The Importance of Sleep Variability as a Predictor of Type 2 Diabetes in Primary Care

As primary care clinicians, we often focus on established risk factors for type 2 diabetes, such as obesity, physical inactivity, and family history. However, emerging evidence suggests that irregular sleep patterns, or sleep variability, may play a significant role in the development of this condition. A recent study published in Diabetes Care sheds light on this lesser-known contributor to metabolic health, emphasizing the importance of understanding sleep variability as a predictor of diabetes, particularly for patients without a strong genetic predisposition.

This article aims to help clinicians—including nurses, nurse practitioners, and physicians—reflect on how insights from this study could be integrated into their practice. By addressing sleep variability, we may find additional opportunities to prevent diabetes, especially in patients who may not traditionally be considered high-risk.

Understanding Sleep Variability and Its Impact on Diabetes Risk

Sleep variability refers to the day-to-day fluctuations in sleep duration and timing. While one night a person may sleep for six hours, the next they might sleep for eight or nine. Over time, this inconsistency in sleep patterns can disrupt the body’s circadian rhythm, which is essential for regulating glucose metabolism, insulin sensitivity, and overall metabolic health.

In the study by Kianersi and colleagues , data were drawn from over 84,000 participants in the UK Biobank, using wrist-worn accelerometers to measure sleep patterns over seven days. The researchers found that individuals with greater variability in their sleep duration—specifically those whose sleep duration fluctuated by more than 60 minutes from night to night—had a 34% higher risk of developing type 2 diabetes over the seven-year follow-up period.

Interestingly, the study also revealed that this association was stronger among individuals who had a lower genetic predisposition to diabetes, as measured by polygenic risk scores. This suggests that even for patients without a family history of diabetes, sleep variability may significantly increase their risk of developing the condition.

Insights from the Study: Strengths and Considerations

One of the strengths of the study lies in its use of objective data collected from accelerometers, rather than relying on self-reported sleep patterns, which can often be inaccurate. This method allowed for precise measurement of sleep duration variability, offering a clearer picture of how inconsistent sleep impacts health.

The prospective nature of the study also allowed the researchers to follow participants over several years, providing stronger evidence for a relationship between sleep variability and diabetes risk. This long-term approach helps establish a temporal relationship, suggesting that irregular sleep patterns precede the development of diabetes rather than being a consequence of it.

However, there are limitations to consider. The cohort was predominantly White and based in the UK, which may limit the generalizability of the findings to more diverse populations. Additionally, the sleep data were collected over a seven-day period, which may not fully capture long-term sleep patterns or reflect the challenges faced by those with shift work or social jetlag.

Despite these limitations, the study presents valuable insights that can be applied to primary care, particularly when considering patients who may not have traditional risk factors for diabetes.

Relevance to Primary Care: Screening and Patient Education

For those of us working in primary care, this research underscores the importance of incorporating sleep assessments into routine screenings. Traditionally, we focus on factors like BMI, physical activity levels, and family history when assessing a patient's risk for diabetes. However, given the strong association between sleep variability and diabetes, we should also consider asking patients about their sleep patterns, especially those who may not appear at high risk based on other factors.

Wearable technology offers a practical way for patients to self-monitor their sleep variability. Devices like Fitbit, Apple Watch, and Garmin can track sleep duration and provide data on nightly fluctuations. By encouraging patients to use these devices, we can gather objective sleep data that may highlight inconsistencies that would otherwise go unnoticed.

For example, a patient who reports getting "enough" sleep each night might still have significant variability in their sleep patterns, which could increase their diabetes risk. By reviewing their wearable data, we can identify patterns of irregular sleep and discuss how these might impact their long-term health.

This approach could be especially useful for patients who don't have a family history of diabetes but exhibit irregular sleep patterns. Since the study showed that sleep variability poses a greater risk for individuals without a strong genetic predisposition, monitoring sleep may serve as an early intervention tool in this population.

Improving Sleep Hygiene to Reduce Variability

Once sleep variability is identified as a concern, helping patients improve their sleep hygiene becomes a key component of diabetes prevention. Consistent sleep patterns can help regulate circadian rhythms, improve insulin sensitivity, and reduce the risk of metabolic disturbances.

Here are some practical strategies that we can recommend to patients:

1. Set a Consistent Sleep Schedule: Encourage patients to go to bed and wake up at the same time every day, including weekends. Consistency is crucial for maintaining a stable circadian rhythm. Patients should aim to minimize fluctuations in their sleep duration, staying within a 60-minute range each night.
2. Create a Relaxing Pre-Sleep Routine: Advise patients to wind down before bed by engaging in relaxing activities such as reading, gentle stretching, or meditation. Reducing screen time and exposure to bright lights in the evening can also help improve sleep quality and consistency.
3. Limit Stimulants and Large Meals in the Evening: Caffeine, nicotine, and heavy meals can disrupt sleep. Encourage patients to avoid these substances at least a few hours before bedtime, and opt for light snacks if needed.
4. Promote Physical Activity: Regular exercise is associated with better sleep, but it's important to remind patients to avoid vigorous activity too close to bedtime. A balanced routine of physical activity during the day can help stabilize sleep patterns.
5. Address Mental Health and Stress: Many patients with sleep variability also struggle with anxiety, depression, or stress, which can exacerbate irregular sleep. Collaborating with mental health professionals or offering stress management techniques, such as mindfulness or cognitive-behavioural therapy (CBT), can help patients improve both their mental health and sleep consistency.
6. Manage Shift Work and Social Jetlag: Patients who work irregular hours or night shifts may face unique challenges when it comes to sleep consistency. In these cases, it's important to work with them to establish a sleep routine that aligns with their work schedule, while also minimizing the impact of shift work on their health.

Practical Application in Primary Care

As primary care clinicians, we are often at the front line of managing and preventing chronic diseases like diabetes. Integrating sleep assessments into our practice could provide us with additional insights into our patients' overall health and risk factors. For instance, a simple set of questions about sleep habits or the use of wearable data during routine checkups could offer us a more comprehensive view of a patient's lifestyle.

Encouraging patients to monitor their own sleep through wearables can also empower them to take an active role in managing their health. By reviewing sleep data with patients, we can help them understand how their sleep patterns may be impacting their diabetes risk and provide personalized recommendations for improvement.

While addressing sleep variability may not be a standard part of diabetes screening today, this research points to the potential value it offers. By incorporating this relatively simple metric into our evaluations, we may be able to catch early signs of metabolic disruption and intervene before diabetes develops.

Conclusion

The association between sleep variability and type 2 diabetes presents a compelling case for expanding how we assess diabetes risk in primary care. By paying attention to sleep patterns and helping patients recognize the importance of consistent sleep, we can provide a more holistic approach to diabetes prevention. Wearable technology offers an accessible way for patients to monitor their sleep at home, and we can use this data to guide interventions that improve sleep hygiene, reduce sleep variability, and ultimately lower the risk of type 2 diabetes. This is an actionable and evidence-based addition to our toolkit in managing and preventing this increasingly prevalent disease.

Dr. Joel Brown, MD