of sleep. Studies have found that low protein intake is associated with difficulty initiating sleep and poor quality of sleep. One study, found that apnea hypopnea index (AHI) was significantly associated with total calories consumed. Low quality dietary intake and low quality of sleep are currently common health issues among the U.S. population. People who do not obtain sufficient sleep or sleep irregular hours, may have inadequate diet, weight gain, and poorer health in general than those who maintain adequate sleep quality. Conversely, low or inadequate diet may have an effect on quality of sleep.
In the present study, the association between macronutrients (protein, fat, carbohydrates, monounsaturated fatty acids (MUFA), saturated fatty acids (SFA), and polyunsaturated fatty acids (PUFA)) and alcohol with apnea hypopnea index (AHI) were evaluated. Data from the longitudinal assessment of daily activity patterns on weight change after involuntary job loss: the ADAPT study was used.
Results from this study showed differences in nutrient intake by gender and associations with AHI scores. Although these results were not statistically significant, there were variances with positive associations for SFA, MUFA, and PUFA with AHI scores for women while there were negative associations for SFA, MUFA, and PUFA with AHI scores for males.
The present study showed initial analysis for potential association between dietary nutrients and AHI and that these associations may differ by gender. Further studies with larger number of participants and which include participants with moderate and severe SDB could produce results to further elucidate these associations.
3
Background
Research has shown that there are associations between macronutrient intake and quality of sleep. For instance, a study found that low protein intake (<16% as compared to ≥16% of total energy) was associated with difficulty initiating sleep (OR 1.24, 95% CI: 0.99 – 1.56) and poor quality of sleep (OR 1.24, (5% CI 1.04 – 1.48). However, high protein intake (≥19% vs <19%) was associated with difficulty maintaining sleep (DMS) (OR 1.4, 95% CI: 1.12-1.76) and low carbohydrate intake (<50% vs ≥50% of total energy) was associated with DMS (OR 1.19, 95% CI; .97-1.45) (Tanaka et al., 2013). Another study, including 42 obese subjects aged 10 to 16.9 years, found that apnea hypopnea index (AHI) was significantly associated with total calories consumed and the grams of fat and carbohydrates, but not with protein (Beebe, Miller, Kirk, Daniels, & Amin, 2011). It was found that protein macronutrients did not correlate with sleep duration during a week nor the night before the meal. It was concluded that obstructive sleep apnea (OSA) was associated with an increased preference for calorie-dense foods that are high in fat and carbohydrates.
Additional research has been conducted to determine whether gender confounds the sleep apnea diagnosis (Young, Hutton, Finn, Badr, & Palta, 1996). This has been a topic of study due to the hypothesis that women with sleep apnea are frequently misdiagnosed due to the clinical guidelines for the evaluation and diagnosis of sleep apnea. Young et.al, found that disregarding the severity level, women with sleep apnea did not report symptoms that were significantly different than those of men. For both genders, snoring was the most sensitive and strongest predictor of sleep apnea (Young et al., 1996).
Low quality dietary intake and low quality of sleep are currently common health issues among the U.S. population. People who do not obtain sufficient sleep or sleep irregular hours, may have inadequate diet, weight gain, and poorer health in general than those who maintain adequate sleep quality (Gangwisch, Malaspina, Boden-Albala, & Heymsfield, 2005; Silva et al., 2009). Conversely, low or inadequate diet may have an effect on quality of sleep. This topic is
4
relevant because many people lose their jobs every year and it is important to understand how the stress of job loss and lack of daily routines can affect overall health, sleep, and diet. This topic is of interest to me because I believe that it is important to have a better understanding of how nutrition can affect the quality of participant’s sleep. It is especially interesting to me to evaluate how nutrition can affect a person’s sleep, and therefore the effect of the person's sleep affects their overall health.
In the present study, I evaluated the association between macronutrients (protein, fat, carbohydrates, monounsaturated fatty acids, saturated fatty acids, and polyunsaturated fatty acids) and alcohol with apnea hypopnea index (AHI). Studies which investigate the associations between macronutrient intakes and obstructive sleep apnea are desired, to explore these associations I used data from the longitudinal assessment of daily activity patterns on weight change after involuntary job loss: the ADAPT study, (Haynes et al., 2017) in which I participated in data collection. Studies have shown that males are at a higher risk for sleep disordered breathing than females (Young). I will control for potential confounding by gender.
My research will seek to answer:
Specific Aim 1: To evaluate the association between dietary micronutrients and AHI scores.
Hypothesis 1: Participants with poorer dietary choices (i.e. higher fats and carbohydrates and lower protein), will have greater likelihood of mild obstructive sleep apnea (OSA, AHI ≥5-15) than subjects with better dietary choices.
Recruitment Methods and Research Procedures For the present thesis data was used from the longitudinal assessment of daily activity patterns on weight change after involuntary job loss: the ADAPT study (Haynes et al., 2017). The overall objective of the ongoing ADAPT study is to explore whether social rhythms and sleep operate as mechanisms of weight gain following involuntary job loss. Briefly, this cohort
5
research design examines social rhythms, sleep, dietary intake, energy expenditure, waist circumference, and weight gain during six scheduled visits over 18 months in individuals who have sustained involuntary job loss. Approximately 332 participants who lost their job within the last 3 months are targeted for recruitment to investigate changes in social rhythms, sleep, and weight gain. The present analysis includes 51 participants from the baseline evaluation of the ADAPT study who completed the Apnealink study to record their AHI and a food frequency assessment (vide infra). The primary approach to recruitment for ADAPT is through the Arizona Department of Economic Security (AZDES). On a weekly basis, recruitment letters describing the study are included in packets of information sent out to all individuals applying for Unemployment Insurance (UI) within Tucson and neighboring metropolitan areas (approximately 380 per week). Recruitment flyers are also posted at the local libraries and on Craigslist, a popular U.S. website for individuals searching for employment, in order to expedite the start of recruitment. Recruitment began in October 2015 with the goal of recruiting 322 participants. Potential participants are given a phone screen, the first screening visit is then scheduled and participants are consented in person at that visit and have the opportunity to ask questions at the time of the screening visit. Research staff explain that consenting is voluntary (Haynes et al., 2017). Screening Procedures: Participants are between the ages of 25-60 years, as age above or below this range may have substantial impact on sleep and social rhythms. To complete forms and participate in interviews, individuals are required to fluently speak, read, and write English. To detect a change in social rhythms associated with job loss, individuals must have been working at their previous job for at least 6 months for 30 or more hours per week and have been laid off or terminated from their place of employment within the last 90 days.
6
Participants are given instructions on the use of the ApneaLink Plus™ device (ResMed), which they wear while sleeping in their home for one night. Study staff retrieve the ApneaLink Plus™ device the following morning for immediate scoring by a registered polysomnographic technologist and interpretation by a physician board-certified in sleep medicine (SFQ). During the baseline (V0) interview, participants are also given the Arizona Activity Frequency Questionnaire (AAFQ) and other questionnaires to complete and bring back to their next scheduled interview (Visit 1, V1). Participants are informed on the procedure for the 3-day USDA multi-pass dietary recall. Recalls are scheduled for 3 days over the next 2 weeks, and participants are instructed to take pictures of meals, beverages, and snacks before and after consumption to facilitate their memory and intake assessment. My responsibilities as a Research Assistant included helping conduct the V0 evaluations of the study. I took the wrist, neck, and waist measurements of participants. As well as collected weight, height, and body mass index (BMI) of the participant. After this was collected, a short STOP-BANG Questionnaire (Chung et al., 2008) was taken for each participant and all data was recorded in REDCap, a secure web application for building and managing online surveys and databases.
Data Analysis
(AHI) [scores of <5 no apnea, and ≥5 – 15 mild apnea] and dietary 24-hour recall interviews. AHI data was used from the screening visit zero (V0), during this visit participants wore the device called ApneaLink to assess the participants’ AHI score. Participants with AHI scores <15 were included in the ADAPT study and invited back for a follow-up visit (V1). Participants with AHI scores ≥ 15 were excluded from further participation in the study. Participants in V1 completed the three USDA-approved 24-hour diet recall phone interviews over a period of 14 days, two interviews during the weekdays and one weekend interview. Other variables from the baseline
7
evaluation included in the present analysis were age, gender, ethnic background, number of children, marital status, alcohol consumption, and smoking habits. All the data was inputted into the RedCap Database. SPSS version 24 was used for data analysis. The parent study was approved by the University of Arizona Institutional Review Board.
Descriptive statistics were performed, including means and standard deviations for continuous variables, and frequency distributions for categorical variables. Differences in proportions for categorical variables were determined using Chi squared test, and the Mann–Whitney U test to compare differences in continuous variables. Scatter plots were produced to examine associations between Apnea Hypopnea Index and macronutrients by gender. Linear regressions models adjusted for gender were used to evaluate associations between Apnea Hypopnea Index and macronutrients.
Results
There were 34% (n=17) males and 66% (n=33) females (Table 1). Forty two percent were Hispanic and 58% Non-Hispanic or Latino. Most participants were single (46%, n=23) or married (20%, n=10), Approximately half had children (54%, n=27). Six percent described having asthma, emphysema or chronic bronchitis and 40% were obese. Most participants (57.6%, n=19) reported having one alcoholic drink per week and 6.1% one alcoholic drink per day. There were no significant differences when comparisons were made between males and females.
Table 2 shows differences for continuous variables, median age was 40 years (min-max: 24-60). There were significant differences between males and females for monounsaturated fatty acids (MUFA), but no other differences were seen by gender. For MUFA, males had mean consumption of 0.005 (SD: 0-0.22) and females had a mean of 0.004 (SD: 0-0.15) (p-value 0.047). Males had an increased energy (kcal) intake (M=1628, SD: 850-2769) compared to females (M=1498, SD:671-3600). Males had an increased total fat intake (M= 67, SD: 36-122)
8
compared to females (M=57, SD:19-164), and males had an increased calorie intake from fat (%) (M= 38, SD: 28-51) compared to females (M= 34, SD: 15-51). However, females had increased total carbohydrates intake with a median of 177 (SD: 57-426) compared to males (M=170, SD: 74-389). Women had an increased alcohol consumption with a median of 0.05 (SD: 0-80) compared to men (M<0.001, SD: 0-14).
We used linear regression analysis looking at associations between AHI and percent calories from fat, carbohydrates, protein, and alcohol; as well as MUFA, saturated fatty acids (SFA), and polyunsaturated fatty acids (PUFA) all adjusted for gender (Tables 3.1 – 3.7). These analyses showed no significant difference adjusted for gender. Even though associations were not significant, there were positive and negative trends. An increased consumption of percent calories from fat were associated with an increased AHI score (coefficient =.054). An increase consumption of carbohydrates was associated with a lower AHI score (coefficient = -.029). An increased consumption of protein was associated with lower AHI scores (coefficient =-.064). An increase in alcohol consumption was associated with increased AHI score (coefficient = 0.060). Increased consumption of MUFA was associated with increased AHI score (coefficient = 3.437). An increased consumption of SFA was associated with increased AHI score (coefficient = .333). An increased consumption of PUFA was associated with a decreased AHI score (coefficient = -2.004).
The Scatterplots:
I ran scatter plots looking at associations between AHI, SFA, MUFA, and PUFA separate for males and females (Figure 1A-F). The scatter plots showed negative associations with SFA, MUFA, and PUFA for males. While the scatter plots showed positive