Understanding the impact of night-to-night sleep variations on glucose regulation in healthy young adults: Insights from Ng et al. (2024)
Year: 2024
Type: article
Abstract: In the September issue of the SLEEP, Ng et al. [1] present two related studies examining how night-to-night variations in sleep affect glucose regulation in healthy young adults. The study’s use of wearable technology—specifically the Oura Ring 2 for sleep monitoring and the FreeStyle Libre Pro for continuous glucose monitoring (CGM)—is noteworthy. This real-world approach addresses a key challenge in sleep and metabolic research: capturing naturalistic sleep behaviors and their impact on glucose metabolism in free-living conditions. The problem Ng et al. address is the gap in understanding how moderate, everyday sleep variations influence glucose regulation, especially when previous research has focused on extreme sleep deprivation or in tightly controlled laboratory environments. Unlike previous reports linking poor sleep quality and short sleep duration to insulin resistance and increased diabetes risk [2–7], the findings from Ng et al. reveal that moderate nightly sleep variations did not significantly affect next-day glucose profiles, nor did a modest sleep restriction (about 1.5 h) impair glucose levels or insulin sensitivity the following morning [1]. These results suggest that, in young, healthy adults, glucose regulation may be more resilient to moderate, real-world sleep fluctuations than previously thought [1]. Ng et al.’s findings challenge the traditional view that insufficient sleep disrupts glucose regulation. Previous research has often focused on middle-aged or older adults [8–14] or individuals with pre-existing metabolic conditions [15, 16], where metabolic effects of sleep disturbance are more pronounced. For example, as individuals age, total sleep time decreases [17], and glucose regulation becomes less efficient due to changes in body composition (e.g. a gradual loss of muscle mass and increase in adipose tissue), declining beta-cell function, and increased inflammation [18]. Epidemiological studies suggested that younger adults may be more resilient to sleep fluctuations, while older adults are more vulnerable to the metabolic consequences of insufficient or excessive sleep [19]. This resilience in younger populations, such as the Asian university students in Ng et al.’s study (mean age ~22.2 years), likely explains the lack of significant metabolic disruption following a single night of moderate sleep loss [18]. Ng et al.’s findings align with earlier research by Spiegel et al., which examined the effects of sleep restriction on glucose metabolism in young, healthy, white men [10]. Spiegel’s study restricted participants to 4 h of sleep per night for six consecutive nights, resulting in a 40 per cent reduction in glucose clearance and a 30 per cent decrease in the acute insulin response to glucose [10]. Although glucose levels were higher after breakfast following sleep restriction, there was no significant difference in glucose and insulin responses at lunch and dinner [10]. The difference between these findings and those of Ng et al. may be due to the methods used for glucose testing. Spiegel used intravenous glucose tolerance tests, which capture more immediate insulin responses, while Ng et al. used oral glucose tolerance tests, reflecting glucose regulation over a longer period. Additionally, ethnic differences in visceral adiposity and insulin sensitivity may explain the variability in responses across the two studies [20]. It is also essential to consider that the 1.5-h sleep restriction in Ng et al.’s study was not sufficient to cause significant circadian misalignment, which typically arises from larger shifts in sleep timing, such as those experienced by shift workers [21]. Studies in individuals who experience significant circadian misalignment (e.g. 12-h shifts) have shown increased metabolic disturbances, including elevated glucose and insulin levels, and, over time, an increased risk of insulin resistance and diabetes [22–24]. However, these conditions were not present in Ng et al.’s study, where participants’ sleep reduction was brief and sleep remained within their habitual windows. Recent data from the UK Biobank cohort further support the role of irregular sleep patterns, rather than acute sleep restriction, in metabolic health [25]. In a prospective cohort involving 84 421 middle-aged to older adults, greater night-to-night variability in sleep duration, rather than just short sleep, was associated with a higher risk of type 2 diabetes [25]. This research contrasts with Ng et al.’s focus on short-term, controlled sleep restriction, highlighting that chronic sleep variability may pose more significant metabolic risks than occasional sleep loss. These findings suggest that a single night of sleep loss may not meaningfully affect glucose metabolism in young, healthy individuals, while chronic sleep irregularity might have more substantial consequences. In a crossover study, So-ngern et al. [2] evaluated whether extending sleep time could improve glucose metabolism in chronically sleep-deprived individuals. Participants who regularly slept less than 6 h per night were asked to extend their sleep by at least 1 h per night over 2 weeks [2]. Metabolic improvements were only observed in those who successfully extended their sleep to over 6 h per night, emphasizing that a critical threshold of sleep is necessary to benefit glucose metabolism. These findings, combined with Ng et al.’s work, reinforce the importance of not only aiming for longer sleep durations but also maintaining regular sleep patterns for optimal metabolic health. Finally, underlying genetic factors might contribute to the resilience observed in this cohort. Genome-wide association studies have identified multiple genetic variants linked to insulin sensitivity and diabetes risk, suggesting that genetic predisposition influences an individual’s susceptibility to metabolic disturbances [26–29]. It is well understood that insulin sensitivity and glucose regulation are polygenic traits influenced by several genes involved in insulin production, signaling pathways, and glucose uptake [30–32]. For example, genetic variants affecting pancreatic beta-cell function can alter insulin secretion in response to blood glucose changes [33]. In Ng et al.’s study, fewer than 10 per cent of participants reported a family history of diabetes, indicating a low genetic risk [1, 34]. The low risk may have buffered the impact of sleep variations on glucose regulation in these healthy young adults. In contrast, findings from the UK Biobank study showed that the association between sleep duration variation and diabetes risk was strongest among those with a low genetic risk (measured by diabetes polygenic risk score), while individuals with a high genetic risk appeared less impacted by night-to-night sleep variation [25]. Interestingly, such gene-sleep interaction was not observed when genetic risk was assessed by family history [25]. These results may suggest that genetic background may either exacerbate or mitigate the metabolic effects of sleep variability. While the interaction between sleep variability and genetic predisposition remains an emerging area of research, understanding how these factors interplay could be pivotal in tailoring sleep recommendations for different risk groups. Ultimately, the role of genetics, in conjunction with aging, health status, and lifestyle factors such as diet and physical activity, should be explored in future research to elucidate why some individuals demonstrate resilience to sleep variability while others show marked metabolic disturbances. Until then, the current study by Ng et al. serves as a compelling example of how a young, healthy population might be more adaptable to real-world sleep variations—an insight that challenges the conventional view of sleep as a universally critical factor for metabolic health. Financial disclosure: AZ is a consultant for Restful Robotics Inc. and receives grant support from ResMed corporation. Non-financial disclosure: QD, BSW, and AZ have no relevant non-financial relationships to disclose. There are no new data associated with this article.
Source: SLEEP
Authors: Qinglan Ding, Brian Wojeck, Andrey Zinchuk
Institutions: Purdue University West Lafayette, Yale University
Language: English
Topic: Sleep and related disorders
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,607
OpenAlex ID: W4403782136