Personalized Prevention: Using Genetics and Biomarkers to Guide Cardiovascular Risk Reduction

Cardiovascular diseases are the leading cause of death worldwide, responsible for nearly 17.9 million deaths every year, according to WHO. Public health programs have historically focused on blanket, one-size-fits-all interventions such as encouraging healthy diets, regular physical activity, smoking cessation, and screening for known risk factors like obesity, hypertension, and high cholesterol. While this approach has saved millions of lives, it ignores a critical dimension: genetic makeup. Not everyone has the same biological risk.  Traditional models can overlook individuals who appear healthy on paper but may carry hidden vulnerabilities.

Genomics and biomarkers, when paired with traditional approaches, can create a path toward more effective personalized prediction and prevention of cardiovascular disease. These tools can help clinicians move beyond conventional risk calculators to tailor interventions based on an individual’s biological makeup. “Genomics will change the way doctors approach cardiac disease prevention,” says Dr. Bhaskar Semintha, a cardiologist and cardiothoracic surgeon with Fortis Hospital. “We will be able to stratify patients not by what they present with but by what lies beneath the surface.”

Dr. Semintha explains that in his practice, he routinely uses genomic profiling and specialized biomarkers to assess cardiovascular risk, especially in patients who don’t exhibit traditional warning signs. He finds that adding these tests helps him uncover risks that would otherwise go undetected.  “In addition to blood pressure and lipid panels, I look at high-sensitivity C-reactive protein, lipoprotein(a), and apolipoprotein B,” he says. “These markers provide deeper insights into inflammation and lipid metabolism, which play a major role in premature atherosclerosis.”

In one recent case, he describes managing a 42-year-old man with no obvious risk factors. “He had a normal BMI, didn’t smoke, no diabetes, and an LDL of 112 mg/dL,” Dr. Semintha explains. “Standard calculators placed him at low risk.” However, the patient had a strong family history of early cardiac events. Based on that, Dr. Semintha ordered an extended panel that revealed elevated lipoprotein(a), high hs-CRP, and a polygenic risk score (PRS) in the 90th percentile for coronary artery disease. “A coronary CT angiogram confirmed early-stage plaque,” he says. “We started statin therapy and made lifestyle changes. Without this insight, he might have had a heart attack in a few decades to come.”

This case illustrates the unique strength of personalized prevention—it doesn’t just identify who is at risk, but uncovers how they are at risk. “Two patients with similar LDL levels may metabolize fats differently depending on their APOE genotype,” Dr. Semintha notes. For example, someone with the APOE4 variant might be at higher risk even with borderline LDL levels, while another with the PCSK9 variant might need more aggressive lipid-lowering therapy. By identifying these underlying genetic pathways, clinicians can recommend more precise treatment plans, potentially years before symptoms arise.

Among the most promising tools for cardiovascular risk prediction is the polygenic risk score. This score aggregates the effects of many common genetic variants, known as single-nucleotide variants (SNVs), each of which may contribute a small amount of risk individually. When weighted and combined, these variants can produce a powerful estimate of an individual’s inherited susceptibility to diseases like coronary artery disease, atrial fibrillation, or venous thromboembolism.

Recent genome-wide association studies (GWAS) have identified hundreds of SNVs linked to cardiometabolic traits. And studies are underway to fully understand exactly how they contribute to cardiovascular disease. “Polygenic scores are exciting because they allow us to move from population-based to precision-based prevention,” says Dr. Semintha. “They’re not yet perfect, but the predictive power continues to improve.”

Artificial intelligence is also helping to accelerate the integration of genomic data into clinical workflows. Dr. Ethan Cohen, a cardiologist and researcher, believes that machine learning algorithms can help extract meaningful patterns from vast genomic datasets. “Finding combinations of genes that contribute to disease was once a massive challenge,” he explains. “Now, with AI, more is possible.”

Dr Cohen isn’t the only one who sees the potential for AI to be deployed here. Researchers in India were able to deploy AI, along with polygenic scores, and compare it to traditional methods. They found that AI has enormous potential to enhance personalized cardiovascular care. However, they noted the need for more diverse and representative data sets. As the technology develops, more integration will be possible.

This points to a significant limitation. Many polygenic risk models are based on genomic data from individuals of European ancestry. As a result, they may be less accurate in other groups. This lack of diversity raises concerns about equity and the potential for misclassification in underrepresented populations.

Moreover, the integration of biomarkers and genetic tools must align with broader ethical considerations, including informed consent, data privacy, and potential psychological impacts of risk disclosure. Patients need clear explanations of what these scores mean—and what they do not.

Real-world implementation of biomarkers and genomics also faces barriers. One major challenge is access. Not all healthcare systems are equipped to provide genetic screening, and insurance coverage for polygenic risk testing remains inconsistent. Additionally, many physicians are still unfamiliar with how to interpret or act on this data. In a scientific statement, the American Heart Association (AHA) advised that efficacy, harm, and logistics be considered before using polygenic scores in any healthcare setting.

Still, the long-term potential remains clear: biomarkers and genomics can make a life-saving difference for millions. They have already made a meaningful impact on clinical decision-making and will continue to evolve as science advances. They do not replace traditional risk models but complement and refine them. As genomics, biomarker science, and digital tools continue to evolve, they offer a powerful opportunity to reimagine how cardiovascular disease is detected, predicted, and prevented. For patients and providers alike, the future of heart health may well lie not just in what we see but in what is hidden in our genes.