Panomics: New Databases for Advancing Cardiology

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Integrating Coronary Atherosclerosis Burden and Progression with Coronary Artery Disease Risk Factors to Guide Therapeutic Decision Making

IMPORTANCE

Although atherosclerosis represents the primary driver of coronary artery disease, evaluation and treatment approaches have historically relied upon indirect markers of atherosclerosis that include surrogates (cholesterol), signs (angina), and sequelae (ischemia) of atherosclerosis. Direct quantification and characterization of atherosclerosis may encourage a precision heart care paradigm that improves diagnosis, risk stratification, therapeutic decision-making, and longitudinal disease tracking in a personalized fashion.

OBSERVATIONS

The American College of Cardiology Innovations in Prevention Working Group introduce the Atherosclerosis Treatment Algorithms that personalize medical interventions based upon atherosclerosis findings from coronary computed tomography angiography (CTA) and cardiovascular risk factors. Through integration of coronary CTA-based atherosclerosis evaluation, clinical practice guidelines, and contemporary randomized controlled trial evidence, the Atherosclerosis Treatment Algorithms leverage patient-specific atherosclerosis burden and progression as primary targets for therapeutic intervention. After defining stages of atherosclerosis severity by coronary CTA, Atherosclerosis Treatment Algorithms are described for worsening stages of atherosclerosis for patients with lipid disorders, diabetes, hypertension, obesity, and tobacco use. The authors anticipate a rapid pace of research in the field, and conclude by providing perspectives on future needs that may improve efforts to optimize precision prevention of coronary artery disease. Importantly, the Atherosclerosis Treatment Algorithms are not endorsed by the American College of Cardiology, and should not be interpreted as a statement of American College of Cardiology policy.

CONCLUSIONS AND RELEVANCE

We describe a precision heart care approach that emphasizes atherosclerosis as the primary disease target for evaluation and treatment. To our knowledge, this is the first proposal to use coronary atherosclerosis burden and progression to personalize therapy selection and therapy changes, respectively.

DISCLOSURE

The American College of Cardiology Foundation has made an investment in Cleerly, Inc., makers of a software solution that utilizes coronary CT angiography findings to evaluate coronary artery disease.

KATP channel dependent heart multiome atlas

Plasmalemmal ATP sensitive potassium (K_ATP) channels are recognized metabolic sensors, yet their cellular reach is less well understood. Here, transgenic Kir6.2 null hearts devoid of the K_ATP channel pore underwent multiomics surveillance and systems interrogation versus wildtype counterparts. Despite maintained organ performance, the knockout proteome deviated beyond a discrete loss of constitutive K_ATP channel subunits. Multidimensional nano-flow liquid chromatography tandem mass spectrometry resolved 111 differentially expressed proteins and their expanded network neighborhood, dominated by metabolic process engagement. Independent multimodal chemometric gas and liquid chromatography mass spectrometry unveiled differential expression of over one quarter of measured metabolites discriminating the Kir6.2 deficient heart metabolome. Supervised class analogy ranking and unsupervised enrichment analysis prioritized nicotinamide adenine dinucleotide (NAD⁺), affirmed by extensive overrepresentation of NAD⁺ associated circuitry. The remodeled metabolome and proteome revealed functional convergence and an integrated signature of disease susceptibility. Deciphered cardiac patterns were traceable in the corresponding plasma metabolome, with tissue concordant plasma changes offering surrogate metabolite markers of myocardial latent vulnerability. Thus, Kir6.2 deficit precipitates multiome reorganization, mapping a comprehensive atlas of the K_ATP channel dependent landscape.

Big Data in Cardiology: State-of-Art and Future Prospects

Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.