linking phenotype changes to internal/external longitudinal time series in a single human

“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 MonitoringOrlando, FL

August 18, 2016

Dr. Larry SmarrDirector, California Institute for Telecommunications and Information Technology

Harry E. Gruber Professor, Dept. of Computer Science and Engineering

Jacobs School of Engineering, UCSDhttp://lsmarr.calit2.net

1

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.

Over the Last Decade, I Have Used a Variety of Personal SensorsTo Quantify My Body & Drive Behavioral Change

Withings/iPhone-Blood Pressure

Zeo-SleepAzumio-Heart Rate

MyFitnessPal-Calories Ingested

FitBit -Daily Steps &

Calories Burned

Withings WiFi Scale -Daily Weight

Wireless Monitoring Produced Time Series That Helped Me Improve My Health

Since Starting November 3, 2011Total 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, 2016137

42

I Increased Walking,

Aerobic, and Resistance Training,

All of WhichHave Health

Benefits

From Measuring Macro-Variables to Measuring Your Internal Variables

www.technologyreview.com/biomedicine/39636

As a Model for the Precision Medicine Initiative, I Have Tracked My Internal Biomarkers To Understand My Body’s Dynamics

My Quarterly Blood DrawCalit2 64 Megapixel VROOM

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

Adding Stool Tests RevealedOscillatory 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

TypicalLactoferrin Value

for Active

Inflammatory Bowel Disease

(IBD)

Descending Colon

Sigmoid ColonThreading 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 ColonLiver

Small Intestine

Diseased Sigmoid ColonCross SectionMRI Jan 2012

Severe ColonWall Swelling

Time Series Reveals Oscillations in Immune BiomarkersAssociated with Time Progression of Autoimmune Disease

Immune &Inflammation

Variables

Weekly Symptoms

PharmaTherapies

StoolSamples

2009 20142013201220112010 2015

What Can We Learn From the Gut Microbiome Time Series In an Individual?

Your Microbiome is Your “Near-Body” Environment

and its CellsContain 100x as Many DNA GenesAs 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 BodyWill Radically Alter Medicine

Evolving Microbiome Environmental Pressures: Dynamical Innate and Adaptive Immune Oscillations in Colon

Normal <600

Innate Immune System

Normal 50 to 200

Adaptive Immune SystemThese Must Be Coupled to

A Dynamic Microbiome Ecology

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

LS Weekly Weight During Period of 16S Microbiome AnalysisAbrupt Change in Weight and in Symptoms at January 1, 2014

Lialda

Uceris

Frequent IBD SymptomsWeight Loss

Few IBD SymptomsWeight Gain

Source: Larry Smarr, UCSD

My Microbiome Ecology Time Series Over 3 Years

Source Justine Debelius, Knight Lab, UC San Diego

Coloring Samples Before (Blue) and After (Red) January 2014Reveals Clustering


An Apparent Sudden Phase Change Occurs


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 SymptomsWeight 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 SymptomsWeight Gain 1/1/14 to 8/1/15

Red Balls on Diagram to the Right

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

What I Have Measured Is Rapidly Being Supersededto Include Deep Characterization of the Human Body

The Future Foundation of Medicine is an Exponential Scaling-Up of the Number of Deeply Quantified Humans

Source: @EricTopolTwitter 9/27/2014

Thanks to Our Great Team!

Calit2@UCSD Future Patient TeamJerry SheehanTom DeFanti Joe Keefe John GrahamKevin PatrickMehrdad YazdaniJurgen Schulze Andrew Prudhomme Philip Weber Fred RaabErnesto Ramirez

JCVI TeamKaren Nelson Shibu Yooseph Manolito Torralba

AyasdiDevi RamananPek Lum

UCSD Metagenomics TeamWeizhong Li Sitao Wu

SDSC TeamMichael Norman Mahidhar Tatineni Robert Sinkovits

UCSD Health Sciences TeamDavid BrennerRob Knight Lab Justine Debelius Jose Navas Gail Ackermann Greg HumphreyWilliam J. Sandborn Lab Elisabeth Evans John Chang Brigid Boland

Dell/R SystemsBrian KucicJohn Thompson

linking phenotype changes to internal/external longitudinal time series in a single human

Data & Analytics