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

Abstract

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.

Epigenetic and Transcriptional Variability Shape Phenotypic Plasticity

“…A precise understanding of cell‐ and individual‐intrinsic variability, the environmental sources of variability, and how these effects are propagated and accumulate throughout life will also allow to reveal to what extent environmental or behavioral intervention could have an impact on modulating disease risks . This way, personalized monitoring and adaptation of lifestyle factors such as diet or physical activity could enable the combination of precision medicine with disease prevention, the ultimate goal of successful health care management. That this is achievable has been successfully exemplified by the Pioneer 100 Wellness Project (P100) and the Blue Zones Vitality Project, which showed that modulating the environment and behavior of a population can indeed lead to improved health and life‐expectancy.…”

Integrative omics for health and disease

“…To further qunatify these differences, projects have been initiated to extend such analyses to thousands of individuals, characterizing preterm births, inflammatory bowel disease and type 2 diabetes 53 . In a similar vein, two separate groups recently profiled genetic and metabolomics data: one of these calculated polygenic risk scores for over 100 individuals and correlated these with measurements of metabolites 54 , whereas the other identified rare deleterious variants in healthy volunteers that correlated with outliers of individual metabolites and metabolic pathways 55 . Additionally, as reference databases of omics data for healthy individuals become available (as are already available for exome 56 , genome (for example, the Genome Aggregation Database (gnomAD)) and RNA-seq 57 data), it will become easier to interpret individual-level data in the context of these control cohorts.…”

“…However, the adoption of clinical genomics has expanded rapidly over the past few years from the first successful diagnosis 1 to multi-institutional and international adoption 72 . In the same vein, longitudinal multi-omics profiling, with its first recent research examples 6,54 , may similarly emerge as a clinical tool.…”

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

“…Thus, where statistical powering is a key consideration in research metabolomics, clinical metabolomics is a true n‐of‐1 analysis. Untargeted clinical metabolomic profiling of individual patient samples has found early application in the screening of inherited metabolic diseases, but other uses, such as probing of undiagnosed cases for disease insights, as well as health and wellness screenings, have started to emerge …”

“…In addition to combining metabolomic and genomic data, a larger trend is to seamlessly integrate all patient information, such as test results from traditional clinical biochemistry (eg, enzymatic assays, protein assays), clinical ‘omics, and microbiome profiling. The combination of clinical metabolomics testing results, clinical biochemistry, and other ‘omics data streams has been shown to offer superior diagnostic power, in both IEM and wellness screenings, relative to the individual methods performed in isolation.…”