New model IDs narcolepsy as key driver of excessive daytime sleepiness

A new data-driven model has identified narcolepsy — a condition characterized by the brain’s inability to regulate sleep-wake cycles — as a key driver of excessive daytime sleepiness (EDS), which is marked by chronic, intense drowsiness that can occur throughout the day, a U.S. study found.

The model, generated using data from a large U.S. sleep study, also identified a wide range of other factors that could cause EDS, including persistent difficulty sleeping (insomnia), depression, and breathing issues during sleep, as the most significant, according to the researchers.

While most of the identified factors, including narcolepsy, have known links to EDS, the scientists nonetheless believe this new model provides a tool to further probe these relationships. The ultimate goal, according to the team, is to find ways to stop EDS from occurring.

“This model can serve as a reference framework for researchers to prevent EDS and further investigate the identified pathways before developing clinical interventions for EDS,” the researchers wrote.

The study, “Excessive Daytime Sleepiness Prevention Using Causality Network Driven by Score-based Bayesian Network Structure Learning Algorithms,” was published in the journal Smart Health.

Causes of excessive daytime sleepiness remain poorly understood

EDS, characterized by an intense and irresistible urge to sleep throughout the day, is a core symptom of several sleep disorders, including narcolepsy. People with narcolepsy may experience EDS in the form of sudden, so-called sleep attacks that occur at any time of day.

Despite extensive research, the underlying mechanisms of EDS remain poorly understood, hindering the development of strategies to prevent it, according to the research team.

“Detecting its causality is central to EDS prevention,” the researchers wrote. “This understanding allows health professionals to act effectively to prevent or lessen future EDS risks.”

Unlike many conventional medical studies that merely reveal associations, the approach used in this study can directly uncover causal medical etiologies [origins].

In their study, the team aimed to develop a model to identify potential causes of EDS. It was based on Bayesian networks, which offer a mathematical representation of how various factors are likely to influence one another — essentially, providing a map of cause-and-effect relationships.

“Unlike many conventional medical studies that merely reveal associations, the approach used in this study can directly uncover causal medical etiologies [origins],” the researchers explained.

To build the model, the scientists analyzed clinical and biomedical data from 1,881 individuals who participated in the Stanford Technology Analytics and Genomics in Sleep study across six U.S. centers. Participants were ages 13 to 84, and were nearly evenly split by sex.

EDS was assessed using the Epworth Sleepiness Scale (ESS), a patient-reported questionnaire that assesses the likelihood of falling asleep during daily activities.

New tool named narcolepsy, depression, insomia, apnea as top factors

An algorithm was then used to visualize the relationships between ESS score and a range of demographic and clinical factors. Ultimately, the analysis used data from 1,783 participants with available ESS scores and considered 52 possible EDS-related variables, according to the study.

The work identified several factors that may directly or indirectly contribute to EDS, including but not limited to age, sex, smoking, high cholesterol, psychiatric or mental health issues, depression, fatigue, and alcohol consumption. A number of sleep conditions were also on the list, including narcolepsy, insomnia, and sleep apnea, in which breathing repeatedly stops and starts during sleep.

The researchers noted that, among these factors, high cholesterol was the only one that hadn’t been previously reported in the literature. As such, it warrants further study.

Further analysis, however, revealed that of all the identified factors, four — insomnia, depression, sleep apnea, and narcolepsy — were the most powerful drivers of EDS.

Researchers ID 6 possible levels of sleep disorder

Narcolepsy was categorized into six possible levels. The sleep disorder was found to strongly influence EDS severity at most intermediate levels, while the association was weaker at the extremes, that is, in people with no narcolepsy symptoms or in those with the most severe symptoms. Most levels of depression and sleep apnea also directly influenced EDS, as did the presence or absence of insomnia, the analysis found.

While the study identifies several factors linked to the development of EDS, “additional relevant … variables are likely to exist,” the researchers wrote. “Further studies … are needed to investigate the influence of these … factors on the development and severity of EDS and to explore additional causal relationships.”