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GetPersonalized! Estonian approach - from biobanking to precision medicine, Andres Metspalu

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Estonian approach - from biobanking to precision medicine, Andres Metspalu, Director, Estonian Genome Center, (Estonia), GetPersonalized! summit in Helsinki on 25 May 2015

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GetPersonalized! Estonian approach - from biobanking to precision medicine, Andres Metspalu

  1. 1. Estonian approach - from biobanking to precision medicine Andres Metspalu Professor & Director Estonian Genome Center (Estonia)
  2. 2. Estonian approach: from biobanking to precision medicine Andres Metspalu, M.D., Ph.D Estonian Genome Center University of Tartu Helsinki May 25th, 2015 GetPersonalized! Summit in Helsinki
  3. 3. Per Med Definition in EU documents • Personalised medicine refers to a medical model using molecular profiling for tailoring the right therapeutic strategy for the right person at the right time, and/or to determine the predisposition to disease and/or to deliver timely and targeted prevention This is the EU H2020 definition of the Per Med.
  4. 4. For personalized medicine we’ll need: E-infrastructure (e-health, digital prescriptions, digital signature, electronic health records etc.) Public support Primary care providers support Population based biobank DNA analysis technology Databases on genotype phenotype associations
  5. 5. Estonian Biobank • Estonian Genome Center, University of Tartu • A prospective, longitudinal, population-based database with health records and biological materials • 52,000 participants - 5% of the adult population of Estonia • Individuals are recruited by GPs, physicians in the hospitals and medical personnel in the EGCUT recruitment offices • Estonian Human Genes Research Act (HGRA)
  6. 6. § 3. Chief processor of Gene Bank • (1) The chief processor of the Gene Bank is the University of Tartu, whose objectives as the chief processor are to: • 1) promote the development of genetic research; • 2) collect information on the health of the Estonian population and genetic information concerning the Estonian population; • 3) use the results of genetic research to improve public health.
  7. 7. -0.05 -0.03 -0.01 0.01 0.03 0.05 0.07 -0.05 -0.03 -0.01 0.01 0.03 0.05 P21(4.68%) PC1 (8.65%) Austria Bulgaria Czech Republic Estonia Finland (Helsinki) Finland (Kuusamo) France Northern Germany Southern Germany Hungary Northern Italy Southern Italy Latvia Lithuania Poland Russia Spain Sweden Switzerland -0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 -0.12 -0.07 -0.02 0.03 PC223.8% PC1 36.6% Europe CHB JPT YRI CEU
  8. 8. Uuringubaas: TÜ Eesti Geenivaramu geenidoonorite kohort ca 52000 isikut
  9. 9. Public opinion and awareness of the EGCUT 2001- 2014 18% 16% 13% 19% 18% 16% 22% 33% 28% 36% 59% 55% 67% 70% 38% 39% 40% 36% 43% 39% 44% 33% 35% 36% 30% 33% 27% 27% 4% 4% 3% 4% 4% 2% 2% 2% 3% 2% 2% 2% 2% 1% June, 2001 Sept, 2002 Feb, 2002 March, 2003 March, 2004 Sept, 2004 Dec, 2005 May, 2007 Apr, 2008 July, 2009 June, 2010 April, 2011 July, 2013 Sept, 2014 In favor of the idea of EGCUT Never heard of EGCUT Against it Annely AllikKellele: Kuupäev: Tingimused: Tähtaeg: Kliendi nr.: 7 päeva 29.06.2010 013 SUMMAARVE SISU 24 900,00Uuring: Eesti elanike arvamus Geenivaramu projektist Leping nr. C-201-10, 24.05.2010.a. Tartu Ülikooli Eesti Geenivaramu Tiigi 61b Tartu 50410 Maksja: 22.06.2010 2902552Arve nr. Kaidi KandlaEsindaja: 24 900,001 Hind tk. Ühik Kogus EEK SUMMA EUR 1 591,40
  10. 10. Estonian Government • 16.12. 2014. the Cabinet approved the Pilot Program of Personalized Medicine” • Current government has “Personalized Medicine” as one of its activities for the next period • From 1.03.15. the Ministry of Social Affairs has a new position “Deputy Secretary General for E- services and Innovation”
  11. 11. e-Health
  12. 12. The Estonian ID card • The ID card is a mandatory ID document for all Estonian residents from the age of 15 • Enables secure digital authentication and signing • A digital signature has the same legal consequences as a hand-written signature • Does not have any additional information – No bank account, no health information etc. • Active cards: 1 214 428 (31.12.2013) – Estonian Population 1 286 540 (01.01.2013) – Estonia has been issuing electronic ID cards from January 1st 2002 – Also Mobile-ID
  13. 13. PHARMACIS AND FAMILY DOCTORS 2009 X-Roads, ID-card, State IS Service Register HEALTHCAREBOARD -Healthcareproviders -Healthprofessionals -Dispensingchemists STATEAGENCYOFMEDICINES -CodingCentre -Handlersofmedicines POPULATIONREGISTER PHARMACIS 2010january BUSINESSREGISTER HOSPITALS 2009 FAMILYDOCTORS 2009 SCHOOLNURSES 2010september EMERGENCYMEDICALSERVICE 2013 NATIONAL HEALTH INFORMATION SYSTEM 2008 december PRESCRIPTION CENTRE 2010 january PATIENT PORTAL 2009 XROADS GATEWAY SERVICE 2009 Architectural “Big picture” From Artur Novek (eHealth)
  14. 14. Documents total – 10.8 mio 1.1 mio persons medical data (growth 20% during 2012) National Patient Portal
  15. 15. 15 Acceptance • ePrescription covers 94% of issued prescriptions. • Over 90% of Hospital discharge letters – digital • Over 97% of stationary case summaries have sent to the central e-Health DB 1 mio person have documents (82% of population)
  16. 16. Do we have enough information to start?
  17. 17. Health Insurance Fund Estonian Myocardial Infarction Registry Tuberculosis Registry Estonian Cancer Registry Estonian Causes of Death Registry Population Registry Tartu University Hospital North Estonia Medical Center National Digital Health Record Database The broad informed consent. Phenotype data Phenotype database Coding center High security area. Data collector: baseline phenotype data (demographics, health, behavior,...) From questionnaires to the national registries 17
  18. 18. Example of an individual disease trajectory 18 Birth B01 Varicella [chickenpox] B05 Measles B26 Mumps L23.9 Allergic contact dermatitis, unspecified cause K02 Dental caries A37 Whooping cough J18.0 Bronchopneumonia, unspecified B00 Herpesviral [herpes simplex] infections J30 Vasomotor and allergic rhinitis B35 Dermatophytosis J01 Acute sinusitis J31 Chronic rhinitis, nasopharyngitis and pharyngitis F32.1 Moderate depressive episode G44.2 Tension-type headacheI10 Essential (primary) hypertension J20 Acute bronchitis . 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Birth J01 M54 G50.0 S51.0 J35.0 J38.4 H65.1 I21.9 K29.9 R49.0 F32.01 F43.22 D23.6 G44.0 J31.0 . 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Recruitment Birth F43.22 K29.9 . 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Male, born 1970, joined 2007 Smoker, BMI=27.5, low physical activity Health Insurance Fund NE Med Center
  19. 19. 19 . J01 Acute sinusitis M54 Dorsalgia G50.0 Trigeminal neuralgia S51.0 Open wound of elbow J35.0 Chronic tonsillitis J38.4 Oedema of larynx H65.1 Otitis media, acute and subacute allergic I21.9 Acute myocardial infarction, unspecified F43.22 Mixed anxiety and depressive reaction K29.9 Gastroduodenitis, unspecified R49.0 Dysphonia F32.01 Depressive episode with somatic syndrome F43.22 Mixed anxiety and depressive reaction K29.9 Gastroduodenitis, unspecified D23.6 Benign neoplasms of skin G44.0 Cluster headache syndrome J31.0 Chronic rhinitis . 2007 2008 2009 2010 2011 2012 2013
  20. 20. Polygenic risk scores: questions to address • Which markers to include? – We use 7500 independent markers for prediction of genetic risk of Type 2 diabetes and 10200 markers for Coronary Artery Disease • Optimal weights? - We show that optimal risk scores are obtained with double weights: regression coefficients from a large-scale meta- analysis are additionally weighted, to give more weight to more significant markers (correction for „winners curse“)
  21. 21. Polygenic risk scores Calculated as S = β1X1 + β2X2 + … + βkXk, X2,…, Xk - allele dosages for k independent markers (SNP-s), β1, β2, … , βk – weights Individuals at high genetic risk Polygenic risk score for type II diabetes: histogram of the score in 7462 individuals (Estonian Biobank) Score Frequency 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 050010001500
  22. 22. Practical usefulness of the polygenic risk score? BMI <25 BMI 25..30 BMI 30..35 BMI >35 T2D prevalence in individuals aged 45-80 0% 5% 10% 15% 20% 25% Below-average (<50%) genetic risk Above-average (50-90%) genetic risk Highest 10% of the genetic risk 24 27 5 80 134 52 119 198 84 116 154 47 (n=1810) (n=2020) (n=1337) (n=685)
  23. 23. Incident T2D: analysis of 264 incident cases in overweight individuals free of T2D at recruitment 0 1 2 3 4 5 6 0.00 0.02 0.04 0.06 0.08 0.10 0.12 Follow-up time (years) Cumulative risk of T2D in 3421 individuals BMI at recruitment >24 kg/m2, age 35-74 Highest genetic risk score quintile (>80%) Average risk score quintiles (20-80%) Lowest risk score quintile (<20%) p 8.3 10 8 81 cases p 1.9 10 3 153 cases 30 cases No significant sex difference, genetic risk score is the strongest predictor after BMI (fasting glycose and insulin measurements are not available for this cohort)
  24. 24. ROC curves (BMI=25..35)
  25. 25. Model-based predictions T2D
  26. 26. Coronary Artery Disease Males Females CAD prevalence in individuals aged 40-75 0% 5% 10% 15% 20% 25% Below-average genetic risk Above-average (50-90%) gen. risk Highest decile of the gen. risk 146 144 52 81 97 34
  27. 27. Incident Myocardial Infarction A weaker, but still significant effect seen in women: p=0.005
  28. 28. What is Clinical Decision Support Software (CDSS)? • CDSS provides clinicians with knowledge presented at appropriate times • It encompasses a variety of tools such as computerized alerts, clinical guidelines, and order sets • CDSS has the potential provide the necessary level of personalized guidance to providers at the point of care that will be necessary in the era of genomic medicine • This is a tool to advise PCP (like radiology report)
  29. 29. Virtuous Cycle of Clinical Decision Support Measure Guideline CDS Practice Registry http://www2.eerp.usp.br/Nepien/DisponibilizarArquivos/tomada_de_decis%C3%A3o.pdf
  30. 30. Approved at the Estonian Government Research and Development Council on 17.12.2013. - Health care - Educating health care professionals - Educating the patients - Further development of the eHealth incl. decision support systems - Research and Development - Sequencing individuals from the Estonian Biobank (ongoing), decision support analysis software, microarray analyze of 50 000 people from the biobank and return the important information to all who want by the PCP or in hospital Commercialization - IPR - Business agreements The Estonian Program for Personal Medicine I
  31. 31. Currently the pre-PILOT PROJECT is underway until the end of June 2015. Then the pilot will last up to 2018. The Estonian Program for Personal Medicine II The precision medicine method should be seenbasic as a additional “instrument” for physicians in diagnosing, treating and preventing disease, but also for research and clinical investigations, drug trials etc.
  32. 32. *Committee on a Framework for Developing a New Taxonomy of Disease; ‘Towards Precision Medicine’, National Research Council November 2011 Future Clinical Care Research Cohort From Prof. Böttinger
  33. 33. Evidence Generating Medicine • The next step beyond evidence-based medicine • The systematic incorporation of research and quality improvement considerations into the organization and practice of healthcare • To advance biomedical science and thereby improve the health of individuals and populations.
  34. 34. Challenges and issues • Awareness executives, doctors and patients • New technologies and data empower patient with more possibilities to manage own health • Ethical issues – Right to know and right not to know – Treatable and non-treatable conditions – Big data • Not enough knowledge about associations between DNA variants and diseases • Large work-load to keep database of known risk markers updated
  35. 35. Conclusions • Estonia has great potential to plan and implement personalized medicine solutions for the whole country, starting with the pilot project for 50 000 gene donors – Genetic research with 5% of population genetic and continuously updated phenotype information – Nation wide Health Information Exchange platform – 10 years of experience of national level e-services (PKI, X-Road, ID-card, security framework) – High level public trust and acceptance Estonian government: 25. We will establish a course towards personalised medicine based on modern gene technology.
  36. 36. Thank you! www.biobank.ee
  37. 37. Timeline: linking to registries 38 2007 2008 2009 2010 2011 2012 2013 2014 Pop reg 03/2010 Pop Reg 11/2010 Pop reg 10/2011 Pop reg 4/2012 Pop reg 1/2013 NDHRD 2/2014 Pop reg 3/2014 HIF 6/2014 Cancer Reg 12/2010 Cancer Reg 6/2012 Tub Reg 12/2012 University Hospital 8/2013 MI Reg 2/2014 North Estonia Medical Center 5/2014 01/2002 - 31/2010 Baseline 1/2011 - 6/2014 2nd visit 1/2003 - ... Electronic follow-up 2002 Death Reg 9/2010 Death Reg 5/2011 Death Reg 1/2012 Death Reg 12/2012 Death Reg 8/2013 Death Reg 6/2014
  38. 38. Prevented deaths of CHD in Finland By treating 2144 persons who have >20% risk when the genetic information is known 135 deaths would be prevented in 10 years! Tikkanen, E. 2013. Genetic risk profiles for coronary heart disease
  39. 39. GD in national registries • Population Registry 51800 GD 99.8% • Health Insurance Fund 51607 GD 99.5% • Estonian eHealth 39880 GD 76.9% • Tartu University Hospital 22492 GD 43.4% • North Estonia Medical Center 21202 GD 40.9% • Cancer Registry 2644 GD 5.1% • Causes of Death Registry 2349 GD 4.5% • Myocardial Infarction Registry 945 GD 1.8% • Tuberculosis Registry 260 GD 0.5% 40

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