Healthcare Solutions – 1 12/12/2013

Healthcare Solutions

Big Data, Personal Analytics and Health Engineering

Utah HIMSS Salt Lake City, UT

David L. Riley Chief of Informatics Nov 6, 2013

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Motivation

• With my departure from ONC in Oct 2010 I discovered I could afford to take some time to do something new… P4 Medicine caught my attention.

• Healthcare is becoming: – Personalized – Predictive – Preventive – Participatory

• But what does it really mean? • May 2011, I experienced a singular moment of clarity during a

routine primary care office visit. • Shocked out of my complacency and with a new found sense of

urgency I launched myself whole-heartedly into P4 Medicine…my life now depended on getting it right.

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THE GRAND ADVENTURE BEGINS

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The Approach

• Educate myself

• Acquire Genetic Datasets: – 23&Me SNP profile – latest chip set

• ~ 1 million SNPs – Exome 80X

• 50 million base pairs of data • ~180,000 exons that make up our proteins

• Conduct a series of N=1 Clinical Trials – Start with diabetes – Formulate and test hypotheses – Take a data driven approach to decision making

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Getting an Education

• Genomics • Transcriptomics • Proteomics • Metabolomics • Metabanomics • Microbiomics • Pharmacogenomics • Toxicogenomics • Metagenomics

In the past 3 years I have read thousands of pages in the literature in:

• Nutrigenomics • Epigenomics • Exposomics • Glycomics • Phenomics • Statistical Mechanics of

Networks • Food Science • Pharmacognosy

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What we know from my Exome

• Variant Frequencies – Novel – 403 (variant has not been observed in dbSNP or 1000

Genomes dataset) – Unknown 473 (variant observed in dbSNP but not in the 1000 Genomes

dataset) – Rare – 946 (variant with an allele frequency < 1%) – Somewhat rare – 1,701 (variant with a frequency of 1-5%) – Common – 42,107 (variant with a frequency > 5%)

• Variant Impact – High impact – 235 (Frame shift (indel not multiple of 3), splice site, stop

gain, start loss, stop loss) – Moderate impact – 7423 (nonsynonymous substitution, codon indel –

multiple of 3) – Low impact – 8459 (synonymous substitution, start gain, synonymous

stop) – Unknown – 29,513 (variant falls in intron, UTR non-coding transcript or

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N=1 Clinical Trial Patient

• Subject is a 54 y/o wm with the following: • Active Problem List

– Dyslipidemia dx in 1978 – Coronary Artery Disease dx in 1987 S/P MI in 2004 – Non-Alcoholic Fatty Liver Disease dx in 1992 confirmed by Liver Biopsy

in 2010 – Hypertension dx in 1993 – Metabolic Syndrome dx in 1994 – Obesity dx in 1997 – Congestive Heart Failure/Secondary Pulmonary Hypertension dx in

1994 – Obstructive Sleep Apnea Syndrome dx in 1996 – Narcolepsy dx in 1996 – Type 2 DM Adult onset dx in 2000 – Hashimoto’s Thyroiditis dx by FNA in 2000

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N=1 Clinical Trial Patient

• PMH: – Multiple Renal Calculi 1978, 1983, 1984 – Anaphylaxis to Contrast Agent 1984 – Atorvastatin induced Rhabdomyolysis 2000 – Appendicitis with rupture 2000 – Post surgical wound infection requiring I&D 2000 – Multiple Pulmonary Emboli – 2006 – Severely Dysplastic Adenoma situated on the Papillae of Vater – 2006 – Bowel Obstruction/Temporary Colostomy– 2009

• PSH: – Wisdom Tooth Extraction x4 1986 – Appendectomy 2000 – Post-surgical wound infection I&D 2000 – Laparotomy with temporary colostomy 2009 – Ostomy take down with re-anastamosis 2009

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N=1 Clinical Trial Patient

• Procedure History: – Cardiac Catheterizations 1987, 1994, 2001, 2004 x3, 2008, 2013 – Angioplasty with 33mm Sirolimus Eluting Stent 2004 – CT guided liver biopsy 2010 – EGD 1987, 2006 x2 with loop excision of adenoma in duodenum – ERCP 2006, 2010

• Medications – Furosemide – Triamterene/HCTZ – Lisinopril – Metoprolol – Aspirin – Loratidine – Metformin – B12 (methylcobalamin)

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Care Team

• Primary Care – Physician – Diabetic Nurse – Coumadin Clinic Pharmacist – Cardiology Lipid Nurse – Dietitian

• General Surgeon • Specialists

– Cardiologist – Pulmonologist – Endocrinologist – Diabetologist – Hepatologist – Podiatrist – Gastroenterologist – Hematology/Oncologist

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In Summary

• Classic PPP Syndrome Patient • Multiple common complex disease co-morbidities

– Typical of many patients seen in the system today. – I have most of the chronic diseases that drive costs in healthcare today. – Care consumes a lot of resources = high costs.

• Highly motivated to improve my health outcomes – Much to be gained even with a modicum of success. – What did I have to lose? ...and I didn’t need to get IRB approval to start.

• Diabetes not optimally controlled (What is optimal?) – HgbA1C - 7.2% – Start with Diabetes

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Starting with diabetes

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Genes with SNPs relevant to Type 2 Diabetes

• The genotype for these SNPs decreased my risk for T2DM – TCF7L2 – IGF2BP2 – HHEX – SLC30A8 – WFS1

• The genotype for these SNPS increased my risk for T2DM – PPARG – involved in formation of fat storage cells/plays a role in insulin

resistance – KCNJ11 –Decreased insulin response to glucose – CDKAL1 – Impaired 1st phase insulin response – CDKN2A/B – Impaired baseline insulin secretion – KCNQ1 – Impaired baseline insulin secretion – MTNR1B – increased fasting plasma glucose levels

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Collect and collate the data

• Hgb A1C – used the A1CNow kit for monthly tracking of A1C values

• Baseline glucose monitoring – 7 data points each day: – Upon waking, prior to each meal (x3), 2 hours post-prandial (x3) and at

bedtime

• Food glucose performance curves – For a given food item and quantity of food

• Baseline glucose determination prior to commencing eating the food item

• Immediately upon completion of the food item obtain glucose reading and one every 10 minutes until glucose returns to baseline

• Plot glucose curve for that food item • Collect data on glucose response curves for specific foods and

specific combinations of foods • Calculate the AUC the curve and look for patterns in the data to

better understand the nutridynamics

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Set some targets

• What is the optimal target for glucose levels to support optimal health? – Optimal vs. normal

• Targets for optimal health – Fasting glucose <86 mg/dL – 2 Hour post-prandial glucose <126 mg/dL – 4 Hour post-prandial glucose – returned to fasting baseline – Hgb A1C <4.9%

• Utilizing these targets, develop a simple algorithm for making decisions about what to eat and what not to eat: – Reduce the amount or eliminate any foods that cause more than a 40

mg/dl spike in glucose levels or – That cause an elevation in glucose that takes more than 2 hours to drop

below 126 or – That cause an elevation that takes more than four hours to return to

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Getting started

• Identify and test a combination of medications, nutraceuticals, herbals and functional foods to address the specific problems driven by my personal data findings to: – Improve first phase insulin response – Increase glucose clearance via non-insulin mediated means – Modulate liver production of glucose – Delay digestion and absorption of sucrose. – Moderate alpha-amylase enzyme activity to reduce the absorption of

carbohydrates into the bloodstream. – Reduce alpha-glucosidase enzyme activity to further slow the

absorption of starches and sugars into the bloodstream. – Improve leptin sensitivity to stimulate lipolysis. – Modulate levels of adiponectin to help maintain healthy insulin

sensitivity. – Moderate glycerol-3-phosphate dehydrogenase enzyme activity to

reduce the amount of ingested starches that are converted to triglycerides and stored as fat.

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As my understanding deepened I expanded the therapeutic targets

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Outcomes

• Identified a set of pharmaceuticals, nutraceuticals, herbals and functional foods to use in my therapeutic program for diabetes

• Proved the utility of the food decision algorithm – Achieved fasting morning glucose levels in the 80s. – Designed meals where peak post-prandial glucose response was less

than 126 mg/dL

• Improved health outcomes – Optimized HgBA1C (4.9%) – ~50 lb weight loss – Significant improvement in Lipid values – Blood pressure improved to optimal values – Improved inflammatory markers – Improved quality of life – Improved exercise tolerance

• Improved understanding of diabetes

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What have I learned?

• To personalize care we need to: – Use convergent genomics for patient stratification – Identify a biomarker that can be monitored to drive therapy – Make data driven day to day decisions using a simple algorithm (give

the patient a simple rule to make decisions based on the biomarker being monitored)

– Monitor the biomarker and alter therapy accordingly

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Where am I today?

• In May I decided to stop the various compounds I was taking (except for core prescription medications) and monitor for a few months to see what happened. – It took from May to October to see significant elevations in am fasting

glucose levels, increased post-prandial excursions both in peak and length of time before returning to fasting baseline, slowly creeping up on Hgb A1C

– In August experienced a sudden increase in the amount of hypnogogic hallucinations and significantly fragmented sleep architecture with increased daytime hypersomnolence – this was a unexpected • Implemented daily review of CPAP data, pulse-oximetry data and

Zeo Sleep data, hacked Zeo to extract actual EEG/EOG/EMG waveforms to apply FFT to perform spectral power analysis and more detailed analysis of sleep epochs to verify severity of sleep fragmentation

– Experienced 12-15 lb weight gain

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A revised Equation: A new Understanding

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Now Becomes:

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The New Biology

• Sees the human organism as a robust complex adaptive system

• Utilizes high through put analytic tools to assay biological systems

• Models biology using networks and analyzes them using statistical mechanics

• In order to better understand how disease-associated variations in DNA actually contribute to development of disease we need to organize genomic sequencing and genome-wide activity data into coherent functional units represented by networks.

• Empirically derived networks are necessary for describing the molecular mechanisms and biological processes that drive disease under the influences of inherited risk factors (genetic markers) and environmental risk factors.

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Co-expression network

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Tissue specific and cross-tissue molecular networks

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“Omics” datasets

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Genetic and Environmental Risk

• To unearth the 85-95% of common complex disease risk that is not explained by current analyses of GWAS data sets, it will be important to address contexts defined by macro- and microenvironmental factors

• Macroenvironmental factors – mainly those that affect the individual through exposure to toxins, food intake, and other life-style factors vary over time and alter the microenvironments within distinct tissues and cell types.

• The predominant microenvironment determines which DNA variants promote disease risk and the extent to which they do so.

• The effects of most disease-linked DNA variants probably vary with the phenotypic situation, as reflected by exposures to shifting macro- and microenvironments.

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Genetic and Environmental Risk

• Some genetic risk variants are environment independent and their disease associations may be detected early in life

• Other genetic risk variants are age-related (such as epigenetic changes)

• Some genetic risk variants are dependent on contexts in the macroenvironment such as life-style factors exposures to environmental toxins, foods, etc.

• Over time these macroenvironmental factors transform microenvironments (in cell and tissue types) to activate DNA variants that then exert their risk-promoting effects on certain common complex diseases.

• Macroenvironments alter microenvironments over time as such environment dependent DNA risk variants become increasingly important for common complex diseases later in life.

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Disease networks

• Complete disease networks ideally consist of all relevant genes and thus represent the entire complexity of a given disease and capture all pathological perturbations that alter disease-related processes.

• The network senses alterations in the microenvironments and the presence of certain DNA variants reacts accordingly, leading to increased or decreased disease risk and development.

• Disease networks both sense and mediate the effects of microenvironmental contexts and the genetic variations relevant to those contexts.

• Most disease linked DNA sequence variations exert their effects in specific contexts. The effects of most DNA sequence variants linked to type 2 diabetes in Caucasians are manifested only in patients with a body mass index above 26

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Intermediate Phenotypes

• Intermediate phenotypes arise from the activity of groups of functionally associated genes (e.g., in physiological pathways or networks) that are directly affected by disease DNA loci (disease genotypes) and act to mediate the disease risk represented by the DNA loci.

• Changes to the intermediate phenotypes appear before the full-blown disease phenotype is manifested and can be captured at an omics level through screens of RNA transcripts, proteins, and metabolites in relevant cell types and tissues.

• Depending on whether the omics data are gathered from healthy or diseased tissues (or at intermediate steps), these networks represent molecular states of physiology transforming into pathology.

• Using these insights we can efficiently define key disease processes that drive common complex diseases.

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Focusing on health

• The existence of intermediate phenotypes enables us to shift our focus from disease to defining health

• If we turn our focus on disease to a focus on health using these approaches we can quantitatively define an optimal state of health and the intermediate phenotypes that progress from optimal health to disease to advanced stages of systemic disease

• The health continuum can then be modeled as a measurable set of intermediate phenotypes that looks something like this: – Optimal – Adaptive – Dysadaptive – Maladaptive – Pre-disease – Disease

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The health continuum

• Health can be modeled as a continuum of states • Each category is a stable state with measurable characteristics • There are defined transitions between states • When an allostatic load is applied to the system compensatory

mechanisms are invoked to maintain stability, if the load is severe enough or lasts long enough the system will shift to a new attractor basin and assume a new stability around a different set point.

• Stability is dynamic and buffering capacity is reflected in variability

• Our genetic individuality affects our buffering capacity and the ability of the system to maintain stability in the face of perturbations and/or to re-acquire stability in the event of a shift in state space to a new attractor basin.

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What is Health Engineering?

• It is a clinical discipline of P4 Medicine • Grounded in systems biology, medical science and engineering

practice • Health is defined quantitatively around system state and

whether or not adaptive responses and/or subsystems are active rather than simply “the lack of disease”

• Recognizes that complex biological systems are dynamic, fluid systems able to reconfigure themselves as conditions demand.

• It is focused on identifying an optimal state of function for an individual with a given set of genetics in a particular environmental niche

• Founded in biological individuality • Focused on optimization

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What emerges out of all of this?

• An approach to understanding how to identify the pathway between a genetic marker and its impact on the clinical finding via understanding the gene product, its role and the impact the altered gene product has on its ability to fulfill that role in metabolism.

• An approach to stratification of patients using genetic markers.

• An approach to doing randomized, placebo-controlled, double-

blind, cross-over clinical trials where n=1. – May be used to determine if a therapeutic agent is effective for a given

patient, or – May be used to determine optimal dosing for a given patient for a given

therapeutic agent

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What emerges out of all of this?

• We will not do less testing in the future…it will just be done in the patient’s setting using more and more sophisticated sensor technology generating enormous amounts of data events

• The patient will need to couple this data with simple to understand and use algorithms for making decisions to improve outcomes

• Gene expression, protein expression and metabolic expression testing and functional metagenomic testing will move into the setting of the primary care provider

• Personalization has its foundation in stratification based on genetic profiling and understanding its link to the functional physiology.

• We will intervene earlier based on this understanding of genetics and its impact on function with a goal of optimizing function for health.

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Impacts on Health IT

• Future systems will need to be able to process large amounts of sensor data

• Computational pragmatics will become a more defined discipline developing sophisticated filters to handle the massive amounts of data

• The need for standard interoperable computable data will grow and become a critical element in being able to handle the ever growing amounts of data

• The provenance and management of evidential records become more critical

• We will need to be able to reliably manage the identity of not only people but also the sensor devices being used to collect the data this will include strong authentication and reliable non-repudiation of sources

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DAVID L. RILEY DRILEY03@HARRIS.COM

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