Researchers found that postmenopausal women who consume a diet high in refined carbohydrates can develop insomnia


An estimated 30% of adults experience insomnia, and a new study by researchers at Columbia University Vagelos College of Physicians and Surgeons suggests that diet may be partly to blame.

The study found that postmenopausal women who consumed a diet high in refined carbohydrates – particularly added sugars – were more likely to develop insomnia.

Women whose diet included higher amounts of vegetables, fiber, and whole fruit (not juice) were less likely to develop problems with insomnia.

“Insomnia is often treated with cognitive behavioral therapy or medications, but these can be expensive or carry side effects,” says the study’s senior author James Gangwisch, Ph.D., assistant professor at Columbia University Vagelos College of Physicians and Surgeons.

“By identifying other factors that lead to insomnia, we may find straightforward and low-cost interventions with fewer potential side effects.”

The findings were published Dec. 11 online in The American Journal of Clinical Nutrition.

Link Between Diet and Sleep Is an Emerging Research Area

Previous studies have explored a possible link between refined carbohydrates and insomnia, but results have been inconsistent. And because the studies didn’t follow individuals over time, it’s not clear if a diet that’s high in refined carbs triggered the onset of insomnia, or if insomnia caused individuals to eat more sweets.

One way to determine if carb intake is causing sleep problems is to look for the emergence of insomnia in people with different diets.

In the current study, Gangwisch and his team gathered data from more than 50,000 participants in the Women’s Health Initiative who had completed food diaries.

The researchers looked at whether women with higher dietary glycemic index were more likely to develop insomnia.

All Carbs Aren’t Created Equally

Different kinds and amounts of carbohydrates increase blood sugar levels to varying degrees. Highly refined carbohydrates—such as added sugars, white bread, white rice, and soda—have a higher glycemic index, and cause a more rapid increase in blood sugar.

“When blood sugar is raised quickly, your body reacts by releasing insulin, and the resulting drop in blood sugar can lead to the release of hormones such as adrenaline and cortisol, which can interfere with sleep,” Gangwisch says.

The researchers hypothesized that the rapid spikes and troughs in blood sugar after eating refined carbs may trigger insomnia.

Refined Carbs Triggered Insomnia

They found that the higher the dietary glycemic index – particularly when fueled by the consumption of added sugars and processed grains – the greater the risk of developing insomnia.

They also discovered that women who consumed more vegetables and whole fruits (not juices) were less likely to develop insomnia.

“Whole fruits contain sugar, but the fiber in them slow the rate of absorption to help prevent spikes in blood sugar,” says Gangwisch.

“This suggests that the dietary culprit triggering the women’s insomnia was the highly processed foods that contain larger amounts of refined sugars that aren’t found naturally in food.”

Since most people, not just postmenopausal women, experience a rapid rise in blood sugar after eating refined carbohydrates, the authors suspect that these findings may also hold true in a broader population.

“Based on our findings, we would need randomized clinical trials to determine if a dietary intervention, focused on increasing the consumption of whole foods and complex carbohydrates, could be used to prevent and treat insomnia,” says Gangwisch.

The paper is “High glycemic index and glycemic load diets as risk factors for insomnia: Analyses from the Women’s Health Initiative.”

Adequate and quality sleep are both crucial to ideal health conditions. The recommended hours of sleep by the National Sleep Foundation are 7–9 hr for adults to maintain health (Hirshkowitz et al., 2015).

A growing body of evidence suggests that poor sleep quality elevates the risk of overweight or adiposity (Cappuccio et al., 2008) and other metabolic diseases (Ju & Choi, 2013; Shan et al., 2015).

It is especially noteworthy that short sleep duration (generally the amount of time in bed spent asleep less than 7 hr; Dashti et al., 2016) is linked to high levels of inflammation, low levels of cognition, and all‐cause mortality (Campanini et al., 2017; Dashti et al., 2016; Huang, Wahlqvist, & Lee, 2013).

A large number of researches imply that the prevalence of poor sleep quality is between 6.6% and 29.4% among Chinese adults, compared with that between 25.6% and 48.1% reported by researches abroad (Del Brutto et al., 2016; Katagiri, Asakura, Kobayashi, Suga, & Sasaki, 2014; Lohsoonthorn et al., 2013).

Therefore, the high prevalence and unfavorable outcomes have rendered poor sleep quality, an increasing concern for public health. Apart from genetic factors, a plenty of studies have demonstrated that many other factors are related to sleep, such as gender, age, occupation, psychological state (Haseli‐Mashhadi et al., 2009; Knutson, 2013). Nevertheless, it has been recognized that dietary modification is implicated in sleep alteration. One diet intervention study found that lower fiber and high saturated fat intake were associated with less slow wave sleep in normal adults (St‐Onge, Roberts, & Shechter, 2016).

Another randomized clinical trial among obese men with chronic insomnia showed that compared to the controls, the diet group, in which 300–500 kcal/day less energy intake and optimized nutrient composition were recommended, had shorter objective sleep onset latency after intervention (Tan et al., 2016). A cohort study in Rotterdam found that increase in each percent of sleep efficiency was related to reduction of 11.1kcal energy (Dashti et al., 2016).

One cross‐sectional study among Japanese workers indicated that there was an association between self‐reported poor sleep quality and higher percentage energy intake for protein (Tanaka et al., 2013). Another three studies, respectively, suggested that poor sleep quality was related to less consumption of vegetables, fish, or both (Del Brutto et al., 2016; Huang et al., 2013; Katagiri et al., 2014). What is more, exploring the correlation of sleep quality and diet is essential because sleep quality cannot be autonomously improved easily while diet quality can (Katagiri et al., 2014).

However, most epidemiology studies with respect to exploring the relationship between sleep quality and diet use only one way of diet assessment. In addition, diet in China is distinct from that in western countries, even other Asian countries. Hence, in the background of Chinese diet our study aimed to determine whether an association between diet and sleep quality existed in Chinese urban adults with evaluating both short‐term and long‐term diet via diet quality, certain food consumption, and dietary nutrients intake.


Sociodemographic factors, lifestyle factors, and health‐related indicators of the participants included in this analysis are shown between two groups in Table ​Table1.1. Overall, 25.2% (390) of the total had a poor sleep quality. Women, individuals above 45‐year‐old, or with normal BMI and above, current‐smokers tended to have a higher risk of poor sleep quality. Besides, poor sleep quality was significantly associated with hypertension, sub‐health, or illness of self‐assessment compared with health.

There was also a positive association between the prevalence of poor sleep quality and the frequency of self‐rated mental stress. No significant association was seen between sleep quality and other characteristics.

Table 1

Basic characteristics of Chinese urban adults by sleep quality (N %)

 Sleep qualityOR(95% CI)a
N = 1,158N = 390
Sociodemographic factors
Male436 (37.7)116 (29.7)Ref
Female722 (62.3)274 (70.3)1.04 (0.84–1.30)
Age (y)
18–30228 (19.7)38 (9.7)Ref
30–45219 (18.9)42 (10.8)1.56 (1.04–2.34)
45–65366 (31.6)157 (40.3)3.47 (2.46–4.92)
≥65345 (29.8)153 (39.2)4.05 (2.85–5.75)
First‐tier cities374 (32.3)126 (32.3)Ref
Second‐tier cities267 (23.1)75 (19.2)0.89 (0.64–1.24)
Third‐tier cities517 (44.6)189 (48.5)1.19 (0.91–1.56)
Han Nationality1,131 (97.7)380 (97.4)Ref
1.11 (0.52–2.35)
Hon‐Han Nationality27 (2.3)10 (2.6) 
Occupational statusb
Employed478 (41.3)114 (29.2)Ref
Unemployed57 (4.9)20 (5.1)1.19 (0.68–2.09)
Retired440 (38)195 (50)1.12 (0.79–1.58)
Never work183 (15.8)60 (15.4)1.17 (0.79–1.73)
Education level
Junior high school or below376 (32.5)176 (45.1)Ref
Senior high school445 (38.4)138 (35.4)0.81 (0.61–1.06)
Bachelor’s degree or above337 (29.1)76 (19.5)0.79 (0.55–1.12)
Marital statusc
Unmarried193 (16.7)32 (8.2)Ref
Married891 (76.9)316 (81)1.10 (0.50–2.46)
Divorced71 (6.1)41 (10.5)1.36 (0.56–3.35)
Household monthly income (rmb: yuan)
<5,000561 (48.4)206 (52.8)Ref
5,000–9999361 (31.2)116 (29.7)0.91 (0.70–1.20)
>10,000236 (20.4)68 (17.4)0.80 (0.58–1.10)
Lifestyle factors
Self‐rated health condition
Healthy485 (41.9)91 (23.3)Ref
Sub‐healthy523 (45.2)201 (51.5)2.07 (1.56–2.74)
Diseased98 (8.5)81 (20.8)3.41 (2.33–5.00)
Recovering of diseases22 (1.9)9 (2.3)2.03 (0.90–4.59)
Unknown30 (2.6)8 (2.1)1.58 (0.69–3.61)
Self‐rated mental stress
Always15 (1.3)22 (5.6)Ref
Often257 (22.2)99 (25.4)0.29 (0.14–0.61)
Sometimes520 (44.9)154 (39.5)0.21 (0.10–0.42)
Seldom351 (30.3)110 (28.2)0.15 (0.07–0.31)
Unknown15 (1.3)5 (1.3)0.21 (0.06–0.73)
Intensity of physical activities
High280 (24.2)99 (25.4)Ref
Medium608 (52.5)194 (49.7)0.88 (0.66–1.18)
Low270 (23.3)97 (24.9)1.07 (0.77–1.50)
Never smoke858 (74.1)296 (75.9)Ref
Previous smoker139 (12)34 (8.7)0.94 (0.57–1.53)
Current smoker152 (13.1)59 (15.1)1.61 (1.05–2.46)
Unknown9 (0.8)1 (0.3)0.38 (0.05–3.02)
Health‐related indicators
Yes343 (29.6)150 (38.5)Ref
0.83 (0.64–1.06)
No815 (70.4)240 (61.5) 
Yes386 (33.3)179 (45.9)Ref
0.75 (0.58–0.98)
No772 (66.7)211 (54.1) 
Yes143 (12.3)60 (15.4)Ref
0.96 (0.69–1.35)
No1,015 (87.7)330 (84.6) 
Underweight73 (6.3)9 (2.3)Ref
Normal569 (49.1)188 (48.2)2.16 (1.04–4.46)
Overweight or obese516 (44.6)193 (49.5)2.15 (1.03–4.48)

Abbreviation: Ref, reference; OR, odds ratios; CI, confidence interval.aAll the sociodemographic factors, lifestyle factors, and health‐related indicators were presented as N (%), and differences between groups were compared using binary logistics regression adjusted for gender, age, or both.bOne missing value in the poor sleep quality group.cThree missing values in the good sleep quality group and one in the poor sleep quality group.

Diet quality

Total and individual score of CHDI between two sleep quality groups are shown in Table ​Table2.2. The total score of CHDI was found significantly higher in the group of good sleep quality. Moreover, the components of CHDI which showed significant correlations with sleep quality were food variety, fruits and fish, shellfish, and mollusk.

Table 2

Total and individual CHDI score by sleep quality (Median (P25, P75))

 Sleep qualityp a
N = 1,158N = 390
CHDI(score)56.8 (48.1, 64.7)54.2 (46.5, 62.6)0.002
CHDI items(score)
Food variety7.1 (4.3, 10.0)5.7 (2.9, 10.0)0.004
Refined grains5.0 (5.0, 5.0)5.0 (5.0, 5.0)0.141
Whole grain, dry bean, and tuber1.8 (0.0, 5.0)2.4 (0.0, 5.0)0.213
Total vegetables3.5 (2.0, 5.0)3.8 (1.9, 5.0)0.380
Dark green and orange vegetables1.5 (0.0, 4.1)1.1 (0.0, 3.9)0.226
Fruit5.1 (0.0, 10.0)3.1 (0.0, 10.0)0.006
Dairy0.0 (0.0, 0.7)0.0 (0.0, 0.8)0.317
Soybean0.0 (0.0, 10.0)0.0 (0.0, 10.0)0.330
Meat and egg5.0 (3.4, 5.0)5.0 (3.2, 5.0)0.757
Fish, shellfish, and mollusk0.0 (0.0, 2.3)0.0 (0.0, 0.0)0.018
Calories from SFA10.0 (10.0, 10.0)10.0 (10.0, 10.0)1.000
Sodium4.7 (0.9, 7.2)4.7 (1.0, 7.4)0.854
Empty calories10.0 (9.1, 10.0)10.0 (9.0, 10.0)0.463

Abbreviations: SFA, saturated fatty acid.aThe total and individual CDHI cores were presented as median (P25‐P75), and differences between groups were compared using nonparametric test.

In multivariable logistic regression analysis of CHDI and sleep quality, a higher total score of CHDI was inversely related to poor sleep quality in the crude model. After adjustment for gender and age, this correlation still remained, and it was the same case with fully adjusted model (Table ​(Table5).5). The associations between the individual score of 3 CHDI components and sleep quality were likewise statistically significant in fully adjusted model, which denoted greater food varieties, higher consumption of fruits, fish, shellfish, and mollusk were correlated with lower risk of poor sleep quality.

Table 5

Unadjusted and adjusted odds ratios and 95% confidence intervals for poor sleep quality among 1,548 Chinese urban adults

 ModelQ1 (Lowest)Q2Q3Q4 (Highest)p for trenda
N = 387N = 387N = 387N = 387
CHDI(score)Model1bRef1.01 (0.74, 1.38)0.78 (0.56, 1.07)0.60 (0.43, 0.84)*0.003
 Model2cRef0.97 (0.70, 1.33)0.76 (0.55, 1.06)0.58 (0.41, 0.82)*0.006
 Model3dRef0.95 (0.68, 1.33)0.80 (0.57, 1.12)0.62 (0.43, 0.88)*0.031
CHDI items
Food variety  Model1bRef0.82 (0.60, 1.13)0.81 (0.57, 1.14)0.63 (0.46, 0.85) *0.004
Model2cRef0.81 (0.58, 1.12)0.82 (0.58, 1.17)0.65 (0.47, 0.89) *0.010
Model3dRef0.83 (0.59, 1.17)0.85 (0.59, 1.22)0.70 (0.50, 0.97) *0.043
Fruit  Model1bRef0.78 (0.54, 1.13)0.65 (0.47, 0.90)*0.71 (0.53, 0.94) *0.006
Model2cRef0.70 (0.48, 1.02)0.58 (0.42, 0.81)*0.65 (0.48, 0.87) *0.001
Model3dRef0.69 (0.46, 1.01)0.60 (0.42, 0.84)*0.71 (0.52, 0.97) *0.012
Fish, shellfish, and mollusk  Model1bRef0.91 (0.47, 1.77)0.70 (0.53, 0.92) * 0.012
Model2cRef1.00 (0.51, 1.98)0.71 (0.53, 0.95) * 0.019
Model3dRef1.01 (0.50, 2.03)0.73 (0.54, 0.98) * 0.036
Total energy(kJ)Model1bRef0.86 (0.63, 1.17)0.75 (0.54, 1.03)0.61 (0.44, 0.84)*0.002
Model2cRef0.87 (0.63, 1.19)0.77 (0.56, 1.07)0.73 (0.52, 1.04)0.074
Model3dRef0.85 (0.61, 1.19)0.79 (0.56, 1.11)0.70 (0.49, 1.00)*0.055
Food categories
Fresh vegetables
(g/1,000 kcal)
Model1bRef0.78 (0.55, 1.10)1.38 (1.00, 1.91)*1.42 (1.03, 1.95)*0.002
Model2cRef0.66 (0.46, 0.95)*1.10 (0.79, 1.55)1.03 (0.73, 1.44)0.243
Model3dRef0.71 (0.49, 1.03)1.12 (0.79, 1.59)1.11 (0.78, 1.57)0.149
(g/1,000 kcal)
Model1bRef0.69 (0.39, 1.22)0.89 (0.67, 1.18)0.67 (0.50, 0.90)*0.014
Model2cRef0.76 (0.42, 1.37)0.96 (0.72, 1.29)0.68 (0.50, 0.91)*0.012
Model3dRef0.73 (0.40, 1.34)1.02 (0.75, 1.37)0.68 (0.49, 0.93)*0.015
(g/1,000 kcal)
Model1bRef1.27 (0.90, 1.77)1.25 (0.89, 1.75)1.70 (1.23, 2.36)*0.002
Model2cRef1.16 (0.82, 1.64)1.10 (0.78, 1.55)1.45 (1.04, 2.04)*0.033
Model3dRef1.22 (0.85, 1.74)1.17 (0.82, 1.68)1.55 (1.09, 2.20)*0.020
(ml/1,000 kcal)
Model1bRef0.74 (0.57, 0.97)*  
Model2cRef1.22 (0.90, 1.65)  
Model3dRef1.20 (0.87, 1.64)  

Abbreviations: Q, quartile; Ref, reference.aThe results of multivariable binary logistic regression were showed as unadjusted and adjusted odds ratios and 95% confidence intervals for poor sleep quality. Testing for linear trend was performed by applying the median of every dietary variable to each category and analyzing them as continuous variables in logistic regression.* indicated p < 0.05.bModel1 was unadjusted.cModel2 was adjusted for gender and age.dModel3 was adjusted for gender, age, self‐rated health condition, self‐assessed mental stress, smoking, hypertension, BMI.

Food groups

Table ​Table33 presents the distribution of food consumption between two sleep quality groups. Consumption of fresh vegetables, seafood, eggs, and beverage differentiated significantly between two groups. Poor sleep quality group reported lower intake of seafood and beverage, contrary to higher intake of fresh vegetables and eggs.

Table 3

Consumption of food by sleep quality (Median (P25, P75))

 Sleep qualityp a
N = 1,158N = 390
Cereals(g/1,000 kcal)162.8 (96.9, 234.2)170.5 (102.7, 264.0)0.112
Tubers(g/1,000 kcal)10.3 (3.0, 26.0)10.6 (3.3, 27.6)0.634
Fresh vegetables(g/1,000 kcal)151.4 (73.2, 271.2)188.3 (87.1, 314.0)0.002
Fruits(g/1,000 kcal)68.2 (29.4, 139.0)76.0 (28.0, 147.3)0.639
Poultry and livestock
(g/1,000 kcal)
35.6 (14.3, 68.3)34.5 (13.0, 71.2)0.650
Seafood(g/1,000 kcal)1.3 (0.0, 8.9)0.2 (0.0, 5.8)0.006
Freshwater products
(g/1,000 kcal)
4.8 (0.3, 14.8)4.3 (0.0, 12.0)0.235
Eggs(g/1,000 kcal)23.8 (10.3, 40.5)27.7 (14.3, 45.4)0.005
Milk and dairy products
(g/1,000 kcal)
36.9 (0.0, 114.8)44.2 (0.0, 125.2)0.427
Soybeans and soybean products
(g/1,000 kcal)
13.5 (3.8, 33.4)14.3 (4.2, 37.6)0.415
Nuts(g/1,000 kcal)3.6 (0.0, 13.3)3.3 (0.0, 13.4)0.464
Water(ml/1,000 kcal)800.0 (375.0, 1525.0)775.0 (275.0, 1,500.0)0.322
Salt(g/1,000 kcal)5.8 (3.1, 9.3)5.7 (3.2, 10.0)0.600
Cooking oil(g/1,000 kcal)14.5 (7.1, 24.0)14.9 (7.1, 27.1)0.503
Pickled food(g/1,000 kcal)0.6 (0.0, 3.9)0.7 (0.0, 4.5)0.469
Sugar‐sweetened beverage
(ml/1,000 kcal)
0.0 (0.0, 0.0)0.0 (0.0, 0.0)0.044
Tea(ml/1,000 kcal)4.3 (0.0, 133.0)0.0 (0.0, 175.5)0.422
Coffee(ml/1,000 kcal)0.0 (0.0, 0.0)0.0 (0.0, 0.0)0.168
Alcoholic drinks(ml/1,000 kcal)0.0 (0.0, 3.8)0.0 (0.0, 1.1)0.225

The consumption of food groups was presented as median (P25‐P75), and differences between groups were compared using nonparametric test.

The results of multivariable logistic regression analysis of food consumption and sleep quality are indicated in Table ​Table55 as well. Lower seafood consumption and higher eggs consumption significantly increased the risk of poor sleep quality in both unadjusted and adjusted analyses. Intake of fresh vegetables was positively associated with the risk of poor sleep quality in the unadjusted model, while this association did not exist in model 2 and model 3 after adjustment for potential confounders. Participants who reported to have consumed beverage had an elevated risk of poor sleep quality in the crude model, whereas this correlation did not meet the threshold for statistical significance in both partially adjusted and fully adjusted models.


Distribution of nutrients intake between two sleep quality groups is shown in Table ​Table4.4. Total energy intake was found lower in poor sleep quality group,apart from which no association of other nutrients with sleep quality was observed in univariate analysis. In the following multivariable logistic regression, a positive correlation was identified between total energy intake and the risk of poor sleep quality in both unadjusted and fully adjusted models (Table ​(Table55).

Table 4

Intake of nutrients by sleep quality (Median P25, P75)

 Sleep qualityp a
N = 1,158N = 390
Energy (kJ)6,726.46 (5,045.55, 8,950.48)6,178.39 (4,706.71, 8,271.08)0.002
Protein (%E)0.14 (0.12, 0.17)0.14 (0.12, 0.17)0.353
Fat (%E)0.34 (0.26, 0.42)0.35 (0.25, 0.43)0.885
Carbohydrate (%E)0.58 (0.48, 0.66)0.57 (0.47, 0.67)0.824
Diet fiber (g/1,000 kcal)5.41 (3.80, 7.90)5.46 (3.85, 7.72)0.660
Cholesterol (g/1,000 kcal)169.29 (54.37, 285.61)156.08 (45.33, 303.34)0.784
Vitamin A (μg/1,000 kcal)196.28 (116.84, 327.8)202.09 (112.81, 307.85)0.577
Retinol (μg/1,000 kcal)69.32 (23.17, 123.44)71.6 (17.01, 130.3)0.920
Thiamin (mg/1,000 kcal)0.48 (0.39, 0.59)0.49 (0.38, 0.61)0.401
Riboflavin (mg/1,000 kcal)0.44 (0.34, 0.59)0.45 (0.33, 0.63)0.402
Niacin (mg/1,000 kcal)6.64 (5.43, 8.46)6.5 (5.38, 8.3)0.495
Vitamin C (mg/1,000 kcal)35.59 (18.99, 60.91)35.18 (17.05, 60.73)0.590
Vitamin E (mg/1,000 kcal)13.02 (8.43, 19.41)13.38 (8.74, 19.95)0.249
α‐Vitamin E (mg/1,000 kcal)3.83 (2.58, 5.49)3.99 (2.69, 5.5)0.363
Folic acid (μg/1,000 kcal)109.34 (77.44, 163.93)110.63 (76.66, 169.67)0.949
Vitamin B6 (mg/1,000 kcal)0.53 (0.41, 0.66)0.52 (0.4, 0.63)0.167
Vitamin B12
(μg/1,000 kcal)
1.11 (0.45, 2.21)1.07 (0.38, 1.95)0.181
Vitamin D (μg/1,000 kcal)0.35 (0.05, 2.33)0.31 (0.06, 1.95)0.489
Ca (mg/1,000 kcal)208.28 (138.82, 313.59)204.07 (124.73, 334.49)0.672
P (mg/1,000 kcal)512.18 (436.21, 581.78)513.06 (435.73, 591.89)0.371
K (mg/1,000 kcal)906.93 (711.22, 1,142.26)901 (718.19, 1,140.98)0.905
Na (mg/1,000 kcal)2,590.3 (1837.43, 3,718.88)2,597.3 (1779.5, 3,701.27)0.807
Mg (mg/1,000 kcal)144.23 (118.92, 175.3)143.68 (120.85, 178.45)0.596
Fe (mg/1,000 kcal)10.25 (8.77, 12.09)10.11 (8.73, 12.16)0.861
Zn (mg/1,000 kcal)5.38 (4.77, 6.2)5.42 (4.67, 6.46)0.620
Se (μg/1,000 kcal)20.57 (15.89, 27.15)20.84 (15.19, 27.3)0.760
Cu (mg/1,000 kcal)0.94 (0.77, 1.17)0.94 (0.78, 1.18)0.556
Mn (mg/1,000 kcal)2.81 (2.28, 3.45)2.81 (2.29, 3.52)0.653

The intake of nutrients was presented as median (P25, P75), and differences between groups were compared using nonparametric test.


The present study examined the associations between dietary factors and sleep quality. Our results suggest that better sleep quality is significantly related to better diet quality, which features greater food varieties, higher consumption of fruits and fish, especially seafood, along with lower ingestion of eggs and higher total energy intake. Furthermore, all of the abovementioned associations were dose‐dependent and still exhibited statistical significance after adjustment for a series of potential confounding variables.

Prior studies mainly focused on individual nutrients and foods, so they may not explore their synergistic effects. In addition, as different countries have different dietary patterns and dietary intake, it is reasonable to examine the diet holistically on a basis of dietary patterns or diet quality, which have been suggested to better reflect the complexity of dietary intake (Hu, 2002). Further, studies focusing on food groups and overall diet are also necessary, as they would provide an approachable benchmark for alternations in food choice and lifestyle (Katagiri et al., 2014).

The current study provided new evidence of a positive association between diet quality, food diversity, and sleep quality; however, we only found a similar result in one study (van Lee et al., 2017). This study came to a conclusion that good sleep quality was associated with a better diet quality, which is consistent with our study. Although it was derived from a cohort of healthy pregnant women instead of general population in Singapore, this result was still comparable to ours. It is similar to ours that sleep quality was also assessed by PSQI, and diet quality was also evaluated by a kind of diet index—Healthy Eating Index in this cohort study. Among the findings, our study suggested that the correlation of diet with sleep quality overlaps that of sleep efficiency or sleep duration in other studies, both of which belong to the seven components of sleep quality measured by PSQI. Short sleep duration (generally the amount of time in bed spent asleep <7 hr; Dashti et al., 2016) and low sleep efficiency (generally the percentage of sleep duration in bed <85%; St‐Onge, Mikic, & Pietrolungo, 2016) commonly denote poor sleep quality. Compared to adequate sleep, the relationship between short sleep duration and poor diet quality as assessed with various diet quality scores has been extensively shown in Iranian students (Haghighatdoost, Karimi, Esmaillzadeh, & Azadbakht, 2012), European adolescents (Bel et al., 2013), and postmenopausal women (Stern et al., 2014). In a cohort‐based study, healthier diet quality assessed by Alternative Healthy Eating Index diet quality (AHEI) was associated with both an increase in sleep duration and sleep efficiency (Mossavar‐Rahmani et al., 2017). Additionally, short sleepers were also reported to tend to have lower food diversity evaluated by Dietary Diversity Score (Haghighatdoost et al., 2012).

With regard to the score of individual item in CHDI, we noticed a significant correlation of a higher score of fruit consumption with lower risk of poor sleep quality, which is supported by a study indicative of increased whole fruit intake as one component of AHEI associated with higher sleep efficiency (Mossavar‐Rahmani et al., 2017). Fruits consumption was likewise found to be positively related to sleep duration (Gong et al., 2017; Haghighatdoost et al., 2012). The findings that higher adherence to Mediterranean diet (Campanini et al., 2017) or Chinese modern dietary patterns (Yu et al., 2017), both of which are characterized by high intake of fruits and other specific foods, were associated with better sleep quality strengthen our result. Furthermore, a review implied fruits may promote sleep duration and efficiency (Peuhkuri, Sihvola, & Korpela, 2012). The mechanisms underlying the association of fruits with sleep quality may be due to melatonin or serotonin, and antioxidant capacity of fruits. Previous studies have reported a correlation between disordered sleep and oxidative stress (Tsaluchidu, Cocchi, & Tonello, 2008), and it has been established that melatonin, the sleep‐promoting neurosecretory hormone, and the neurotransmitter serotonin, an intermediary product of melatonin, regulate sleep cycle (Peuhkuri et al., 2012). Therefore, fresh fruits with high dietary concentration of melatonin or serotonin and antioxidant substance, such as kiwifruits (Lin, Tsai, & Fang, 2011), tart cherries (St‐Onge, Mikic, et al., 2016), may be beneficial in improving sleep quality.

There have been other studies revealing an inverse association between the PSQI score and oily fish among adults living in rural coastal Ecuador (Del Brutto et al., 2016) and Japanese female workers (Katagiri et al., 2014). A cross‐sectional study among Japanese adults reported that intake of fish and shellfish was likewise correlated with sleep duration in male (Komada et al., 2017). In addition, another cohort study found that higher compliance with Mediterranean diet, which also features moderate, or even high, consumption of fish, olive oil, fruits, and vegetables, was associated with better sleep quality (Campanini et al., 2017). Our results are consistent with these studies. The results derived from both daily intake of seafood based on the semi‐FFQ and individual score of fish from CHDI calculated by the 24‐hr dietary recall suggested a significant association between fish, especially seafood, and sleep quality in the present study. Several factors could account for this association. First, in animal foods, melatonin concentrations were found higher in fish and eggs than those in meat (Meng et al., 2017). Moreover, seafood, especially fatty fish, is good resources of long‐chain omega‐3 polyunsaturated fatty acids and vitamin D, both of which have been reported to be positively correlated with sleep quality (Del Brutto et al., 2016; Hansen et al., 2014). Omega‐3 fatty acids play an important role in the secretion of serotonin, which is a biogenic amine involved in sleep regulation (Monti, 2011). Vitamin D is implicated in sleep–wake cycle (Gominak & Stumpf, 2012).

Interestingly, our study was suggestive that eggs intake appeared to be conversely with sleep quality. So far, there is a lack of researches regarding the correlation of eggs ingestion and sleep quality. Only two cross‐sectional studies conducted in Japan and China, respectively, reported that individuals with higher compliance with certain dietary pattern which features high consumption of eggs and other foods were less likely to have insomnia symptoms (Kurotani et al., 2015; Yu et al., 2017). Possible explanations for the effect of eggs on sleep may be related to cholesterol. Although eggs are rich in melatonin as mentioned before, eggs are the most principal source of cholesterol for Chinese in recent decade (Su, Jia, Wang, Wang, & Zhang, 2015) and we also found higher egg consumption contributed to more cholesterol intake significantly in our study population (Spearman correlation coefficient = 0.283, p < 0.001). Furthermore, a negative correlation was found between dietary cholesterol and sleep duration in men (Santana et al., 2012), although this correlation had not been captured in our study. Therefore, further studies on the associations between eggs and sleep quality and underlying mechanisms are needed.

There has been emerging information surrounding higher sugar‐sweetened beverage consumption related to short sleep duration (Katagiri et al., 2014; Kleiser et al., 2017). However, in the current study the negative correlation of sugar‐sweetened beverage consumption with sleep quality did not exhibit significance after considering gender, age, and other confounders. The reason for this phenomenon may attribute to an age‐effect that beverages are generally targeted to young adult consumers, as consumers of sugar‐sweetened beverages were significantly younger than nonconsumers among the participants for analysis( median age 34.1 years vs. 61.8 years, p < 0.01). Moreover, our results showed that the middle‐aged and the elder were more likely to be poor sleep quality.

As to the association between total energy intake and sleep quality, our finding is in conflict with most existing studies implying that more energy intake was related to shorter sleep duration and deteriorated sleep parameters such as sleep onset latency (Haghighatdoost et al., 2012; Stern et al., 2014; Tan et al., 2016), yet one study in India reported that insomniacs without co‐morbid compared to normal sleepers consumed significantly less energy (Zadeh & Begum, 2011). Hence, longitudinal studies are imperative to elucidate this discrepancy which remains unclear.

Cow’s milk (Gong et al., 2017) and coffee (Kleiser et al., 2017) have been traditionally considered as sleep‐promoting and sleep‐inhibiting beverage, separately, and have been found to be correlated with sleep duration. The lack of signals from milk and dairy products or coffee on sleep quality in our study is noteworthy, but it has to be interpreted with caution due to the large number of nonconsumers among Chinese residents, particularly milk and dairy products consumption (median absolute consumption 71.4 g) far below the recommended daily intake of 300 g.

The current study is on the basis of a cross‐sectional design and is subject to several limitations. First, we observed a couple of dietary factors associated with sleep quality, whereas the inherent limitation of a cross‐sectional study did not allow for causality inference. Second, PSQI is recognized as the most common subjective method to assess sleep quality in large population‐based epidemiology studies all around the world. However, given bias and measurement accuracy, objective methods are warranted in the following researches. Third, regarding a few discrepancies between the present study and others, there still may be residual confounding variables we did not include in the finally adjusted model resulting from either sleep‐influencing ingredients in healthcare products that we failed to identify or the impact of drugs for other chronic conditions on sleeping.

Journal information: American Journal of Clinical Nutrition


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