Examine the association of nutrients,lifestyle,and related factors with the risk of depression in a well-nourished over-55-years old community

Abstract

BACKGROUND:

The older adult population is increasing, and depression is commonly observed within this community.

OBJECTIVE:

Examine the association of nutrients and lifestyle with depression in a well-nourished over-55-years old community.

METHOD:

The risk of depression was evaluated by the Beck Depression Inventory. Lifestyle, health-related quality of life, and physical activity were assessed. Dietary nutrient intake was recorded and adjusted to body weight.

RESULT:

58% of the population had mild to severe depression. The differences between depression groups for age, gender, quality of life, and nearly all social-economic factors were significant. The impact of age, physical activity, sports, economic level, and smoking on depression was independent of all other factors. Most adjusted nutrients and energy intake were inversely associated with depression (protein (p = 0.043), fiber (p = 0.037), iron (p = 0.041), vitamin B6 (p = 0.011), and caffeine (p = 0.009) was independent of the energy intake). The predictor decision tree model for depression showed working in males and having sport, economics, and living with someone in females were the main depression predictors.

CONCLUSION:

Working for males and sports in females along with a high-quality lifestyle with a diet rich in protein, fibre, iron, vitamin B6, and caffeine were associated with a lower risk of depression in this population.

Keywords

Depression lifestyle nutrition prevalence ageing

1 Introduction

Depression is known as a complex mental condition. It is a treatable disorder that can negatively affect how people feel, think, and act [1 –3]. Evidence suggests depression significantly lowers people’s performance during their day, which can negatively affect both quality of life and health [1 –3]. Depression can occur at any age, but it is more common among teenagers and older adults [1, 2].

The relationship between depression and its risk factors is bi-directional [1, 3]. In other words, the risk factors can elevate the risk of depression, and the resulting depression can affect its factors. In return, The affected factors increase the severity of the depression [1, 3]. Some factors that can cause or exacerbate depression are ageing, social-economic status, lifestyle quality, and environmental and personal factors like low self-esteem, experiencing violence, neglect, abuse, or poverty [4, 5]. Additionally, previous literature has demonstrated the importance of overall dietary intake on the risk of depression [6, 7]. However, none of these findings has resulted from a well-nourished nearly healthy population that can have differences.

Despite the range of works on depression, the lack of sufficient studies that assess these factors in a healthy retirement-age population is counting. There is low consideration to assess depression in healthy people that made mild to moderated levels of this disease undiagnosed for years [8]. Retirement age is the time of change into an unscheduled lifestyle with experiencing the first major health issues even in healthy adults [8]. Some of these experiencing issues during retirement are close to the risk factors of depression that can be linked to the high prevalence of depression and severe depression in older adults [6 –8]. Considering this, studying depression and its risk factors in these healthy populations can have a meaningful impact on early diagnosis and controlling this disorder in geriatrics.

To the authors’ knowledge, there is no current data on the prevalence of depression in the over 55 years old community-dwelling, well-nourished elderly healthy population. In addition, the predictors of depression to prevent or early diagnosis of depression in this population are still theoretical. To address these gaps in the literature, the authors performed this study to elucidate the effect of social-economic status, lifestyle, and nutrient intake on depression in healthy older adults to draft an early-stage preventive strategy.

2 Material and method

2.1 Data collection

The data was collected in two assessment centres; one at the Department of Nutrition clinical assessment institute in Varastegan Institute for Medical Sciences and the other at a private nutrition clinic in Mashhad. This study was performed in collaboration with the Nutrition Department of Varastegan Institute for Medical Sciences, the Welfare Organization of Khorasan Razavi Province, and the Khorasan Razavi Retirement Association.

2.2 Population

The sample size was estimated by assuming the prevalence of depression to be 50% among the retired population in Mashhad with a confidence level of 95% (margin error: 3.5%). The data collection was performed through a random cluster sampling method in municipal areas of Mashhad-Iran. The inclusion criteria were the age of 55 or higher, currently living in one of the 13-municipal areas of Mashhad, settled in Mashhad during the last 20 years, and having no physical (disability to walk or stand), mental and psychological health disorders (approved depression, eating disorders, Alzheimer, and personality disorders), chronic diseases (cancer, heart disease, chronic kidney disease, hypertension grade 2 or more, diabetes, and arthritis), and hospitalization during the last year. Excluding criteria were diagnosis of malnutrition using Mini Nutritional Assessment–Short Form (MNA^®-SF) and having high risk of chronic diseases according to the clinical assessments by an expert decision. This exclusion was made to ensure all the populations included in the current study are as healthy as possible. All the individuals used an online registration system to affirm their eligibility prior to attending the study.

2.3 Questionaries

Demographic data (e.g., age, gender, sleep, living with, marital status, education, working situation) was recorded, and a short form of the Beck Depression Inventory (BDI-13) which was adjusted for the Iranian population with high sensitivity, was used, for assessment of the depression [9]. BDI has been shown to be a good instrument for surveying depression in community-based studies [10]. According to the BDI-13 questionary with 0 to 39 scores, people with a score less than 4 are non-depressed (control), and with scores of 5 to 7, 8 to 15, and more than 15 are categorized as mild, moderate, and severe depression, respectively. The Medical Outcomes Study Health Survey Questionnaire 36-Item Short Form (SF-36), measures eight health-related quality of life domains, including physical functioning, role limitation because of physical problems, body pain, general health, vitality, social functioning, role limitation because of an emotional problem, and mental health by twelve criteria was used [11]. All the criteria scored between zero and 100, and the reported health score was between –2 to +2. The financial status of each individual was determined according to the family’s monthly income in comparison with the regular expenses for an ordinary family in Iran by the time of assessment that the Iran National Bank published. In addition, International Physical Activity Questionnaire (IPAQ) was used to classify the individuals’ physical activity [12]. Weight, basal metabolic rate (BMR) and body mass index (BMI) were measured during the assessment using an Inbody 270 made in Japan in 2018.

2.4 Nutritional intake assessment

Some general questions were recorded regarding the usual kind and frequency intake of grain, dairy, vegetable, fruit, and meat, meals per day, special diet, or allergies. Then, according to the recorded data, the nutritional intake of each individual was recorded using the one-day recall method by a professional registered nutritionist and dietitian (RDN). The recall was recorded based on a usual working day for people who did not have a significant change in dietary habits on days of a week and according to the general questions. For people who had a change in their intake, a 3-day recall (2 working days and 1 holiday) was recorded along with the 1-day recall. This method is a standard, short-time consumption method to evaluate the nutritional intake of a healthy population with no specific dietary intake change under special situations which was considered in this study [13, 14]. In this method, each individual was asked about the all-oral intakes of the last 24 hours, including all foods, drinks, and medicines during breakfast, lunch, dinner, before sleep, and the space between each. Furthermore, at the end of the nutrition recording, the individuals were asked again about their regular food consumption during the last month by a second nutritionist based on the first general question sheet. If any difference were found, the changes were applied by an experienced registered nutritionist to cover the biases and weaknesses of this method.

After observing the dietary recalls, each individual’s nutrient intake was observed using software designed for this purpose by summing the calculated nutrients of each food. The nutrients of each food were calculated based on the kind of food, the nutrient composite of each food, and the gram of consumed food using the same software. The recalls were analyzed using the United States Department of Agriculture (USDA) food composite database, which was updated in December 2019 (https://fdc.nal.usda.gov) [15]. For checking the validity of the recalls and under and over reporting, the calculated energy, protein, Iron, calcium, and fibre intake was compared to the biological needs of each individual according to their total energy expenditure (TEE), and the result of the recorded general questions. If the difference between the data and the biological need was within standard range, and the trend of intake was similar between the recall and general questions the data was considered valid.

After calculating the nutrients and checking their validity, the dietary intakes of nutrients according to the 1-day recall were divided into individuals’ weights (nutrients weight ÷ body weight) collected during the assessment with the accuracy of 100 grams. Weight plays the most crucial role in the quantity of daily food consumption of individuals, and higher food intake results in higher nutrient intake [16]. This division was made to remove the impact of weight on nutrients and its possible confounding effect on the impact of nutrients on depression.

2.5 Statistical analysis

All statistical analyses were performed using the software package IBM SPSS Statistics for Windows version 20.0 (IBM Co., Armonk, NY, USA). Homogeneity and adequacy of variances were assessed using Levene’s test and Kaiser-Meyer-Olkin (KMO), and Bartlett’s tests. After determining normality using the Shapiro-Wilk test, the comparison of continuous variables was ascertained using the One-Way Analysis of variance (ANOVA) test or Mann-Whitney U test (non-normal distribution). Chi-square test or Fisher’s exact test was used for qualitative variables, and odds ratios (95% confidence intervals) indicated as OR between non-depressed (control) and depression groups (classified as mild, moderate, or severe) were obtained using binary logistic regression. Multivariate Analysis of covariance (ANCOVA) was used for adjusting multivariable Analysis; stepwise regression analysis and Two-tailed Bivariate Correlation were used to assess the correlation (R) between BDI-13 scores and assessed variables. A decision tree is illustrated using CHAID (Chi-Square Automatic Interaction Detector) method. P-value <0.05 was considered statistically significant.

3 Result

For 53 individuals who had a difference between the 1-day recall and trend of intake according to the general questions, a 3-day recall was filled. The recorded recalls showed the same trend of dietary intake in comparison with general questions. The difference between recorded energy intake and TEE (energy intake to TEE = 97.3±8.5%) indicates no significant under or over-reporting within the population. Of 766 individuals (65.14±6.84-year-old/ 256 male vs 510 female) who filled BDI-13, 322 (42%) of them didn’t have depression (BDI-13 score = 1.71±1.33); 254 (34%) individuals had mild (BDI-13 score = 5.85±0.56), 131 (17%) moderate (BDI-13 score = 10.50±2.33) and 59 (7%) of them had severe (BDI-13 score = 19.47±3.22) depression.

The differences between depression groups for age, gender and nearly all the social-economic factors, including current working condition, physical activity level, having sports activity, living with someone, education, marriage, economic level, and smoking, were significant between groups (p < 0.05) without any adjustment (Table 1). The findings indicated that females had a higher OR of having one of three stages of depression than males (OR = 2.229 (95% CI: 1.641–3.026), p < 0.005) while higher age was directly associated with depression (OR = 1.073 (95% CI: 1.049–1.097), p < 0.005). Some specific educational degrees (BSc, PhD), living with a wife and children, higher physical activity, and higher income had lesser OR of depression while the OR was increased for those who are smoking and widowed. Furthermore, people who were employed in shop keeping (OR = 0.487 (95% CI = 0.273–0.869), p = 0.015), office works (OR = 0.385 (95% CI = 0.264–0.562), p < 0.005) and others including freelancers, programmers and managers (OR = 0.417 (95% CI = 0.187–0.932), p = 0.033) had a lower OR of depression compare with people who are not working or housewives; while the OR of depression in teachers, military personnel, academic personnel, medical staffs and drivers was not significantly differed from people who were not working or housewives.

Table 1
The relation between social-economic factors and the risk of depression

Factors Values overall non-depressed Depressed (n = 444) OR (95% CI)^a P-value P-value^c

(n = 766) (n = 322) Mild (n = 254) Moderate (n = 131) Severe (n = 59)

BDI-13 score – 5.95±5.27 1.71±1.33 5.85±0.56 10.50±2.33 19.47±3.22 – – –

Age year 65.14±6.84 63.31±6.17 66.59±6.62 66.15±7.44 66.58±7.65 1.073 (1.049–1.097)^ <0.001 0.035

Gender Male 256 (100%) 141 (55%) 56 (22%) 44 (17%) 15 (6%) Ref <0.001 0.390

Female 510 (100%) 181 (35%) 198 (39%) 87 (17%) 44 (9%) 2.229 (1.641–3.026)^

BMI¹ Kg/m² 26.5±4.4 26.1±4.2 27.0±4.3 26.5±4.8 27.2±4.4 1.039 (0.999–1.077) 0.057 0.142

Still Working No 563 (100%) 197 (35%) 213 (38%) 103 (18%) 50 (9%) Ref <0.001 0.971

Part-time 85 (100%) 49 (58%) 19 (22%) 13 (15%) 4 (5%) 0.395 (0.249–0.629)^

Full-time 118 (100%) 76 (64%) 22 (19%) 15 (13%) 5 (4%) 0.297 (0.196–0.450)^

Physical Activity low 158 (100%) 44 (28%) 43 (27%) 46 (29%) 25 (16%) Ref <0.001 0.001

Low-moderate 236 (100%) 93 (39%) 91 (39%) 35 (15%) 17 (7%) 0.593 (0.384–0.917)^*

Moderate 283 (100%) 137 (48%) 99 (35%) 32 (11%) 15 (5%) 0.411 (0.271–0.625)^

Over- moderate 89 (100%) 48 (54%) 21 (24%) 18 (20%) 2 (2%) 0.330 (0.192–0.567)^

sport activity Yes 388 (100%) 193 (50%) 128 (33%) 47 (12%) 20 (5%) Ref <0.001 <0.001

No 378 (100%) 129 (34%) 126 (33%) 84 (22%) 39 (10%) 1.910 (1.428–2.556)^**

Living with someone alone 72 (100%) 30 (42%) 17 (24%) 16 (22%) 9 (13%) Ref <0.001 0.091

With wife 365 (100%) 145 (40%) 140 (38%) 57 (16%) 23 (6%) 1.084 (0.649–1.811)

With children 109 (100%) 31 (28%) 44 (40%) 24 (22%) 10 (9%) 1.0797 (0.960–3.363)

With wife and children 220 (100%) 116 (53%) 53 (24%) 34 (15%) 17 (8%) 0.640 (374–1.097)^*

Sleep duration Hrs 7.01±1.58 7.12±1.61 6.88±1.51 6.92±1.69 7.23±1.4 0.930 (0.849–1.018) 0.185 0.437

Education Up to high school 241 (100%) 89 (37%) 99 (41%) 35 (15%) 18 (7%) Ref 0.005 0.669

High school 130 (100%) 43 (33%) 47 (36%) 31 (24%) 9 (7%) 1.185 (0.756–1.857)

Diploma 250 (100%) 109 (44%) 75 (30%) 45 (18%) 21 (8%) 0.757 (0.527–1.088)

BSc 81 (100%) 46 (57%) 19 (23%) 9 (11%) 7 (9%) 0.446 (0.267–743)^**

MSc 30 (100%) 14 (47%) 7 (23%) 5 (17%) 4 (13%) 0.669 (0.312–1.436)

PhD 34 (100%) 21 (62%) 7 (21%) 6 (18%) 0 (0%) 0.362 (0.173–0.759)^*

Marriage Married 601 (100%) 268 (45%) 198 (33%) 93 (15%) 42 (7%) Ref 0.047 0.872

Single 12 (100%) 4 (33%) 5 (42%) 3 (25%) 0 (0%) 1.610 (0.480–5.403)

Device 17 (100%) 6 (35%) 8 (47%) 3 (18%) 0 (0%) 2.475 (539–4.042)

Widowed 136 (100%) 44 (32%) 43 (32%) 32 (24%) 17 (13%) 1.683 (1.132–2.494)^

Economic level No 252 (100%) 76 (30%) 102 (40%) 54 (21%) 20 (8%) Ref <0.001 <0.001

Lower 132 (100%) 44 (33%) 41 (31%) 27 (20%) 20 (15%) 0.864 (0.550–1.356)

Same level 279 (100%) 137 (49%) 86 (31%) 43 (15%) 13 (5%) 0.448 (0.313–0.640)^

Higher 103 (100%) 65 (63%) 25 (24%) 7 (7%) 6 (6%) 0.252 (0.156–0.409)

Smoke No 582 (100%) 252 (43%) 187 (32%) 100 (17%) 43 (7%) Ref <0.001 <0.001

Past 90 (100%) 39 (43%) 42 (47%) 7 (8%) 2 (2%) 0.999 (0.638–1.563)

Yes 94 (100%) 31 (33%) 25 (27%) 24 (26%) 14 (15%) 1.552 (0.980–2.459)^*

Factors	Values	overall	non-depressed	Depressed (n = 444)	OR (95% CI)^a	P-value	P-value^c
BDI-13 score	–	5.95±5.27	1.71±1.33	5.85±0.56	10.50±2.33	19.47±3.22	–	–	–
Age	year	65.14±6.84	63.31±6.17	66.59±6.62	66.15±7.44	66.58±7.65	1.073 (1.049–1.097)^**	<0.001	0.035
Gender	Male	256 (100%)	141 (55%)	56 (22%)	44 (17%)	15 (6%)	Ref	<0.001	0.390
	Female	510 (100%)	181 (35%)	198 (39%)	87 (17%)	44 (9%)	2.229 (1.641–3.026)^**
BMI¹	Kg/m²	26.5±4.4	26.1±4.2	27.0±4.3	26.5±4.8	27.2±4.4	1.039 (0.999–1.077)	0.057	0.142
Still Working	No	563 (100%)	197 (35%)	213 (38%)	103 (18%)	50 (9%)	Ref	<0.001	0.971
	Part-time	85 (100%)	49 (58%)	19 (22%)	13 (15%)	4 (5%)	0.395 (0.249–0.629)^**
	Full-time	118 (100%)	76 (64%)	22 (19%)	15 (13%)	5 (4%)	0.297 (0.196–0.450)^**
Physical Activity	low	158 (100%)	44 (28%)	43 (27%)	46 (29%)	25 (16%)	Ref	<0.001	0.001
	Low-moderate	236 (100%)	93 (39%)	91 (39%)	35 (15%)	17 (7%)	0.593 (0.384–0.917)^*
	Moderate	283 (100%)	137 (48%)	99 (35%)	32 (11%)	15 (5%)	0.411 (0.271–0.625)^**
	Over- moderate	89 (100%)	48 (54%)	21 (24%)	18 (20%)	2 (2%)	0.330 (0.192–0.567)^**
sport activity	Yes	388 (100%)	193 (50%)	128 (33%)	47 (12%)	20 (5%)	Ref	<0.001	<0.001
	No	378 (100%)	129 (34%)	126 (33%)	84 (22%)	39 (10%)	1.910 (1.428–2.556)^**
Living with someone	alone	72 (100%)	30 (42%)	17 (24%)	16 (22%)	9 (13%)	Ref	<0.001	0.091
	With wife	365 (100%)	145 (40%)	140 (38%)	57 (16%)	23 (6%)	1.084 (0.649–1.811)
	With children	109 (100%)	31 (28%)	44 (40%)	24 (22%)	10 (9%)	1.0797 (0.960–3.363)
	With wife and children	220 (100%)	116 (53%)	53 (24%)	34 (15%)	17 (8%)	0.640 (374–1.097)^*
Sleep duration	Hrs	7.01±1.58	7.12±1.61	6.88±1.51	6.92±1.69	7.23±1.4	0.930 (0.849–1.018)	0.185	0.437
Education	Up to high school	241 (100%)	89 (37%)	99 (41%)	35 (15%)	18 (7%)	Ref	0.005	0.669
	High school	130 (100%)	43 (33%)	47 (36%)	31 (24%)	9 (7%)	1.185 (0.756–1.857)
	Diploma	250 (100%)	109 (44%)	75 (30%)	45 (18%)	21 (8%)	0.757 (0.527–1.088)
	BSc	81 (100%)	46 (57%)	19 (23%)	9 (11%)	7 (9%)	0.446 (0.267–743)^**
	MSc	30 (100%)	14 (47%)	7 (23%)	5 (17%)	4 (13%)	0.669 (0.312–1.436)
	PhD	34 (100%)	21 (62%)	7 (21%)	6 (18%)	0 (0%)	0.362 (0.173–0.759)^*
Marriage	Married	601 (100%)	268 (45%)	198 (33%)	93 (15%)	42 (7%)	Ref	0.047	0.872
	Single	12 (100%)	4 (33%)	5 (42%)	3 (25%)	0 (0%)	1.610 (0.480–5.403)
	Device	17 (100%)	6 (35%)	8 (47%)	3 (18%)	0 (0%)	2.475 (539–4.042)
	Widowed	136 (100%)	44 (32%)	43 (32%)	32 (24%)	17 (13%)	1.683 (1.132–2.494)^**
Economic level	No	252 (100%)	76 (30%)	102 (40%)	54 (21%)	20 (8%)	Ref	<0.001	<0.001
	Lower	132 (100%)	44 (33%)	41 (31%)	27 (20%)	20 (15%)	0.864 (0.550–1.356)
	Same level	279 (100%)	137 (49%)	86 (31%)	43 (15%)	13 (5%)	0.448 (0.313–0.640)^**
	Higher	103 (100%)	65 (63%)	25 (24%)	7 (7%)	6 (6%)	0.252 (0.156–0.409)
Smoke	No	582 (100%)	252 (43%)	187 (32%)	100 (17%)	43 (7%)	Ref	<0.001	<0.001
	Past	90 (100%)	39 (43%)	42 (47%)	7 (8%)	2 (2%)	0.999 (0.638–1.563)
	Yes	94 (100%)	31 (33%)	25 (27%)	24 (26%)	14 (15%)	1.552 (0.980–2.459)^*

^aAnalysis using Binary Logistic Regression between non-depressed and depressed (including mild, moderate and severe) groups. For nominal variables, O.R reported as in comparison with the first row. The reference factor is the first row for all factors. ^bAnalyzed using ANOVA, Mann-Whitney U. Chi-square or Fisher’s exact tests between non-depressed, mild, moderate and severe groups. ^cANCOVA, adjusted to other main factors: SF-36 score, gender, age, working, education, economic level, marriage, smoking, physical activity and living with someone. Percentage reported in the clinical stage of depression for each factor. Effect estimates with a p-value <0.05 are indicated in bold for both O.R and p.value. OR with P.value <0.05 reported ^* and <0.005 reported as ^** (the P.value is not reported); 1. Body Mass Index.

After adjustment of all other factors, the impact of age, physical activity level, sports activity, economic level and smoking were independent of all other factors associated with depression (p < 0.05). From lifestyle indicators, the level of physical activity showed that having higher levels of physical activity and sports activities were negatively associated with depression independently from other factors (Table 1). However, the difference in sleep duration in these people was not significant before and after adjustment. The findings are also suggesting people with no income are more affected by factors affecting depression than other people (Table 2). At the same time, depression and any other factors were not associated together within people with higher income.

Table 2

The association between main social-economic factors and the risk of depression in quartiles of financial level

		None	Low level income	Same level income	Higher level income
Still Working	No	Ref	Ref	Ref	Ref
	Part-time	–	0.24 (0.06–0.953) ^*	1.098 (0.494–2.441)	0.46 (0.062–3.431)
	Full-time	–	0.378 (0.093–1.545)	0.839 (0.348–2.021)	2.291 (0.374–14.037)
Physical Activity	low	Ref	Ref	Ref	Ref
	Low-moderate	0.381 (0.148–0.982)^*	0.439 (0.165–1.169)	0.675 (0.332–1.37)	2.239 (0.49–10.231)
	Moderate	0.448 (0.179–1.122)	0.386 (0.129–1.151)	0.46 (0.231–0.917)^*	0.488 (0.118–2.013)
	Over- moderate	0.317 (0.102–0.992)^*	0.379 (0.093–1.552)	0.773 (0.294–2.035)	0.228 (0.043–1.204)
sport activity	Yes	Ref	Ref	Ref	Ref
	No	3.666 (2.001–6.717)^**	2.849 (1.275–6.365)^*	1.431 (0.871–2.354)	1.98 (0.816–4.805)
Education	Up to high school	Ref	Ref	Ref	Ref
	High school	2.142 (0.819–5.6)	1.357 (0.445–4.137)	1.317 (0.559–3.104)	1.192 (0.272–5.225)
	Diploma	–	1.799 (0.644–5.022)	1.248 (0.609–2.554)	1.057 (0.323–3.465)
	BSc	0.502 (0.264–0.954)^*	0.943 (0.255–3.482)	0.794 (0.338–1.869)	3.041 (0.577-16.038)
	MSc	–	2.195 (0.302–15.935)	1.47 (0.374–5.783)	2.933 (0.543–15.842)
	PhD	0.148 (0.023–0.968)^*	1.001 (0.088–11.407)	0.572 (0.161–2.034)	0.724 (0.141–3.72)
Smoke	No	Ref	Ref	Ref	Ref
	Past	0.401 (0.169–0.953)^*	4.722 (0.954–23.379)	1.144 (0.556–2.356)	1.057 (0.264–4.238)
	Yes	1.634 (0.446–5.991)	14.514 (1.824–115.471)^*	1.344 (0.685–2.638)	1.818 (0.409–8.091)

^aAll the factors are adjusted to age and gender. ^bAnalysis using Binary Logistic Regression between non-depressed and depressed (including mild, moderate and severe) groups. For nominal variables, O.R reported as in comparison with the first row. The reference factor is the first row for all factors. ^cEffect estimates with a p-value <0.05 are indicated in bold for both O.R and p.value. OR with P.value <0.05 reported ^* and <0.005 reported as ^**.

The comparison between lifestyle quality indicators and depression according to SF-36 (Table 3) also showed a higher overall score on the SF-36 questionnaire had a negative association with depression, meaning a higher quality of life is associated with a lesser risk of depression using this tool (OR = 0.971 (95% CI: 0.963–0.979), p < 0.005). The findings also show the OR of having depression significantly (p < 0.005) ranging from 0.615 (95% CI: 0.502–0.754) in reported health score (the lowest) to 0.984 (95% CI: 0.979–0.989) in Role of Physical score (the highest).

Table 3

The relation between lifestyle quality indicators (SF-36) and the risk of depression. The association between SF-36 score with depression is independent of other factors (gender, age, working, education, income, marriage, smoking, physical activity and living with) with p = 0.008

Factors	overall	non-depressed	Depressed (n = 303)			OR (95% CI)^a	P-value
	(n = 522)	(n = 219)	Mild (n = 169)	Moderate (n = 91)	Severe (n = 43)
Physical Function score	60.36±32.13	76.79±25.79	55±29.02	39.94±29.88	31.49±30.64	0.966 (0.959–0.973)^**	<0.001
Role of Physical score	71.89±37.42	83.3±30.48	67.96±39.39	56.06±38.86	56.73±42.23	0.984 (0.979–0.989)^**	<0.001
Body Pain score	60.24±32.18	71.92±29.05	56.1±32.69	45.34±28.64	42.19±29.68	0.978 (0.972–0.984)^**	<0.001
General Health score	63.89±27.03	77.81±21.4	58.82±25.53	48.66±24.33	37.08±23.59	0.958 (0.949–0.966)^**	<0.001
Vitality score	68.38±26.62	81.66±19.81	64.3±25.48	52.42±25.75	42.65±25.55	0.960 (0.952–0.968)^**	<0.001
Social Function score	70.83±25.67	83.85±18.81	65.28±25.26	56.14±24.92	50.46±25.72	0.955 (0.945–0.964)^**	<0.001
Role of Emotional score	65.59±39.34	85.97±29.88	59.11±38.64	39.57±35.54	30.76±32.05	0.972 (0.967–0.978)^**	<0.001
Mental Health score	67.16±25.72	78.1±18.65	65.14±24.67	52.36±27.7	43.78±28.55	0.964 (0.955–0.973)^**	<0.001
Reported Health score	–0.3±0.89	–0.08±0.97	–0.45±0.81	–0.43±0.81	–0.59±0.64	0.615 (0.502–0.754)^**	<0.001
Physical Health score	62.59±25.09	76.58±20.98	58.35±22.74	44.43±18.49	38.49±19.09	0.954 (0.946–0.963)^**	<0.001
Mentally Health score	64.56±23.71	79.6±18.26	60.76±20.09	44.09±16.01	37.43±15.62	0.938 (0.928–0.949)^**	<0.001
Total Score	61.01±22.26	68.72±20.86	61.15±20.53	49.14±19.3	40.92±20.54	0.971 (0.963–0.979)^**	<0.001

^an analyzed using ANOVA test between non-depressed, mild, moderate and severe groups. ^banalyzed using Binary Logistic Regression between non-depressed and Depressed (include mild, moderate and severe) groups. Effect estimates with a p-value <0.05 are indicated in bold for both O.R and p.value. OR with P.value <0.05 reported ^* and <0.005 reported as ^** (the P.value is not reported).

Within nutrients intake, after dividing intakes by weight to understand the required metabolically intake, the findings of the current study show the overall higher intake of nearly all nutrients except copper, cholesterol and vitamin B12 has a lower OR of depression. In addition, the relation only for protein (p = 0.043), fibre (p = 0.037), iron (p = 0.041), vitamin B6 (p = 0.011) and caffeine (p = 0.009) was independent of the overall energy intake. At the same time, the impact of other nutrients was dependent on the overall energy intake (Table 4). Despite there being a correlation between energy and physical activity (r = 0.645, p < 0.001) and, energy and financial status (r = 0.164, p < 0.001), dietary intake had an independent association with depression after adjusting physical activity and financial status (adjusted p = 0.001). Furthermore, the very low OR can be explained by the small value of the variables, which means these ORs can only use for the direction of the effect that is reconfirmed in Table 5.

Table 4

The relation between dietary nutrient intake and the risk of depression

Factors	overall	non-depressed	Depressed (n = 444)			OR (95% CI)^a	P-value
	(n = 766)	(n = 322)	Mild (n = 254)	Moderate (n = 131)	Severe (n = 59)
Water (g/kg)	19.52±5.88	20.51±5.85	19.25±5.73	18.88±6.29	16.79±4.63	0.952 (0.928–0.976)^**	<0.001
Energy (Kcal/kg)	26.05±5.03	27.44±5.19	25.28±4.44	25.3±5.23	23.5±3.87	0.906 (0.878–0.934)^**	<0.001
Protein (g/kg)	0.88±0.21	0.92±0.23	0.85±0.19	0.84±0.21	0.82±0.21	0.176 (0.87–0.355)^**	<0.001
Total lipid (g/kg)	1±0.32	1.07±0.4	0.95±0.2	0.95±0.25	0.98±0.33	0.254 (0.143–0.453)^**	<0.001
saturated fatty acid (g/kg)	0.26±0.07	0.28±0.08	0.26±0.07	0.26±0.07	0.25±0.06	0.013 (0.002–0.099)^**	<0.001
Cholesterol (mg/kg)	3.785±1.88	3.936±2.042	3.638±1.681	3.762±1.897	3.638±1.703	–	0.261
Carbohydrate (g/kg)	3.506±0.756	3.699±0.776	3.399±0.688	3.436±0.796	3.071±0.519	0.547 (0.447–0.670)^**	<0.001
Sugars (g/kg)	1.224±0.389	1.291±0.424	1.174±0.322	1.23±0.431	1.056±0.259	0.456 (0.310–0.669)^**	<0.001
Starch (g/kg)	0.643±0.284	0.685±0.283	0.63±0.286	0.606±0.287	0.556±0.236	0.404 (0.239–0.684)^**	0.002
Dietary fibre (g/kg)	0.303±0.083	0.324±0.086	0.29±0.074	0.29±0.085	0.275±0.075	0.005 (0.001–0.030)^**	<0.001
Caffeine (mg/kg)	0.697±0.305	0.727±0.311	0.702±0.297	0.675±0.325	0.563±0.221	0.574 (0.357–0.923)^*	0.002
Calcium (mg/kg)	10.97±3.16	11.53±3.40	10.65±2.71	10.61±3.20	10.07±3.11	0.906 (0.864–0.950)^**	<0.001
Iron (mg/kg)	0.199±0.053	0.21±0.054	0.192±0.052	0.194±0.052	0.179±0.045	0.001 (0.000–0.019)^**	<0.001
Copper (μg/kg)	0.021±0.01	0.021±0.009	0.02±0.01	0.021±0.012	0.02±0.011	–	0.312
Magnesium (mg/kg)	3.889±0.953	4.124±1.008	3.752±0.829	3.745±1	3.512±0.73	0.626 (0.535–0.739)^*	<0.001
Phosphorus (mg/kg)	15.14±3.72	15.95±3.94	14.65±3.15	14.63±3.91	14.0±3.54	0.901 (0.865–0.939)^**	<0.001
Potassium (mg/kg)	33.83±8.76	35.91±9.33	32.46±7.22	32.63±9.63	31.04±7.23	0.953 (0.936–0.970)^**	<0.001
Selenium (μg/kg)	1.573±0.463	1.641±0.451	1.53±0.46	1.558±0.497	1.417±0.402	0.572 (0.414–0.789)^*	0.001
Vitamin C (mg/kg)	1.384±0.666	1.482±0.775	1.331±0.576	1.289±0.572	1.285±0.498	0.674 (0.536–0.847)^**	0.005
Vitamin A (IU/kg)	88.54±43.88	93.03±45.67	85.66±41.28	85.11±43.8	84.03±43.9	0.0996 (0.993–0.999)^*	0.117
Vitamin D (IU/kg)	0.516±0.362	0.55±0.424	0.492±0.308	0.507±0.323	0.452±0.268	–	0.120
Vitamin E (mg/kg)	0.133±0.038	0.142±0.042	0.128±0.031	0.125±0.038	0.124±0.032	0.000 (0.000–0.001)^*	<0.001
Vitamin B6 (μg/kg)	0.021±0.005	0.023±0.006	0.021±0.005	0.02±0.005	0.02±0.004	0.000 (0.000–0.001)^**	<0.001
Vitamin B12 (μg/kg)	0.11±0.15	0.1±0.12	0.1±0.15	0.12±0.19	0.12±0.17	–	0.545

^an analysis using Binary Logistic Regression between non-depressed and Depressed (including mild, moderate and severe) groups. Non-significant OR are not reported to simplify the table. ^banalyzed using the ANOVA test between non-depressed, mild, moderate and severe groups. ^cANCOVA, adjusted to energy intake (Kcal). All nutrients are divided into individuals’ weight (kg). The high OR for both Trans fatty acid (g/kg) and copper (μg/kg) is related to their very small value. Effect estimates with a p-value <0.05 are indicated in bold for both O.R and p.value. OR with P.value <0.05 reported ^* and <0.005 reported as ^** (the P.value is not reported).

Table 5

Correlations of the assessed numeric variables with the BDI-13 Scoring

Factors	Correlation	p.value
Age	0.168^**	<0.001
Sleep	0.010	0.99
SF-36 Physical Function score	–0.497^**	<0.001
SF-36 Role of Physical score	–0.272^**	<0.001
SF-36 Body Pain score	–0.335^**	<0.001
SF-36 General Health score	–0.495^**	<0.001
SF-36 Vitality score	–0.494^**	<0.001
SF-36 Social Function score	–0.457^**	<0.001
SF-36 Role of Emotional score	–0.490^**	<0.001
SF-36 Mental Health score	–0.448^**	<0.001
SF-36 Reported Health score	–0.177^**	<0.001
SF-36 Physical Health score	–0.547^**	<0.001
SF-36 Mentally Health score	–0.632^**	<0.001
SF-36 Total Score	–0.404^**	<0.001
Water (g/kg)	–0.161^**	<0.001
Energy (Kcal/kg)	–0.222^**	<0.001
Protein (g/kg)	–0.148^**	<0.001
Total lipid (g/kg)	–0.107^**	<0.001
saturated fatty acid (g/kg)	–0.118^**	<0.001
Trans fatty acid (g/kg)	–0.101^**	0.01
Cholesterol (mg/kg)	–0.04	0.27
Carbohydrate (g/kg)	–0.225^**	<0.001
Sugars (g/kg)	–0.156^**	<0.001
Starch (g/kg)	–0.122^**	<0.001
Dietary fibre (g/kg)	–0.184^**	<0.001
Caffeine (mg/kg)	–0.118^**	<0.001
Calcium (mg/kg)	–0.134^**	<0.001
Iron (mg/kg)	–0.156^**	<0.001
Magnesium (mg/kg)	–0.188^**	<0.001
Phosphorus (mg/kg)	–0.155^**	<0.001
Potassium (mg/kg)	–0.177^**	<0.001
Selenium (μg/kg)	–0.115^**	<0.001
Vitamin C (mg/kg)	–0.110^**	<0.001
Vitamin A (IU/kg)	–0.07	0.07
Vitamin D (IU/kg)	–0.072^*	0.05
Vitamin E (mg/kg)	–0.162^**	<0.001
Vitamin B6 (μg/kg)	–0.171^**	<0.001
Vitamin B12 (μg/kg)	0.05	0.16

The negative symbol (–) shows an inverse relationship between the factor and the BDI-13 score. Effect estimates with a p-value <0.05 are indicated in bold. This table shows the difference between the BDI-13 score with variables and is different from the depression classification.

The Table 5 is showing the correlation between the BDI-13 score and scaled variables which can confirm the findings. The strongest correlation was for SF-36 indicators, which shows a moderate correlation, while the weakest was related to nutrient intake, which was significant but very weak.

The decision tree (Fig. 1) also illustrated the impact of risk factors on depression. Working was the best risk factor for depression in males, while having any sports activity was more important in females. For females who had sports, living with someone, and for those who did not have sports, the income level can predict the depression risk.

Fig. 1

Decision tree model for predictors of depression in a healthy population. The model included the impact of gender, age, education, working, living with someone, having sports activity, income Classification, Physical activity, and smoking.

4 Discussion

This study is one of the first to assess depression in a well-nourished over-55 years old population without any considerable disease. Generally, this idea that a healthy population is at low risk of depression prevents researchers from assessing the depression within this population. However, the current study’s findings showed that the risk and prevalence of depression, even in a healthy population, can be notable. This study hazards the high risk of depression in healthy populations.

The findings show the impact of important dependent and independent risk factors on depression. The decision tree also provided an excellent insight into predicting high depression-risk populations for further studies, especially early diagnosis and preventive strategizing. The findings suggested that working is the best predictor for males while sports, income and living with someone play essential roles in females. However, in detail, there is more to consider.

4.1 Prevalence

In this healthy population, 58% of the population had one of three stages of depression. The reported prevalence in different countries was estimated to be about 1.5% in China to 15.2% in India before the pandemic, with a mean of 7% globally regardless of age [17, 18]. However, this prevalence can be higher within the older adult population. As a result, one systematic review in 2019 estimated the prevalence of depression to be about 34.4% in the Indian 60 years old and above population [19]. Another study in Brazil reported a prevalence of 11.1% and 25.6% for late-life depression and clinically significant depressive symptoms in individuals older than 70 years old [20].

High-quality population-based prevalence studies on the same population in Iran are not available. No recent data is available to estimate the prevalence of depression in Mashhad either. However, one study in Yazd, Iran, reported a prevalence of 29% for depression in 2019 (11.1% mild, 12.2% moderate, and 6.9% severe depression) [21]. Nevertheless, in a systematic review from 2001 to 2015 in Iranian people aged 50–90 years, the prevalence of severe depression and overall depression was estimated to be 8.2% and 43%, indicating a relative prevalence of depression with the current study [22].

4.2 Age

previous literature has established that increased age is one of the main risk factors for depression, reconfirmed with the current study [23 –25]. One of the newest hypotheses to explain this situation is brain ageing [25, 26]. In this thesis, when people stop routine activities, the brain may face some uncontrolled mental burdens, including anxiety and worries. This pressure and lowered brain annalistic power generally lead to mental health disorders, especially depression [25, 26]. Based on this new thesis, the effect of age on depression can be through brain ageing, and age itself may have no independent effect on mental health disorders [26]. In this new hypothesis, keeping the mind active lowers the process of brain ageing, mental disorders, and depression as a result [26]. However, it is a thesis, and more work, especially trials, is required.

4.3 Gender

In the current study, females had a significantly higher prevalence of depression when compared to males. The different studies’ findings vary; some studies suggested males [23, 27], and others suggest females are at a higher risk of depression [18 , 28]. The gender-related finding depends on the population, age, culture, and occupation change. The higher prevalence of depression in females in this population can be explained by the women’s limitations in Islamic nations, especially Iran, which can lead to depression; limitations like systematic gender discrimination, hijab, religious patriarchy and considering lower values for females [29 –31]. However, this relationship is not absolute, and during the last few years, society started to provide more equality between genders [30].

4.4 Smoking

Current smokers were at least 1.5 times more likely to be depressed in the current population, while there was no difference between those who never smoked or quit smoking. Smoking has always been a fixed risk factor in nearly all diseases, especially depression [32, 33]. The relationship between smoking and depression can be bidirectional [32]. The findings suggest that quitting smoking can be considered a sign of lower depression. However, still more works are required to understand the relationship between smoking and depression as the effect direction is still unclear.

4.5 Marriage and living with someone

Marital status is another indication of depression in the current population. As expected, currently married people have a significantly lower prevalence than widowed people. The other studies’ findings also confirmed that married people are at a lower risk of depression in comparison with people who had divorced, widowed, or never gotten married [18 , 34]. In addition to marriage, the presence of family can be effective in lowering the risk of depression that can link the impact of marriage to depression [18, 23]. The current study also confirmed, people living with their partner and children had a significant 40% lower OR of depression, while this difference was not significant for other categories. Furthermore, living with someone was the main depression predictor in females who had sports activity. These two simple findings show the importance of family and their presence in preventing depression. All the findings highlight the importance of family support in mental health within the retired population, which requires further study.

4.6 Working and physical activity

The findings in the current population indicated that the effect of physical and sports activity on depression is independent of other factors. People who engage in higher physical activities have a considerably lower OR of depression. The finding of previous studies also reported that inactive people were significantly at higher risk of depression [18 , 35]. In addition, in the current population, people who had any sports activity ranging from swimming and volleyball to yoga and aerobics had a significantly lower OR of depression than the inactive population as previse findings [35]. In addition, the sport was the first depression predictor among females, showing its impact.

Regarding working, There is evidence that people who are employed or currently working are at lower risk of depression [21, 24]. The current study also showed that unemployed individuals have a higher OR of depression compared with those working full-time or part-time. In addition, this factor was the main predictor of depression in males, which shows the importance of working in this gender. This decreased likelihood of having depression in employed individuals can be related to the positive influence of occupational engagement and beneficial effects that being in the workforce has on their mental well-being [36, 37]. Being employed typically provides individuals with a routine program, purpose, social network, support system, financial stability and security which are associated with a protective effect on mental health [36].

The result suggests that Iranian people who are employed in shop keeping, office works, and other professions such as freelancers, programmers, and managers have a OR of depression. On the other hand, the OR of depression in teachers, military personnel, academic personnel, and medical staff, and drivers did not significantly differ from individuals who were not working or housewives. One possible reason for this finding could be the level of job satisfaction and stress associated with different professions [36, 37]. It is known that individuals in certain occupations may have higher levels of job satisfaction and lower stress levels, which can contribute to better mental health. In Iran, shop keeping, office work, freelancing, programming, and management positions may offer more stability, job autonomy, financial stability and a sense of control, which can positively impact psychological well-being. On the other hand, teaching, military service, academia, medical professions, and driving may entail higher levels of stress, long working hours, exposure to traumatic events, or lack of job control, which might contribute to a similar level of depression as people who are not employed or are housewives. These professions often have demanding responsibilities and may be associated with higher levels of burnout, which can increase the risk of depression [36 –38].

4.7 Education and economic level

Generally, education and economic level have a direct relationship, and people with higher educational levels are more likely to occupy better-income occupations [39, 40]. In the current population, as was expected, people with higher incomes had a lower risk of depression as reported elsewhere [18, 34]. In addition, the current study’s findings showed that having an equal or higher income than expenses can have less prevalence of depression, as previous findings reported that Americans with less than 5000$ in savings are at a higher risk of depression [34]. However, in the current study, there was no significant difference in the risk of depression between people with low income or without income; but people with moderate- or higher-income levels have significant 66% and 75% less OR of depression. In addition, income was the main predictor/risk factor among females who were not having any sports activity, which shows the importance of this factor.

Despite the inverse relationship between educational level and the risk of depression [24, 34], the educational level was only significant for PhD and BSc. Some reports suggest that the graduated population has a lower risk of depression than the under-graduated population, reconfirmed in the current study [18, 34]. However, there was evidence that illiterate people are at the lowest risk of depression in other studies [21, 34]. The best explanation for this factor can be related to the impact of education on net income [39, 40]. However, still more investigations are required.

This study also assessed the impact of the main social economic factors in different quartiles of the income classifications within this population. This study highlights a concerning trend where individuals with no income are more susceptible to depression due to various lifestyle factors compared to those with higher incomes. The potential impact of this finding on public health and policy is significant, as it may suggest a need for increased support and resources targeted toward individuals with low income. Moreover, this finding highlights the need to recognize and address the intersectionality of socioeconomic status and mental health. This underscores the importance of reducing inequality in access to resources and opportunities, as these factors can have significant implications for an individual’s mental health [41, 42]. This finding, despite requiring more investigation, can suggest lifestyle modifications may be more effective in people with lower income specially those who don’t have any income including housewives, non-paid retired population and those who had stopped working for a while. This finding also underlines the need for a multidisciplinary approach that considers the complex interplay between income, lifestyle and mental health.

4.8 Health-related quality of life

The relationship between lifestyle factors including marital, economic, education, working, and physical activity status with depression are well established. To the authors’ knowledge and online report of the World Health Organization (WHO), an SF-36 questionnaire is a good tool that considers physical function, general health, vitality, and social, mental, and emotional function [11]. In the current study, a 1% higher quality of life showed about 3.9% significantly lower OR of depression. In addition, all indicators of quality-of-life scores using this tool showed a significant moderate relation for depression. The finding of other studies also reported the inverse relationship between depression and higher quality of life [18 , 43]. Evidence shows that this is one of the first studies that used this tool to score the population’s lifestyle in depression. As a result, the authors suggest SF-36 can be easy to use, reliable, and a proper tool to assess the quality of life in depression risked populations that can consider all aspects of lifestyle quality. However, a validation study in this population is still required.

4.9 Diet and nutrition

In the current study, the nutrient intake was adjusted to the individual’s weight to increase the efficacy and decrease the confounding effect. Weight is known as the main and most effective component of BMR. With this adjustment, a good comparison between groups was made that showed a more accurate effect of nutrient intake compared to the metabolic requirement of people [44]. To the authors’ knowledge, this study is the first to use this method to study the relationship between nutrition and depression.

After adjustments, a significant difference had been found between groups of depression, for the primary nutrients such as water (g/kg), energy (Kcal/kg), protein (g/kg), carbohydrate (g/kg), and total lipid (g/kg) intake, as well as most the micronutrients. The energy intake of the population was considerably lower than the standard (26 Kcal/kg VS 30 Kcal/kg) required energy [44]. However, the lower energy intake in elderly populations was reported in previous studies that confirmed this situation is a trend in the elderly [44 –46].

Based on the findings, despite receiving an overall lower energy intake than the metabolic requirement, the lower energy intake was associated with higher depression in this population. However, the findings regarding the energy intake in depression are varied. Despite some studies confirming the current findings [47 –50], others reported a higher energy intake in depression groups [48, 51]. It is also noted that there was a trend to lower energy intake and malnutrition in depressed Iranians, especially the elderly population [49, 50]. Nevertheless, the importance of the samples’ populations’ age and culture is also noted as the limitation of this varied finding [48].

The inverse association of most other dietary nutrient intakes with depression in the current study can be explained by the impact of malnutrition on depression and the energy compound [47 –51]. Based on the current study’s findings, higher intake of lipids, carbohydrates and most of their subcomponents were negatively associated with depression. Their impact was not independent of the energy intake, indicating that their impact can be related to the 4 Kcal/g and 9 Kcal/g energy density of carbohydrates and lipids [44]. However, the previous studies reported that higher carbohydrate intake, especially sugars, simple carbohydrates, and starch, as well as total lipids, saturated fats, and cholesterol, were associated with higher depression risk in all ages [6 , 52].

Protein was the essential macronutrient that independently was associated with depression in the current population confirming previous studies [44–46 , 54]. Amino acids can explain the possible mechanism behind the anti-depressive impact of protein. Based on the evidence, serotonin and norepinephrine, which have tryptophan and tyrosine as the main components, are confidentially associated with depression [55]. However, more work, especially clinical trials with high-protein diet interventions, are required to understand the practical impacts.

Caffeine was the other nutrient that was independently associated with depression. The impact of caffeine, especially coffee consumption, on lowering the risk of depression is also indicated in the previous studies [44, 56]. In addition to caffeine, according to evidence, higher fibre intake (fruit and vegetable) can lower the risk of depression independently from other nutrients and energy, also reconfirmed in the current study [6 , 52].

Only iron, independent of energy intake, was associated with depression from all the minerals assessed within this study. In addition, one meta-analysis also indicates the inverse associations between dietary zinc and iron intake and the risk of depression, confirming the findings of the current study [57]. However, more work is required regarding minerals to make any firm conclusion [44, 58].

The information regarding the impact of vitamins on depression also follows the same limitations as minerals. There was an independent difference between groups for vitamin B6 in this study, confirming previous reports [59, 60]. Despite the relation between folate and vitamin D with depression being dependent on energy in the current population, there is evidence that a lower intake of these vitamins is associated with depression [58]. In addition, promising findings regarding the anti-depressive impact of vitamin D are reported several times [61].

The impact of food groups on body metabolism is through nutrients [44]. Despite limited work on nutrients, all studies agreed that a healthy diet rich in fruits and vegetables, replacing red meats with white meats, and increasing the beans and lumen intake close to the Mediterranean diet can lower the risk of depression [6 , 52]. As the main limitation, the impact of nutrients in studies using food patterns was not assessed accurately. In addition, the food preparation process and density of nutrients can affect the dietary value of foods which should be considered as the other factor that may impact the results [44].

4.10 Special conditions

The higher prevalence of depression in the current study can be explained by the exceptional condition of Iranians in the past few years. During the last few years, the political and negatively economic changes that were highly affected by the United States sanction made a complicated condition [62, 63]. Sanctions can cause economic hardship and poverty. The lower quality of life resulting from economic inflation, reduced purchasing power, liquidity reduction and livelihood problems can be the main reason for this level of depression [62, 63]. Sanctions can cause social isolation and a breakdown in social support networks by cutting Iranians off from the rest of the world. This can lead to feelings of loneliness, anxiety, and depression. The other outcome of sanctions which can lead people to mental problems is limiting access to health services due to poverty and difficulty to access medicines. It is while this condition can reduce the desire to continue education as it can be difficult for people to travel abroad for education or work. This can limit opportunities and lead to feelings of hopelessness and despair. Finally, when people feel that they have no control over their daily life, they may develop feelings of hopelessness and despair that can lead to depression [62 –64]. In addition, By considering the impact of good and bad news on individuals, daily exposure of Iranians to more disappointing news provides a suitable condition for developing disappointment and depression [65, 66]. However, the authors believe the current study’s findings encourage researchers to assess other healthy populations for depression and other mental health disorders.

4.11 Suggestions, limitations, and strengths

The strengths of the current study are its accurate methodology, comprehensive report of a vast range of depression-related factors, and the big sample size that presents the overall healthy population of Mashhad, Iran, with a shallow risk of biases between groups. As most studies categorize healthy populations as a low-risk groups and do not assess them, this study is one of the first to evaluate a healthy population. Using a single BDI-13 questionnaire was the main limitation; however, these tools have been shown to be reliable in community-based studies [10]. The other limitation was using a dietary recall instate of a Food Frequency Questionnaire (FFQ), but the leading cause was decreasing memory depending on such a detailed tool. However, tools limitation was covered by some specialized dietary questions about dietary habits, adjustments made by the RDN, and the population size. Nevertheless, the present study’s main limitation is its observational nature, which is uninformative on the temporal criterion for judging causality. However, this type of study provides a rationale for future research.

Based on the current study’s findings and the importance of prevention, the authors suggest researchers study healthy populations more. Studying a healthy population have two significant advantages. First, by understanding the risk factors and diagnosing diseases in healthy populations, designing prevention strategies will be more accessible, and second, people with undiagnosed mental disorders will be diagnosed to treatment sooner.

5 Conclusion

Depression can be prevalent even in a healthy population. The overall lifestyle had an important role in depression and age, physical activity, sports, economic level, and smoking independently from other factors associated with depression. Higher dietary intake was associated with lower depression risk and the impact of protein, fibre, iron, vitamin B6 and caffeine on depression was independent of energy intake. Strategizing to concentrate on working for males and sports for females along with modifying other lifestyle indicators and diet can be helpful in controlling depression in a nearly healthy, well-nourished, over-55 years old population.

Ethics approval and consent to participate

The protocol of the current study is approved by an Ethical code: IR.MUMS.REC.1398.229 (http://ethics.research.ac.ir) from Mashhad University of Medical Science- Iran. All methods were carried out in accordance with relevant guidelines and regulations under the supervision of the Varastegan Institute for Medical Sciences and Mashhad University of Medical Sciences. All participants were provided with verbal and written explanations of the study objectives and methodology, and informed consent was obtained from them. The Welfare Organization of Khorasan Razavi Province screened the process of study at all stages.

Consent for publication

The authors declare that this manuscript was available as a preprint with the URL address: https://www.researchsquare.com/article/rs-1531230/v2 which may indicate a high rate of plagiarism between these two versions. The Authors confirm all rights of the current manuscript and its preprint is officially reserved for the current journal from the acceptance date.

Availability of Data and Material (ADM)

The published data is available for review and further investigations. Please contact mrsh13713@gmail.com or Hosseiniz@varastegan.ac.ir to access the data.

Conflict of interest

The authors confirm that there is no conflict of interest.

Financial support

The financial support was provided by the in-house research committee of Varastegan Institute for Medical Sciences.

Author contribution

Study concept and design: ZH, PP, MRSh; Drafting of the manuscript: MRSh, MR, SJ; Study implementation: MRSH, SE, MA, RB; Data validation and dietary intake analysis: MA, RR, MRSh; Statistical Analysis and interpretation of data: MRSH, ZH, RR. All authors gave final approval of the version to be published and agreed to be accountable for all aspects of the work. ZH and RR also accept all responsibility for the manuscript on behalf of all the authors.

Footnotes

Acknowledgments

We want to thank Varastegan Institute for Medical Sciences and Mr. Matin Etemadi, who managed our online registration programs. We acknowledge the Welfare Organization of Khorasan Razavi Provence and Khorasan Razavi Retirement Association for supporting us and all those who help us in our registration, gathering data, and performing this assessment.

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