Cardiovascular
Decoding the Future of Cardiovascular Health: Navigating the Road Ahead with Genomics and Polygenic Risk Scores
Nov
by Dr Firoz Ahmad
Cardiovascular diseases (CVD) as a result of coronary artery diseases (CAD) are a group of health issues that affect the heart and the blood vessels which are essential for optimal heart functioning. It is a leading cause of death worldwide, particularly in India, where it accounts for roughly two-thirds of all non-communicable diseases. It is vital to remember a balanced lifestyle that includes adequate eating, weight management, and optimal physical activity can help to prevent CAD. The most common and classical risk factors for CAD include abnormal lipid profile, high blood pressure, family history, diabetes, smoking, and obesity. Thus, accurate risk determination in CAD prevention is important to save lives by better identifying high-risk individuals as early as possible so that management with counselling and medicines can begin at the earliest. Framingham, INTERHEART, SCORE, WHO/ISH CVD, and Pooled Cohort Equation are some of the CVD risk calculators being used that have been tested on populations. However, risk predictions for a specific population are not applicable in other populations or the same populations later because the mean levels of CVD predictors vary across populations and change over time.
The completion of the human genome project in 2003, followed by refinement in 2022, undoubtedly marked the beginning of a new era in biomedical research. Furthermore, the rapid evolution of DNA sequencing technology, such as the introduction of high throughput next generation sequencing, has proven to be the icing on the cake, since it has greatly reduced the cost of genomic-based tests and made them more commonly accepted in clinics. Genomic data analysis using artificial intelligence based software is reshaping the healthcare landscape. With this advance development in genomics, today, we have started linking the changes in the DNA sequence and its association of various human diseases such as CAD more precisely. CAD is a complex disease which means it occurs because of many genomic variants, coupled with environmental influences (like dietary habits, sleeping patterns, stress, and smoking). They are also referred as “Polygenic” disorders, where “poly” means “many” and “genic” means “involving genetic changes.” The genetic variants are permanent change in the DNA sequence that makes up a gene predisposing to CVD range from rare and deleterious mutations responsible for diseases, like familial hypercholesterolemia, to common polymorphisms that regulate the predisposition to complex diseases with a weak effect at the individual level. Most of these genetic variants are scattered across the genome and not confined to one specific chromosome. Genomic variants associated with complex diseases are identified by comparing the genomes of individuals with and without those diseases. The massive genomic data now available allows researchers to calculate which variants tend to frequent more in groups of people with a given disease. There can be thousands or even millions of genetic variants per disease. This information is then fed into a computer and statistics to estimate how the collection of a person’s variants affects their risk for a certain disease. These are called polygenic risk scores. All of this can be done without knowing the actual genes involved in the complex disease. In fact, advances in polygenic risk scores (PRSs) have triggered speculations in enhancing disease risk prediction by leveraging information on millions of DNA variants across the genome. Thus, PRS represent the total number of genetic variants that an individual possesses to ascertain their heritable risk of developing a certain disease. It was observed that individuals having higher PRS score for CAD derived greater relative and absolute benefit from cholesterol-lowering therapeutic strategies. PRS for CAD could identify individuals who would gain from intensive lifestyle modification, imaging surveillance and early statin therapy. PRS are not yet routinely used by health professionals because of the absence of guidelines for practice and there is still room for more improvement as to how these scores are generated. Nonetheless, private healthcare and direct-to-consumer companies have already begun generating polygenic risk scores for their consumers and they may someday serve as a useful new tool to guide healthcare decisions.
Apart from the enthusiasm for polygenic scores to usher in a new era of preventive clinical medicine the key limitations must also be considered. First, polygenic scores of individuals of non-European ancestry have been underrepresented just like in prior Genome-wide association studies (GWAS) discovery cohorts. GWAS show that multiple common genetic variants predispose to CAD. Efforts are taken to broaden GWAS in more genetically diverse populations and design methods accordingly for enhanced score performance. Second, although available scores associate strongly with the prevalent disease, they do not predict incident disease, which would offer more clinical utility in enabling targeted interventions. Lastly, most risk prediction models so far are based on either genetic or clinical risk factors, but better integration of these modalities and estimation of a clinically actionable risk estimate is required.
The strides taken in human genetics have enabled the heritable quantification of CAD risk, in the form of a PRS. Research observations and the rapidly expanding number of individuals with available genetic data are now able to offer tangible paths for genomic medicine to be employed for CAD prevention. PRS has the ability to improve on existing CAD risk prediction and screening algorithms, better prioritize and select patients for a specific treatment or procedure, and update current prospective clinical trial designs. Thus, scientifically rigorous scoring methods, along with well-thought-out approaches to amalgamate them into medical care, will be critical to the safe and effective use of predicting risk to enhance patients’ lives.
Dr Firoz Ahmad, Head- Clinical Genomics, Unipath Speciality Laboratories Ltd, Ahmedabad
(DISCLAIMER: The views expressed are solely of the author and ETHealthworld does not necessarily subscribe to it. ETHealthworld.com shall not be responsible for any damage caused to any person / organisation directly or indirectly.)
