ObjectivesTo evaluate the prevalence and determinants of anxiety and depression and to assess their impact on glycaemic control in participants with type 2 diabetes mellitus.DesignA cross-sectional study.SettingCommunity-based investigation in Xuzhou, China.Participants893 Chinese men and women aged 18–84 years who fulfilled the inclusion criteria.MethodsPeople with type 2 diabetes completed the Pittsburgh Sleep Quality Index and the Zung Self-Rating Anxiety and Depression Scales. Demographic and physiological characteristics were recorded. Multiple logistic regression was used to evaluate the combined effect of factors associated with anxiety and depression and to assess the effects of anxiety and depression on glycaemic control.ResultsThe prevalence of depressive symptoms and anxiety symptoms was 56.1% and 43.6%, respectively. Multivariate logistic regression analysis indicated that anxiety symptoms were associated with being woman, low income, chronic disease, depressive symptoms and poor sleep quality. Depressive symptoms were associated with being woman, older age, low education level, being single, diabetes complications, anxiety symptoms and poor sleep quality. Glycaemic control was not related to anxiety symptoms (OR=1.31, 95% CIs 0.94 to 1.67) or depressive symptoms (OR=1.23, 95% CI 0.85 to 1.63). A combination of depressive symptoms and anxiety symptoms was associated with poor glycaemic control (relative excess risk due to interaction: 4.93, 95% CI 2.09 to 7.87; attributable proportion due to interaction: 0.27, 95% CI 0.12 to 0.45).ConclusionsThere was a high prevalence of depressive and anxiety symptoms in this Chinese sample of participants, although depression and anxiety were not singly associated with glycaemic control. However, a combination of depressive and anxiety symptoms was negatively correlated with glycaemic control in participants with type 2 diabetes.

No abstract

BackgroundPoor sleep quality and depression negatively impact the health-related quality of life of patients with type 2 diabetes, but the combined effect of the two factors is unknown. This study aimed to assess the interactive effects of poor sleep quality and depression on the quality of life in patients with type 2 diabetes.MethodsPatients with type 2 diabetes (n = 944) completed the Diabetes Specificity Quality of Life scale (DSQL) and questionnaires on sleep quality and depression. The products of poor sleep quality and depression were added to the logistic regression model to evaluate their multiplicative interactions, which were expressed as the relative excess risk of interaction (RERI), the attributable proportion (AP) of interaction, and the synergy index (S).ResultsPoor sleep quality and depressive symptoms both increased DSQL scores. The co-presence of poor sleep quality and depressive symptoms significantly reduced DSQL scores by a factor of 3.96 on biological interaction measures. The relative excess risk of interaction was 1.08. The combined effect of poor sleep quality and depressive symptoms was observed only in women.ConclusionsPatients with both depressive symptoms and poor sleep quality are at an increased risk of reduction in diabetes-related quality of life, and this risk is particularly high for women due to the interaction effect. Clinicians should screen for and treat sleep difficulties and depressive symptoms in patients with type 2 diabetes.

ObjectivesTo explore the interactions of sleep quality and sleep duration and their effects on impaired fasting glucose (IFG) in Chinese adults.DesignCross-sectional survey.SettingCommunity-based investigation in Xuzhou, China.Participants15 145 Chinese men and women aged 18–75 years old who fulfilled the inclusion criteria.Primary and secondary outcome measuresThe Pittsburgh Sleep Quality Index was used to produce sleep quality categories of good, common and poor. Fasting blood glucose levels were assessed for IFG. Sleep duration was measured by average hours of sleep per night, with categories of <6, 6–8 and >8 h. The products of sleep and family history of diabetes, obesity and age were added to the logistic regression model to evaluate the addictive interaction and relative excess risk of interaction (RERI) on IFG. The attributable proportion (AP) of the interaction and the synergy index (S) were applied to evaluate the additive interaction of two factors. Bootstrap measures were used to calculate 95% CI of RERI, AP and S.ResultsThe prevalence of IFG was greatest in those with poor sleep quality and short sleep duration (OR 6.37, 95% CI 4.66 to 8.67; p<0.001) compared with those who had good sleep quality and 6–8 h sleep duration, after adjusting for confounders. After adjusting for potential confounders RERI, AP and S values (and their 95% CI) were 1.69 (0.31 to 3.76), 0.42 (0.15 to 0.61) and 2.85 (2.14 to 3.92), respectively, for the interaction between poor sleep quality and short sleep duration, and 0.78 (0.12 to 1.43), 0.61 (0.26 to 0.87) and −65 (−0.94 to −0.27) for the interaction between good sleep quality and long sleep duration.ConclusionsThe results suggest that there are additive interactions between poor sleep quality and short sleep duration.

No abstract

BackgroundTo describe the prevalence of alcohol dependence and to explore the relationship between alcohol dependence and newly detected hypertension in China.MethodsA multistage stratified cluster sampling method was used to obtain samples from February to June 2013. The Michigan Alcoholism Screening Test was used to estimate alcohol dependence level. A standard questionnaire measured other independent variables. Enumeration data were analyzed using chi-square; quantitative data were analyzed using t-tests. Spearman correlation analysis and multivariate logistic regression analysis were performed to identify the relationship between alcohol dependence and hypertension.ResultsThe alcohol dependence rate was 11.56%; 22.02% of males (3854/17501) and 1.74% of females (324/18656) were classified as alcohol dependent. The newly detected hypertension rate was 9.46% (3422/36157). Significant associations were found between alcohol dependence levels and blood pressure (P < 0.01). Alcohol dependence was positively correlated with systolic blood pressure (r = 0.071, P < 0.01) and diastolic blood pressure (r = 0.077, P < 0.01) and was an independent risk factor for hypertension after adjusting for confounders (low alcohol dependence: odds ratio [OR] = 1.44, 95% confidence intervals [CI] = 1.14–1.81, P < 0.01; light alcohol dependence: OR = 1.35, 95% CI = 1.11–1.64, P < 0.01; medium alcohol dependence: OR = 1.83, 95% CI = 1.40–2.41, P < 0.01).ConclusionAlcohol dependence was high and associated with hypertension. Health education and precautions against alcoholism should be implemented in Xuzhou city.

No abstract

BACKGROUND Poor sleep quality is a common clinical feature in patients with type 2 diabetes mellitus (T2DM), and often negatively related with glycemic control. Cognitive behavioral therapy (CBT) may improve sleep quality and reduce blood sugar levels in patients with T2DM. However, it is not entirely clear whether CBT delivered by general practitioners is effective for poor sleep quality in T2DM patients in community settings. AIM To test the effect of CBT delivered by general practitioners in improving sleep quality and reducing glycemic levels in patients with T2DM in community. METHODS A cluster randomized controlled trial was conducted from September 2018 to October 2019 in communities of China. Overall 1033 persons with T2DM and poor sleep quality received CBT plus usual care or usual care. Glycosylated hemoglobin A1c (HbAlc) and sleep quality [Pittsburgh Sleep Quality Index (PSQI)] were assessed. Repeated measures analysis of variance and generalized linear mixed effects models were used to estimate the intervention effects on hemoglobin A1c and sleep quality. RESULTS The CBT group had 0.64, 0.50, and 0.9 lower PSQI scores than the control group at 2 mo, 6 mo, and 12 mo, respectively. The CBT group showed 0.17 and 0.43 lower HbAlc values than the control group at 6 mo and 12 mo. The intervention on mean ΔHbAlc values was significant at 12 mo ( t = 3.68, P < 0.01) and that mean ΔPSQI scores were closely related to ΔHbAlc values ( t = 7.02, P < 0.01). Intention-to-treat analysis for primary and secondary outcomes showed identical results with completed samples. No adverse events were reported. CONCLUSION CBT delivered by general practitioners, as an effective and practical method, could reduce glycemic levels and improve sleep quality for patients with T2DM in community.