Theragenomic Medicine meets Life- and Health-Assurance

Last Updated on June 4, 2025 by Joseph Gut – thasso

May 25, 2025 – Theragenomic Medicine is a rapidly evolving area in medicine that combines therapeutics, genomics, and personalized medicine to improve disease prevention, diagnosis, and treatment. When integrated into Life- and Health Assurance systems and Public Health programs), it can transform the way care is provided and financed.

What is theragenomic medicine in the first place? Theragenomics refers to the application of genomic data to tailor therapeutic interventions for individual patients. It combines genomic sequencing (e.g., identifying mutations, polymorphisms), pharmaco- and toxico-genomics (how genes affect drug response) with targeted therapies (custom treatments based on genetic profiles and genetically established targets).

Life- and health-assurance on the other side encompasses strategies to guarantee access to healthcare, often through insurance models. The role of theragenomics in life- and health-assurance is in reshaping these processes in several possible ways.

Genetic Risk Stratification (GRS)

The intention of genetic risk stratification (GRS) is to identify individuals at high genetic risk for diseases like cancer, diabetes, or cardiovascular conditions. One of the most impressive examples of the concept stems from the analyse of genetic risk for coronary artery disease (CAD). CAD is a pandemic that could be prevented. Conventional risk factors are inadequate to detect who is at risk early in the asymptomatic stage. In contrast, genetic risk for CAD can be determined already at birth, and those at highest genetic risk have been shown to respond to lifestyle changes and statin therapy with a 40% to 50% reduction in cardiac events. Thus, GRS for CAD should be / is brought to the bedside in an attempt to prevent this pandemic disease. Overall, GRS-concept helps life and health-insurers tailor policies and promote early intervention.

Personalized Prevention and Precision Treatment Plans

The approach is to promote individual lifestyle changes or preventive measures based on genetic predispositions. In fact, both genetic and lifestyle factors contribute to an individual’s disease risk, suggesting a multi-omic approach is essential for personalized prevention. Studies examine now the effectiveness of lifestyle coaching on clinical outcomes since to date  little is known about the impact of genetic predisposition on the response to lifestyle coaching. Thus, a real-world observational study enrolled 2531 participants in a commercial “Scientific Wellness” program, which combines multi-omic data with personalized, telephonic lifestyle coaching. The impact of this program on 55 clinical markers looks at the effect of genetic predisposition on these clinical changes. Sustained improvements in clinical markers related to cardiometabolic risk, inflammation, nutrition, and anthropometrics were observed. Notably, improvements in HbA1c were akin to those observed in landmark trials. Furthermore, genetic markers were associated with longitudinal changes in clinical markers. For example, individuals with genetic predisposition for higher LDL-C had a lesser decrease in LDL-C on average than those with genetic predisposition for average LDL-C. Overall, these results suggest that a program combining multi-omic data with lifestyle coaching produces clinically meaningful improvements, and that genetic predisposition impacts clinical responses to lifestyle change.

Cost-Efficiency and Data-Driven Policy Making

Long-term savings for life- and health-assurance providers due to better health outcomes and less chronic disease management becomes a reality which may run under the term of “Data-Driven Policy Making“. It may serve policy makers, government agencies, and life- and health-assurances in the same way in that population genomic data can guide public health strategies and funding decisions.

Challenges and Considerations

There are issues, however around privacy and ethics, involving protecting genetic information from misuse or discrimination. Furthermore there remain issues  on  cost and accessibility: Genetic testing must be affordable and equitable.
Also, data integration in health systems need robust infrastructure to use genomic data effectively. and there need to be regulatory oversight: in ensuring safety, efficacy, and fairness in personalized care models. Needless to say to pose one essential question in this context: Whom do my personal genetic and clinical data belong to?

Real-World Examples

Existing real world application of integration of the discussed concepts would include BRCA testing in breast cancer to determine preventive care options., and generally in oncology where targeted cancer therapies are largely based on tumor genomics. There are also insurance models in place that incentivize genetic screening for high-risk populations. Taking together, one could say that in the real world, theragenomic medicine not only meets life-and health assurance but that they become more and more integrative.

Thasso had in the past already some posts concerned with theragenomics in patient populations here, here, here, here, and here. See also this article by the NIH on lifeomics.

See here a sequence addressing personalised/precision/theragenomic medicine which all have the same goal:

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