Cardiometabolic risk reductions in patients with type 2 diabetes mellitus newly treated with a sodium-glucose cotransporter 2 inhibitor versus a dipeptidyl peptidase-4 inhibitor: A real-world administrative database study in Japan

Cardiovascular and renal outcomes between sodium-glucose cotransporter 2 inhibitors and dipeptidyl peptidase 4 inhibitors in patients with chronic kidney disease stages 4 and 5: a population-based study

AIMS

Limited evidence exists regarding the benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in cardiovascular and renal outcomes in patients with advanced chronic kidney disease (CKD) with an estimated glomerular filtration rate (eGFR) < 30 mL/min per 1.73 m2.

METHODS

We enrolled patients with type 2 diabetes mellitus and eGFR < 30 mL/min per 1.73 m2 who were prescribed SGLT2i or dipeptidyl peptidase 4 inhibitors (DPP4i) from 2016 to 2022 (n = 117,924). The primary cardiovascular outcomes included cardiovascular death, myocardial infarction, ischemic stroke, and heart failure-related admission. Renal outcomes encompassed an eGFR decline of >50 %, a doubling of serum creatinine levels, and progression to dialysis.

RESULTS

The study included 6,730 participants [SGLT2i, n = 1,086; DPP4i, n = 5,644]. In both groups, the composite cardiovascular events developed at a rate of 13.2 events per 100 person-years (PYs) [hazard ratio (HR), 0.92; 95 % confidence interval (CI) 0.71-1.19]. The composite of renal events occurred at a rate of 18.5 and 16.2 events per 100 PYs in the SGLT2i and DPP4i groups, respectively [subdistribution HR 1.12; 95 % CI 0.91-1.38].

CONCLUSIONS

Compared to DPP4i, SGLT2i did not show superiority in the reduction of cardiovascular or renal events in CKD stage 4-5 patients.

Risk of insulin initiation with sodium-glucose cotransporter-2 inhibitors versus dipeptidyl peptidase-4 inhibitors in patients with type 2 diabetes mellitus: A real-world claims database study in Japan

AIMS

Insulin therapy is a cornerstone in type 2 diabetes mellitus (T2DM) management, but its use is associated with several barriers, including hypoglycaemia, fear of injections and high costs. We compared the risk of insulin initiation and other treatment intensification between patients with T2DM newly treated with a sodium-glucose cotransporter-2 inhibitor (SGLT2i) versus those newly treated with a dipeptidyl peptidase-4 inhibitor (DPP4i).

MATERIALS AND METHODS

This Japanese retrospective cohort study was conducted between 1 January 2015 and 31 March 2023 using the JMDC Claims Database. Patients with T2DM newly treated with an SGLT2i or a DPP4i were matched 1:1 using propensity score (n = 18 488 each). Incidence rates (IR) of insulin initiation, other antidiabetic drugs (OAD) and antihypertensive drugs added onto baseline treatment were calculated for each treatment group. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using a Cox proportional hazards model.

RESULTS

The IR of insulin initiation was 0.95 and 2.12 per 1000 person-years in the SGLT2i and DPP4i groups, respectively, with significantly lower risk in the SGLT2i group than in the DPP4i group (HR 0.46, 95% CI: 0.28-0.74, p = 0.001). The risks of OAD (HR 0.66, 95% CI: 0.64-0.69, p < 0.001) and antihypertensive drugs (HR 0.90, 95% CI: 0.85-0.95, p < 0.001) added onto baseline treatment were lower in the SGLT2i group than in the DPP4i group.

CONCLUSIONS

The risk of insulin initiation was lower in patients with T2DM newly treated with an SGLT2i than in those newly treated with a DPP4i. SGLT2i may reduce or delay the need for insulin therapy.

Comparison of the Risk of Diabetic Retinopathy Between Sodium-Glucose Cotransporter-2 Inhibitors and Dipeptidyl Peptidase-4 Inhibitors in Patients with Type 2 Diabetes Mellitus in Japan: A Retrospective Analysis of Real-World Data

INTRODUCTION

Diabetic retinopathy (DR), a microvascular complication of type 2 diabetes mellitus (T2DM), is a leading cause of blindness and has detrimental effects on patients' quality of life. We compared the risk of DR diagnosis with sodium-glucose cotransporter-2 inhibitors (SGLT2i) versus dipeptidyl peptidase-4 inhibitors (DPP-4i) in patients with T2DM in Japan.

METHODS

This Japanese retrospective cohort study used the JMDC Claims Database (data collected from January 2015 to September 2022). Patients with T2DM and no record of microvascular or macrovascular diseases who were newly treated with an SGLT2i (23,061 patients) or a DPP-4i (53,986 patients) were matched 1:1 using propensity score (10,166 per matched group). Incidence rates (IRs) and cumulative IRs of DR diagnosis were calculated for each treatment group; hazard ratio (HR) and its 95% confidence interval (CI) were calculated using Cox proportional hazard models to compare the risk between the groups.

RESULTS

The IR of DR diagnosis was 46.23 and 57.12 per 1000 person-years in the SGLT2i and DPP-4i groups, respectively, with a significantly lower risk in the SGLT2i group than in the DPP-4i group (HR 0.83, 95% CI 0.75-0.92, P = 0.0003).

CONCLUSIONS

In this study, the risk of DR diagnosis was lower with SGLT2i compared with DPP-4i in patients with T2DM without microvascular and macrovascular diseases in Japan. Findings suggest that early SGLT2i treatment may be beneficial in preventing DR development in early-stage T2DM. Graphical abstract available for this article.

Healthcare resource utilization and healthcare costs in patients with type 2 diabetes mellitus initiating sodium-glucose cotransporter 2 inhibitors vs dipeptidyl peptidase-4 inhibitors in Japan: A real-world administrative database analysis

AIMS/INTRODUCTION

Healthcare resource utilization (HCRU) and healthcare costs are important factors to consider when selecting appropriate treatment for type 2 diabetes mellitus. We compared the HCRU and healthcare costs of sodium-glucose cotransporter 2 inhibitors (SGLT2i) vs dipeptidyl peptidase-4 inhibitors (DPP4i) in patients with type 2 diabetes mellitus in Japan.

MATERIALS AND METHODS

This was a Japanese retrospective cohort study conducted using the JMDC Claims Database (January 1, 2015-December 31, 2021). Patients newly treated with an SGLT2i (31,872 patients) or a DPP4i (73,279 patients) were matched 1:1, using propensity score, after excluding patients without continuous SGLT2i or DPP4i prescriptions after the index date. HCRU and healthcare costs were compared between the treatment groups in the full cohort and subcohorts/subgroups of different baseline characteristics, including body mass index (BMI).

RESULTS

After matching, patient characteristics were well balanced (17,767 patients each). Patients receiving an SGLT2i vs those receiving a DPP4i had significantly lower numbers of hospitalizations per person per month (PPPM) and outpatient visits PPPM, and had shorter lengths of stay per hospitalization. Healthcare costs, including all-cause overall healthcare costs PPPM and all-cause hospitalization costs PPPM, were generally lower in patients receiving an SGLT2i than those receiving a DPP4i. Similar results were observed among patients with a higher BMI but not among patients with a lower BMI.

CONCLUSIONS

SGLT2i were associated with lower HCRU and healthcare costs than DPP4i, suggesting economic benefits with SGLT2i vs DPP4i in type 2 diabetes mellitus management.

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

BACKGROUND

Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.

METHODS

We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.

RESULTS

Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.

CONCLUSION

Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.