Introduction
Clinical medicine aims to develop novel treatments to fight disease and as a result, must constantly adapt and evolve to stay one step ahead of the curve. While this ongoing battle can be difficult, researchers and medical practitioners have developed methods to try and predict a person's risk of developing certain diseases. Here, we discuss two methods of predicting disease risk:
- Polygenic risk scores (PRS) - a longstanding method with reasonable and potentially actionable results
and
- Methylation risk scores (MRS) - a relatively new method that is proving increasingly valuable in the field of precision medicine.
Precision Medicine and Disease-Causing Factors
Precision medicine aims to determine appropriate preventive measures and treatment plans that consider variations in a patient's genes, environmental conditions, and lifestyle habits. Human disease is a complex phenomenon characterized by various factors widely classified into two primary risk factors: internal and external. Internal factors like genetic predisposition, inherited mutations, and family history significantly determine a person's susceptibility to certain diseases. In contrast, external factors include environmental toxins (i.e., air pollution, pesticides, and compromised drinking water), lifestyle habits (i.e., dietary choices and exercise), exposure to infectious agents (i.e., viruses, bacteria, and fungi), and occupational hazards, which can each significantly affect disease susceptibility. Identifying the interaction between internal and external risk factors is critical in understanding the complex nature of disease and composing effective strategies for preventive measures and treatment management.
Disease Risk Prediction
Precision medicine experts take into account several factors when developing appropriate treatments for diseases, including population risk or incidence, the underlying genetic cause and potential impact on the patients and their families.
When determining what diseases to target for risk prediction, it's crucial to analyze diseases that are the leading causes of worldwide deaths. One of the primary goals of medicine is to save lives, and this can be accomplished by taking preventive measures against the illnesses responsible for contributing to these worldwide deaths like ischemic heart disease or stroke. This will have downstream impacts on healthcare cost and resource utilization. For example, the Center for Disease Control and Prevention estimates there are more than 877,500 fatalities in the United States each year from stroke or heart disease, resulting in a financial burden on the American health care system of approximately $216 billion, along with losing $147 billion in loss of job productivity. Due to various disease-causing factors, medical practitioners have developed risk prediction methods and models to determine a person's likelihood of developing specific conditions.
What are Polygenic Risk Scores (PRS)?
One such method for assessing a person’s risk is through polygenic risk scores, also known as PRS, which is designed to determine a person's level of risk for developing a disease based on a combination of genetic variants. Using statistical analysis and computer modeling, researchers can use a person's genetic profile to determine which diseases they are at risk of developing. It's important to note that PRS can only estimate the relative risk of developing a particular disease; that is, PRS cannot estimate an individual's likelihood of developing a disease with absolute certainty. An Illumina 2023 case study discussed how medical practitioners use PRS to enable more efficient medical treatments for patients.
What are Methylation Risk Scores (MRS)?
PRS has a long history of use in precision medicine but lacks the precision needed to give a patient or physician a sense of certainty. However, a new method, Methylation Risk Scores (MRS), presents the opportunity to provide more accurate results in determining disease risk. Like PRS, MRS still provides a relative risk, however, instead of evaluating specific genetic variants, it involves analysis of how DNA is expressed in an individual. This expression, also known as epigenetic markers, changes over time and is impacted by both internal and external factors.
MRS enables the development of novel applications to combat a myriad of diseases, resulting in enhanced development of epigenetic tools used in epigenomics research.
PRS vs. MRS
As noted, MRS and PRS are used with electronic health record (EHR) data to determine a person's disease risk. Datasets from both PRS and MRS can be used individually or in combination to help assess a person's disease risk, along with diagnoses, disease tracking, and treatment development and advancement.
What does this mean for predicting disease risk?
While PRS has a proven history of estimating a person's risk of developing certain conditions/diseases, MRS can potentially establish a new method of predicting disease risk while working in tandem with PRS for more comprehensive results. This could lead to more informed decision-making, early and accurate diagnosis, and improved screening, all of which can lead to improved patient outcomes. In fact, a single study showed that MRS data can be more accurate at predicting disease risk than PRS and EHR data alone (UCLA paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9411568/).
MRS Development Strategy
Due to the enormous impact MRS could make on human health, Illumina is devoted to promoting the development of MRS. By providing high quality epigenetic tools like InfiniumTM Methylation Microarrays, Illumina supports leading research in MRS and precision medicine. They also continue to support community efforts in increasing awareness around the value of MRS by communicating customer success stories and aiding in MRS design and development. Illumina anticipates that further development of MRS could lead to various benefits.
For research use only. Not for use in diagnostic procedures.
Additional Sources: CDC, Nature, Epigenetics (1), F1000Research, Epigenetics (2), Allergy, Illumina
Document Number: M-GL-02570
