BackgroundLipids and lipid ratios are associated with complications of diabetes mellitus type 2 (T2DM), such as cardiovascular disease, but the relationship between blood glucose levels and lipid or lipid ratios is not fully understood in T2DM patients. This study assesses the association between blood glucose levels and lipid or lipid ratios in a cohort of T2DM patients.MethodsA total of 1,747 Chinese T2DM patients from the Ningxia province of China were included in this cross-sectional study. Lipid parameters, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C), and fasting blood glucose levels were measured quantitatively using standard methods. Fasting blood glucose was divided into three groups. A multiple mixed-effect linear regression model was conducted to identify a potential association between blood glucose and lipid parameters.ResultsThere was a positive association between blood glucose and TG levels (β=0.34, 95% CI: (0.20, 0.48), p<0.01); every 1 mmol/L increase in blood glucose levels resulted in a 0.34 mmol/L increase in TG. Blood glucose levels were also associated with high LDL (β=0.08, 95% CI: (0.02, 0.14), p<0.01), TG/HDL-C (β=0.31, 95% CI: (0.13, 0.49), p<0.01), and LDL-C/HDL-C (β=0.13, 95% CI: (0.06, 0.20), p<0.01) levels. After controlling for demographic variables, health-related behaviors, and physical health variables, a positive association between blood glucose levels and TG (β=0.31, 95% CI: (0.17, 0.45), p<0.01) and LDL-C (β=0.08, 95% CI: (0.02, 0.13), p<0.01) levels and an in increase in TG/HDL-C (β=0.28, 95% CI: (0.09, 0.46), p<0.01) and LDL-C/HDL-C (β=0.11, 95% CI: (0.04, 0.18), p<0.01) ratios was found.ConclusionA correlation between blood glucose levels and serum lipids or lipid ratios has been established in this study. Blood glucose levels were positively associated with TG and LDL-C levels and elevated TG/HDL-C and LDL-C/HDL-C ratios.

Purpose This study aimed to evaluate the relationship between social capital (SC) and hypertension among type 2 diabetes mellitus (T2DM) patients, considering the moderation effects of depressive symptoms. Patients and Methods A total of 1761 Chinese T2DM patients completed measure scales of social capital and epidemiological survey depression scale (CES-D). The Bootstrap methods PROCESS program is employed to test the moderation model. Results The prevalence of hypertension among T2DM patients was 39.3%. The SC was negatively correlated with the CES-D score (r=−0.18, P<0.01); the SC was also negatively correlated with diastolic blood pressure (r=−0.05, P<0.05); and the CES-D score was positively correlated with systolic blood pressure (r=0.05, P<0.05). Both logistic regression analysis and the Bootstrap method showed that depressive symptoms weakened the protective effect of SC on hypertension, there existed a moderating effect of depressive symptoms on the relationship between SC and hypertension among T2DM patients. Conclusion Depressive symptoms may be one crucial moderator of the relationship between SC and hypertension in a representative sample of Chinese diabetes patients. The findings indicate that improving SC and mental health may help manage hypertension among T2DM patients.

Purpose Few studies have explored the association between neighborhood social cohesion (NSC), a type of social capital, and the quality of life of patients with type 2 diabetes mellitus (T2DM). In addition, the potential mechanism for this association remains unclear. The current study examined the mediation effect of depressive symptoms on the relationship between NSC and quality of life among diabetes patients in China. Patients and Methods A cross-sectional study of 1747 T2DM patients was conducted. The specific quality of life (DSQL), Center for Epidemiological Survey Depression (CES-D), and social capital scales were administered using a face-to-face survey. Partial correlation analysis and a linear regression model were employed to explore the relationship between NSC, depressive symptoms, and quality of life. Bootstrap analysis using PROCESS was used to test the mediation model. Results After controlling for covariates, NSC was negatively correlated with depressive symptoms (r=−0.24, P<0.01) and DSQL score (r=−0.20, P<0.01) while depressive symptoms were positively correlated with DSQL score (r=0.46, P<0.01). Linear regression analysis also found that NSC was negatively associated with the DSQL score, while depressive symptoms were positively associated with the DSQL score. Depressive symptoms mediated the relationship between NSC and quality of life in T2DM patients (explaining 50.7% of the total variance). Conclusion NSC was positively associated with improved quality of life among Chinese T2DM patients in this study, and depressive symptoms were likely to partially explain this relationship. These findings may be used to help maintain a good quality of life among at-risk individuals. Additional prospective studies are needed to confirm these findings.

Background: Traditional Chinese medicine (TCM) has certain advantages in treating diabetes via TCM syndromes differentiation, and health-related behaviors can regulate TCM syndromes. This study aimed to identify the clusters of TCM syndromes in type 2 diabetes mellitus (T2DM) patients and to explore the association between health-related behaviors and those TCM syndromes clusters. Methods: This was a cross-sectional study of 1761 T2DM patients from the Ningxia Province. The TCM syndromes (11 TCM syndromes in total) scale was used to collect the syndrome information. Health-related behaviors, including smoking, alcohol use, tea drinking, the intensity of physical activity, sleep quality, and sleep duration, were collected via a face-to-face interview questionnaire. Latent profile analysis was employed to identify clusters of 11 TCM syndromes. Multinomial logistic regression was employed to examine the relationships between health-related behaviors and clusters of TCM syndromes. Results: TCM syndromes in T2DM patients were classified into three profiles using latent profile analysis: light, moderate, and heavy. Participants with poor health-related behaviors were more likely to have heavy 1.49 (95% CI: 1.12, 1.99) or moderate 1.75 (95% CI: 1.10, 2.79) profiles than those with good health-related habits. Smokers, tea drinkers, and those with poor sleep quality were more likely to have a moderate profile and heavy profile than a light profile. Compared with heavy physical activity, moderate activity 0.24 (95% CI: 0.07, 0.88) was negatively associated with a heavy profile. Conclusion: Results showed that most participants had light or moderate levels of TCM syndromes, and those with poor healthrelated behaviors were more likely to have heavy or moderate profiles. In the context of precision medicine, these results have important implications for understanding the prevention and treatment of diabetes via changing lifestyles and behaviors to regulate TCM syndromes.

Although some studies have shown the association between sleep duration and cognitive impairment is positive, the mechanism explaining how sleep duration is linked to cognition remains poor understood. The current study aims to explore it among Chinese population. A cross-sectional study of 12,589 participants aged 45 or over was conducted, cognition was assessed by three measures to capture mental intactness, episodic memory, and visuospatial abilities. The Center for Epidemiologic Studies Depression Scale 10 (CES-D10) was administered during the face-to-face survey to assess depressive status. Sleep duration was reported by the participants themselves. Partial correlation and linear regression were used to explore the association between sleep duration, cognition, and depression. The Bootstrap methods PROCESS program was used to detect the mediation effect of depression. Sleep duration was positively correlated with cognition and negatively with depression (p < 0.01). The CES-D10 score (r = − 0.13, p < 0.01) was negatively correlated with cognitive function. Linear regression analysis showed sleep duration was positively associated with cognition (p = 0.001). When depressive symptoms were considered, the association between sleep duration and cognition lost significance (p = 0.468). Depressive symptoms have mediated the relationship between sleep duration and cognitive function. The findings revealed that the relationship between sleep duration and cognition is mainly explained by depressive symptoms and may provide new ideas for interventions for cognitive dysfunction.