Founded by Stéphane Ragusa, who has a qualification from the Ecole Polytechnique and from ENSAE (École nationale de la statistique et de l’administration économique (in English, the National School of Statistics and Economic Administration), as well as a PhD in Biology, Predilife develops innovative solutions in the field of predictive medicine combining proven medical techniques (genetic testing, medical imaging, etc.) and mathematical models using a large amount of statistical data, which could enable individuals to define their risk profile regarding the occurrence of a wide range of serious diseases.
Predilife develops predictive tests for medicine of the future: Predictive, Preventive, Personalised and Participatory (P4). Predictive medicine involves predicting the future of an individual in order to act in a timely manner thanks to personalised prevention, either by lowering the risks (known as primary prevention), or by identifying the illness sooner (secondary prevention). Major diseases, cancers and cardiac arrest in particular, could be prevented in a more effective manner, thereby decreasing their death rate by at least 30%. Still, each person’s health trajectory is needed to act whilst there’s still time.
For 15 years, we have been working on the “prospective cohorts” of hundreds of thousands of people who have been monitored for years, enabling diseases to be linked to their causes: nutrition, physical activity, blood parameters, genetics, etc. There are a restricted number of these cohorts in the world, as they are expensive to establish and must be monitored for several years. In practice, only the academic sector has reliable and secure data. Predilife was able to access these databases thanks to its academic origin, providing access to INSERM cohorts as well as a major American cohort. Today, our technology enables us to predict an individual’s risk using these cohorts, by integrating all types of parameters: laboratory tests, images, genetic tests.
Our first test concerns breast cancer for which this approach should reduce the number of advanced cancers and thus have a long-term impact on mortality, which is today 40 000 deaths / year in the US and 500 000 / year in the world. We will offer in the future a generalist approach to predict the main individual risks with quantified means to reduce each one of them.