Association of SGLT2 inhibitors with lower incidence of death in type 2 diabetes mellitus and causes of death analysis

Sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide 1 receptor agonists and cancer mortality. A real-world registry

INTRODUCTION AND OBJECTIVES

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1ra) reduce cardiovascular events through different mechanisms, but their association with cancer remains unclear. The aim of this study was to compare the effect of combined treatment (SGLT2i and GLP1ra) and monotherapy (SGLT2i or GLP1ra) on hospitalization and/or death from cancer in a general population and a subgroup of patients with cardiovascular disease (CVD).

METHODS

We conducted a nonconcurrent observational prospective study of patients prescribed SGLT2i, GLP1ra, or both. Multinomial propensity scores were performed in the entire population and in a subgroup of patients with CVD. A multivariate Cox regression analysis was used to determine the hazard ratio (HR) for age, sex, risk factors, and treatment for each outcome.

RESULTS

We included 14 709 patients (11366 with SGLT2i, 1016 with GLP1ra, and 2327 with both treatments) from treatment initiation. Diabetes was present in 97% of the patients. The subgroup with CVD included 4957 (33.7%) patients. After a median of 33 months of follow-up, the risk of adverse cancer events was similar between patients with and without CVD (3.4% or 3.7%, respectively). The main risk factors for cancer mortality were male sex and age. Combined treatment and its duration reduced the risk of cancer mortality compared with monotherapy with SGLT2i or GLP1ra in the overall population (HR, 0.2216; 95%CI, 0.1106-0.4659; P<.001; and HR, 0.1928; 95%CI, 0.071-0.5219; P=.001, respectively) and in the subgroup of patients with CVD (HR, 0.2879; 95%CI, 0.0878-0.994; P<.049; and HR, 0.1329; 95%CI, 0.024-0.6768; P=.014, respectively).

CONCLUSIONS

Initiation of combined therapy (SGLT2i and GLP1ra) vs monotherapy with SGLT2i or GLP1ra was associated with a lower risk of cancer mortality, mostly in diabetic patients with or without CVD. Although clinical trials are needed, these results might be explained by the complementary mechanisms of these drugs, including their antiproliferative, anti-inflammatory, and metabolic effects. Future clinical trials and mechanistic studies will clarify the possible role of these drugs in carcinogenesis.

Treatment with SGLT2 Inhibitors in Patients with Diabetes Mellitus and Extensive Coronary Artery Disease: Mortality and Cardiovascular Outcomes

INTRODUCTION

Sodium-glucose type 2 cotransporter inhibitors (SGLT2-I) have shown solid benefits in reducing cardiovascular mortality and admissions for heart failure in patients with type 2 diabetes mellitus (T2DM) and cardiovascular disease. However, no specific studies exist in patients with high-risk coronary artery disease (CAD).

METHODS

Single-center, retrospective, observational study including patients with T2DM and a new diagnosis of extensive CAD (defined as left main disease or three main coronary vessel disease). Patients were recruited from 2015 until 2020, with a follow-up of at least 12 months. The primary outcome was to compare all-cause mortality in patients treated with or without SGLT2-I at discharge and adjusted by inverse probability of treatment weighting (IPTW) propensity score.

RESULTS

A total of 420 patients were included: 104 (24.7%) were treated with SGLT2-I and 316 (75.3%) were not (non-SGLT2-I group). The presentation was acute coronary syndrome in 44.3%. The mean age was 71.2 ± 10.5 years. The mean left ventricular ejection fraction was 51.5 ± 12.5%, and the mean estimated glomerular filtration rate was 73.9 ± 22 ml/min. After a mean follow-up of 3 ± 1.6 years, all-cause mortality was 16.4%, and cardiovascular mortality was 9.5%. After IPTW, the risk of all-cause death was lower in the SGLT2-I group with a hazard ratio of 0.32 (95% confidence interval 0.12-0.81), p = 0.016. With regard to secondary outcomes, patients in the SGLT2-I group were associated with less renal function deterioration but an increase in unplanned revascularizations.

CONCLUSIONS

In patients with T2DM and extensive CAD, treatment with SGLT2-I after discharge was associated with a reduced risk of all-cause death.

Sodium-glucose cotransporter 2 inhibitors versus dipeptidyl peptidase 4 inhibitors on new-onset overall cancer in Type 2 diabetes mellitus: A population-based study

BACKGROUND

Cancer is currently the second leading cause of death globally. There is much uncertainty regarding the comparative risks of new-onset overall cancer and pre-specified cancer for Type 2 diabetes mellitus (T2DM) patients on sodium-glucose cotransporter 2 inhibitors (SGLT2I) versus DPP4I.

METHODS

This population-based cohort study patients included patients who were diagnosed with T2DM and administered either SGLT2 or DPP4 inhibitors between 1 January 2015 and 31 December 2020 in public hospitals of Hong Kong.

RESULTS

This study included 60,112 T2DM patients (mean baseline age: 62.1 ± 12.4 years, male: 56.36%), of which 18,167 patients were SGLT2I users and 41,945 patients were dipeptidyl peptidase 4 inhibitor (DPP4I) users. Multivariable Cox regression found that SGLT2I use was associated with lower risks of all-cause mortality (HR: 0.92; 95% CI: 0.84-0.99; p= 0.04), cancer-related mortality (HR: 0.58; 95% CI: 0.42-0.80; p ≤ 0.001) and new diagnoses of any cancer (HR: 0.70; 95% CI: 0.59-0.84; p ≤ 0.001). SGLT2I use was associated with a lower risk of new-onset breast cancer (HR: 0.51; 95% CI: 0.32-0.80; p ≤ 0.001), but not of other malignancies. Subgroup analysis on the type of SGLT2I, dapagliflozin (HR: 0.78; 95% CI: 0.64-0.95; p = 0.01) and ertugliflozin (HR: 0.65; 95% CI: 0.43-0.98; p = 0.04) use was associated with lower risks of new cancer diagnosis. Dapagliflozin use was also linked to lower risks of breast cancer (HR: 0.48; 95% CI: 0.27-0.83; p = 0.001).

CONCLUSION

Sodium-glucose cotransporter 2 inhibitor use was associated with lower risks of all-cause mortality, cancer-related mortality and new-onset overall cancer compared to DPP4I use after propensity score matching and multivariable adjustment.

Sodium-glucose cotransporter-2 inhibitors and cancer outcomes: A systematic review and meta-analysis of randomized controlled trials

AIMS

Concerns regarding breast and bladder cancer risk with Sodium-glucose cotransporter-2 (SGLT2) inhibitors remain controversial and its effect on cancer mortality is unknown. We aim to evaluate the association between SGLT2 inhibitors and the risk of cancer outcomes.

METHODS

We searched PubMed, Embase and CENTRAL up to June 20th, 2022, for randomized controlled trials of SGLT2 inhibitors in adults, with a minimum follow-up of 48 weeks. Researchers extracted study-level data and assessed within-study risk of bias with the RoB 2.0 tool and quality of evidence with GRADE. We performed meta-analyses summarizing the relative risks (RRs) of cancer outcomes.

RESULTS

Seventy-six trials encompassing 116,375 participants were selected. Overall risk of bias was low. SGLT2 inhibitors did not reduce/increase the overall risk of cancer (RR, 1.03; 95% confidence interval [CI], 0.96-1.10) and cancer mortality (RR, 0.99; 95% CI, 0.85-1.16). SGLT2 inhibitors likely result in little to no difference in the risk of breast (RR, 1.01; 95% CI 0.77-1.32) and bladder cancers (RR, 0.93; 95% CI 0.71-1.21). Trial sequential analysis provided evidence that the sample size was sufficient to avoid missing alternative results.

CONCLUSIONS

SGLT2 inhibitors are not associated with an increased risk of cancer outcomes, providing reassuring data regarding previous safety concerns.

Utilizing shared frailty with the Cox proportional hazards regression: Post discharge survival analysis of CHF patients

Understanding patients' survival probability as well as the factors affecting it constitute a significant concern for researchers and practitioners, in particular for patients with severe chronic illnesses such as congestive heart failure (CHF). CHF is a clinical syndrome characterized by comorbidities and adverse medical events. Risk stratification to identify patients most likely to die shortly after hospital discharge can improve the quality of care by better allocating organizational resources and personalized interventions. Probability assessment improves clinical decision-making, contributes to personalized care, and saves costs. Although one of the most informative indices is the time to an adverse event for each patient, commonly analyzed using survival analysis methods, these are often challenging to implement due to the complexity of the medical data. Numerous studies have used the Cox proportional hazards (PH) regression method to generate the survival distribution pattern and factors affecting survival. This model, although advantageous for survival analysis, assumes the homogeneity of the hazard ratio across patients and independence of the observations in terms of survival time. These assumptions are often violated in real-world data, especially when the dataset is composed of readmission data for chronically ill patients, since these recurring observations are inherently dependent. This study ran the Cox PH regression on a feature set selected by machine learning algorithms from a rich hospital dataset. The event modeled here was patient mortality within 90 days post-hospital discharge. The sample was composed of medical records of patients hospitalized in the Israeli Sheba Medical Center more than once, with CHF as the primary diagnosis. We modeled the survival of CHF patients using the Cox PH regression with and without the shared frailty correction that addresses the shortcomings of the Cox Model. The results of the two models of the Cox PH regression - with and without the shared frailty correction were compared. The results demonstrate that the shared frailty correction, which was statistically significant in our analysis, improved the performance of the basic Cox PH model. While this is the main contribution, we also show that this model outperforms two commonly used measures (ADHERE and EFFECT) for predicting early mortality of CHF patients. Thus, the results illustrate how applying advanced analytics can outperform traditional methods. An additional contribution is the feature set selected using machine-learning methods that is different from those used in the extant literature.