Researchers at the University of California, San Diego have uncovered a link between how people sleep and the risk of several illnesses, including diabetes and infectious diseases. The findings, published in npj Digital Medicine, suggest that the way sleep changes over time may provide early signals of health problems long before they become obvious through other symptoms.
The study examined the sleep data of 33 thousand individuals, tracking nightly rest across more than five million nights. Using the Oura Ring, a wearable device that records heart rate, physical activity, and skin temperature, researchers gathered a rich dataset on sleep quality, duration, and the timing of awakenings. The objective was to see how shifts in sleep patterns align with shifts in health status, offering a window into whether sleep signals might foretell disease development.
From this extensive dataset, scientists identified five distinct sleep phenotypes. The first phenotype is deemed healthy and features roughly eight hours of continuous sleep for at least six consecutive days. The second phenotype involves a night where sleep is largely continuous but interrupted by periods of less than three hours, indicating fragmented rest. The third phenotype centers on predominantly uninterrupted sleep with only one night per week showing intermittent wakefulness. The fourth phenotype describes mostly continuous sleep with rare nights of longer awakenings that disrupt the routine. The fifth phenotype stands out as the rarest and is characterized by severely disrupted sleep, with frequent and pronounced interruptions.
Key results show that changes in one’s sleep phenotype often precede the emergence of several health issues. Diabetes, sleep apnea, and viral illnesses such as COVID-19 and influenza have all been associated with alterations in sleep patterns. The researchers propose that the pattern and frequency of transitions among sleep phenotypes could yield valuable information for early disease detection, potentially offering earlier insights than relying on a single, static sleep measurement. In other words, dynamic sleep trajectories might serve as a barometer for evolving health conditions, allowing clinicians to intervene sooner and more effectively.
Beyond identifying associations, the study highlights a practical approach for leveraging sleep data in population health monitoring. By observing how often individuals shift between phenotypes and how rapidly those shifts occur, health professionals may gain a more nuanced understanding of risk profiles. This perspective aligns with a growing emphasis on longitudinal health data, where continuous streams of information can illuminate trends that static snapshots miss. The work underscores the value of wearable technology in providing scalable, real-world insights into sleep health and its relationship to disease, inviting broader discussion about how sleep data can be integrated into routine medical surveillance and preventive care.
For researchers, the findings prompt further questions about causality and mechanism. Do changes in sleep phenotype directly contribute to disease pathways, or are they early indicators of underlying physiological processes already underway? What role do lifestyle factors, such as caffeine intake, physical activity, stress, and chronotype, play in shaping phenotype transitions? Answering these questions will require multidisciplinary collaboration, longer follow-up periods, and diverse study populations. Nevertheless, the UCSD study adds a compelling piece to the puzzle of how sleep influences health and how we might harness that knowledge to improve early diagnosis and intervention.
In summary, the UCSD investigation demonstrates that sleep phenotypes are not merely descriptive labels of rest quality. They function as dynamic biomarkers that evolve over time and may foreshadow serious health outcomes. By focusing on the tempo and direction of sleep-trajectory changes, clinicians and researchers can gain richer, more actionable insights into disease risk, paving the way for proactive strategies in preventive medicine. The work invites ongoing exploration of how wearable sleep analytics can be integrated into clinical practice and large-scale health monitoring, ultimately supporting better health outcomes for people in Canada, the United States, and beyond.