Investigating the interaction between major dietary patterns and psychological disorders in association with sleep quality and quantity among Iranian adults: YaHS-TAMYZ study

Abstract

Objectives

The aim of the present study was to investigate if major dietary patterns (DPs) interact with psychological disorders (PDs) in association with sleep quality and quantity among a large sample of Iranian adults living in Yazd, Iran.

Methods

This cross-sectional investigation was conducted on the baseline data of a population-based Iranian cohort study (Yazd Health Study-Taghzieh Mardom-e-YaZd: YaHS-TAMYZ). All data regarding dietary intakes, PDs, and sleep status were collected via validated questionnaires. The interaction between major DPs and PDs in relation to sleep parameters was determined using multivariable adjusted binary logistic regression.

Results

A total of 6048 adults participated in this study. Compared to subjects with the lowest adherence to each DP and without any severe PDs: a) individuals with the highest adherence to the “western” DP and with severe anxiety had higher risk of “short sleep duration” (P = 0.030) and “insufficient sleep” (P = 0.020); b) subjects in the “western” DP’s top tertile and with severe depression had lower chance of having “short sleep duration” (P = 0.029) and “insufficient sleep” (P = 0.029); c) those with the highest adherence to the “high animal protein” DP and with severe anxiety had significantly greater odds of “long sleep duration” (P = 0.044); d) a lower chance of “low sleep quality” was observed in participants in the “high animal protein” DP’s top tertile and with severe stress (P = 0.05).

Conclusions

The “western” and “high animal protein” DPs might interact with PDs in association with the quality and duration of sleep. Further investigations are needed to confirm our findings.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01494-4.

Introduction

Maintaining physical, mental, and emotional wellbeing depends on getting enough sleep [1]. There is accumulating evidence that serious mental and metabolic health issues, including neurasthenia, depression [2], diabetes [3], and cardiovascular disease [4], may result from sleep disorder-related illnesses in certain extreme circumstances. Furthermore, depending on the severity of the disease, 30–80% of individuals with psychological disorders (PDs) suffer from sleep disturbances [5].

The relationship between sleep and dietary patterns (DPs) has also been highly regarded by researchers [6]. Less than six hours of sleep per night has been associated with lower levels of food quality indicators, such as higher intake of sweets and lower intake of protein, fruits, whole grains, seeds, and vegetables [7, 8]. Moreover, different investigations have revealed that people with short sleep durations might receive more energy, especially from dietary sweets [9] and fats [10], than people with normal sleep durations. In addition, a negative association has been suggested between a “healthy eating pattern” and sleep duration of more than 8 h in a day [11].

Simultaneously, several studies have examined the relationship between diet and PDs. For instance, a direct association has been documented between behavioral manifestations of PDs and inadequate dietary consumption of vegetables and fish [12]. Recent studies have also investigated the association between empirically derived food patterns and mental disturbances. The key findings of a large cohort study revealed that a “low-fat diet” may be positively associated with a reduced risk of depression, anxiety, and stress in both genders [13]. Conversely, a Western-style diet in the Norwegian population was connected to a greater level of anxiety in both genders [14].

Although studies have revealed that both diet and PDs might be independently associated with sleep, their interaction effect on sleep is still unclear. Hence, the purpose of the current study was to detect if major DPs interact with PDs in association with sleep quality and quantity in a large prospective study of Yazd population, Iran.

Methods & materials

Participants

9962 people between the ages of 20 and 70 were recruited from the adult population in Yazd province, Iran, for a prospective cohort study named the Yazd Health Study-Taghzieh Mardom-e-YaZd (YaHS-TAMYZ). The baseline data from YaHS-TAMYZ study were used to perform the current cross-sectional investigation. Trained interviewers completed all the study validated questionnaires via in-person interviews. A detailed description of the YaHS-TAMYZ study’s methodology is available in a separate investigation [15].

Participants with incomplete information on sleep duration or missing answers to more than 70 frequency questionnaire (FFQ) items (n = 850), inexplicable energy consumption (less than 800 kcal/day or more than 6000 kcal/day [16]) (n = 864), irregular sleeping time (< 3 h or > 12 h per night) (n = 250), pregnancy (n = 104), newly diagnosed patients or had history of using medication for mental disorders (n = 91), and had a history of cardiovascular disease, diabetes, and cancer (n = 1719), were excluded from the study. A total of 6084 people were retained to undergo additional assessment. All the subjects provided written consent in order to take part in the aforementioned Iranian cohort study. The ethical committee of Shahid Sadoughi University of Medical Sciences, Yazd, Iran, approved the procedure of present investigation on September 26, 2021 (approval ID: IR.SSU.SPH.REC.1400.111).

Dietary assessment

A comprehensive data regarding individuals’ food intake was assessed using an Iranian validated 178-item FFQ, which contained 168 Iranian conventional food items and 10 specific traditional food items consumed by Yazd population [17]. All the subjects were interviewed to report their frequency and amount of intake of each food item throughout the past year. An extra questionnaire was also given to the participants to determine the frequency with which they took specific dietary supplements, such as multivitamin minerals, vitamin D, calcium, iron, vitamin B9, and fish oil (or omega-3). All relevant data were then transformed to g/day via Iranian household portion sizes [18]. Energy and nutrient intakes were measured using the food composition database provided by the United States Department of Agriculture [19]. All of the dietary components were eventually combined into 31 food groups which were determined by comparing the nutritional profiles and culinary applications of each category (Supplementary Table 1).

Measuring sleep quantity and quality

A nocturnal sleep questionnaire was used to collect information regarding habitual sleep duration. Subjects were divided into tertiles depending on their sleep period. People who slept for more than eight hours a night were in the top tertile of sleep quantity, whereas people who slept for five hours or less were in the lowest tertile. Individuals with a normal sleep duration were defined as those who slept for five to eight hours at night. Finally, three categories were classified based on the quantity of sleep: (i) insufficient sleep (< 5 h vs. ≥5 h), (ii) short duration of sleep (< 5 h vs. 5–8 h), and (iii) long duration of sleep (> 8 h vs. 5–8 h) [20].

All data regarding the participants’ sleep quality was collected by the Pittsburgh questionnaire, which has recently been validated among Iranians [21]. Following its detailed scoring route [16], the main four indicators of the sleep quality were categorized as sleep disorders, medication use for sleep, delay in falling asleep, and sleep duration.

Measuring the psychological profile

The validated Depression, Anxiety, and Stress Scale (DASS-21) was used to assess PDs in the study population [22]. The questionnaire has three separate subscales with seven questions regarding the symptoms of PDs. The responses ranged from 0 (did not seem relevant to me at all) to 3 (exactly mirrored my experience during the previous week). A higher overall score indicates a more severe level of psychological illness. According to their scores, subjects were assigned to one of five main classifications: “no illness”, “mild”, “moderate”, “severe”, or “extremely severe” [23]. Finally, the participants were split into distinguished categories: those with “no or mild or moderate PD symptoms” and those with “severe and extremely severe PD symptoms”.

Anthropometric indices and other study variables

Competent assistants carried out each measurement in accordance with established procedures. While the participants were wearing no shoes and had minimum coverage, height was measured via a nonstretchable tape to the nearest 0.5 cm. Also, an accurate body analyzer (Omron BF511, Omron, Inc., Nagoya, Japan) was used to assess body weight. In addition, body mass index (BMI) was assessed from reported weights and heights based on Quetelet’s index. Additional information regarding the individuals’ demographic and chronic disease history data was obtained via face-to-face interviews.

Statistical analysis

Identification of the DPs was performed using principal component analysis with varimax rotation. In order to select the major DPs, eigenvalues (> 1), scree plots, and factor interpretability were taken into account [24]. Individuals were classified into tertiles in accordance with their adherence scores to the research DPs (tertile 1 (T1): adhered the least, and tertile 3 (T3): adhered the most). Histograms and the Kolmogorov‒Smirnov test were used to determine the normality of the data distribution. Comparisons between continuous variables including the body weight, BMI, physical activity, cell phone use (min/day), and watching TV/movie (min/day) depending on the gender status were carried out implementing independent samples t-test. In addition, all categorical variables were compared according to the gender status via the chi-square test. The general psychological status and sleep duration of the participants across tertiles of DP scores were carried out employing analysis of variance (ANOVA). Age-, gender-, and energy-adjusted dietary food categories and nutrient intake were compared across tertiles of DP scores using analysis of covariance (ANCOVA) with Bonferroni correction.

The interaction between major DPs and PDs in association with sleep quality and duration was investigated using a multivariable adjusted binary logistic regression test. All analyses were adjusted for sex, age, BMI, house ownership status, education rank, occupation, smoking status, marital status, total energy intake, physical activity, and the amount of time spent using a mobile phone or watching TV/movie. The IBM SPSS (version 22.0; SPSS, Inc., Chicago, IL, USA) was used to conduct all the statistical examinations. The threshold for statistical significance was defined as a P value of 0.05 or less.

Results

Participants’ characteristics

The present investigation comprised a total of 6084 eligible individuals (3097 men and 2987 women).

Tables 1 and 2 summarize the overall characteristics of the research participants. As the Table 1 depicts, men were significantly more self-employed and had higher body weight, physical activity, education, and time spent using a cell phone compared to women. However, in comparison with men, women were significantly more government employee and had greater BMI, history of chronic diseases, and time spent watching television & movie.

Table 1.

General characteristics of the participants^†

Variables	Gender Status
	Male	Female	P-value*
Subjects (n)	3097	2987
Body weight (kg)	75.53 ± 16.2	67.5 ± 15.8	< 0.001
BMI (kg/m 2 )	25.85 ± 4.6	27.48 ± 5.7	< 0.001
PA (MET x min/wk)	1097.25 ± 503.2	714.32 ± 319.3	< 0.001
Age (year, %)			0.595
20–29	24.2	23.3
30–39	23.4	23.7
40–49	21.8	23.5
50–59	17.4	16.6
60–69	13.2	12.9
Job Status, %			< 0.001
Unemployed	25	14.4
Government employee	16.4	78.6
Manual worker	6.1	1.1
Self-employed	52.5	5.9
Education (Diploma and Graduate Diploma, %)	34.4	30.7	< 0.001
Married, %	81.7	85.3	< 0.001
Smoking status (Current smoker, %)	19.2	2	< 0.001
Homeowner, %	76.4	76.1	0.342
Chronic disease ‡ (Yes, %)	29.2	47.5	< 0.001
Cell phone use (min/day)	31.25 ± 17.1	24.13 ± 10.8	< 0.001
Watching television & movie (min/day)	221.69 ± 147.8	233.34 ± 153.3	0.003

^† Values are presented as the mean ± SD unless otherwise indicated

^* P-values are resulted from independent samples t-test for quantitative variables and from chi-square test for qualitative variables

^‡ Chronic diseases: high cholesterol levels, hypertension, asthma, Alzheimer’s disease, thyroid dysfunction, osteoporosis, lung disease, arthritis, hepatitis, renal failure, kidney stones, fatty liver, epilepsy, chronic headache, rheumatic arthritis, psychiatric disorders, and multiple sclerosis

Abbreviations: MET: metabolic equivalent, BMI: body mass index, PA: physical activity

Table 2.

General psychological status and sleep duration of the participants^†

Variables	Sugar & Fat Diet			Fruits and Vegetables Diet			Western Diet			High animal protein Diet
	Low adherence (Tertile 1)	High adherence (Tertile 3)	P-value*	Low adherence (Tertile 1)	High adherence (Tertile 3)	P-value*	Low adherence (Tertile 1)	High adherence (Tertile 3)	P-value*	Low adherence (Tertile 1)	High adherence (Tertile 3)	P-value*
Subjects (n)	2028	2028		2028	2028		2028	2028		2028	2028
Anxiety (score)	2.9 ± 3.6	2.7 ± 3.4	0.273	3.0 ± 3.7	2.7 ± 3.4	< 0.001	2.8 ± 3.7	3.0 ± 3.6	0.001	2.8 ± 3.4	2.7 ± 3.6	0.335
Depression (score)	3.1 ± 3.7	3.2 ± 3.6	0.986	3.5 ± 3.9	3.1 ± 3.6	< 0.001	3.2 ± 3.7	3.3 ± 3.8	0.022	3.2 ± 3.6	3.2 ± 3.8	0.445
Stress (score)	5.7 ± 4.6	5.9 ± 4.6	0.264	5.9 ± 4.7	5.8 ± 4.6	0.009	5.8 ± 4.7	5.9 ± 4.6	0.152	5.8 ± 4.5	6.0 ± 4.8	0.008
Sleep time (hours)	6.9 ± 1.6	6.9 ± 1.6	0.650	7.0 ± 1.6	6.8 ± 1.6	0.052	6.9 ± 1.6	7.0 ± 1.6	0.090	6.9 ± 1.6	6.9 ± 1.6	0.888

^† Values are presented as the mean ± SD unless otherwise indicated

^* P-values are resulted from analysis of variance (ANOVA)

According to the Table 2, subjects with the highest adherence to the “fruits and vegetables” DP had lower anxiety, depression, and stress scores (p < 0.05). Participants in the top tertile of the “western” DP had greater anxiety and depression scores (p < 0.05). The highest imitation of the “high animal protein” DP was significantly related to a higher level of stress score (p < 0.05).

Dietary food patterns

Using principal component analysis, four major DPs were found and classified as “sugar & fat”, “fruits and vegetables”, “western”, or “high animal protein”. In total, 23.17% of the variation in food intake was explained by these four DPs. Supplementary Table 2 displays all the food groups and their loading factors for each DP. The “sugar & fat” DP was distinguished by high consumption of sugars, snacks, soft drinks, nuts, broth, condiments, and mayonnaise (explaining 8.32% of the total variation). The “fruits and vegetables” DP was defined by high intake of dried fruits, olives, fruits, vegetables and dairy products (explaining 5.45% of the overall variation). The “western” eating pattern was high in potatoes, French fries, red meat and refined grains (explaining 4.71% of the total variation). Fish, processed meat, organ meat and canned fish were loaded in the “high animal protein” DP, which accounted for 4.67% of the overall variation.

Food and nutrient consumption

Table 3 compares age-, gender-, and energy-adjusted dietary food categories and nutrient consumption according to DP adherence levels.

Table 3.

Comparison of age-, sex-, and energy-adjusted dietary food groups and nutrient intake based on the imitation level of DPs

Variables	Sugar & Fat Diet			Fruits and Vegetables Diet			Western Diet			High animal protein Diet
	Low adherence (Tertile 1)	High adherence (Tertile 3)	P-value*	Low adherence (Tertile 1)	High adherence (Tertile 3)	P-value*	Low adherence (Tertile 1)	High adherence (Tertile 3)	P-value*	Low adherence (Tertile 1)	High adherence (Tertile 3)	P-value*
Total energy (Kcal/day)	2222.5 ± 22.1	3651.9 ± 22.1	< 0.001	2417.05 ± 23.8	3466.8 ± 23.8	< 0.001	2605.9 ± 25.3	3184.9 ± 25.3	< 0.001	2681.2 ± 24.2	3361.7 ± 24.2	< 0.001
Food groups †,‡
Whole grains (g/day)	100.3 ± 1.6	48.2 ± 1.7	< 0.001	66.09 ± 1.6	77.4 ± 1.6	< 0.001	77.6 ± 1.6	73.5 ± 1.6	0.151	90.02 ± 1.5	59.7 ± 1.6	< 0.001
Refined grains (g/day)	268.7 ± 3.9	157.8 ± 4.2	< 0.001	232.8 ± 3.9	216.03 ± 4.1	0.012	125.6 ± 3.4	326.2 ± 3.5	< 0.001	205.6 ± 3.8	230.6 ± 4.02	< 0.001
Low fat dairy products (g/day)	65.9 ± 2.6	48.6 ± 2.8	< 0.001	32.01 ± 2.5	89.8 ± 2.6	< 0.001	52.7 ± 2.5	65.4 ± 2.6	0.003	74.5 ± 2.5	42.5 ± 2.6	< 0.001
High fat dairy products (g/day)	181.00 ± 3.9	170.3 ± 4.2	0.017	139.3 ± 3.8	226.4 ± 3.9	< 0.001	181.4 ± 3.8	171.6 ± 3.8	0.034	190.9 ± 3.8	171.6 ± 3.9	0.001
Nuts (g/day)	12.9 ± 0.6	34.9 ± 0.6	< 0.001	23.1 ± 0.6	19.03 ± 0.6	< 0.001	25.7 ± 0.6	16.4 ± 0.6	< 0.001	24.6 ± 0.6	17.6 ± 0.6	< 0.001
Legumes (g/day)	47.2 ± 1.2	41.3 ± 1.3	< 0.001	50.07 ± 1.2	45.7 ± 1.3	0.005	42.9 ± 1.2	51.4 ± 1.2	< 0.001	36.9 ± 1.2	60.2 ± 1.2	< 0.001
Red meats (g/day)	48.2 ± 1.3	53.07 ± 1.4	< 0.001	51.4 ± 1.2	57.6 ± 1.3	0.005	30.4 ± 1.1	85.1 ± 1.1	< 0.001	75.9 ± 1.2	35.8 ± 1.2	< 0.001
Processed meats (g/day)	15.3 ± 0.6	11.3 ± 0.7	0.001	16.5 ± 0.6	10.6 ± 0.7	< 0.001	18.1 ± 0.6	8.7 ± 0.6	< 0.001	5.3 ± 0.6	26.4 ± 0.6	< 0.001
Fruits (g/day)	761.6 ± 10.8	404.2 ± 11.6	< 0.001	368.7 ± 9.8	904.5 ± 10.3	< 0.001	628.4 ± 10.8	591.09 ± 10.9	0.002	646.7 ± 10.7	540.8 ± 11.2	< 0.001
Vegetables (g/day)	328.4 ± 5.5	218.5 ± 5.9	< 0.001	199.2 ± 5.1	400.8 ± 5.4	< 0.001	238.6 ± 5.3	343.7 ± 5.3	< 0.001	274.1 ± 5.3	303.1 ± 5.6	0.001
Nutrients †,‡
Total fat (g/day)	95.2 ± 0.7	119.2 ± 0.7	< 0.001	111.1 ± 0.7	99.8 ± 0.7	< 0.001	108.5 ± 0.7	101.9 ± 0.7	< 0.001	108.9 ± 0.7	105.6 ± 0.7	< 0.001
Saturated fat (g/day)	28.4 ± 0.2	30.3 ± 0.2	< 0.001	29.3 ± 0.2	30.2 ± 0.2	0.016	28.8 ± 0.2	30.7 ± 0.2	< 0.001	31.5 ± 0.2	28.8 ± 0.2	< 0.001
Mono-unsaturated fat (g/day)	29.6 ± 0.2	35.8 ± 0.3	< 0.001	35.1 ± 0.2	29.6 ± 0.3	< 0.001	32.8 ± 0.2	32.3 ± 0.2	0.292	33.7 ± 0.2	32.6 ± 0.2	< 0.001
Poly-unsaturated fat (g/day)	24.4 ± 0.3	30.9 ± 0.4	< 0.001	27.6 ± 0.3	26.9 ± 0.3	0.503	27.6 ± 0.3	26.5 ± 0.3	0.033	29.5 ± 0.3	24.9 ± 0.3	< 0.001
Total protein (g/day)	117.9 ± 0.7	96.07 ± 0.8	< 0.001	104.02 ± 0.7	116.9 ± 0.8	< 0.001	107.6 ± 0.7	116.7 ± 0.7	< 0.001	108.05 ± 0.7	116.2 ± 0.8	< 0.001
Total carbohydrate (g/day)	395.6 ± 1.8	391.09 ± 2.00	< 0.001	390.3 ± 1.8	387.9 ± 1.9	0.664	394.6 ± 1.8	378.3 ± 1.8	< 0.001	374.9 ± 1.7	396.9 ± 1.8	< 0.001
Simple sugars (g/day)	262.9 ± 10.3	290.6 ± 11.05	0.159	232.6 ± 10.00	315.6 ± 10.4	< 0.001	326.1 ± 9.8	196.9 ± 10.00	< 0.001	245.8 ± 9.8	306.3 ± 10.3	< 0.001
Total dietary fiber (g/day)	27.2 ± 0.3	26.7 ± 0.4	< 0.001	24.9 ± 0.3	28.04 ± 0.3	< 0.001	28.7 ± 0.3	23.6 ± 0.3	< 0.001	24.9 ± 0.3	28.05 ± 0.3	< 0.001
Vitamin C (µm/d)	247.8 ± 3.4	149.1 ± 3.6	< 0.001	134.4 ± 3.1	296.9 ± 3.2	< 0.001	206.5 ± 3.3	207.4 ± 3.4	0.029	209.1 ± 3.3	197.7 ± 3.5	0.067
Vitamin E (mg/d)	11.9 ± 0.2	9.1 ± 0.2	< 0.001	8.7 ± 0.2	14.1 ± 0.2	< 0.001	9.2 ± 0.2	12.9 ± 0.2	< 0.001	13.3 ± 0.2	8.6 ± 0.2	< 0.001
Thiamine (mg/d)	2.4 ± 0.01	1.8 ± 0.01	< 0.001	2.03 ± 0.01	2.3 ± 0.01	< 0.001	1.9 ± 0.01	2.4 ± 0.01	< 0.001	2.1 ± 0.01	2.2 ± 0.01	< 0.001
Riboflavin (µm/d)	2.3 ± 0.01	2.2 ± 0.01	< 0.001	2.1 ± 0.01	2.5 ± 0.01	< 0.001	2.2 ± 0.01	2.4 ± 0.01	< 0.001	2.2 ± 0.01	2.3 ± 0.01	< 0.001
Vitamin B3(mg/day)	29.5 ± 0.1	24.1 ± 0.2	< 0.001	26.5 ± 0.1	28.2 ± 0.1	< 0.001	26.6 ± 0.1	29.04 ± 0.1	< 0.001	26.8 ± 0.1	28.6 ± 0.1	< 0.001
Vitamin B5(mg/day)	6.5 ± 0.05	6.08 ± 0.06	< 0.001	5.4 ± 0.05	7.5 ± 0.05	< 0.001	5.8 ± 0.05	6.9 ± 0.05	< 0.001	6.7 ± 0.05	5.9 ± 0.05	< 0.001
Vitamin B6 (mg/d)	2.4 ± 0.02	2.4 ± 0.02	0.928	2.2 ± 0.02	2.6 ± 0.02	< 0.001	2.5 ± 0.02	2.3 ± 0.02	< 0.001	2.3 ± 0.02	2.4 ± 0.02	0.003
Folic Acid (µg/d)	374.8 ± 3.3	341.7 ± 3.5	< 0.001	331.5 ± 3.1	413.02 ± 3.3	< 0.001	353.7 ± 3.2	380.9 ± 3.2	< 0.001	349.2 ± 3.2	378.1 ± 3.3	< 0.001
Vitamin B12 (µg/d)	5.9 ± 0.1	5.3 ± 0.1	< 0.001	5.1 ± 0.1	6.7 ± 0.1	< 0.001	5.3 ± 0.1	6.6 ± 0.1	< 0.001	5.3 ± 0.1	7.02 ± 0.1	< 0.001
Magnesium (mg/day)	337.5 ± 2.06	305.4 ± 2.2	< 0.001	290.2 ± 1.8	372.2 ± 1.9	< 0.001	317.1 ± 1.9	337.8 ± 2.01	< 0.001	316.1 ± 1.9	338.2 ± 2.06	< 0.001
Calcium (mg/day)	979.6 ± 7.5	864.5 ± 8.07	< 0.001	821.9 ± 7.04	1084.2 ± 7.3	< 0.001	902.04 ± 7.2	981.1 ± 7.3	< 0.001	975.7 ± 7.2	902.4 ± 7.5	< 0.001
Iron (mg/day)	41.9 ± 1.6	40.5 ± 1.7	0.692	33.4 ± 1.6	53.8 ± 1.6	< 0.001	40.5 ± 1.6	44.4 ± 1.6	0.142	42.3 ± 1.5	42.07 ± 1.6	0.816
Zinc (mg/day)	11.6 ± 0.08	10.7 ± 0.08	< 0.001	10.6 ± 0.07	12.7 ± 0.08	< 0.001	10.4 ± 0.07	13.1 ± 0.07	< 0.001	12.1 ± 0.07	11.2 ± 0.08	< 0.001

^† Values are adjusted for age, sex and total energy

^‡ Values are reported as the mean ± standard error (SE)

^* P-values are obtained from analysis of covariance (ANCOVA)

Individuals with the highest adherence to the “sugar & fat” DP consumed a notably greater quantity of energy, nuts, red meats, saturated and total fat, as well as mono- and polyunsaturated fat (p < 0.05), whereas they had lower intakes of grains, processed meats, legumes, fruits, vegetables, total protein, total carbohydrate, vitamin C, vitamin E, vitamins B1, B2, B3, B5, B9, B12, magnesium, calcium, zinc, and total dietary fiber (p < 0.05) than did subjects with the lowest adherence to this DP.

Compared with subjects in the lowest tertile of “fruits and vegetables” DP, individuals in the highest tertile consumed significantly more total energy, whole grains, red meats, fruits, vegetables, saturated fat, total protein, simple sugars, vitamins C, E, B1, B2, B3, B5, B6, B9, B12, magnesium, calcium, iron, zinc, and total dietary fiber (P < 0.05) and consumed fewer refined grains, nuts, legumes, processed meats, mono-unsaturated and total fat (P < 0.05).

Further imitation of the “western” DP was strongly related to increased intake of total energy, refined grains, low-fat dairy products, legumes, red meats, vegetables, saturated fat, total protein, vitamins C, E, B1, B2, B3, B5, B9, B12, magnesium, calcium, and zinc (p < 0.05). However, in comparison with the individuals in the first tertile, subjects in the top tertile ingested fewer high-fat dairy products, nuts, processed meats, fruits, total carbohydrates, polyunsaturated fats, total fat, simple sugars, vitamin B6, and total dietary fiber (p < 0.05). The highest imitation to the “high animal protein” DP was found to be connected with higher intakes of total energy, refined grains, legumes, processed meats, vegetables, total protein, total carbohydrate, simple sugar, vitamins B1, B2, B3, B6, B9, B12, magnesium, and total dietary fiber (p < 0.05) and lower consumption of whole grains, nuts, red meat, fruits, saturated, mono- and polyunsaturated and total fat, vitamin E, vitamin B5, calcium, and zinc (p < 0.05).

The interactions between major DPs and the psychological profile in association with sleep quantity and quality

The interactions between DPs and PDs in association with sleep duration, sleep quality components, and overall sleep quality are explained in Tables 4, 5 and 6, respectively.

Table 4.

Odds ratios for sleep quantity factors based on DPs’ score and psychological profile

Variables	Sugar & Fat Diet				Fruits and Vegetables Diet				Western Diet				High animal protein Diet
	Tertile 1†,‡	Tertile 2†,‡	Tertile 3†,‡	P-int*	Tertile 1†,‡	Tertile 2†,‡	Tertile 3†,‡	P-int*	Tertile 1†,‡	Tertile 2†,‡	Tertile 3†,‡	P-int*	Tertile 1†,‡	Tertile 2†,‡	Tertile 3†,‡	P-int*
	Short sleep duration (< 5 h vs. 5–8 h)
Severe anxiety				0.920				0.237				0.030				0.775
No	1	1.21 (0.9–1.5)	1.07 (0.8–1.3)		1	0.72 (0.5–0.9)	0.93 (0.7–1.1)		1	0.93 (0.7–1.1)	0.81 (0.6–1.02)		1	0.92 (0.7–1.1)	1.01 (0.8–1.2)
Yes	1.56 (0.7–3.3)	0.88 (0.2–2.6)	0.77 (0.2–2.6)		1.39 (0.6–2.7)	0.37 (0.07–1.8)	1.51 (0.5–4.2)		1.04 (0.4–2.4)	0.41 (0.07–2.2)	2.70 (0.9–7.9)		1.15 (0.4–2.7)	1.15 (0.3–3.8)	1.51 (0.4–4.8)
Severe depression				0.398				0.948				0.029				0.199
No	1	1.22 (0.9–1.5)	1.07 (0.8–1.3)		1	0.70 (0.5–0.8)	0.94 (0.7–1.1)		1	0.95 (0.7–1.1)	0.90 (0.7–1.1)		1	0.90 (0.7–1.1)	1.02 (0.8–1.2)
Yes	1.24 (0.4–3.6)	0.70 (0.1–3.1)	0.20 (0.02-2.0)		0.80 (0.3–2.04)	0.69 (0.07–6.4)	0.92 (0.2–4.2)		2.29 (0.9–5.4)	0.14 (0.01–1.3)	0.08 (0.009-0.6)		0.25 (0.03–1.9)	6.30 (0.6–58.3)	2.46 (0.2–25.1)
Severe stress				0.819				0.151				0.572				0.802
No	1	1.19 (0.9–1.4)	1.05 (0.8–1.3)		1	0.70 (0.5–0.8)	0.97 (0.7–1.2)		1	0.94 (0.7–1.1)	0.87 (0.6–1.09)		1	0.92 (0.7–1.1)	1.01 (0.8–1.2)
Yes	1.2 (0.4–3.3)	1.46 (0.4–5.09)	1.13 (0.3–4.1)		1.92 (0.9–4.1)	1.29 (0.4–4.04)	0.31 (0.07–1.2)		2.16 (1.003-4.6)	0.58 (0.1–1.9)	0.57 (0.1–1.9)		1.19 (0.4–3.1)	1.39 (0.3–5.1)	1.50 (0.4–5.2)
Long sleep duration (> 8 h vs. 5–8 h)
Severe anxiety				0.565				0.844				0.304				0.044
No	1	0.90 (0.7–1.1)	0.82 (0.6–1.06)		1	0.95 (0.7–1.2)	0.98 (0.7–1.2)		1	1.11 (0.8–1.4)	1.25 (0.9–1.5)		1	0.98 (0.7–1.2)	0.94 (0.7–1.1)
Yes	0.92 (0.3–2.2)	1.15 (0.3–3.8)	1.82 (0.5–5.9)		1.20 (0.6–2.4)	0.78 (0.2–2.5)	1.14 (0.3–3.5)		1.69 (0.8–3.4)	0.76 (0.2–2.3)	0.39 (0.1–1.2)		0.83 (0.3–2.1)	0.69 (0.1–2.8)	2.86 (0.8–9.1)
Severe depression				0.853				0.225				0.103				0.666
No	1	0.90 (0.7–1.1)	0.84 (0.6–1.09)		1	0.93 (0.7–1.1)	0.99 (0.7–1.2)		1	1.08 (0.8–1.3)	1.23 (0.9–1.5)		1	0.96 (0.7–1.2)	0.97 (0.7–1.2)
Yes	1.63 (0.6–4.06)	0.88 (0.2–3.05)	0.67 (0.1–2.7)		1.09 (0.4–2.4)	2.68 (0.7–9.3)	0.93 (0.2–3.5)		1.82 (0.6–4.8)	1.37 (0.3–4.9)	0.33 (0.07–1.3)		0.93 (0.3–2.6)	1.70 (0.4–7.2)	1.78 (0.4–6.6)
Severe stress				0.997				0.176				0.677				0.379
No	1	0.90 (0.7–1.1)	0.83 (0.6–1.07)		1	0.94 (0.7–1.1)	1.01 (0.8–1.2)		1	1.12 (0.8–1.4)	1.20 (0.9–1.5)		1	0.98 (0.7–1.2)	0.96 (0.7–1.2)
Yes	1.91 (0.8–4.4)	1.00 (0.3–3.3)	10.04 (0.3–3.2)		2.25 (1.08–4.6)	1.34 (0.4–3.9)	0.39 (0.1–1.4)		2.16 (0.9–4.8)	0.62 (0.1–2.1)	1.03 (0.3–3.06)		1.63 (0.6-4.00)	0.78 (0.2–2.9)	1.70 (0.5–5.2)
Insufficient sleep (< 5 h vs. ≥5 h)
Severe anxiety				0.856				0.245				0.020				0.899
No	1	1.23 (0.9–1.5)	1.09 (0.8–1.4)		1	0.72 (0.5–0.9)	0.93 (0.7–1.1)		1	0.93 (0.7–1.1)	0.80 (0.6-1.00)		1	0.92 (0.7–1.1)	1.01 (0.8–1.2)
Yes	1.59 (0.7–3.4)	0.84 (0.2–2.4)	0.70 (0.2–2.3)		1.37 (0.6–2.7)	0.37 (0.07–1.8)	1.48 (0.5–4.08)		0.97 (0.4–2.2)	0.43 (0.08–2.3)	2.96 (1.01–8.6)		1.16 (0.4–2.7)	1.18 (0.3–3.9)	1.31 (0.4–4.1)
Severe depression				0.406				0.877				0.029				0.214
No	1	1.23 (0.9–1.5)	1.10 (0.8–1.4)		1	0.71 (0.5–0.8)	0.94 (0.7–1.1)		1	0.94 (0.7–1.1)	0.88 (0.7–1.1)		1	0.91 (0.7–1.1)	1.02 (0.8–1.2)
Yes	1.19 (0.4–3.4)	0.71 (0.1–3.1)	0.21 (0.02-2.0)		0.81 (0.3–2.07)	0.56 (0.06–5.1)	0.91 (0.2–4.1)		2.19 (0.9–5.1)	0.13 (0.01–1.1)	0.08 (0.01–0.7)		0.26 (0.03–1.9)	5.83 (0.6–53.5)	2.28 (0.2–23.1)
Severe stress				0.825				0.226				0.544				0.835
No	1	1.21 (0.9–1.5)	1.07 (0.8–1.3)		1	0.70 (0.5–0.8)	0.97 (0.7–1.2)		1	0.93 (0.7–1.1)	0.86 (0.6–1.07)		1	0.92 (0.7–1.1)	1.01 (0.8–1.2)
Yes	1.20 (0.4–3.09)	1.43 (0.4–4.9)	1.10 (0.3–3.9)		1.76 (0.8–3.6)	1.17 (0.3–3.5)	0.34 (0.08–1.3)		1.99 (0.9–4.2)	0.60 (0.1–2.02)	0.54 (0.1–1.8)		1.11 (0.4–2.8)	1.44 (0.3–5.2)	1.36 (0.4–4.6)

† Data are odds ratios (95% CIs)

^‡ Adjusted for gender (category), age (category), BMI (category), housing ownership status (category), education status (category), job status (category), smoking status (category), marital status (category), total energy (continuous), physical activity (continuous), duration of cell phone use (continuous) and watching television & movie (continuous)

^* P-values are obtained from binary logistic regression

Table 6.

Odds ratio for “low sleep quality” based on DPs’ score and psychological profile

Variables	Sugar & Fat Diet				Fruits and Vegetables Diet				Western Diet				High animal protein Diet
	Tertile 1†,‡	Tertile 2†,‡	Tertile 3†,‡	P-int*	Tertile 1†,‡	Tertile 2†,‡	Tertile 3†,‡	P-int*	Tertile 1†,‡	Tertile 2†,‡	Tertile 3†,‡	P-int*	Tertile 1†,‡	Tertile 2†,‡	Tertile 3†,‡	P-int*
Low sleep quality
Severe anxiety				0.080				0.880				0.865				0.905
No	1	0.77 (0.4–1.3)	0.95 (0.5–1.7)		1	0.90 (0.5–1.4)	0.66 (0.3–1.1)		1	0.80 (0.4–1.3)	1.05 (0.6–1.7)		1	0.82 (0.4–1.3)	1.02 (0.6–1.7)
Yes	6.32 (2.5–15.6)	2.45 (0.7–8.2)	0.53 (0.1–2.2)		7.05 (3.1–15.6)	1.30 (0.3–4.3)	0.94 (0.2–3.5)		6.27 (2.6–14.6)	1.28 (0.3–4.9)	1.37 (0.4–4.5)		7.90 (3.2–19.4)	1.08 (0.3–3.8)	0.81 (0.2–2.9)
Severe depression				0.390				0.860				0.086				0.356
No	1	0.86 (0.5–1.4)	0.96 (0.5–1.7)		1	0.97 (0.6–1.5)	0.68 (0.3–1.1)		1	0.71 (0.4–1.2)	1.17 (0.7–1.9)		1	0.86 (0.5–1.4)	1.07 (0.6–1.7)
Yes	7.34 (2.5–21.2)	1.34 (0.3–5.5)	0.38 (0.06–2.4)		7.20 (3.04–17.03)	1.09 (0.2–5.2)	0.68 (0.1–3.2)		6.50 (2.2–18.8)	2.58 (0.5–11.3)	0.42 (0.08–2.1)		11.50 (4.1–31.9)	0.60 (0.1–2.8)	0.32 (0.07–1.4)
Severe stress				0.613				0.723				0.449				0.05
No	1	0.89 (0.5–1.4)	0.81 (0.4–1.4)		1	0.91 (0.5–1.4)	0.59 (0.3–1.03)		1	0.85 (0.5–1.4)	1.25 (0.7–2.03)		1	0.93 (0.5–1.5)	1.21 (0.7–2.01)
Yes	3.28 (0.9–11.6)	1.83 (0.3–10.0)	2.22 (0.4-11.05)		5.24 (1.8–14.5)	0.75 (0.1–3.8)	1.47 (0.3–6.5)		7.44 (2.9–18.6)	0.88 (0.1–4.2)	0.37 (0.07–1.7)		13.35 (5.2–34.2)	0.37 (0.07–1.8)	0.12 (0.02–0.6)

† Data are odds ratios (95% CIs)

^‡ Adjusted for gender (category), age (category), BMI (category), housing ownership status (category), education status (category), job status (category), smoking status (category), marital status (category), total energy (continuous), physical activity (continuous), duration of cell phone use (continuous) and watching television & movie (continuous)

^* P-values are obtained from binary logistic regression test

Table 5.

Odds ratios for sleep quality factors based on DPs’ score and psychological profile

Variables	Sugar & Fat Diet				Fruits and Vegetables Diet				Western Diet				High animal protein Diet
	Tertile 1†,‡	Tertile 2†,‡	Tertile 3†,‡	P-int*	Tertile 1†,‡	Tertile 2†,‡	Tertile 3†,‡	P-int*	Tertile 1†,‡	Tertile 2†,‡	Tertile 3†,‡	P-int*	Tertile 1†,‡	Tertile 2†,‡	Tertile 3†,‡	P-int*
Sleep disorders
Severe anxiety				1.000				0.244				0.704				0.264
No	1	0.92 (0.5–1.6)	0.90 (0.4–1.7)		1	0.36 (0.1–0.7)	0.85 (0.4–1.5)		1	0.59 (0.3–1.1)	0.93 (0.5–1.6)		1	1.35 (0.7–2.5)	1.75 (0.9–3.3)
Yes	9.99 (3.9–25.0)	1.01 (0.2–3.7)	1.00 (0.2–3.8)		7.75 (3.4–17.4)	2.87 (0.7–10.9)	0.97 (0.2–3.6)		7.47 (3.02–18.4)	1.76 (0.4–7.1)	1.46 (0.4–5.1)		6.96 (1.9–24.3)	2.40 (0.5–10.6)	0.93 (0.1–4.4)
Severe depression				0.761				0.515				0.217				0.794
No	1	0.95 (0.5–1.6)	1.00 (0.5–1.8)		1	0.42 (0.2–0.8)	0.87 (0.5–1.5)		1	0.76 (0.4–1.3)	1.11 (0.6–1.9)		1	1.75 (0.9–3.2)	1.63 (0.8–3.07)
Yes	10.90 (3.7–31.7)	0.73 (0.1–3.2)	0.53 (0.09–2.8)		7.30 (2.9–18.1)	2.09 (0.4–10.7)	0.72 (0.1–3.4)		15.43 (5.9–40.3)	0.16 (0.01–1.6)	0.40 (0.09–1.6)		9.72 (2.6–35.9)	0.57 (0.09–3.5)	0.93 (0.1–4.6)
Severe stress				0.335				0.221				0.435				0.490
No	1	0.97 (0.5–1.7)	0.99 (0.5–1.8)		1	0.36 (0.1–0.6)	0.77 (0.4–1.3)		1	0.65 (0.3–1.1)	1.14 (0.6–1.9)		1	1.62 (0.8-3.00)	1.84 (0.9–3.4)
Yes	9.37 (3.2–27.0)	0.98 (0.2–4.7)	0.32 (0.06–1.6)		3.56 (1.1–10.9)	4.28 (0.8–22.1)	1.76 (0.3–8.6)		7.79 (2.9–20.4)	1.22 (0.2–6.2)	0.41 (0.08–2.01)		8.24 (2.2–30.7)	1.10 (0.2–6.04)	0.45 (0.08–2.5)
Had to use medication for sleep
Severe anxiety				0.206				0.734				0.486				0.227
No	1	0.71 (0.5-1.00)	0.90 (0.6–1.3)		1	0.81 (0.5–1.1)	0.77 (0.5–1.1)		1	0.76 (0.5–1.07)	0.91 (0.6–1.3)		1	0.79 (0.5–1.1)	1.06 (0.7–1.5)
Yes	3.27 (1.5–7.02)	2.41 (0.8–6.7)	1.19 (0.3–3.7)		3.95 (2.03–7.6)	1.51 (0.5–4.2)	1.19 (0.3–3.5)		4.63 (2.3–9.2)	1.47 (0.5–4.2)	0.73 (0.2–2.1)		2.47 (0.9–6.1)	2.20 (0.6–7.1)	2.67 (0.8–8.3)
Severe depression				0.153				0.839				0.954				0.698
No	1	0.72 (0.5–1.01)	0.91 (0.6–1.3)		1	0.87 (0.6–1.2)	0.80 (0.5–1.1)		1	0.77 (0.5–1.08)	0.89 (0.6–1.2)		1	0.86 (0.6–1.2)	1.12 (0.7–1.6)
Yes	2.51 (0.9–6.8)	3.05 (0.8–10.8)	1.08 (0.2–5.2)		4.58 (2.2–9.4)	0.83 (0.1–3.5)	0.67 (0.1–2.5)		4.30 (1.7–10.6)	1.02 (0.2–3.9)	0.84 (0.2–3.07)		3.78 (1.3–10.4)	0.74 (0.1–3.3)	1.33 (0.3–4.8)
Severe stress				0.719				0.698				0.704				0.188
No	1	0.77 (0.5–1.09)	0.92 (0.6–1.3)		1	0.81 (0.5–1.1)	0.78 (0.5–1.1)		1	0.79 (0.5–1.1)	0.94 (0.6–1.3)		1	0.83 (0.5–1.1)	1.23 (0.8–1.7)
Yes	5.23 (2.3–11.6)	1.25 (0.3-4.00)	0.74 (0.2–2.5)		5.75 (2.7–12.1)	1.04 (0.3–3.2)	0.62 (0.1–2.1)		6.02 (2.8–12.6)	0.93 (0.2–3.07)	0.61 (0.1-2.00)		5.43 (2.3–12.7)	1.61 (0.4–5.2)	0.52 (0.1–1.8)
Delay in falling asleep
Severe anxiety				0.332				0.985				0.538				0.373
No	1	0.96 (0.7–1.2)	1.02 (0.7–1.3)		1	0.71 (0.5–0.8)	0.85 (0.6–1.07)		1	0.93 (0.7–1.1)	0.82 (0.6–1.03)		1	1.01 (0.8–1.2)	0.99 (0.7–1.2)
Yes	3.21 (1.7–5.8)	1.14 (0.4–2.6)	0.57 (0.2–1.4)		2.93 (1.7–4.9)	0.92 (0.3–2.2)	0.94 (0.3–2.2)		2.56 (1.4–4.6)	0.92 (0.3–2.3)	1.48 (0.6–3.4)		3.02 (1.5–5.8)	1.28 (0.5–3.06)	0.67 (0.2–1.7)
Severe depression				0.724				0.805				0.105				0.523
No	1	0.94 (0.7–1.1)	0.99 (0.7–1.2)		1	0.72 (0.5–0.9)	0.86 (0.6–1.08)		1	0.93 (0.7–1.1)	0.88 (0.7–1.1)		1	1.03 (0.8–1.2)	0.98 (0.7–1.2)
Yes	2.97 (1.4–6.2)	1.47 (0.5–3.9)	1.12 (0.3–3.2)		3.65 (2.04–6.5)	1.11 (0.3–3.2)	0.77 (0.2-2.00)		6.17 (3.04–12.5)	0.65 (0.2–1.8)	0.33 (0.1–0.9)		4.20 (2.04–8.6)	1.07 (0.3–2.9)	0.63 (0.2–1.7)
Severe stress				0.937				0.090				0.339				0.518
No	1	0.9 (0.7–1.2)	0.9 (0.7–1.2)		1	0.66 (0.5–0.8)	0.83 (0.6–1.05)		1	0.95 (0.7–1.1)	0.88 (0.7–1.1)		1	1.02 (0.8–1.2)	0.97 (0.7–1.2)
Yes	5.11 (2.6–9.8)	0.94 (0.3–2.3)	1.10 (0.4–2.6)		3.95 (2.1–7.2)	2.40 (0.9–5.8)	1.00 (0.4–2.4)		7.47 (4.06–13.7)	0.61 (0.2–1.5)	0.53 (0.2–1.2)		4.93 (2.5–9.5)	1.38 (0.5–3.5)	0.82 (0.3–2.03)

† Data are odds ratios (95% CIs)

^‡ Adjusted for gender (category), age (category), BMI (category), housing ownership status (category), education status (category), job status (category), smoking status (category), marital status (category), total energy (continuous), physical activity (continuous), duration of cell phone use (continuous) and watching television & movie (continuous)

^* P-values are obtained from binary logistic regression test

As the Table 4 depicts, severe stress was linked to a greater likelihood of short sleep duration in those with the lowest adherence to the “western” DP (P value: 0.047, OR = 2.16, 95% CI: 1.003-4.6). Furthermore, in individuals with the lowest adherence to the “fruits and vegetables” DP, severe stress was linked to a greater risk of long sleep duration (P value: 0.029, OR = 2.25, 95% CI: 1.08–4.6). Participants with the highest imitation to the “western” DP and with severe anxiety had a considerably greater chance of having “short sleep duration” (P for interaction: 0.030) and “insufficient sleep” (P for interaction: 0.020) than did those with the lowest adherence and no anxiety.

However, it was discovered that subjects in the “western” DP’s top tertile and with severe depression showed significantly lower odds of having “short sleep duration” (P for interaction: 0.029) and “insufficient sleep” (P for interaction: P = 0.029) than individuals with the lowest imitation of this DP and without severe depression.

Those with the highest score on the “high animal protein” DP and with severe anxiety had a significantly increased risk of “long sleep duration” compared to those with the lowest score and without anxiety (p for interaction: 0.044).

As Table 5 shows, severe anxiety, depression, and stress were accompanied by greater odds of sleep disorders in subjects with the lowest adherence to each of the four study DPs (all p < 0.05).

Moreover, severe anxiety was associated with a greater likelihood of utilizing medication for sleep in people with the lowest imitation to “sugar & fat” (P value: 0.002, OR = 3.27, 95% CI: 1.5–7.02), “fruits and vegetables” (P value < 0.001, OR = 3.95, 95% CI: 2.03–7.6), and “western” (P value < 0.001, OR = 4.63, 95% CI: 2.3–9.2) DPs. Additionally, severe depression was related to a higher chance of using medication for sleep in those with the lowest adherence to “fruits and vegetables” (P value < 0.001, OR = 4.58, 95% CI: 2.2–9.4), “western” (P value: 0.002, OR = 4.30, 95% CI: 1.7–10.6), and “high animal protein” (P value: 0.01, OR = 3.78, 95% CI: 1.3–10.4) DPs. Moreover, severe stress was linked to higher odds of using medication for sleep in individuals with the lowest imitation of each DP (all p < 0.05).

It was observed that all severe anxiety, depression, and stress were associated with a greater chance of delay in falling asleep in subjects with the minimum imitation score in each study DP (p < 0.05). Notably, severe depression was significantly accompanied by a reduced risk of delay in falling asleep in the “western” DP’s top tertile (P value: 0.035, OR = 0.33, 95% CI: 0.1–0.9).

According to Table 6, regarding the findings on the low sleep quality parameter, severe anxiety and severe depression were related to a greater risk of low sleep quality in people with the lowest adherence to “each of the study DPs” (all p < 0.05). In addition, severe stress was related to increased odds of low sleep quality in those with the lowest imitation of “fruits and vegetables” (P value: 0.002, OR = 5.24, 95% CI: 1.8–14.5), “western” (P value < 0.001, OR = 7.44, 95% CI: 2.9–18.6) and “high animal protein” (P value < 0.001, OR = 13.35, 95% CI: 5.2–34.2) DPs. Participants with the highest adherence to the “high animal protein” DP and severe stress had significantly lower odds of having “low sleep quality” than did those without stress and the lowest adherence to the diet (P for interaction: 0.05). There were no additional links between DPs and PDs in terms of sleep quality and duration.

Discussion

As far as we are concerned, the current cross-sectional study provides the primary analytical evidence about the important impact of interaction between major DPs and PDs in association with sleep quality and quantity. Our main findings revealed that the highest imitation of the “western” DP may interact with severe anxiety in relation to the higher risk of “short sleep duration” and “insufficient sleep”. However, possible interactions were detected between the top tertile of the “western” DP and severe depression in association with reduced odds of “short sleep duration” and “insufficient sleep”. Furthermore, the “high animal protein” DP’s top tertile and severe anxiety were shown to be interacted in relation to the higher chance of “long sleep duration”. In addition, an interaction was observed between the top tertile of the “high animal protein” DP and severe stress in association with the lower odds of “low sleep quality”.

Sleep is one of the foundational health cycles, the quality and quantity of which can be bidirectionally impacted by various aspects of the human psychological state [25, 26]. Diet is also closely related to brain plasticity and function, systemic inflammation, and oxidative stress, all of which have been linked to the development of PDs [13]. Hence, the importance of preventive strategies such as changing lifestyle behaviors and eating patterns in the development of PDs has become a significant point of interest for healthcare researchers [27].

In participants with severe anxiety, our results revealed that level of imitation to the “western” DP was related to a greater likelihood of short sleep duration and insufficient sleep. However, this interaction was shown to be inverse in those with severe depression. In line with our results, there is accumulating evidence relating to the increased chance of anxiety and depression in people with higher dietary intake of meat, French fries, soft drinks, chocolate, and fried potatoes [14, 28]. The latest research on more than 140,000 people during an 11-year period revealed that western DP with frequent fried food consumption, specifically French fries, was connected to an elevated likelihood of depression by 7% and anxiety by 12%. Scholars have reported that long-term acrylamide exposure may be a significant factor in the development of anxiety and depression symptoms due to the following trajectories: (a) neuroinflammation caused by oxidative stress and (b) acrylamide-induced lipid metabolism disturbance facilitated by the PPAR signaling pathway in the brain [29]. In agreement with our findings, the scientists have recently found that a western DP consisting of a large amount of confectionery and desserts, pizza, energy drinks, snacks, red meat, mayonnaise, and coffee was strongly linked to short sleep duration [30]. Earlier investigations have also shown a reciprocal relationship between eating unhealthy calorie-rich foods and inadequate sleep duration [31]. It appears that the underlying mechanisms might be triggered by alterations in food preferences due to restricted sleep duration [30]. According to the results of previous experiments on insufficient sleep periods, greater stimulation of the insular cortex, orbitofrontal cortex, and dorsolateral prefrontal cortex, which are specific food-related brain areas, has been reported as a result of poor dietary habits rather than nutritious meals [32, 33]. Moreover, dysregulation of hunger-related hormones, such as reduced leptin and elevated ghrelin, has been detected in individuals with sleep deprivation, which may cause further consumption of food from carbohydrate sources [34].

Notably, the difference in the current interaction results between subjects with anxiety and/or depression might be mediated by the nature of the depression disorder. Although both diseases are predominantly accompanied by insufficient sleep, hypersomnia is considered an important subtype feature of depression [35, 36]. Furthermore, longer sleep duration can lead to more fragmented sleep and interrupted sleep via numerous breaks of wakefulness [37], which has been shown to be linked with greater intake of calorie-dense meals [38].

In the present study, it was observed that a “high animal protein” DP may result in higher odds of long sleep duration and higher sleep quality in subjects with severe anxiety and severe stress, respectively. A greater susceptibility to impaired sleep issues resulting from experiencing anxiety and psychological stress has been highlighted in previous investigation [39]. Incidentally, convincing evidence suggests that sleep quality is directly connected with dietary protein consumption [40]. Also, it has been established that appropriate dietary protein consumption may be essential for mental health maintenance due to the production of important neurotransmitters in the brain [41]. Incidentally, Haghighatdoost et al. [42] reported that there may be benefits in terms of psychological distress, anxiety, and depression from eating a diet higher in protein and lower in refined carbohydrates. Moreover, it has been documented that a high-protein low-fat DP during a short-term weight loss period may have further beneficial effects on alleviating mood disturbance, fatigue, and stress than a moderate-protein moderate-fat DP in male weightlifters [43]. Multiple pathways could account for such associations. According to previous experiments, amino acids derived from dietary proteins, such as lysine, arginine, beta-alanine, tryptophan, and tyrosine, have been found to act as potential anxiolytic agents [44]. In addition, tryptophan and tyrosine are the precursors of serotonin and dopamine, which are the foremost brain neurotransmitters involved in the psychological improvement and regulation of sleep rhythmicity [45, 46].

Based on the current findings, a favorable interaction effect between greater imitation of “high animal protein” DP and psychological stress in relation to sleep quality can be speculated. However, as both short and long sleep durations may have bidirectional relationships with PDs, the interactions related to the “western” and “high animal protein” DPs should be interpreted with caution.

The main strength of the present investigation is that we studied a large number of participants and considered a number of possible confounders when analyzing the data. Nevertheless, the following limitations should be addressed when interpreting our findings. First, there might be more accurate and comprehensive questionnaires for detecting people’s psychological state than DASS-21, such as DASS-42 or duke-anxiety-depression-scale. Moreover, as a result of the cross-sectional methodology of the current study, causal and temporal effects are impossible to discern. Additionally, FFQs are susceptible to measurement inaccuracy, misclassification, and recall bias.

Conclusion

Our results demonstrated that the “western” DP may interact with severe anxiety in relation to the higher chance of short sleep duration and insufficient sleep. However, “western” DP may interact with severe depression in association with lower chance of these sleep disturbances. Additionally, it was detected that “high animal protein” DP may interact with severe anxiety and severe stress in relation to the higher odds of long sleep duration and lower chance of low sleep quality, respectively. Robust investigations are needed in the future to corroborate our findings.

Electronic supplementary material

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Author contributions

S.SH: Original draft, Data curation, Formal analysis, Investigation, Software, Visualization, Review & Editing. F.M: Methodology, Formal analysis, Validation, Visualization, Review & Editing. M.H: Supervision, Data curation, Formal analysis, Visualization, Review & Editing. M.MO: Methodology, Data curation, Validation, Visualization, Review & Editing. M.MI: YaHS founder, Methodology, Data curation, Validation, Visualization, Review & Editing. H.KB: Methodology, Data curation, Validation, Visualization, Review & Editing A.SA: Conceptualization, Supervision, Data curation, Formal analysis, Methodology, Project administration, Validation, Visualization, Original draft, Review & Editing.

Funding

The current study was funded by the Shahid Sadoughi University of Medical Sciences.

Data availability

The datasets used and/or analyzed during the present study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval

The procedure of this project has been approved by the ethics committee of Shahid Sadoughi University of Medical Sciences, Yazd, Iran (ethics code: IR.SSU.SPH.REC.1400.111). Informed consent was received from all participants. Participation in the study was voluntary, as individuals could refuse to participate at any time during the investigation. All methods were performed in compliance with the relevant guidelines and regulations in practice.

Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.