Sleep duration is a significant predictor of cardiac autonomic neuropathy in type 2 diabetes mellitus

Effect of fear of hypoglycaemia on sleep quality of patients with type 2 mellitus diabetes: The mediating role of alexithymia

Background

Patients with type 2 diabetes mellitus (T2DM) commonly experience poor sleep quality. This study aimed to investigate whether alexithymia mediates the association between fear of hypoglycaemia (FoH) and sleep quality in patients with T2DM.

Methods

From September 2021 to November 2021, a cross-sectional survey was conducted on 407 patients with T2DM in China. Data collection was made possible through the administration of the Chinese Version of the Worry Scale, Toronto Alexithymia Scale and Chinese version of the Pittsburgh Sleep Quality Index (CPSQI). Multiple linear regression analyses were also performed.

Results

A total of 65.6% of the participants were male, and 75.7% were aged 18-40 years. FoH showed a moderate and positive correlation with CPSQI scores (r = 0.308, p < 0.001). Alexithymia was weakly and positively correlated with CPSQI scores (r = 0.185, p < 0.001). Meanwhile, FoH exhibited a moderate and positive correlation with alexithymia (r = 0.422, p < 0.001), and difficulty in identifying (r = 0.414, p < 0.001) and describing feelings (r = 0.416, p < 0.001) and a weak and positive correlation with externally oriented thinking (r = 0.221, p < 0.001). The total effect (β = 0.408, p < 0.001) of FoH on CPSQI comprised not only the direct (β = 0.293, 95% confidence interval: 0.174-0.411, p < 0.001) but also the indirect effect (β = 0.115, p < 0.001) of alexithymia.

Conclusions

Alexithymia can mediate the association between FoH and sleep quality. Clinicians should recognize the potential effect of alexithymia and incorporate it in intervention planning and care. Addressing the affective disturbances arising from FoH can enhance emotional expression and sleep quality among T2DM patients.

Predictors of Cardiac Autonomic Dysfunction in Obesity-Related Hypertension

INTRODUCTION

Cardiac Autonomic Dysfunction (CAD) is an overlooked cardiovascular risk factor in individuals with obesity-related hypertension. Despite its clinical significance, there is a notable lack of clarity regarding the pathophysiological correlates involved in its onset and progression.

AIM

The present study aimed to identify potential predictors of CAD in obesity-related hypertension.

METHODS

A total of 72 participants (34 men and 38 women) were enrolled. Comprehensive evaluations were conducted, including cardiac autonomic function assessments, body composition estimation and biochemical analysis. Participants were categorized as CAD-positive or CAD-negative based on Ewing's criteria for autonomic dysfunction. Univariate logistic regression analysis was performed to identify potential predictors for CAD. Multivariate logistic regression models were further constructed by adjusting clinically relevant covariates to identify independent predictors of CAD.

RESULTS

Multivariate logistic regression analysis revealed that resting heart rate (HRrest), (odds ratio, confidence interval: 0.85, 0.78-0.93; p = 0.001) and percentage body fat (BF%), (odds ratio, confidence interval: 0.78, 0.64-0.96; p = 0.018) were significant independent predictors of CAD. Receiver Operating Characteristic curve analysis depicted optimal cut-off values for HRrest and BF% as > 74.1 bpm and > 33.6%, respectively. Multicolinearity analysis showed variance inflation factors (VIF) below the cautionary threshold of 3.

CONCLUSIONS

The HRrest and BF% emerged as significant independent predictors of CAD in obesity-related hypertension. Therapeutic strategies should target HRrest < 74.1 bpm and BF% < 33.6% to mitigate CAD risk in this population. Future trials are required to establish causal relationships and may consider additional confounding variables in obesity-related hypertension.

Sleep quality and heart rate variability in adolescents with type 1 or type 2 diabetes

INTRODUCTION

Decreased sleep quality and lower heart rate variability (HRV) have both independently been associated with diabetes and may contribute to risks for cardiovascular disease. Although poor sleep quality has been associated with lower HRV in adults with type 2 diabetes (T2D), studies of sleep quality in adolescents with (T2D) or studies examining the possible association of poor sleep quality with lower HRV in adolescents with T2D or T1D are not available.

AIM

Thus, we conducted a secondary analysis of data from an existing study to determine if there were associations between sleep quality and HRV in adolescents with T1D or T2D.

METHODS

Adolescents with T1D (n = 101) or T2D (n = 37) completed 24-h HRV Holter monitoring and analysis and a self-reported global measure of sleep quality.

RESULTS

Poor sleep quality was significantly associated with lower HRV, a known predictor for CV risk. Those with T2D had lower measures of HRV.

CONCLUSIONS

The evaluation of sleep quality and early signs of cardiovascular autonomic changes should be considered in routine assessments of adolescents with diabetes. Future research is warranted to examine more robust measures of sleep and HRV in adolescents with diabetes.

Leisure-Time Physical Activity and Glycemic Control Independently Predicts Cardiac Autonomic Neuropathy in Type 2 Diabetes Mellitus

BACKGROUND

Though cardiac autonomic neuropathy (CAN) is a common complication of type 2 diabetes mellitus (T2DM); still, there is lack of clarity on pathophysiological correlates for its onset and progression. Therefore, the purpose of this study was to investigate the predictive ability of lifestyle and cardiometabolic risk factors for CAN in T2DM patients.

METHODS

A total of 105 Indian T2DM patients were recruited in the present study. Cardiometabolic risk factors, such as glycemic control, lipids, resting heart rate, systolic and diastolic blood pressure, and lifestyle risk parameters, such as physical activity levels and sleep quality were assessed. Standard cardiovascular autonomic reflex tests were performed for diagnosing CAN by Ewing's criteria.

RESULTS

Leisure-time physical activity and glycosylated hemoglobin were significant independent predictors of CAN in T2DM. Leisure-time physical activity and glycosylated hemoglobin predicted the occurrence of CAN at cutoff values ≤4.68 metabolic equivalent-hours per week (P = .007) and >7.5% (P = .002), respectively.

CONCLUSIONS

The T2DM patients should be encouraged to engage in leisure-time physical activity of at least 4.68 metabolic equivalent-hours per week (equivalent to 1.2 h of walk or 0.6 h of jog per week) and therapeutic strategies for controlling hyperglycemia in T2DM should aim to reduce glycosylated hemoglobin below 7.5% to reduce CAN occurrence.

Sleep Quality and Associated Factors in Adults with Type 2 Diabetes: A Retrospective Cohort Study

Purpose: Sleep disturbance is one of the major complaints among patients with diabetes. The status of diabetes control and associated complications may contribute to sleep disturbance. This study explored night time sleep and excessive daytime sleepiness in adults with type 2 diabetes and examined the association of diabetes control and associated complications on their sleep quality. Methods: A retrospective cohort study design was used. Type 2 diabetic patients (87 females and 79 males, aged 63.1 ± 10.5 years) were recruited from the outpatient clinics of the endocrine department. Sleep quality was assessed by the Pittsburg Sleep Quality Index and the Epworth Sleepiness Scale. Diabetes control and complications were obtained by retrospectively reviewing patients' medical records over 1 year prior to study enrollment. Results: 72.3% of recruited patients had poor glycemic control, and 71.1% had at least one diabetic complication. 56.0% of patients experienced poor sleep quality, and 24.1% had excessive daytime sleepiness. Those who were female (OR = 3.45) and who had ophthalmological problems (OR = 3.17) were associated with poor night time sleep quality, but if they did exercise to the point of sweating (OR = 0.48) reduced the risk of poor sleep quality. Furthermore, poor sleep quality (OR = 4.35) and having nephropathy (OR = 3.78) were associated with a higher risk of excessive daytime sleepiness. Conclusions: Sex, ophthalmological problems, nephropathy, and no exercise to the point of sweating are associated with sleep problems in patients with type 2 diabetes. Both lifestyle behaviors and diabetic complications affect sleep disturbances in patients with diabetes.

Sleep Quality of Students from Elementary School to University: A Cross-Sectional Study

BACKGROUND

Sleep affects a wide array of health outcomes and is associated with the quality of life. Among students, sleep quality is affected by school stage and grade; however, data regarding the different sleep-related problems students experience at different school stages are limited. In this study, we aimed to explore sleep quality among a student sample ranging from elementary school to university level.

METHODS

Overall, data were examined for 9392 subjects aged 9-22 years. Information on sociodemographic characteristics and other variables were collected through self-administered questionnaires. Sleep quality on school nights was evaluated using the standard Pittsburgh Sleep Quality Index; global score >5 was classified as poor sleep quality. For the high school sample, logistic regression analysis was used to estimate associations between sleep quality and certain factors.

RESULTS

Of the elementary school, middle school, vocational high school, senior high school, and university students, 7.5%, 19.2%, 28.6%, 41.9%, and 28.5%, respectively, showed poor sleep quality. The high school students reported the highest prevalence of shorter sleep duration (70.8%), day dysfunction (84.7%), and subjective poor sleep quality (17.2%). The elementary school students showed the highest prevalence of poor sleep efficiency (17.9%). The university students showed the highest prevalence of sleep medication use (6.4%). The vocational high school students reported the highest prevalence of sleep latency (6.3%) and sleep disturbance (7.4%). Logistic regression modeling indicated that sleep quality is positively associated with school stage, grade, family atmosphere, academic pressure, and number of friends.

CONCLUSION

Sleep quality and sleep features change greatly from elementary school to university. Interventions to improve sleep quality should consider targeting the specific issues students experience at each school stage. Alarmed by the high prevalence of poor sleep quality among high school students, it is recommended that high school students should be informed of their sleep matter and the consequences.