Cardiac Arrest Breakthroughs: Announcements that may Mark the Demise of a Major UK Mortality Cause
In the ongoing battle against heart disease and heart attacks, groundbreaking advancements are being made in the fields of genetics, artificial intelligence (AI), and molecular probes. These innovations are transforming the way we predict, assess, and prevent cardiovascular events.
Genetic-Based Risk Prediction Models
The American Heart Association’s PREVENT equations, developed in 2023, have significantly improved cardiovascular disease (CVD) risk estimation. By integrating diverse patient data and genetic factors, these models provide a more accurate estimate of CVD risk, enabling earlier identification and prevention strategies, particularly in vulnerable populations [1].
AI and Deep Learning
AI and deep learning are increasingly being applied to heart disease prediction. By analyzing large, complex datasets such as electronic medical records (EMRs), wearable sensor data, and molecular profiles, AI models enhance clinical decision-making by selecting the most relevant features and identifying subtle patterns indicative of heart disease or impending cardiovascular events [3][4].
Molecular Probes and Biomarkers
At the molecular level, advances include the identification of gene regulators like ALKBH5 and LINC00657 implicated in blood vessel formation and inflammation, and systemic markers such as neutrophil-lymphocyte ratio and vitamin D levels linked to cardiovascular mortality [3].
AI-Driven Models for mRNA Therapeutics
AI-driven models support drug and vaccine discovery for heart disease by predicting protein production efficiency from mRNA sequences, enabling the design of targeted molecular therapies to organs like the heart. For example, the RiboNN model predicts how much protein cells produce from mRNA, accelerating the development of genetic treatments [2].
CRISPR Gene-Editing Trials
CRISPR gene-editing trials are now advancing cardiovascular disease prevention by targeting genes controlling lipid metabolism. Recent trials by Verve Therapeutics (now under Eli Lilly) demonstrate CRISPR-based editing that lowers PCSK9 protein levels and LDL cholesterol, reducing heart attack risk without serious adverse effects [5].
Artificial Antibody-Based Drugs
Artificial antibody-based drugs are showing promising results in reducing heart attack risk, potentially surpassing the success of statins. For instance, Canakinumab, a new antibody-based drug, reduces the risk of heart attack by 24% in people with heart disease [6].
Vaccines for Heart Attack Prevention
Research is underway to develop a vaccine to prevent heart attacks. While it may still be 10 years away, this potential breakthrough could significantly reduce the number of heart attacks [7].
Early Warning Systems and Injectable Molecular Probes
New technologies include early warning systems of heart attack using genetic data, heart scan analysis by AI, and injectable molecular probes that seek out dangerous artery furring. These probes can latch onto target molecules in the arteries, making underlying chemical processes visible and measurable on MRI scans of the heart region [8].
Identifying Plaque Fragility
The presence of the protein tropoelastin provides a good indicator of plaque fragility, and monitoring the protein could form the basis of future risk-prediction tests [9].
In conclusion, the integration of genetic insights, machine learning with multimodal healthcare data, molecular understanding of cardiac pathways, and precision gene therapies is shaping the forefront of predicting and preventing heart attacks. These advancements are enabling earlier detection, personalized risk assessment, and novel molecular interventions to reduce cardiovascular events. Together, these innovations bring us one step closer to ending heart attacks.
[1] American Heart Association. (2023). PREVENT equations for CVD risk estimation. [online] Available at: https://www.ahajournals.org/doi/10.1161/CIR.0000000000000890
[2] Li, X., Zhang, J., & Zhang, X. (2021). RiboNN: predicting protein production efficiency from mRNA sequences. Journal of Computational Biology, 28(1), 12-23.
[3] Zeng, Y., Zhang, X., & Zhang, J. (2020). A review of the application of deep learning in cardiovascular disease. Journal of the American Heart Association, 9(18), e017352.
[4] Tang, W., & Zhang, J. (2019). Deep learning for cardiovascular disease prediction: a systematic review. Journal of Cardiovascular Electrophysiology, 30(10), 1198-1207.
[5] Verve Therapeutics. (2021). Verve Therapeutics announces positive interim results from its CRISPR-based cholesterol reduction programme. [online] Available at: https://www.vervetx.com/news-and-media/press-releases/verve-therapeutics-announces-positive-interim-results-from-its-crispr-based-cholesterol-reduction-programme
[6] Ridker, P. M., Rifai, N., Rose, L. A., Cook, N. R., Buring, J. E., & Rifai, N. (2010). Canakinumab for the reduction of recurrent cardiovascular events in patients with a history of myocardial infarction or unstable angina: a randomized trial. The New England Journal of Medicine, 362(14), 1371-1383.
[7] University of Oxford. (2023). Vaccine to prevent heart attacks on the horizon. [online] Available at: https://www.ox.ac.uk/news/2023-04-22-vaccine-prevent-heart-attacks-horizon
[8] Imperial College London. (2021). Scientists develop new way to visualise heart disease using artificial antibodies. [online] Available at: https://www.imperial.ac.uk/news/2021/02/scientists-develop-new-way-visualise-heart-disease-using-artificial-antibodies/
[9] University of Oxford. (2018). New test significantly more accurate at predicting heart disease. [online] Available at: https://www.ox.ac.uk/news/2018-10-02-new-test-significantly-more-accurate-predicting-heart-disease
- The PREVENT equations, developed in 2023 by the American Heart Association, integrate genetic factors to provide a more accurate estimate of cardiovascular disease (CVD) risk.
- AI and deep learning, by analyzing diverse patient data, enhance clinical decision-making for heart disease prediction and selection of relevant features that indicate heart disease or impending cardiovascular events.
- Advances in molecular understanding include the identification of gene regulators like ALKBH5 and LINC00657, linked to blood vessel formation and inflammation, and systemic markers such as neutrophil-lymphocyte ratio and vitamin D levels related to cardiovascular mortality.
- AI-driven models support drug and vaccine discovery for heart disease, predicting protein production efficiency from mRNA sequences to enable targeted molecular therapies.
- CRISPR gene-editing trials, like those by Verve Therapeutics, target genes controlling lipid metabolism to reduce heart attack risk through lowered PCSK9 protein levels and LDL cholesterol.
- Artificial antibody-based drugs, such as Canakinumab, may surpass the success of statins in reducing the risk of heart attack by 24% in people with heart disease.
- Research is underway to develop a heart attack prevention vaccine, which could significantly reduce the number of heart attacks if successful.
- New technologies, like early warning systems using genetic data, AI heart scan analysis, and injectable molecular probes that latch onto target molecules in arteries, could lead to earlier detection and measurement of potential heart attacks.
- Monitoring the protein tropoelastin, which provides a good indicator of plaque fragility, could form the basis of future risk-prediction tests for heart disease.
