Identification of potentially common loci between childhood obesity and coronary artery disease using pleiotropic approaches.

Childhood obesity remains one of the most important issues in global health, which is implicated in many chronic diseases. Converging evidence suggests that a higher body mass index during childhood (CBMI) is significantly associated with increased coronary artery disease (CAD) susceptibility in adulthood, which may partly arise from the shared genetic determination. Despite genome-wide association studies (GWASs) have successfully identified some loci associated with CBMI and CAD individually, the genetic overlap and common biological mechanism between them remains largely unexplored. Here, relying on the results from the two large-scale GWASs (n = 35,668 for CBMI and n = 547,261 for CAD), linkage disequilibrium score regression (LDSC) was used to estimate the genetic correlation of CBMI and CAD in the first step. Then, we applied different pleiotropy-informed methods including conditional false discovery rate ([Formula: see text]) and genetic analysis incorporating pleiotropy and annotation (GPA) to detect potentially common loci for childhood obesity and CAD. By integrating the genetic information from the existing GWASs summary statistics, we found a significant positive genetic correlation ([Formula: see text] = 0.127, p = 2E-4) and strong pleiotropic enrichment between CBMI and CAD (LRT = 79.352, p = 5.2E-19). Importantly, 28 loci were simultaneously discovered to be associated with CBMI, and 13 of them were identified as potentially pleiotropic loci by [Formula: see text] and GPA. Those corresponding pleiotropic genes were enriched in trait-associated gene ontology (GO) terms "amino sugar catabolic process", "regulation of fat cell differentiation" and "synaptic transmission". Overall, the findings of the pleiotropic loci will help to further elucidate the common molecular mechanisms underlying the association of childhood obesity and CAD, and provide a theoretical direction for early disease prevention and potential therapeutic targets.

PEGylated Gold Nanoparticle Toxicity in Cardiomyocytes: Assessment of Size, Concentration, and Time Dependency.

Gold Nanoparticles (GNPs) have shown promising capabilities for use in many in-vivo applications such as gene and drug delivery, photothermal ablation of tumors, and tracking in many imaging modalities. Yet GNPs have thus far had limited use in cardiovascular medicine. Polyethylene glycol functionalized (PEGylated) GNPs have been extensively studied in a wide array of in vitro and in vivo models with results showing no apparent toxicity, but to our knowledge an investigation has never been performed to determine direct cardiomyocyte toxicity. In this study, we assessed if PEGylated GNPs exhibited direct toxicity to a primary culture of neonatal rat cardiomyocytes in order to establish PEGylated GNPs for potential future use in cardiovascular medicine applications. We present novel results that demonstrate both a particle size and concentration dependent relationship on cell viability. Cell viability was found to be significantly enhanced for many concentrations and sizes as compared to the control and increased linearly as a function of particle diameter. Additionally, viability increased in a parabolically dependent manner as a function of decreasing particle concentration. These new results could advance understanding of nanoparticle-cell interactions and lead to the development of new applications involving the use of gold nanoparticles in cardiovascular medicine.

PCSK9 regulates pyroptosis via mtDNA damage in chronic myocardial ischemia.

Proprotein convertase subtilisin/Kexin type 9 (PCSK9) and pyroptosis both play important roles in myocardial infarction. This study was designed to test the hypothesis that PCSK9 regulates pyroptosis in cardiomyocytes during chronic myocardial ischemia. Primary cardiomyocytes were isolated from WT and PCSK9-/- mice. HL-1 cardiomyocytes were used to set up PCSK9-deficient (PCSK9-/-) and PCSK9-upregulated (PCSK9CRISPRa) cardiomyocyte cell line with CRISPR/Cas9 knockout or activation plasmid. Additional studies were performed with chronic myocardial ischemia in WT and PCSK9-/- mice. We observed that PCSK9 initiates mitochondrial DNA (mtDNA) damage, activates NLRP3 inflammasome signaling (NLRP3, ASC, Caspase-1, IL-1ß, and IL-18), and subsequently induces Caspase-1-dependent pyroptosis. There was an intense expression of PCSK9 and pyroptosis marker, GSDMD-NT, in the zone bordering the infarct area. PCSK9-/- significantly suppressed expression of NLRP3 inflammasome signaling, GSDMD-NT, and LDH release. Furthermore, serum levels of PCSK9, NLPR3 inflammasome signaling, and pyroptosis (GSDMD and LDH release) were significantly elevated in patients with chronic myocardial ischemia as compared to those in age-matched healthy subjects. Human hearts with recent infarcts also showed high expression of PCSK9 and GSDMD-NT in the border zone similar to that in the infarcted mouse heart. These observations provide compelling evidence for the role of PCSK9 in regulating Caspase-1-dependent pyroptosis via mtDNA damage and may qualify pro-inflammatory cytokines and pyroptosis as potential targets to treat PCSK9-related cardiovascular diseases.

Additional common loci associated with stroke and obesity identified using pleiotropic analytical approach.

Stroke is a complex disease with multiple etiologies. Numerous studies suggest an established association between obesity and stroke, which may partly arise from the shared genetic components between the two phenotypes. Despite genome-wide association studies (GWASs) have identified some loci associated with stroke and obesity individually, the estimated genetic variability explained by these loci is limited (especially for stroke) and the pleiotropic loci between them are largely unknown. In this study, we jointly applied the pleiotropy-informed conditional false discovery rate (cFDR) method and the genetic analysis incorporating pleiotropy and annotation (GPA) method on summary statistics of two large GWASs to detect the genetic overlap between stroke (n = 446,696) and obesity (n = 681,275). Stratified Q-Q and fold-enrichment plots showed strong pleiotropic enrichment between the two phenotypes. With cFDR < 0.05 and fdr.GPA < 0.2, we identified 24 (16 novel) stroke-associated SNPs and 12 (10 novel) of them to be potentially pleiotropic SNPs for both phenotypes. The corresponding genes were enriched in trait-associated gene ontology (GO) terms "brain development" and "negative regulation of transport". In conclusion, our study demonstrated the feasibility and effectivity of the two pleiotropic methods which successfully improved the genetic discovery by incorporating related GWAS datasets and validated the genetic intercommunity between stroke and obesity. The identification of pleiotropic loci may provide us any new insights into potential genetic and etiology mechanism between them for the further studies.

Development and validation of prediction models for hypertension risks in rural Chinese populations.

BACKGROUND: Various hypertension predictive models have been developed worldwide; however, there is no existing predictive model for hypertension among Chinese rural populations. METHODS: This is a 6-year population-based prospective cohort in rural areas of China. Data was collected in 2007-2008 (baseline survey) and 2013-2014 (follow-up survey) from 8319 participants ranging in age from 35 to 74 years old. Specified gender hypertension predictive models were established based on multivariate Cox regression, Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), and Classification and Regression Tree (CART) in the training set. External validation was conducted in the testing set. The estimated models were assessed by discrimination and calibration, respectively. RESULTS: During the follow-up period, 432 men and 604 women developed hypertension in the training set. Assessment for established models in men suggested men office-based model (M1) was better than others. C-index of M1 model in the testing set was 0.771 (95% confidence Interval (CI) = 0.750, 0.791), and calibration χ2 = 6.3057 (P = 0.7090). In women, women office-based model (W1) and ANN were better than the other models assessed. The C-indexes for the W1 model and the ANN model in the testing set were 0.765 (95% CI = 0.746, 0.783) and 0.756 (95% CI = 0.737, 0.775) and the calibrations χ2 were 6.7832 (P = 0.1478) and 4.7447 (P = 0.3145), respectively. CONCLUSIONS: Not all machine-learning models performed better than the traditional Cox regression models. The W1 and ANN models for women and M1 model for men have better predictive performance which could potentially be recommended for predicting hypertension risk among rural populations.