No abstract

Variation in the human gut microbiome can reflect host lifestyle and behaviors and influence disease biomarker levels in the blood. Understanding the relationships between gut microbes and host phenotypes are critical for understanding wellness and disease. Here, we examine associations between the gut microbiota and ~150 host phenotypic features across ~3,400 individuals. We identify major axes of taxonomic variance in the gut and a putative diversity maximum along the Firmicutes-to-Bacteroidetes axis. Our analyses reveal both known and unknown associations between microbiome composition and host clinical markers and lifestyle factors, including host-microbe associations that are composition-specific. These results suggest potential opportunities for targeted interventions that alter the composition of the microbiome to improve host health. By uncovering the interrelationships between host diet and lifestyle factors, clinical blood markers, and the human gut microbiome at the population-scale, our results serve as a roadmap for future studies on host-microbe interactions and interventions.

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.