Associations Among Cardiometabolic Risk Factors, Sleep Duration, and Obstructive Sleep Apnea in a Southeastern US Rural Community: Cross-Sectional Analysis From the SLUMBRx-PONS Study (Preprint)
Authors: Knowlden Adam P , Winchester Lee J , MacDonald Hayley V , Geyer James D , Higginbotham John C 
Published: February 2024

BACKGROUND

Short sleep and obstructive sleep apnea are underrecognized strains on the public health infrastructure. In the United States, over 35% of adults report short sleep and more than 80% of individuals with obstructive sleep apnea remain undiagnosed. The associations between inadequate sleep and cardiometabolic disease risk factors have garnered increased attention. However, challenges persist in modeling sleep-associated cardiometabolic disease risk factors.

OBJECTIVE

This study aimed to report early findings from the Short Sleep Undermines Cardiometabolic Health-Public Health Observational study (SLUMBRx-PONS).

METHODS

Data for the SLUMBRx-PONS study were collected cross-sectionally and longitudinally from a nonclinical, rural community sample (n=47) in the southeast United States. Measures included 7 consecutive nights of wrist-based actigraphy (eg, mean of 7 consecutive nights of total sleep time [TST<sub>7N</sub>]), 1 night of sleep apnea home testing (eg, apnea-hypopnea index [AHI]), and a cross-sectional clinical sample of anthropometric (eg, BMI), cardiovascular (eg, blood pressure), and blood-based biomarkers (eg, triglycerides and glucose). Correlational analyses and regression models assessed the relationships between the cardiometabolic disease risk factors and the sleep indices (eg, TST<sub>7N</sub> and AHI). Linear regression models were constructed to examine associations between significant cardiometabolic indices of TST<sub>7N</sub> (model 1) and AHI (model 2).

RESULTS

Correlational assessment in model 1 identified significant associations between TST<sub>7N</sub> and AHI (<i>r</i>=–0.45, <i>P</i>=.004), BMI (<i>r</i>=–0.38, <i>P</i>=.02), systolic blood pressure (<i>r</i>=0.40, <i>P</i>=.01), and diastolic blood pressure (<i>r</i>=0.32, <i>P</i>=.049). Pertaining to model 1, composite measures of AHI, BMI, systolic blood pressure, and diastolic blood pressure accounted for 25.1% of the variance in TST<sub>7N</sub> (<i>R</i><sup>2</sup><sub>adjusted</sub>=0.25; <i>F</i><sub>2,38</sub>=7.37; <i>P</i>=.002). Correlational analyses in model 2 revealed significant relationships between AHI and TST<sub>7N</sub> (<i>r</i>=–0.45, <i>P</i><.001), BMI (<i>r</i>=0.71, <i>P</i><.001), triglycerides (<i>r</i>=0.36, <i>P</i>=.03), and glucose (<i>r</i>=0.34, <i>P</i>=.04). Results from model 2 found that TST<sub>7N</sub>, triglycerides, and glucose accounted for 37.6% of the variance in the composite measure of AHI and BMI (<i>R</i><sup>2</sup><sub>adjusted</sub>=0.38; <i>F</i><sub>3,38</sub>=8.63; <i>P</i><.001).

CONCLUSIONS

Results from the SLUMBRx-PONS study highlight the complex interplay between sleep-associated risk factors for cardiometabolic disease. Early findings underscore the need for further investigations incorporating the collection of clinical, epidemiological, and ambulatory measures to inform public health, health promotion, and health education interventions addressing the cardiometabolic consequences of inadequate sleep.

INTERNATIONAL REGISTERED REPORT

RR2-10.2196/27139