Researchers have successfully pinpointed five unique “sleep profiles,” detailing distinct ways individuals experience sleep.

A recent study has expanded our understanding of what constitutes quality sleep, suggesting it encompasses more than just the length or whether the night was restless. Researchers concluded that an individual’s sleep quality is significantly influenced by both genetic predispositions and emotional states, including feelings of anxiety, stress, sadness, or general low mood. Each distinct sleep profile identified in their analysis was also found to correlate with how effectively people functioned in various aspects of their daily lives. These findings were detailed in a study published on October 7 in the journal *PLOS Biology*.

Aurore Perrault, lead author of a recent study and a researcher at the Woolcock Institute of Medical Research in Sydney, is urging clinicians to adopt a more comprehensive approach when evaluating patients’ sleep during initial assessments.

In an email to Live Science, Perrault emphasized that practitioners should delve deeper than superficial inquiries. Rather than simply asking if sleep is “good or bad” or inquiring about its duration, she advocates for a broader range of questions to gain a more complete understanding of a patient’s sleep health.

Scientific consensus underscores the critical role of quality sleep in overall health. Researchers have long established that individuals suffering from insufficient or poor-quality sleep face significantly elevated risks for a range of serious conditions. These include mental health challenges such as depression and anxiety, physical ailments like cardiovascular disease, and declines in cognitive function. Furthermore, a consistent pattern of inadequate sleep is also linked to a reduced lifespan, according to the authors’ findings.

Previously, the precise mechanisms and causal pathways governing these connections have largely remained elusive. Moreover, earlier investigations often failed to provide a granular classification of sleep patterns, limiting their comprehensive analysis.

To gain a more detailed understanding of this challenge, researchers harnessed data from the Human Connectome Project. This advanced initiative meticulously maps the intricate, nerve fiber-level connections throughout the human brain, offering crucial insights. Their objective was to develop a clearer portrait of sleep quality and its fundamental relationship with overall well-being.

This comprehensive dataset integrates brain imaging results with a wealth of self-reported participant data. It meticulously documents aspects ranging from individual lifestyles and mental and physical health conditions to personality traits and crucial sleep characteristics. Key details on sleep include typical duration, reported difficulties in maintaining sleep, and the frequency of sleep aid usage.

The study deliberately excluded individuals with a formal diagnosis of clinical depression. However, participants experiencing subclinical symptoms of anxiety or depression were included, provided these conditions did not impair their daily functioning. Researchers specifically focused on a dataset of 770 individuals, ranging in age from 22 to 36, a demographic chosen, according to Perrault, because their sleep patterns were likely still unaffected by the natural aging process.

Researchers employed unsupervised machine learning – an advanced artificial intelligence technique that independently processes data to uncover hidden patterns without requiring human-defined categories – to reveal significant statistical relationships among various lifestyle factors, brain imaging data, and sleep behaviors.

The study utilized the Pittsburgh Sleep Quality Index (PSQI) to evaluate participants’ sleep quality over the preceding month. On the PSQI’s 0-to-19 point scale, a score of 5 or less is the benchmark for what is considered a “good sleeper.” Researchers noted that the average score among the surveyed individuals was 5.14, indicating a collective sleep quality that slightly exceeded this ideal threshold.

Researchers deliberately focused their analysis on individuals whose sleep scores exceeded a threshold of five, thereby excluding those who reported sound sleep. This selective approach yielded the identification of five distinct sleep profiles.

Among these, “poor sleepers” were characterized by persistent difficulties in sleeping alongside reported mental health symptoms, particularly feelings of anxiousness. A more severe designation, “disturbed sleepers,” applied to individuals whose sleep was so severely disrupted that it began to adversely affect both their physical health and cognitive functioning.

Each individual’s profile is uniquely tied to a “neural signature” – a distinct brain response. This signature captures crucial data regarding the individual’s past experiences, encompassing physiological indicators like body temperature, psychological phenomena such as nightmares, and even shifts in hormonal balance.

According to Perrault, these observations underscore a crucial dual relationship: an individual’s unique sleep characteristics are intimately linked to both their brain’s inherent neural circuitry and key indicators of their overall physical and mental well-being.

Dr. Henry Yaggi, a professor of internal medicine and the director of the Yale Centers for Sleep Medicine, lauded the study’s advanced methodology. Though not involved in the research, Yaggi characterized its approach as a significant leap forward in understanding nocturnal patterns.

“It’s a much more comprehensive assessment of sleep than I think we’ve had in the past,” Yaggi told Live Science. He further stressed the individualized nature of rest, asserting, “This is not a one-size-fits-all.” Yaggi concluded by highlighting a critical insight: “There are profiles of sleep with connections to mental health,” underscoring the nuanced relationship between sleep patterns and psychological well-being.

The five distinct sleep profiles identified could hold significant potential as biomarkers, according to Perrault, serving as measurable indicators that signal the possible onset of future health conditions. However, Yaggi emphasizes the need for extensive additional research. Given that these profiles were initially characterized in a cohort of healthy young adults, further study is crucial to validate their utility as biomarkers, particularly in potentially spotting the developing stages of anxiety and depression.

The emergence of five distinct sleep profiles holds significant promise for revolutionizing clinical interventions for sleep-related issues, according to experts Perrault and Yaggi. They envision these profiles guiding more targeted treatments, potentially improving patient outcomes.

For instance, specific therapies like talk therapy, specialized sleep applications, or continuous positive airway pressure (CPAP) machines might prove more efficacious for individuals categorized as “short sleepers” compared to those identified as “sleep-aid users.”

Beyond personalized treatment, this line of research could offer crucial insights into a persistent clinical challenge: why approximately 40% of patients do not respond to cognitive behavioral therapy for insomnia (CBT-I), a widely utilized talk and behavioral therapy. Understanding the underlying sleep profile of these non-responders could pave the way for more effective alternative strategies.