Associations of midpoint of sleep and night sleep duration with type 2 diabetes mellitus in Chinese rural population: the Henan rural cohort study

Zhai, Zhihan; Liu, Xiaotian; Zhang, Haiqing; Dong, Xiaokang; Niu, Miaomiao; Pan, Mingming; Wang, Chongjian; Wang, Xiaoqiong; Li, Yuqian

doi:10.1186/s12889-021-10833-6

Cited by 14 publications

(8 citation statements)

References 46 publications

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“…Later sleep timing has been associated with a higher risk for cardiometabolic factors in many studies [ 69 , 70 , 71 ]. However, in a rural Chinese cohort, both late and early midpoints of sleep were associated with a higher prevalence of diabetes [ 72 ], probably due to a mismatch between internal circadian rhythms and work schedule obligations in early sleepers [ 73 ]. We have shown that later sleep timing characteristics are associated with taste perception, specifically a lower bitter taste perception and TAS2R38-rs713598, and with a higher taste intensity for salty and sour tastes.…”

Section: Discussionmentioning

confidence: 99%

“…In our work on sleep and eating patterns, we found a modulating effect only on the midpoint of sleep for all three genes. The midpoint of sleep was correlated with the dim light melatonin onset (DLMO), a marker of the internal circadian phase and the onset of the biological night [ 72 , 85 , 86 ]. Eating closer or after DLMO was related to higher body fat [ 86 ] and glucose levels [ 87 ], possibly due to a prolonged glucose spike during the night [ 85 ].…”

Section: Discussionmentioning

confidence: 99%

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Timing of Meals and Sleep in the Mediterranean Population: The Effect of Taste, Genetics, Environmental Determinants, and Interactions on Obesity Phenotypes

et al. 2023

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Circadian rhythms regulate the sleep–wake and feeding–fasting cycles. Sleep and feeding constitute a complex cycle that is determined by several factors. Despite the importance of sleep duration and mealtimes for many obesity phenotypes, most studies on dietary patterns have not investigated the contribution of these variables to the phenotypes analyzed. Likewise, they have not investigated the factors related to sleep or mealtimes. Thus, our aims were to investigate the link between taste perception and eating/sleep patterns and to analyze the effect of the interactions between sleep/meal patterns and genetic factors on obesity phenotypes. We conducted a cross-sectional analysis on 412 adults from the Mediterranean population. We measured taste perception (bitter, sweet, salty, sour, and umami) and assessed sleep duration and waketime. The midpoint of sleep and social jetlag was computed. From the self-reported timing of meals, we estimated the eating window, eating midpoint, and eating jetlag. Adherence to the Mediterranean diet was measured with a validated score. Selected polymorphisms in the TAS2R38, CLOCK, and FTO genes were determined, and their associations and interactions with relevant phenotypes were analyzed. We found various associations between temporal eating, sleep patterns, and taste perception. A higher bitter taste perception was associated with an earlier eating midpoint (p = 0.001), breakfast time (p = 0.043), dinner time (p = 0.009), waketime (p < 0.001), and midpoint of sleep (p = 0.009). Similar results were observed for the bitter taste polymorphism TAS2R38-rs713598, a genetic instrumental variable for bitter perception, increasing the causality of the associations. Moreover, significant gene–sleep interactions were detected between the midpoint of sleep and the TAS2R38-rs713598 (p = 0.032), FTO-rs9939609 (p = 0.037), and CLOCK-rs4580704 (p = 0.004) polymorphisms which played a role in determining obesity phenotypes. In conclusion, our study provided more information on the sleep and mealtime patterns of the general Spanish Mediterranean population than on their main relationships. Moreover, we were able to show significant associations between taste perception, specifically bitter taste; sleep time; and mealtimes as well as an interaction between sleep time and several genetic variants linked to obesity phenotypes. However, additional research is needed to better characterize the causality and mechanisms behind these associations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Timing of Meals and Sleep in the Mediterranean Population: The Effect of Taste, Genetics, Environmental Determinants, and Interactions on Obesity Phenotypes

et al. 2023

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“…91,92 Furthermore, having a later midpoint of sleep has been linked to increased insulin resistance as measured with HOMA-IR, 93,94 and increased risk of type 2 diabetes. 95 In treatment-naive individuals with type 2 diabetes, higher fasting glucose levels were associated with a later bedtime. 93 Conversely, another large study found no association between chronotype and HbA1c levels in patients with type 2 diabetes, 96 although the investigators did not control for diabetes medication.…”

Section: Desynchrony Due To Mistimed Sleepmentioning

confidence: 99%

Circadian desynchrony and glucose metabolism

Speksnijder,

Bisschop,

Siegelaar

et al. 2024

Journal of Pineal Research

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The circadian timing system controls glucose metabolism in a time‐of‐day dependent manner. In mammals, the circadian timing system consists of the main central clock in the bilateral suprachiasmatic nucleus (SCN) of the anterior hypothalamus and subordinate clocks in peripheral tissues. The oscillations produced by these different clocks with a period of approximately 24‐h are generated by the transcriptional‐translational feedback loops of a set of core clock genes. Glucose homeostasis is one of the daily rhythms controlled by this circadian timing system. The central pacemaker in the SCN controls glucose homeostasis through its neural projections to hypothalamic hubs that are in control of feeding behavior and energy metabolism. Using hormones such as adrenal glucocorticoids and melatonin and the autonomic nervous system, the SCN modulates critical processes such as glucose production and insulin sensitivity. Peripheral clocks in tissues, such as the liver, muscle, and adipose tissue serve to enhance and sustain these SCN signals. In the optimal situation all these clocks are synchronized and aligned with behavior and the environmental light/dark cycle. A negative impact on glucose metabolism becomes apparent when the internal timing system becomes disturbed, also known as circadian desynchrony or circadian misalignment. Circadian desynchrony may occur at several levels, as the mistiming of light exposure or sleep will especially affect the central clock, whereas mistiming of food intake or physical activity will especially involve the peripheral clocks. In this review, we will summarize the literature investigating the impact of circadian desynchrony on glucose metabolism and how it may result in the development of insulin resistance. In addition, we will discuss potential strategies aimed at reinstating circadian synchrony to improve insulin sensitivity and contribute to the prevention of type 2 diabetes.

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“…A few studies have noted these issues and further analyzed the possible association between different sleep patterns and T2DM. For example, Zhai et al found that compared to those with an intermediate chronotype and 7-8 h of night sleep duration, individuals with a night chronotype and >9 h of night sleep duration had the highest risk of T2DM [18]. A meta-analysis showed that short objective sleep duration (<6 h) was significantly associated with T2DM only in the presence of insomnia symptoms [19].…”

Section: Introductionmentioning

confidence: 99%

Association of Sleep Patterns with Type 2 Diabetes Mellitus: A Cross-Sectional Study Based on Latent Class Analysis

Liu

Ahmed

Zhuo

et al. 2022

IJERPH

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Sleep duration, sleep quality and circadian rhythm disruption indicated by sleep chronotype are associated with type 2 diabetes. Sleep involves multiple dimensions that are closely interrelated. However, the sleep patterns of the population, and whether these sleep patterns are significantly associated with type 2 diabetes, are unknown when considering more sleep dimensions. Our objective was to explore the latent classes of sleep patterns in the population and identify sleep patterns associated with type 2 diabetes. Latent class analysis was used to explore the best latent classes of sleep patterns based on eleven sleep dimensions of the study population. Logistic regression was used to identify sleep patterns associated with type 2 diabetes. A total of 1200 participants were included in the study. There were three classes of sleep patterns in the study population: “circadian disruption with daytime dysfunction” (class 1), “poor sleep status with daytime sleepiness” (class 2), and “favorable sleep status” (class 3). After controlling for all confounding factors, people in class 2 have significantly higher prevalence of type 2 diabetes than those in class 3 (OR: 2.24, 95% CI 1.26–4.00). Sleep problems have aggregated characteristics. People with sleep patterns involving more or worse sleep problems have higher significantly prevalence of T2DM.

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Associations of midpoint of sleep and night sleep duration with type 2 diabetes mellitus in Chinese rural population: the Henan rural cohort study

Cited by 14 publications

References 46 publications

Timing of Meals and Sleep in the Mediterranean Population: The Effect of Taste, Genetics, Environmental Determinants, and Interactions on Obesity Phenotypes

Timing of Meals and Sleep in the Mediterranean Population: The Effect of Taste, Genetics, Environmental Determinants, and Interactions on Obesity Phenotypes

Circadian desynchrony and glucose metabolism

Association of Sleep Patterns with Type 2 Diabetes Mellitus: A Cross-Sectional Study Based on Latent Class Analysis

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