 27 (15.2) | 21 (11.8) | |
| Higher than diploma | 166 (93.8) | 151 (84.8) | 157 (88.2) | |
| Physical activity (HEPA) [n, (%)] | 0.41 | |||
| Inactive | 109 (61.6) | 101 (56.7) | 92 (51.7) | |
| Minimally active | 57 (32.2) | 62 (34.8) | 70 (39.3) | |
| Active | 11 (6.2) | 15 (8.4) | 16 (9.0) | |
| Smoking status [n, (%)] | 0.16 | |||
| Non-smoker | 167 (94.4) | 164 (92.1) | 172 (96.6) | |
| Ex-smoker | 3 (1.7) | 7 (3.9) | 5 (2.8) | |
| Smoker | 7 (4.0) | 7 (3.9) | 1 (0.6) | |
| House ownership [n, (%)] | 0.33 | |||
| Tenant | 36 (20.3) | 44 (4.7) | 48 (27.0) | |
| Owner | 141 (79.7) | 134 (75.3) | 130 (73.0) | |
| Approximate income of the family [n, (%)] | 0.89 | |||
| Very low | 22 (12.4) | 22 (12.4) | 26 (14.6) | |
| Low | 61 (34.5) | 56 (31.5) | 55 (30.9) | |
| Moderate | 65 (36.7) | 68 (38.2) | 60 (33.7) | |
| High | 29 (16.4) | 32 (18.0) | 37 (20.8) | |
| Antidepressantc usage [n, (%)] | 0.20 | |||
| Non-user | 165 (93.2) | 167 (93.8) | 173 (97.2) | |
| User | 12 (6.8) | 11 (6.2) | 5 (2.8) | |
| BMI status [n, (%)] | 0.71 | |||
| Normal weight | 55 (31.1) | 56 (31.5) | 62 (34.8) | |
| Overweight or obese | 122 (68.9) | 122 (68.5) | 116 (65.2) | |
Abbreviations:n Number, BMI Body mass index, MDA Malonaldehyde, GPx Glutathione peroxidase, hs-CRP High-sensitive C-reactive protein, HEPA Health-enhancing physical activity
aContinuous variables are reported as mean ± SD, categorical variables are reported as count (percentage)
bObtained from one-way ANOVA and the Pearson chi-square test for continuous and categorical variables, respectively
cIncluding nortriptyline, amitriptyline, imipramine, fluoxetine, citalopram, and fluvoxamine
Details of participants’ dietary intakes are shown in Table 2. As presented, the mean protein and fat composition, along with total energy intake, were significantly higher in the third tertile compared to first one. In contrast, the mean carbohydrate was significantly higher in the first tertile. Regarding micronutrients, vitamin E, B2, and B12 intakes were significantly higher in the third tertile, whereas B1 intake was higher in the first tertile. Moreover, vegetable consumption was higher in the third tertile of egg consumption. No other significant differences were observed for micronutrients or food groups.
Table 2.
Dietary intakes (energy, macro/micronutrients, and food groups) of study participants across tertiles of egg intakea
| Egg tertiles | P b | |||
|---|---|---|---|---|
| T1 (n = 177) |
T2 (n = 178) |
T3 (n = 178) |
||
| Egg consumption (g/d) | < 15.13 | 15.13–29.78 | > 29.78 | |
| Total energy (kcal/d) | 2391.72 ± 49.22 | 2021.06 ± 49.27 | 2411.45 ± 49.15 | < 0.001 |
| Macronutrients (% of Total energy) | ||||
| Protein | 13.89 ± 0.21 | 14.18 ± 0.21 | 14.64 ± 0.21 | 0.04 |
| Carbohydrate | 61.88 ± 0.61 | 61.57 ± 0.91 | 58.97 ± 0.61 | 0.01 |
| Fat | 26.14 ± 0.50 | 26.19 ± 0.50 | 28.41 ± 0.50 | 0.01 |
| Micronutrients (mg/d unless stated) | ||||
| Potassium | 3658.51 ± 79.32 | 3805.83 ± 80.94 | 3821.24 ± 79.38 | 0.27 |
| Calcium | 926.11 ± 28.13 | 918.05 ± 28.70 | 920.10 ± 28.15 | 0.98 |
| Magnesium | 280.67 ± 5.00 | 284.08 ± 5.10 | 286.36 ± 5.00 | 0.72 |
| Phosphorus | 1184.18 ± 25.42 | 1181.55 ± 25.94 | 1239.04 ± 25.44 | 0.20 |
| Vitamin E | 6.23 ± 0.23 | 6.62 ± 0.24 | 7.36 ± 0.23 | 0.04 |
| Vitamin B1 | 2.11 ± 0.03 | 2.02 ± 0.03 | 1.97 ± 0.03 | 0.01 |
| Vitamin B2 | 1.87 ± 0.04 | 1.98 ± 0.05 | 2.09 ± 0.04 | 0.01 |
| Vitamin B3 | 23.32 ± 0.34 | 23.15 ± 0.35 | 22.32 ± 0.34 | 0.09 |
| Vitamin B6 | 1.75 ± 0.04 | 1.83 ± 0.04 | 1.82 ± 0.04 | 0.29 |
| Vitamin B9 (µg/d) | 328.36 ± 8.46 | 344.50 ± 8.63 | 352.12 ± 8.46 | 0.13 |
| Vitamin B12 (µg/d) | 3.91 ± 0.13 | 4.01 ± 0.14 | 4.44 ± 0.13 | 0.01 |
| Vitamin C | 198.83 ± 7.60 | 204.75 ± 7.75 | 190.94 ± 7.61 | 0.45 |
| Food groups (g/d) | ||||
| Whole grains | 115.00 ± 6.02 | 114.14 ± 6.14 | 106.78 ± 6.03 | 0.57 |
| Fruits | 559.81 ± 23.98 | 580.55 ± 24.46 | 521.34 ± 23.99 | 0.22 |
| Vegetables | 324.64 ± 17.32 | 327.47 ± 17.67 | 380.89 ± 17.33 | 0.04 |
| Nuts, soy, and legumes | 50.65 ± 2.83 | 47.95 ± 2.89 | 54.13 ± 2.83 | 0.32 |
| Low-fat dairy | 251.56 ± 18.84 | 251.34 ± 19.23 | 253.41 ± 18.86 | 0.99 |
| Red and processed meat | 65.64 ± 3.40 | 67.79 ± 3.47 | 70.07 ± 3.40 | 0.65 |
| White meat | 38.15 ± 2.50 | 40.06 ± 2.55 | 38.00 ± 2.50 | 0.82 |
Abbreviations: n Number, g Grams, d Day
aValues are presented as mean ± SE. Intakes of energy and macronutrients were adjusted for age and sex, all other values were adjusted for age, sex, and energy intake
bObtained from ANCOVA
Prevalence of depression and high psychological distress was significantly higher among participants in the first tertile of egg consumption (P = 0.01), as illustrated in Fig. 1; however, the same case was not evident for anxiety prevalence (P = 0.34).
Fig. 1.
Prevalence of depression, anxiety, and high psychological distress across tertiles of energy-adjusted egg consumption. P-values were obtained via chi-square test
Multivariable-adjusted results on the association of egg tertiles with depression, anxiety, and high psychological distress are presented in Table 3. Participants in the third tertile of egg consumption were 56% less likely to have depression, according to crude model (OR = 0.44; 95% CI: 0.25–0.77). After adjusting for multiple confounders and food groups, results remained significant, and participants in the top tertile (T3) had 55% lower odds of depression than those in the first tertile (T1) (OR = 0.45; 95% CI: 0.24–0.82). Additionally, each tertile increase in egg consumption was associated with 33% lower odds of depression, both in crude (OR = 0.67; 95% CI: 0.51–0.88) and fully adjusted model (OR = 0.67; 95% CI: 0.49–0.91). Regarding high psychological distress, participants in the third tertile were 53% less likely to be afflicted based on crude model (OR = 0.47; 95% CI: 0.30–0.75). Adjusting for confounders strengthened these results, and in fully adjusted model, participants in the third tertile were 56% less likely to be highly distressed (OR = 0.44; 95% CI: 0.27–0.72). Similar to depression, each tertile increase in egg consumption was associated with 30% lower odds of high psychological distress based on crude model (OR = 0.70; 95% CI: 0.56–0.87). Again, this finding was confirmed by fully adjusted model (OR = 0.67; 95% CI: 0.52–0.85). However, we did not find any significant association between egg consumption and anxiety, either in crude, or in adjusted models.
Table 3.
Multivariable-adjusted odds ratio (OR) and 95% confidence interval (CI) for being depressed, anxious, and highly psychologically distressed across tertiles of egg intakea
| Egg tertiles | Each tertile increase | P trend | |||
|---|---|---|---|---|---|
| T1 (n = 177) |
T2 (n = 178) |
T3 (n = 178) |
|||
| Egg consumption (g/d) | < 15.13 | 15.13–29.78 | > 29.78 | ||
| Depression | |||||
| Cases (n) | 43 | 36 | 22 | ||
| Crude | 1.00 | 0.79 (0.48–1.31) | 0.44 (0.25–0.77) | 0.67 (0.51–0.88) | 0.01 |
| Model 1 | 1.00 | 0.74 (0.44–1.26) | 0.45 (0.26–0.80) | 0.68 (0.51–0.90) | 0.01 |
| Model 2 | 1.00 | 0.67 (0.38–1.18) | 0.44 (0.24–0.82) | 0.66 (0.49–0.90) | 0.01 |
| Model 3 | 1.00 | 0.68 (0.39–1.20) | 0.45 (0.24–0.82) | 0.67 (0.49–0.91) | 0.01 |
| Anxiety | |||||
| Cases (n) | 12 | 6 | 9 | ||
| Crude | 1.00 | 0.48 (0.18–1.31) | 0.73 (0.30–1.78) | 0.84 (0.52–1.35) | 0.46 |
| Model 1 | 1.00 | 0.48 (0.17–1.34) | 0.76 (0.31–1.85) | 0.85 (0.53–1.37) | 0.50 |
| Model 2 | 1.00 | 0.43 (0.14–1.31) | 0.81 (0.30–2.19) | 0.89 (0.53–1.51) | 0.67 |
| Model 3 | 1.00 | 0.42 (0.14–1.31) | 0.81 (0.30–2.19) | 0.89 (0.53–1.51) | 0.67 |
| High psychological distress | |||||
| Cases (n) | 70 | 66 | 42 | ||
| Crude | 1.00 | 0.90 (0.59–1.38) | 0.47 (0.30–0.75) | 0.70 (0.56–0.87) | 0.01 |
| Model 1 | 1.00 | 0.82 (0.53–1.28) | 0.48 (0.30–0.76) | 0.70 (0.55–0.87) | 0.01 |
| Model 2 | 1.00 | 0.78 (0.49–1.24) | 0.46 (0.28–0.75) | 0.68 (0.54–0.87) | 0.01 |
| Model 3 | 1.00 | 0.76 (0.48–1.21) | 0.44 (0.27–0.72) | 0.67 (0.52–0.85) | 0.01 |
Model 1: Adjusted for age, sex, and energy intake
Model 2: Additionally, adjusted for marital status, education level, physical activity (HEPA), smoking status, antidepressant usage (nortriptyline, amitriptyline, imipramine, fluoxetine, citalopram, and fluvoxamine), house ownership, approximate income of the family per month, and daily intakes of whole grains, fruits, vegetables, nuts, soy, legumes, and low-fat dairy
Model 3: Additionally, adjusted for body mass index (BMI)
Abbreviations: n Number
aAll values are odds ratios and 95% confidence intervals. P-trend was obtained by considering indices’ tertiles as a continuous variable rather than categorical
Results for sex-stratified analyses on the association of egg consumption with depression, anxiety, and elevated psychological distress are presented in Table 4. Men in the third tertile were 67% less likely to be depressed than those in the reference tertile (T1) (OR = 0.33; 95% CI: 0.14–0.75). The results were robust to confounders, as fully adjusted model suggested that men in the third tertile had 74% lower odds of depression than those in the first tertile (OR = 0.26; 95% CI: 0.10–0.66). Additionally, each tertile increase showed 42% (OR = 0.58; 95% CI: 0.39–0.87) and 49% (OR = 0.51; 95% CI: 0.32–0.81) reductions in depression odds in men, based on crude and fully adjusted models, respectively. None of the results regarding depression were significant among women (P > 0.05). Crude analysis between egg consumption and high psychological distress showed an insignificant result for men in the third tertile compared with the first one (OR = 0.55; 95% CI: 0.29–1.02). The fully adjusted model, though, confirmed a statistically significant finding, such that men in the third tertile were 55% less likely to be distressed than those in the first tertile (OR = 0.45; 95% CI: 0.22–0.89). The same case was observed for each tertile increase in egg consumption among men, as crude analysis suggested insignificance (OR = 0.75; 95% CI: 0.55–1.01), the fully-adjusted model suggested a 33% significant (OR = 0.67; 95% CI: 0.48–0.94) high-distress odds-decline for each tertile increase in egg consumption among men. Both crude (OR = 0.41; 95% CI: 0.21–0.81) and fully-adjusted models (OR = 0.34; 95% CI: 0.16–0.73) showed significant results for the association between egg consumption and high distress in women. Each tertile increase in egg consumption was associated with 41% lower odds of high distress among women, based on the fully-adjusted model (OR = 0.59; 95% CI: 0.41–0.86). Again, sex-stratified analysis failed to find any significant association between egg consumption and anxiety (P > 0.05).
Table 4.
Sex-stratified multivariable-adjusted odds ratio (OR) and 95% confidence interval (CI) for being depressed, anxious, and highly psychologically distressed across tertiles of egg intakea
| Egg tertiles | Each tertile increase | P trend | |||
|---|---|---|---|---|---|
| T1 | T2 | T3 | |||
| Egg consumption (g/d) | < 15.13 | 15.13–29.78 | > 29.78 | ||
| Depression | |||||
| Men | |||||
| Case/participants (n) | 21/94 | 14/88 | 9/105 | ||
| Crude | 1.00 | 0.66 (0.31–1.39) | 0.33 (0.14–0.75) | 0.58 (0.39–0.87) | 0.01 |
| Model 1 | 1.00 | 0.60 (0.27–1.35) | 0.32 (0.14–0.74) | 0.57 (0.37–0.86) | 0.01 |
| Model 2 | 1.00 | 0.51 (0.21–1.24) | 0.25 (0.10–0.66) | 0.50 (0.32–0.81) | 0.01 |
| Model 3 | 1.00 | 0.52 (0.21–1.27) | 0.26 (0.10–0.66) | 0.51 (0.32–0.81) | 0.01 |
| Women | |||||
| Case/participants (n) | 22/83 | 22/90 | 13/73 | ||
| Crude | 1.00 | 0.90 (0.45–1.78) | 0.60 (0.28–1.30) | 0.78 (0.54–1.14) | 0.21 |
| Model 1 | 1.00 | 0.88 (0.43–1.77) | 0.63 (0.29–1.37) | 0.80 (0.54–1.17) | 0.25 |
| Model 2 | 1.00 | 0.95 (0.43–2.08) | 0.61 (0.25–1.53) | 0.80 (0.51–1.24) | 0.32 |
| Model 3 | 1.00 | 0.95 (0.43–2.08) | 0.61 (0.25–1.52) | 0.80 (0.51–1.24) | 0.32 |
| Anxiety | |||||
| Men | |||||
| Case/participants (n) | 7/94 | 1/88 | 4/105 | ||
| Crude | 1.00 | 0.14 (0.02–1.19) | 0.49 (0.14–1.74) | 0.64 (0.31–1.32) | 0.23 |
| Model 1 | 1.00 | 0.15 (0.02–1.34) | 0.49 (0.14–1.73) | 0.65 (0.32–1.32) | 0.24 |
| Model 2 | 1.00 | 0.11 (0.01–1.17) | 0.48 (0.12–1.96) | 0.65 (0.30–1.43) | 0.28 |
| Model 3 | 1.00 | 0.10 (0.01–1.04) | 0.46 (0.11–1.87) | 0.64 (0.29–1.41) | 0.27 |
| Women | |||||
| Case/participants (n) | 5/83 | 5/90 | 5/73 | ||
| Crude | 1.00 | 0.92 (0.26–3.29) | 1.15 (0.32–4.13) | 1.07 (0.56–2.06) | 0.84 |
| Model 1 | 1.00 | 0.89 (0.24–3.26) | 1.19 (0.33–4.30) | 1.09 (0.56–2.11) | 0.80 |
| Model 2 | 1.00 | 1.04 (0.23–4.81) | 2.48 (0.47–13.01) | 1.57 (0.66–3.70) | 0.31 |
| Model 3 | 1.00 | 1.03 (0.22–4.75) | 2.49 (0.47–13.15) | 1.57 (0.66–3.72) | 0.31 |
| High psychological distress | |||||
| Men | |||||
| Case/participants (n) | 33/94 | 31/88 | 24/105 | ||
| Crude | 1.00 | 1.05 (0.55–1.85) | 0.55 (0.29–1.02) | 0.75 (0.55–1.01) | 0.06 |
| Model 1 | 1.00 | 0.93 (0.49–1.77) | 0.54 (0.29–1.01) | 0.74 (0.54–1.01) | 0.06 |
| Model 2 | 1.00 | 0.84 (0.42–1.68) | 0.51 (0.26–0.99) | 0.71 (0.51–0.99) | 0.04 |
| Model 3 | 1.00 | 0.76 (0.38–1.53) | 0.45 (0.22–0.89) | 0.67 (0.48–0.94) | 0.02 |
| Women | |||||
| Case/participants (n) | 37/83 | 35/90 | 18/73 | ||
| Crude | 1.00 | 0.79 (0.43–1.45) | 0.41 (0.21–0.81) | 0.65 (0.46–0.91) | 0.01 |
| Model 1 | 1.00 | 0.73 (0.39–1.36) | 0.41 (0.21–0.82) | 0.65 (0.46–0.91) | 0.01 |
| Model 2 | 1.00 | 0.73 (0.37–1.43) | 0.34 (0.16–0.73) | 0.59 (0.41–0.86) | 0.01 |
| Model 3 | 1.00 | 0.73 (0.37–1.43) | 0.34 (0.16–0.73) | 0.59 (0.41–0.86) | 0.01 |
Model 1: Adjusted for age and energy intake
Model 2: Additionally, adjusted for marital status, education level, physical activity (HEPA), smoking status, antidepressant usage (nortriptyline, amitriptyline, imipramine, fluoxetine, citalopram, and fluvoxamine), house ownership, approximate income of the family per month, and daily intakes of whole grains, fruits, vegetables, nuts, soy, legumes, and low-fat dairy
Model 3: Additionally, adjusted for body mass index (BMI)
Abbreviations: n Number
aAll values are odds ratios and 95% confidence intervals. P-trend was obtained by considering indices’ tertiles as a continuous variable rather than categorical
Results for BMI-stratified analyses are presented in Table 5. Crude findings suggested that only participants with normal weight in the third tertile of egg consumption had lower odds of depression (OR = 0.05; 95% CI: 0.01–0.42) and high psychological distress (OR = 0.20; 95% CI: 0.09–0.46). Fully-adjusted analyses also confirmed these findings; adults with normal-weight in the third tertile were 96% (OR = 0.04; 95% CI: 0.00-0.41) and 84% (OR = 0.16; 95% CI: 0.06–0.43) less likely to be depressed and highly-distressed, respectively, compared to those in the reference tertile. Moreover, each tertile increase in egg consumption was associated with 62% (OR = 0.38; 95% CI: 0.19–0.76) and 59% (OR = 0.41; 95% CI: 0.25–0.66) decline in odds of depression and distress, respectively, in fully adjusted model.
Table 5.
BMI-stratified multivariable-adjusted odds ratio (OR) and 95% confidence interval (CI) for being depressed, anxious, and highly psychologically distressed across tertiles of egg intakea
| Egg tertiles | Each tertile increase | P trend | |||
|---|---|---|---|---|---|
| T1 | T2 | T3 | |||
| Egg consumption (g/d) | < 15.13 | 15.13–29.78 | > 29.78 | ||
| Depression | |||||
| Normal weight | |||||
| Case/participants (n) | 13/55 | 12/56 | 1/62 | ||
| Crude | 1.00 | 0.88 (0.36–2.15) | 0.05 (0.01–0.42) | 0.39 (0.21–0.69) | 0.01 |
| Model 1 | 1.00 | 0.83 (0.32–2.15) | 0.05 (0.01–0.42) | 0.37 (0.20–0.69) | 0.01 |
| Model 2 | 1.00 | 1.18 (0.40–3.51) | 0.04 (0.00-0.41) | 0.38 (0.19–0.76) | 0.01 |
| Overweight/obese | |||||
| Case/participants (n) | 30/122 | 24/122 | 21/116 | ||
| Crude | 1.00 | 0.75 (0.41–1.38) | 0.68 (0.36–1.27) | 0.82 (0.60–1.12) | 0.22 |
| Model 1 | 1.00 | 0.71 (0.37–1.36) | 0.73 (0.38–1.39) | 0.85 (0.61–1.18) | 0.33 |
| Model 2 | 1.00 | 0.55 (0.27–1.12) | 0.67 (0.32–1.36) | 0.81 (0.56–1.17) | 0.26 |
| Anxiety | |||||
| Normal weight | |||||
| Case/participants (n) | 6/55 | 4/56 | 2/62 | ||
| Crude | 1.00 | 0.63 (0.17–2.36) | 0.27 (0.05–1.41) | 0.54 (0.25–1.16) | 0.11 |
| Model 1 | 1.00 | 0.66 (0.17–2.50) | 0.24 (0.04–1.36) | 0.52 (0.24–1.14) | 0.10 |
| Model 2 | 1.00 | 0.81 (0.12–5.41) | 0.22 (0.02–2.93) | 0.51 (0.16–1.65) | 0.26 |
| Overweight/obese | |||||
| Case/participants (n) | 6/122 | 2/122 | 7/116 | ||
| Crude | 1.00 | 0.32 (0.06–1.63) | 1.24 (0.41–3.81) | 1.14 (0.60–2.16) | 0.69 |
| Model 1 | 1.00 | 0.30 (0.06–1.61) | 1.36 (0.44–4.27) | 1.20 (0.63–2.29) | 0.57 |
| Model 2 | 1.00 | 0.18 (0.03–1.10) | 1.19 (0.33–4.30) | 1.14 (0.56–2.36) | 0.72 |
| High psychological distress | |||||
| Normal weight | |||||
| Case/participants (n) | 30/55 | 23/56 | 12/62 | ||
| Crude | 1.00 | 0.58 (0.27–1.23) | 0.20 (0.09–0.46) | 0.45 (0.30–0.68) | < 0.001 |
| Model 1 | 1.00 | 0.56 (0.26–1.19) | 0.20 (0.09–0.47) | 0.46 (0.30–0.69) | < 0.001 |
| Model 2 | 1.00 | 0.49 (0.20–1.19) | 0.16 (0.06–0.43) | 0.41 (0.25–0.66) | < 0.001 |
| Overweight/obese | |||||
| Case/participants (n) | 40/122 | 43/122 | 30/116 | ||
| Crude | 1.00 | 1.12 (0.66–1.90) | 0.72 (0.41–1.25) | 0.85 (0.65–1.12) | 0.26 |
| Model 1 | 1.00 | 1.01 (0.58–1.77) | 0.70 (0.40–1.24) | 0.84 (0.64–1.11) | 0.23 |
| Model 2 | 1.00 | 0.90 (0.50–1.62) | 0.62 (0.34–1.14) | 0.79 (0.59–1.07) | 0.12 |
Model 1: Adjusted for age, sex, and energy intake
Model 2: Additionally, adjusted for marital status, education level, physical activity (HEPA), smoking status, antidepressant usage (nortriptyline, amitriptyline, imipramine, fluoxetine, citalopram, and fluvoxamine), house ownership, approximate income of the family per month, and daily intakes of whole grains, fruits, vegetables, nuts, soy, legumes, and low-fat dairy
Abbreviations: BMI Body mass index, n Number
aAll values are odds ratios and 95% confidence intervals. P-trend was obtained by considering indices’ tertiles as a continuous variable rather than categorical
Figure 2 illustrates the linear association between each tertile increase in egg consumption with MDA (nmol/mL), GPx (mU/mL), SOD (unit), and CRP (mg/L). Fully-adjusted analysis suggested that each tertile increase in egg consumption was not significantly associated with MDA levels (B or unstandardized β = -8.455 nmol/mL; 95% CI: -18.034, 1.124). The fully-adjusted model for SOD also yielded insignificant results (B or unstandardized β = 0.045 units; 95% CI: -0.007, 0.096). Also, there were no significant linear associations between egg consumption tertiles and GPx or CRP in the total study population, in either crude or adjusted models (P > 0.05). Sex-stratified analyses suggested a strong negative association between egg consumption and MDA in women, with each tertile increase in egg consumption associated with 16.784 nmol/mL lower serum MDA in the fully adjusted model (B or unstandardized β = -16.784 nmol/mL; 95% CI: -30.090, -3.479) (see Fig. 3). There was also a significant association between SOD and egg among women in the fully adjusted model, with a 0.084 unit increase in SOD per each tertile increase in egg consumption (B or unstandardized β = 0.084 unit; 95% CI: 0.008, 0.160). There was no association between egg consumption and GPx or CRP among women (P > 0.05). Ultimately, as presented in Fig. 4, there was no significant association between energy-adjusted egg consumption and MDA, SOD, GPx, or CRP among men (P > 0.05).
Fig. 2.
Linear association between each tertile increase in egg consumption and malonaldehyde (MDA), glutathione peroxidase (GPx), superoxide dismutase (SOD), and high-sensitive C-reactive protein (hs-CRP) among the total study population. Values are B (or unstandardized β) regression coefficients (95% confidence intervals) for MDA (nmol/mL), GPx (nU/mL), SOD (Unit), and hs-CRP (mg/L) per one tertile increase in egg consumption. Results were obtained via linear regression test. Model 1: Adjusted for age and sex; Model 2: More adjustment for body mass index (BMI)
Fig. 3.
Linear association between each tertile increase in egg consumption and malonaldehyde (MDA), glutathione peroxidase (GPx), superoxide dismutase (SOD), and high-sensitive C-reactive protein (hs-CRP) among women. Values are B (or unstandardized β) regression coefficients (95% confidence intervals) for MDA (nmol/mL), GPx (nU/mL), SOD (Unit), and hs-CRP (mg/L) per one tertile increase in egg consumption. Results were obtained via linear regression test. Model 1: Adjusted for age; Model 2: More adjustment for body mass index (BMI)
Fig. 4.
Linear association between each tertile increase in egg consumption and malonaldehyde (MDA), glutathione peroxidase (GPx), superoxide dismutase (SOD), and high-sensitive C-reactive protein (hs-CRP) among men. Values are B (or unstandardized β) regression coefficients (95% confidence intervals) for MDA (nmol/mL), GPx (nU/mL), SOD (Unit), and hs-CRP (mg/L) per one tertile increase in egg consumption. Results were obtained via linear regression test. Model 1: Adjusted for age; Model 2: More adjustment for body mass index (BMI)
Discussion
The present study examined the association between egg consumption in adults and depression, anxiety, high psychological distress symptoms, inflammation, and oxidative stress. Our results suggested that individuals consuming more eggs were less likely to be depressed or highly distressed. These findings were also dose-related. Additionally, we observed a substantial association between egg consumption and MDA and SOD (two oxidative stress biomarkers) among women.
Based on our findings, individuals who were consuming an egg daily or more were less likely to suffer from depression and high psychological distress, as compared to those who only consumed one egg per week or less. But a recent meta-analysis suggested that higher egg consumption may be associated with a higher risk of cardiovascular diseases (CVD) (in US and European cohorts, but not in Asian cohorts), all-cause mortality and CVD-specific mortality [27]. Therefore, although our findings suggest that dietitians could include more egg consumption in their clients’ dietary plans (especially for those at risk of common affective disorders, or oxidative stress), this approach should be applied with caution. As our cross-sectional study by nature is unable to detect causality, and concerning the available literature regarding CVD, future prospective studies must evaluate our hypothesis, both among the general populations and those with CVD, before it can be treated as a clinical intervention.
Looking at our findings across sex groups, egg consumption was significantly associated with depression only among men, not women, even though this disorder was more prevalent among women, and total protein and egg consumption were similar between the two groups. These findings may be explained by the hormonal differences that make women more vulnerable to depression, lowering the protective effect of dietary factors on this disorder [28]. Higher egg consumption was protective against elevated psychological distress among individuals of both sexes. Additionally, higher egg consumption was more protective against depression and high psychological distress among individuals with a desirable weight, as compared to those with overweight/obesity. This can be to somewhat explained as obesity is a potent risk factor for common affective disorders, neutralizing the protective effects that dietary factors can have on mood [29]. Ultimately, with regard to biomarkers in subgroups, we found that higher egg intake was associated with SOD and MDA only among women, not among men. These results support the growing body of evidence on sex differences in oxidative stress and its response [30]. All results regarding anxiety were null, as the prevalence of anxiety was too small in our sample, making the analysis in that regard less reliable.
Looking at available literature, in contrast to our findings, Shafiei et al. did not find any significant association between egg consumption and depression, anxiety, or psychological distress in their cross-sectional study among adults in Iran, either in the total sample, or among men or women separately [18]. However, on closer inspection, in the mentioned study, a dish-based FFQ as used, which may have failed to provide an accurate and reliable assessment of egg consumption, especially in case of mixed dishes [31]. Another cohort study on Chinese elderly suggested that a higher frequency of egg consumption may be associated with a lower risk of depression, after a follow-up of six years, supporting our findings [19]. Also, similar to our results, a meta-analysis observed no significant effect of egg consumption on inflammation, combining nine randomized controlled trials [20]. No previous study had evaluated the association between egg consumption and oxidative stress biomarkers.
Egg is a high-nutrient food item, containing considerable amounts of omega-3 fatty acids, magnesium, zinc, thiamin, vitamin E, riboflavin, niacin, and high-quality protein, alongside other nutrients [13]. Vitamin E acts as an antioxidant, neutralizing free radicals [14]. The brain contains a large amount of fatty acids and is highly reliant on sufficient oxygen for regular activity; thus, it is not surprising that oxidative stress can easily disrupt brain processes and cause affective and psychological disorders [15]. Nutrients in eggs, including B vitamins, magnesium, protein, and omega-3 fatty acids, are also critical for pathways involved in emotion regulation, serotonin production, and brain energy metabolism [16, 17].
This study was the first to comprehensively cover the association between egg consumption and depression, anxiety, high psychological distress, oxidative stress, and inflammation. However, a cross-sectional study cannot detect causal relationships by nature; hence, cohort studies are required to evaluate the findings of our study. We considered and adjusted for multiple confounders; however, other mediating factors may remain to be discovered, potentially influencing the findings. Moreover, egg intake may reflect broader dietary habits, though we adjusted for multiple food groups; dietary habits such as breakfast skipping may still alter the results, and future studies should also consider them. Although the FFQ we used was validated for Iranian population and included egg as a separate item, recall bias was unavoidable with this method.
In conclusion, individuals who consumed one egg or more daily were less likely to have lower odds of depression or be highly distressed, compared to those consuming one per week or less. These reduced odds followed a dose-related fashion. The findings were more potent among individuals with a normal weight. Additionally, we observed an association between this dietary item and reduced oxidative stress among women.
Acknowledgements
None.
Abbreviations
- MDA
Malondialdehyde
- SOD
Superoxide dismutase
- GPx
Glutathione dismutase
- hs-CRP
High-sensitivity C-reactive protein
- FFQ
Food frequency questionnaire
- ELISA
Enzyme-linked immunosorbent assay
- IPAQ
International Physical Activity Questionnaire
- ANOVA
Analysis of variance
- ANCOVA
Analysis of covariance
Authors’ contributions
M.M., Z.M., P.R., F.S., and P.S. contributed to conception, design, data collection, interpretation, manuscript drafting, data analyses, approval of the manuscript, and agreed on all aspects of the work.
Funding
This study is supported by the Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Data availability
The data supporting this study’s findings are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study was conducted in accordance with the Declaration of Helsinki and the STROBE checklist. All study participants provided written informed consent. The local Ethics Committee of Isfahan University of Medical Sciences approved the study protocol.
Consent for publication
All the authors agreed to submit the manuscript to the journal. No third-party material requiring approval was used in the preparation of the current paper.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Feng L, Zhou N, Li Z, Fu D, Guo Y, Gao X, Liu X. Co-occurrence of gut microbiota dysbiosis and bile acid metabolism alteration is associated with psychological disorders in Crohn’s disease. Faseb j. 2022;36(1):e22100. [DOI] [PubMed] [Google Scholar]
- 2.Uher R, Payne JL, Pavlova B, Perlis RH. MAJOR DEPRESSIVE DISORDER IN DSM-5: IMPLICATIONS FOR CLINICAL PRACTICE AND RESEARCH OF CHANGES FROM DSM-IV. Depress Anxiety. 2014;31(6):459–71. [DOI] [PubMed] [Google Scholar]
- 3.Moabedi M, Aliakbari M, Erfanian S, Milajerdi A. Magnesium supplementation beneficially affects depression in adults with depressive disorder: a systematic review and meta-analysis of randomized clinical trials. Front Psychiatry. 2023;14:1333261. [DOI] [PMC free article] [PubMed]
- 4.Lee B, Wang Y, Carlson SA, Greenlund KJ, Lu H, Liu Y, Croft JB, Eke PI, Town M, Thomas CW. National, State-Level, and County-Level Prevalence Estimates of Adults Aged ≥ 18 Years Self-Reporting a Lifetime Diagnosis of Depression - United States, 2020. MMWR Morbidity Mortal Wkly Rep. 2023;72(24):644–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Saneei P, Esmaillzadeh A, Keshteli AH, Reza Roohafza H, Afshar H, Feizi A, Adibi P. Combined Healthy Lifestyle Is Inversely Associated with Psychological Disorders among Adults. PLoS ONE. 2016;11(1):e0146888. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Global regional. and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9(2):137–150. [DOI] [PMC free article] [PubMed]
- 7.Chen NH, Liu LM, Liu HY, Hsieh IC, Tsai CC. Psychological distress among first-year health science students in Taiwan. Heliyon. 2022;8(8):e10121. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Moabedi M, Milajerdi A. The effect of co-administration of vitamin E and C supplements on plasma oxidative stress biomarkers and antioxidant capacity: a GRADE-assessed systematic review and meta-analysis of randomized controlled trials with meta-regression. Front Immunol. 2025;16:1547888. [DOI] [PMC free article] [PubMed]
- 9.Arulselvan P, Fard MT, Tan WS, Gothai S, Fakurazi S, Norhaizan ME, Kumar SS. Role of antioxidants and natural products in inflammation. Oxid Med Cell Longev. 2016;2016(1):5276130. [DOI] [PMC free article] [PubMed]
- 10.Michopoulos V, Powers A, Gillespie CF, Ressler KJ, Jovanovic T. Inflammation in Fear- and Anxiety-Based Disorders: PTSD, GAD, and Beyond. Neuropsychopharmacology. 2017;42(1):254–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Hassan W, Noreen H, Castro-Gomes V, Mohammadzai I, da Rocha JB, Landeira-Fernandez J. Association of Oxidative Stress with Psychiatric Disorders. Curr Pharm Des. 2016;22(20):2960–74. [DOI] [PubMed] [Google Scholar]
- 12.Aucoin M, LaChance L, Naidoo U, Remy D, Shekdar T, Sayar N, Cardozo V, Rawana T, Chan I, Cooley K. Diet and Anxiety: A Scoping Review. Nutrients. 2021;13(12):4418. [DOI] [PMC free article] [PubMed]
- 13.Tolik D, Poawska E, Charuta A, Nowaczewski S, Cooper R. Characteristics of egg parts, chemical composition and nutritive value of Japanese quail eggs–a review. Folia Biologica (Kraków). 2014;62(4):287–92. [DOI] [PubMed] [Google Scholar]
- 14.Mummadi MK, Sapavat S, Ettey I, Appala T, Geddam JJB. Antioxidant Nutrients and Diabetes and Its Complications: A Narrative Review on the Roles of Vitamin E, Vitamin C, and Selenium. Cureus. 2025;17(10). [DOI] [PMC free article] [PubMed]
- 15.Salim S. Oxidative stress and psychological disorders. Curr Neuropharmacol. 2014;12(2):140–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Merino del Portillo M, Clemente-Suarez VJ, Ruisoto P, Jimenez M, Ramos-Campo DJ, Beltran-Velasco AI, Martinez-Guardado I, Rubio-Zarapuz A, Navarro-Jimenez E, Tornero-Aguilera JF. Nutritional modulation of the gut–brain axis: A comprehensive review of dietary interventions in depression and anxiety management. Metabolites. 2024;14(10):549. [DOI] [PMC free article] [PubMed]
- 17.Depeint F, Bruce WR, Shangari N, Mehta R, O’Brien PJ. Mitochondrial function and toxicity: role of the B vitamin family on mitochondrial energy metabolism. Chemico-Biol Interact. 2006;163(1–2):94–112. [DOI] [PubMed] [Google Scholar]
- 18.Shafiei F, Keshteli AH, Pouraram H, Afshar H, Salari-Moghaddam A, Esmaillzadeh A, Adibi P. Egg consumption and prevalence of psychological disorders in adults. Eur J Nutr. 2019;58(5):1923–32. [DOI] [PubMed] [Google Scholar]
- 19.Li F, Li X, Gu X, Zhang T, Xu L, Lin J, Chen K. Egg consumption reduces the risk of depressive symptoms in the elderly: findings from a 6-year cohort study. BMC Psychiatry. 2023;23(1):44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Sajadi Hezaveh Z, Sikaroudi MK, Vafa M, Clayton ZS, Soltani S. Effect of egg consumption on inflammatory markers: a systematic review and meta-analysis of randomized controlled clinical trials. J Sci Food Agric. 2019;99(15):6663–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Poursalehi D, Shahdadian F, Hajhashemy Z, Lotfi K, Moradmand Z, Rouhani P, Mohammadi S, Mokhtari E, Saneei P. Diet in relation to Metabolic, sleep and psychological health Status (DiMetS): protocol for a cross-sectional study. BMJ Open. 2023;13(12):e076114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study. Public Health Nutr. 2010;13(5):654–62. [DOI] [PubMed] [Google Scholar]
- 23.Ghaffarpour M, Houshiar-Rad A, Kianfar H. The manual for household measures, cooking yields factors and edible portion of foods. Tehran: Nashre Olume Keshavarzy. 1999;7(213):42–58. [Google Scholar]
- 24.Montazeri A, Vahdaninia M, Ebrahimi M, Jarvandi S. The Hospital Anxiety and Depression Scale (HADS): translation and validation study of the Iranian version. Health Qual Life Outcomes. 2003;1:14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Montazeri A, Harirchi AM, Shariati M, Garmaroudi G, Ebadi M, Fateh A. The 12-item General Health Questionnaire (GHQ-12): translation and validation study of the Iranian version. Health Qual Life Outcomes. 2003;1:66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Moghaddam MHB, Aghdam F, Asghari Jafarabadi M, Allahverdipour H, Nikookheslat S, Safarpour S. The Iranian Version of International Physical Activity Questionnaire (IPAQ) in Iran: Content and Construct Validity, Factor Structure, Internal Consistency and Stability. World Appl Sci J. 2012;18:1073–80. [Google Scholar]
- 27.Zhao B, Gan L, Graubard BI, Männistö S, Albanes D, Huang J. Associations of Dietary Cholesterol, Serum Cholesterol, and Egg Consumption With Overall and Cause-Specific Mortality: Systematic Review and Updated Meta-Analysis. Circulation. 2022;145(20):1506–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Albert KM, Newhouse PA. Estrogen, Stress, and Depression: Cognitive and Biological Interactions. Annual Review of Clinical Psychology. 2019;15(1):399–423. [DOI] [PMC free article] [PubMed]
- 29.Monsalve FA, Fernández-Tapia B, Arriagada OC, González DR, Delgado-López F. Obesity and Depression: A Pathophysiotoxic Relationship. Int J Mol Sci vol. 2025;26:11590. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Brunelli E, Domanico F, La Russa D, Pellegrino D. Sex differences in oxidative stress biomarkers. Curr Drug Targets. 2014;15(8):811–5. [DOI] [PubMed] [Google Scholar]
- 31.Keshteli A, Esmaillzadeh A, Rajaie S, Askari G, Feinle-Bisset C, Adibi P. A Dish-based Semi-quantitative Food Frequency Questionnaire for Assessment of Dietary Intakes in Epidemiologic Studies in Iran: Design and Development. Int J Prev Med. 2014;5(1):29–36. [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Data Availability Statement
The data supporting this study’s findings are available from the corresponding author upon reasonable request.