Association of treatment-achieved HbA1c with incidence of coronary artery disease and severe eye disease in diabetes patients

Impact of Medication Adherence and Glycemic Control on the Risk of Micro- and Macrovascular Diseases in Patients with Diabetes

PURPOSE

Our purpose in the research was to clarify the impact of medication adherence to oral hypoglycemic agents during a 1-year period and subsequent glycemic control on the risk of micro- and macrovascular diseases.

METHODS

Examined was a nationwide claims database on 13,256 individuals with diabetic eye disease without requiring prior treatment, 7,862 without prior initiation of dialysis, 15,556 without prior coronary artery disease, 16,243 without prior cerebrovascular disease, and 19,386 without prior heart failure from 2008 to 2016 in Japan. Medication adherence was evaluated by the proportion of days covered. Patients were considered to have poor adherence if the proportion of days covered was <80%. Multivariate Cox regression model identified risks of micro- and macrovascular diseases.

RESULTS

In each group, mean age was 53 to 54 years, HbA1c was 7.1% to 7.2%, and median follow-up period was 4.6 to 5.1 years, and the percentage of poor adherence was approximately 30%. During the study period, 532 treatment-requiring diabetic eye disease, 75 dialysis, 389 coronary artery disease, 316 cerebrovascular disease, and 144 heart failure events occurred. Multivariate Cox regression model revealed that the hazard ratio (95% confidence interval) of dialysis in the poor adherence group was 2.04 (1.27-3.30) compared with the good adherence group. The hazard ratios in the poor adherence/poor glycemic control group were 3.34 (2.63-4.24) for treatment-requiring diabetic eye disease, 4.23 (2.17-8.26) for dialysis, 1.69 (1.23-2.31) for coronary artery disease, and 2.08 (1.25-3.48) for heart failure compared with the good adherence/good glycemic control group.

CONCLUSIONS

Poor medication adherence was an independent risk factor for the initiation of dialysis, suggesting that clinicians must pay close attention to these patients.

Current trends in diabetes mellitus database research in Japan

With the widespread use of electronic medical records and administrative claims databases, analytic results from so-called real-world data have become increasingly important in healthcare decision-making. Diabetes mellitus is a heterogeneous condition that involves a broad spectrum of patients. Real-world database studies have been recognised as a powerful tool to understand the impact of current practices on clinical courses and outcomes, such as long-term glucose control, development of microvascular or macro-vascular diseases, and mortality. Diabetes is also a major global health issue and poses a significant social and economic burden worldwide. Therefore, it is critical to understand the epidemiology, clinical course, treatment reality, and long-term outcomes of diabetes to determine realistic solutions to a variety of disease-related issues that we are facing. In the present review, we summarise the healthcare system and large-scale databases currently available in Japan, introduce the results from recent database studies involving Japanese patients with diabetes, and discuss future opportunities and challenges for the use of databases in the management of diabetes.

Risk of coronary artery disease according to glucose abnormality status and prior coronary artery disease in Japanese men

OBJECTIVE

Although glucose abnormality status (GAS), prior coronary artery disease (CAD), and other traditional risk factors affect the incidence of subsequent CAD, their impact in the same cohort has been scantly studied.

RESEARCH DESIGN AND METHODS

We analyzed data from a nationwide claims database in Japan that was accumulated during 2008-2016 involving 138,162 men aged 18-72 years. Participants were classified as having normoglycemia, borderline glycemia, or diabetes mellitus (DM) with prior CAD (CAD+) or without prior CAD (CAD-). Cox regression model identified variables related to the incidence of CAD.

RESULTS

Among CAD-, management of traditional risks differed from those with and without subsequent CAD events. On the other hand, such differences were weaker in borderline glycemia and DM CAD+, and the influence of traditional risk factors on subsequent CAD was not observed. Cox regression model showed that borderline glycemia and DM confer approximately 1.2- and 2.8-fold excess risks of CAD, respectively, compared with CAD- with normoglycemia. CAD+ confers approximately a 5- to 8-fold increased risk. The impacts of DM and prior CAD additively reached a hazard ratio (HR) of 15.74 (95% confidence interval [CI]: 11.82-21.00). However, the HR in those with borderline glycemia and CAD+ was 7.20 (95% CI: 5.01-10.34), which was not different from those with normoglycemia and CAD+.

CONCLUSION

Control status of traditional risk factors and impact on subsequent CAD differ among categories of glycemic status with and without prior CAD. Individualizing treatment strategies is needed in consideration of risk factors, such as GAS and CAD+.