Researchers at Rutgers University are using artificial intelligence (AI) to study the genes in patients’ DNA to predict their risk of developing cardiovascular disease.
This new method could help to find people at risk of diseases such as arterial fibrillation and heart failure, which are major contributors to heart disease-related deaths.
According to the World Health Organization, heart disease is the leading cause of death globally, and it is estimated that more than 75% of premature heart disease is preventable.
Despite significant advancements in heart disease diagnostics, prevention, and treatment, around half of those affected die within five years of receiving a diagnosis, often due to genetic and environmental factors.
The researchers conducted a study to investigate the genes that are associated with the most common cardiovascular diseases, such as atrial fibrillation and heart failure.
They analyzed the genes of healthy individuals and those diagnosed with heart disease, and used AI and machine-learning models to identify a group of genes that were strongly associated with heart disease.
The researchers also found significant differences in cardiovascular disease risks among race, gender, and age factors.
Age and gender factors correlated with heart failure, while age and race factors correlated with atrial fibrillation.
For instance, the older the patient, the more likely they were to have a cardiovascular disease.
The team says with the successful execution of our model, we predicted the association of highly significant cardiovascular disease genes tied to demographic variables like race, gender, and age.
This study shows the potential of using AI and machine learning to accelerate the identification of genes associated with cardiovascular disease.
It may help to improve diagnoses, treatments, and prevention strategies for individuals at risk of developing these diseases.
The researchers suggest that future research should analyze the full set of genes in patients with cardiovascular disease, which may reveal important biomarkers and risk factors associated with susceptibility to cardiovascular disease.
Atrial fibrillation (AFib) is a type of irregular heartbeat or arrhythmia, that affects the heart’s upper chambers, known as the atria.
In AFib, the electrical signals that control the rhythm and pace of the heart become irregular, causing the heart to beat too quickly, too slowly, or in an unpredictable pattern.
This can lead to symptoms such as palpitations, shortness of breath, fatigue, and chest pain, and can also increase the risk of complications such as stroke and heart failure.
AFib is a common condition, affecting millions of people worldwide, and can be managed with medication, lifestyle changes, and medical procedures.
Heart failure is a medical condition in which the heart is unable to pump enough blood to meet the body’s needs.
It occurs when the heart muscle becomes weakened or damaged, leading to a decrease in the heart’s ability to effectively pump blood to the rest of the body.
As a result, fluid may accumulate in the lungs and other tissues, leading to symptoms such as shortness of breath, fatigue, swelling in the legs, and a rapid or irregular heartbeat.
Heart failure can be caused by various underlying conditions such as coronary artery disease, hypertension, and heart valve disease.
It is a serious condition that requires medical attention and management.
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The study was conducted by Zeeshan Ahmed et al and published in Genomics.
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