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

2. 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

3. 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

4. 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

5. 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