Nightingale Health Plc has published a pre-print showcasing the accuracy and performance of its blood biomarker-based risk prediction models on a scale that has not been done before. The now-announced population study includes three national biobanks totaling half a million blood samples from Estonia, Finland, and the United Kingdom. In early 2022, Nightingale Health announced its expansion to genetics by acquiring a Finnish genetic testing company and launching an international center of excellence for genomic data analysis.

As part of this initiative, Nightingale Health has now completed a study combining blood biomarkers and genetics on an unprecedented scale. The population study findings show that Nightingale Health's risk prediction models, based on blood biomarkers, have better risk detection capabilities for quality-of-life lowering diseases than polygenic risk scores alone, which reflect inherited risks. The study results show that Nightingale Health's risk prediction models successfully identify more high-risk individuals than polygenic risk scores for 10 of the 12 diseases studied.

The diseases included in the study are the twelve leading causes of disability-adjusted life years (DALYs) in high-income countries: myocardial infarction, ischemic stroke, intracerebral hemorrhage, lung cancer, type 2 diabetes, COPD, Alzheimer's disease, dementia, depressive disorders, alcoholic liver disease, cirrhosis of the liver, and colon and rectum cancers. These diseases are also the biggest source of healthcare costs and, therefore, the most important to predict and prevent. Polygenic risk scores have received recent attention as a key enabler of preventative health.

However, the new study shows that genomic data alone is not a solution for accurate disease risk prediction. Risk models based on blood biomarkers provide more accurate and actionable information, and when combined with polygenic risk scores, they provide a superior tool for finding people at high risk and targeting them with preventative actions. The combination of blood biomarkers and genetic data takes into account both inherited static risks and dynamic risks that reflect lifestyle.

Additionally, the study confirms the company's risk prediction models can be used for continuous health monitoring and measuring the effects of preventative actions. A subset of individuals in the study had donated two blood samples five years apart, and Nightingale Health's risk prediction models successfully captured changes in disease risks after the individuals had made lifestyle changes. By contrast, since polygenic risk scores remain the same throughout life, they cannot show progress in response to better lifestyle choices.

The company's risk prediction models are generated from a single standard clinical blood sample and include clinically validated biomarkers, many of which are already familiar in clinical practice, such as cholesterol, glucose, and creatinine. In addition to 39 clinically validated biomarkers, the company can provide over 200 additional biomarkers for research use – all of which have been analyzed from the same standard blood sample in one test. The study demonstrates the expansion of the company's risk prediction models to large-scale use, thereby enabling a broad implementation of preventative healthcare strategies worldwide.