Ventricular arrhythmias in mouse models of diabetic kidney disease

Chest CT radiomics is feasible in evaluating bone changes in chronic kidney diseases

BACKGROUND

Non-invasive imaging methods are still lacking for evaluating bone changes in chronic kidney diseases (CKD).

PURPOSE

To investigate the feasibility of chest CT radiomics in evaluating bone changes caused by CKD.

MATERIAL AND METHODS

In total, 75 patients with stage 1 CKD (CKD1) and 75 with stage 5 CKD (CKD5) were assessed using the chest CT radiomics method. Radiomics features of bone were obtained using 3D Slicer software and were then compared between CKD1 and CKD5 cases. The methods of maximum correlation minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were used to establish a prediction model to determine CKD. The receiver operating characteristic (ROC) curve was used to determine the performance of the model.

RESULTS

Cases of CKD1 and CKD5 differed in 40 radiomics features (P <0.05). Using the mRMR and LASSO methods, five features were finally selected to establish a predication model. The area under the receiver operating characteristic curve of the model in the determination of CKD1 and CKD5 was 0.903 and 0.854, respectively, for the training and validation cohorts.

CONCLUSION

Chest CT radiomics is feasible in evaluating bone changes caused by CKD.

Rodent models to study type 1 and type 2 diabetes induced human diabetic nephropathy

INTRODUCTION

Diabetic nephropathy (DN), an outcome of prolonged diabetes, has affected millions of people worldwide and every year the incidence and prevalence increase substantially. The symptoms may start with mild manifestations of the disease such as increased albuminuria, serum creatinine levels, thickening of glomerular basement membrane, expansion of mesangial matrix to severe pathological symptoms such as glomerular lesions and tubulointerstitial fibrosis which may further proceed to cardiovascular dysfunction or end-stage renal disease.

PERSPECTIVE

Numerous therapeutic interventions are being explored for the management of DN, however, these interventions do not completely halt the progression of this disease and hence animal models are being explored to identify critical genetic and molecular parameters which could help in tackling the disease. Rodent models which mostly include mice and rats are commonly used experimental animals which provide a wide range of advantages in understanding the onset and progression of disease in humans and also their response to a wide range of interventions helps in the development of effective therapeutics. Rodent models of type 1 and type 2 diabetes induced DN have been developed utilizing different platforms and interventions during the last few decades some of which mimic various stages of diabetes ranging from early to later stages. However, a rodent model which replicates all the features of human DN is still lacking. This review tries to evaluate the rodent models that are currently available and understand their features and limitations which may help in further development of more robust models of human DN.

CONCLUSION

Using these rodent models can help to understand different aspects of human DN although further research is required to develop more robust models utilizing diverse genetic platforms which may, in turn, assist in developing effective interventions to target the disease at different levels.

Cellular interplay between cardiomyocytes and non-myocytes in diabetic cardiomyopathy

Patients with Type 2 diabetes mellitus (T2DM) frequently exhibit a distinctive cardiac phenotype known as diabetic cardiomyopathy. Cardiac complications associated with T2DM include cardiac inflammation, hypertrophy, fibrosis and diastolic dysfunction in the early stages of the disease, which can progress to systolic dysfunction and heart failure. Effective therapeutic options for diabetic cardiomyopathy are limited and often have conflicting results. The lack of effective treatments for diabetic cardiomyopathy is due in part, to our poor understanding of the disease development and progression, as well as a lack of robust and valid preclinical human models that can accurately recapitulate the pathophysiology of the human heart. In addition to cardiomyocytes, the heart contains a heterogeneous population of non-myocytes including fibroblasts, vascular cells, autonomic neurons and immune cells. These cardiac non-myocytes play important roles in cardiac homeostasis and disease, yet the effect of hyperglycaemia and hyperlipidaemia on these cell types are often overlooked in preclinical models of diabetic cardiomyopathy. The advent of human induced pluripotent stem cells provides a new paradigm in which to model diabetic cardiomyopathy as they can be differentiated into all cell types in the human heart. This review will discuss the roles of cardiac non-myocytes and their dynamic intercellular interactions in the pathogenesis of diabetic cardiomyopathy. We will also discuss the use of sodium-glucose cotransporter 2 inhibitors as a therapy for diabetic cardiomyopathy and their known impacts on non-myocytes. These developments will no doubt facilitate the discovery of novel treatment targets for preventing the onset and progression of diabetic cardiomyopathy.

Prediction of sudden cardiac arrest in the general population: Review of traditional and emerging risk factors

Sudden cardiac death (SCD) is the most common and devastating outcome of sudden cardiac arrest (SCA), defined as an abrupt and unexpected cessation of cardiovascular function leading to circulatory collapse. The incidence of SCD is relatively infrequent for individuals in the general population, in the range of 0.03-0.10% per year. Yet, the absolute number of cases around the world is high due to the sheer size of the population at risk, making SCA/SCD a major global health issue. Based on conservative estimates, there are at least 2 million cases of SCA occurring worldwide on a yearly basis. As such, identification of risk factors associated with SCA in the general population is an important objective from a clinical and public health standpoint. This review will provide an in-depth discussion of established and emerging factors predictive of SCA/SCD in the general population beyond coronary artery disease and impaired left ventricular ejection fraction. Contemporary studies evaluating the association between age, sex, race, socioeconomic status and the emerging contribution of diabetes and obesity to SCD risk beyond their role as atherosclerotic risk factors will be reviewed. In addition, the role of biomarkers, particularly electrocardiographic ones, on SCA/SCD risk prediction in the general population will be discussed. Finally, the use of machine learning as a tool to facilitate SCA/SCD risk prediction will be examined.