This is based on the concept that precision medicine can be applied to improve individual well-being. The concept combines the study of the genome with a diverse range of biochemical and microbiological data.
The ability to integrate the information that our biomarkers provide us with our genetic information can be crucial in initiating a specific preventative treatment or attempts to improve our health. For example, this concept would be essential to a program which sought to improve the glycemic profile in cases where our genes show that a specific type of individual or group have a propensity to suffer from diabetes.
Dr. Moisés de Vicente – Medical Director Neolife Madrid
A study published in the Nature Biotechnology journal suggests that the continuous collection of personal biological data can improve our understanding of health and diseases.
Each person is unique and in essence is unrepeatable. Not only on an intellectual level but also on a sentimental and ‘way of being’ level. Each individual contains an immense amount of biological data that make us different from each other. This data set includes our genomic information, metabolites, proteins, microbiome composition, etc. The interaction of all these systems that coexist in our body, and how they react to external or internal aggression, is what will determine our disease state (human condition). In this way, if we could understand the data in greater detail, as well as the intrinsic relationships which exist between them then we could obtain some crucial information to allow us to preserve our health.
BIBLIOGRAPHY
(1) Price ND, et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat Biotechnol. 2017 Jul 17. doi: https://dx.doi.org/10.1038/nbt.3870 (2) Institute for Systems Biology and Arivale “Pioneer 100 Study” Establishes Foundation for New Industry of Scientific Wellness. https://www.systemsbiology.org/news/2017/07/17/pioneer-100-study/ (3) Cross R. Scientific wellness’ study—and a famed biologist’s spinoff company—divide researchers. 2017. Doi: https://dx.doi.org/10.1126/science.aan7123