The Potential Impact of microRNA-Related Functional Polymorphisms in the Pathogenesis of Coronary Heart Disease - Cytology and Genetics

Abstract MicroRNAs (miRNAs) are widely referred to as gene regulators for different diseases. Single nucleotide polymorphism (SNP) in miRNA genes affects miRNA transcription, maturation, target specificity, and interaction, contributing to coronary heart disease (CHD). This case-control study aimed to investigate the association of five miRNA SNPs—rs2292832, rs3746444, rs11614913, rs1044165, and rs767649—with CHD risk in the Pakistani population using the TaqMan assay. Among these, rs3746444 showed a significant association with CHD under co-dominant, dominant, heterozygous, and additive inheritance models. Similarly, rs11614913 was linked to CHD under co-dominant, dominant, recessive, and additive models. The SNP rs767649 was associated with CHD across co-dominant, dominant, recessive, heterozygous, and additive models. A strong association between rs1044165 and CHD was observed under the heterozygous model. Variants in MIR499A, MIR196A2, MIR155, and MIR223 emerged as significant genetic risk factors for CHD, whereas MIR149 did not show a meaningful association in this cohort. These findings suggest a potential role of miRNA polymorphisms in CHD pathogenesis; however, further studies with larger sample sizes are required to validate these associations.

Hemodynamic Evaluation of an Enhanced External Counterpulsation Strategy for Coronary Heart Disease with a Geometric Multiscale Model

ChatGPT provides inconsistent risk-stratification of patients with atraumatic chest pain

Background ChatGPT-4 is a large language model with promising healthcare applications. However, its ability to analyze complex clinical data and provide consistent results is poorly known. Compared to validated tools, this study evaluated ChatGPT-4’s risk stratification of simulated patients with acute nontraumatic chest pain. Methods Three datasets of simulated case studies were created: one based on the TIMI score variables, another on HEART score variables, and a third comprising 44 randomized variables related to non-traumatic chest pain presentations. ChatGPT-4 independently scored each dataset five times. Its risk scores were compared to calculated TIMI and HEART scores. A model trained on 44 clinical variables was evaluated for consistency. Results ChatGPT-4 showed a high correlation with TIMI and HEART scores (r = 0.898 and 0.928, respectively), but the distribution of individual risk assessments was broad. ChatGPT-4 gave a different risk 45–48% of the time for a fixed TIMI or HEART score. On the 44-variable model, a majority of the five ChatGPT-4 models agreed on a diagnosis category only 56% of the time, and risk scores were poorly correlated (r = 0.605). Conclusion While ChatGPT-4 correlates closely with established risk stratification tools regarding mean scores, its inconsistency when presented with identical patient data on separate occasions raises concerns about its reliability. The findings suggest that while large language models like ChatGPT-4 hold promise for healthcare applications, further refinement and customization are necessary, particularly in the clinical risk assessment of atraumatic chest pain patients.

Genetic Factors for Coronary Heart Disease and Their Mechanisms: A Meta-Analysis and Comprehensive Review of Common Variants from Genome-Wide Association Studies

Genome-wide association studies (GWAS) have discovered 163 loci related to coronary heart disease (CHD). Most GWAS have emphasized pathways related to single-nucleotide polymorphisms (SNPs) that reached genome-wide significance in their reports, while identification of CHD pathways based on the combination of all published GWAS involving various ethnicities has yet to be performed. We conducted a systematic search for articles with comprehensive GWAS data in the GWAS Catalog and PubMed, followed by a meta-analysis of the top recurring SNPs from ≥2 different articles using random or fixed-effect models according to Cochran Q and I2 statistics, and pathway enrichment analysis. Meta-analyses showed significance for 265 of 309 recurring SNPs. Enrichment analysis returned 107 significant pathways, including lipoprotein and lipid metabolisms (rs7412, rs6511720, rs11591147, rs1412444, rs11172113, rs11057830, rs4299376), atherogenesis (rs7500448, rs6504218, rs3918226, rs7623687), shared cardiovascular pathways (rs72689147, rs1800449, rs7568458), diabetes-related pathways (rs200787930, rs12146487, rs6129767), hepatitis C virus infection/hepatocellular carcinoma (rs73045269/rs8108632, rs56062135, rs188378669, rs4845625, rs11838776), and miR-29b-3p pathways (rs116843064, rs11617955, rs146092501, rs11838776, rs73045269/rs8108632). In this meta-analysis, the identification of various genetic factors and their associated pathways associated with CHD denotes the complexity of the disease. This provides an opportunity for the future development of novel CHD genetic risk scores relevant to personalized and precision medicine.