**Abstract:** Background: The relationship between food insecurity and the experience of sleep disturbance has received little attention among researchers, although food insecurity is associated with poor physical and mental health globally. This study aimed to investigate the relationship between food security status and sleep disturbance among adults 20–64 years old. Methods: The study's population-based sample included 20,212 Indonesian adults who participated in the fifth wave of the Indonesia Family Life Survey (IFLS5) in 2014. Dietary intake data, gathered using a food frequency questionnaire (FFQ), were used to assess the food security status. Sleep disturbance was assessed using the 10-item Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaire. We used multiple linear and logistic regression models to test the study hypothesis. Results: A higher likelihood of experiencing sleep disturbance was recorded in people aged older than 56 years (OR = 1.78, 95% CI: 1.17–2.72, *p* = 0.007), people with depressive symptoms (OR = 3.57, 95% CI: 2.77–4.61, *p* < 0.001), and food-insecure people (OR = 1.32, 95% CI: 1.02–1.70, *p* = 0.036). A lower likelihood of experiencing sleep disturbance was recorded in people with low educational attainment (OR = 0.41, 95% CI: 0.30–0.57, *p* < 0.001). Sleep disturbance was dependent on the food consumption groups and food security status among men (*p* = 0.004). Conclusions: Sleep disturbance may be affected by the food-insecure status of adults, and later, may lead to serious health outcomes.

**Keywords:** food insecurity; sleep disturbance; adults; cross-sectional study

#### **1. Introduction**

Food insecurity is a public health problem that exists globally, including in developing countries. Food insecurity is the disruption of dietary patterns or nutrient intake because of a lack of finances and other resources [1]. In the USA, researchers have estimated that food insecurity affects 9–14% of adults

aged 24–32 years, and shows higher rates among women and low-income adults [2–4]. Meanwhile, developing countries, such as Indonesia, have reported a high prevalence of food insecurity, with as many as 19.4 million people out of a population of 258.7 million who were unable to meet their dietary requirements [5]. Food-insecure people may encounter anxiety or stress from uncertainty about fulfilling their food requirements or other necessities. The anxiety and stress may lead to depressive symptoms and affect their quality of sleep [6,7]. Poor sleep quality, such as short sleep duration, the presence of sleep disturbance, or difficulty getting to sleep, is associated with the possibility of chronic disease later in life [8–10]. Sleep disturbance is known to be a big problem in developed countries. In the United States, the National Institutes of Health stated that in 2017, about thirty percent of people reported getting less than seven hours of sleep each night [11]. The problem has begun to slide to low-to-middle-income countries, which reported about ten percent of people having severe sleeping problems [12,13]. People with sleep disturbances have reported difficulties with concentrating during the day and feeling restless, anxious, and depressed [14,15]. On the other hand, depressed people may also experience economic difficulties, which leads to food insecurity [16,17]. People with food insecurity are known to be vulnerable and more likely to develop chronic diseases or experience poorer mental health. However, the relationship between food insecurity and sleep disturbance is rather vague. This study's objective was to determine the relationship between food security statuses and sleep disturbance using a nationally representative sample of adults aged 20–64 years in Indonesia.

#### **2. Materials and Methods**

#### *2.1. Study Participants and Data Source*

This cross-sectional study used the secondary longitudinal data of the fifth wave of the Indonesian Family Life Survey (IFLS5) that was conducted in 2014. The initial survey (IFLS1) was conducted in 1993, representing about 80 percent of the Indonesian population. The IFLS5 datasets were anonymous, included participants of all ages, and were available for researchers who met the criteria based on the RAND Corporation guidelines about the dataset usage [18]. The institutional review board (IRB) review of the IFLS studies went through the sufficient and appropriate review that followed the IRB guidelines and was approved by both the RAND Corporation and Indonesia's Institutions, in particular, the Survey Meter institution for the IFLS5 study [18,19]. The total number of participants for the IFLS5 were 34,464 people, aged from zero to older than 80 years old. The present study included participants who had complete data relating to food, anthropometric characteristics, sociodemographic characteristics, blood pressure, depressive symptoms, and sleep disturbance. We excluded participants who had been diagnosed with cancer, chronic diseases (e.g., diabetes and cardiovascular diseases), had a disability, or who were breastfeeding or pregnant in order to minimize the probability of sampling bias. Therefore, 20,212 participants aged 20–64 years old were included in the present study.

#### *2.2. Assessment of Sleep Disturbance*

The assessment of sleep disturbance was based on the guidelines of the Patient-Reported Outcomes Measurement Information System (PROMIS) [20,21]. The IFLS5 used self-reported answers to ten questions based on the PROMIS questionnaire for the assessment of sleep deprivation and sleep quality [18]. Each question on the questionnaire was rated using a one-to-five scale (never, rarely, sometimes, often, and always, respectively). The total score range of the ten questions was then summarized with a range from 10 to 50, which was the so-called total raw score. We used a t-score table to identify the t-score that related to every participant's total raw score and the information was attached to a t-score row based on its value. The t-scores were interpreted as "none to slight" for the participants with scores <55, "mild" for the participants with scores 55.0–59.9, and a combination of "moderate to severe" for the participants with scores >60 [20–22]. For the purposes of this study, we also categorized the data into two groups: participants whose sleep disturbance was "mild or less" or "greater than mild."

#### *2.3. Assessment of Food Security Status*

The assessment of food insecurity is associated with a person's lack of secure access to fulfilling their need for a nutritious diet in a sufficient amount to keep an active healthy life, considering both the food frequency and food diversity [23–25]. There are several ways to assess food insecurity at the individual level [26–28]. One of the assessments of the food insecurity concept was developed using the food frequency questionnaire (FFQ). The World Food Programme (WFP) introduced this concept to assess food consumption analysis by producing food consumption scores (FCSs) and cut-off points for food insecurity [24,25]. The FCSs allow data to be categorized into three food consumption groups (FCGs) consisting of poor, borderline, and acceptable groups. Furthermore, the ten food items listed on the IFLS5 food frequency questionnaire were included in the food consumption analysis. The IFLS5 FFQ asked about ten food types that were eaten by the participants during the last seven days before the interview. We then grouped these ten food types into five food groups. The first group was the staple group, consisting of sweet potato. The second group was the protein group, consisting of eggs, fish, and meats. The third group was dairy products. The fourth group was the fruit group, consisting of banana, mango, and papaya. The last group was the vegetable group, consisting of green leafy vegetables and carrots [23–25]. The score for each food group was then summed as the food consumption score in the form of continuous data. The "poor" food consumption group included participants who had an FCS lower than 21. The "borderline" FCG included participants who had food consumption scores that lay between 21 and 35. The "acceptable" FCG included participants who had food consumption scores higher than 35 [24]. The "poor" FGC and "borderline" FGC participants were defined as food-insecure persons [25,29].

#### *2.4. Assessment of Covariates*

The covariates of the present study were anthropometric characteristics, blood pressure, physical activity, and sociodemographic measurements. We used the body mass index, and additionally, for the participants aged 40 years and older, the measurements of the waist circumference and the body shape index [30–32] were added. Furthermore, the adult body mass indices (in kg/m2 ) adopted the Indonesian cut-off points [33]. The body mass index (BMI) was categorized into "normal weight" for participants with a BMI between 18.5 to 25.0, "overweight" for participants with a BMI between 25.1 to 27.0, and "obese" for participants with a BMI higher than 27.0 [34]. The definition of abdominal obesity used the waist circumference measurement with two cut-off points (for men: >90 cm, for women: >80 cm). Trained nurses performed the anthropometric and blood pressure measurements. For the blood pressure measurements, the participants were in the seated position. Participants were defined as having hypertension if the systolic blood pressure (SBP) was ≥140 mmHg or the diastolic blood pressure (DBP) was ≥90 mmHg, or if they had been diagnosed by paramedics before the interview or were currently consuming blood pressure-lowering medication [32].

Furthermore, physical activity was assessed using the number of days on which respondents had done two types of physical activity (i.e., vigorous and moderate) within the last seven days before the survey. We considered the volume of physical activity (PA) to be a continuous variable in the analysis. Respondents answered the self-reported questionnaires in terms of whether they had engaged in physical activities for at least ten minutes continuously during the last seven days. If the respondents said yes, then they were further asked about the number of days on which they had done each type of physical activity. Eight and four metabolic equivalent of tasks (METs), respectively, were then multiplied by the minutes and days of each type of physical activity to form the physical activity volume (in METs minutes/week) [35]. For example, the vigorous physical activity volume formula was the minutes/day multiplied by days/week of doing vigorous PA multiplied by eight METs.

The sociodemographic variables included smoking habits, educational attainment, living area, and marital status, which were presented as categorical data. The smoking habits of the participants were categorized into never (never had a smoking habit), current smoker (currently has a smoking

habit), and former smoker (has stopped a smoking habit). The participants' educational attainment was categorized into low: <12 years of school attainment, and high: ≥12 years of school attainment.

Depressive symptoms were defined using the score of the questionnaire about mental health. The IFLS5 used the ten-item self-reported questionnaire from the Center for Epidemiologic Studies–Depression (CES-D) to assess the mental health of the adult participants. Some prior researchers have used the 10-CES-D questionnaire to assess adults' depressive symptoms [36,37]. The form of the responses to the 10-CES-D questionnaire was based on four scale items: less than one day (rarely or never), one to two days (some days), three to four days (occasionally), and five to seven days (most of the time). The scores of the 10 questions were then added, resulting in a score ranging from ten to forty. Furthermore, the score was rebased so that the lowest score was zero and the highest score was thirty. The highest score identified people with the most symptomatology of depression [38]. The cut-off point for defining a person as having a risk of heightened depressive symptoms was a score higher than or equal to ten [39,40].

#### *2.5. Statistical Analysis*

The present study used secondary data from the IFLS5 (2014). The characteristics of participants were presented as a mean and standard deviation for the continuous data and as numbers with percentages for the categorical data. We used one-way ANOVA for the continuous data, with the Bonferroni post hoc test or chi-squared test for the categorical data to compare the values between groups. Furthermore, we used the regression model to assess the relationship between sleep disturbance and depressive symptoms by food consumption group. Furthermore, we also used the sleep disturbance score and CES-D-10 score as continuous data in the linear regression analysis and as categorical data in the logistic regression analysis. To assess the relations of interest, we used a linear and logistic regression model, which was presented using an exponentiated beta coefficient or odds ratio and a 95% confidence interval, respectively. Moreover, this study used three models that accounted for various potential confounders in the multiple logistic regression model. The three models were an unadjusted model, a model with adjustment for age and sex, and a model with a complete adjustment; the complete adjustment was an adjustment for age, sex, educational attainment, marital status, BMI, living area, blood pressure, smoking habit, and physical activity volume. We used a similar sequence of adjustments for potential confounders, which were also used for the linear regression models. Statistical significance was designated as a *p*-value < 0.05. We conducted a multivariate test to identify the characteristics related to sleep disturbance and depressive symptoms, which were analyzed in a separate model. Covariates in these two models included sex, age group, living area, educational attainment, smoking status, blood pressure, physical activity volume, and body mass index. All the analyses were conducted using STATA statistical software (v16.1; StataCorp LP, College Station, TX, USA).

#### **3. Results**

This cross-sectional study included 20,212 participants (women = 10,070, men = 10,142) from the IFLS5 dataset (Table 1). The flowchart related to the selection of the participants is shown in Figure S1. The mean age of the participants was 39 (standard deviation (SD): 11) years old. Most of the participants had low educational attainment, were currently or ever married, living in urban areas, and had never had a smoking habit. Additionally, most of the participants aged 40 years and older had abdominal obesity, which was observed in 4494 (51.54%) people. The prevalence of food insecurity in this study was 53.86%, or 10,886 of the total participants. Among the food-insecure participants, women represented as many as 5602 (51.46%) people, low education attainment was found in 6832 (62.76%) people, people who were currently married or had a marriage experience constituted 9707 (89.17%) people, and the number of people who lived in urban areas was 6041 (55.49%). Most of the food-insecure participants, i.e., 6544 (60.11%) people, never had a smoking habit, whereas 3944 (36.23%) people reported that they had quit smoking. We found that 191 (1.75%) food-insecure participants were

−

taking blood-pressure-lowering medication and 61 (0.56%) people were taking cholesterol-lowering medication. Furthermore, 2294 (49.95%) people aged 40 years old and older among the food-insecure groups had abdominal obesity. We found that the number of people with depressive symptoms among the food-insecure group was 3808 (34.98%). Furthermore, 595 (56.67%) of the food-insecure participants were experiencing "mild" sleep disturbance, whereas 168 (62.92%) of the food-insecure participants were experiencing "moderate-to-severe" sleep disturbance. The means of the systolic blood pressures and vigorous physical activity volumes of the food-insecure participants were higher than those in the food-secure group (*p* < 0.001). off values ≥10.

Figure 1 shows the prevalence of sleep disturbance by FCG for women and men. Among men, participants reported a "none-to-slight" sleep disturbance level for 94.17% (*n* = 4575) of the acceptable FCG, 93.54% (*n* = 3260) of the borderline FCG, and 92.22% (*n* = 1659) of the poor FCG. Participants reported a "mild" sleep disturbance level for 5.00% (*n* = 243) of the acceptable FCG, 4.99% (*n* = 174) of the borderline FCG, and 6.06% (*n* = 109) of the poor FCG. Furthermore, participants reported a "moderate-to-severe" sleep disturbance level for 0.82% (*n* = 40) of the acceptable FCG, 1.46% (*n* = 51) of the borderline FCG, and 1.72% (*n* = 31) of the poor FCG. The level of sleep disturbance was dependent on the food consumption group or food security status among men (*p* = 0.004). participants reported a "none slight" sleep disturbance level for 94.17% ( reported a "mild" sleep disturbance level for 5.00% ( "moderate " sleep disturbance level for 0.82% (

**Figure 1.** Prevalence of sleep disturbance among men (left) and women (right) by food consumption group. Indonesia Family Life Survey (IFLS) 2014 data were used in the analysis. \* Significance: *p* < 0.05.

participants reported a "none slight" sleep disturbance level for 93.93% ( poor FCG. Participants reported a "mild" sleep disturbance level for 4.74% ( participants reported a "moderate severe" sleep disturbance level for 1.32% ( Among women, participants reported a "none-to-slight" sleep disturbance level for 93.93% (*n* = 4197) of the acceptable FCG, 92.80% (*n* = 3370) of the borderline FCG, and 93.06% (*n* = 1825) of the poor FCG. Participants reported a "mild" sleep disturbance level for 4.74% (*n* = 212) of the acceptable FCG, 5.58% (*n* = 203) of the borderline FCG, and 5.56% (*n* = 109) of the poor FCG. Furthermore, participants reported a "moderate-to-severe" sleep disturbance level for 1.32% (*n* = 59) of the acceptable FCG, 1.62% (*n* = 59) of the borderline FCG, and 1.38% (*n* = 27) of the poor FCG. The level of sleep disturbance was independent of the food consumption group or food security status among women (*p* = 0.301).

Table 2 shows the characteristics related to sleep disturbance, as determined by the regression model. When the confounding variables were taken into account in the multivariate regression model, several characteristics remained independently related to sleep disturbance. A higher likelihood of experiencing sleep disturbance was recorded in participants aged older than 56 years (OR = 1.78, 95% CI: 1.17–2.72, *p* = 0.007), participants with depressive symptoms (OR = 3.57, 95% CI: 2.77–4.61, *p* < 0.001), and food-insecure participants (OR = 1.32, 95% CI: 1.02–1.70, *p* = 0.036). However, a lower likelihood of experiencing sleep disturbance was recorded in participants with low educational attainment (OR = 0.41, 95% CI: 0.30–0.57, *p* < 0.001).

**Table 1.** The participants' characteristics.

Abbreviations: SD, standard deviation; METs min/w, metabolic equivalent of tasks for minutes per week; CES-D-10, 10 items of the Center for Epidemiological Studies Depression questionnaire; PA, physical activity. Note: The categorical data are presented using *n* (%) and the continuous data are presented using mean (SD). a The definition of abdominal obesity for women and men used the waist circumference with cut-off points of >80 cm or >90 cm, respectively. b The body mass index used the adult categorization of body mass index for the Indonesian population. c The definition of having depressive symptoms used the score of the CES-D-10 with cut-off values ≥10. d The sleep disturbance level was defined using the t-score of the Patient-Reported Outcomes Measurement Information System (PROMIS) guidelines of the sleep disturbance questionnaire. Significance was set to *p* < 0.05.

**Table 2.** Characteristics related to the sleep disturbance level, as determined by the regression model.

Abbreviations: Ref., reference; OR, odds ratio; 95% CI, 95% confidence interval. Note: \* Sleep disturbance was categorized into greater than mild or not greater than mild. The models were adjusted for age, gender, body mass index, education attainment, marital status, living area, smoking habits, physical activity volume, blood pressure value, food consumption score, and CES-D-10 score. Statistical significance was set to *p* < 0.05.

Furthermore, Table S1 shows that a higher likelihood of experiencing sleep disturbance after adjusting for age and gender in our study was reported in participants with depressive symptoms (OR = 3.50, 95% CI: 2.72–4.52, *p* < 0.001) and participants with food insecurity (OR = 1.47, 95% CI: 1.15–1.89, *p* = 0.002). On the other hand, a lower likelihood of experiencing sleep disturbance was reported in participants with low educational attainment (OR = 0.38, 95% CI: 0.28–0.51, *p* < 0.001). Furthermore, Table S2 presents the logistic regression between characteristics related to the sleep disturbance level. A higher likelihood of experiencing sleep disturbance among men in our study was reported in participants with depressive symptoms (OR = 3.16, 95% CI: 2.17–4.60, *p* < 0.001) and participants with food insecurity (OR = 1.75, 95% CI: 1.19–2.58, *p* = 0.005). Meanwhile, a higher likelihood of experiencing sleep disturbance among women in our study was reported in participants with depressive symptoms (OR = 3.16, 95% CI: 2.17–4.60, *p* < 0.001) and in participants aged older than 36 years (OR = 1.73–2.54, 95% CI: 1.00–2.72, *p* = 0.016–0.002). On the other hand, a lower likelihood of experiencing sleep disturbance among men in our study was reported in current smoker participants (OR = 0.35, 95% CI: 0.12–0.99, *p* = 0.048) and in participants with low educational attainment (OR = 0.46, 95% CI: 0.30–0.71, *p* < 0.001). Moreover, a lower likelihood of experiencing sleep disturbance among women in our study was reported in participants with low educational attainment (OR = 0.37, 95% CI: 0.22–0.61, *p* < 0.001).

#### **4. Discussion**

The present study demonstrates the relationship between food insecurity and sleep disturbance. This study's results show that most of the food-insecure participants were women, people who had marriage experience, people with low educational attainment, and people who lived in urban areas. In addition, the middle-aged participants (older than 40 years old) were more likely to have abdominal obesity. Furthermore, the means of the systolic blood pressure and vigorous physical activity volume were higher among food-insecure participants compared to food-secure participants. Moreover, participants who were more likely to experience sleep disturbance were aged older than 56 years old, had depressive symptoms, and a food-insecurity status, but this was not the case for the people who had low educational attainment.

Food insecurity is associated with chronic diseases and poor mental health [41–43]. The burden of food insecurity and marriage experience, in particular for women, synergistically contributes to the development of depressive symptoms [44]. A potential explanation may be the stressful decision-making that most food-insecure women have to do related to their financial situation, and women may be more susceptible to harm from life stresses and other environmental factors [4,42,45]. The findings from this study also confirmed previous research that suggests the higher prevalence of food insecurity among women [44,46]. Furthermore, our study results are in line with previous findings showing that most people with low education levels are more likely to encounter food insecurity. One of the possible mechanisms is the contribution of the financial hardship of food-insecure people. The financial hardship of food-insecure people may be affected by the low wage or minimum payment from intensive work. Low-educated people are more likely to do manual labor and to receive a minimum payment, which may affect the difficulty in fulfilling their nutrition requirements through adequate meals [47,48]. Another explanation is that low education may lead to less exposure to nutrition education explaining how to maintain a well-balanced and nutritious diet [49].

Food-insecure people who live in the urban areas may try to keep up with their environment, which includes needs relating to food, or cultural or financial situations [50–52]. People who live in urban areas might also be exposed to more processed food options [53,54]. The problem with processed foods or fast foods is the lack of consumers' ability to control the amount of calories, sodium, fat, and sugar [55]. In particular, for people with inadequate nutrition education, they may prefer to buy the processed foods in a lower price range, which may contain high calories, fat, high sodium, or high sugar, instead of buying well-balanced meals [56]. For food-insecure people, the difficulty of maintaining their nutritional dietary needs may affect their body weight, which leads to being overweight or obese [57]. The findings of the present study support the previous finding that the numbers of food-insecure participants who live in urban areas and who have excess weight were greater than those in the food-secure group. Furthermore, people with food insecurity may encounter sleep disturbance, which is also associated with an increase in systolic blood pressure. The systolic blood pressure may be a result of jobs involving heavy work or of continuous vigorous physical activity [58–60]. A population-based study in a rural area of China found that older age, unemployment, lower-income, disability, and chronic disease comorbidities were significant factors associated with an increased risk of poor sleep quality for both men and women [61]. The significant interactions with race/ethnicity indicate that the relationship between sleep complaints and marital status, income, and employment differs between groups for men, and the relationship with education differs between groups for women [62]. Food insecurity is related to "poor" sleep quality, which may develop from anxiety, stress, or feelings of uncertainty about providing food and other necessities for themselves and their families [6,7].

Moreover, a combination of biological and psychosocial factors is involved in the mechanism of the relationship between food insecurity and poor mental health [9,63]. Meanwhile, the relationship between depression or having depressive symptoms and the experience of sleep disturbance is also known to be closely linked [15]. Food insecurity is related to depressive symptoms [29,64,65], and having depressive symptoms increases the odds of experiencing sleep disturbance in adults [9,14,66]. Another explanation is the compensatory mechanism of leptin, which reduces appetite and increases energy expenditure through the hypothalamic receptors [67,68]. Low leptin levels are associated with increased body mass index, lower quality of sleep, and a higher propensity toward having depressive symptoms [66]. The presence of sleep disturbance is associated with the effect of high

levels of ghrelin and low levels of leptin. On the other hand, lower quality of sleep also causes greater neuronal activation in response to food stimuli, which results in increased motivation to seek food to achieve a high-energy intake, particularly by eating energy-dense foods that are high in fat and sugar [66,69]. Food-insecure adults may have diets that are deficient in nutrients, such as folic acid and tryptophan [70,71], which could influence mood and immune functions [72–74], which, in turn, may have an effect on their sleep.

The present study has some limitations. First, the usage of a cross-sectional study limited us from seeing the causal relationship between variables. However, our data were nationally representative for almost a majority of the adult Indonesian population. Second, although we controlled for covariates in the analysis, there remain several sociodemographic factors, such as individual income or house environmental factors that may contribute to sleep disturbance, which we could not include. Third, the sleep disturbance questions in the IFLS survey did not provide more potential sources of sleep disturbance. Thus, we were unable to specifically explain the type of sleep disturbance that the participants experienced (e.g., insomnia, sleep apnea, duration of sleep, and the latency of sleep). The use of self-reported data for sleep disturbance and physical activity variables is likely to suffer from a response bias and may affect the study results [75]. However, the PROMIS self-reported sleep disturbance has been used and validated in former research among adults [20–22]. A possible bidirectional relationship between depressive symptoms and sleep disturbance that we could not test in both directions may also be a limitation of the present work. For the study's purpose, we only focused on the relationship between the exposure (i.e., depressive symptom) and sleep disturbance (as the outcome) because of the general assumption that depression treatment would also resolve the associated symptoms, such as sleep disturbance [76]. Lastly, since we used the food consumption score in the food security assessment, although the outcome investigated may not fully represent food insecurity, this method has been used widely in former studies [29,77,78]. Therefore, the interpretation of the study result must be taken cautiously.

#### **5. Conclusions**

In conclusion, the results of this study suggest that sleep disturbance may be affected by food insecurity in adults and may later lead to serious health outcomes. A potential solution that could overcome this problem is to encourage food-insecure people to participate in nutritional education programs that are conducted by health experts that also incorporate advice about the benefits of sleep quality.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2072-6643/12/11/3411/s1, Figure S1: Flowchart of the participant sampling process; Table S1: The logistic regression between covariate variables and the outcome; Table S2: The logistic regression between the characteristics related to the sleep disturbance level, stratified by gender; Table S3. The regression test between depressive symptoms and sleep disturbance by food security status.

**Author Contributions:** E.R.I. and S.-H.Y. conceived and designed the study; E.R.I. and Y.-C.C. performed the data analyses; E.R.I., Y.-C.C., and S.-H.Y. wrote the paper. Y.-C.C., H.-Y.S., and S.-H.Y. provided the supervision. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Taipei Medical University Hospital (109TMU-TMUH-30).

**Acknowledgments:** E.R.I. was supported by Hibah Riset Mandat Dosen Muda year 2020, Universitas Airlangga.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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*Article*

### **Diet Quality and Health Service Utilization for Depression: A Prospective Investigation of Adults in Alberta's Tomorrow Project**

**Shelby Marozo**ff**, Paul J. Veugelers , Julia Dabravolskaj, Dean T. Eurich , Ming Ye and Katerina Maximova \***

School of Public Health, University of Alberta, Edmonton, AB T6G 1C9, Canada<EMAIL_ADDRESS>(S.M<EMAIL_ADDRESS>(P.J.V<EMAIL_ADDRESS>(J.D<EMAIL_ADDRESS>(D.T.E<EMAIL_ADDRESS>(M.Y.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +1-780-248-2076

Received: 14 July 2020; Accepted: 11 August 2020; Published: 13 August 2020

**Abstract:** Depression is a leading cause of disability and economic burden worldwide. Primary prevention strategies are urgently needed. We examined the association of diet quality with depression in a large provincial cohort of adults. A past year food frequency questionnaire was completed by Alberta's Tomorrow Project (ATP) participants enrolled between 2000–2008 (*n* = 25,016; average age 50.4 years) and used to calculate Healthy Eating Index-Canada (HEI-C) 2015 scores. The number of physician visits for depression 2000–2015 was obtained via linkage with administrative health records. Negative binomial regression models assessed the relationship between HEI-C 2015 scores and physician visits for depression, adjusting for confounders. Every 10-unit increase in HEI-C 2015 scores was associated with 4.7% fewer physician visits for depression (rate ratio (RR): 0.95; 95% Confidence Interval (CI): 0.92–0.98). This relationship persisted when participants with physician visits for mental illness prior to cohort enrollment were excluded. Higher quality diets were associated with a lower number of physician visits for depression. Results highlight diet may be an important prevention strategy for reducing the burden of health service utilization for depression.

 Introduction**

Depression is the leading global cause of disability, affecting over 300 million individuals worldwide [1]. In Canada, the lifetime prevalence of depression is over 11% [2]. Mental illnesses, of which depression represents approximately 42.5% of the burden [3], place a substantial strain on health care systems. In 2011, mental illnesses cost the Canadian economy over \$22.6 billion dollars in direct costs, a value which is predicted to increase to \$105.6 billion over the following 30 years [4]. About one in seven (14%) Canadians access health care for a mental illness annually [5]. Mental illnesses accounted for 25.5% of all acute care hospital days and diagnoses of mental illness in 2009/10 and were associated with hospital stays over 2.5 times as long as those not involving a mental health diagnosis [6]. Given the large personal, societal, and economic burden of depression, primary prevention strategies are urgently needed.

Diet has recently received attention as a promising intervention target for prevention of depression. Poor quality diets are a leading contributor to the burden of chronic disease in Canada [7,8]. Compliance with dietary recommendations is poor among Canadians, and consumption of beneficial food groups, such as vegetables and fruit, as well as milk and alternatives, has decreased in recent years [7]. While there have been two systematic reviews that synthesized evidence from randomized controlled trials

(RCTs) on the diet-depression relationship [9,10], RCTs included in both reviews focused on dietary interventions in relation to existing depression symptoms (i.e., secondary prevention). To the best of our knowledge, there are no RCTs that examined diet as a potential target for primary prevention of depression. There are several factors that limit the feasibility of the RCT design when it comes to the diet-depression relationship. Complex exposures, such as diet, affect disease risk through multiple systems and pathways, and these effects aggregate over long periods of time [11], thus requiring sufficient follow-up time [12]. This results in additional costs, as well as non-compliance and high dropout rates among participants. Blinding is also problematic (if at all possible) in RCTs of dietary interventions, and these interventions are often examined in high-risk populations (e.g., obese and overweight) rather than the general population.

Given these challenges, evidence from high-quality observational studies is required to understand if diet is a novel intervention target for primary prevention of depression. Emerging evidence from observational studies indicates that this might be true. For example, several systematic reviews and meta-analyses report the link between adherence to high quality, healthy diets with high intakes of vegetables, fruit, whole grains, and healthy fats and reduced consumption of saturated fat, sugar, and red meat (e.g., whether healthy/prudent, Mediterranean, pro-vegetarian, or Tuscan) and as much as 33% lower risk of incident depression [13–17]. While most of the studies appropriately assessed diet quality (as opposed to individual food constituents) using a variety of tools, the assessment of depression has been limited by the use of symptom severity scales rather than other outcome measures, such as healthcare utilization. For example, available observational studies most commonly included symptom severity scales (e.g., Center for Epidemiologic Studies Depression Scale [18], Beck Depression Inventory-II [19], Composite International Diagnostic Interview Short Form [20]), antidepressant use, or self-report of physician diagnoses, with mixed results that vary by age, sex, and diagnosis [14,15].  There is value in examining whether the association between diet quality and symptoms of depression extends to individuals seeking health care for depression. Filling this gap can contribute to our understanding of the potential for using dietary approaches to reduce the health service utilization burden.

There is an acknowledged need for large prospective investigations that account for a range of relevant confounders, and focus on population-based samples without a previous history of mental health problems [14]. The aim of the present study was to fill this gap and examine the association between diet quality and health service utilization for depression in a large prospective study of community dwelling adults, accounting for a range of relevant confounders.

#### **2. Materials and Methods**

From 2000 to 2008, Alberta's Tomorrow Project (ATP) enrolled 29,876 participants into a population-based prospective cohort study of cancer and chronic disease. Participants were considered eligible if they were ages 35–69 years, with no personal history of cancer other than non-melanoma skin cancer, plans to stay in Alberta for at least one year, and able to complete written questionnaires in English [22]. Participants were recruited using random digit dialing mapped to Regional Health Authority boundaries to facilitate balanced recruitment throughout the province. Eligible participants were mailed baseline questionnaires: the Health and Lifestyle Questionnaire (HLQ), the Canadian Diet History Questionnaire (CDHQ-I), and the Past-Year Total Physical Activity Questionnaire (PYTPAQ), which assessed sociodemographic characteristics, diet, and physical activity, respectively [22]. ATP methods have been previously published [22–24]. ATP data were linked to Alberta Health administrative health care databases via Personal Health Numbers and covered the years 2000 to 2015. Over 99% of ATP participants were successfully linked after providing valid Personal Health Numbers and consenting to data linkage [25]. ATP study procedures were approved

by the Health Research Ethics Board of Alberta (HREBA)—Cancer Committee (HREBA.CC-17-0461). Ethics approval for the linkage to Alberta Health databases and current analyses was obtained from the University of Alberta Health Research Ethics Board (Pro00058561). All ATP participants provided written consents to participating in ATP and allowing healthcare data linkage and long-term follow-ups at the time of enrolment.

The CDHQ-I, a 124-item past year food frequency questionnaire (FFQ) of foods, beverages, and dietary supplements was based on the Diet History Questionnaire from the U.S. National Cancer Institute [26] and adapted to Canadian food availability, brand names, nutrient composition, and food fortification [23,27]. Responses were analyzed using Diet\*Calc (version 1.4.2) software (National Cancer Institute, Bethesda, MD, USA) with a nutrient database adapted for the CDHQ-I to measure each participant's mean daily intake of energy, nutrients, foods, and supplement use. Diet quality was assessed by the Healthy Eating Index Canada (HEI-C) 2015 (Table S1), based on the American Healthy Eating Index (HEI) 2015 scoring criteria [28] and adapted to age- and sex-specific recommendations from Canada's Food Guide (CFG) 2007 [29]. The HEI-C 2015 assesses two major components of diet: adequacy (sufficiency of intake of healthy foods and nutrients (e.g., total fruits, whole fruits, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, and fatty acids)) and moderation (excess consumption of unhealthy foods and nutrients (e.g., refined grains, sodium, added sugars, and saturated fats)). Moderation components are reverse-scored to reward the restriction of consumption the unhealthy components. The HEI-C 2015 score ranges 0–100, with higher scores indicating better diet quality and greater adherence to dietary recommendations.

We also calculated the Modified Mediterranean Diet Score (MMDS), which has nine components and total index score ranges from zero to nine, with higher scores indicating greater adherence to the Mediterranean diet [30]. Each of the nine components is scored either zero or one based on the sex-specific median, with the exception of alcohol which is based on set consumption values. The MMDS components are constituents of the traditional Mediterranean diet, which is associated with a number of beneficial chronic disease outcomes [31,32].

Health service utilization for depression was estimated using hospital discharge abstracts, physician claims, and prescription medication data between cohort enrollment (2000–2008) and 2015. International Classification of Diseases (ICD), Ninth and 10th Revision codes from administrative health databases of hospital inpatient datasets and the primary, secondary, and tertiary diagnosis fields from physician claims were used to identify depression. Additionally, Anatomical Therapeutic Chemical Classification (ATC) codes for antidepressants or mood stabilizers from Alberta Blue Cross (ABC) pharmacy claims or Pharmaceutical Information Network (PIN) dispenses were used (see Table S2 for ICD-9/10 and ATC codes). The codes for hospital discharge abstracts, physician claims, and prescription medication data were drawn from a validated algorithm from the Manitoba Centre for Health Policy's Regional Health Authority Indicators Atlas [33,34] to identify health service utilization for depression.

We considered a range of potential confounders. Sociodemographic factors included sex; age; region of residence (urban or rural) determined by postal code; family history of gambling, alcohol, or drug addiction (yes/no); household income (<\$30,000, \$30,000–59,999, \$60,000–99,999, ≥\$100,000); highest level of education (high school, some university, post-graduate); marital status (attached or unattached); and employment status (full-time, part-time, other). Lifestyle factors included body weight status (underweight/normal weight, overweight, obese); leisure time moderate-to-vigorous physical activity (MVPA) (rounded quartiles: <70.0 min/week, 70.0–209.9 min/week, 210.0–389.9 min/week, ≥390.0 min/week); smoking status (present, former, never); alcohol intake (g/week); use of supplements (yes/no), specifically vitamin D and other (vitamin A, beta carotene, vitamin E, vitamin C, thiamin, riboflavin, niacin, folic acid, calcium, magnesium, iron, zinc, copper, selenium). The Charlson comorbidity index (0, 1, ≥2 comorbidities) was calculated at baseline, using administrative health records and the ICD-9/10 coding algorithm for administrative healthcare data [35].

#### *Data Analyses*

Given the overdispersion (*p* < 0.05) of physician visits for depression, associations between diet quality and health service utilization for depression were calculated using Negative Binomial Regression Models (NBM). The effect of each of the HEI, moderation, adequacy, and MMDS were evaluated sequentially using four separate models. Rate ratios and 95% confidence intervals were derived from unadjusted, parsimonious, and fully adjusted NBMs. Parsimonious models adjusted for covariates commonly identified in the literature: age, sex, annual household income, educational attainment, physical activity, Body Mass Index (BMI), and energy intake. Fully adjusted models included all covariates from the parsimonious models plus rural/urban residence, marital status, family history of addiction, smoking status, disease comorbidities, vitamin D supplement use, other supplement use, and alcohol intake. Missing values for confounding variables were treated as separate covariate categories in the multivariable NBMs, but their estimates are not presented.

Participants were excluded if they had daily caloric intakes (<500 or >5000 kcal) [36], had not completed the three baseline questionnaires (HLQ, PYTPAQ, and CDHQ-I), or had missing dietary data (>10 missing responses on the CDHQ-I). In order to exclude prevalent cases of depression, we excluded participants with one or more physician visits for mental illness (see Table S3 for ICD-9/10 and ATC codes) during four time periods prior to enrollment in the ATP cohort, including three periods of fixed length (six months, one year and two years prior to cohort enrollment (*n* = 2597, 5391 and 12,657, respectively)) and one period of variable length (from October 2000 to cohort enrollment (*n* = 7681)). These "washout" periods were implemented to increase the chance that depression episodes were incident and are recommended for examining chronic, episodic disorders in administrative health databases [37]. Analyses also included models that excluded physician visits for mental illness in the three months following enrollment to account for the possibility of increased health-consciousness immediately following enrollment in the ATP study. All analyses were conducted using STATA statistical software (Stata Corp LP, College Station, Texas, USA. 2007, Release 14). Statistical significance was set at *p* < 0.05.

#### **3. Results**

A total of 25,016 participants were available for analysis (Supplementary Materials Figure S1). Excluded participants did not significantly differ from those included in the analytic sample in terms of age, gender, geographic residence, education and presence of comorbidities (data not presented). Baseline characteristics of ATP participants are presented in Table 1. Of 25,016 participants included in analysis, 62.8% were female and 65.7% were overweight or obese. More than one-third (31.1%) of participants had one or more physician visits for depression between enrollment in the ATP cohort and end of follow-up in 2015 (Table 1). Participants' HEI-C 2015 score was, on average, 61.6 ± 11.0 out of 100 (Table 2), while the MMDS score was, on average, 4.3 ± 1.7 out of 10. Overall, 31.1% of participants saw a physician for depression following enrollment.

**Table 1.** Baseline characteristics of ATP participants (*n* = 25,016).

**Table 1.** *Cont.*

ATP, Alberta's Tomorrow Project; BMI, Body Mass Index; SD, Standard Deviation.

ATP, Alberta's Tomorrow Project; HEI-C 2015, Health Eating Index-Canada 2015; SD, Standard Deviation.

Table 3 presents the estimated reduction in the number of physician visits for depression for every 10-unit increase in HEI-C 2015 and component scores. After adjusting for all covariates, every 10-unit increase in HEI-C 2015 score was associated with 4.68% fewer physician visits for depression (rate ratio (RR): 0.95; 95% Confidence Interval (CI): 0.92–0.98). Dietary adequacy was also negatively associated with physician visits, leading to 8.78% fewer visits for depression (RR: 0.91; 95% CI: 0.87–0.96). For each 1-unit increase in MMDS score, there was a 2.48% reduction in the number of physician visits

for depression (RR: 0.98; 95% CI: 0.96–0.99) (Table 3). Results capturing the association of diet quality measured with HEI-C 2015 or MMDS and health service utilization for depression remained robust regardless of the covariates adjusted for in parsimonious and fully adjusted models.

A similar reduction in the number of physician visits for depression associated with 10-unit increases in HEI-C 2015 scores was observed when participants were excluded in different periods prior to cohort enrollment. In fully adjusted models, participants had 5.39% (RR: 0.95; 95% CI: 0.91–0.98), 5.38% (RR: 0.95; 95% CI: 0.91–0.98), and 8.34% (RR: 0.92; 95% CI: 0.88–0.96) fewer physician visits when we excluded visits in the six months, one year, or variable-length period from 2000 to cohort enrollment, respectively (Table 4). However, the results were no longer significant after excluding physician visits for mental health in the two years prior to enrollment. Lastly, after excluding physician visits in the three months after cohort enrollment, every 10-unit increase in HEI-C 2015 score and adequacy component score was associated with 4.68% (RR: 0.95; 95% CI: 0.92–0.98) and 8.78% (RR: 0.91; 95% CI: 0.87–0.96) fewer physician visits for depression, respectively (Table 4).

 95% CI, 95% Confidence Interval; HEI-C 2015, Health Eating Index-Canada 2015; MMDS, Modified Mediterranean Diet Score; RR, rate ratio; a Adjusted for sex, age, BMI, leisure time moderate-to-vigorous physical activity (MVPA), household income, educational attainment, caloric intake; b Adjusted for sex, age, BMI, urban/rural location, family history of addiction, leisure time MVPA, smoking status, household income, educational attainment, marital status, employment status, chronic disease comorbidities, use of vitamin D supplements, use of other supplements, weekly alcohol intake, caloric intake;c Per 10-unit increase in score;dPer 1-unit increase in score.

**Table 4.**Associations of increases in HEI-C 2015 and MMDS scores with number of physician visits for depression following exclusions.

**Table 4.** *Cont.*

95% CI, 95% Confidence Interval; HEI-C 2015, Health Eating Index-Canada 2015; MMDS, Modified Mediterranean Diet Score; RR, rate ratio; a Adjusted for sex, age, BMI, leisure time MVPA, household income, educational attainment, caloric intake; b Adjusted for sex, age, BMI, urban/rural location, family history of addiction, leisure time MVPA, smoking status, household income, educational attainment, marital status, employment status, chronic disease comorbidities, use of vitamin D supplements, use of other supplements, weekly alcohol intake, caloric intake;c Per 10-unit increase in score;d Per 1-unit increase in score.

#### **4. Discussion**

Our study provides evidence that a high-quality diet in adulthood is associated with reduced health service utilization, where every 10-unit increase in diet quality as measured with the HEI-C 2015 was associated with a 4.68% reduction in the number of physician visits for depression. We also observed a similar 2.48% reduction in physician visits for depression for every 1-unit increase in MMDS score, which measures adherence to the Mediterranean diet, high in vegetables, fruit, grains, and legumes, and low in meat and dairy. In addition, our results remained robust when participants with pre-existing physician-diagnosed mental illness were excluded from analyses.

The association between diet quality indices and depression in adults was recently summarized in two systematic reviews. One of the two reviews was limited to 29 prospective studies and found that adherence to high-quality (i.e., healthy/prudent, Mediterranean, pro-vegetarian, or Tuscan) and anti-inflammatory diets was associated with 23% and 19% lower risk of depression, respectively [14]. Another systematic review and meta-analysis of 41 observational studies found that individuals in the highest category of adherence to high-quality and Mediterranean diets had 24% and 33% lower risk of incident depression, respectively, compared to those with lowest adherence [15]. The coefficients from our analyses translate into a difference of up to 17.26% for higher versus lower adherence to the HEI-C 2015 (scores >75 vs. <50) and up to 9.38% for the MMDS (scores >6 vs. <4). These results are of similar magnitude to earlier studies and further corroborate the previously reported associations.

Previous studies often have not accounted for baseline depression status [38–40], with findings pointing to complex and bidirectional relations between diet, other lifestyle behaviors, and mental disorders and depression in particular. Given the chronic and episodic nature of depression, our exclusion of participants using four periods prior to cohort enrollment sought to mitigate the issue of reverse causation. It is compelling that our results remained significant with exclusions of six months (*n* = 22,419), one year (*n* = 19,625), and all visits between 2000 and cohort enrollment (*n* = 17,335). Although our results were no longer significant following exclusion of participants with any physician visits for mental illness in the two years prior to cohort enrollment, this is likely due to additionally excluding all participants enrolled in the first two years of ATP. This resulted in a substantial reduction of study sample (12,359/25,016 or approximately half) and a loss of statistical power. As suggested by Molendijk et al. (2018), the correction for depression status at baseline may negate the cumulative effects of lifestyle behaviors in the years before the study. Our work in children demonstrated that adherence to established recommendations for diet, physical activity, sleep, and sedentary behavior at age 10–11 years was associated with 56% fewer physician visits for mental illness in the subsequent four years [41]. This reduction was of similar magnitude with and without correction for mental illness at baseline, i.e., exclusion of all children with a mental illness diagnosis before the baseline. As less than 10% of children in the sample met this exclusion criteria, the similar magnitude of the association with and without correction for baseline mental illness corroborates the findings from this study, and suggests that the lack of association after excluding participants with physician visits in the two years prior to cohort enrollment may indeed be a statistical power issue.

We found that the adequacy component of the HEI-C 2015 remained significant following adjustment for confounders, but the moderation component did not. This finding is interesting and corroborates existing literature. In a recent meta-analysis, the highest category of adherence to healthy diets was associated with less depressive symptoms; however, adherence to unhealthy dietary patterns and food groups was not associated with incident depressive symptoms [14]. Taken together, these findings suggest that a diet sufficient in beneficial foods and nutrients may be more important than a diet restrictive in foods and nutrients recommended to be consumed in moderation. To our knowledge, other studies that have used versions of the HEI to investigate the relationship between diet quality and mental illness have not examined or reported the components of adequacy or moderation.

Existing studies also relied predominantly on cross-sectional design and often did not include several potential confounding variables, such as obesity, energy intake, baseline socioeconomic status (income, parents' educational level), and medical conditions (e.g., diabetes, food allergies, hypothyroidism) that may be correlated with mood or diet [14,15]. Diet is the product of the interplay among a large number of factors and a lack of consistency in the adjustment for confounders leads to errors in inference and makes comparison across studies difficult [14]. We adjusted for a range of relevant confounders identified in the literature and observed that even after the inclusion of confounders in the models, the results remained robust.

The large sample of the general adult population of Alberta with a diverse range of demographic and behavioral characteristics, followed prospectively for up to 14 years of follow-up and low attrition, the nearly complete linkage with administrative databases, little missing data due to rigorous quality control measures, and the use of multiple measures of diet quality are major strengths. Several limitations warrant consideration. First, although we included a wide range of relevant covariates, the possibility of residual confounding remains. Second, the exposures of interest and confounders, with the exclusion of disease comorbidities, were based on self-report. Nonetheless, the HLQ, CDHQ-I, and PYTPAQ were either composed of existing items used in other studies, adapted from a validated questionnaire, or specifically developed and tested for validity and reliability in this cohort, respectively [22]. Third, since ATP recruited participants when they were already aged 35–69 years, this precludes our ability to observe lifetime incidence of depression.  Those who encountered barriers to care or received care outside of the medical system (e.g., psychological counselling through community mental health services) were not included, thus underestimating the burden of depression. Additionally, individuals of higher socioeconomic status may have chosen to seek mental health support through private psychological services, which was also not included. However, if misclassification of outcome did occur, the literature suggests that the result is typically an estimate biased toward the null [42].

#### **5. Conclusions**

We observed an inverse relationship between diet quality measured with two different dietary indices and health service utilization for depression, independent of socio-economic status (SES), other lifestyle behaviors, and disease comorbidities. Given that the diet quality of many Canadians is of poor quality and either stagnating or declining [7,8], interventions to improve the diets of Canadians at the population level may have important implications for reducing the health care burden for depression in addition to reducing the established risk for chronic disease.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2072-6643/12/8/2437/s1: Figure S1: Flow chart for the inclusion and exclusion of Alberta's Tomorrow Project (ATP) cohort (2000–2008); Table S1: Scoring Criteria for the Healthy Eating Index-Canada 2015; Table S2: ICD 9/10 and ATC Codes Identifying Physician Visits for Depression; Table S3: ICD 9/10 and ATC Codes Identifying Physician Visits for Mental Illness.

**Author Contributions:** S.M. conceived, designed, and conducted the analysis and drafted the manuscript; K.M. and P.J.V. conceived and designed the analysis, assisted in interpreting the data, and critically reviewed the manuscript; J.D. conducted literature reviews, drafted the manuscript, and critically reviewed the manuscript for important intellectual content; M.Y. and D.T.E. assisted in interpreting the data and critically reviewed the manuscript for important intellectual content. All authors have read and agreed to the published version of the manuscript.

**Funding:** Alberta's Tomorrow Project is funded by Alberta Health, Alberta Cancer Foundation, Canadian Partnership against Cancer and Health Canada, and substantial in kind funding from Alberta Health Services.

**Acknowledgments:** Alberta's Tomorrow Project is only possible because of the commitment of its research participants, its staff and its funders: Alberta Health, Alberta Cancer Foundation, Canadian Partnership against Cancer and Health Canada, and substantial in kind funding from Alberta Health Services. The views expressed herein represent the views of the author(s) and not of Alberta's Tomorrow Project or any of its funders. This study is based in part on data provided by Alberta Health. The interpretation and conclusions contained herein are those of the researchers and do not necessarily represent the views of the Government of Alberta. Neither the Government nor Alberta Health express any opinion in relation to this study.

**Conflicts of Interest:** The authors have no conflicts of interest relevant to this article to disclose.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*

## **Specific Dietary (Poly)phenols Are Associated with Sleep Quality in a Cohort of Italian Adults**

**Justyna Godos 1 , Ra**ff**aele Ferri 1 , Sabrina Castellano 2 , Donato Angelino 3 , Pedro Mena 4 , Daniele Del Rio 5,6 , Filippo Caraci 1,7, Fabio Galvano 8 and Giuseppe Grosso 8,\***

Received: 28 March 2020; Accepted: 24 April 2020; Published: 26 April 2020

**Abstract:** Background: Diet has been the major focus of attention as a leading risk factor for non-communicable diseases, including mental health disorders. A large body of literature supports the hypothesis that there is a bidirectional association between sleep and diet quality, possibly via the modulation of neuro-inflammation, adult neurogenesis and synaptic and neuronal plasticity. In the present study, the association between dietary total, subclasses of and individual (poly)phenols and sleep quality was explored in a cohort of Italian adults. Methods: The demographic and dietary characteristics of 1936 adults living in southern Italy were analyzed. Food frequency questionnaires (FFQs) were used to assess dietary intake. Data on the (poly)phenol content in foods were retrieved from the Phenol-Explorer database. The Pittsburg Sleep Quality Index was used to measure sleep quality. Multivariate logistic regression analyses were used to test the associations. Results: A significant inverse association between a higher dietary intake of lignans and inadequate sleep quality was found. Additionally, individuals with the highest quartile of hydroxycinnamic acid intake were less likely to have inadequate sleep quality. When individual compounds were taken into consideration, an association with sleep quality was observed for naringenin and apigenin among flavonoids, and for matairesinol among lignans. A secondary analysis was conducted, stratifying the population into normal weight and overweight/obese individuals. The findings in normal weight individuals showed a stronger association between certain classes of, subclasses of and individual compounds and sleep quality. Notably, nearly all individual compounds belonging to the lignan class were inversely associated with inadequate sleep quality. In the overweight/obese individuals, there were no associations between any dietary (poly)phenol class and sleep quality. Conclusions: The results of this study suggest that a higher dietary intake of certain (poly)phenols may be associated with better sleep quality among adult individuals.

**Keywords:** polyphenol; sleep; mental health; cohort; antioxidant; cognitive; brain; Sicily; population

#### **1. Introduction**

Diet has been the focus of major attention as a leading risk factor for non-communicable diseases [1,2]. These estimates are based on convincing evidence that dietary factors may play a role in the risks of cardiovascular diseases and certain cancers [3–8]. A more intriguing hypothesis recently explored is that diet may also influence brain health and mental disorders [9,10]. A large body of literature supports the hypothesis that there is an association between sleeping patterns and diet quality, possibly mediating weight status and obesity-related disorders [11]. Generally, most of the evidence relies on the positive association between sleep quality or duration and diet quality, but relatively recent studies suggest that a bidirectional relationship may exist, with dietary factors influencing sleep features [12]. Several mechanisms have been hypothesized to explain this association, including inflammation, oxidative stress, the gut microbiome, epigenetic modifications and the direct effects of nutrients and non-nutrients on neuroplasticity [13]. Among the healthy dietary patterns suggested for their putative influence on sleep quality, plant-based foods, including vegetables, grains, nuts, seeds, legumes and fruits, have demonstrated to have a mechanistic relationship with better mental health, potentially influencing sleep features [12]. Those foods are rich sources of bioactive compounds that, in the context of a healthy lifestyle, may play a potential role in preventing subclinical low-grade inflammation, a starting point for several chronic non-communicable diseases as well as for impaired sleep quality and duration [14,15]. There is, in fact, evidence that inflammation may mediate a variety of brain disorders involving sleep quality but also stress, depression, dementia and Alzheimer's disease [16]. Thus, it is crucial to understand whether diet may affect the level of inflammation and which compounds should be of major interest.

Dietary (poly)phenols represent a group of compounds present in plant-derived foods that, based on their biochemical structure, may play a pivotal role in radical scavenging and in mediating inflammation processes [17–19]. Several families are commonly consumed when adhering to healthy dietary patterns, including flavonoids (mostly contained in fruits, vegetables, tea and cocoa products), phenolic acids (contained in fruits, coffee, pulses and nuts), stilbenes (mainly contained in wine) and phytoestrogens (including isoflavones and lignans, contained in soy products and legumes). Dietary (poly)phenols have been related to several potential health benefits [20]: recently, they have been hypothesized to also play a role in brain health [21,22]. We previously reported that individuals more adherent to healthy dietary patterns (i.e., the Mediterranean diet) and to a diet with low inflammatory potential were more likely to have higher sleep quality [23,24]. In the present study, we aimed to test whether total, subclasses of and individual (poly)phenols may be candidate molecules associated with sleep quality in a cohort of Italian adults.

#### **2. Materials and Methods**

#### *2.1. Study Population*

The MEAL study is an observational study aiming to investigate the association between the nutritional and lifestyle habits characterizing the classical Mediterranean area and non-communicable diseases. The baseline data included a sample of 2044 men and women aged 18 or more years old. Individuals were randomly selected in the main districts of the city of Catania, Sicily, Italy. The enrolment and data collection were performed between 2014 and 2015. Details of the study protocol are published elsewhere [25]. All participants were informed about the aims of the study and provided written informed consent. All the study procedures were carried out in accordance with the Declaration of Helsinki (1989) of the World Medical Association. The study protocol has been reviewed and approved by the concerning ethical committee of the Municipal Health Authority (protocol number: 802/23 December 2014).

#### *2.2. Data Collection*

Electronic data collection was performed by face-to-face assisted personal interviews, using tablet computers. In order to visualize the response options, participants were provided with a paper copy of the questionnaire. However, final answers were registered directly by the interviewer. The demographic data included gender, age at recruitment, highest educational degree achieved, occupation (specifying the nature of the most important employment during the year before the investigation) or last occupation before retirement, and marital status. Educational status was categorized as (i) low (primary/secondary), (ii) medium (high school), and (iii) high (university). Occupational status was categorized as (i) unemployed, (ii) low (unskilled workers), (iii) medium (partially skilled workers), and (iv) high (skilled workers). Physical activity status was evaluated using International Physical Activity Questionnaires (IPAQ) [26], which demonstrated an acceptable validity for the Italian population (the Cronbach's alpha values were 0.73 and 0.60 for the short and long versions, respectively) [27]: the instrument included a set of questionnaires (five domains) investigating the time spent being physically active in the last 7 days. Based on the IPAQ guidelines, the final score allows categorizing physical activity levels as (i) low, (ii) moderate, and (iii) high. Smoking status was categorized as (i) non-smoker, (ii) ex-smoker, and (iii) current smoker. Alcohol consumption was categorized as (i) none, (ii) moderate drinker (0.1–12 g/d) and (iii) regular drinker (>12 g/d). Anthropometric measurements have been collected following standard procedures [28]. Arterial blood pressure was measured in sitting position and after at least 5 min of rest, at the end of the physical examination. Because of the possibility of differences in blood pressure measurements, the measurements were taken three times at the right arm, relaxed and well supported by a table, with an angle of 45◦ from the trunk. A mean of the last two measurements was considered for inclusion in the database. Patients were considered hypertensive when their average systolic/diastolic blood pressure levels were higher than or equal to 140/90 mm Hg, they were taking anti-hypertensive medications, or they had previously been diagnosed with hypertension.

#### *2.3. Dietary Assessment*

The dietary assessment was performed by the administration of two food frequency questionnaires (FFQ, long and short versions) that had been previously tested for validity and reliability for individuals living in Sicily [29,30]. For the purposes of this study, the data from the most comprehensive FFQ including 110 food items were used. The identification of food intake, energy content and macro- and micro-nutrient intake was performed through comparison with the food composition tables from the Research Center for Foods and Nutrition [31]. The intake of seasonal foods referred to consumption during the period in which the food was available, proportionally adjusted by its intake in one year. The instrument showed a good relative validity (all major food groups with the exception of bread and soft drinks had significant Person's correlation coefficients, over 0.60, and the highest correlation coefficients for coffee (*R* = 0.96 in men and women), tea (*R* = 0.79 in men and 0.80 in women) and alcoholic beverages (*R* = 0.83 in men and 0.88 in women)) and reliability (all major food groups besides bread had significant Person's correlation coefficients, over 0.60, and the highest correlation coefficients for coffee (*R* = 0.97 in men and *R* = 0.96 in women), tea (*R* = 0.82 in men and 0.84 in women) and alcoholic beverages (*R* = 0.87 in men and 0.92 in women)). FFQs with unreliable intakes (<1000 or >6000 kcal/d) were excluded from the analyses (*n* = 107), leaving a total of 1936 individuals included in the analysis.

#### *2.4. Estimation of Polyphenol Intake*

The process of the estimation of habitual (poly)phenol intakes has been previously described in detail [32]. Briefly, data on the (poly)phenol content in foods were retrieved from the Phenol-Explorer database (www.phenol-explorer.eu) [33]. A new version of the Phenol-Explorer database containing data on the effects of cooking and food processing on (poly)phenol contents was used whenever possible, in order to apply (poly)phenol-specific retention factors [34]. Foods that contained no (poly)phenols were excluded from the calculation, leaving a total of 75 items included in the analyses. Food weight loss or gain during cooking was corrected using yield factors [35]. Average food consumption was calculated (in g or mL) by following the standard portion sizes used in the study and then converted to 24 h intake. Finally, a search was carried out in the Phenol-Explorer database to retrieve the mean content values for all (poly)phenols contained in the selected foods. Next, (poly)phenol intake from each food was calculated by multiplying the content of each (poly)phenol class by the daily consumption of each food. The total (poly)phenol intake was considered as the sum of all the main classes and subclasses. Finally, (poly)phenol intake was adjusted for total energy intake (kcal/d) using the residual method [36].

#### *2.5. Sleep Quality*

The Pittsburg sleep quality index (PSQI) [37] was used to assess participants' sleep quality and disturbances in the past six months, which has also been demonstrated to be a good and reliable tool in the Italian population (the internal consistency was represented by a Cronbach's alpha of 0.835) [38]. It consists of 19 items that are rated on a four-point scale (0–3) and grouped into seven components (sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, the use of sleeping medications, and daytime dysfunction). The item scores in each component were summed and converted to component scores ranging from 0 (better) to 3 (worse) based on guidelines. The total PSQI score was calculated as the summation of seven component scores, ranging from 0 to 21, where a higher score indicates a worse condition. A score of <5 on for total global PSQI is indicative of adequate sleep quality.

#### *2.6. Statistical Analysis*

Frequencies are expressed as absolute numbers and percentages; continuous variables are expressed as means and standard deviations. Individuals were divided into quartiles of dietary (poly)phenol intake and the distributions of background characteristics were compared between the groups. Differences were tested with Chi-square tests for categorical variables, ANOVA for continuous variables distributed normally, and Kruskall–Wallis tests for variables not normally distributed. Energy-adjusted multivariate logistic regression models were used to test the association between the variables of exposure (including total (poly)phenols, their classes, subclasses and individual compounds) and inadequate sleep quality; a multivariate model adjusted for all other background characteristics (body mass index, physical activity, educational status, occupational status, smoking status, alcohol consumption, occurrence of hypertension, diabetes, dyslipidemias, cardiovascular disease, cancer, and menopausal status) was also used to test whether the observed associations were independent from the aforementioned variables. All reported *P*-values were based on two-sided tests and compared to a significance level of 5%. Bonferroni correction was applied and *P*-values meeting the threshold of 0.05 divided by the number of polyphenol quartiles were noted. The SPSS 17 (SPSS Inc., Chicago, IL, USA) software was used for all the statistical calculations.

#### **3. Results**

The baseline characteristics of the study population by quartiles of energy-adjusted total (poly)phenol intake are presented in Table 1. The distributions of certain variables, such as age and education level, did not follow linear trends, as individuals in the middle quartiles were significantly older and had lower educational levels than the others. The participants in the highest quartile of total (poly)phenol intake had moderate levels of physical activity and were moderate or regular alcohol drinkers, and they also had a lower prevalence of hypertension, while concerning type 2 diabetes, the distribution was not linear and higher rates were registered in the middle quartiles (Table 1). High total (poly)phenol intake was also correlated with higher total energy intake (Table 1).

**Table 1.** Background characteristics of participants in the MEAL cohort by quartiles of total (poly)phenol intake (energy-adjusted).

A total of 509 individuals (32.4%) reported inadequate sleep quality. No association between total or individual major classes of (poly)phenols and sleep quality was found, with the exception of lignans, for which participants in the third quartile of intake were less likely to have inadequate sleep quality after adjusting for potential confounding factors (OR = 0.62; 95% CI: 0.43, 0.88; Table 2). None of the flavonoid subclasses showed an association with sleep quality. Conversely, individuals with the highest quartile of hydroxycinnamic acid intake were less likely to have inadequate sleep quality (OR = 0.67; 95% CI: 0.46, 0.98; Table 2). When individual compounds were taken into consideration, inverse associations with inadequate sleep quality were observed for naringenin (OR = 0.66; 95% CI: 0.46, 0.95) and apigenin (OR = 0.63; 95% CI: 0.44, 0.90) among flavonoids, and for matairesinol (OR= 0.66; 95% CI: 0.46, 0.96) among lignans (Table 2).

**Table 2.** Odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between (poly)phenol intake (total, main classes, subclasses and individual compounds) and adequate sleep quality in the MEAL cohort.

Adjusted for total energy intake (continuous), body mass index (continuous), physical activity (low/medium/high), educational status (low/medium/high), occupational status (unemployed/low/medium/high), smoking status (current/former/never), alcohol consumption (none/moderate/regular), occurrence of hypertension, diabetes, dyslipidemias, cardiovascular disease, cancer (yes/no), and menopausal status (women only, yes/no). a *P*-value meeting threshold for Bonferroni correction.

A secondary analysis was conducted, stratifying the population into normal weight and overweight/obese individuals. The findings in normal weight individuals showed a stronger association between certain classes, subclasses and individual compounds and sleep quality (Table 3). Notably, nearly all individual compounds belonging to the lignan class (secoisolariciresinol, matairesinol and pinoresinol) were inversely associated with inadequate sleep quality (Table 3).

Adjusted for total energy intake (continuous), body mass index (continuous), physical activity (low/medium/high), educational status (low/medium/high), occupational status (unemployed/low/medium/high), smoking status (current/former/never), alcohol consumption (none/moderate/regular), occurrence of hypertension, diabetes, dyslipidemias, cardiovascular disease, cancer (yes/no), and menopausal status (women only, yes/no).

To the contrary, in the overweight/obese individuals, there were no associations between any dietary (poly)phenol class and sleep quality. Among the individual components, only apigenin was significantly associated with sleep quality (OR = 0.53; 95% CI: 0.31, 0.90) (Table 4).

Adjusted for total energy intake (continuous), body mass index (continuous), physical activity (low/medium/high), educational status (low/medium/high), occupational status (unemployed/low/medium/high), smoking status (current/former/never), alcohol consumption (none/moderate/regular), occurrence of hypertension, diabetes, dyslipidemias, cardiovascular disease, cancer (yes/no), and menopausal status (women only, yes/no).

#### **4. Discussion**

In this article, we tested whether dietary (poly)phenols were associated with sleep quality in a cohort of Italian adults. Individuals showing a higher intake of some flavonoid subclasses (flavanones and flavones), phenolic acids (such as hydroxycinnamic acids) and lignans were significantly less likely to have inadequate sleep quality. These findings suggest that some classes of (poly)phenol may play a specific role when exploring their relationship with brain and mental health. Interestingly, the associations were more evident when stratifying the cohort by weight status, showing significant results in normal weight individuals, but no confirmed associations for overweight/obese participants.

To date, only one recent study has investigated the relationship between dietary (poly)phenols derived from fruit and vegetables and sleep duration [39]. The study was conducted on 13,958 women with about 4 years of follow-up in the UK Women's Cohort Study: total fruit and vegetable consumption and their estimated content of total polyphenols were directly associated with sleep duration, while individual (poly)phenol classes were not associated with the outcome of interest [39]. Despite no other studies being focused on polyphenols, some studies reported a direct relationship between sleep duration and quality, and fruit and vegetable intake [40,41]. Other studies showed the role of certain polyphenol-rich foods (i.e., black tea and cocoa products) in improving sleep quality [42,43]. Despite there being no other studies specifically conducted on (poly)phenols and sleep quality, there is consistent evidence from the literature suggesting a potential role of dietary (poly)phenols in improving mental health and preventing conditions that are associated with sleep disorders. For instance, some cohort studies showed that individuals with higher intakes of the same flavonoid, hydroxycinnamic and lignan classes found to be significantly associated with better sleep quality in this study were less likely to have depressive symptoms [44,45]. Other studies also showed an inverse association between fruit and vegetable intake and depressive symptoms and perceived stress, despite most of them have been conducted on students [46–49].

From a general mechanistic point of view, (poly)phenol circulating metabolites are able to pass through the blood–brain barrier to various extents, depending on their degree of lipophilicity, with less polar (poly)phenol metabolites capable of greater brain uptake than more polar ones [50,51]. The main potential beneficial effects of dietary (poly)phenols in the central nervous system include the suppression of neuronal apoptosis, modulation of signaling pathways implicated in neuron survival, and stimulation of adult neurogenesis [52–54]. With special regard to specific mechanisms related to sleep features, dietary (poly)phenols have been shown to improve resilience after sleep deprivation [55]; some individual molecules, such as apigenin, are able to reduce locomotor activity, prolong sleep time, increase sleep rate increase and sleep time in combined administration with a GABA(A) receptor agonist, and show synergic effects in potentiating sleep onset in animal models [56–58]. Additionally, derivates of hydroxycinnamic acids have been identified as agonists for both gamma-amino butyric acid (GABA) receptors and act synergistically with 5-hydroxytryptophan (5-HTP), both of which play a role in sleep quality, including having sedative effects on locomotion activity, prolonging sleeping time and shortening sleep latency [59–61].

Dietary (poly)phenols have been shown to decrease systemic inflammation [62] but also exert anti-neuroinflammatory properties and reduce oxidative stress and inflammation-related conditions [63]. Several studies have shown that molecules of interest from our study, including some flavonoids (i.e., apigenin) and hydroxycinnamic acids, improve cell antioxidant activity against oxidative stress in the central nervous system [64,65]. Additionally, lignans have been demonstrated to exert anti-oxidative and anti-inflammatory properties in neurons and protect the blood–brain barrier against inflammatory cells by reducing oxidative stress, inflammation and permeability [66–68]. Dietary (poly)phenols may ameliorate poor endothelial function [69] and help to control blood pressure [70], which, in turn, has been associated with measures of sleep quality together with decreases in the percentage of REM sleep and increases in REM sleep latency [71–73]. Previous epidemiological studies have shown an inverse association between the intake of specific classes of (poly)phenol (in line with the findings shown in the present study)—including flavones and flavanones among flavonoids [74], and hydroxycinnamic acids—and the occurrence of hypertension [75,76]. The mechanisms underlying these relationships are still under investigation; besides, regarding the direct effect of (poly)phenols (especially hydroxycinnamic acids) on low-grade inflammation, which, in turn, may affect endothelial function [77,78], an intriguing hypothesis involves nitric oxide-mediated vasodilation in the brain, which has been shown to facilitate REM sleep [79].

An emerging body of literature investigates the double interexchange of information between the gut microbiota and the brain through a complex system of signals involving neural, endocrine and inflammatory mechanisms [80]. In fact, the gut microbiota has been shown to affect brain and behaviors related to anxiety and depressive symptoms depending on bacterial family ratios, dysbiosis, and subsequent modulation through dietary (poly)phenol intake [81]; the status of the pro- and anti-inflammatory balance in the gut has been demonstrated to have an impact at the systemic level and on the central nervous system [82,83]. Recent studies show that dietary (poly)phenols may play a role in the modulation of gut microbiota metabolism and that variations in the gut microbiota can affect (poly)phenol activity [84]. This hypothesis is particularly valid in light of our results stratified by weight status; in the intestinal microbiota of obese people, a specific increase in the proportion of

class Firmicutes to class Bacteroidetes has been shown compared to in normal-weight individuals [85], which may affect (poly)phenol transformation and absorption in the gut [86] and their anti-oxidant effects [87–89]. However, current evidence is still limited, largely based on cell and animal studies, and future studies conducted on humans are needed to identify specific metabotypes associated with activity in the brain.

To the best of our knowledge, this is the first study investigating such a comprehensive group of compounds in order to identify key (poly)phenol molecules of potential interest to improve sleep quality. Moreover, based on our previous results [32], no unique food source is responsible for specific compounds or classes of (poly)phenols; thus, the present analysis is able to detect the potential role of (poly)phenols rather than of the individual foods underlying their consumption. However, the findings presented in this study should be considered in light of some limitations. Firstly, this study provided evidence from a cross-sectional analysis, which cannot exclude reverse causation nor describe a causal relation. Secondly, all methods used to assess food consumption and dietary polyphenol intake provide only estimations, while true intake cannot be estimated without measuring biomarkers or metabolites. Despite the use of the Phenol-Explorer database being validated and widespread, this method cannot take into account molecular transformation or interaction. Moreover, recall bias and unmeasured confounding factors (i.e., jobs requiring night shifts) should be considered as potential limitations. However, these methods are commonly used in the current scientific literature, representing the standard for scientific research until new methods are validated and made available. Thirdly, no other aspects related to sleeping problems or other mental health issues have been considered, while they may be associated with sleep quality. Thus, the potential mediating effect of such intermediary conditions should be taken into account.

#### **5. Conclusions**

Our study suggests that a higher dietary intake of certain (poly)phenols may be potentially associated with better sleep quality. However, further epidemiological studies are needed to confirm the presented hypothesis, with a major focus on sleep quality. Several aspects should be further considered in future studies, such as the use of caffeinated beverages or the timing of food and alcohol intake. Future studies should additionally focus on the inter-individual variation in response to the consumption of (poly)phenols and thus investigate the associations not only for their dietary intake but also for the true internal exposure to their metabolites. In this context, attention to the gut microbiota composition should also be paid as differences in microbial species may condition (poly)phenol metabolite formation and bioactivity. Finally, intervention studies will be needed to explore the level of absorption and bioavailability of dietary (poly)phenols and the characterization of biologically available (poly)phenol metabolites responsible for the promotion of resilience against cognitive impairment in response to poor sleep quality.

**Author Contributions:** Conceptualization, J.G., R.F., F.C., F.G., G.G.; methodology J.G., R.F., F.C., F.G., G.G.; validation, J.G., R.F., S.C., F.C., F.G., G.G.; formal analysis, J.G. and G.G.; investigation, J.G., R.F., F.C., F.G., G.G.; data curation, J.G. and S.C.; writing—original draft preparation, J.G., R.F., F.C., F.G., G.G.; writing—review and editing, J.G., R.F., S.C., D.A., P.M., D.D.R., F.C., F.G., G.G.; supervision, R.F., F.C., F.G., G.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was partially supported by a fund from the Italian Ministry of Health "Ricerca Corrente" (RC n. 2751594) (Ferri, Caraci and Godos).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

1. Afshin, A.; Sur, P.J.; Fay, K.A.; Cornaby, L.; Ferrara, G.; Salama, J.S.; Mullany, E.C.; Abate, K.H.; Abbafati, C. Health effects of dietary risks in 195 countries, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. *Lancet* **2019**, *393*, 1958–1972. [CrossRef]

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*4.13. Luteolin*

Luteolin (LT) (3,4,5,7-tetrahydroxy flavone) is a flavonoid in many plants including broccoli, carrots, perilla leaves, seeds, and celery. It possesses anti-allergy, antiinflammatory, anti-cancer, antioxidant, and anti-microbial properties [96–98]. In various cancers (including lung, GBM, BC, CRC, PC), LT inhibits cell proliferation and tumor growth, promotes cancer cell apoptosis and cell cycle arrest, reduces drug resistance, and reduces cancer cell invasiveness and metastasis [99]. LT can also stop EMT from occurring, shrinking in the cytoskeleton, increasing the expression of E-cadherin, and decreasing the expression of CDH2, Snail, and VIM [100]. LT inhibits the Smad 2/3 pathway and the Wnt/-catenin pathway by inhibiting the synthesis of Snail and Slug by downregulating the production of β-catenin. Doing so prevents metastasis by upregulating CDH2, Zo 1, and claudin 1 and downregulating CDH2, fibronectin, VIM, and MMP-2 [99,101]. The mechanism of the action of LT is demonstrated in Figure 4.

**Figure 4.** Possible mechanisms of luteolin, Carnosic acid, and N-Phenethylacetamide inhibit EMT via different pathways [94,95].