Recent scientific and technological developments in genomics, proteomics, and metabolomics are opening unprecedented opportunities to understand health and disease. The promise of personalised healthcare enabled by the era of omics aided diagnostics can be seen as a technological vision. But how far is the future of omics sciences in routine healthcare? Nightingale CEO Teemu Suna shares his insight.
The discussion is very positive as there are great opportunities with the omics sciences. There has never been a broader or more detailed view to human biology. However, as with all new technologies, there is a risk of over promising. In general, this is not very harmful but sometimes it may shift the focus off from creating major impact in healthcare. For example, there is a great potential with low cost genomics but it is often omitted that the genomic data is effective mainly for rare diseases. When looking at global heath impact, rare diseases are a minor problem.
Gladly, the role of different omics technologies is being continuously clarified. It is understood that no single technology is a silver bullet. Instead, the next generation healthcare is created by combining different advanced technologies of genomics, proteomics, and metabolomics. All of which have their pros and cons.
Let’s look at some of the biggest global healthcare problems, heart disease and diabetes. These diseases are both very systemic and complex diseases. The complexity of the diseases is expressed by hundreds or thousands of genes. Therefore, trying to link genomic data with clinical data creates a problem where recognising and understanding the impact of individual genes in the development of systemic diseases is generally not leading to actionable diagnosis or influencing patient treatment.
Moreover, heart disease and diabetes are greatly affected by living habits. This is not directly visible in genomic data but can be understood with the tools of metabolomics. Metabolomics provide molecular level information on how the metabolism is working at the time of a patient examination, like a snapshot of the health state. This is a far more accurate window to understand for example heart disease and diabetes than by just looking at clinical data representing diagnoses during patient visits.
The best scientific understanding and implementation to healthcare can be achieved by combining the benefits of the different omics. By putting all the data together, we have genetic understanding on the inherent disease risk, molecular level understanding of how the systemic metabolism works and the actual disease outcomes visible for the individuals. The approach enables prediction and prevention in the key chronic diseases, and it is the way towards global impact in healthcare.
Currently, genomics is already benefitting treatment of rare cancers. But omics technologies are not used for common diseases that affect hundreds of millions of people. These diseases are daily routine and main source of costs in healthcare worldwide. The key gap for omics sciences is implementation to routine healthcare and providing actionable impact on global health. To address this, we need novel technologies that can scale, provide a cost-neutral solution and integrate to current healthcare systems.
This is a challenge particularly for genomics and proteomics at their current stage of development. However, the recent advancements in metabolomics demonstrate that the era of omics aided diagnostics is not only a technological vision but can be a reality already today. It is already possible to measure hundreds of metabolic biomarkers in absolute concentrations from one blood sample and maintain the cost level comparable to routine cholesterol tests in clinical scale. This has never been possible in the history of medicine and has great impact potential in healthcare.
The discussions about changing healthcare many times overlook the current ways of working. It is not realistic to think that a major change in healthcare will happen overnight and driven solely by technology. Patient safety comes first and it sets boundaries for development and innovation processes. I see changes in healthcare as a continuous road map where tight integration and incremental steps create the future. This must be respected in order to achieve a change.
For example, there is a very long tradition in clinical diagnostics. New technologies need to integrate into the current clinical workflows in terms of sample processing and result delivery. When this can be achieved, the additional value provided by the new technology is mostly welcomed warmly. When there is no risk moving towards the new technology, there are more reasons to embrace the change rather than overlook the opportunity.
Original article published on Pan European Networks – Horizon2020