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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;

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Presentation on theme: "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;"— Presentation transcript:

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 T2 TM test for 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? 40 yo M, BMI <30, no FH DM, not from higher-risk group; wants screening for diabetes ‘elevated’ risk baseline risk annual OGTT (or Hgb A1c) screening + stepped-up lifestyle change screening w/ FPG every 3y if physically inactive,  45,  chol, HTN + usual lifestyle recs prediabetic? initiate early treatment w/ metformin urge lifestyle change, consider early treatment w/ metformin 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

23 Additional Slides

24 Genomics, Direct-to-Consumer


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