Association between the quality of primary care, insurance coverage, and diabetes-related health outcomes in a cohort of older adults in China: results from the China Health and Retirement Longitudinal Study

The association between variability of risk factors and complications in type 2 diabetes mellitus: a retrospective study

The variability in diabetes risk factors, such as uric acid and lipids, may influence the development of complications. This study aimed to investigate the influence of such variability on the occurrence of diabetic complications. A retrospective analysis of electronic medical records was conducted with type 2 diabetic patients who received treatment at a tertiary care hospital in Chengdu, Sichuan Province, between 2013 and 2022. The risk factor variability is presented as the standard deviation (SD). The associations between the variability and complications were examined using a binary logistic regression model. The study included 369 patients with type 2 diabetes. The findings revealed that outpatient special disease management served as a protective factor against the development of complications [OR = 0.53, 95% confidence interval (CI) (0.29-0.10)], particularly for the prevention of diabetic peripheral neuropathy [OR = 0.51, 95% CI (0.30-0.86)]. Variability in total cholesterol (TC-SD) was found to be a risk factor for the development of complications [OR = 2.42, 95% CI (1.18-4.97)] and acted as a risk factor for diabetic peripheral vasculopathy [OR = 2.50, 95% CI (1.25-5.02)]. TC-SD is a risk factor for the occurrence of diabetic peripheral neuropathy and diabetic peripheral vasculopathy, whereas outpatient special disease management functions as a protective factor against complications and diabetic peripheral neuropathy. Thus, in addition to glycaemic control, the regulation of lipid levels should be emphasized, particularly among patients without outpatient special disease management, to delay the onset of complications.

Acarbose reduces Pseudomonas aeruginosa respiratory tract infection in type 2 diabetic mice

BACKGROUND

Type 2 diabetes mellitus (T2DM) is widely prevalent worldwide, and respiratory tract infections (RTIs) have become the primary cause of death for T2DM patients who develop concurrent infections. Among these, Pseudomonas aeruginosa infection has been found to exhibit a high mortality rate and poor prognosis and is frequently observed in bacterial infections that are concurrent with COVID-19. Studies have suggested that acarbose can be used to treat T2DM and reduce inflammation. Our objective was to explore the effect of acarbose on P. aeruginosa RTI in T2DM individuals and elucidate its underlying mechanism.

METHODS

High-fat diet (HFD) induction and P. aeruginosa inhalation were used to establish a RTI model in T2DM mice. The effect and mechanism of acarbose administered by gavage on P. aeruginosa RTI were investigated in T2DM and nondiabetic mice using survival curves, pathological examination, and transcriptomics.

RESULTS

We found that P. aeruginosa RTI was more severe in T2DM mice than in nondiabetic individuals, which could be attributed to the activation of the NF-κB and TREM-1 signaling pathways. When acarbose alleviated P. aeruginosa RTI in T2DM mice, both HIF-1α and NF-κB signaling pathways were inhibited. Furthermore, inhibition of the calcium ion signaling pathway and NF-κB signaling pathway contributed to the attenuation of P. aeruginosa RTI by acarbose in nondiabetic mice.

CONCLUSIONS

This study confirmed the attenuating effect of acarbose on P. aeruginosa RTIs in T2DM and nondiabetic mice and investigated its mechanism, providing novel support for its clinical application in related diseases.

A study of factors influencing long-term glycemic variability in patients with type 2 diabetes: a structural equation modeling approach

Aim

The present study aims to utilize structural equation modeling (SEM) to investigate the factors impacting long-term glycemic variability among patients afflicted with type 2 diabetes.

Method

The present investigation is a retrospective cohort study that involved the collection of data on patients with type 2 diabetes mellitus who received care at a hospital located in Chengdu, Sichuan Province, over a period spanning from January 1, 2013, to October 30, 2022. Inclusion criteria required patients to have had at least three laboratory test results available. Pertinent patient-related information encompassing general demographic characteristics and biochemical indicators was gathered. Variability in the dataset was defined by standard deviation (SD) and coefficient of variation (CV), with glycosylated hemoglobin variation also considering variability score (HVS). Linear regression analysis was employed to establish the structural equation models for statistically significant influences on long-term glycemic variability. Structural equation modeling was employed to analyze effects and pathways.

Results

Diabetes outpatient special disease management, uric acid variability, mean triglyceride levels, mean total cholesterol levels, total cholesterol variability, LDL variability, baseline glycated hemoglobin, and recent glycated hemoglobin were identified as significant factors influencing long-term glycemic variability. The overall fit of the structural equation model was found to be satisfactory and it was able to capture the relationship between outpatient special disease management, biochemical indicators, and glycated hemoglobin variability. According to the total effect statistics, baseline glycated hemoglobin and total cholesterol levels exhibited the strongest impact on glycated hemoglobin variability.

Conclusion

The factors that have a significant impact on the variation of glycosylated hemoglobin include glycosylated hemoglobin itself, lipids, uric acid, and outpatient special disease management for diabetes. The identification and management of these associated factors can potentially mitigate long-term glycemic variability, thereby delaying the onset of complications and enhancing patients' quality of life.