Vitamin D gene polymorphisms and risk of acute cardiovascular events

Random Forests Highlight the Combined Effect of Environmental Heavy Metals Exposure and Genetic Damages for Cardiovascular Diseases

Heavy metals are a dangerous source of pollution due to their toxicity, permanence in the environment and chemical nature. It is well known that long-term exposure to heavy metals is related to several chronic degenerative diseases (cardiovascular diseases, neoplasms, neurodegenerative syndromes, etc.). In this work, we propose a machine learning framework to evaluate the severity of cardiovascular diseases (CVD) from Human scalp hair analysis (HSHA) tests and genetic analysis and identify a small group of these clinical features mostly associated with the CVD risk. Using a private dataset provided by the DD Clinic foundation in Caserta, Italy, we cross-validated the classification performance of a Random Forests model with 90 subjects affected by CVD. The proposed model reached an AUC of 0.78 ± 0.01 on a three class classification problem. The robustness of the predictions was assessed by comparison with different cross-validation schemes and two state-of-the-art classifiers, such as Artificial Neural Network and General Linear Model. Thus, is the first work that studies, through a machine learning approach, the tight link between CVD severity, heavy metal concentrations and SNPs. Then, the selected features appear highly correlated with the CVD phenotype, and they could represent targets for future CVD therapies.

Vitamin D levels and vitamin D receptor genetic variants in Egyptian cardiovascular disease patients with and without diabetes

Background

25-Hydroxyvitamin D (Vit.D) levels associated with cardiovascular disease (CVD) may vary according to genetic variants in the vitamin D receptor (VDR) gene. However, the existing results are not conclusive in the Egyptian population, where diabetes mellitus is a common CVD risk factor. The purpose of the study was to evaluate the role of VDR polymorphism in Egyptian patients with CVD by studying the association of the rs2228570 (FokI) and rs1544410 (BsmI) single nucleotide polymorphisms (SNPs) of the VDR gene and serum levels of Vit.D with several CVD risk factors in patients with and without diabetes mellitus. We studied the genotypes for rs2228570 (FokI) and rs1544410 (BsmI) SNPs of the VDR gene in 382 Egyptian patients (120 CVD patients with diabetes, 124 CVD patients without diabetes, 69 diabetic patients without CVD and 69 healthy individuals). We also determined the serum levels of Vit.D, insulin, lipids, fasting blood glucose (FBG), and the body mass index (BMI).

Results

The distribution of genotypes and allelic frequencies of the rs2228570 (FokI) and rs1544410 (BsmI) SNPs of the VDR gene was significant in CVD patients (p < 0.001). The level of Vit.D was significantly lower in patients with CVD and diabetes compared to those without diabetes (p < 0.001). Moreover, there was a significant association between Vit.D level and the selected SNPs with serum lipids, BMI, FBG, and insulin levels in CVD patients with or without diabetes.

Conclusion

The level of Vit.D and the distribution of VDR polymorphisms are associated with risk of CVD in Egyptian patients with or without diabetes. These results suggest that VDR polymorphisms may be potential diagnostic biomarkers for CVD susceptibility.

Novel molecular markers of cardiovascular disease risk in type 2 diabetes mellitus

Diabetes represents the leading risk factor for the development of cardiovascular disease (CVD). Chronic hyperglycemia and/or acute post-prandial changes in blood glucose determine an increase in reactive oxygen species (ROS), which play a fundamental role in endothelial dysfunction and in the nuclear transport of pro-atherogenic transcription factors that activate the "inflammasome". In addition, the glycemic alteration favors the formation and stabilization of atherosclerotic plaque through the mechanism of non-enzymatic glycation of different molecules, with the establishment of the so-called "advanced glycosylation end products" (AGE). Laboratory information provided by the level of biomarkers could make a quantitative and qualitative contribution to the clinical process of screening, prediction, prevention, diagnosis, prognosis and monitoring of cardiovascular (CV) risk linked to diabetes. This review describes the importance of specific biomarkers, with particular focus on novel ones, for stratifying and management of diabetes CV risk.