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Sleep Features Linked to Physical, Lifestyle Measures in Phenome-Wide Association Study

NEW YORK – Using data from the Human Phenotype Project (HPP) and a phenome-wide association study approach, a team from Israel, Switzerland, and Dubai has tracked down physical characteristics and lifestyle factors with ties to sleep-related biomarkers such as obstructive sleep apnea, sleep position, or snoring.

"Together, lifestyle factors contributed to the prediction of over 50 percent of the sleep characteristics," senior and corresponding author Eran Segal, a researcher affiliated with the Weizmann Institute of Science, Pheno.AI, and the Mohamed bin Zayed University of Artificial Intelligence, and coauthors wrote in Nature Medicine on Monday, adding that the new study "lays the groundwork for exploring the associations of sleep traits with body characteristics and developing predictive models based on sleep monitoring."

For the PheWAS, the investigators brought together sleep data collected on 6,366 HPP participants between the ages of 40 and 75 years old, mainly comprised of individuals from the Ashkenazi Jewish population, who were followed every two years for more than two decades. Through home sleep apnea test monitoring done over more than 16,800 nights, they explained, they identified 448 sleep characteristics that were subsequently analyzed alongside physical, clinical, and lifestyle profiles.

Along with questionnaires on participants' medical, medication, and lifestyle factors, for example, the team considered measurements such as body composition, blood pressure, diet, frailty, grip strength, insulin resistance, immune cell counts, and mass spectrometry-based blood serum lipidomics. The group also performed metagenomics-based microbiome profiling on stool samples collected in the days following participants' sleep monitoring tests, while blood testing was done on samples collected months later.

"[O]ur aim was to map all the phenotypes associated with sleep characteristics and rank them by importance across diverse body systems," the authors explained, adding that "we investigated the ability of sleep features to predict medical conditions beyond sleep or cardiovascular disorders."

When they considered the 448 sleep traits in combination with body characteristic clues representing 16 body systems, the researchers highlighted an association between participants' peripheral apnea-hypopnea index and visceral adipose tissue measurements, among thousands of other associations.

The team also used the PheWAS data to find sleep-related features that could be used to predict a range of physical or clinical features, including blood triglyceride measurements, cardiovascular characteristics, and insulin resistance.

"Notably, sleep characteristics contributed more to the prediction of certain insulin resistance, blood lipids … and cardiovascular measurements than to the characteristics of other body systems," the authors explained, noting that the contribution "was independent of [visceral adipose tissue], as sleep characteristics outperformed age, BMI, and [visceral adipose tissue] as predictors for these measurements in both male and female participants."

The team also saw ties between diet, gut microbial community features, and clinical obstructive sleep apnea signs such as sleepiness, and suggested that gut microbes and diet could help to predict such sleep symptoms, particularly in women in the HPP cohort.

Likewise, lifestyle factors such as watching TV, smoking, or physical activity were linked to a sleep measure known as the peripheral apnea-hypopnea index, while lifestyle, frailty, and body composition coincided with a measure of overall health known as nocturnal pulse rate variability.

Based on their findings so far, the authors suggested that "this holistic approach may assist in future research, discovery of biomarkers, development of predictive models, or understanding of the underlying metabolic mechanisms related to sleep."