Associations between whole and low-fat dairy products consumption,physical performance and mental health

Abstract

BACKGROUND:

Military staff are required to be in a proper condition of mental health and weight standards and have a specific level of physical performances (PP). Dairy products are rich sources of essential nutrients. It was demonstrated that Iranians consume much less milk and its products and there is no exception in the military staff.

OBJECTIVES:

To examine the association between consumption of low or whole-fat dairy and psychological disorders and PP in military personnel.

METHODS:

This cross-sectional study was conducted in four military zones of Iran. Dairy products intake were evaluated by using a semi-quantitative food-frequency questionnaire (FFQ). The Persian version of Depression, Anxiety, Stress Scale-21 (DASS-21) were used to assess participants’ depression, anxiety and stress and PP were assessed by a 2-mile run, sit-up, push-up and pull-up.

RESULTS:

There was a significant negative relationship between the tertiles of total and low-fat dairy consumption and depression (OR 0.28, 95% CI 0.10 to 0.79, P < 0.05), and low-fat dairy consumption and stress (OR 0.34, 95% CI 0.13 to 0.93, P < 0.05). Also, there was a positive association between the last tertiles of low-fat dairy consumption and suitable PP (OR 5.28, 95% CI 1.08 to 25.71, P < 0.05).

CONCLUSIONS:

We concluded that consumption of dairy products and particularly low-fat products are related to less psychological problems and a better PP level.

Keywords

Dairy consumption psychological disorders depression anxiety stress physical performances military

1 Introduction

Jobs with hard responsibilities have important negative effects on the individual’s health. The military community is a unique population in contrast to the civilian. Military staff are required to be in a proper condition of mental health and weight standards and have a specific level of physical performances (PP).

Psychological flexibility is a vital feature for military employees as they frequently experience job-related stressors, such as separation from their family, deployment and hard physically and cognitively operational trainings [1]. Stress, anxiety and depression are three common mental complaints.

The prevalence of psychological disorders (PD) are increasing worldwide [2]. It has been estimated that 17.1%, 20.8% and 21.0% of Iranian adults are suffering from stress, anxiety and depression, respectively [3]. Previously, the prevalence of stress, anxiety and depression was reported 15.9%, 10.6% and 27.2% in military service [4]. Mental abnormalities may result in disability, premature death [5] and harm to others or themselves such as suicide [6].

Appropriate nutrition is one of the important factors in promoting health and evidenced its role as an influential mediator in chronic diseases. Over the past century, the international community has been moving towards changing nutritional patterns and physical activity related to chronic diseases [7]. Nutrition and military performance are principally entwined. Quantitative and qualitative nutrition and appropriate hydration are required to guarantee both cognitive performance and physical capacity in military recruits at optimal levels [8, 9].

The ministry of health and medical education statistics demonstrated that Iranians consume much less milk and its products than the world population and there is no exception in the military staff [10]. Dairy products are rich sources of essential nutrients. This food group contains essential amino acids and adequate intake of both minerals (i.e., calcium, zinc, etc.) and vitamins (i.e., B2, B12 and etc.) [11]. Various biological properties were found in dairy products, including anti-oxidative and anti-inflammatory activities [12, 13].

Previous studies have investigated the association of poor diet to PDs [14, 15]. Higher consumption of low-fat dairy products was associated with a lower incidence of depression [16]. Similar results were observed for yogurt and calcium in pregnant women [17].

Cuenca-García et al. have reported a strong positive relationship between the consumption of dairy products and CRF levels in both genders of European adolescents [18].

To our knowledge, few studies have described the relationship between dairy consumption, cognitive health and physical performances [16 , 20], but the results are inconsistent. Also, there is no study in military staff in worldwide. Thus, we considered a cross-sectional study aimed to examine the association between consumption of two types of low or whole-fat dairy and PD and PP in military personnel.

2 Method and materials

2.1 Design and participants

This cross-sectional study was conducted between October and December 2019 in four military zones of Iran. The minimum sample size (155 participants) was calculated by the previous study [21] and using the formula: n = [(Z_1-α/2 +Z_1-β)/0.5×ln[1 + r/1-r]]² + 3 where r is the correlation between a healthy diet and PP (0.26), α error = 0.05 and power = 90% (1-β). Accordingly, five hundred eighty male military staff with age of over 18 years have participated in the study. Sampling was done by interview with all people who were willing to participate in the study. Then, we excluded one hundred ninety six participants who had chronic disorder, energy intake out of 800–4200 kcal/day range or follow a special diet [19]. Furthermore, we excluded one hundred and four participants because of having missing data for FFQ, DASS questionnaire and PP indices. Finally, two hundred and thirty contributors were included in the final analysis. A written informed consent received from participants before the study.

2.2 Ethical considerations

The protocol of this study was approved by the ethics committee of Baghiatallah University of Medical Sciences, Tehran, Iran (NO: IR.BMSU.REC.1398.067 in July 1, 2019).

2.3 Anthropometric measurements

Anthropometric indices, including height, weight, hip and waist circumference were measured. Weight was recorded to the nearest 0.1 kg using digital scales. Height was assessed in the standing position to the nearest 0.5 cm using a stadiometer. Hip and waist circumferences were recorded with an inelastic tape to the nearest 0.5 cm and waist to hip ratio (WHR) was calculated. Waist circumference was recorded central between the lower rib and the iliac crest. Hip circumference was measured at the largest circumference of bottoms. BMI was computed using the “weight (kg)/height² (m)” equation.

2.4 Dietary assessment

Dietary intakes of contributors were evaluated by using a semi-quantitative food-frequency questionnaire (FFQ) of 147 food items. It is validated for Iranian society [22]. Standard serving sizes that usually used by Iranians were included in questionnaires. Participants answered how often they consumed food items as the number of times yearly, monthly, weekly or daily through the past year. Selected dairy products were whole- or low-fat milk, yogurt, cheese, and other milk products (included puddings, chocolate milk, etc.).The portion size of food items was changed to grams per day and the intake of each nutrient was evaluated using the US Department of Agriculture’s (USDA) national nutrient databank [23].

2.5 Assessment of mental health

The Persian version of Depression, Anxiety, Stress Scale-21 (DASS-21) were used to assess participants’ depression, anxiety and stress [24]. Cronbach’s alpha coefficient for was 0.77 for depression, 0.79 for anxiety and 0.78 for stress. Each items of depression, anxiety and stress comprises seven questions with a score between 0 and 3, depending to the severity. Then the total points for each item multiplied by 2 and each item provides a score between 0 to 42. Higher scores specify more severity of mental distress. At the end, the participants were classified into two groups of normal or unmoral.

2.6 Physical performance tests

PP tests were aimed to test the CRF, endurance and muscular strength of military staff. This test is designed by Iranian military service to evaluate basic readiness. It is similar to the Army Physical Fitness Test (APFT) that is designed for the Iranian military by gender and age [25]. This assessment comprised four parts: run time of 3200 m (2-mile), number of sit-up in 90 seconds, number of push-up in 120 seconds and the total number of pull-up. PP tests were completed according to the sport standard procedures by an expert in physical education. Each test was rated on a scale of 0 to 100. Each participant who scored at least fifty in all items passed the whole test.

2.7 Statistical analysis

Data were analyzed by SPSS software (SPSS Inc., Chicago, IL, USA), v.23. Descriptive statistics were used to analyze the normality of variables. Qualitative and quantitative data were presented as frequency (percent) and mean±SD, respectively. First, participants were categorized based on tertiles of dairy consumption. We used chi-square and one-way ANOVA tests to consider the differences in demographic features, nutrients intakes, mental and PP indices. The potential effect of dairy consumption on depression, anxiety, stress and PP level was evaluated by binary logistic regression analysis (adjusted for confounders) and odds ratios (OR) with 95% confidence intervals (CI) were considered. Age (continuous) was adjusted in the first model. Further adjustment was used for marital status (married and unmarried), education level (high school, diploma, associate degree, bachelor and master degree), WHR (below and above the 0.94), BMI (normal weight and overweight) and dietary intakes in the final model. In all analyses, the first tertile of dairy scores was considered as the reference set. We considered the tertile of dairy consumption as an ordinal variable to determine the overall trend of odds ratios. P-value less than 0.05 was considered to be significant.

3 Results

Table 1 shows the demographic, anthropometric, psychological and fitness characteristics of the study participants in the tertiles of total dairy consumption. A total of 230 men with a mean age of 32.34±5.11 contributed to the study. 2.1% were underweight (n = 5) and 40.8% were obese or overweight (n = 94), although there was no significant difference. Also, half of the participants (49.5%) had a college education. As shown in the table, there was not any significant difference in demographic, and anthropometric indices among the dairy consumption tertiles. In our military participants 30.4% (n = 70), 23.0% (n = 53), 23.0% (n = 53) were suffered from stress, anxiety and depression. Among the fitness indices, we found a positive difference in pull-up across the dairy consumption tertiles.

Table 1
General characteristics, anthropometrical, mental health and physical fitness indices of subjects across tertiles dairy consumption

Total T₁ T₂ T₃ P value

Age (year)^†a 33.05±4.67 32.74±4.53 33.31±4.54 33.10±4.94 0.792

Marital status, n (%) ^††b 0.090

Married 209 (90.8) 66 (85.7) 73 (96.1) 70 (90.9)

Education level, n (%) ^††b 0.507

High school 3 (1.3) 1 (1.3) 1 (1.3) 1 (1.3)

Diploma 113 (49.1) 39 (50.6) 35 (46.1) 39 (50.6)

Associate degree 55 (23.9) 16 (20.8) 15 (19.7) 24 (31.2)

Bachelor 53 (23.1) 19 (24.7) 23 (30.3) 11 (14.3)

Master degree 6 (2.6) 2 (2.6) 2 (2.6) 2 (2.6)

Anthropometrical indices

Weight (kg)^†a 76.39±10.98 76.82±10.28 76.88±10.19 75.49±12.48 0.756

Height (cm)^†a 176.11±7.61 177.50±6.58 176.03±8.84 174.80±7.43 0.056

BMI, n (%) ^††b 0.313

Underweight 5 (2.1) 2 (2.6) 1 (1.3) 2 (2.6)

Normal 131 (56.9) 48 (62.3) 42 (55.3) 41 (53.2)

Overweight 86 (37.4) 25 (32.5) 30 (39.5) 31 (40.3)

Obese 8 (3.4) 2 (2.6) 3 (3.9) 3 (3.9)

WC (cm)^†a 89.69±10.01 89.81±10.30 89.26±9.32 90.00±10.43 0.896

HC (cm)^†a 98.45±13.11 97.75±9.63 96.89±15.65 100.72±14.05 0.422

WHR, n (%) ^††b 0.496

Normal 185 (80.4) 59 (76.6) 64 (84.2) 62 (80.5)

Unormal 45 (19.6) 18 (23.4) 12 (15.7) 15 (19.5)

Mental health indices

Stress, n (%) ^††b 0.251

Unormal 70 (30.4) 29 (37.7) 20 (26.3) 21 (27.3)

Anxiety, n (%) ^††b 0.980

Unormal 53 (23.0) 18 (23.4) 18 (23.7) 17 (22.1)

Depression, n (%) ^††b 0.003

Unormal 53 (23.0) 28 (36.4) 13 (17.1) 12 (15.6)

Physical fitness indices^†a

Sit-up (number) 46.69±5.52 45.89±4.71 47.37±5.74 46.81±6.13 0.307

Pull-up (number) 7.95±3.25 7.85±3.00 7.83±3.55 8.19±3.20 0.029

Push-up (number) 37.69±5.41 36.21±5.23 38.73±5.86 38.15±5.16 0.740

Run (minute) 16.95±1.12 17.17±0.95 16.82±1.25 16.87±1.16 0.186

	Total	T₁	T₂	T₃	P value
Age (year)^†a	33.05±4.67	32.74±4.53	33.31±4.54	33.10±4.94	0.792
Marital status, n (%) ^††b					0.090
Married	209 (90.8)	66 (85.7)	73 (96.1)	70 (90.9)
Education level, n (%) ^††b					0.507
High school	3 (1.3)	1 (1.3)	1 (1.3)	1 (1.3)
Diploma	113 (49.1)	39 (50.6)	35 (46.1)	39 (50.6)
Associate degree	55 (23.9)	16 (20.8)	15 (19.7)	24 (31.2)
Bachelor	53 (23.1)	19 (24.7)	23 (30.3)	11 (14.3)
Master degree	6 (2.6)	2 (2.6)	2 (2.6)	2 (2.6)
Anthropometrical indices
Weight (kg)^†a	76.39±10.98	76.82±10.28	76.88±10.19	75.49±12.48	0.756
Height (cm)^†a	176.11±7.61	177.50±6.58	176.03±8.84	174.80±7.43	0.056
BMI, n (%) ^††b					0.313
Underweight	5 (2.1)	2 (2.6)	1 (1.3)	2 (2.6)
Normal	131 (56.9)	48 (62.3)	42 (55.3)	41 (53.2)
Overweight	86 (37.4)	25 (32.5)	30 (39.5)	31 (40.3)
Obese	8 (3.4)	2 (2.6)	3 (3.9)	3 (3.9)
WC (cm)^†a	89.69±10.01	89.81±10.30	89.26±9.32	90.00±10.43	0.896
HC (cm)^†a	98.45±13.11	97.75±9.63	96.89±15.65	100.72±14.05	0.422
WHR, n (%) ^††b					0.496
Normal	185 (80.4)	59 (76.6)	64 (84.2)	62 (80.5)
Unormal	45 (19.6)	18 (23.4)	12 (15.7)	15 (19.5)
Mental health indices
Stress, n (%) ^††b					0.251
Unormal	70 (30.4)	29 (37.7)	20 (26.3)	21 (27.3)
Anxiety, n (%) ^††b					0.980
Unormal	53 (23.0)	18 (23.4)	18 (23.7)	17 (22.1)
Depression, n (%) ^††b					0.003
Unormal	53 (23.0)	28 (36.4)	13 (17.1)	12 (15.6)
Physical fitness indices^†a
Sit-up (number)	46.69±5.52	45.89±4.71	47.37±5.74	46.81±6.13	0.307
Pull-up (number)	7.95±3.25	7.85±3.00	7.83±3.55	8.19±3.20	0.029
Push-up (number)	37.69±5.41	36.21±5.23	38.73±5.86	38.15±5.16	0.740
Run (minute)	16.95±1.12	17.17±0.95	16.82±1.25	16.87±1.16	0.186

^†Expressed as mean±SD; ^††indicate number (percent); ^aanalysis of variance (ANOVA) test; ^bChi-square test. BMI, body mass index; WC, waist circumference; HC, hip circumference; WHR, waist to hip ratio.

Nutrients intake were summarized in Table 2. Subjects with higher consumption of dairy products reported higher intakes of dietary groups, macronutrients and micronutrients. In the first tertile, participants received an average of 115.79±61.80 g, in the second tertile, participants received an average of 224.12±73.77 g and in the third tertile, participants received an average of 430.00±173.94 g of dairy products. Participants in the third tertile of dairy products consumption received more protein, carbohydrates, fats, fiber, calcium, iron, phosphorus, cereals, vegetables, fruits, meats, etc. than participants in the first and second tertile of dairy consumption (p < 0.05).

Table 2

Dietary intakes of selected nutrients of study participants across tertiles dairy consumption^†a

	T₁ (115.79 g)	T₂ (224.12 g)	T₃ (430.00 g)	P value
Energy (kcal/day)^†a	2249.93±681.37	2801.12±655.57	3234.55±755.39	< 0.001
Protein (g/day)^†a	72.56±21.16	95.90±20.42	115.46±32.56	< 0.001
Carbohydrate (g/day)^†a	367.20±1124.25	436.66±104.96	493.81±112.90	< 0.001
Fat (g/day)^†a	60.51±29.39	79.27±28.27	94.78±33.33	< 0.001
Cereals (g/day)^†a	579.58±186.58	662.21±200.70	666.89±164.19	0.005
Vegetables (g/day)^††^b	148.56 (20.95–543.00)	198.32 (36.62–467.16)	253.38 (74.66–1330.51)	< 0.001
Fruits (g/day)^††^b	169.69 (10.41–1831.40)	214.58 (0.00–1128.04)	334.81 (10.63–1523.86)	< 0.001
Red meat (g/day)^††^b	3.17 (0.00–24.22)	5.00 (0.00–66.43)	5.00 (0.00–66.00)	0.011
White meat (g/day)^††^b	17.00 (0.00–85.00)	24.28 (0.00–170.00)	24.28 (2.83–425.00)	0.002
Fish (g/day)^††^b	12.74 (0.00–54.63)	20.45 (0.00–147.57)	27.31 (0.00–208.40)	< 0.001
SFA (g/day)^†a	16.57±7.05	21.65±6.80	28.05±11.38	< 0.001
MUFA (g/day)^††^b	16.80 (5.80–64.29)	22.26 (9.58–49.55)	25.76 (8.06–59.47)	< 0.001
PUFA (g/day)^††^b	13.32 (4.78–54.92)	17.72 (8.45–35–84)	19.83 (9.78–38.22)	< 0.001
Fiber (g/day)^†a	35.02±9.36	34.55±9.14	30.56±8.22	0.004
Calcium (mg/day)^†a	868.04±258.65	1195.65±274.43	1478.78±401.88	< 0.001
Iron (mg/day)^†a	20.48±6.08	25.19±5.58	27.69±6.19	< 0.001
Phosphorous (mg/day)^†a	1159.52±377.90	1553.66±320.85	1944.86±551.45	< 0.001
Zinc (mg/day)^†a	9.17±3.26	11.91±2.57	14.95±4.12	< 0.001
Magnesium (mg/day)^†a	322.77±134.05	417.29±103.67	529.43±164.50	< 0.001
Manganese (mg/day)^†a	5.54±2.56	6.63±2.13	7.62±2.52	< 0.001
Copper (mg/day)^†a	1.94±0.64	2.45±0.58	2.77±0.68	< 0.001
Selenium (mg/day)^†a	119.43±42.75	144.32±33.91	169.52±47.29	< 0.001

^†Expressed as mean±SD; ^aanalysis of variance (ANOVA) test; ^††expressed as median (Min-Max); ^bKruskal-Wallis test. SFA, saturated fatty acid; MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid.

Multivariable-adjusted odds ratios for PD and PP indices across tertiles of total and low-fat dairy consumption are indicated in Table 3 and 4. There was a significant negative relationship between the tertiles of total and low-fat dairy consumption and depression both in crude and confounders adjusted models (OR 0.28, 95% CI 0.10 to 0.79, pv = 0.016 and OR 0.28, 95% CI 0.09 to 0.84, pv = 0.023 (for multivariable model), respectively). Also, in about stress, it was found that individuals in the third tertiles of low-fat dairy consumption (after adjusting for confounders) had 0.34 lower odds ratio to be stressed compared with those in the baseline score of low-fat dairy consumption (OR 0.34, 95% CI 0.13 to 0.93, pv = 0.036). But the relationship between total and low-fat dairy consumption and anxiety and total fat dairy and stress were not statistically significant (p > 0.05) and it remained non-significant after adjustment for potential confounders (p > 0.05).

Table 3

Odds ratio (95% CI) for mental health according tertiles dairy consumption

	Tertiles of total dairy consumption					Tertiles of low dairy consumption
	T₁	T₂		T₃		T₁	T₂		T₃
	(115.79 g)	(224.12 g)		(430.00 g)		(74.61 g)	(173.32 g)		(338.19 g)
	OR	OR	(95% CI), Pvalue	OR	(95% CI), Pvalue	OR	OR	(95% CI), Pvalue	OR	(95% CI), Pvalue
Stress
Crude	1.00	0.59	(0.29 to 1.17), 0.134	0.66	(0.33 to 1.30), 0.232	1.00	1.03	(0.53 to 2.00), 0.914	0.50	(0.24 to 1.03), 0.062
Model 1	1.00	0.58	(0.28 to 1.20), 0.145	0.67	(0.33 to 1.37), 0.277	1.00	0.91	(0.45 to 1.83), 0.919	0.41	(0.19 to 0.88), 0.023
Model 2	1.00	0.57	(0.25 to 1.28), 0.177	0.59	(0.25 to 1.43), 0.249	1.00	0.92	(0.42 to 2.02), 0.845	0.34	(0.13 to 0.93), 0.036
Anxiety
Crude	1.00	1.01	(0.48 to 2.14), 0.964	0.92	(0.43 to 1.97), 0.848	1.00	1.20	(0.58 to 2.49), 0.611	0.75	(0.34 to 1.64), 0.477
Model 1	1.00	0.99	(0.46 to 2.16), 0.995	0.83	(0.38 to 1.82), 0.650	1.00	1.09	(0.51 to 2.33), 0.810	0.67	(0.29 to 1.52), 0.347
Model 2	1.00	0.92	(0.38 to 2.24), 0.864	0.60	(0.22 to 1.63), 0.323	1.00	0.93	(0.38 to 2.24), 0.873	0.42	(0.14 to 1.26), 0.123
Depression
Crude	1.00	0.36	(0.16 to 0.76), 0.008	0.32	(0.14 to 0.69), 0.004	1.00	0.53	(0.25 to 1.11), 0.094	0.39	(0.18 to 0.86), 0.020
Model 1	1.00	0.34	(0.15 to 0.75), 0.008	0.30	(0.13 to 0.67), 0.004	1.00	0.47	(0.22 to 1.02), 0.058	0.34	(0.15 to 0.79), 0.012
Model 2	1.00	0.38	(0.15 to 0.92), 0.033	0.28	(0.10 to 0.79), 0.016	1.00	0.41	(0.17 to 1.00), 0.051	0.28	(0.09 to 0.84), 0.023

Model 1, adjusted for age, BMI, WHR, marital status and education level; Model 2, additional adjustment for other food groups.

Table 4

Odds ratio (95% CI) for physical performance according tertiles dairy consumption

	Tertiles of total dairy consumption					Tertiles of low dairy consumption
	T₁	T₂		T₃		T₁	T₂		T₃
	(115.79 g)	(224.12 g)		(430.00 g)		(74.61 g)	(173.32 g)		(338.19 g)
	OR	OR	(95% CI), Pvalue	OR	(95% CI), Pvalue	OR	OR	(95% CI), Pvalue	OR	(95% CI), Pvalue
Crude	1.00	1.06	(0.43 to 2.65), 0.885	1.17	(0.46 to 2.97), 0.732	1.00	1.36	(0.58 to 3.16), 0.475	3.00	(1.02 to 8.86), 0.046
Model 1	1.00	0.84	(0.31 to 2.25), 0.740	1.04	(0.38 to 2.85), 0.993	1.00	1.56	(0.61 to 3.94), 0.345	2.99	(0.95 to 9.43), 0.061
Model 2	1.00	0.93	(0.31 to 2.71), 0.895	0.97	(0.29 to 3.28), 0.969	1.00	1.87	(0.63 to 5.53), 0.258	5.28	(1.08 to 25.71), 0.039

Model 1, adjusted for age, BMI, WHR, marital status and education level; Model 2, additional adjustment for other food groups.

Although no significant differences were found between PP indices (except for pull-up) in any of total dairy consumption tertiles (Table 1) but referring to the total performance in the PP tests, there was a positive association between tertiles of low-fat dairy consumption and suitable PP in the crude and adjusted models in the third tertile. After the potential confounders were occupied, staff in the third tertile had 5.28 more odds to have better PP compared with those in the first tertile of low-fat dairy consumption (OR 5.28, 95% CI 1.08 to 25.71, pv = 0.039). But it was not observed any significant correlation in tertiles of total dairy consumption.

4 Discussion

The present study examined the relationship between dairy products consumption and especially low-fat dairy products with mental problems and physical function of military staff. So far, no such study has been conducted in the military. Few studies in other groups have had contradictory results.

We showed that consumption of dairy products and particularly low-fat products are related to less psychological problems and a better PP level.

4.1 Mental problems

Our hypothesis was that dairy consumption might be inversely associated with PD, and it was recognized by the results. A few studies have recognized an association between dairy products and PD. To our knowledge, this is the first study to examine the relationship between the consumption of dairy products in two types of low and whole-fat and PD in military staff.

A cross-sectional study of Japanese adults aged 19 years and older indicated that higher intake of low-fat dairy products were linked to a lower incidence of depression but whole-fat dairy consumption was not associated with the frequency of depressive symptoms [16]. Consumption of low-fat cheese was associated with lower stress levels and better social function in the Australian population [26]. Also, whole-fat dairy products intakes (cream and ice-cream) were associated with increased stress, anxiety and depression [26].

In a cross-sectional study of pregnant women, there was an inverse link between depressive symptoms and yogurt consumption, but no significant relationship was found with milk consumption [17]. In contrast to our study, in a study on Iranian men and women, Salehi-Abargouei et al. did not find a significant relationship between the daily intake of grains and dairy products and the prevalence of anxiety, depression and psychological distress, based on Hospital-Anxiety- and Depression Scale (HADS) and General Health Questionnaire (GHQ) [19]. Also, dairy products intake was not associated with the perceived stress and depressive symptoms among male and female students in three UK countries [27].

Dairy products may decrease the prevalence of depressive symptoms due to their beneficial contents, such as tryptophan. Tryptophan is an essential amino acid that can be converted into 5-hydroxytryphtophan and then produce the neurotransmitter serotonin. Therefore, intake of the dairy product may recover mental symptoms by the accumulation of the level of serotonin in the brain [28]. Also, the recent results suggest that a higher intake of dietary calcium, magnesium, iron, and zinc is linked with a lower incidence of depression in Japanese staff [29] which was similar to our study.

In relation to low-fat products, whole-fat milk is a primary source of trans-fatty acid (TFA). TFAs can potentially increase inflammation and oxidative stress [30, 31] and also, their intake has been associated with mental problems [32].

4.2 Physical performance

Also, we investigated the relationship between dairy consumption and PP; it was shown that the higher consumption of dairy products was positively related to PP. In a cross-sectional study, higher milk consumption and B-vitamin derived from milk was positively associated with better CRF in children, after adjustment of several potential confounders. The vital roles of B-vitamins in energy production, substrate oxidation and erythropoiesis might be a basis for these relationships [33]. Also, in this regard, Cuenca-García et al. have reported a strong positive relationship between the consumption of dairy products and CRF levels in both genders of European adolescents [18]. Magnussen et al. found a positive association between greater daily intake of calcium and dairy products and muscular power in male boys, however, this association with muscular strength was not significant [34].

A study in Spanish children had contradictory results. Lahoz-García et al. measured the associations between dairy intake and cardiometabolic risk factors in the Cuenca Study. In this study, whole fat milk had a positive association and skimmed milk had a negative association with CRF test [35]. López-Sánchez et al. analyzed the association between the fitness level and dietary patterns in 186 Polish and Spanish male students of sports sciences but no association was found between the dairy products consumption and the level of PP [20].

Whole-fat dairy products are a primary source of trans-fatty acid (TFA). The TFA content of whole-fat dairy products is 3–6 times higher than low-fat products [32]. Trans fatty acids can potentially increase inflammation and oxidative stress [30, 31] which affect body function and decrease the physical ability for better performance [36, 37]. Consequently, it may explain the suitability of low-fat dairy products instead of high-fat ones.

These findings support the complicated relation of lifestyle and nutrition which might moderately explain the two-sided results for epidemiological studies. The reasons of the inconsistency between the results could be described based on differences in gender, assessment of dietary intakes, dissimilarity of participants, variation in confounders and statistical analysis [38].

4.3 Strength and limitation of the study

This is the first study that efforts to exclusively assess the combined relationships of dairy consumption and PD and PP in all populations and specifically in military personnel. Though our efforts might be considered as a primary outcome, but this can serve as an important beginning for understanding the power of dairy consumption on PD and PP in the military. A standard procedure of PP measurement and good quality in sampling are the other strengths of this study.

Our study had some limitations. The cross-sectional design of our study was the main limitation that cannot recognize the causality. Therefore, a prospective cohort or randomized controlled clinical trial could be more conclusive. Moreover, it should be noted that the sample size of our study was low, although sampling at a higher volume was not possible due to sensitive protective conditions. Also, in our country, most of the military staff are men and evaluation in women is also very important in other countries. Consequently, the generalizability of the findings should be with caution.

5 Conclusion

Our study adds to the restricted works studying the relationships between dietary intakes, PP and PD. Future interventional and longitudinal examinations are needed to prove these associations. In conclusion, those with more consumption of dairy products and particularly low-fat products have less PD and better PP level. Finally, military employees are one of the populated occupations in any community and this investigation will be very important in terms of public health.

Footnotes

Acknowledgments

There was no funding provided for this research. The authors appreciate the cooperation of the participants.

Funding

There was no funding provided for this research.

Conflict of interest

The authors have no conflict of interest to report.

References

Xie

, Peng

, Zuo

, Li

. The psychometric evaluation of the Connor-Davidson resilience scale using a Chinese military sample. PLoS One. 2016;11.

Whiteford

, Degenhardt

, Rehm

, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. The Lancet . 2013;382:1575–86.

Noorbala

, Akhondzadeh

. Mental health study process into prevalence of mental disorders in Iran. Arch Iran Med . 2015;18:74–5.

Rahmani

, Milajerdi

, Dorosty-Motlagh

. Association of the Alternative Healthy Eating Index (AHEI-2010) with depression, stress and anxiety among Iranian military personnel. J R Army Med Corps . 2018;164:87–91.

Olesen

, Gustavsson

, Svensson

, et al. The economic cost of brain disorders in Europe. Eur J Neurol . 2012;19:155–62.

Naifeh

, Mash

HBH

, Stein

, Fullerton

, Kessler

, Ursano

. The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS): progress toward understanding suicide among soldiers. Mol Psychiatry . 2019;24:34–48.

Bishwajit

. Nutrition transition in South Asia: the emergence of non-communicable chronic diseases. F1000Research. 2015;4.

McClung

, Karl

, Cable

, et al. Randomized, double-blind, placebo-controlled trial of iron supplementation in female soldiers during military training: effects on iron status, physical performance, and mood. Am J Clin Nutr . 2009;90:124–31.

Cobo-Cuenca

, Garrido-Miguel

, Soriano-Cano

, Ferri-Morales

, Martínez-Vizcaíno

, Martín-Espinosa

. Adherence to the Mediterranean Diet and Its Association with Body Composition and Physical Fitness in Spanish University Students. Nutrients . 2019;11:2830.

10.

Soroush

. Dairy consumption and related disease in personnel of military educating hospital and dormitory. Journal of Epidemiology Annals of Military and Health Sciences Research . 2008;6:159–62.

11.

Prentice

. Dairy products in global public health. Am J Clin Nutr . 2014;99:1212S–6S.

12.

, Lee

, et al. The dual effects of Maillard reaction and enzymatic hydrolysis on the antioxidant activity of milk proteins. J Dairy Sci . 2013;96:4899–911.

13.

Chatterton

, Nguyen

, Bering

, Sangild

. Anti-inflammatory mechanisms of bioactive milk proteins in the intestine of newborns. Int J Biochem Cell Biol . 2013;45:1730–47.

14.

Hosseinzadeh

, Vafa

, Esmaillzadeh

, et al. Empirically derived dietary patterns in relation to psychological disorders. Public Health Nutr . 2016;19:204–17.

15.

, Lv

M-R

, Wei

Y-J

, et al. Dietary patterns and depression risk: a meta-analysis. Psychiatry Res . 2017;253:373–82.

16.

Cui

, Huang

, Momma

, et al. Consumption of low-fat dairy, but not whole-fat dairy, is inversely associated with depressive symptoms in Japanese adults. Soc Psychiatry Psychiatr Epidemiol . 2017;52:847–53.

17.

Miyake

, Tanaka

, Okubo

, Sasaki

, Arakawa

. Intake of dairy products and calcium and prevalence of depressive symptoms during pregnancy in Japan: a cross-sectional study. BJOG . 2015;122:336–43.

18.

Cuenca-García

, Ortega

, Huybrechts

, et al. Cardiorespiratory fitness and dietary intake in European adolescents: the Healthy Lifestyle in Europe by Nutrition in Adolescence study. Br J Nutr . 2012;107:1850–9.

19.

Salehi-Abargouei

, Esmaillzadeh

, Azadbakht

, et al. Do patterns of nutrient intake predict self-reported anxiety, depression and psychological distress in adults? SEPAHAN study. Clin Nutr . 2019;38:940–7.

20.

López-Sánchez

, Radzimiński

, Skalska

, et al. Body composition, physical fitness, physical activity and nutrition in Polish and Spanish male students of Sports Sciences: Differences and correlations. Int J Env Res Public Health . 2019;16:1148.

21.

Chan

, Yau

, Yu

, Woo

. The Role of Dietary Patterns in the Contribution of Cardiorespiratory Fitness in Community-Dwelling Older Chinese Adults in Hong Kong. J Am Med Dir Assoc . 2019;20:558–63.

22.

Esfahani

, Asghari

, Mirmiran

, Azizi

. Reproducibility and relative validity of food group intake in a food frequency questionnaire developed for the Tehran Lipid and Glucose Study. J Epidemiol . 2010;20:150–8.

23.

Food

, Administration

. Whole-grain foods authoritative statement claim notification. Docket 99P-2209, Washington, DC. 1999.

24.

Sahebi

, Asghari

, Salari

. Validation of depression anxiety and stress scale (DASS-21) for an Iranian population. 2005.

25.

Knapik

. The Army Physical Fitness Test (APFT): a review of the literature. Mil Med . 1989;154:326–9.

26.

Crichton

, Murphy

, Bryan

. Dairy intake and cognitive health in middle-aged South Australians. Asia Pac J Clin Nutr . 2010;19:161.

27.

El Ansari

, Adetunji

, Oskrochi

. Food and mental health: relationship between food and perceived stress and depressive symptoms among university students in the United Kingdom. Cent Eur J Public Health . 2014;22:90–7.

28.

Delgado

. Depression: the case for a monoamine deficiency. J Clin Psychiatry. 2000.

29.

Miki

, Kochi

, Eguchi

, et al. Dietary intake of minerals in relation to depressive symptoms in Japanese employees: the Furukawa Nutrition and Health Study. Nutrition . 2015;31:686–90.

30.

Ganguly

, Pierce

. The toxicity of dietary trans fats. Food Chem Toxicol . 2015;78:170–6.

31.

Guillocheau

, Legrand

, Rioux

. Benefits of natural dietary trans fatty acids towards inflammation, obesity and type 2 diabetes: defining the n-7 trans fatty acid family. OCL Oilseeds and fats crops and lipids. 2019;26.

32.

Aro

, Antoine

, Pizzoferrato

, Reykdal

, Van Poppel

. Transfatty acids in dairy and meat products from 14 European countries: the TRANSFAIR study. J Food Compost Anal . 1998;11:150–60.

33.

Moschonis

, Van den Heuvel

, Mavrogianni

, Singh-Povel

, Leotsinidis

, Manios

. Associations of Milk Consumption and Vitamin B2 and B12 Derived from Milk with Fitness, Anthropometric and Biochemical Indices in Children. The Healthy Growth Study. Nutrients. 2016;8:634.

34.

Magnussen

, Fraser

, Smith

. Dietary calcium and dairy intake and muscular fitness phenotypes in Australian children. J Sports Sci. 2020:1-2.

35.

Lahoz-García

, Milla-Tobarra

, García-Hermoso

, Hernández-Luengo

, Pozuelo-Carrascosa

, Martínez-Vizcaíno

. Associations between Dairy Intake, Body Composition, and Cardiometabolic Risk Factors in Spanish Schoolchildren: The Cuenca Study. Nutrients . 2019;11:2940.

36.

I-C

, Chang

H-Y

, Hsu

C-C

, et al. Association between dietary fiber intake and physical performance in older adults: a nationwide study in Taiwan. PLoS One. 2013;8.

37.

, Houston

, Locher

, et al. Higher Healthy Eating Index-2005 scores are associated with better physical performance. J Gerontol A Biol Sci Med Sci . 2012;67:93–9.

38.

Willett

. Nutritional epidemiology. Oxford university press, 2012.