Systems Medicine and P4 Medicine: Transforming Healthcare through Wellness Lee Hood Logan Center 3-24-15 The grand challenge for biology and medicine is deciphering biological complexity.
Download ReportTranscript Systems Medicine and P4 Medicine: Transforming Healthcare through Wellness Lee Hood Logan Center 3-24-15 The grand challenge for biology and medicine is deciphering biological complexity.
Systems Medicine and P4 Medicine: Transforming Healthcare through Wellness Lee Hood Logan Center 3-24-15 1 The grand challenge for biology and medicine is deciphering biological complexity 2 6 Blind Men and an Elephant Systems Approach 3 Paradigm Changes Drive Radical Changes in Science 4 I Participated in Five Paradigm Changes in Biology Dealing with Complexity over 45 Years Leading to Systems Medicine and P4 Medicine • Brought engineering to biology – Developed 6 instruments that led to high-throughput biology and big data in biology (1970 - present) • The Human Genome Project – Invented enabling technology, advocate, participant, applying genomics to P4 medicine (1990-2003)—complete parts list human genes • Cross-disciplinary biology – Created 1st cross-disciplinary department: enabled technology development to be driven by biology (1992-2000) • Systems biology – Created 1st systems biology institute: deciphering the complexities of biology and disease (2000 – present) • Systems medicine / emergence of proactive P4 medicine – Early advocate and pioneer of a P4 medicine that will transforming healthcare (2001 – present) – Pioneered systems driven technologies and strategies for P4 – 100,000 person wellness project (2013—present) 5 Central features of systems medicine 6 Dynamical personalized clouds of billions of data points—capturing both genetic and environmental contributions to wellness and disease TeleHealth 11010100 01010101 01101010 10100100 01011010 10001 Phenome Na143 K 3.7 BP 110/70 HCT32 BUN 12.9 Pulse 110 PLT150 WBC 92 Social Media Epigenome 11010100 01010101 01101010 10100100 01011010 10001 11010100 01010101 01101010 10100100 01011010 10001 Genome Transcriptome GCGTAG ATGCGTA GGCATGC ATGCCAT TATAGCTT CCA UUAGUG AUGCGU CUAGGC AUGCAU GCC Proteome arg-his-progly-leu-serthr-ala-trptyr-valmet-phe- iPS Cells Transactional 11010100 01010101 01101010 10100100 Single Cell 11010100 01010101 01101010 10100100 11010100 01010101 01101010 10100100 7 Systems Medicine Biological networks carry information and mediate development, physiology and aging Disease-perturbed networks mediate disease • Integration of patient data will reveal biological networks that specify health and are altered in disease • Understanding differences in normal and diseaseperturbed networks will provide fundamental insights into disease mechanisms • These insights are essential for developing more effective diagnostic and therapeutic approaches 8 Evolution of the Vision for Systems Medicine and P4 Medicine 2001 - Present • By 2006, the vision of Systems Medicine/P4 medicine had been clearly articulated by ISB – Key question: How to bring P4 to the healthcare system? • In 2008, ISB formed a 5 year $100M strategic partnership with Luxembourg pushed us to a tipping point for medicine – Developed about 10 new systems-driven technologies and strategies – Placed P4 medicine at a tipping point for transforming the practice of healthcare • In 2013, ISB first proposed the P4 pilot project to carry out a longitudinal, high-dimensional data study 100,000 well people – Bringing P4 medicine to the contemporary healthcare system • Obama’s precision medicine initiative—presents a unique funding opportunity for the 100 K wellness project 9 The convergences leading to a medicine that is predictive, preventive, personalized and participatory 10 The Emergence of P4 Medicine Predictive, Preventive, Personalize, Participatory Systems Biology & Systems Medicine Consumer-Driven Social Networks P4 MEDICINE Digital Revolution Big Data Converging Megatrends Driving the transformation of healthcare for patients 11 P4 medicine vs. contemporary medicine • Proactive vs. reactive • Focus on individual vs. populations • Focus on wellness and disease vs. just disease • Generate personalized data clouds • Use personalized data clouds rather than averaged populations of patients for clinical trials (N-1 experiments) • Employ patient (consumer) activated social networks for education, crowd sourcing and advocacy 12 Conceptual Themes of P4 Medicine P4 Medicine Predictive Preventive Personalized Participatory Wellness Quantified Wellness Industry Disease Demystified Disease Industry 13 Understanding Wellness is Key Developed World If the trend of the last 10 years of increases in life expectancy continue, more than half of all children born today in developed countries can expect to celebrate their 100th birthday. Christensen, Ageing Populations: The Challenges Ahead, Lancet , 2009 14 A Framingham-like digital-age study of wellness in 100,000 (100K Project) patients longitudinally -- 20-30 years 2014 P4 Pilot Project Hundred Person Wellness Project (March--December 2014) Nathan Price—ISB--CoPI 15 Assays / Measurements Whole Genome Sequencing Detailed lab tests 3x (blood, urine, saliva) Database of actionable possibilities that will grow exponentially over time Continual self-tracking & lifestyle monitoring Gut Microbiome 3x 16 Health: What do we really want to understand from 100,000 well patients? Wellness Wellness Disease transition Time 17 Actionable Traits, Coaches and Positive Reinforcement • Actionable possibilities permit individuals to optimize wellness or avoid disease • Actionable possibilities from single or integrated data types • Coaches with MD advisors – Bringing actionable opportunities to each individual – Nourish relationship based accountability for participants • Positive reinforcement / immediate gratification – Individuals can see improvement of their high-dimensional data clouds within a three-month period (from one blood draw or other sample to the next) 18 107 Individuals Nature, Feb 2014 19 Preliminary stories about actionable possibilities for the 107 Pioneers Nathan Price--ISB 20 New Discoveries: Four types of transitions from single and integrated data Identifying four early transitions to optimize wellness and avoid disease: Whole Genome Sequencing DISCOVERY Quantified Self/Traits Data Integration & Correlations Gut Microbiome Lab Tests Blood, saliva, urine • • • • Less to more well More to less well Wellness to disease Disease to wellness 21 Initial Clinical Labs Discovery: High Rate of Actionable Clinical Lab Results • The 107 “well” participants had a high rate of initial abnormal lab results Cardiovascular 59% Diabetes Risk 54% Baseline Blood Results Inflammation • 100% of the participants had actionable recommendations from their blood results 68% Nutrient Abnormalities 91% 22 22 Clinical Labs Discovery: Behavior modification reduces heavy metal toxicity 1. Baseline: High mercury levels in blood 2. Coached to modify diet - eight weeks of eating salmon sushi vs. tuna sushi (3x a week) 3. Reduced mercury levels in three months 23 23 Actionable possibilities from two or more integrated data types 24 Wellness to disease transition: hemachromatosis 25 Genetics and Clinical Labs: Hemochromatosis Detected risk of a deadly disease in two participants 250.0 Ferritin levels 200.0 150.0 100.0 50.0 0.0 Zero copies of rare variant (86 individuals) One copy of rare Two copies of rare variant variant (12 individuals) (2 individuals) Baseline 3 months • Blood + Genetics illuminated the effects of increasing copies of the Hemochromatosis variant • Left untreated, this disorder could lead to cartilage damage, liver cancer, diabetes, and heart disease: Easily treated by regular blood donations to reduce the iron stores • One participant ALREADY had cartilage damage from his undiagnosed disease • Subsequent family genetic testing detected other family members at risk 26 26 The power of integrative analysis on multiple quadrants where N=100 We have calculated more than 30 thousand statistically-significant correlations between all of our five data quadrants for 107 individuals. 40 135 LDL Cholesterol 35 125 30 25 115 20 105 15 95 10 85 5 0 75 Very Low Low Med ium Genetic Risk High Very H igh We have demonstrated that genome data can successfully predict risk for traits such as high LDL cholesterol, Crohn’s disease, vitamin D deficiency and type 2 diabetes. We believe it can predict risk for all GWAS diseases. We have begun to identify the next generation of early-stage disease biomarkers and the first metrics for wellness. 27 Enormous potential for genetics (GWAS variants) to be early indicators of disease transition risks ADHD COPD 2000 Genomes Crohn's disease Distribution from Obesity Alzheimer's disease 140 Anorexia Esophageal cancer 120 Asthma Gout Atrial fibrillation 80 Grave's disease Variant Bone mineral density rs59583 Variant rs111393 Inflammation Ovarian cancer Hypertension20 Parkinson's disease Hypothyroidism0 Primary biliaryRisk cirrhosis Cumulative Inflammatory bowel disease Prostate cancer Variant rs5837 Variant rs6827 Macular degeneration Psoriasis Estimated risk for the Rheumatoid arthritis disease or trait relative Schizophrenia to a population Stroke Magnesium levels Type 1 Diabetes Chronic kidney disease Metabolic syndrome Type 2 Diabetes Colorectal cancer Migraine Ulcerative colitis Coronary heart disease Multiple sclerosis Urate levels Variant rs68883 Calcium Var iant Variant Celiac disease Variant Variant Cholesterol levels rs68883 Variant rs14445 rs6837 Beneficial Variant Iron levels Lung Cancer Variant rs0994 Cardiovascular disease rs9769 Variant rs9769 Osteoporosis 100 Pancreatic cancer Variant rs1352 Blood pressure Osteoarthritis Hematocrit 40 rs68279 Bipolar disorder Variant rs6827 Below average 60 Breast cancer Variant Variant rs5837 Variant rs8572 Myopia Variant rs14445 Lupus Detrimental Variant 28 Enormous potential for genetics to be early indicators of disease transition risks—inform individual’s physician 150 Alzheimer’s disease Below average risk 100 50 0 Variant rs68279 Variant rs5837 Variant rs1352 Variant rs8572 Cardiovascular disease Variant rs5837 Variant rs59583 Variant rs6827 150 Cumulative Risk Variant rs111393 Above average risk 100 Variant rs6827 50 Variant rs68883 Var iant Variant rs0994 Variant rs9769 Variant rs14445 Variant rs6837 Variant rs9769 Variant rs14445 Variant rs68883 0 150 Cumulative Risk Type 2 Diabetes Average risk 100 50 Beneficial Variant Detrimental Variant 0 Cumulative Risk 29 Personalized Dynamical Data Clouds Growing exponentially over time By examining only one data type, it was determined that 100% of the 107 Pioneers have actionable traits: • Hence, virtually every person will have multiple, actionable traits as their data are aggregated and integrated • These actionable possibilities will change as the environment changes, as reflected by dynamically changing personalized data clouds 30 107 Pioneers insights • Your genome does not control your destiny—rather just you potential. • I can take control of my health with the proper data. • Almost all of the Pioneers want to continue the longitudinal wellness study in its next phase. 31 100K Project: Transforming Healthcare • Identify vast array of actionable possibilities • Analytics to optimize wellness and avoid (reduce) disease for each individual patient—optimize human potential • Create a data base of wellness measurements to mine for the “multiparameter wellness metrics”—define fundamental human features of wellness—physiological and psychological • Generate a data base from individuals that will allow us to follow early disease mechanisms in the transitions from wellness to disease for major diseases—diabetes, cardiovascular, cancer, neurodegeneration and diseases of pregnancy—enable early transition back to wellness • Drive the development of improved old and new assays and analytics—parallelize, miniaturize, increase throughput, reduce cost, point of contact—digitalization through smart phone format • Database of wellness and disease transitions catalyze innovation for the wellness industry industry • Bring P4 medicine into the healthcare system – Improving the quality of healthcare – Decreasing the cost of healthcare – Promoting innovation in Healthcare 32 The ISB Wellness Project Has Two Directions • The 100,000 person wellness project— academic—discovery science—pioneer assays (to the smart phone) and pioneer the integrative and modeling analytics—seek Congressional funding—focus on both wellness and early disease transitions • The company—Wellness Sciences— consumer directed—may really lead the large-scale adoption of P4 medicine and the democratization of healthcare 33 Consumer Wellness and the Digitalization of Healthcare Will Help Democratize Healthcare 34 Essence of the vision for the future of US healthcare • Generate 350 million dynamical personal data clouds— capture genetic and environmental data • Develop analytics to – Integrate and model individual data cloud to optimize wellness and avoid disease – Develop metrics for wellness • Develop a database to – Identify wellness and disease transitions – Transform how pharma and nutrition companies carry our research – Catalyze the newly emerging wellness industry • Bring P4 medicine to healthcare systems – Focus on wellness—improve human potential and use disease transitions to quickly reverse disease – Improve quality of healthcare – Decrease costs • Democratize healthcare—bring to developing as well as developed nations 35 36