Healthy eating lowers risk of sleep apnea

By Pooja Toshniwal PahariaReviewed by Benedette Cuffari, M.Sc.Apr 27 2023

In a recent study published in the journal Nutrients, researchers in the United States analyze the relationship between diet quality and various sleep outcomes in the Bogalusa Heart Study (BHS).

Study: Diet Quality and Sleep Characteristics in Midlife: The Bogalusa Heart Study. Image Credit: Prostock-studio / Shutterstock.com

Background

Cardiovascular diseases (CVD) are the leading cause of death in the United States, with one person dying from CVD every 34 seconds in this country. With about 122 million adults currently living with CVD in the U.S., it is imperative to identify risk factors associated with the disease to reduce its occurrence.

Certain lifestyle-associated risk factors, such as diet and sleep, can be modified to lower CVD risks. In fact, a balanced diet has been shown to prevent CVD development, whereas sleep apnea and insomnia have been repeatedly linked to an increased risk of CVD. Studies have largely focused on the association between these activities and health outcomes; therefore, data on the interaction between nutrition and sleep are limited.

Short-term studies support the concept that diet and sleep are cyclical, as sleep affects next-day food intake, and diet impacts sleep through metabolic pathways. In fact, high-quality diets are reportedly associated with improved outcomes of sleep.

However, many of these studies included non-diverse groups such as males or females only and Mediterranean diet followers only. Another common limitation of these studies is that many only evaluated one sleep parameter, such as sleep duration, with a single measure to determine dietary quality.

About the study

In the present cross-sectional study, researchers investigate the association between diet quality and different sleep outcomes, such as sleep apnea, healthy sleep patterns, and insomnia, in the BHS study cohort of white and black adults of both sexes. The researchers also determined whether the impacts varied by socioeconomic status, race, and sex.

The study cohort consisted of individuals who participated in the BHS study between 2013 and 2016 and completed sleep questionnaires and diet assessments. The Lower Mississippi Delta Nutrition Intervention Research Initiative’s (Delta NIRI) Food Frequency Questionnaire (FFQ) was used to assess diet. Over a six-month period, the participants provided four one-day diet recalls.

The team matched Delta NIRI components to the U.S. Department of Agriculture (USDA) Food Patterns Equivalent Database (FPED) data published between 2015 and 2016. The Nutrient Database for Scientific Research was used to estimate nutrient intake. The Healthy Eating Index (HEI) 2015, Alternate Healthy Eating Index (AHEI) 2010, and Alternate Mediterranean Dietary Pattern (aMed) were used to construct diet patterns.

The Women's Health Initiative Insomnia Rating Scale (WHIIRS) was used to assess insomnia, whereas the Berlin Questionnaire was used for snoring and sleep apnea, and the healthy sleep pattern scores for overall sleep health. The International Physical Activity Questionnaire (IPAQ) was used to assess physical activity, and the total metabolic equivalent of task (MET) minutes per week was determined. The Centres for Epidemiologic Studies Depression (CESD) scale assessed depressive symptoms.

Multivariate Poisson regression modeling was performed using generalized estimating equations (GEE) to estimate prevalence rate ratios (PRRs). Models were adjusted for covariates such as age, sex, body mass index (BMI), race, physical exercise, level of education, status of employment status, caffeine consumption, depressive symptoms, having bed partners, and frequent usage of sleeping pills.

Data adjustments were also made for household size, census tract Index of Concentration at the Extremes (ICE), pediatric individuals in households, census tract Modified Retail Food Environment Index (mRFEI), smoking habits, alcohol consumption, and illicit use of drugs.

Study findings

Out of 1,298 individuals, 224 individuals with implausible calorie intake exceeding 4,800 kcal/day, 43 individuals with a prior history of stroke or heart attack, 123 worked in shifts, seven individuals with missing sleep or diet data, and 77 individuals with missing covariate data were excluded from the analysis. As a result, the final sample cohort included 824 individuals.

The participants' mean age was 48 years, 30% were black, 36% were men, about 30% had depression, and over 50% were obese. Among the participants, 44% had an increased risk of insomnia and sleep apnea, whereas sleeping patterns were considered good among 23% of the participants.

A mean score of 45 was obtained for AHEI-2010. Higher AHEI-2010 quintiles were more likely to be observed among older individuals, females, more educated individuals, never-smokers, alcohol drinkers, and individuals who were not depressed.

Higher food quality, as determined using AHEI 2010 scores, was related to a reduced sleep apnea risk score post-adjustment. There was no significant relationship between HEI 2015, aMed, insomnia symptoms, or a good sleep score.

AHEI-2010 and HEI-2015 were inversely related to a high risk of sleep apnea. Individuals in Q5 of the AHEI-2010 had a 41% lower likelihood of being at high risk for sleep apnea than those in Q1. Analyzing AHEI-2010 as a continuous variable showed that the correlation was significant, with a 12% reduced prevalence of high sleep apnea score per 10-point rise in AHEI-2010.

A greater AHEI-2010 was related to being positive on the sleepiness component but not the snoring component. In the unadjusted model, the researchers found a modest association between healthy sleep patterns and the AHEI-2010.

The relationship between AHEI-2010 and sleep apnea risk was greater among women than men. Notably, this relationship was also stronger among those with a higher level of education.

Fruits, long-chain omega-3 fatty acids, sugar-loaded beverages, red and processed meats, and alcohol had significant inverse associations when comparing Q5 to Q1. The sensitivity analysis, which was performed by excluding BMI from the covariates, yielded similar results.

Conclusions

Higher food quality was associated with reduced sleep apnea risk scores among semi-rural, lower-income populations in the southeastern U.S., where health disparities are common. These findings elucidate how diet quality affects sleep and, as a result, support the incorporation of high-quality food products into health interventions that may improve the efficacy of sleep therapies while simultaneously reducing the risk of developing CVD.