Inferring networks from personal, dense, dynamic data clouds of biological and quantified-self data

Determinants of Health in U.S. Health Care 10% Behavior & environment 30% Genetics For the large part, western health care focuses on the diagnosis and treatment of disease. Medical doctors often have little expertise in making personal recommendations around optimizing health through diet, exercise and behavioral factors. According to Schroeder et al, the impact the US health care system makes on health is minimal while behavioral and environmental factors have the greatest impact. What’s more is these factors are modifiable, but the knowledge and motivation to make impactful changes aren’t built into everyday life. Arivale’s mission is to help people understand and improve health and wellness and understand how science (e.g. genetics) plays a role. Schroeder S et al. New Engl J Med 2007

Creating a new paradigm: Scientific Wellness Arivale is a behavioral intervention based in data and implemented through coaching. Our coaches are licensed health professionals, typically registered dieticians, are trained to interpret personal scientific data (e.g. microbiome diversity and polygenic risk scores), develop health goals tailored to the individual, recommend lifestyle and behavior changes (e.g. diet or sleep) to address the individual’s health goals Through regular contact, hold individuals accountable for making change Data + Coaching + Action = Results

Members Receive Personalized Results Examples of scientific data a member of the program receives include clinical lab tests, collected every 6 months: Cardiovascular biomarkers, including HDL/LDL cholesterol Diabetes risk biomarkers, including hbA1C Inflamation biomarkers, including IL-6 and IL-8 Nutriontional biomarkers, including vitamin D and Omega-3s Stress levels, measured from cortisol through saliva Other types of data we collect and utilize include Genotype Proteomics collected from blood Quantified self data, collected from wearable fitness devices such as FitBit Gut microbiome collected from mail-in kits Metabolomics collected from blood

Research Leverages Longitudinal Multi-omic Data Clinical Labs Blood, Saliva Lifestyle/ Quantified Self Fitbit, Assessments SNP or Whole Genome Sequencing Blood Gut Microbiome Stool LabCorp – Blood (2X/year) Heart health Diabetes risk Inflammation Nutrition Oxidative stress Chem14 CBC ZRT – Saliva (1X/year) Cortisol LabCorp (2X/year) Blood pressure HumanAPI (Continuous) Activity Sleep Heart Rate Weight Health/Lifestyle Assessments Diet Happiness Stress Personal and Family Health History WuXi/Nextcode (1X) Illumina, 30X coverage: GATK, deCODE tools, Multi-ethnic Global Array In-house processing: Polygenic scores, CNV calling, ancestry analysis DNA Genotek (1X/year) 16S rRNA sequencing In-house processing: OTU abundances, diversity metrics, KEGG ontology pathways All of the data we collect is interesting and helps guide the coaching process, however we also leverage this data for research. This slide give a more complete picture of the size of our data and where the data come from. We also do in-house processing, including polygenic risk score creation and build pipelines to process the raw data (e.g. microbiome sequencing data to diversity metrics and taxa abundance). Metabolomics Blood Proteomics Blood Metabolon ~1,000 small metabolites: GC/MS, LC/MS, GC-FID (Lipids) Olink 266 proteins / 3 panels: 2 cardiovascular + 1 inflammation