Quantifying the Effect of Environmental Interventions Based on Genetic Risk Score and Developing Personalized Prevention Indicators for Seven Diseases
Background
In recent years, genomic analysis technology has advanced rapidly, and polygenic risk scores (PRS) are emerging as a key tool for predicting an individual's risk of disease. This approach, which comprehensively analyzes millions of genetic variants to calculate the probability of developing a specific disease, has expanded the horizons of preventive medicine.
However, there is a general consensus that simply knowing that one has a high genetic risk is not sufficient to fully predict the onset of a disease. An individual's health status is closely intertwined with their daily lifestyle, including diet, exercise, and smoking habits, as well as their inherent genetic information. Therefore, even individuals with a high genetic risk can reduce their risk of developing a disease by maintaining a healthy lifestyle, while those with a low genetic risk may still be susceptible to disease if exposed to harmful environmental factors.
Despite this gene-environment interaction, previous studies have been limited in their ability to quantitatively assess the specific benefits of particular preventive measures for individual patients. They have often been limited to presenting the relative risk ratio for an entire group, making it difficult for clinicians to intuitively determine what specific lifestyle modifications to prescribe to patients. As a result, there is a growing need in clinical practice for new evaluation indicators that can more precisely measure the correlation between individual genetic risk and environmental factors.
Key Findings
To address these issues, an international research team published a new mathematical index in the latest issue of Nature Genetics that quantifies the interaction between genetic risk and environmental context. The study analyzed a large cohort of patients with seven major chronic diseases registered in a large-scale biobank to closely examine the association between polygenic risk scores and lifestyle factors.
The researchers introduced the "Proportion Needed to Benefit" (PNB) index, which calculates the expected effectiveness of preventive interventions based on genetic risk score. The PNB intuitively shows the proportion of individuals who would benefit from a specific environmental intervention, tailored to their genetic risk level. This is a model that adapts the "Number Needed to Treat" (NNT) concept, used to assess treatment efficacy in clinical trials, to the field of genomic medicine.
The analysis revealed that individuals with a high genetic risk experienced significantly greater preventive benefits from improving environmental factors compared to those with a low genetic risk. For example, in a high-risk group of individuals with a cardiovascular disease genetic risk score in the top 10%, the PNB value decreased significantly when they combined smoking cessation and weight management. The research team explained that the lower this value, the more pronounced the preventive benefits of lifestyle improvements in a smaller number of individuals.
Furthermore, the research team designed the study to move beyond simply considering genetic risk and to evaluate the combination of environmental conditions in a multidimensional way. The synergistic effect of a rapid increase in disease incidence when individuals with a high genetic risk are exposed to adverse environmental conditions is a noteworthy finding. This precisely quantified data is expected to serve as a strong scientific basis for healthcare professionals to combine a patient's genetic information and lifestyle to assess individual risk.
Significance and Prospects
This study is recognized for its value in providing a roadmap for personalized medicine. It goes beyond simply providing genetic test results as static numbers representing the probability of developing a disease, and quantitatively demonstrates the expected effectiveness when combined with environmental interventions. This can serve as a powerful motivator for patients to adopt healthy behavioral changes.
However, there are still challenges to be overcome before it can be widely applied in clinical practice. Additional studies are needed to validate the PNB model for various genetic diseases and cancers beyond the seven chronic diseases used in this analysis. Furthermore, if the study does not include a diverse group of individuals, it may lead to healthcare disparities in specific populations. The establishment of standardized monitoring technology to track and record complex environmental factors in real-time is also a prerequisite.
Nature Genetics, Published online: 13 July 2026; doi:10.1038/s41588-026-02674-zThis study examines interactions between polygenic scores and pairs of environmental contexts across seven diseases and introduces the proportion needed to benefit as a metric to quantify the expected effectiveness of interventions as a function of polygenic risk.
This study is expected to have a significant impact on primary care settings and the digital healthcare industry. For example, when a patient visits a hospital and receives a genetic test, instead of receiving an abstract advice such as "You have a high genetic risk for heart disease, so be careful," they may receive a quantified prescription based on the PNB value, such as "If you consistently engage in aerobic exercise three times a week, you can reduce the risk of developing the disease by four times more effectively than the general population."
In addition, it can be applied to the development of personalized health management applications linked to wearable devices. A business model can be created in which users' genetic risk scores and real-time environmental data (daily activity levels, sleep patterns, etc.) are combined to provide optimized behavioral guidance on a daily basis. From the perspective of public health authorities, it is expected that they can significantly reduce national healthcare expenditures by prioritizing the allocation of limited resources to specific high-risk groups with maximized PNB efficiency, rather than investing in random prevention programs.