Global Assessment of the Impact of Type 2 Diabetes on Sleep through Specific Questionnaires. A Case-Control Study

Effects of Physical and Mind-body Exercise on Sleep Quality in Individuals With Diabetes Mellitus: A Systematic Review and Meta-analysis

OBJECTIVES

Physical and mind-body exercises represent distinct intervention strategies that may improve sleep quality by influencing physiological and psychological factors. Nevertheless, their effectiveness in individuals with diabetes is not well-established. This systematic review and meta-analysis aimed to examine the impacts of physical and mind-body exercise interventions on sleep quality in patients with diabetes mellitus.

METHODS

Six randomized controlled trials (RCTs) that met the inclusion criteria were identified from PubMed, CINAHL, Embase, Scopus, Web of Science, Cochrane, and Ovid-Medline Library. The effect size for sleep quality was calculated using the standardized mean difference (SMD) with a 95% confidence interval (CI), employing a random-effects model. Heterogeneity and publication bias were also examined, and subgroup, meta-regression, and sensitivity analyses were performed.

RESULTS

Physical and mind-body exercise interventions significantly improved sleep quality, with an SMD of -1.040 (95% CI, -1.686 to -0.394). Subgroup analysis revealed significant differences with respect to the type of intervention (p=0.047), or its duration (p=0.282). Meta-regression analysis indicated that mean hemoglobin A1c level was the only factor to be significantly related to the effect size for sleep quality, demonstrating a negative association (p=0.033). The assessment of publication bias and the sensitivity analysis suggested that the findings were reliable and robust.

CONCLUSIONS

Physical and mind-body exercises may serve as effective interventions for patients with diabetes mellitus who experience poor sleep quality. However, to substantiate these findings, additional rigorous RCTs with larger sample sizes, longer follow-up periods, and standardized interventions are required.

Quality of Sleep and Its Determinants Among People with Diabetes Mellitus in Sub-Saharan Africa: A Systematic Review and Meta-Analysis

BACKGROUND

Poor sleep quality is a serious concern among diabetes mellitus patients, adversely affecting glycemic management and the prognosis of diabetic patients. Therefore, the main aim of this study was to estimate the overall pooled prevalence of poor sleep quality and its associated factors among diabetic patients in Sub-Saharan Africa.

METHODS

Several search databases were employed to retrieve the eligible studies. A standardized data extraction format was used to extract the data, and the analysis was conducted using STATA version 14. To determine heterogeneity, the I2 test was conducted, and both the funnel plot and Egger's regression test were used to assess the publication bias. A random effects model meta-analysis was performed to estimate the overall prevalence and associated factors of poor sleep quality.

RESULTS

In the current meta-analysis, a total of 11 studies with 3,766 study participants were included. The overall pooled prevalence of poor sleep quality among diabetic patients was 53.22% (95% CI: 36.26, 70.19). Poor sleep quality was significantly associated with longer duration of diabetes (OR = 2.94, 95% CI: 1.85, 4.67), poor glycemic control (OR = 2.24, 95% CI: 1.51, 3.32), depression (OR = 4.09, 95% CI: 2.07, 8.05) and comorbidity (OR = 1.94, 95% CI: 1.31, 2.88).

CONCLUSION

In this study, more than half of diabetic patients in Sub-Saharan Africa had poor sleep quality. Therefore, healthcare providers should give more attention to sleep disorders when screening diabetic patients to improve their poor sleep quality.

Prevalence of sleep disturbance and its associated factors among diabetes type-2 patients in Saudi Arabia

Introduction

This study investigated the prevalence of sleep disturbances among people living with type 2 diabetes in Saudi Arabia.

Methods

A four-stage sampling method was used to recruit 479 participants from various parts of the country. A self-administered questionnaire was used to collect demographic, sleep disruption, and related clinical characteristics. Data analysis included reporting sleep disturbance prevalence as frequency and percentage, chi-square tests to examine categorical variable associations, and multiple logistic regression analysis to identify independent factors associated with sleep disturbances.

Results

The data showed that most participants were male (59.3%), married (78.5%), and Diploma/ Bachelor's degree holders (41.5%). The study found that most respondents had subjective sleep quality issues, with 55.3% of Sleep Latency participants having trouble falling asleep, 62.4% having trouble sleeping, 65.1% experiencing serious sleep efficiency impairment, 85.2% reporting sleep difficulties, 88.5% using sleep medication without difficulty, 5.4% having moderate difficulty, and 6.1% having considerable difficulties. The Global Pittsburgh Sleep Quality Index (PSQI) showed that all participants had sleep problems. Marital status, nationality, HBA1C, education, region, comorbidity, and monthly income were significantly associated with sleep disturbance characteristics.

Conclusion

The study concluded that diabetes itself can increase the risk of sleep problems, as it is associated with various sleep-related issues such as insomnia, sleep apnea, and restless legs syndrome. Encouraging social support within the realm of healthcare services holds significance in reducing the occurrence of inadequate sleep. For individuals diagnosed with type 2 diabetes mellitus, managing their blood sugar levels effectively is crucial for enhancing the quality of their sleep. Furthermore, engaging in regular physical activity is essential for preventing subpar sleep quality.

Assessment of dyspneic sensation in patients with type 2 diabetes

Introduction

Individuals with type 2 diabetes (T2D) should be considered a susceptible group for pulmonary dysfunction. So, we aimed to evaluate the sensation of breathlessness in this population by administering two well-validated questionnaires.

Methods

This is a crosssectional study with 592 people without known respiratory disease (353 with T2D) who answered the modified Medical Research Council (mMRC) questionnaire. In addition, 47% also responded to the St George Respiratory Questionnaire, a specific instrument designed to be applied to patients with obstructive airway disease.

Results

Patients with T2D showed a higher mMRC score in comparison to the control group [1.0 (0.0 - 4.0) vs. 0.0 (0.0 - 4.0), p<0.001]. A higher prevalence of subjects with mMRC ≥2 was observed in T2D that in the control group (20.2% vs. 11.6%, p=0.004). Participants with T2D and mMRC ≥2 showed a higher HbA1c (8.2 ± 1.6% vs. 7.8 ± 1.6%, p=0.048), longer T2D evolution and higher prevalence of nephropathy. In the multivariate analysis, the presence of T2D [OR=1.95 (1.19 to 3.22), p=0.008] in all the population, and HbA1c [OR=1.19 (1.01 to 1.41), p=0.034] and the presence of diabetic nephropathy [OR=2.00 (1.14 to 3.52), p=0.015] in patients with T2D, predicted a mMRC ≥2. Finally, no differences were observed regarding the SGRQ score among groups.

Conclusions

Patients with T2D showed a greater sensation of dyspnea than subjects with normal carbohydrate metabolism. Risk factors included poor metabolic control and the presence of renal disease.

Prediabetes Is Associated with Increased Prevalence of Sleep-Disordered Breathing

Type 2 diabetes leads to severe nocturnal hypoxemia, with an increase in apnea events and daytime sleepiness. Hence, we assessed sleep breathing parameters in the prediabetes stage. A cross-sectional study conducted on 966 middle-aged subjects without known pulmonary disease (311 patients with prediabetes and 655 controls with normal glucose metabolism) was conducted. Prediabetes was defined by glycated hemoglobin (HbA1c), and a nonattended overnight home sleep study was performed. Participants with prediabetes (n = 311) displayed a higher apnea−hypopnea index (AHI: 12.7 (6.1;24.3) vs. 9.5 (4.2;19.6) events/h, p < 0.001) and hypopnea index (HI: 8.4 (4.0;14.9) vs. 6.0 (2.7;12.6) events/h, p < 0.001) than controls, without differences in the apnea index. Altogether, the prevalence of obstructive sleep apnea was higher in subjects with prediabetes than in controls (78.1 vs. 69.9%, p = 0.007). Additionally, subjects with prediabetes presented impaired measurements of the median and minimum nocturnal oxygen saturation, the percentage of time spent with oxygen saturations below 90%, and the 4% oxygen desaturation index in comparison with individuals without prediabetes (p < 0.001 for all). After adjusting for age, sex, and the presence of obesity, HbA1c correlated with the HI in the entire population (r = 0.141, p < 0.001), and the presence of prediabetes was independently associated with the AHI (B = 2.20 (0.10 to 4.31), p = 0.040) as well as the HI (B = 1.87 (0.61 to 3.14), p = 0.004) in the multiple linear regression model. We conclude that prediabetes is an independent risk factor for an increased AHI after adjusting for age, sex, and obesity. The enhanced AHI is mainly associated with increments in the hypopnea events.

Type 2 diabetes mellitus and sleep characteristics: a population-based study in Tumbes, Peru

OBJECTIVE.

To determine if there is an association between type 2 diabetes mellitus (T2DM) and some sleep characteristics: duration, sleep difficulties and quality, in a population aged between 30 and 69 years in Tumbes.

MATERIALS AND METHODS.

Cross-sectional study. The outcomes were sleep difficulty (sometimes/ almost never vs. frequently), sleep duration (normal, short, and long), and sleep quality (good and bad). The exposure of interest was the presence of T2DM assessed using the glucose tolerance test (without T2DM, with T2DM but without previous diagnosis, and with T2DM and with previous diagnosis). Poisson regression models were used to report prevalence ratio (PR) and 95% confidence intervals (95%CI).

RESULTS.

A total of 1604 subjects were analyzed and the mean age was 48.2 years; 50.3% were women, 71 (4.4%) had T2DM without a previous diagnosis, and 105 (6.5%) had T2DM with a previous diagnosis. Regarding sleep characteristics, 12.0% had short sleep, 8.2% had long sleep, 3.7% had sleep difficulties, and 19.5% presented poor sleep quality. In a multivariable model, T2DM with previous diagnosis was associated with sleep difficulty (PR= 2.20; 95%CI: 1.13 - 4.27) and bad sleep quality (PR= 1.40; 95%CI: 1.05 - 1.92) compared to those without T2DM.

CONCLUSIONS.

Individuals with previous diagnosis of T2DM had greater probability of presenting sleep difficulties and poor sleep quality. These results suggest the need to evaluate periodically the sleep characteristics of patients with T2DM.

Using Cognitive Behavioral Therapy for Insomnia in People with Type 2 Diabetes, Pilot RCT Part I: Sleep and Concomitant Symptom

OBJECTIVE/BACKGROUND

The primary aim of this study was to examine the effect of Cognitive Behavioral Therapy for Insomnia (CBT-I) on the severity of insomnia in people with Type 2 diabetes (T2D) compared to a health education (HE) control group. The secondary aim was to explore the effect of CBT-I on other sleep outcomes and concomitant symptoms.

PARTICIPANTS

Twenty-eight participants with T2D were randomly assigned to CBT-I (n = 14) or HE (n = 14).

METHODS

Validated assessments were used at baseline and post intervention to assess sleep outcomes and concomitant symptoms. In addition, actigraph and sleep diaries were used to measure sleep parameters. Independent sample t tests and Mann-Whitney U tests were utilized to measure between-group differences in the mean change scores.

RESULTS

Participants in the CBT-I group showed higher improvements in the following mean change scores compared to the HE group: insomnia symptoms (d = 1.78; p < .001), sleep quality (d = 1.53; p =.001), sleep self-efficacy (d = 1.67; p < .001). Both actigraph and sleep diary showed improvements in sleep latency and sleep efficiency in the CBT-I group as compared to the HE group. In addition, participants in the CBT-I group showed greater improvement in the mean change scores of depression symptoms (d = 1.49; p = .002) and anxiety symptoms (d = 0.88; p = .04) compared to the HE group.

CONCLUSION

This study identified a clinically meaningful effect of CBT-I on sleep outcomes and concomitant symptoms in people with T2D and insomnia symptoms. Further work is needed to investigate the long-term effects of CBT-I in people with T2D and insomnia symptoms.

Circadian Patterns of Patients with Type 2 Diabetes and Obstructive Sleep Apnea

Sleep apnea, a condition that modifies sleep and circadian rhythms, is highly prevalent in patients with diabetes. However, it is not known if there is an association between sleep apnea, circadian alterations and glycemic regulation in this type of patient. Here, a polysomnographic study was carried out on 21 women and 25 men (mean age = 64.3 ± 1.46 years) with diagnoses of type 2 diabetes to detect the presence of sleep apnea. Moreover, patients wore an actigraph and a temperature sensor on the wrist for one week, to study the manifestation of the circadian rhythms. The correlations of circadian and polysomnographic variables with the severity of apnea, measured by the apnea-hypopnea index, and with glycemic dysregulation, measured by the percentage of glycated hemoglobin, were analyzed. The mean apnea-hypoapnea index of all the participants was 39.6 ± 4.3. Apnea-hypoapnea index correlated with % N1, negatively with % N3, and also the stability of the active circadian rhythm. However, no significant correlation was found between the apnea-hypopnea index and wrist temperature rhythm and glycated hemoglobin. Glycated hemoglobin levels were negatively associated with the percentage of variance explained by the wrist temperature circadian rhythm (calculated via 24 and 12 h rhythms). This association was independent of body mass index and was strongest in patients with severe apnea. In conclusion, patients with diabetes showed altered circadian rhythms associated with a poor glycemic control and this association could partially be related to the coexistence of sleep apnea.

Sleep Quality and Associated Factors Among Diabetes Mellitus Patients in a Follow-Up Clinic at the University of Gondar Comprehensive Specialized Hospital in Gondar, Northwest Ethiopia: A Cross-Sectional Study

INTRODUCTION

Diabetic mellitus has a negative impact on the quality of sleep. It is one of the leading public health conditions which can result in poor sleep quality. Poor sleep quality is an unreported and unrecognized problem which can affect the prognosis of diabetes patients.

OBJECTIVE

The aim of this study is to assess the prevalence of poor sleep quality and its associated factors among patients with diabetes mellitus attending follow-up clinics at the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia, 2019.

METHODS

An institution-based cross-sectional study was conducted among 430 diabetes mellitus patients at the University of Gondar Comprehensive Specialized Hospital from February 1, 2020 to March 28, 2020. A systematic random sampling method was used to reach the study subjects. An interviewer-administered questionnaire was used for data collection. Pittsburgh sleep quality index was used for assessing sleep quality. To explain study variables, frequency tables and percentages were used. A binary logistic regression was conducted to see the relation between dependent and independent variables.

RESULTS

A total of 430 diabetes mellitus patients participated in the study with a response rate of 100%. The overall prevalence of poor sleep quality was 47.2%. Drinking alcohol (AOR = 2.45, 95% CI: 1.28-4.69), smokers (AOR = 6.26, 95% CI: 2.04-19.21), comorbidity (AOR = 1.80, 95% CI: 1.10-2.96), BMI ≥ 30 (AOR = 4.87, 95% CI: 1.07-22.09), having type 2 diabetes mellitus (AOR = 2.16, 95% CI: 1.04-4.50), poor glycemic control (AOR = 2.61, 95% CI: 1.81-4.81) and having depression (AOR = 9.95, 95% CI: 4.85-20.38) were associated with poor sleep quality.

CONCLUSION

In this study, nearly half of the patients had poor sleep quality. Drinking alcohol, smoking, comorbidities, higher BMI, type 2 diabetes mellitus, poor glycemic control and having depression were factors in poor sleep quality. Creating awareness of the need for weight reduction, minimizing alcohol intake, cessation of smoking, and improving sleep hygiene for DM patients would be effective management for improving poor sleep quality.

The Phenotype of Elderly Patients with Type 2 Diabetes Mellitus and Poor Sleep Quality

BACKGROUND

Sleep disturbances are a common problem among patients with Type 2 diabetes mellitus (T2DM). The aim of the study was to identify the phenotype of T2DM patients with poor sleep quality.

METHODS

An observational, cross-sectional study was conducted between May 2013 and August 2015. One hundred and sixty consecutive patients with T2DM: 74 women and 86 men, with a median age of 69.50 years (59.00; 79.50 years) were enrolled in the study. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI) questionnaire.

RESULTS

Poor sleep quality was noted in 85 (53%) patients. Sleep disorders were associated with older age, as well as female gender, longer duration of diabetes, lower level of fasting plasma glucose, glycated hemoglobin A1c, estimated glomerular filtration rate, triglycerides, waist-to-hip ratio, and the presence of nephropathy. A multivariate logistic regression revealed that sleep disorders were associated with older age (Odd Ratio (OR) = 1.11, 95% Confidence Interval (CI) 1.07-1.15). Fifty-one patients (31.87%) were treated with sleeping pills. We found that older age, female gender, longer duration of diabetes, lower level of fasting plasma glucose, glycated hemoglobin A1c, estimated glomerular filtration rate, triglycerides, and the presence of nephropathy were linked with more frequent usage of hypnotics. A multivariate logistic regression demonstrated that older age (OR = 1.09, 95% CI 1.05-1.14) and nephropathy (OR = 2.79, 95% CI 1.24-6.28) were associated with a more frequent receiving the hypnotics, whereas male gender (OR = 0.30, 95% CI 0.13-0.71) has less frequent hypnotics usage.

CONCLUSION

Although, we assessed a wide range of patients' characteristics, age had the most negative impact on the quality of sleep in patients with T2DM. We detected more frequent use of hypnotics in older females, with coexisting nephropathy.

Sleep Efficiency and Total Sleep Time in Individuals with Type 2 Diabetes with and without Insomnia Symptoms

There is increasing awareness of the high prevalence of insomnia symptoms in individuals with type 2 diabetes (T2D). Past studies have established the importance of measuring sleep parameters using measures of central tendency and variability. Additionally, subjective and objective methods involve different constructs due to the discrepancies between the two approaches. Therefore, this study is aimed at comparing the averages of sleep parameters in individuals with T2D with and without insomnia symptoms and comparing the variability of sleep parameters in these individuals. This study assessed the between-group differences in the averages and variability of sleep efficiency (SE) and total sleep time (TST) of 59 participants with T2D with and without insomnia symptoms. Actigraph measurements and sleep diaries were used to assess sleep parameter averages and variabilities calculated by the coefficient of variation across 7 nights. Mann-Whitney U tests were utilized to compare group differences in the outcomes. Validated instruments were used to assess the symptoms of depression, anxiety, and pain as covariates. Objective SE was found to be statistically lower on average (85.98 ± 4.29) and highly variable (5.88 ± 2.57) for patients with T2D and insomnia symptoms than in those with T2D only (90.23 ± 6.44 and 3.82 ± 2.05, respectively). The subjective average and variability of SE were also worse in patients with T2D and insomnia symptoms, with symptoms of depression, anxiety, and pain potentially playing a role in this difference. TST did not significantly differ between the groups on averages or in variability even after controlling for age and symptoms of depression, anxiety, and pain. Future studies are needed to investigate the underlying mechanisms of worse averages and variability of SE in individuals with T2D and insomnia symptoms. Additionally, prompting the associated risk factors of insomnia symptoms in individuals with T2D might be warranted.

Effect of Glucose Improvement on Nocturnal Sleep Breathing Parameters in Patients with Type 2 Diabetes: The Candy Dreams Study

Type 2 diabetes exerts a negative impact on sleep breathing. It is unknown whether a long-term improvement in glycemic control ameliorates this effect. We conducted an interventional study with 35 patients with type 2 diabetes and obstructive sleep apnea (OSA) to explore this. At home, sleep breathing parameters were assessed at baseline and after a 4-month period in which antidiabetic therapy was intensified. Patients who decreased their body mass index ≥2kg/m² were excluded. Those with an HbA1c reduction ≥0.5% were considered good responders (n = 24). After the follow-up, good responders exhibited an improvement in the apnea–hypopnea index (AHI: 26-1 (95% IC: 8.6–95.0) vs. 20.0 (4.0–62.4) events/hour, p = 0.002) and in time with oxygen saturation below 90% (CT90: 13.3 (0.4–69.0) vs. 8.1 (0.4–71.2) %, p = 0.002). No changes were observed in the group of non–responders (p = 0.722 and p = 0.138, respectively). The percentage of moderate and severe OSA decreased among good responders (p = 0.040). In the wider population, the change in HbA1c correlated positively to decreases in AHI (r = 0.358, p = 0.035) and negatively to increases in the minimum arterial oxygen saturation (r = −0.386, p = 0.039). Stepwise multivariate regression analysis showed that baseline AHI and the absolute change in HbA1c independently predicted decreased AHI (R² = 0.496). The improvement of glycemic control exerts beneficial effects on sleep breathing parameters in type 2 diabetes, which cannot be attributed merely to weight loss.

Relationship between subjective sleep disturbances and glycaemia in Chinese adults with type 2 diabetes: findings from a 1.5-year follow-up study

We wanted to determine whether subjective sleep disturbance was associated with serum glycated hemoglobin (HbA1c) in people with type 2 diabetes mellitus. In total, 944 randomly-selected people with diabetes completed the Chinese version of the Pittsburgh Sleep Quality Index (PSQI). Participants' glycaemia was assessed using HbA1c in March 2016 and September 2017. The PSQI score and the change in score(△PSQI), and the HbA1c and its change (△HbAlc) were analysed by sex and age (30-45, 46-60, 61-75, and 76-89 years). Associations between time point PSQI and △PSQI with static HbA1c and △HbA1c were analysed using multiple linear regression. The results showed subjective sleep disturbance among people with diabetes was not correlated with serum HbAlc (β coefficient = 0.032, P = 0.103). However, cross-sectional multiple linear regression showed the relationship was present in women (β coefficient = 0.163, P < 0.01). In multiple linear regression, △PSQI score was correlated with △HbAlc value (β coefficient = 0.142, P < 0.01). The regression coefficient (β) for the relationship between △PSQI score and △HbA1c in men was greater than that in women, and for age was β_61-75years < β_46-60years < β_30-45years. The strongest relationship between △PSQI and △HbA1c was in men aged 30-45 years (β = 0.452, P < 0.01). Subjective sleep disturbance among people with diabetes was not related to glycaemic status in the whole sample, but there was a correlation in women. The change in subjective sleep disturbance correlated with the change in glycaemia, most strongly in younger participants, especially men aged 30-45 years.

Active Time-Restricted Feeding Improved Sleep-Wake Cycle in db/db Mice

People with diabetes are more likely to experience sleep disturbance than those without. Sleep disturbance can cause daytime sleepiness in diabetic patients, which may impair their daytime performance or even lead to workplace injuries. Therefore, restoring the normal sleep-wake cycle is critical for diabetic patients who experience daytime sleepiness. Previous data on a diabetic mouse model, the db/db mice, have demonstrated that the total sleep time and sleep fragmentation are increased and the daily rhythm of the sleep-wake cycle is attenuated. Accumulating evidence has shown that active time-restricted feeding (ATRF), in which the timing of food availability is restricted to the active-phase, is beneficial to metabolic health. However, it is unknown whether ATRF restores the normal sleep-wake cycle in diabetes. To test that, we used a non-invasive piezoelectric system to monitor the sleep-wake profile in the db/db mice with ad libitum feeding (ALF) as a baseline and then followed with ATRF. The results showed that at baseline, db/db mice exhibited abnormal sleep-wake patterns: the sleep time percent during the light-phase was decreased, while during the dark-phase it was increased with unusual cycling compared to control mice. In addition, the sleep bout length during both the light-phase and the full 24-h period was shortened in db/db mice. Analysis of the sleep-wake circadian rhythm showed that ATRF effectively restored the circadian but suppressed the ultradian oscillations of the sleep-wake cycle in the db/db mice. In conclusion, ATRF may serve as a novel strategy for treating diabetes-induced irregularity of the sleep-wake cycle.

Sleep questionnaires for the screening of obstructive sleep apnea in patients with type 2 diabetes mellitus compared with non-diabetic patients

BACKGROUND

Several tools have been used to screen for obstructive sleep apnea (OSA). Evaluation of the predictive performance of different questionnaires is essential in patients with type 2 diabetes mellitus (T2DM) because the prevalence of OSA in this population is high. The aim of this study was to evaluate different sleep questionnaires to identify T2DM patients with OSA, and to compare the predictive values of these questionnaires with a matched sample of non-diabetic patients.

METHODS

The study was a retrospective study of two patients groups (n = 350 with T2DM, n = 350 without T2DM) visiting a sleep clinic and matched by age, gender, body mass index, and the apnea-hypopnea index (AHI). Symptoms of OSA and Epworth Sleepiness Scale (ESS), STOP-Bang, Berlin questionnaire, and Athens insomnia scale (AIS) scores were compared, and sleep studies were performed.

RESULTS

Diabetic patients with OSA complained more frequently of nocturia (P = 0.025), morning headaches (P = 0.04), restless sleep (P = 0.002), and leg movements (P = 0.01) than non-diabetic patients with OSA. Most predictive values of the sleep questionnaires did not differ significantly between the two groups; however, the AIS was higher only in T2DM women (P = 0.01). In both groups, the Berlin and STOP-Bang questionnaires had the highest sensitivity. The ESS had the highest specificity in T2DM patients and the STOP and S-B questionnaires had the highest specificity in non-diabetics.

CONCLUSIONS

The predictive performance of the questionnaires was similar in both groups, especially in the case of moderate and severe OSA.

Aiding sleep in type 2 diabetes: therapeutic considerations

Insomnia and obstructive sleep apnoea (OSA) are more prevalent in patients with type 2 diabetes than in the general population. Both insomnia and OSA have been linked to cardiometabolic alterations (eg, hypertension, increased activity of the sympathetic nervous system, and systemic insulin resistance) that can exacerbate the pathophysiology of type 2 diabetes. Improvement of sleep in patients with diabetes could therefore aid the treatment of diabetes. To help health practitioners choose the best clinical tool to improve their patients' sleep without detrimentally affecting glucose regulation, this Review critically analyses the effects of common treatments for insomnia and OSA on both sleep and glucose metabolism in patients with type 2 diabetes. These treatments include pharmaceutical sleep aids (eg, benzodiazepine receptor agonists, melatonin) and cognitive behavioural therapy for insomnia, continuous positive airway pressure for OSA, and lifestyle interventions.

The relationship between sleep disturbance and glycaemic control in adults with type 2 diabetes: An integrative review

AIMS AND OBJECTIVES

To explore and synthesise current research to assess the state of science about the relationship between sleep disturbance and glycaemic control in adults with type 2 diabetes.

BACKGROUND

Sleep disturbance is suggested a risk factor for type 2 diabetes. Diabetes alone is a leading cause of death, but when coupled with sleep disturbance poses additional health risks. However, little is known about the relationship between sleep disturbance and glycaemic control in people with overt diabetes.

DESIGN

An integrative review.

METHODS

Whittemore and Knafl's methodology guided this integrative review. Original studies published before October 2016 were identified through systematic searches of seven databases using terms: diabet*; sleep or insomnia; glycem* or glucose or A1C or HbA1c or sugar; and their combinations. The matrix and narrative synthesis were employed to organise and synthesise the findings, respectively. The Crowe Critical Appraisal Tool was used to evaluate the study quality.

RESULTS

A total of 26 studies were identified; 17 of which reported significant relationships between sleep measures and glycaemic control. In 13 studies, sleep duration was associated with glycaemic control in both linear (n = 2) and nonlinear (n = 3) relationships; however, eight studies reported no significant relationships. Sleep quality was significantly related to glycaemic control in 14 of 22 studies. Nine studies found no relationship between any measure of sleep and glycaemic control.

CONCLUSIONS

There is strong evidence supporting the relationship between sleep quality and glycaemic control but further examination of the relationship between sleep duration and glycaemic control is warranted. Sleep disturbance, particularly impaired sleep quality, could potentially influence glycaemic control in adults with type 2 diabetes.

RELEVANCE TO CLINICAL PRACTICE

Nurses who treat patients with diabetes should include assessment of sleep, education for healthy sleep, and referral for treatment of sleep disturbance in order to maximise the potential for achieving good glycaemic control.

Pulmonary Function and Sleep Breathing: Two New Targets for Type 2 Diabetes Care

Population-based studies showing the negative impact of type 2 diabetes (T2D) on lung function are overviewed. Among the well-recognized pathophysiological mechanisms, the metabolic pathways related to insulin resistance (IR), low-grade chronic inflammation, leptin resistance, microvascular damage, and autonomic neuropathy are emphasized. Histopathological changes are exposed, and findings reported from experimental models are clearly differentiated from those described in humans. The accelerated decline in pulmonary function that appears in patients with cystic fibrosis (CF) with related abnormalities of glucose tolerance and diabetes is considered as an example to further investigate the relationship between T2D and the lung. Furthermore, a possible causal link between antihyperglycemic therapies and pulmonary function is examined. T2D similarly affects breathing during sleep, becoming an independent risk factor for higher rates of sleep apnea, leading to nocturnal hypoxemia and daytime sleepiness. Therefore, the impact of T2D on sleep breathing and its influence on sleep architecture is analyzed. Finally, the effect of improving some pathophysiological mechanisms, primarily IR and inflammation, as well as the optimization of blood glucose control on sleep breathing is evaluated. In summary, the lung should be considered by those providing care for people with diabetes and raise the central issue of whether the normalization of glucose levels can improve pulmonary function and ameliorate sleep-disordered breathing. Therefore, patients with T2D should be considered a vulnerable group for pulmonary dysfunction. However, further research aimed at elucidating how to screen for the lung impairment in the population with diabetes in a cost-effective manner is needed.