Sleep Biol. Rhythms DOI 10.1007/s41105-017-0123-9
ORIGINAL ARTICLE
The association between sleep pattern and nutrients intake pattern in healthy overweight and obese adults Mona Norouzi1 · Banafshe Hosseini2 · Mehdi Yaseri3 · Mahboobeh Heydari Araghi1 · Kosar Omidian4 · Kurosh Djfarian1
Received: 20 December 2016 / Accepted: 6 September 2017 © Japanese Society of Sleep Research 2017
Abstract Few studies have investigated the association between sleep pattern and nutrient intake pattern. This study was conducted to examine the associations between patterns of nutrient intake and sleep pattern. 108 overweight and obese individuals were recruited to participate in the present cross-sectional study. Participant underwent sleep evaluation through ActiGraph. A 3-day food dietary record was obtained to estimate food intake for each participant. The average of total sleep duration was 7.07 h, average of wake after sleep onset was 0.43 h, average of sleep latency was 0.14 h, and finally, average of sleep efficacy was 93.66%. Moreover, based on principal component analysis, six nutrient intake patterns were identified: the first and second patterns accounting for 53.88% of the total variance and the third and fourth patterns made up 13.6% of the total variance. Totally, the six patterns constitute 74.8% of the total variance. Our results showed that the second nutrient pattern had a negative correlation with total sleep time (P = 0.03); it was positively correlated with sleep latency (P = 0.004). The sixth nutrient pattern was negatively associated with total sleep time (P = 0.007). It was observed that higher intake * Kurosh Djfarian
[email protected] 1
Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran
2
School of Biomedical Sciences and Pharmacy, Faculty of Health and Medicine, University of Newcastle, Newcastle, Australia
3
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
4
College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada
of the fourth pattern had a negative correlation with total sleep time (P = 0.03). Higher intake of the fifth pattern was positively associated with sleep latency (P = 0.05). In summary, we found that nutrient patterns are correlated with sleep pattern. Keywords Sleep pattern · Nutrient pattern · Nutrition · Principal component analysis · Sleep efficacy
Introduction Sleep is a physiological and mental process occurring in human and animal and comes along with reduced attention and awareness to the environment. Body function is one of the most important roles of sleep [1]. The extent of required sleep varies in different individuals and depends on several factors such as age, sex, level of physical activity, light, intensity as well as diet [2]. For instance, evidence suggested that aging is associated with sleep latency and nightmares [3]. Adults need to sleep approximately 7–8 h per day [4], whereas children and adolescents need more hours of sleep. Robust evidence has reported that approximately 28.3% of adults sleep 6 h or less per day [5]. In the developed countries, the prevalence of the insufficient sleep (<6 h of sleep per day) has been increased in the last decade [5, 6]. Several epidemiological studies have shown that both insomnia and over sleeping are associated with poor health outcomes including obesity [7, 8], type 2 diabetes [9, 10], coronary heart disease [11, 12], and hypertension [13, 14]. In addition, it has been reported that insomnia leads to reduced nutrient intake [15–21]. Several cross-sectional studies have shown an association between body mass index (BMI) and both insomnia and over sleeping [22–24]. Weiss et al. [25] showed that in adolescent girls suffering from insufficient
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sleep (defined as <8 h sleep on weekdays), the proportion of calories from fats and carbohydrates were higher and lower, respectively, compared to their counterparts sleeping 8 or more hours on average on weekdays [25]. It has been reported that people with insomnia are more likely to skip the breakfast meal compared to the normal subjects. Findings from several studies have shown that sleep disorders can increase the risk of mortality and morbidity. Similarly, epidemiological and methodological studies have reported that sleep duration is associated with health outcomes [26, 27]. Patients suffering from sleep disorders are more likely to consume high-fat, low-carb diet, and particularly consume less fruits and vegetables [28]. Several studies have shown that dietary intake of macro- and micronutrients is associated with sleep factors such as sleep duration, number of awakening, sleep onset, wake after sleep onset as well as total hours of napping [29–37]. Accordingly, our study aimed to determine the association between nutrient intake patterns and sleep pattern and the contribution of diet in the correlation between sleep duration and related diseases.
Materials and methods The present cross-sectional study was conducted between May 2014 and January 2015 in Imam Khomeini Hospital in Iran. One hundred and eight participants (67 men, 41 women) between 22 and 50 years took part in this study. Participants recruited from those who registered as a first visit in obesity and fitness clinic. The inclusion criteria includes adults in both genders between 20 and 55 years with the BMI ≥25 kg/m2 and the exclusion criteria, namely, were pregnancy, lactation, menopause, history of cancer, untreated thyroid diseases, cardiovascular diseases, being on a special diet for any reason, consuming antihistamines, sleeping or antidepressant medications, consuming nutritional supplements, professional athletes, and smoking more than two times per week. The subjects did not suffer from sleep disturbances. Written consent forms were provided for all participants prior to the study. The demographic data including age, marital status, education level, occupation, and the family history of obesity were collected from all participants through interview. The present study was approved by the local ethics committee of Tehran Medical Science University. Anthropometric assessment Anthropometric measurements including height, weight, waist, and hip circumferences were assessed. Standing height (without shoes) was measured by seca height measuring fixed to the wall and with 0.1 cm precision. Body weight
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was measured by digital equipment (TANITA, BC-418, Japan), while participants had minimum clothing. BMI was calculated by dividing the body weight (in kilograms) to the height (in meters) squared (BMI = weight/height2). According to the standard guidelines [38], overweight and obesity were defined as having BMI ≥25(kg/m²), and BMI ≥30(kg/ m²), respectively. Waist circumference was measured midway between the iliac crest and lower rib margin in the standing position and normal breathing by metric tape and ±0.5 cm accuracy. Hip circumference was measured by metric tape measure at the level of the greatest protrusion of the gluteal muscles. The NHANES protocol was used to evaluate the anthropometric status. To prevent measurement errors, each site was measured three times, and finally, the mean was obtained and considered in the analysis. All measurements were collected and recorded by only one person to avoid errors due to various measurement methods between individuals. Dietary assessment Dietary data were collected by a 3-day food dietary record (using 3 non-consecutive days). To help participants in completing the questionnaire, a comprehensive list of foods and dishes commonly consumed by Iranian adults was constructed. Finally, we computed daily intakes of all food items and then converted them to grams per day using household measures., The following variables were used in the food analysis to identify nutrient patterns: protein, starch, total dietary fiber, glucose, fructose, sucrose, total saturated fatty acids (SFAs), total monounsaturated fatty acids (MUFAs), total polyunsaturated fatty acids (PUFAs), total trans fatty acids (TFAs), cholesterol, vitamin B 12, vitamin A, vitamin D, vitamin E, vitamin K, thiamin, riboflavin, niacin, pantothenic acid, pyridoxin, folate, vitamin C, theobromine, caffeine, choline, betaine, sodium, potassium, phosphorus, magnesium, iron, selenium, calcium, manganese, copper, zinc, and fluoride,. The N4 software was used for the analyses of macro- and micronutrient intakes. Nutritionist IV (N4, Salem, Or: N-Squared Computing, version 3.5) diet analysis and nutrition evaluation software was used to determine the nutritional value of dietary pattern intake. Each food item was coded and added to N4, so the nutritional value of dietary pattern intake was calculated for every individual. Sleep assessment Habitual sleep was assessed using ActiGraphy (GT3X, USA), a device similar to a belt which was worn continuously for 7 days on the waist. The ActiGraphy GT3X has been validated against Polysomnography with excellent correlations in different patient populations [39]. We based our analyses on four measures: total sleep time, wake after sleep
Sleep Biol. Rhythms
onset, sleep latency, and sleep efficacy. Data were analyzed using the Actilife 4.0 software.
Table 1 Demographic data according to gender Variable
Men
Statistical analysis We used Kolmogorov–Smirnov test and Q–Q plot to assess the normal distribution of data. Normally distributed variables were reported as mean ± SD, while, non-parametric data was described as median (interquartile range). To determine the relationship between variables, Pearson and Spearman tests were applied. To obtain the nutrient patterns, we used PCA on standardized value of macro- and micro-nutrients. Principal component analysis (PCA) is a common method for assessing dietary patterns. Since the PCA output results in a large number of factor solutions (as many as there are food groups), it is essential to identify the key dietary patterns [40]. Each participant received a factor score for each identified pattern. We categorized the subjects based on the tertile of nutrient pattern scores. To obtain the simultaneous association between variables and different sleep factors, multivariate analysis of covariance (MANCOVA) was used. All statistical analyses performed by the SPSS software (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0 Armonk, NY: IBM Corp.). P value less than 0.05 considered as statistically significant.
Results In the present study, 108 healthy adults were enrolled. Demographic and anthropometric data are shown in Tables 1 and 2. We found an association between weight (P = 0.001) and height (P = 0.001) with gender. Using a principal component analysis, we identified six major components that characterize the overall nutrients intake patterns in our study population. Nutrient data with absolute scoring coefficients higher than 0.1 were considered as an important contributor of patterns. This analysis was tested by KMO and Bartlett test. The first nutrient pattern includes high intake of niacin, isoleucine, threonine, methionine-to-valine substitution, lucien, zinc, thiamine, tryptophan, cysteine, tyrosine, lysine, phenylalanine, and pantothenic acid, which accounting for 44.85% of the total nutrient pattern variance. The second pattern includes high intake of vitamin C, sucrose, iron, calorie, carbohydrate, oleic acid, potassium, pyridoxine, biotin, and fiber which totally made up 9.03% of the total variance. The third pattern includes high intake of arginine, alanine, proline, glycine, serine, and also low intake of maltose and caffeine which constitutes 8.7% of the total variance. In total, these three patterns composed 62% of the total variance. The fourth nutrient pattern includes high intake of vitamin K, vitamin E, chromium, beta carotene, and folate,
Gender
Age 36.42 ± 7.71 Education Less than high school n = 13 (19.4%) High school n = 30 (44.8%) Associate diploma n = 7 (10.4%) University graduate n = 17 (25.4%) Marriage status Married n = 58 (86.6%) Single n = 7 (10.4%) Other n = 2 (3%) Occupation Housewife n = 0 (0%) Worker n = 38 (56.8%) Employee n = 25 (37.3%) Other n = 4 (6%) Family history of obesity Yes n = 50 (74%) No n = 17 (25%) † ‡
P value Women 33.68 ± 6.71 n = 6 (14.6%) n = 15 (36.6%) n = 3 (7.3%) n = 17 (41.4%) n = 30 (73.2%) n = 9 (22%) n = 2 (4.9%) n = 30 (73.2%) n = 9 (22%) n = 15 (36.6%) n = 1 (2.4%) n = 32 (80%) n = 8 (20%)
0.39† 0.17‡
0.21‡
<0.00001‡
0.62‡
Based on one-way ANOVA Based on χ² Tests
which accounting for 4.9% of the total variance. The fifth nutrient pattern includes high intake of fat, linoleic acid, cholesterol, alpha tocopherol, while low intake of vitamin K, fiber, and biotin. This nutrient pattern accounted for 4.3% of the total variance. Finally, the sixth pattern was loaded positively with high intake of manganese, fluorine, and caffeine and negatively with the intake of nutrients such as molybdenum, sodium, and oleic acid. This pattern made up 3.2% of the total variance. Totally, all six major patterns form 74.8% of the total variance (Tables 3, 4). Our results show a significant association between total sleep time and BMI and hip circumstances HP (P = 0.01, P = 0.02, respectively). We also found a significant association between sleep latency and weight as well as waist circumstances (P = 0.001, P = 0.03, respectively). No significant association was observed between wake after sleep onset as well as sleep efficacy and anthropometric characteristics. Our study demonstrated that the second nutrient pattern was positively correlated with sleep latency (P = 0.004), while it had negative correlation with total sleep time (P = 0.03). The fourth nutrient pattern had a significant negative correlation with total sleep time (P = 0.03). The fifth nutrient pattern was positively associated with latency sleep. It was shown that the sixth nutrient pattern had negative correlation with total sleep time (P = 0.007).
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Table 2 Anthropometric data according to gender
Variable
Gender
Height (cm) Weight (kg) BMI (kg/m²) †
Table 3 Association between sleep pattern and anthropometric data
Men
Women
173.37 ± 6.57 95.6 ± 14.23 31.40 ± 3.51
161.40 ± 5.62 82.58 ± 12.64 31.71 ± 5.02
P value†
168.83 ± 8.51 90.32 ± 14.91 31.52 ± 4.31
0.001 0.001 0.87
Based on Mann–Whitney test
Sleep pattern
Total sleep time (h) Wake after sleep onset (h) Sleep latency (h) Sleep efficacy (%) †
Total
Weight (kg)
Waist circumstance (cm)
Hip circumstance Body mass (cm) index (kg/m²)
r†
P
r†
P
r†
P
r†
P
0.04 0.03 0.31 −0.07
0.62 0.75 0.001 0.44
0.02 −0.009 0.20 −0.03
0.80 0.92 0.03 0.77
0.22 0.003 0.05 −0.05
0.02 0.97 0.55 0.56
0.23 0.03 0.16 0.003
0.01 0.97 0.08 0.97
Spearman correlation
Table 4 Association between sleep pattern and nutrient intake pattern Sleep pattern
Total sleep time (h) Wake after sleep onset (h) Sleep latency (h) Sleep efficacy (%)
Component 1
Component 2
Component 3
Component 4
Component 5
Component 6
r†
P
r†
P
r†
P
r†
P
r†
P
r†
P
−0.12ª −0.04 0.001 0.006
0.21 0.65 0.98 0.50
−0.1 −0.01 0.20 0.03
0.03 0.16 0.004 0.74
−0.07 0.09 −0.06 −0.11
0.48 0.35 0.52 0.26
−0.1 0.07 −0.05 0.01
0.03 0.45 0.61 0.86
0.11 0.04 0.009 0.42
0.26 0.65 0.05 0.08
−0.27‡ −0.04 −0.008 0.59
0.007 0.65 0.93 0.05
†
Spearman correlation
‡
Pearson correlation
Table 5 shows that tertile of the second nutrient pattern was positively correlated with wake after sleep onset (WASO) after adjusting for sex, BMI, family history of obesity, and marital status. In addition, tertile of the sixth nutrient was negatively correlated with total sleep time before and after adjusting for gender, BMI, family history of obesity, and marital status.
Discussion Using principal component analysis in a sample of 108 adult men and women, six dietary components were identified that characterize their nutrient intake. Instead of looking at individual nutrients or foods, pattern analysis examines the effects of overall nutrients and represents a broad spectrum of nutrient consumption. Furthermore, this type of analysis reveals similarities and captures extremes in nutrients intake. Several foods and nutrients have been traditionally associated with sleep status. The effects of food on sleep have not been clear yet. However, there is evidence that an adequate
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sleep pattern acts to protect against a series of nutritional and metabolic disorders [7–10]. These disorders probably occur as sleep plays an important role in the metabolic control of the body. In our study, we found that second nutrient pattern was negatively associated with total sleep duration, while, it had a positive association with sleep latency. The fourth nutrient pattern had negative correlation with total sleep time. In addition, we found that higher intake of the fifth nutrient pattern was positively associated with sleep latency. Finally, higher intake of the sixth nutrient pattern had a negative correlation with total sleep duration. In this study, we did not observe any correlation between nutrient intake pattern and wake after sleep onset as well as sleep efficacy. To the best of our knowledge, no previous study has addressed the association between dietary intake pattern and sleep pattern, and for the first time, we studied the associations between different nutrient intakes and sleep pattern. It would be of great interest to understand whether this matter would promote an effect on sleep patterns. All the previous studies only investigated the association between a single nutrient and sleep pattern. To illustrate, Weiss et al.
Sleep Biol. Rhythms Table 5 Sleep pattern and nutrient pattern across tertile of nutrient pattern scores
S1
First nutrient pattern Total sleep time (h) Wake after sleep onset (h) Sleep latency (h) Sleep efficacy (%) Second nutrient pattern Total sleep time (h) Wake after sleep onset (h) Sleep latency (h) Sleep efficacy (%) Third nutrient pattern Total sleep time (h) Wake after sleep onset (h) Sleep latency (h) Sleep efficacy (%) Fourth nutrient pattern Total sleep time (h) Wake after sleep onset (h) Sleep latency (h) Sleep efficacy (%) Fifth nutrient pattern Total sleep time (h) Wake after sleep onset (h) Sleep latency (h) Sleep efficacy (%) Sixth nutrient pattern Total sleep time (h) Wake after sleep onset (h) Sleep latency (h) Sleep efficacy (%) *
S3
Diff
P value P value*
95% CI Lower
Upper
7.2 ± 0.9 0.45 ± 0.22 0.15 ± 0.05 93.4 ± 2.7
7 ± 1 0.41 ± 0.2 0.14 ± 0.06 94 ± 2.7
0.2291 0.0360 0.0065 −0.5947
−0.2519 −0.0670 −0.0202 −1.9474
0.7101 0.1389 0.0331 0.07580
0.345 0.488 0.629 0.383
0.988 0.156 0.42 0.554
7.2 ± 1.28 0.46 ± 0.22 0.14 ± 0.05 93.4 ± 2.9
6.8 ± 0.8 0.39 ± 0.14 0.16 ± 0.05 94 ± 2.2
0.3995 0.0711 −0.0161 −0.6684
−0.0604 −0.0193 −0.0429 −1.9341
0.8594 0.1615 0.0107 0.5972
0.087 0.121 0.235 0.295
0.195 0.044 0.458 0.157
7 ± 0.9 0.45 ± 0.17 0.15 ± 0.04 93.7 ± 2.2
7.2 ± 1 0.45 ± 0.21 0.14 ± 0.05 93.3 ± 2.6
−0.2100 0.0021 0.0070 0.4553
−0.6626 −0.0937 −0.0135 −0.7295
0.2426 0.0979 0.0275 1.6401
0.357 0.966 0.496 0.446
0.777 0.096 0.675 0.545
7.1 ± 0.9 0.41 ± 0.18 0.15 ± 0.06 94 ± 2.7
7 ± 1 0.45 ± 0.2 0.14 ± 0.03 93.5 ± 2.4
0.0522 −0.0327 0.0021 0.4908
−0.4174 −0.1287 −0.0214 −0.7856
0.5218 0.0634 0.0257 1.7672
0.825 0.499 0.857 0.445
0.344 0.513 0.621 0.418
7.1 ± 0.9 0.45 ± 0.2 0.15 ± 0.06 93.4 ± 2.5
7.2 ± 0.8 0.43 ± 0.2 0.16 ± 0.04 93.7 ± 2.8
−0.1223 0.0248 −0.0078 −0.3111
−0.5432 −0.0733 −0.0319 −1.6435
0.2986 0.1229 0.0163 1.0212
0.563 0.616 0.519 0.642
0.933 0.549 0.811 0.238
7.2 ± 0.9 0.45 ± 0.22 0.15 ± 0.05 93.4 ± 2.7
7.3 ± 1 0.45 ± 0.21 0.14 ± 0.05 93.3 ± 2.6
−0.6975 0.0041 −0.0032 −0.4603
0.2335 −0.0864 −0.0287 −1.7720
1.1615 0.0946 0.0224 0.8513
0.004 0.928 0.805 0.486
0.023 0.404 0.989 0.882
Adjusted for gender, BMI, family history of obesity and marital status
observed that decrease in total sleep time in adolescents was associated with a relatively higher caloric intake from fat. Another study demonstrated that those with insufficient sleep are more likely to consume energy-rich foods (such as fats or carbohydrates) [25]. Similarly, another study shown that energy density (the amount of energy in a given weight of food) can affect the sleep patterns of individuals in both genders [41]. In one study, it has been reported that dietary vitamin C was negatively associated with sleep duration [42]. Recent sleep restriction study of Markwald et al. [43] showed that food intake, particularly carbohydrates, was higher in short sleepers. Thus, recent evidence suggests that a change in appetite hormones is probably not the main mechanism through which sleep restriction affects food intake. It is well documented that iron absorption is affected by a variety of dietary factors. The amount of iron consumed indicates the possibility of low bioavailability and
anemia among short sleeper [44]. One study reported that iron intake was negatively associated with late midpoint of sleep [45]. Another study found that infants who received iron supplementation had greater sleep duration compared to those who did not receive [46]. Little is known about the effects of mineral nutrients on human sleep pattern. High potassium levels have been shown to shorten the period of circadian rhythms in a variety of organisms. Sato-mito et al. [45] showed that potassium had negative association with late midpoint of sleep. In contrast, another study found no significant relation between potassium supplement and total sleep time [47]. Moreover, consuming fiber-rich foods such as vegetables and fruits is one strategy for decreasing the energy density of the diet [48]. One study shown that low fiber diet is associated with lighter, less restorative sleep with more arousals [49]. In the same line with these data, epidemiologic studies have been indicated that short sleep
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duration is associated with higher caloric consumption and poor dietary quality [32, 50, 51], which can be explained by the mechanism that decreased total sleep time can increase food intake by modulating key appetite hormones such as cortisol, leptin, and ghrelin to the extent that the sensation of hunger is enhanced. We found that some nutrient patterns were associated with sleep pattern. Although data about nutrient intake and sleep pattern are scarce, association between dietary intakes of MUFA, PUFA, SFA, and alpha tocopherol has been reported previously [44]. We found for the first time that anthropometric variables were significantly correlated with sleep latency; however, these correlations were not clinically strong enough. one of the study shows that short sleep appears independently associated with weight gain, particularly in younger age group. This study show that finding in both cross-sectional and cohort studies suggested that short sleep duration is strongly and consistently associated with concurrent and future obesity [52]. The other study shows that in older men and women, actigraphy-ascertained reduced sleep durations are strongly associated with greater adiposity [53]. Therefore, further investigations are required to elucidate correlations between sleep latency and anthropometric variables. In addition, a positive correlation was found between BMI and total sleep time. However, there was no significant relation between any anthropometric variables and wake after sleep onset as well as sleep efficacy in our study. Similarly, other study showed a negative association between total sleep duration and body mass index [54]. Another study has shown that total sleep time had a positive correlation with BMI [44]. Our study has several limitations. First, as a cross-sectional study, we could not find cause–effect relationships between sleep pattern and nutrient pattern. Second, our target study population was overweight and obese people, so it may not be representative of the general population. Finally, our study was conducted on a small sample size, and therefore, further studies with a large sample size are needed to address the correlation between sleep pattern and nutrient pattern. Acknowledgements The authors would like to appreciate all the participants of the study who carefully and patiently took part in the study. This research received no grant from any funding agency. Compliance with ethical standards Conflict of interest All authors have no conflict of interest regarding this paper. Ethical Approval Ethical Committee Permission 91012717318. This study investigated the association between intakes of both macronutrient and micronutrient with quality and quantity of sleep. We divided all nutrients to six groups and investigated the role of each group in sleep patterns. We found that nutrient patterns are correlated with sleep
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Sleep Biol. Rhythms pattern. We believe our work will be of interest to the readers in the areas of sleep patterns and nutrients intake studies. This research was not supported financially from any institute.
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