Personalized intervention to improve stress and sleep patterns for glycemic control and weight management in obese Emirati patients with type 2 diabetes: a randomized controlled clinical trial

Poor Sleep Quality Increases Gestational Weight Gain Rate in Pregnant People: Findings from the MADRES Study

Background

Poor sleep quality is associated with weight gain in non-pregnant populations, but evidence in pregnant people is lacking. Our study examined the association between early-to-mid pregnancy sleep quality and weekly gestational weight gain (GWG) rate during mid-to-late pregnancy by pre-pregnancy body mass index (BMI).

Method

Participants were 316 pregnant participants from the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) study. During early-to-mid pregnancy, participants reported their sleep quality which was used to construct four categories: very poor, poor, good, and very good. Linear growth curve models examined the association between early-to-mid pregnancy sleep quality and weekly rate of GWG (kg/week) during mid-to-late pregnancy (> 20 weeks gestation), with a three-way cross-level interaction between gestational age, sleep quality, and pre-pregnancy BMI category. Models adjusted for ethnicity by birthplace, hypertensive disorders, perceived stress score, and physical activity level.

Results

Overall, poorer early-to-mid pregnancy sleep quality was associated with increased weekly weight gain during mid-to-late pregnancy. For example, amongst normal weight participants, mid-to-late pregnancy weight gain was, on average, 0.39 kg (95% CI: 0.29, 0.48) per week for those with very good sleep quality, 0.53 kg (95% CI: 0.44, 0.61) per week for those with poor sleep quality, and 0.54 kg (95% CI: 0.46, 0.62) per week for those with very poor sleep quality during early-to-mid pregnancy. This difference in GWG rate was statistically significantly comparing very good to poor sleep (0.14 kg/week, 95% CI: 0.01, 0.26) and very good to very poor sleep (0.15kg/week, 85% CI: 0.02, 0.27). This association between sleep quality and GWG rate did not statistically differ by pre-pregnancy BMI.

Conclusion

Our study found very poor early-to-mid pregnancy sleep quality was associated with higher mid-to-late pregnancy GWG rate. Incorporating pregnancy-specific sleep recommendations into routine obstetric care may be a critical next step in promoting healthy GWG.

Association of sleep patterns and cardiovascular disease risk is modified by glucose tolerance status

AIMS

To investigate whether the association between sleep patterns and cardiovascular disease (CVD) risk differs according to glucose tolerance status.

MATERIALS AND METHODS

This prospective study included 358,805 participants initially free of CVD from the UK Biobank. We created a sleep score based on five sleep factors (sleep duration, chronotype, insomnia, snoring, and daytime sleepiness) with one point for each unhealthy factor. Cox proportional hazards models were used to examine the association between sleep and incident CVD, including coronary heart disease (CHD) and stroke, according to normal glucose tolerance (NGT), prediabetes, and diabetes.

RESULTS

During a median follow-up of 12.4 years, 29,663 incident CVD events were documented. There was a significant interaction between sleep score and glucose tolerance status on CVD (P for interaction = 0.002). Each 1 point increment in sleep score was associated with a 7% (95% confidence interval 6%-9%), 11% (8%-14%), and 13% (9%-17%) higher risk of CVD among participants with NGT, prediabetes, and diabetes, respectively. Similar interaction patterns were observed for CHD and stroke. Among the individual sleep factors, sleep duration and insomnia significantly interacted with glucose tolerance status on CVD outcomes (all P for interaction <0.05). All five unhealthy sleep factors accounted for 14.2% (8.7%-19.8%), 19.5% (7.4%-31.0%), and 25.1% (9.7%-39.3%) of incident CVD cases among participants with NGT, prediabetes, and diabetes, respectively.

CONCLUSIONS

The CVD risk associated with a poor sleep pattern was exacerbated across glucose intolerance status. Our findings emphasise the importance of integrating sleep management into a lifestyle modification programme, particularly in people with prediabetes or diabetes.

Nutrition Situation Analysis in the UAE: A Review Study

This review study aimed to assess the nutrition situation in the UAE using published data from 2010 to 2022. It highlights the gaps and challenges that prevail in addressing the nutrition-related problems in the UAE and the opportunities that have been overlooked. The available literature indicates that the UAE is burdened with more than one form of nutrition-related problems, including being underweight, being overweight, obesity, micronutrient deficiencies, and nutrition-related chronic diseases. It is clear that data on micronutrient deficiencies, protein-energy malnutrition, obesity, diabetes, and other nutrition-related diseases among the UAE population are extremely scarce. The UAE has a high prevalence of obesity and diabetes; however, limited studies have been conducted to document this nutritional phenomenon. Few examples of published data are available assessing the burden of stunting, wasting, and being underweight among children under five years of age. Despite the importance of protein-energy malnutrition, no recent publications analyze its prevalence within the UAE population. Therefore, future studies must be conducted, focusing on malnutrition. Based on the literature, and bearing in mind the magnitude of the health issues due to the UAE population's nutrition negligence, there is an urgent need to assess the population's nutrient behaviors, to aid policy decision-makers in developing and implementing effective health policies and strategies.

Effects of sleep intervention on glucose control: A narrative review of clinical evidence

BACKGROUND

Optimizing sleep has been recently gained exposure as a promising lifestyle consideration to aid in the control of diabetes. The evidence to support the impact of sleep quantity and quality on blood glucose control is largely acknowledged. This study aimed to review all published randomized controlled trials (RCTs) investigating the relationship between sleep and glucose control to synthesize an accurate overview.

METHOD

Literature from PubMed and Google Scholar was searched using the listed search terms to obtain RCTs on the role of sleep in glucose homeostasis. Seven RCTs were eligible and included in our review. References in these RCTs were screened for the presentation of the pathophysiology of metabolic disturbances relating to the sleep duration, and the relevant factors affecting blood glucose concentration.

RESULTS

Sleep deprivation and poor sleep quality are connected with blood glucose disturbance and reduction of insulin sensitivity. This leaves diabetic patients at an increased risk of glucose level fluctuations. However, the function of β-cells was likely to be conserved after 14-days of sleep deprivation. Sleep extension from 7 to 14 days improved blood glucose control and insulin sensitivity in both healthy and diabetes participants. Diabetes sleep education and personalized interventions that reduced stress and improved sleep quality contributed to glucose homeostasis in diabetic patients. Overall improving one's sleep hygiene was found to improve glucose control in diabetic patients.

CONCLUSION

Longer or short-term sleep deprivation may negatively affect glucose homeostasis, although the body temporarily compensates for the impaired function of β-cells when reduced sleep lasted up to 14 days. Thus, we recommend optimum sleep duration and optimistic sleep duration and sleep quality for decreasing risk and progression of diabetes.

High body mass index and sleep problems during pregnancy: A meta-analysis and meta-regression of observational studies

Despite the well-established correlation of weight and sleeping problems, little is known about the nature of the association. The present study examined whether pregnant women with high body mass index have a risk of developing sleep problems, and identified any covariates that affect this relationship. We systematically searched electronic databases, specialized journals, various clinical trial registries, grey literature databases and the reference list of the identified studies. All observational studies were obtained from inception until 9 August 2020. The Newcastle-Ottawa Scale was adopted to assess the quality of studies. Stata software was used to conduct meta-analysis and meta-regression. Forty-six observational studies involving 2,240,804 participants across 16 countries were included. Quality assessment scores ranged from 4 to 10 (median = 6). Meta-analyses revealed that the risk of sleep apnea, habitual snoring, short sleep duration and poor sleep quality is increased in pregnant women with high body mass index, but not for daytime sleepiness, insomnia or restless legs syndrome. Subgroup differences were detected on body mass index between different regions, nature of population, year of publication, age group and study quality. Random-effects meta-regression analyses showed that year and quality of publication were covariates on the relationships between pre-pregnant body mass index and sleep apnea risk. Our review shows that sleep apnea, habitual snoring, short sleep duration and poor sleep quality are important concerns for pregnant women with high body mass index. Developing screening and targeted interventions is recommended to promote efficacious perinatal care.

Poor sleep efficiency and daytime napping are risk factors of depersonalization disorder in female university students

Objectives

Depersonalization is characterized by feelings of detachment from reality and has been associated with anxiety and depression, both of which have a bi-directional relationship with sleep. To date, few studies have directly examined the potential relationship between sleep and depersonalization, which was the primary objective of our study.

Design/methods

A cross-sectional study of female, Emirati, university students (n = 100) was conducted. Participants completed the Pittsburgh Sleep Quality Index (PSQI), the Cambridge Depersonalization Scale (CDS) and the Hospital Anxiety and Depression Scale (HADS). Additionally, 36 of the 100 participants wore wrist actigraphy for two consecutive weekdays. Average sleep duration, and average sleep efficiency (SE; %) across the two nocturnal sleep episodes were calculated. Total number of sleep episodes were obtained from wrist actigraphy and sleep logs.

Results

A significant, positive relationship was observed between PSQI global score and CDS total score (r = 0.21, p = 0.04). Actigraphy-estimated average nocturnal sleep duration was not significantly associated with the CDS. Compared to nocturnal sleepers only, those who undertook daytime naps had almost three times the risk of meeting the criteria for depersonalization disorder (OR = 2.95, 95% CI: 1.04–8.41), after adjustment. For each 1% increase in SE a 23% decreased risk of depersonalization was observed (OR = 0.77, 95% CI: 0.61–0.96), after adjustment.

Conclusions

Sleep screening in young adults may help to ensure better detection and management of psychological health outcomes. Our findings need to be confirmed prospectively in larger samples and amongst different populations but reiterate the importance of sleep habits pertaining to mental health.

•

We show a novel relationship between depersonalization and sleep in a non-clinical sample.

•

Actigraphy determined poor sleep efficiency was significantly associated with subjective reports of depersonalization.

•

Daytime nappers were ~3 times more likely to report depersonalization symptoms and meet the diagnositic criteria.

The future of sleep health: a data-driven revolution in sleep science and medicine

In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.