13/11/2019 COPE WEBINAR SERIES FOR HEALTH PROFESSIONALS November 13, 2019 Using g e ne tic informa tion to pre dic t a nd tre a t obe sity: Ar e we r e ady for pr e c ision me dic ine ? Moderator: Lisa Diewald, MS, RD, LDN Program Manager MacDonald Center for Obesity Prevention and Education M. Louise Fitzpatrick College of Nursing Nursing Education Continuing Education Programming Research 1 FINDING SLIDES FOR TODAY’S WEBINAR www.villanova.edu/COPE Click on Loos webinar description page Nursing Education Continuing Education Programming Research 2 DID YOU USE YOUR PHONE TO ACCESS THE WEBINAR? If you are calling in today rather than using your computer to log on, and need CE credit, please email cope@villanova.edu and provide your name so we can send your certificate. Nursing Education Continuing Education Programming Research 3 1
13/11/2019 OBJECTIVES • Discuss genetic testing directly-to-consumers (DTC) services, advertised as providing genetically matched diets based on genotype data. • Explain the basic principles of prediction and the limitations of using genetic information in personalizing diet and exercise prescriptions. • Identify other opportunities for personalizing health related behaviors. Nursing Education Continuing Education Programming Research 4 CE DETAILS Villanova University College of Nursing is accredited as a provider of continuing nursing education by the American Nurses Credentialing Center Commission on Accreditation Villanova University College of Nursing Continuing Education/COPE is a Continuing Professional Education (CPE) Accredited Provider with the Commission on Dietetic Registration Nursing Education Continuing Education Programming Research 5 NUTRITION FUTURE FORWARD: ARE WE READY FOR OUT OF THE BOX THINKING? March 6, 2020 9 AM-4 PM Driscoll Hall Auditorium Villanova University RNs: 6 contact hours RD/ RDN/ DTR: 6 CPEUs Villanova.edu/cope 6 2
13/11/2019 CE CREDITS • This webinar awards 1 contact hour for nurses and 1 CPEU for dietitians • Suggested CDR Learning Need Codes: 2000, 2050, 5370, and 9020 • Level 2 • CDR Performance Indicators: 6.2.5, 8.3.6, 8.3.7 Nursing Education Continuing Education Programming Research 7 Using genetic information to predict and treat obesity: Are we ready for precision medicine? Ruth Loos, PhD. Charles Bronfman Professor in Personalized Medicine Icahn School of Medicine Wednesday, November 13, 2019 12-1 PM EST 8 DISCLOSURE The planners and presenter of this program have no conflicts of interest to disclose. Accredited status does not imply endorsement by Villanova University, COPE or the American Nurses Credentialing Center of any commercial products or medical/nutrition advice displayed in conjunction with an activity. 9 3
13/11/2019 Using g e ne tic informa tion to pre dic t a nd tre a t obe sity: Ar e we r e ady for pr e c ision me dic ine ? Ruth L oos Charle s Bro nfman Pro fe sso r in Pe rso nalize d Me dic ine Cha rle s Bro nfma n Institute fo r Pe rso na lize d Me dic ine Mindic h Child He a lth a nd De ve lo pme nt Institute Ic a hn Sc ho o l o f Me dic ine a t Mo unt Sina i Ne w Yo rk ruth.lo o s@ mssm.e du Vi l l anova COPE Webi nar , November 13 t h 2019 10 NCD Risk F ac to r Co llab o ratio n L anc e t 2017 Ove rwe ig ht/ obe sity pre va le nc e in USA sinc e 1980’s Wo me n Me n Girls Bo ys 11 Obe sity “runs” in fa milie s Ca na da F itne ss Surve y N = 15,245, a g e d 7 – 69 yrs, fro m 6,377 fa milie s Ge ne s Sha re d e nviro nme nt Sha re d e nviro nme nt Katzmarzyk e t al. AJE , 1999 12 4
13/11/2019 Both g e ne s a nd e nvironme nt c ontribute to obe sity risk T win studie s F amily studie s MZ DZ Bo rje so n Ac ta Pae d Sc and 1976 h 2 = 40- 70% 13 Obe sity is he rita ble , more tha n 1,000 loc i ha ve be e n ide ntifie d 1000 Childhood Obesity and BMI Extreme and early onset Obesity Overweight and obesity 800 BMI (African) BMI (Asian) Cumulative number of loci identified BMI (predominantly European) 600 400 200 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 14 Why study g e ne tic s of obe sity ? Ge ne - disc ove ry Biolog y & me c ha nisms Pre dic tion & Dia g nostic s Speliotes et al. Nature Genetics 2010 Siljee et al. Nature Genetics 2018 Khera et al. Nature Genetics 2018 15 5
13/11/2019 Using g e ne tic informa tion throug hout the life -c ourse Pre dic t & pre ve nt dise a se Dia g nose & tre a t dise a se 16 Pre c ision me dic ine to tre a t obe sity, ma inta in he a lthy we ig ht 17 Pr e c ision me dic ine a nd c ommon obe sity Monoge nic for ms of obe sity Common obe sity MC4R L E P POMC L E PR F a ro o q i e t a l. NE JM 2003 F a ro o q i e t a l. NE JM 1999 K rud e e t a l. Na t Ge n 1998 Sa e e d e t a l. Ob e sity 2014 • mutation in o ne g e ne tha t affe c ts a • Many ge ne tic var iants in many g e ne s tha t One spe c ific pathway a nd ha ve a lar ge e ffe c t . a c t thro ug h multiple pathways tha t ha ve • L ife style a nd e nviro nme nt ha ve mino re ffe c t. small e ffe c ts . • L ife style a nd e nviro nme nt a re impo rta nt. • He rita b ility 40-70% L P muta tions Re c ombinant human le ptin E muta tions in POMC , L ,… MC4R a g onists E PR 18 6
13/11/2019 Pr e c ision me dic ine in the g e nome e r a – ta ilor e d tr e a tme nt High fibe r die t L ow satur ate d fat die t Ae robic e xe rc ise L ow glyc e mic die t High- fat die t Sc re e ning T re atme nt base d • E xpo so me on profile (life style , e nviro nme nt) • Ge no me • T ra nsc ripto me , … So urc e : MD Ande rso n 19 T he Blood-T ype die t • e vo lve d a fte r the sta rt o f • the b e ne fits a nd into le ra nc e s o f type s A a nd B a g ra ria n so c ie ty • b e st o ff a s ve g e ta ria n • e me rg e d a s huma ns • ne e d to e a t me a t virtua lly mig ra te d to wa rd e ve ry da y to sa tisfy yo ur a nc ie nt hunte r-g a the re r g e ne s c o lde r c lima te s • a vo id o a ts, whe a t, a nd mo st • the mo st va rie d die t, g ra ins inc luding me a t • do e s we ll with da iry pro duc ts 20 21 7
13/11/2019 Dir e c t- to- c onsume r c ompa nie s ma ke bold c la ims 22 A c a se study – he a lthy woma n se e ks a dvic e • 48 ye a r o ld wo ma n • Ge ne ra lly he a lthy, physic a lly a c tive , he a lthy die t, do e s no t smo ke ,… • Wa nts a dvic e fo r he a lthy a g ing DT C te st tha t inc lud e s te sts o n we llne ss Ruth L o o s 23 A c a se study – the e vide nc e • • No da ta o n we ig ht g a in No da ta o n we ig ht g a in • • One va ria nt – wha t a b o ut o the r One va ria nt – wha t a b o ut o the r va ria nts ? va ria nts ? • • Wha t is the a dvic e fo r T Wha t is the a dvic e fo r T -a lle le -a lle le c a rrie rs ? c a rrie rs ? • • Sig nific a nt inte ra c tio n <> Sig nific a nt inte ra c tio n <> pre d ic tive pre d ic tive F r amingha m Offspr ing Pue r to Ric a n Ce nte r s on PHHD Pue r to Ric a n Ce nte r s on PHHD study study 24 8
13/11/2019 A c a se study – the e vide nc e • Ho w muc h “pre c isio n” do e s this • Ho w muc h “pre c isio n” do e s this info rma tio n a dd in the c o nte xt o f info rma tio n a dd in the c o nte xt o f pre c isio n me dic ine ? pre c isio n me dic ine ? 25 Cha lle ng e s for pre c ision me dic ine in c ommon obe sity • Sma ll (inte ra c tio n) e ffe c ts b ig g e r e ffe c ts that disc riminate b e twe e n g e no type s • One SNP a t a time , o ne e xpo sure (d ie t, phys a c t, …) a t a time “F ull pic ture ” (”b ig data”) is ne e de d • F e w studie s tha t e xa mine “c ha ng e ” in re spo nse to a n e xpo sure L o ng itudinal studie s o n we ig ht c hang e , inte rve ntio ns (L o o k AHE AD, DPP, POUNDS L OST , …) 26 Pre c ision me dic ine to pre dic t obe sity 27 9
13/11/2019 28 SNPs – Sing le Nuc le otide Polymorphisms Gly Ala Gly Arg Ser Ile Ser Trp Ala Trp Trp Ala Cys Val GGC GCC GGA CGC TCC ATC TCC TGG GCC TGG TGG GCA TGT GTG T T/T GGC GCC GGA CGC TCC ATC TCC TGG GCC TGG TGG GCA TGT GTG T Gly Ala Gly Arg Thr Ile Ser Trp Ala Trp Trp Ala Cys Val GGC GCC GGA CGC ACC ATC TCC TGG GCC TGG TGG GCA TGT GTG A A/T GGC GCC GGA CGC TCC ATC TCC TGG GCC TGG TGG GCA TGT GTG T GGC GCC GGA CGC TCC ATC TCC TGG GCC TGG TGG GCA TGT GTG T T/T T GGC GCC GGA CGC TCC ATC TCC TGG GCC TGG TGG GCA TGT GTG GGC GCC GGA CGC ACC ATC TCC TGG GCC TGG TGG GCA TGT GTG A A/A GGC GCC GGA CGC ACC ATC TCC TGG GCC TGG TGG GCA TGT GTG A GGC GCC GGA CGC TCC ATC TCC TGG GCC TGG TGG GCA TGT GTG T A/T GGC GCC GGA CGC ACC ATC TCC TGG GCC TGG TGG GCA TGT GTG A SNP Single Nucleotide Polymorphism 29 Polyg e nic risk sc ore s One SNP- a ssoc ia tion 29 PRS 7 8 4 12 7 8 28 SNP 1 0 0 1 2 0 2 27.9 BMI (kg / m 2 ) SNP 2 1 2 2 2 1 0 27 SNP 3 2 0 1 0 2 2 26.6 26 SNP 4 … SNP 5 … 25 25.2 SNP 6 … 24 SNP 7 … 0 1 2 T / T A/ T A/ A SNP 8 … SNP - Ge notype SNP … … SNP … … So urc e : RGA 30 10
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