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Genomic Testing for Type 2 Diabetes Risk: A Prototype for Personalized Preventive Medicine?

Genomic Testing for Type 2 Diabetes Risk: A Prototype for Personalized Preventive Medicine?. Alex Cho MD, MBA; Scott Joy MD; Julianne O’Daniel MS, CGC; Ley Killeya-Jones, PhD; Susanne Haga PhD; Isaac Lipkus PhD; Geoff Ginsburg MD, PHD Center for Genomic Medicine (IGSP) Duke University.

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Genomic Testing for Type 2 Diabetes Risk: A Prototype for Personalized Preventive Medicine?

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  1. Genomic Testing for Type 2 Diabetes Risk: A Prototype for Personalized Preventive Medicine? Alex Cho MD, MBA; Scott Joy MD; Julianne O’Daniel MS, CGC; Ley Killeya-Jones, PhD; Susanne Haga PhD; Isaac Lipkus PhD; Geoff Ginsburg MD, PHD Center for Genomic Medicine (IGSP) Duke University

  2. Clinical Vignette 40 yo M, BMI <30, no FH DM, not from higher-risk group; wants screening for diabetes

  3. Genomics, Direct-to-Consumer

  4. Why Prevention? • Genomic discovery finding new associations with disease risk • Genomics can deliver what prevention requires • Conditions w/ significant burden of suffering • Conditions w/ suitable natural history • Acceptable screening procedures • Treatments that work better early than late • Benefits outweigh harms • Benefits come at reasonable cost • Genomics thus a potentially powerful tool to help rationalize imprecise practice

  5. At Reasonable Cost? • Multiplex technologies have built-in economies of scale • $1,000 for 1,000,000 SNPs ~ 0.1¢ per SNP; no expiration date • even on a per-condition basis, price is reasonable • vs. CT scan of the head for HA = $425, good for 12h? • variant on 9p21 assoc w/ incr risk of intracranial aneurysm (OR = 1.3); price = $195, includes VTE, AF too • Cost issue is as much about the orientation of our healthcare system as it is about the actual cost of these technologies

  6. Why Diabetes? • Huge burden • Onset can be delayed, even prevented • Knowledge isn’t enough, but can be motivating • Current screening practice imprecise • FPG most common; will be replaced by Hgb A1c • USPSTF recs screening only those w/ HTN, chol; ADA recs screening more broadly • NHANES III found FPG alone misclassifies 20% of patients w/ diabetes, prediabetes as being nondiabetic • Oral glucose tolerance testing (OGTT) is “gold standard,” but more expensive and less convenient • Clinical inertia around borderline results • Well-studied markers (~20 SNPs), GWA studies • Even an RCT showing benefit (DPP)

  7. TCF7L2 • TCF7L2 encodes for entero-endocrine transcription factor • Role in Wnt signaling pathway • Regulates peptide hormone made by enteroendocrine cells (glucagon-like peptide 1)

  8. TCF7L2 & Diabetes Prevention Program Diabetes Prevention Program (DPP) not only showed association with risk of progression, but also that intervention reduces risk for higher-risk genotypes. Source: Florez et al. N Engl J Med 2006;355:241-50.

  9. TCF7L2 & Diabetes Prevention Program Source: Florez et al. N Engl J Med 2006;355:241-50.

  10. Potential impact on patient behavior? • We know that knowing is not enough • Adherence to lifestyle changes proven to reduce risk for T2DM poor • Family history motivating for some • e.g., study of 1100 African Americans found those aware of +FH T2DM were more likely to make healthier food choices • REVEAL study • finding of ApoE4+ led to increased AD-specific behavior change • Survey of patients and physicians re: their enthusiasm for the use of genetic information for T2DM risk • 71% of patients said this information would be motivating • 23% of providers said it would Source: Baptiste-Roberts et al. 2007; Chao et al. 2008; Florez et al. 2009

  11. Issue #1: What does this add to what we already know? • Recent studies suggest the addition of genomic testing to standard risk assessment adds little to risk prediction • Meigs et al. and Lyssenko et al. found that the contribution of specific genetic information only slightly improved upon the ability of a constellation of other clinical factors to predict who would progress to T2D. • These factors included systolic blood pressure, high-density lipoprotein levels, and triglycerides in the former; and diastolic blood pressure, triglycerides, liver enzymes in the latter. • However, Lyssenko et al. also pointed out that the risk prediction model from Meigs et al. performed worse than one based on genomic risk alone. Source: Meigs et al. N Engl J Med 2008; Lyssenko et al. N Engl J Med 2008.

  12. Issue #2: What if it “contradicts” what we already know?

  13. Issue #3: What happens when risk estimates change? Source: http://exploringmygenes.blogspot.com

  14. A Three-Part Approach • Identify markers (i.e., SNPs) • Systematic review (TCF7L2, PPARg2, KCNJ11) • deCODE T2D (TCF7L2, PPARg2, CDKAL1, CDKNA2A/B) • Build a clinical prototype • Duke Executive Health module • Other Duke clinics (e.g., Pickett) • Build a research program • ‘Clinical utility’ RCT pilot • CHSRPC (Durham VAMC) study • Multiplex pilot??

  15. deCODE T2TMtestfor T2DM risk in 1 care • Panel of 4 SNPs associated w/ increased risk of developing T2DM • Retails for $300 • Analyzed in a CLIA-certified lab • Can only be ordered by physician Source: deCODE genetics.

  16. T2DM Genomic Risk Pilot Study

  17. Risk Profile

  18. Role for DNA testing in DM risk assessment? annual OGTT (or Hgb A1c) screening + stepped-up lifestyle change initiate early treatment w/ metformin ‘elevated’ risk prediabetic? screening w/ FPG every 3y if physically inactive, 45,  chol, HTN + usual lifestyle recs urge lifestyle change, consider early treatment w/ metformin baseline risk 40 yo M, BMI <30, no FH DM, not from higher-risk group; wants screening for diabetes OGTT = oral glucose tolerance testing, FPG = fasting plasma glucose

  19. Acknowledgements • Study Team • Ley Killeya-Jones • Marylou Bembe • Dana Baker • Michael Scott • Sarah McBane • Gloria Trujillo • The Duke Endowment • deCODE genetics

  20. Resources • National Human Genome Research Institute (NIH) • www.genome.gov • National Office of Public Health Genomics (CDC) • HuGE Net • Nat’l Coalition for Health Professional Education in Genetics (NCHPEG) • Gene Tests (www.genetests.org) • Online Mendelian Inheritance in Man (OMIM) • HapMap (www.hapmap.org/whatishapmap.html) • Wellcome Trust (genome.wellcome.ac.uk) • Guilford County Genomedical Connection (http://www.aheconnect.com/genomic_medicine) • Duke IGSP (www.genome.duke.edu)

  21. Thank You

  22. Additional Slides

  23. Genomics, Direct-to-Consumer

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