Dapagliflozin Pretreatment Prevents Cardiac Electrophysiological Changes in a Diet and Streptozotocin Induction of Type 2 Diabetes in Rats: A Potential New First-Line?

Purpose

Dapagliflozin exerts cardioprotective effects in Type 2 Diabetes Mellitus (T2DM). However, whether these effects prevent electrocardiographic changes associated with T2DM altogether remain unknown. Our aim was to investigate the prophylactic effect of dapagliflozin pretreatment on the rat ECG using a high-fat, high-fructose (HFHf) diet and a low dose streptozotocin (STZ) model of T2DM.

Methods

Twenty-five (25) rats were randomized into five (5) groups: normal control receiving a normal diet while the other groups received an 8-week HFHf and 40mg/kg STZ on day 42, and either: saline for the diabetic control (1 mg/kg/d), low dose (1.0 mg/kg/d) and high dose dapagliflozin (1.6 mg/kg/d), or metformin (250 mg/kg/d). Oral glucose tolerance (OGT), electrocardiograms (ECGs), paracardial adipose mass, and left ventricular fibrosis were determined. Data were analyzed using GraphPad version 9.0.0.121, with the level of significance at p < 0.05.

Results

Compared to the diabetic control group, a high dose of dapagliflozin preserved the OGT (p = 0.0001), QRS-duration (p = 0.0263), QT-interval (p = 0.0399), and QTc intervals (p = 0.0463). Furthermore, the high dose dapagliflozin group had the lowest paracardial adipose mass (p = 0.0104) and fibrotic area (p = 0.0001). In contrast, while metformin showed favorable effects on OGT (p = 0.0025), paracardial adiposity (p = 0.0153) and ventricular fibrosis (p = 0.0291), it did not demonstrate significant antiarrhythmic effects.

Conclusion

Pretreatment with higher doses of Dapagliflozin exhibits prophylactic cardioprotective characteristics against diabetic cardiomyopathy that include antifibrotic and antiarrhythmic qualities. This suggests that higher doses of dapagliflozin could be a more effective initial therapeutic option in T2DM.

Associations between Oral Glucose-Lowering Agents and Increased Risk for Life-Threatening Arrhythmias in Patients with Type 2 Diabetes Mellitus-A Literature Review

Background and Objectives: The relationship between type 2 diabetes mellitus (T2DM) and cardiovascular (CV) morbidity and mortality is well-established. Ventricular arrhythmias (VA) are frequently diagnosed in patients with T2DM, especially in those with associated coronary syndrome, non-ischemic dilated cardiomyopathy (NIDCM), and heart failure (HF). In these patients, VA and sudden cardiac arrest (SCA) are considered responsible for more than 50% of CV deaths. Newly developed glucose-lowering agents (GLA) seem not only to ameliorate CV morbidity and mortality, but also to reduce the risk of VA and SCA. Materials and Methods: We researched the medical literature on Pub-Med, Clarivate, and Google Scholar for original articles published in the last five years that debated the possible effects of various GLA on ventricular arrhythmias. Results: We identified nineteen original articles, nine of them debating the antiarrhythmic effects of sodium-glucose cotransporter-2 inhibitors (SGLT2i); Conclusions: The results concerning the impact of various GLA on VA/SCA were heterogeneous depending on the pharmacological class studied, with some of them having neutral, positive, or negative effects. Although it appears that SGLT2i reduces the prevalence of atrial fibrillation and SCA, their effect on VA is not conclusive.

The Role of Echocardiography in Evaluating Cardiovascular Diseases in Patients with Diabetes Mellitus

Patients with diabetes mellitus are highly susceptible to cardiovascular complications, which are directly correlated with cardiovascular morbidity and mortality. In addition to coronary artery disease, there is growing awareness of the risk and prevalence of heart failure (HF) in patients with diabetes. Echocardiography is an essential diagnostic modality commonly performed in patients with symptoms suggestive of cardiovascular diseases (CVD), such as dyspnea or chest pain, to establish or rule out the cause of symptoms. Conventional echocardiographic parameters, such as left ventricular ejection fraction, are helpful not only for diagnosing CVD but also for determining severity, treatment strategy, prognosis, and response to treatment. Echocardiographic myocardial strain, a novel echocardiographic technique, enables the detection of early changes in ventricular dysfunction before HF symptoms develop. This article aims to review the role of echocardiography in evaluating CVD in patients with diabetes mellitus and how to use it in patients with suspected cardiac diseases.

Predictive Model for Acute Heart Failure in Patients with Acute Myocardial Infarction and Type 2 Diabetes Mellitus Based on Energy and Adipokine Metabolism Indicators

BACKGROUND: Acute heart failure (AHF) is one of the early complications of acute myocardial infarction (AMI) in diabetic patients. Evaluation of biomarkers of energy and adipokine metabolism can help in the early identification of diabetic patients at risk of AHF. AIM: The present study is aimed to predict the development of AHF in diabetic patients with AMI based on energy and adipokine metabolism parameters. METHODS: A total of 74 diabetic patients with AMI were examined between September 1, 2018, and December 31, 2020. Serum adropin, irisin, and C1q/TNF-related protein 3 (CTRP3) levels were measured by enzyme-linked immunosorbent assay. To predict AHF development in AMI patients, generalized linear mixed model (GLMM) was applied. RESULTS: The serum concentrations of adropin, irisin, and CTRP3 have been found to be reduced in diabetic patients with AMI and AHF. The accuracy of predicting AHF Killip Class 1 was 96.7%, and the accuracy of prediction for AHF Killip Class 2 was 57.1%, that is, the model was poorly sensitive to this level of complications. The prediction accuracy for AHF Killip Class 3 was 80%, that is, the model was highly sensitive to complications of this level, and for AHF Killip Class 4 – 100% being the maximum level of the model sensitivity. CONCLUSIONS: Low serum concentrations of adropin, irisin, and CTRP3 indicate an imbalance in energy and adipokine homeostasis. The constructed model predicts the probability of AHF development with high accuracy of 91.9% in diabetic patients with AMI.

Machine learning-based risk prediction of malignant arrhythmia in hospitalized patients with heart failure

AIMS

Predicting the risk of malignant arrhythmias (MA) in hospitalized patients with heart failure (HF) is challenging. Machine learning (ML) can handle a large volume of complex data more effectively than traditional statistical methods. This study explored the feasibility of ML methods for predicting the risk of MA in hospitalized HF patients.

METHODS AND RESULTS

We evaluated the baseline data and MA events of 2794 hospitalized HF patients in the HF cohort in Anhui Province and randomly divided the study population into training and validation sets in a 7:3 ratio. The Lasso-logistic regression, multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and eXtreme gradient boosting (XGBoost) algorithms were used to construct risk prediction models in the training set, and model performance was verified in the validation set. The area under the receiver operating characteristic curve (AUC) and Brier score were employed to evaluate the discrimination and calibration of the model, respectively. Clinical utility of the Lasso-logistic regression model was analysed using decision curve analysis (DCA). The median (Q1, Q3) age of the study population was 70 (61, 77) years, and 39.5% were female. MA events occurred in 117 patients (4.2%) during hospitalization. In the training set (n = 1964), the AUC of the XGBoost model was 0.998 [95% confidence interval (CI) 0.997-1.000], which was higher than the other models (all P < 0.001). In the validation set (n = 830), there was no significant difference in AUC of Lasso-logistic model 1 [AUC: 0.867 (95% CI 0.819-0.915)], Lasso-logistic model 2 [AUC: 0.828 (95% CI 0.764-0.892)], MARS model [AUC: 0.852 (95% CI 0.793-0.910)], RF model [AUC: 0.804 (95% CI 0.726-0.881)], and XGBoost model [AUC: 0.864 (95% CI 0.810-0.918); all P > 0.05], which were higher than that of CART model [AUC: 0.743 (95% CI 0.661-0.824); all P < 0.05]. Brier scores for all prediction models were less than 0.05. DCA results showed that the Lasso-logistic model had a net clinical benefit. Oral antiarrhythmic drug, left bundle branch block, serum magnesium, d-dimer, and random blood glucose were significant predictors in half or more of the models.

CONCLUSIONS

The current study findings suggest that ML models based on the Lasso-logistic regression, MARS, RF, and XGBoost algorithms can effectively predict the risk of MA in hospitalized HF patients. The Lasso-logistic model had better clinical interpretability and ease of use than the other models.

Medium & long-chain acylcarnitine's relation to lipid metabolism as potential predictors for diabetic cardiomyopathy: a metabolomic study

Background

Acylcarnitine is an intermediate product of fatty acid oxidation. It is reported to be closely associated with the occurrence of diabetic cardiomyopathy (DCM). However, the mechanism of acylcarnitine affecting myocardial disorders is yet to be explored. This current research explores the different chain lengths of acylcarnitines as biomarkers for the early diagnosis of DCM and the mechanism of acylcarnitines for the development of DCM in-vitro.

Methods

In a retrospective non-interventional study, 50 simple type 2 diabetes mellitus patients and 50 DCM patients were recruited. Plasma samples from both groups were analyzed by high throughput metabolomics and cluster heat map using mass spectrometry. Principal component analysis was used to compare the changes occurring in the studied 25 acylcarnitines. Multivariable binary logistic regression was used to analyze the odds ratio of each group for factors and the 95% confidence interval in DCM. Myristoylcarnitine (C14) exogenous intervention was given to H9c2 cells to verify the expression of lipid metabolism-related protein, inflammation-related protein expression, apoptosis-related protein expression, and cardiomyocyte hypertrophy and fibrosis-related protein expression.

Results

Factor 1 (C14, lauroylcarnitine, tetradecanoyldiacylcarnitine, 3-hydroxyl-tetradecanoylcarnitine, arachidic carnitine, octadecanoylcarnitine, 3-hydroxypalmitoleylcarnitine) and factor 4 (octanoylcarnitine, hexanoylcarnitine, decanoylcarnitine) were positively correlated with the risk of DCM. Exogenous C14 supplementation to cardiomyocytes led to increased lipid deposition in cardiomyocytes along with the obstacles in adenosine 5′-monophosphate (AMP)-activated protein kinase (AMPK) signaling pathways and affecting fatty acid oxidation. This further caused myocardial lipotoxicity, ultimately leading to cardiomyocyte hypertrophy, fibrotic remodeling, and increased apoptosis. However, this effect was mitigated by the AMPK agonist acadesine.

Conclusions

The increased plasma levels in medium and long-chain acylcarnitine extracted from factors 1 and 4 are closely related to the risk of DCM, indicating that these factors can be an important tool for DCM risk assessment. C14 supplementation associated lipid accumulation by inhibiting the AMPK/ACC/CPT1 signaling pathway, aggravated myocardial lipotoxicity, increased apoptosis apart from cardiomyocyte hypertrophy and fibrosis were alleviated by the acadesine.

A Network Pharmacology Approach to Investigate the Mechanism of Erjing Prescription in Type 2 Diabetes

Erjing prescription (EJP) was an ancient formula that was recorded in the General Medical Collection of Royal Benevolence of the Song Dynasty. It has been frequently used to treat type 2 diabetes mellitus (T2DM) in the long history of China. The formula consists of Lycium barbarum L. and Polygonatum sibiricum F. Delaroche with a ratio of 1 : 1. This study aimed to identify the potential effects and mechanisms of EJP treatment T2DM. The target proteins and possible pathways of EJP in T2DM treatment were investigated by the approach of network pharmacology and real-time PCR (RT-PCR). 99 diabetes-related proteins were regulated by 56 bioactive constituents in EJP in 26 signal pathways by Cytoscape determination. According to GO analysis, 606 genes entries have been enriched. The PPI network suggested that AKT1, EGF, EGFR, MAPK1, and GSK3β proteins were core genes. Among the 26 signal pathways, the PI3K-AKT signal pathway was tested by the RT-PCR. The expression level of PI3K p85, AKT1, GSK3β, and Myc mRNA of this pathway was regulated by EJP. The study based on network pharmacology and RT-PCR analysis revealed that the blood sugar level was regulated by EJP via regulating the PI3K-AKT signal pathway. Plenty of new treatment methods for T2DM using EJP were provided by network pharmacology analysis.

Impact of Diabetes on Cardiac Function in Patients with High Blood Pressure

Background: Although the combination of high blood pressure (HBP) and type 2 diabetes (T2DM) increases the risk of left ventricular (LV) dysfunction, the impact of T2DM on LV geometry and subclinical dysfunction in hypertensive patients and normal ejection fraction (EF) has been infrequently evaluated. Methods: Hypertensive patients with or without T2DM underwent cardiac echocardiography coupled with LV global longitudinal strain (GLS) assessment. Results: Among 200 patients with HBP (mean age 61.7 ± 9.7 years) and EF > 55%, 93 had associated T2DM. Patients with T2DM had a higher body mass index (29.9 ± 5.1 kg/m² vs. 29.3 ± 4.7 kg/m², p = 0.025), higher BP levels (158 ± 23/95 ± 13 vs. 142 ± 33/87 ± 12 mmHg, p = 0.003), a higher LV mass index (115.8 ± 32.4 vs. 112.0 ± 24.7 g/m², p = 0.004), and higher relative wall thickness (0.51 ± 0.16 vs. 0.46 ± 0.12, p = 0.0001). They had more frequently concentric remodeling (20.4% vs. 16.8%, p < 0.001), concentric hypertrophy (53.7% vs. 48.6%, p < 0.001), elevated filling pressures (25.8 vs. 12.1%, p = 0.0001), indexed left atrial volumes greater than 28 mL/m² (17.2 vs. 11.2%, p = 0.001), and a reduced GLS less than −18% (74.2 vs. 47.7%, p < 0.0001). After adjustment for BP and BMI, T2DM remains an independent determinant factor for GLS decline (OR = 2.26, 95% CI 1.11–4.61, p = 0.023). Conclusions: Left ventricular geometry and subclinical LV function as assessed with GLS are more impaired in hypertensive patients with than without T2DM. Preventive approaches to control BMI and risk of T2DM in hypertensive patients should be emphasized.

Protective effects of SGLT-2 inhibitors across the cardiorenal continuum: two faces of the same coin

The cardiovascular and renal systems are closely interconnected in health and disease. Disorders affecting one of these systems frequently involve the other. Both diseases progress through a continuous chain of events, defined as the 'cardiorenal continuum', which is initiated by risk factors that lead to subclinical disease, clinical events, and ultimately to heart failure and end-stage kidney disease. Previous studies have shown that interventions anywhere along this chain of events can interrupt the pathophysiological cascade and provide cardiovascular and/or kidney 'protection'. More recently, clinical trials with SGLT-2 inhibitors (SGLT2i) have shown a significant reduction in cardiovascular and kidney outcomes. Evidence from EMPA-REG OUTCOME, CANVAS Program, DECLARE-TIMI 58, VERTIS-CV, CREDENCE, and more recently DAPA-HF, EMPEROR-Reduced, and DAPA-CKD show that the beneficial effects of SGLT2i are observed across all stages of the cardiorenal continuum, ranging from patients with diabetes and multiple risk factors to those with established cardiovascular disease and even independently of diabetes status. This review provides a critical appraisal of the efficacy and safety of SGLT2i, demonstrating that this is a novel way to disrupt the chain of pathological events in the cardiorenal continuum and prevent cardiovascular and kidney disease in patients with and without diabetes.

Novel antidiabetic drugs and risk of cardiovascular events in patients without baseline metformin use: a meta-analysis

AIMS

To evaluate the effect of sodium-glucose cotransporter-2 (SGLT-2) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1RAs) on major cardiovascular events (MACE) in metformin-naïve patients with type 2 diabetes (T2D).

METHODS AND RESULTS

A meta-analysis was performed of randomized controlled clinical trials of GLP-1RAs and SGLT-2 inhibitors on T2D populations, after searching the PubMed/MEDLINE, Embase, and Cochrane Controlled Trials databases. The primary endpoint was MACE. The secondary endpoint, explored in the subgroup of SGLT-2 inhibitors studies, was cardiovascular death or hospitalization for heart failure. A random-effects meta-analysis model was applied. Six eligible trials (three studies of SGLT-2 inhibitors and three trials of GLP-1RAs), including 13 049 patients, were identified and considered eligible for the analyses. The new antidiabetic drugs were associated with a significant reduction in MACE [odds ratio (OR): 0.80, 95% confidence interval: 0.70-0.93; I2: 53%]. The subgroup analysis showed the following findings: GLP-1RAs group, OR: 0.77 (95% confidence interval 0.67-0.88); SGLT-2 inhibitors, OR: 0.85 (95% confidence interval 0.63-1.15). SGLT-2 inhibitors were associated with a significant reduction in hospitalization for heart failure or cardiovascular mortality incidence (OR: 0.67, 95% confidence interval: 0.47-0.95; I2: 78%).

CONCLUSION

In this meta-analysis, new antidiabetic drugs reduced the incidence of MACE in metformin-naïve T2D patients. The beneficial effect was especially observed in the GLP-1RAs subgroup. The use of SGLT-2 inhibitors was associated with a reduction in cardiovascular death or hospitalization for heart failure. These results support the fact that metformin would not be indispensable to obtain positive cardiovascular effects when new antidiabetic drugs are administered.