1. “Linking Phenotype Changes to Internal/External Longitudinal Time Series in a Single Human”
Invited Presentation at EMBC ‘16 38th International Conference of the IEEE Engineering in Medicine and Biology Society
Symposium: The Quantified Self: Visions for the Next Decade of Persistent Physiological Monitoring
Orlando, FL
August 18, 2016
Dr. Larry Smarr
Director, California Institute for Telecommunications and Information Technology
Harry E. Gruber Professor,
Dept. of Computer Science and Engineering
Jacobs School of Engineering, UCSD

2. Abstract
Taking the point of view that the human body is a dynamical coupled system, I have been involved in an experiment for most of the last decade to gather time series data on key body variables. By taking blood and stool samples on a regular basis (bimonthly to quarterly), I have developed a detailed longitudinal time series of ~200 biomakers as well as the microbiome ecology. To define phenotype changes, I have daily weight and symptom data, as well as wireless sensors. Since I have colonic Crohn’s autoimmune disease, one sees episodic variation in these variables with excursions of 10x to 100x above healthy values, demonstrating that single values of these variables randomly taken in time (i.e. traditional medical care) is nearly meaningless. By following the dynamics of my gut microbiome ecology, we have discovered an abrupt shift in the microbiome ecology that is strongly coupled to changes in prescription medicines and external variables such as weight and autoimmune symptoms. This experiment provides a window into the future of personalized precision medicine.

3. MyFitnessPal- Calories Ingested
Over the Last Decade, I Have Used a Variety of Personal Sensors To Quantify My Body & Drive Behavioral Change
Withings/iPhone-
Blood Pressure
MyFitnessPal- Calories Ingested
FitBit - Daily Steps &
Calories Burned
Azumio-Heart Rate
Zeo-Sleep
Withings WiFi Scale - Daily Weight

4. Wireless Monitoring Produced Time Series That Helped Me Improve My Health
Since Starting November 3, 2011
Total Distance Tracked 6180 miles = Round Trip San Diego to Nome, Alaska
Total Vertical Distance Climbed 190,000 ft. = 6.5x Mt. Everest
Elliptical
Walking
I Increased Walking, Aerobic, and Resistance Training,
All of Which
Have Health Benefits
Sunday January 17, 2016
137 42
My Resting Heartrate Fell from 70 to 40!

5. From Measuring Macro-Variables to Measuring Your Internal Variables

6. My Quarterly Blood Draw
As a Model for the Precision Medicine Initiative, I Have Tracked My Internal Biomarkers To Understand My Body’s Dynamics
My Quarterly Blood Draw
Calit2 64 Megapixel VROOM

7. Episodic Peaks in Inflammation Followed by Spontaneous Drops
Only One of My Blood Measurements Was Far Out of Range--Indicating Chronic Inflammation
27x Upper Limit
Episodic Peaks in Inflammation Followed by Spontaneous Drops
Normal Range <1 mg/L
Complex Reactive Protein (CRP) is a Blood Biomarker for Detecting Presence of Inflammation

8. Active Inflammatory Bowel Disease
Adding Stool Tests Revealed Oscillatory Behavior in an Immune Variable Which is Antibacterial
Typical
Lactoferrin Value for
Active Inflammatory Bowel Disease
(IBD)
124x Upper Limit for Healthy
Normal Range
<7.3 µg/mL
Lactoferrin is a Protein Shed from Neutrophils -
An Antibacterial that Sequesters Iron

9. Confirming the IBD (Colonic Crohn’s) Hypothesis: Finding the “Smoking Gun” with MRI Imaging
Liver
I Obtained the MRI Slices From UCSD Medical Services
and Converted to Interactive 3D Working With Calit2 Staff
Transverse Colon
Small Intestine
Descending Colon
Sigmoid Colon
Threading Iliac Arteries
Major Kink
Diseased Sigmoid Colon
MRI Jan 2012
Cross Section
Severe Colon
Wall Swelling

10. Time Series Reveals Oscillations in Immune Biomarkers Associated with Time Progression of Autoimmune Disease
Immune &
Inflammation
Variables
2009
2010
2011
2012
2013
2014
2015
Weekly Symptoms
Pharma
Therapies
Stool
Samples

11. Your Microbiome is Your “Near-Body” Environment and its Cells
What Can We Learn From the Gut Microbiome Time Series In an Individual?
To Understand the Autoimmune Dynamics of the Immune System We Must Consider the Human Microbiome
Your Microbiome is Your “Near-Body” Environment
and its Cells
Contain 100x as Many DNA Genes
As Your Human DNA-Bearing Cells
Inclusion of the “Dark Matter” of the Body Will Radically Alter Medicine

12. These Must Be Coupled to A Dynamic Microbiome Ecology
Evolving Microbiome Environmental Pressures: Dynamical Innate and Adaptive Immune Oscillations in Colon
These Must Be Coupled to
A Dynamic Microbiome Ecology
Adaptive Immune System
Normal 50 to 200
Innate Immune System
Normal <600

13. Larry’s 40 Stool Samples Over 3.5 Years to Rob’s lab on April 30, 2015
We are Genomically Analyzing My Stool Time Series in a Collaboration with the UCSD Knight Lab
Larry’s 40 Stool Samples Over 3.5 Years to Rob’s lab on April 30, 2015

14. LS Weekly Weight During Period of 16S Microbiome Analysis Abrupt Change in Weight and in Symptoms at January 1, 2014
Source: Larry Smarr, UCSD
Lialda
Uceris
Frequent IBD Symptoms
Weight Loss
Few IBD Symptoms
Weight Gain

15. My Microbiome Ecology Time Series Over 3 Years
Source Justine Debelius, Knight Lab, UC San Diego

16. Coloring Samples Before (Blue) and After (Red) January 2014 Reveals Clustering
Source Justine Debelius, Knight Lab, UC San Diego

17. An Apparent Sudden Phase Change Occurs
Source Justine Debelius, Knight Lab, UC San Diego

18. Blue Balls on Diagram to the Right Red Balls on Diagram to the Right
My Gut Microbiome Ecology Shifted After Drug Therapy Between Two Time-Stable Equilibriums Correlated to Physical Symptoms
Frequent IBD Symptoms
Weight Loss
7/1/12 to 12/1/14
Blue Balls on Diagram to the Right
Weekly Weight
Few IBD Symptoms
Weight Gain
1/1/14 to 8/1/15
Red Balls on Diagram to the Right
12/1/13 to 1/1/14
12/1/13-1/1/14
Lialda & Uceris
Principal Coordinate Analysis of Microbiome Ecology
PCoA by Justine Debelius and Jose Navas, Knight Lab, UCSD
Weight Data from Larry Smarr, Calit2, UCSD

19. 8x Compute Resources Over Prior Study
To Expand IBD Project the Knight/Smarr Labs Were Awarded ~ 1 CPU-Century Supercomputing Time
Smarr Gut Microbiome Time Series
From 7 Samples Over 1.5 Years
To 50 Samples Over 4 Years
IBD Patients: From 5 Crohn’s Disease and 2 Ulcerative Colitis Patients to ~100 Patients
50 Carefully Phenotyped Patients Drawn from Sandborn BioBank
43 Metagenomes from the RISK Cohort of Newly Diagnosed IBD patients
New Software Suite from Knight Lab
Re-annotation of Reference Genomes, Functional / Taxonomic Variations
Novel Compute-Intensive Assembly Algorithms from Pavel Pevzner
8x Compute Resources Over Prior Study

20. What I Have Measured Is Rapidly Being Superseded to Include Deep Characterization of the Human Body

21. The Future Foundation of Medicine is an Exponential Scaling-Up of the Number of Deeply Quantified Humans
Twitter 9/27/2014

22. Thanks to Our Great Team!
Future Patient Team
Jerry Sheehan
Tom DeFanti
Joe Keefe
John Graham
Kevin Patrick
Mehrdad Yazdani
Jurgen Schulze
Andrew Prudhomme
Philip Weber
Fred Raab
Ernesto Ramirez
JCVI Team
Karen Nelson
Shibu Yooseph
Manolito Torralba
Ayasdi
Devi Ramanan
Pek Lum
UCSD Metagenomics Team
Weizhong Li
Sitao Wu
SDSC Team
Michael Norman
Mahidhar Tatineni
Robert Sinkovits
Dell/R Systems
Brian Kucic
John Thompson
UCSD Health Sciences Team
David Brenner
Rob Knight Lab
Justine Debelius
Jose Navas
Gail Ackermann
Greg Humphrey
William J. Sandborn Lab
Elisabeth Evans
John Chang
Brigid Boland