Cardiovascular Disease In Diabetic Patients Research Articles

Summary of the 2023 European Society of Cardiology clinical guidelines on the management of cardiovascular disease in patients with diabetes mellitus

The prevalence of cardiovascular diseases (CVDs) is well known. According to the World Health Organization (WHO), almost 18 million people die from CVDs worldwide every year, accounting for 31% of all causes of death [1]. CVDs often develop concomitantly with diabetes mellitus (DM), with approximately 20% of cardiovascular deaths attributed to elevated blood glucose levels [2]. Notably, CVDs are the leading cause of death among patients with type 2 diabetes (T2DM). Based on data from the Federal Register of Diabetes 2022 in Russia, chronic heart failure was the direct cause of death in 24.2% of T2DM cases, followed by acute heart failure (13.1%), cerebrovascular events (10.0%), and myocardial infarction (3.7%) [3].The pathophysiological interplay between atherosclerotic cardiovascular disease and DM has led to a situation where cardiologists are increasingly involved in the treatment of patients with DM, while endocrinologists are encountering a growing number of patients with CVDs. This association has become so apparent that in a recent article published in the European Journal of Cardiology, Yu. Braunwald speculated about the emergence of a new subspecialty - diabetocardiology [4]. Unfortunately, experts predict that the global number of diabetic patients will reach 783 million [5].Recent data on the CV benefits of certain hypoglycemic drugs (primarily, certain SGLT2 inhibitors, several GLP-1 receptor agonists, and a novel non-steroidal mineralocorticoid receptor antagonist finerenone) prove the need for a unified interdisciplinary approach to managing CVDs and DM.Given the importance of integrated and coordinated efforts in managing patients with CVD and DM, the Task Force of the -European Society of Cardiology (ESC) updated, formulated, and published clinical guidelines on the treatment of CVD in diabetic patients in 2023 [6]. This article provides a concise overview of the key provisions outlined in the guidelines.

Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

Machine learning (ML) improves operations in many industries, including medicine. It affects the prognosis of several disorders, including heart disease. If predicted, it may provide doctors with new insights and allow them to treat each patient individually. If anticipated, it may provide medical practitioners with valuable information. Our team uses machine learning algorithms to study heart disease risk. This research will compare decision trees, AdaBoost, support vector machines, artificial neural networks (ANN), and customized ANN. The study will include this analysis. The given model will leverage the dataset of general information and medical test results. Our model uses particle swarm optimization (PSO) and k-nearest neighbors (KNN). Algorithm for feature selection. The model reduces dimensionality using evolutionary algorithms and neural networks. We compared the numerous assessment criteria to the current models, our model, and earlier models. Because of this, the suggested model's suitability was rated with the highest accuracy.

Physiological Evaluation of Endothelin-1 and Osteoprotegerin as a Risk Predicator for Cardiovascular Diseases in Diabetic Patients

Physiological Evaluation of Endothelin-1 and Osteoprotegerin as a Risk Predicator for Cardiovascular Diseases in Diabetic Patients

The effect of ethanolic Acalypha Wilkesiana extract on blood sugar, sodium, potassium levels in alloxan-induced diabetic and salt-induced hypertensive Wistar rats

Diabetes Mellitus and hypertension often occur simultaneously. Hypertension affects approximately 70% of patients with diabetes, and the risk of cardiovascular disease in diabetic patients is three times higher than that in healthy individuals. The study was aimed at investigating the problem of diabetes and hypertension, and a possible remedy for it. Twenty-five (25) male wistar rats were assigned into five (5) groups of five (5) rats each. Group A served as negative control, and received water and feed only. Group B served as the positive control, and received alloxan160 mg/kg BW+ salt-loaded diet. Group C-E received alloxan 160 mg/kg BW+ salt-loaded diet + 100 mg/kg BW, 200 mg/kg BW, and 300 mg/kg BW of ethanolic acalypha wilkesiana (EAW) respectively. All administrations were given orally twice daily for 21 days. At the end of the treatment, blood samples were collected to estimate blood glucose, insulin, sodium, and potassium levels. Results are presented as mean±SEM, and were analysed using One-way ANOVA, statistical significance was considered at (P≤0.05). The results showed decrease in blood glucose levels and increase in insulin levels in groups C, D, and E compared to group B. There was also a decrease in sodium ion levels, and a concomitant increase in potassium ion levels in all wistar rat treatment groups when compared to group B. In summary, the results showed improved blood sugar, insulin, sodium, and potassium ion levels following EAW administration. It is therefore concluded that EAW administration was effective in improving diabetic and hypertensive complications in wistar rats.

Investigating the Cardiovascular Benefits of Dapagliflozin: Vasodilatory Effect on Isolated Rat Coronary Arteries

Dapagliflozin, a sodium–glucose co-transporter 2 (SGLT2) inhibitor, is an antidiabetic medication that reduces blood glucose. Although it is well known that dapagliflozin has additional benefits beyond glycemic control, such as reducing blood pressure and lowering the risk of cardiovascular events, no sufficient research data are available on the direct effect of dapagliflozin on cardiovascular function. Thus, in this study, we investigated the direct vascular effect of dapagliflozin on isolated rat coronary arteries. The left descending coronary arteries of 13-week-old male Sprague Dawley rats were cut into segments 2–3 mm long and mounted in a multi-wire myography system to measure isometric tension. Dapagliflozin effectively reduced blood vessel constriction induced by U-46619 (500 nM) in coronary arteries regardless of the endothelium. Treatment with an eNOS inhibitor (L-NNA, 100 μM), sGC inhibitor (ODQ, 5 μM), or COX inhibitor (indomethacin, 3 μM) did not affect the vasodilation induced by dapagliflozin. The application of a Ca2+-activated K+ channel (KCa) blocker (TEA, 2 mM), voltage-dependent K+ channel (KV) blocker (4-AP, 2 mM), ATP-sensitive K+ channel blocker (KATP) glibenclamide (3 μM), and inward-rectifier K+ channel (KIR) blocker (BaCl2, 30 μM) did not affect the dapagliflozin-induced vasodilation either. The treatment with dapagliflozin decreased contractile responses induced by the addition of Ca2+, which suggested that the extracellular Ca2+ influx was inhibited by dapagliflozin. Treatment with dapagliflozin decreased the phosphorylation level of the 20 kDa myosin light chain (MLC20) in vascular smooth muscle cells. In the present study, we found that dapagliflozin has a significant vasodilatory effect on rat coronary arteries. Our findings suggest a novel pharmacologic approach for the treatment of cardiovascular diseases in diabetic patients through the modulation of Ca2+ homeostasis via dapagliflozin administration.

MMP9 Gene Polymorphism (rs3918242) Increases the Risk of Cardiovascular Disease in Type 2 Diabetes Patients.

Matrix metalloproteinase 9 (MMP-9) C(-1562)T gene polymorphism has been considered a risk factor for cardiovascular disease (CVD). Our study aimed to evaluate the association between this polymorphism and CVD in diabetes patients. The genotyping was performed in 740 patients with T2DM and 400 healthy subjects. A significant difference in the polymorphism distribution was revealed between patients and controls. The T allele and TT homozygote were associated with increased risk of diabetes (OR 1.88, p < 0.0001 and OR 3.77, p = 0.0002, respectively). The comparison between CVD+ and CVD- subgroups showed a much higher frequency of the T allele in patients with CVD (OR 2.87, 95% CI 2.14-3.85, p < 0.0001). Patients with the TT genotype had a higher prevalence of CVD (OR 3.19, 95% CI 1.55-6.56, p = 0.0015). The carrier genotypes (CT/TT) were correlated with HDL levels in both CVD+ and CVD- subgroups (p < 0.001 for both). In the logistic regression analysis, only C(-1562)T SNP was a significant predictor of CVD in diabetic patients (p < 0.001). In conclusion, our study suggests an association between MMP-9 C(-1562)T polymorphism and an increased risk of CVD in T2DM. If replicated in other studies, it could be considered a genetic marker for predicting risk of T2DM and its cardiovascular comorbidity.

The management of cardiovascular diseases in diabetic patients according to ESC guidelines

The management of cardiovascular diseases in diabetic patients according to ESC guidelines

A systematic review and meta-analysis of cardiovascular diseases and associated factors among diabetes mellitus patients in Ethiopia

BackgroundCardiovascular disease (CVD) is the most prevalent complication and the leading cause of death and disability among patients with diabetes mellitus (DM). Over time, diabetes-related cardiovascular disease has become more common worldwide. The aim of this study was to determine the cumulative prevalence of cardiovascular disease and associated factors among diabetic patients in Ethiopia.ObjectiveThe main aim of this review was to estimate the pooled prevalence of cardiovascular disease and its associated factors among diabetic patients in Ethiopia.Methods and materialsThis review was searched using PubMed, Google, and Google Scholar search engines, and was accessed using medical subject heading (MeSH) terms for studies based in Ethiopia. Excel was used to extract the data. With a random-effects model, STATA Version 14 was used for all statistical analyses. The studies' heterogeneity and funnel plot were both examined. The study domain and authors' names were used in the subgroup analysis.ResultsIn this systematic review, 12 studies totaling 2,953 participants were included. The estimated overall prevalence of cardiovascular disease among diabetic patients in Ethiopia was 37.26% (95% CI: 21.05, 53.47, I2 = 99.3%, P ≤ 0.001). Study participants’ age older than 60 years (AOR = 4.74, 95%CI: 1.05, 8.43), BMI > 24.9kg/m2 (AOR = 4.12, 95% CI: 2.33, 5.92), triglyceride > 200mg/dl (AOR = 3.05, 95% CI: 1.26, 4.83), Hypertension (AOR = 3.26, 95% CI: 1.09, 5.43) and duration of DM > 4 years (AOR = 5.49, 95% CI: 3.27, 7.70) were significantly associated with cardiovascular disease.ConclusionsIn conclusion, diabetic patients face a serious public health risk from cardiovascular disease. This review found the following factors, which is independent predictors of cardiovascular disease in diabetic patients: age over 60, BMI > 24.9kg/m2, triglycerides > 200 mg/dl, hypertension, and diabetes duration > 4 years. The results emphasize the need for a prospective study design with a longer follow-up period to assess the long-term effects of CVD predictors in diabetic patients as well as the significance of paying attention to cardiovascular disease in diabetic patients with comorbidity.

Uncovering Predictors of Lipid Goal Attainment in Type 2 Diabetes Outpatients Using Logic Learning Machine: Insights from the AMD Annals and AMD Artificial Intelligence Study Group.

Identifying and treating lipid abnormalities is crucial for preventing cardiovascular disease in diabetic patients, yet only two-thirds of patients reach recommended cholesterol levels. Elucidating the factors associated with lipid goal attainment represents an unmet clinical need. To address this knowledge gap, we conducted a real-world analysis of the lipid profiles of 11.252 patients from the Annals of the Italian Association of Medical Diabetologists (AMD) database from 2005 to 2019. We used a Logic Learning Machine (LLM) to extract and classify the most relevant variables predicting the achievement of a low-density lipoprotein cholesterol (LDL-C) value lower than 100 mg/dL (2.60 mmol/L) within two years of the start of lipid-lowering therapy. Our analysis showed that 61.4% of the patients achieved the treatment goal. The LLM model demonstrated good predictive performance, with a precision of 0.78, accuracy of 0.69, recall of 0.70, F1 Score of 0.74, and ROC-AUC of 0.79. The most significant predictors of achieving the treatment goal were LDL-C values at the start of lipid-lowering therapy and their reduction after six months. Other predictors of a greater likelihood of reaching the target included high-density lipoprotein cholesterol, albuminuria, and body mass index at baseline, as well as younger age, male sex, more follow-up visits, no therapy discontinuation, higher Q-score, lower blood glucose and HbA1c levels, and the use of anti-hypertensive medication. At baseline, for each LDL-C range analysed, the LLM model also provided the minimum reduction that needs to be achieved by the next six-month visit to increase the likelihood of reaching the therapeutic goal within two years. These findings could serve as a useful tool to inform therapeutic decisions and to encourage further in-depth analysis and testing.

Association Between Uric Acid to HDL Cholesterol Ratio and Diabetic Complications in Men and Postmenopausal Women

Previous studies have implicated the uric acid to high-density lipoprotein cholesterol (HDL-C) ratio (UHR) was associated with type 2 diabetes. However, the association between UHR and diabetes-related vascular damages is still unclear. The total of 4551 patients with type 2 diabetes from the cross-sectional Environmental Pollutant Exposure and Metabolic Diseases in Shanghai study (METAL study) were enrolled. UHR was calculated as uric acid to HDL-C ratio. Cardiovascular disease (CVD) was defined as previously diagnosed with stroke, coronary heart disease, or peripheral arterial disease. Chronic kidney disease (CKD) was defined as estimated glomerular filtration rate ≤60 mL/min/1.73 m2 and/or urinary albumin to creatinine ratio ≥30 mg/g. Fundus image was examined by trained individuals and degree of diabetic retinopathy (DR) was evaluated. UHR was positively correlated with CVD (OR = 1.28, 95% CI: 1.02-1.61) and CKD (OR = 1.78, 95% CI: 1.39-2.27) after adjusting for all confounders. No association was found between UHR and DR. In stratified analyses, UHR was predominantly correlated with CVD in diabetic patients with age older than 65 (OR = 1.41, 95% CI: 1.08-1.85), female (OR = 1.43, 95% CI: 1.06-1.94) and BMI≥24kg/m2 (OR = 1.57, 95% CI: 1.17-2.11). A 1-SD increment of UHR was also positively associated with CVD (OR 1.26, 95% CI 1.03, 1.15) and CKD (OR 1.28, 95% CI 1.20,1.39). UHR was positively associated with CKD in all subgroups analysis. No significant interaction effect was observed between UHR and all subgroup variables in CVD and CKD risk. Our study reported a positive association between the UHR and diabetic-related vascular complications in men and postmenopausal women. The relationship between the UHR and DR seems to be uncertain and requires further investigation. And no significant interaction effect was observed between the UHR and all subgroup variables in CVD and CKD risk.

Healthy dietary patterns and risk of cardiovascular disease in diabetic patients: a prospective cohort study.

Purpose: Evidence is limited regarding the associations of different dietary patterns with cardiovascular disease (CVD) risk among the population with diabetes. Thus, we aimed to explore the associations between three dietary patterns and CVD incidence among the population with diabetes. Materials and methods: We prospectively followed 22 473 diabetic patients from the UK Biobank at the baseline. The healthy dietary pattern was derived from a food frequency questionnaire; meanwhile, the Alternate Mediterranean Diet (AMED) and the Dietary Approaches to Stop Hypertension (DASH) diet were assessed based on the Oxford WebQ online 24 h dietary questionnaire. Results: During an average of 10.8 years of follow-up, 5209 incident CVD cases, including 3552 coronary heart disease (CHD) events and 881 strokes, were documented. After multivariate adjustment, a higher healthy diet score was negatively associated with CVD, CHD, and stroke incidence. The hazard ratios and 95% confidence intervals across increasing quintiles of healthy diet score were 0.86 (0.79-0.95), 0.83 (0.75-0.93), and 0.71 (0.57-0.88) for CVD, CHD, and stroke, respectively. A similar protective association with CVD incidence was found for the AMED but not the DASH diet. Conclusions: Adherence to healthy dietary patterns is related to a lower risk of developing CVD among diabetic patients. Our findings further provide vigorous evidence for formulating dietary adherence guidelines for diabetic patients to reduce the burden of CVD complications.

Prediction of Arterial Stiffness Risk in Diabetes Patients through Pulse Wave Velocity and Deep Learning Techniques

Diabetes and arterial stiffness are the primary health concerns related to each other. The understanding of both factors provides efficient disease prevention and avoidance. For the development of cardiovascular disease, arterial stiffness and Diabetes are pathological process considerations. The existing researchers reported the association of these two factors and the complications of arterial stiffness with Diabetes are still in research. Arterial stiffness is measured through pulse wave velocity (PWV), which influences cardiovascular disease in diabetic patients. Moreover, this study developed a medical prediction model for arterial stiffness through the machine and deep learning models to predict the patients who are high-risk factors. Brachial–ankle pulse wave velocity (baPWV) and fasting blood glucose (FBG) are the consideration of baseline. Gaussian-Least absolute shrinkage and selection operator (LASSO) with whale optimization is proposed for feature selection. Initially, key features are extracted from the wave measurement using LASSO, and Principal component analysis (PCA) has been used to remove the outliers. Second, Gaussian regression chooses the PWV-based relevant features from the LASSO identified features. The parts are the critical points to increasing the accuracy of the prediction model. Hence, the selected features are further improved with an evolutionary algorithm called the cat optimization approach. Third, the prediction model is constructed using three machine and deep learning algorithms such as a Support vector machine (SVM), a convolution neural network (CNN), and Gated Recurrent Unit (GRU). The performance of these methods is compared through the area under the receiver operating characteristic curve metric in the dataset. The model with the best performance was selected and validated in an independent discovery dataset (n = 912) from the Dryad Digital Repository (https://doi.org/10.5061/dryad.m484p). From the experimental evaluation, LSTM performs better than other algorithms in classifying arterial stiffness with the AUROC of 0.985 and AUPRC of 0.976.

Risk of cardiovascular disease and mortality in patients with diabetes and acute pancreatitis history: a nationwide cohort study

Patients with acute pancreatitis (AP) may have an increased risk of cardiovascular disease (CVD). Few studies have dealt with the association between AP and the risk of CVD in diabetic patients. This study aimed to investigate the risk of CVD and mortality in patients with diabetes and AP history by analyzing a large-scale national claims database in Korea. Data from the Korean National Health Insurance Service database was analyzed. A total of 2,746,988 participants with type 2 diabetes mellitus that underwent a general health examination between 2009 and 2012 were enrolled. The participants were divided into two groups according to AP history (yes or no) prior to the examination date, and follow-up data until 2018 was analyzed. The primary endpoint was the occurrence of stroke, myocardial infarction (MI), or death. The Cox proportional hazards regression analysis was used to evaluate the association between AP history and the risk of stroke, MI, and mortality. After exclusion, the included number of participants with and without AP history were 3,810 and 2,258,910, respectively. The presence of AP history showed a significantly higher incidence of stroke, MI, and mortality. The adjusted hazard ratios (95% confidence interval) for the risk of stroke, MI, and mortality were 1.534 (1.342–1.753), 1.998 (1.733–2.303), and 2.353 (2.200–2.515), respectively. Age < 65, male sex, current smoking, and drinking significantly increased the risk of death in the subgroup analyses. The risk of stroke, MI, and mortality was significantly higher in diabetic participants with AP history than those without AP history at 9-year follow-up. This suggests that active management of cardiovascular risk factors is necessary in diabetic patients with AP history.

Cobalt loaded electrospun poly(ε-caprolactone) grafts promote antibacterial activity and vascular regeneration in a diabetic rat model

Diabetes has been associated with postoperative complications, such as increased risk of tissue infection and impaired tissue repair caused by destabilization of hypoxia-inducible factor-1α (HIF-1α). Consequently, it is imperative to fabricate anti-bacterial and pro-regenerative small-diameter vascular grafts for treating cardiovascular disease in diabetic patients. Herein, we developed electrospun cobalt ion (Co2+)-loaded poly (ε-caprolactone) (PCL) microfiber vascular grafts (PCL-Co grafts). The released Co2+ significantly increased the stabilization of HIF-1α in high-glucose (HG)-treated HUVECs (HG-HUVECs) and macrophages (HG-macrophages). This resulted in enhanced cell migration, nitric oxide production, and secretion of bioactive factors by HG-HUVECs, and polarization of HG-macrophages toward M2 phenotypes in vitro. The Co2+ also conferred anti-bacterial properties to the grafts, while not perturbing the inherent anti-bacterial activities of HG-macrophages. Following abdominal artery implantation into type 2 diabetes mellitus (T2DM) rats, PCL-Co grafts were evaluated for performance in infection (grafts pre-contaminated with Staphylococcus aureus) and prophylaxes models (grafts alone). PCL-Co grafts prevented the incidence of subsequent infection in prophylaxes model and effectively inhibited the bacterial growth in the infection model. PCL-Co grafts also significantly enhanced cellularization, vascularization, endothelialization, contractile SMC regeneration and macrophages polarization in both models. Collectively, PCL-Co grafts exhibited the potential to combat infection and improve tissue regeneration under diabetes conditions.

Machine Learning System for Predicting Cardiovascular Disorders in Diabetic Patients

Introduction. Patients with diabetes are exposed to various cardiovascular risk factors, which lead to an increased risk of cardiac complications. Therefore, the development of a diagnostic system for diabetes and cardiovascular disease (CVD) is a relevant research task. In addition, the identification of the most significant indicators of both diseases may help physicians improve treatment, speed the diagnosis, and decrease its computational costs.Aim. To classify subjects with different diabetes types, predict the risk of cardiovascular diseases in diabetic patients using machine learning methods by finding the correlational indicators.Materials and methods. The NHANES database was used following preprocessing and balancing its data. Machine learning methods were used to classify diabetes based on physical examination data and laboratory data. Feature selection methods were used to derive the most significant indicators for predicting CVD risk in diabetic patients. Performance optimization of the developed classification and prediction models was carried out based on different evaluation metrics.Results. The developed model (Random Forest) achieved the accuracy of 93.1 % (based on laboratory data) and 88 % (based on pysicical examination plus laboratory data). The top five most common predictors in diabetes and prediabetes were found to be glycohemoglobin, basophil count, triglyceride level, waist size, and body mass index (BMI). These results seem logical, since glycohemoglobin is commonly used to check the amount of glucose (sugar) bound to the hemoglobin in the red blood cells. For CVD patients, the most common predictors inlcude eosinophil count (indicative of blood diseases), gamma-glutamyl transferase (GGT), glycohemoglobin, overall oral health, and hand stiffness.Conclusion. Balancing the dataset and deleting NaN values improved the performance of the developed models. The RFC and XGBoost models achieved higher accuracy using gradient descending order to minimize the loss function. The final prediction is made using a weighted majority vote of all the decisions. The result was an automated system for predicting CVD risk in diabetic patients.

Apelin and its ratio to lipid factors are associated with cardiovascular diseases: A systematic review and meta-analysis.

BackgroundThe present systematic review and meta-analysis aimed to ascertain if the circulating levels of apelin, as an important regulator of the cardiovascular homeostasis, differ in patients with cardiovascular diseases (CVDs) and controls.MethodsA comprehensive search was performed in electronic databases including PubMed, Scopus, EMBASE, and Web of Science to identify the studies addressing apelin in CVD up to April 5, 2021. Due to the presence of different units to measure the circulating levels of apelin across the included studies, they expressed the standardized mean difference (SMD) and their 95% confidence interval (CI) as summary effect size. A random-effects model comprising DerSimonian and Laird method was used to pool SMDs.ResultsTwenty-four articles (30 studies) comprised of 1793 cases and 1416 controls were included. Pooled results obtained through random-effects model indicated that apelin concentrations in the cases’ blood samples were significantly lower than those of the control groups (SMD = -0.72, 95% CI: -1.25, -0.18, P = 0.009; I2 = 97.3%, P<0.001). New combined biomarkers showed a significant decrease in SMD of apelin/high-density lipoprotein cholesterol (apelin/HDL-C) ratio [-5.17; 95% CI, -8.72, -1.63, P = 0.000; I2 = 99.0%], apelin/low-density lipoprotein cholesterol (apelin/LDL-C) ratio [-4.31; 95% CI, -6.08, -2.55, P = 0.000; I2 = 98.0%] and apelin/total cholesterol (apelin/TC) ratio [-17.30; 95% CI, -22.85, -11.76, P = 0.000; I2 = 99.1%]. However, no significant differences were found in the SMD of apelin/triacylglycerol (apelin/TG) ratio in cases with CVDs compared to the control group [-2.96; 95% CI, -7.41, 1.49, P = 0.000; I2 = 99.2%].ConclusionThe association of apelin with CVDs is different based on the region and disease subtypes. These findings account for the possible usefulness of apelin as an additional biomarker in the diagnosis of CVD in diabetic patients and in the diagnosis of patients with CAD. Moreover, apelin/HDL-c, apelin/LDL-c, and apelin/TC ratios could be offered as diagnostic markers for CVD.

Review on epidemiology, biochemical characteristics, treatment suggestions, and continued studies on diabetes and cardiovascular disease

Diabetes mellitus (DM) has become one of the very frequent and costliest chronic disease. Diabetes mellitus (DM) and cardiovascular disease (CVD), which is also the largest risk factor for mortality in diabetic individuals, have just a close relationship. Obesity, hypertension, and dyslipidemia all seem to be significant CV significant risk factor in people with diabetes, bringing them at greater risk for heart attacks and strokes. Also, several research have shown physiological processes linked to DM that enhance the risk of CVD in diabetic patients within their own. As a result, minimizing CV risk factors in diabetic individuals is essential to minimize the disease's long-term CV problems. This article investigates probable origins of disease progression, current treatment recommendations, and future studies prospects while discussing the relationship between diabetes and CVD.

Heart Rate Fragmentation in Rats with Streptozotocin‐induced Diabetes

Heart rate fragmentation (HRF) is a recently proposed approach to evaluate sino‐atrial instability characterized by the presence of numerous inflections points in a series of successive values of RR intervals (RRi). HRF has been shown to be increased in aging and coronary diseases and seems to be linked to the risk of life‐threatening cardiovascular events and death. We recently demonstrated that cardiac autonomic modulation in involved in HRF in rats. Moreover, HRF is increased in a rat model of heart failure. Diabetes mellitus (DM) markedly alters HRV indices and might affect HRF as well. Therefore, we hypothesize that HRF is altered in streptozotocin‐ (STZ) induced diabetic rats. Male Sprague‐Dawley rats were injected with STZ (50 mg/kg), or vehicle (citrate buffer) into penile vein. Development of diabetes was confirmed 72h after STZ by the presence of hyperglycemia (&gt; 350 mg/dL). Electrocardiographic (ECG) recordings were performed in conscious rats, 7 days (acute) or 4 weeks (chronic) after STZ or vehicle administration. Series of RR intervals were generated and processed by the customized software PyBios as follows: RRi values were symbolized as “‐1”, “0,” or “1” when the differences between successive RRi were negative, null, or positive, respectively. Transitions between symbols “‐1” and “1” were labeled as “hard” (H) inflection points, while those between “‐1” (or “1”) and “0” were labeled as “soft” (S) inflection points. The percentage of inflection points (PIP) was quantified, as well as sequences of 4 consecutive symbols, so‐called “words”, computing the occurrence of words with zero (W0), one (W1), two (W2), or three (W3) inflections points. The occurrence of words with only hard (WH) or only soft transitions (WS) were also quantified. Both groups of diabetic rats were bradycardic as compared to their control counterparts. Data of HRF are shown in the table below. Occurrences (%, mean ± SEM) of PIP, words with 0 to 3 inflection points, and words with only soft or hard transitions of symbols. As hypothesized, diabetes affects HRF, increasing fragmented indices (PIP, W3 and WH) and decreasing fluent ones (W1 and WS). Moreover, our results showed that changes in HRF due to diabetes in this model is time‐dependent. In addition to traditional methods of analyzing heart rate variability, HRF is a new method that can contribute to the diagnosis and prognosis of cardiovascular diseases in diabetic patients. Further studies are necessary to understand better the mechanisms involved in our findings.

Clinical Observation of SGLT2 Inhibitor Therapy for Cardiac Arrhythmia and Related Cardiovascular Disease in Diabetic Patients with Controlled Hypertension.

Sodium-glucose transporter 2 (SGLT2) inhibitors are new glucose-lowering agents that have been proven to be beneficial for patients with cardiovascular diseases, heart failure, and sudden cardiac death. However, the possible protective effects of cardiac arrhythmia have not yet been clarified in clinical practice. In this study, we attempted to demonstrate the effects of SGLT2 inhibitors on cardiac arrhythmia by medical records from a single center. This retrospective study included patients diagnosed with type 2 diabetes mellitus (DM) and controlled hypertension who prescribed the indicated glucose-lowering agents based on medical records from 2016 to 2019 from Kaohsiung Medical University Hospital. These patients were divided into two groups. Group one patients were defined as patients with SGLT2 inhibitor therapy, and group two patients were defined as patients without SGLT2 inhibitor therapy. Baseline characteristics were collected from medical records. Univariate, multivariate, and match-paired statistical analyses were performed for the study endpoints. The primary study outcome was the incidence of cardiac arrhythmias, including atrial and ventricular arrhythmias, after SGLT2 inhibitor therapy. The secondary study outcomes were the incidence of stroke, heart failure, and myocardial infarction after SGLT2 inhibitor therapy. From the initial 62,704 medical records, a total of 9609 people who met our experimental design criteria were included. The mean follow-up period was 51.50 ± 4.23 months. Group one included 3203 patients who received SGLT2 inhibitors for treatment, and group two included 6406 patients who received non-SGLT2 inhibitors for treatment. Multivariate analysis showed that group one patients had significantly lower incidences of total cardiac arrhythmia (hazard ratio (HR): 0.58, 95% confidence interval (CI): 0.38–0.89, p = 0.013) and atrial fibrillation (HR: 0.56, 95% CI: 0.35–0.88, p = 0.013) than those of group two patients. The secondary outcome analysis showed that group one patients also had a significantly lower risk of stroke (HR: 0.48, 95% CI: 0.33–0.7; p < 0.001), heart failure (HR: 0.54, 95% CI: 0.41–0.7, p < 0.001), and myocardial infarction (HR: 0.47, 95% CI: 0.31–0.72, p < 0.001). A time-to-event analysis showed that treatment of type 2 DM patients with SGLT2 inhibitors could reduce the probability of total cardiac arrhythmia and related cardiovascular disease, such as atrial fibrillation, stroke, heart failure, or myocardial infarction, by 0.5%~0.8%. This databank analysis showed that SGLT2 inhibitor therapy reduced the incidence of total cardiac arrhythmia and atrial fibrillation in type 2 DM patients and decreased the incidence of related cardiovascular diseases, such as stroke, heart failure, and myocardial infarction. However, additional investigations are needed to confirm this hypothesis.

A study of electrocardiographic and 2D echocardiographic changes in type 2 diabetes mellitus patients without cardiovascular symptoms.

Context:Cardiovascular diseases in diabetic patients are mostly asymptomatic due to autonomic neuropathy. Many patients with left ventricular dysfunction remain undiagnosed and untreated until advance disease causes disability. This delay could be avoided if screening techniques are used to identify left ventricular dysfunction in its preclinical phase.Aims:This study was undertaken to find out the incidence of electrocardiographic (ECG) and 2D echocardiographic (2 D Echo) abnormalities in diabetic patients without cardiovascular symptoms. The correlation of control of diabetes with these abnormalities was also studied.Settings and Design:A hospital-based, cross-sectional observational study.Methods and Material:Type 2 diabetic patients (outpatient and indoor) without cardiovascular symptoms like palpitations, chest pain, syncope, and breathlessness were included in the study. Their ECG and 2D Echo findings were noted and correlated with their blood sugar levels.Statistical Analysis Used:Chi-square test.Results:Type 2 diabetic patients without cardiovascular symptoms had significant abnormal findings on ECG and 2D Echo. Control of postprandial blood sugar level was of primary importance to prevent cardiovascular abnormalities.Conclusions:Type 2 diabetics without cardiovascular symptoms must be screened for cardiovascular abnormalities so that early interventions can be done to prevent further progression to symptomatic cardiovascular abnormalities. There is a significant number of people having normal ECG but abnormal 2D Echo and vice versa, so not only ECG but also 2D Echo should be done to predict cardiovascular risk in type 2 diabetic patients without cardiovascular symptoms.