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Linking Phenotype Changes to Internal/External Longitudinal Time Series in a Single Human


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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

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Linking Phenotype Changes to Internal/External Longitudinal Time Series in a Single Human

  1. 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 1
  2. 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. 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. 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. 5. From Measuring Macro-Variables to Measuring Your Internal Variables
  6. 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. 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. 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. 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. 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. 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. 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. 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. 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. 15. My Microbiome Ecology Time Series Over 3 Years Source Justine Debelius, Knight Lab, UC San Diego
  16. 16. Coloring Samples Before (Blue) and After (Red) January 2014 Reveals Clustering Source Justine Debelius, Knight Lab, UC San Diego
  17. 17. An Apparent Sudden Phase Change Occurs Source Justine Debelius, Knight Lab, UC San Diego
  18. 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. 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. 20. What I Have Measured Is Rapidly Being Superseded to Include Deep Characterization of the Human Body
  21. 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. 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