A wellness study of 108 individuals using personal, dense, dynamic data clouds

We collected personal, dense, dynamic data for 108 individuals over 9 months, including whole genome sequence; clinical tests, metabolomes, proteomes and microbiomes at three time points; and daily activity tracking. Using these data we generated a correlation network and identified communities of related analytes that were associated with physiology and disease. We demonstrate how connectivity within these communities identified known and candidate biomarkers, e.g. gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease. We calculated polygenic scores from GWAS for 127 traits and diseases, and identified molecular correlates of polygenic risk, e.g. genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine. Finally, behavioral coaching informed by personalized data helped participants improve clinical biomarkers. Personal, dense, dynamic data clouds will improve understanding of health and disease, especially for early transition states. This approach to “scientific wellness” represents an opportunity largely missing in contemporary health care.