Trends and Gaps in Statin Use for Cardiovascular Disease Prevention in Type 2 Diabetes: A Real-World Study in Shanghai, China.

BACKGROUND: Cardiovascular disease (CVD) is the major cause of death among persons with diabetes. As the preventative use of statin has been proved to reduce CVD risks, understanding the current status and the trend in statin use is crucial to improve clinical treatment strategies. OBJECTIVE: Our study aimed to answer the question of what were the status and trend of statin use in Shanghai, China. METHODS: Our study estimated statin use and trends from 2015 to 2021 among 702 727 patients with type 2 diabetes mellitus (T2DM) based on electronic health records from the Shanghai Hospital Link Database. Patients were grouped according to the presence of CVDs, tested separately for statin primary and secondary prevention use, and stratified by age and sex. RESULTS: In the study population, 221 127 patients (31.5%) received statin therapy, and among patients with CVD, 157 622 patients (51.62%) received statin therapy for secondary prevention, but only 15% of patients received statins for primary prevention. The trend in the use of statins was still on the rise from 28.3% in 2015. Statin use increased with age (18-39 years, 14.0%; 40-59 years, 26.8%; 60-74 years, 33.35%; and 75 and over, 36.1%), and women (29.7%, n = 93 977) were less likely to receive statin therapy compared to men (32.9%, n = 127 150). CONCLUSION: Despite the rise in statin use in T2DM in recent decades, a large proportion of subjects with T2DM did not receive statin therapy.

Insulin use and gout risk among patients with type 2 diabetes mellitus: a real-world cohort study in Shanghai, China.

OBJECTIVES: The effect of insulin use on gout risk remains unknown. This study aimed to investigate the association between insulin use and gout risk among patients with type 2 diabetes mellitus (T2DM). METHODS: Based on the Shanghai Link Healthcare Database, patients with newly diagnosed T2DM, with or without insulin exposure, were identified from January 1, 2014 to December 31, 2020, and followed until December 31, 2021. Apart from the original cohort, we also established a 1:2 propensity score-matched cohort. A time-dependent Cox proportional hazards model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for gout incidence associated with insulin exposure. RESULTS: A total of 414,258 patients with T2DM, including 142,505 insulin users and 271,753 insulin non-users, were enrolled in this study. After a median follow-up of 4.08 years (interquartile range, 2.46-5.90 years), the incidence of gout was significantly higher in insulin users than in insulin non-users (319.35 versus 302.20 cases per 100,000 person-years; HR 1.09, 95% CI 1.03-1.16). The results were robust in propensity score-matched cohort, sensitivity analyses, and stratified analysis of aspirin. In other stratified analyses, the association between insulin use and increased gout risk was found only in patients who were female, or aged 40-69 years, or without hypertension, dyslipidemia, ischemic heart disease, chronic lung disease, kidney disease, or not using diuretic. CONCLUSIONS: Insulin use is associated with a significantly increased risk of gout among patients with T2DM. Key Points • The first real-world study to investigate the effect of insulin use on gout risk. • Insulin use is associated with a significantly increased risk of gout among patients with type 2 diabetes mellitus.

Developing a prediction model for all-cause mortality risk among patients with type 2 diabetes mellitus in Shanghai, China.

BACKGROUND: All-cause mortality risk prediction models for patients with type 2 diabetes mellitus (T2DM) in mainland China have not been established. This study aimed to fill this gap. METHODS: Based on the Shanghai Link Healthcare Database, patients diagnosed with T2DM and aged 40-99 years were identified between January 1, 2013 and December 31, 2016 and followed until December 31, 2021. All the patients were randomly allocated into training and validation sets at a 2:1 ratio. Cox proportional hazards models were used to develop the all-cause mortality risk prediction model. The model performance was evaluated by discrimination (Harrell C-index) and calibration (calibration plots). RESULTS: A total of 399 784 patients with T2DM were eventually enrolled, with 68 318 deaths over a median follow-up of 6.93 years. The final prediction model included age, sex, heart failure, cerebrovascular disease, moderate or severe kidney disease, moderate or severe liver disease, cancer, insulin use, glycosylated hemoglobin, and high-density lipoprotein cholesterol. The model showed good discrimination and calibration in the validation sets: the mean C-index value was 0.8113 (range 0.8110-0.8115) and the predicted risks closely matched the observed risks in the calibration plots. CONCLUSIONS: This study constructed the first 5-year all-cause mortality risk prediction model for patients with T2DM in south China, with good predictive performance.

A network-based study reveals multimorbidity patterns in people with type 2 diabetes.

Patients with type 2 diabetes mellitus (T2DM) are at a heightened risk of living with multiple comorbidities. However, the comprehension of the multimorbidity characteristics of T2DM is still scarce. This study aims to illuminate T2DM's prevalent comorbidities and their interrelationships using network analysis. Using electronic medical records (EMRs) from 496,408 Chinese patients with T2DM, we constructed male and female global multimorbidity networks and age- and sex-specific networks. Employing diverse network metrics, we assessed the structural properties of these networks. Furthermore, we identified hub, root, and burst diseases within these networks while scrutinizing their temporal trends. Our findings uncover interconnected T2DM comorbidities manifesting as emergence in clusters or age-specific outbreaks and core diseases in each sex that necessitate timely detection and intervention. This data-driven methodology offers a comprehensive comprehension of T2DM's multimorbidity, providing hypotheses for clinical considerations in the prevention and therapeutic strategies.

Acute and lag effects of ambient fine particulate matter on the incidence of dyslipidemia in Chengdu, China: A time-series study.

High levels of ambient fine particulate matter (PM2.5) might increase the risk of death due to cardiovascular diseases (CVDs). As a critical risk factor for CVDs, dyslipidemia can cause CVDs or exacerbate pre-existing ones. This study aimed to investigate whether a short-time exposure to PM2.5 leads to dyslipidemia (HyperTC, HyperLDL-C, HyperTG and HypoHDL-C) in adults. The serum lipid data were provided by the Sichuan Provincial People's Hospital Medical Examination Center. We included 309,654 subjects aged 18-79 between May 10, 2015, and May 10, 2017. An advanced distributed lag nonlinear model (DLNM) was applied to investigate the acute and lag effects of ambient PM2.5 on the risk of dyslipidemia. This study was also stratified by sex, age, BMI and season to examine potential effect modification. We observed that the associations between an interquartile increase in PM2.5 (43 µg/m3) and dyslipidemia were [relative risk (RR); 95% confidence interval (CI)]: 1.042 (1.013, 1.071) for HyperLDL-C and 1.027 (1.006, 1.049) for HyperTC at lag0 day. The lag effects were found at lag6 day for HyperLDL-C, in lag4-6 days for HyperTC and lag4-7 days for HyperTG. Short-term exposure to ambient PM2.5 was related to dyslipidemia and the effect modification was observed in the subgroup analysis. The female and normal-weight populations were more susceptible to the risks of PM2.5 on HyperLDL-C and HyperTC.