This document summarizes Dr. Larry Smarr's presentation on quantifying physiological data from his own body over the past decade. Some key points: - Smarr has gathered longitudinal time series data on over 200 biomarkers and microbiome samples to study phenotype changes from his autoimmune disease. - Sensors have tracked daily metrics like weight, activity levels, and symptoms, revealing oscillations and episodes of inflammation. - Imaging and biomarker analysis identified the specific location and nature of his Crohn's disease. - Analysis of his microbiome samples over time uncovered a shift in microbial ecology that correlated with changes in drugs and symptoms. - Expanding this type of personalized, quantitative approach could transform medicine by deeply characterizing individuals

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
http://lsmarr.calit2.net
1

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. Over the Last Decade, I Have Used a Variety of Personal Sensors
To Quantify My Body & Drive Behavioral Change
Withings/iPhone-
Blood Pressure
Zeo-Sleep
Azumio-Heart Rate
MyFitnessPal-
Calories Ingested
FitBit -
Daily Steps &
Calories Burned
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
My Resting Heartrate
Fell from 70 to 40!
Elliptical
Walking
Sunday January 17, 2016
137
42
I Increased
Walking,
Aerobic, and
Resistance
Training,
All of Which
Have Health
Benefits

5. From Measuring Macro-Variables
to Measuring Your Internal Variables
www.technologyreview.com/biomedicine/39636

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

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

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

10. Time Series Reveals Oscillations in Immune Biomarkers
Associated with Time Progression of Autoimmune Disease
Immune &
Inflammation
Variables
Weekly
Symptoms
Pharma
Therapies
Stool
Samples
2009 20142013201220112010 2015

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

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

13. 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
Lialda
Uceris
Frequent IBD Symptoms
Weight Loss
Few IBD Symptoms
Weight Gain
Source: Larry Smarr, UCSD

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

19. 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
Source: @EricTopol
Twitter 9/27/2014

22. Thanks to Our Great Team!
Calit2@UCSD
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
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
Dell/R Systems
Brian Kucic
John Thompson