Introduction

Obesity and overweight are influential factors in today’s world, such that the prevalence of overweight and obesity in low-income countries, as well as in low-income groups in high-income countries, has steadily increased over the past century. In 2016, worldwide, 13% of adults over the age of 18 were reported to be obese and 39% to be overweight [1, 2]. Obesity, as a metabolic disease, is a major risk factor for other metabolic diseases such as coronary heart disease, ischemic stroke, and type 2 diabetes, and the physiological and psychological consequences of obesity, causes greater burden on health systems by disrupting people’s quality of life [3,4,5]. In the etiology of obesity, factors such as genetics [53]. The questionnaire included questions in 5 areas of activity: job-related physical activity; Physical transport activity; Activities for housework and house maintenance; Recreation, exercise and physical activity in leisure time; And the time spent sitting the participant’s pants were asked to think about all the intense and moderate activity they had done in the past 7 days, taking into account the time spent on these activities. It was classified as: low < 600 (MET-h / wk), moderate = 600–3500 (MET-h / wk) and severe > 3500 (MET-h / wk)[54]. A demographic questionnaire was used to collect information on age, marital status, and family history of obesity and overweight, which was defined as BMI ≥ 30 kg/m2 and 25 ≤ BMI ≤ 29.9 kg/m2, respectively [55].

Statistical analysis

The distribution of data was investigated using the Kolmogorov-Smirnov test. Quantitative data were reported as mean and standard deviation and qualitative data were expressed as number and percentage, respectively. We used principal component analysis (PCA) to identify the main dietary patterns based on 31 food groups, based on scree plot inspection and eigenvalues over 1.5. Varimax rotation was used to achieve a simple matrix with better ability to interpret and extract dominant dietary patterns. According to previous studies, and because of the nature of the data and correlations, values with a load factor greater than 0.58 were considered to determine the items of each food pattern. Factor loads illustrate correlation coefficients between food groups and dietary patterns, and a positive charge in a factor indicates a direct relationship with the agent, while a negative charge indicates that the food is inversely related to the agent. Dietary patterns were labeled based on the researchers’ interpretation of the data, and participants were classified based on the mean scores of healthy, unhealthy, which resulted in low and high intakes. Independent t-tests were performed to assess differences in participants’ general characteristics (such as age, anthropometry, and physical activity) in the dietary patterns and depression. The distribution of qualitative variables in the groups was assessed using Chi-square test. To determine the relationship between main dietary patterns and MC4R gene and depression, logistic binary regression was performed in crude and adjusted models. Adjustments were made for age, economic status, night sleep status, a history of weight loss, and physical activity. In all multivariate models, the low adherence of dietary patterns score was considered as reference. Comparison of quantitative variables between quartiles of dietary pattern or genotype was performed using one-way analysis of variance (ANOVA) and analysis of covariance (ANCOVA). The interaction between dietary pattern and genotype on quantitative variables was assessed using linear regression model analysis. All statistical analyses were performed using SPSS software (version 23; SPSS Inc, Chicago IL), with statistical significance accepted, a priori, at P < 0.05.

Result

Study population characteristics

In this cross-sectional study, the demographic characteristics of individuals are reported in Table 1. The means and standard deviation (SD) of age, weight, and BMI of individuals were 36.52 (8.32) years, 78.75 (11.51) kg, and 30.33 (3.65) kg/m 2, respectively. The frequencies of T and C alleles of rs17782313 were 41.8% and 58.2%. The overall prevalence of rs17782313 genotype was 29.4%, 24.8%, and 45.7% for TT, TC, and CC, respectively. Among the participants in terms of marital status, 27.3% were single and 72.6% were married. The majority of study population (99.3%) were employed, and 22.1% were economically weak, 44.2% were moderate, and 33.5% had good economic condition. Among the participants in terms of depression, 53.2% were normal, 13.3% were mild, 17.9% were moderate, 9.3% were severe, and 6.1% were very severe.

Table 1 Characteristics of study participants (n = 289)
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Association between biochemical parameters, body composition among rs17782313 genotype

The general characteristics of the participants among the MC4R genotype are presented in Table 2. A total of 289 Iranian women were categorized based on rs17782313 genotype, and divided into three groups: TT genotype (n = 96), TC genotype (n = 96), and CC genotype (n = 97). Although there was a significant marginal difference in educational status (P = 0.08), among other variables, no significant relationship was observed with MC4R gene variants, even after adjustment for BMI, age, total energy intake, and physical activity (P > 0.05).

Table 2 Characteristics of participants among MC4R gene genotypes (n = 289)
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Association between dietary intakes among rs17782313 genotype

The food group and nutrient intakes according to MC4R rs17782313 genotype are shown in Table 3. The results of this study, after controlling for confounding factor (energy intake), shows that those with CC risk alleles consumed significantly less B carotene than those with TT and TC alleles. Among all micronutrients, only a significant correlation was shown with B carotene (P = 0.05), and this correlation remained significant after adjusting for energy intake (P = 0.03). Among other variables, no significant relationship was observed with MC4R gene varieties and dietary intake, even after adjusting the total energy intake (P > 0.05).

Table 3 Dietary intake in individuals with MC4R gene variant (rs17782313) (n = 289)
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Factor loadings of food groups in identified dietary patterns

Table 4 shows the factor loadings of food groups in identified dietary patterns. Nutrient patterns were derived using PCA with varimax rotation and based on the correlation matrix. Factor load values less than 0.2 have been omitted and the Kaiser—Meyer—Olkin measure of sampling adequacy (KMO) was 0.558. 31 Food group were selected for factor analysis, including vegetables, tomato, low fat dairy, nuts ,garlic ,starchy, fruit, walnut ,fish, banana, red Meat, high energy drink, chicken, nuts ,processed food, high-fat dairy, beans, grain and spices. Factor scores for all participants for each of the extracted factors were calculated by summing the frequency of consumption, multiplied by factor loadings across food groups. By using the factor analysis method and according to the Scree Plot chart review, two dominant dietary patterns were identified in the subjects (Fig. 1). Patterns were named based on food groups as the healthy pattern and unhealthy pattern. The healthy eating pattern included vegetables, tomatoes, low-fat dairy, nuts, garlic, starchy vegetables, fruits, walnuts, fish, bananas, dried red meat, cereals, spices, and legumes (percentage variance = 9.73). The UDP included red meat, high energy drinks, chicken, processed foods, and high fat dairy (percentage of variance = 6.28).

Table 4 Factor loadings of the food groups in the main dietary patterns extracted (n = 289)
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Fig. 1
figure 1

Scree plot of the nutrients and the extracted principal components in overweight and obese women

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Association between biochemical parameters, body composition among adherence of dietary patterns

The general characteristics of the participants among the food groups extracted are presented in Table 5. All participants were divided into two groups based on the healthy and unhealthy food patterns extracted. For the healthy dietary pattern, there was a significant difference in height (P = 0.01), FBS (P = 0.05) and FFM (P = 0.01) in the crude model. After adjusting BMI, age, total energy intake and physical activity, Body weight (P = 0.04), Job (P = 0.03) and educational status (P < 0.001) showed a significant difference. In this regard, the economic status (P = 0.004) maintained its significance after adjusting for confounding factors. There was no significant difference for the other variables in Table 5 compared to the HDP (P > 0.05). But, in terms of following an unhealthy dietary pattern, in the crude model, there was a significant difference with height (P = 0.009), Economic (P = 0.01) and marital status (P = 0.03). After adjusting for BMI, age, total energy intake, and physical activity, a significant difference was observed between groups for HC (P = 0.03). In this regard, Age (P < 0.001) and physical activity (P = 0.01) remained significant after controlling for potential cofounders.

Table 5 Characteristics of participants among dietary patterns (n = 289)
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Interaction between dietary patterns and MC4R gene variants on depression

Interaction between dietary patterns and MC4R gene variants and the effect on depression is shown in Table 6. Using the Generalized linear Model (GLM), the interaction between MC4R polymorphism (rs17782313) and dietary patterns on depression was examined. No Significant interactions were observed between UDP score and rs17782313 SNP in the crude model for people with CC (OR: 0.60, 95%CI: -1.22-2.43, P: 0.51) and CT (OR: 0.95, 95%CI: -1.14-3.06, P: 0.37) genotypes on depression. In adjusted model, participants with the CC genotype, who had the highest adherence to the UPD score, were more likely to develop depression, after controlling for confounders (age, economic status, Night sleep status, a history of weight loss and physical activity) (OR: 8.77, 95%CI: -0.86-18.40, P: 0.07) (Fig. 2). Although the interaction of CT genotype with UDP in the crude model was not significant on depression (OR: 0.95, 95%CI: -1.14-3.06, P: 0.37), a positive logical relationship with depression in was observed adjusted model (OR: 3.78, 95% CI: -5.88-13.45, P: 0.44), after controlling for confounding factors. Regarding the interaction of MC4R gene and HDP on depression (Fig. 3), logical inverse relationships were observed between CT genotype and HDP on depression in both crude and adjusted models (OR: -0.56, 95% CI: -3.69-2.57, P: 0.72) (OR: -4.17, 95% CI: -9.28-0.94, P: 0.11), although this interaction was not statistically significant in both models. In individuals with CC genotype, there was no significant relationship between depression and increased adherence to a HDP in both models.

Table 6 Interaction of MC4R gene (rs17782313) and healthy and unhealthy dietary patterns on depression in obese and overweight women (n = 289)
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Fig. 2
figure 2

Interaction between MC4R genotypes (TT as the reference group) and UDP on depression (The P value for CC genotype: 0.91; P value for UDP: 0.15; P value for interaction between CC genotype and UDP: 0.07)

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Fig. 3
figure 3

Interaction between MC4R genotypes (TT as the reference group) and HDP on depression (The P value for CC genotype: 0.91; P value for HDP: 0.277; P value for interaction between CC genotype and HDP: 0.94)

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Discussion

The aim of this study was to determine the relationship between dominant dietary patterns and MC4R with depression in overweight and obese Iranian adults. After analyzing the variables of dominant dietary patterns by method, unhealthy and healthy dietary patterns were obtained. In the unhealthy dietary pattern, consumption of food items, such as eat red meat, high-energy drinks, chicken, nuts, processed foods, and high-fat dairy was higher. In contrast, in the healthy dietary pattern, people were more likely to consume vegetables, tomatoes, fruits, garlic, starchy vegetables, fruits, nuts, fish, bananas and legumes. The hypothesis of this study regarding the effect of dietary patterns on mental health was that greater adherence to a HDP would be associated with lower odds of develo** depression. In this study, the relationship between dominant dietary patterns and MC4R with the likelihood of depression, after adjusting for the effects of confounders such as age, economic status, night sleep status, a history of weight loss, and physical activity, was seen with increasing adherence to unhealthy dietary pattern. In this investigation, in adjusted model, participants with the CC genotype who had the highest adherence to the UPD score were more likely to develop depression, but no significant relationship was observed in the crude model. On the other hand, in the present study, data analysis showed that HDP in this study was not significantly associated with the score of depression in crude and adjusted models. Also, the HDP was not significantly associated with the odds ratio of depression in the crude and adjusted models. However, the relationship between following a HDP and lower odds ratio of depression in the adjusted model in people with CT alleles was close to significant and in both models, where an inverse relationship with CT gene and consumption of HDP with depression was observed.

So far, various studies have been conducted pertaining to the relationship between diet and the incidence of depression. Kim et al. found that the “Western” dietary pattern, which consisted of refined grains, white potatoes, cheese, meat, oils and fats, and high sugar, was significantly associated with depression in both sexes. Moreover, a healthy dietary pattern, consisting whole grains, fruits, vegetables, fish, nuts, and seeds was inversely associated with depression (OR) in women [56]. A study by Maryam Khosravi et al., performed on 330 depressed patients (case) and healthy individuals (control) (1: 2), found two main dietary patterns using factor analysis. These patterns were an unhealthy dietary pattern, including high-refined grains and breads, high-fat dairy, solid oils, liquid oils and mayonnaise, pickles, snacks, soft drinks, fruits and industrial juices, red meat, poultry, processed meats, and sweets, in addition to a healthy dietary pattern, which included fruits, cruciferous vegetables, yellow, green leaves and other vegetables, low fat dairy products, whole grains, nuts, and olives. Accordingly, findings from this study suggest that healthy and unhealthy dietary patterns may be associated with a higher risk of depression [24]. A prospective study in 2011 revealed a significant dose-dependent and inverse relationship between PUFA and MUFA intake and depression, and participants with the highest trans-FA intake showed significantly higher risk of depression [57]. Furthermore, sugar consumption has also been linked to depression [58]. Possible mechanisms of unhealthy dietary patterns include the presence of more processed foods which are linked to an increased risk of cardiovascular disease, as well as increased inflammation[59, 60], both of which are involved in the pathogenesis of depression [61, 62]. In people with major depressive disorder, the level of proinflammatory cytokines increases, and cytokines can affect the incidence of depression by altering neurotransmitter metabolism, endocrine nerve function, and regional brain activity [63]. Dietary folate deficiency, on the other hand, can reduce access to S-adenosylmethionine, which is responsible for methyl transport in the body, thereby disrupting the formation of myelin, neurotransmitters, and membrane phospholipids by increasing the risk of depression [34].

There are several studies pertaining to the relationship between healthy dietary patterns and depression. For instance, a cohort study of 3,523 people in France found that greater adherence to the Mediterranean dietary pattern in mid-life was associated with a reduced risk of develo** depressive symptoms, especially in men [64]. A study by Skarupski et al. also found that following a diet high in vegetables, fruits, whole grains, fish, and legumes could have a protective effect against the development of depressive symptoms at older ages [65]. In a study of 64 girls, aged 17 − 15 years with normal body mass index (BMI), the relationship between walking exercise and banana consumption was examined for depressive symptoms, where banana consumption, alone and with exercise, was significantly associated with lower depression score than the control group [66].

Previous studies in this field have been limited to investigating the relationship between the dominant food intake pattern and mental disorders [25, 67,68,69,70]. They have also evaluated the interaction of MC4R and different diets on different outcomes (metabolic syndrome, diabetes and obesity) [71, 72]. According to a contemporary approach to nutrition epidemiology, where the role of mediators between diet and genes is considered, we, for the first time in Iran and the world, discerned the interaction between the dominant food intake pattern and MC4R 17782313rs on mood disorders (depression). A study examining the interaction of MC4R rs17782313 with mental stress and energy intake and the risk of obesity found that interactions of mental stress and energy intake with the MC4R minor allele genotype may be associated with an increased risk of obesity in Korean adults [44]. Another study showed that the relationship between MC4R 17782313rs polymorphism and type 2 diabetes depends on diet. In fact, people with a C-risk allele had a lower risk of type 2 diabetes with less adherence to the Mediterranean dietary pattern, whilst people with a high adherence to the Mediterranean dietary pattern had a lower risk of type 2 diabetes [73]. Yilmaz et al. (2015) examined the association between MC4R rs17782313 polymorphism of overeating and depression, and found that BMI was associated with the rs17782313 C allele. Moreover, the authors also noted that rs17782313 was significantly associated with depression and overeating behaviors, and that MC4R leads to weight gain and BMI via depression and overeating behaviors [42]. In the present study, a significant relationship was observed between individuals with CC genotype and consumption of an UDP with depression, while this association was not observed among individuals with CT genotype, despite an increase in OR. This may be because when a dangerous allele is placed next to a harmless allele, they behave differently than when two dangerous alleles are placed next to each other. Also, no significant relationship was observed between MC4R gene interaction and HDP and depression, but a logical inverse relationship was observed between CT genotype and HDP on depression. This may be because most people in the study chose an UDP as their dominant diet, and therefore, the interaction between the MC4R gene and an UDP is more closely linked to depression.

Several limitations must be considered when interpreting the results of the present study. Indeed, the cross-sectional design precludes causal inferences into the interaction between dietary intake and depression in individuals at risk for the MC4R gene allele. Another limitation is that the FFQ for evaluating the diet for accurate reporting is dependent on people’s memory and can be reported incorrectly. Further, based on the study population, this study is only generalizable to overweight and obese women, so it cannot be used for men and women with normal weight. Future research should take into account another limitation—depression-induced changes in eating behavior.

Conclusion

Overall, these findings underline the importance of the UDP in the alarming prevalence of the depression in develo** countries. Although the theoretical models of genetic-dietary patterns-depression interactions clearly need a more empiric foundation, the evidence from this study putatively shows a direct link between the incidence of depressive for people with CC alleles of MC4R gene and greater adherence to an unhealthy dietary pattern. The findings of this study show that the interaction of MC4R variants between individuals and high consumption of UDP can play an important role in the development of depression.