[정보] 2017 디지털 헬스케어 컨퍼런스 리뷰

2017 디지털 헬스케어 컨퍼런스 리뷰 관련 자료입니다.

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2017 디지털 헬스케어 컨퍼런스 리뷰 (v1) from Yoon Sup Choi

1. 2017 디지털 헬스케어 컨퍼런스 리뷰 Health 2.0 Annual Conference DigiMed17 Rock Health Summit 최윤섭 디지털헬스케어 연구소 소장 최윤섭 Sponsored by ?
2. Disclaimer 스타트업 벤처캐피털 저는 위의 회사들과 지분 관계, 자문 계약 등의 이해 관계가 있음을 밝힙니다.
3. Sponsors 여러 회사 및 지인 분들께서 출장 및 이번 행사의 개최에 도움을 주셨습니다 다시 한 번 감사드립니다. ?
4. pelexus.symﬂow.com 질문은 Symflow를 통해서 받겠습니다. 아래의 주소로 접속하셔서, ‘업플로우’에 올려주시면 됩니다. 추천을 많이 받은 질문들을 위주로 답변하겠습니다.
5. Advanced Technology Inc B-LAB frontec KB금융지주 LB인베스트먼트 LG경제연구원 Medigate Siemens Healthineers SKTelecom SPONGY GROUP SPORTRIUM 강북삼성병원 네오젠소프트 노바티스 닥터키친 더웰스인베스트먼트 동아에스티 라이나생명 마콜커뮤니케이션컨설팅 모델라인 베스티안 재단 블루포인트파트너스 삼성경제연구소 삼성서울병원 삼성화재 삼육대학교 약학대학 서울대학교 서울대학교병원 서울의료원 세브란스병원 엠트리케어 예비창업자 와이에스바이오 울산과학기술원UNIST 유비케어 이원다이애그노믹스 인제대학교 일산백병원 인천성모병원 심장내과 인천참사랑병원 인천참사랑병원 전략기획실 인하대병원 일산백병원 순환기내과 질병관리본부 창의사업개발원 카이스트 캐시워크 프론텍 한국 MSD 한국스트라이커 한독 한솔병원 휴레이포지티브 오늘 참석하신 분들
6. 일정 •7:00 - 7:50 1부 발표 •7:50 - 8:00 휴식시간 •8:00 - 8:10 스폰서 세션 (UCB제약) •8:10 - 9:00 2부 발표 앉으셨던 의자는 본인이 직접 카운터 뒤쪽의 저장 공간에 넣어주시기 바랍니다.      (별도의 행사 진행 인력이 없습니다)
7. •시리즈 A 펀딩 단계 스타트업의 컴페티션 •8개의 결승 진출 스타트업이 전문 멘토들 (VC, 제약사 등등)의 멘토링 이후 발표 •심판과 청중이 함께 판정하여 2팀을 시상
8. •시리즈 A 펀딩 단계 스타트업의 컴페티션 •8개의 결승 진출 스타트업이 전문 멘토들 (VC, 제약사 등등)의 멘토링 이후 발표 •심판과 청중이 함께 판정하여 2팀을 시상
9. Klara: Healthcare Communication Made Simple https://www.youtube.com/watch?time_continue=2&v=SgfXgk0kLiw
10. •HIPPA compliant 의료 전문 메시징 플랫폼 •환자가 의료진 (의사, 약사 등등)과 문자 메시지로 커뮤니케이션할 수 있음 •하나의 플랫폼에서 진료 예약, 원격 진료/처방, 검사 결과 확인, 약품 배송 등을 이용할 수 있음 •EMR 연동은 되는지 모르겠음 •국내에서는…
11. •B2B로는 PCP (Primary Care Physician) 들을 주 고객으로 하고 있음 •피부과 전문의가 많은 한국에서는 과연 필요한 기기일까? •B2C로 판매하여 가정에서 보유할 필요가 있는 기기일까?
12. Tell us what’s up. With your own, plain words. https://www.slideshare.net/ForumITESSS/mediktor
13. Answer a few questions about how you feel. https://www.slideshare.net/ForumITESSS/mediktor
14. Chat with the best specialist for your case within minutes. https://www.slideshare.net/ForumITESSS/mediktor
15. SNOO Smart Sleeper
16. •SNOO Smart Sleeper •아기의 수면을 유도하여, 부모의 수면 퀄리티를 높여준다는 컨셉의 제품 •헬스케어 제품의 중요성: 구매 의사 결정권자와 실제 사용자는 다를 수 있다.   •엄마 자궁과 비슷한 환경을 만들어 줘서 수면을 유도 •화이트 노이즈 •흔들어주기 (크게 울수록 더 많이 흔들고, 최대 3분 흔들어서 울음이 멈추지 않으면 stop) •감싸기 등등 •한국에도 사용자 있으며, 비슷한 컨셉의 제품이 더러 있기는 한듯
17. •Cedars-Sinai Accelerator •LA의 Health System인 Cedars-Sinai 와 •테크엑셀러레이터 Techstars가 함께 만든 의료 엑셀러레이팅 프로그램
18. •6-8개월마다 한 기수를 운영 (특이하게도 batch라고 하지 않고, cohort 라고 부름..) •현재 세번째 코호트 운영 •한 코호트당 수백 개의 팀이 지원하여, 그 중 10개의 팀을 선발 •선발된 팀에게 2만불을 보통주 6%에 투자하고, 10만 불을 컨버터블 노트로 제공하는 조건 •마지막에 데모데이를 통해서 졸업시키는 방식
19. •6-8개월마다 한 기수를 운영 (특이하게도 batch라고 하지 않고, cohort 라고 부름..) •현재 세번째 코호트 운영 •한 코호트당 수백 개의 팀이 지원하여, 그 중 10개의 팀을 선발 •선발된 팀에게 2만불을 보통주 6%에 투자하고, 10만 불을 컨버터블 노트로 제공하는 조건 •마지막에 데모데이를 통해서 졸업시키는 방식
20. Health 2.0 2017 Annual Conference VC’s Talk New Trends in Investing
21. •어디를 가나 VC들도 매크로 트렌드에 대해서 신경을 쓰는 것은 마찬가지 •이 세션에서도 오바마케어, FDA 규제 변화, 수가 등등에 대해서 한참 이야기했다. •(얘들이 VC 이야기는 안 할건가? 하고 느낄 정도로..) •웨어러블, VR, 인공지능, FDA Pre-Cert 등의 주요 주제 •우리가 가지고 있는 의견과 방향성에 대한 생각과 크게 다르지 않았음. •우리도 방향을 제대로 잡고 있다는 이야기. •관심 있고, 찾고 있는 스타트업이 매우 specific 하다는 느낌 •미국의 VC들은 아주 많은 pool 의 스타트업 중에서 정말 고르고 고를 수 있기 때문 •하지만 한국은 pool 자체가 한정되어 있어서 결정할 수 있는 범위가 제한적인듯 •GE와 Sanofi 의 CVC 처럼 한국에도 헬스케어 분야의 CVC나 LP가 많이 나왔으면. Health 2.0 2017 Annual Conference VC’s Talk New Trends in Investing Health 2.0 2017 Annual Conference VC’s Talk New Trends in Investing
22. •B2C로 컨슈머에게 직접 파는 서비스는 B2B에 비해서 덜 선호함 •지금까지 B2C 모델을 만들기 위해서 많은 투자가 있었으나, 성공적이었던 것은 별로 없었음 •이는 최근 Rock Health Report의 내용과도 일맥상통 •기존 이해관계자를 mimic하면서 완전히 새로운 모델을 제시하는 야심있는 스타트업 •‘a new-age payer’ or ‘a new-age PBM(Pharmacy Benefit Management)’ •기존의 헬스케어 산업의 범주를 뭉개버리는 스타트업 •Oscar는 기존의 payer 역할에서 provider 까지 진출하고 있음 •23andMe 는 유전정보 분석회사에서 제약회사도 되고 있음 •Ginger.io 는 B2B 헬스케어 스타트업에서 provider도 되었음 어떠한 스타트업을 찾고 있는가? Health 2.0 2017 Annual Conference VC’s Talk New Trends in Investing
23. •‘어떠한 방식으로 돌아가는지 투명하지 않거나, 이해하기 어려운 분야’ •파괴적 혁신을 만들기 좋은 분야이다. •예를 들어, PBM의 risk 계산과 drug formulary 가 어떻게 계산되는지는 아무도 모른다. •VR의 경우에는 좋은 투자처를 찾기가 쉽지 않다. •VR 하드웨어 시장은 이미 너무 establish 되어 있고, •VR 소프트웨어 시장은 아직 충분히 ubiquitous 하지 않다. •예전에는 이와 비슷한 문제를 가지고 있었지만, 이제는 충분히 무르익은 시장: Voice •가정에 하나씩 Alexa, Google Home을 가지게 되면서, •헬스케어에서도 아주 흥미로운 application 등이 가능해짐 어떠한 스타트업을 찾고 있는가? Health 2.0 2017 Annual Conference VC’s Talk New Trends in Investing
24. •디지털 헬스케어도 전형적인 hype cycle을 따르게 될 것 •처음에는 사람들이 흥분하는 분야이지만, 기대하는 것보다 훨씬 더 오래 걸릴 수도 있다. •하지만 결국 많은 사람들이 예상했던 것보다, 더 큰 분야가 될 것이다. •지금이 헬스케어 시스템의 가치를 높이는 크리티컬 시점이다. •앞으로 M&A와 투자가 더욱 활발해지는 싸이클이 올 것이다. •과거에 클라우드나 SAS가 그랬던 것처럼 디지털 헬스케어 분야에서도 퍼펙트 스톰이 오고 있다. •‘Dance among the Giants’ 를 어떻게 하는가가 중요 •실리콘 밸리에서는 많은 창업이 일어나고 있지만, •단순히 기술적인 측면이 아니라, payer, provider 사이의 관계에서 ‘춤추는 것’ 이 중요하다.
25. Gartner Hype Cycle   for Emerging Technologies 2017
26. •디지털 헬스케어도 전형적인 hype cycle을 따르게 될 것 •처음에는 사람들이 흥분하는 분야이지만, 기대하는 것보다 훨씬 더 오래 걸릴 수도 있다. •하지만 결국 많은 사람들이 예상했던 것보다, 더 큰 분야가 될 것이다. •지금이 헬스케어 시스템의 가치를 높이는 크리티컬 시점이다. •앞으로 M&A와 투자가 더욱 활발해지는 싸이클이 올 것이다. •과거에 클라우드나 SAS가 그랬던 것처럼 디지털 헬스케어 분야에서도 퍼펙트 스톰이 오고 있다. •‘Dance among the Giants’ 를 어떻게 하는가가 중요 •실리콘 밸리에서는 많은 창업이 일어나고 있지만, •단순히 기술적인 측면이 아니라, payer, provider 사이의 관계에서 ‘춤추는 것’ 이 중요하다.
27. •어떤 회사를 찾고 있는가? •진료 과정 전체를 virtualize 하는 회사를 찾고 있다. •현재의 원격의료 회사들은 전체 진료에서 ‘환자가 의사를 만난다’는 아주 일부분만 virtualize하고 있다. •B2B vs B2C 모델: ‘디지털 헬스케어에 대해서 B2C 로 성공한 스타트업은 거의 없지 않나?’ •KPCB가 투자한 Kinsa의 경우에도 모바일 체온계라는 B2C 모델이었지만, 진짜 BM은 체온 데이터이다. •Kinsa가 CDC보다 flu를 4주 일찍 파악할 수 있다는 것이 알려지자, 많은 회사들이 이 데이터를 구매하기로 결정함 •향후 대형 기업에 의한 더 많은 M&A가 있을 것이다.
28. Kinsa
29. https://rockhealth.com/reports/streamlining-enterprise-sales-in-digital-health •It’s a B2B World! •Rock Health의 2017년 9월 발표에 따르면, •전체 조사기업의 85%가 B2B 혹은 B2B2C의 모델을 가지고 있음 •B2C의 모델을 유지하고 있는 곳은 전체 14% 밖에 되지 않음 •특히, 처음에 B2C로 시작했던 기업 중 61%가 B2B 혹은 B2B2C로 피보팅했음.   •그만큼 헬스케어에서는 B2C 사업 모델을 만들기가 쉽지 않다는 뜻으로 해석됨 •그렇다면, 한국에서도 미국처럼 이러한 B2B 모델을 만들 수 있는가?
30. D
31. https://www.youtube.com/watch?v=N8g-Dk_EBjY Suggestic Lens - Healthy eating powered by AR.
32. Suggestic Lens - Healthy eating powered by AR.
33. Summary: Health2.0 •Traction •KLARA (messaging) •DermaSensor (Melanoma) •Mediktor (Triage) •SleepTech Summit •SNOO (Smart Sleeper for Babies) •Cedars-Sinai Accelerator •VCs Talk New Trends in Investing •Investing in Health 2.0 Technologies •Launch! •Suggestic (AR for nutrition)
34. DigiMed17
35. •DigiMed17 • 스크립스 중개과학연구소(STSI)의 디지털 헬스케어 학회 • 디지털 헬스케어의 슈퍼스타 에릭 토폴 박사, 스티브 스타인허블 박사의 주도(Nat Digital Med 에디터) • Health 2.0, Connected Health 처럼 규모가 크지는 않지만, 내실있는 행사
36. •DigiMed17 • 상업적인 냄새는 덜하고, 주로 연구자들 위주의 발표와 토론이 이뤄짐 • 2015년 행사에 이어서, 대학 및 기업의 유명 연구자들의 발표 • (스티브 박사님과 이야기해보니) 올해가 아마도 마지막일듯…인력 부족, 스폰서 부족 등등의 이유
37. the manifestations of disease by providing a more comprehensive and nuanced view of the experience of illness. Through the lens of the digital phenotype, an individual’s interaction The digital phenotype Sachin H Jain, Brian W Powers, Jared B Hawkins & John S Brownstein In the coming years, patient phenotypes captured to enhance health and wellness will extend to human interactions with digital technology. In 1982, the evolutionary biologist Richard Dawkins introduced the concept of the “extended phenotype”1, the idea that pheno- types should not be limited just to biological processes, such as protein biosynthesis or tissue growth, but extended to include all effects that a gene has on its environment inside or outside ofthebodyoftheindividualorganism.Dawkins stressed that many delineations of phenotypes are arbitrary. Animals and humans can modify their environments, and these modifications andassociatedbehaviorsareexpressionsofone’s genome and, thus, part of their extended phe- notype. In the animal kingdom, he cites damn buildingbybeaversasanexampleofthebeaver’s extended phenotype1. Aspersonaltechnologybecomesincreasingly embedded in human lives, we think there is an important extension of Dawkins’s theory—the notion of a ‘digital phenotype’. Can aspects of ourinterfacewithtechnologybesomehowdiag- nosticand/orprognosticforcertainconditions? Can one’s clinical data be linked and analyzed together with online activity and behavior data to create a unified, nuanced view of human dis- ease?Here,wedescribetheconceptofthedigital phenotype. Although several disparate studies have touched on this notion, the framework for medicine has yet to be described. We attempt to define digital phenotype and further describe the opportunities and challenges in incorporat- ing these data into healthcare. Jan. 2013 0.000 0.002 0.004 Density 0.006 July 2013 Jan. 2014 July 2014 User 1 User 2 User 3 User 4 User 5 User 6 User 7 Date Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions (probability density functions) are shown for seven individual users over a two-year period. Density on the y axis highlights periods of relative activity for each user. A representative tweet from each user is shown as an example. npg©2015NatureAmerica,Inc.Allrightsreserved. http://www.nature.com/nbt/journal/v33/n5/full/nbt.3223.html
38. “Extended Phenotype”(확장된 표현형)
39. “Extended Phenotype”(확장된 표현형)
40. “Extended Phenotype”(확장된 표현형)
41. “Extended Phenotype”(확장된 표현형)
42. wers, Jared B Hawkins & John S Brownstein phenotypes captured to enhance health and wellness will extend to human interactions with gist Richard cept of the that pheno- o biological esis or tissue l effects that de or outside sm.Dawkins phenotypes can modify odifications sionsofone’s tended phe- e cites damn fthebeaver’s increasingly k there is an theory—the n aspects of mehowdiag- conditions? Jan. 2013 0.000 0.002 0.004 Density 0.006 July 2013 Jan. 2014 July 2014 User 1 User 2 User 3 User 4 User 5 User 6 User 7 Date Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions (probability density functions) are shown for seven individual users over a two-year period. Density on the y axis highlights periods of relative activity for each user. A representative tweet from each user is shown as an example. Your twitter knows if you cannot sleep Timeline of insomnia-related tweets from representative individuals. Nat. Biotech. 2015
43. Ginger.io: your smartphone knows if you are depressed
44. Digital Phenotype: Your smartphone knows if you are depressed J Med Internet Res. 2015 Jul 15;17(7):e175. The correlation analysis between the features and the PHQ-9 scores revealed that 6 of the 10 features were signiﬁcantly correlated to the scores: • strong correlation: circadian movement, normalized entropy, location variance • correlation: phone usage features, usage duration and usage frequency
45. Digital Phenotype: Your smartphone knows if you are depressed J Med Internet Res. 2015 Jul 15;17(7):e175. Comparison of location and usage feature statistics between participants with no symptoms of depression (blue) and the ones with (red). (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay;TT, transition time;TD, total distance; CM, circadian movement; NC, number of clusters; UF, usage frequency; UD, usage duration). Figure 4. Comparison of location and usage feature statistics between participants with no symptoms of depression (blue) and the ones with (red). Feature values are scaled between 0 and 1 for easier comparison. Boxes extend between 25th and 75th percentiles, and whiskers show the range. Horizontal solid lines inside the boxes are medians. One, two, and three asterisks show significant differences at P<.05, P<.01, and P<.001 levels, respectively (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay; TT, transition time; TD, total distance; CM, circadian movement; NC, number of clusters; UF, usage frequency; UD, usage duration). Figure 5. Coefficients of correlation between location features. One, two, and three asterisks indicate significant correlation levels at P<.05, P<.01, and P<.001, respectively (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay; TT, transition time; TD, total distance; CM, circadian movement; NC, number of clusters). Saeb et alJOURNAL OF MEDICAL INTERNET RESEARCH the variability of the time the participant spent at the location clusters what extent the participants’ sequence of locations followed a circadian rhythm. home stay
46. Submitted 23 June 2016 Accepted 7 September 2016 Published 29 September 2016 Corresponding author David C. Mohr, d-mohr@northwestern.edu Academic editor Anthony Jorm Additional Information and Declarations can be found on page 12 DOI 10.7717/peerj.2537 Copyright 2016 Saeb et al. Distributed under Creative Commons CC-BY 4.0 OPEN ACCESS The relationship between mobile phone location sensor data and depressive symptom severity Sohrab Saeb1,2 , Emily G. Lattie1 , Stephen M. Schueller1 , Konrad P. Kording2 and David C. Mohr1 1 Department of Preventive Medicine, Northwestern University, Chicago, IL, United States 2 Rehabilitation Institute of Chicago, Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States ABSTRACT Background. Smartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate andextendpreviousworkusinggeographiclocation(GPS)sensorstoidentifydepressive symptom severity. Methods. We used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate depressive symptom severity at baseline and end-of-study. GPS featureswerecalculatedovertheentirestudy,forweekdaysandweekends,andin2-week blocks. Results. The results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r’s ranging from 0.43 to 0.46, p-values < .05). We also found that these relationships were stronger when GPS features were calculatedfromweekend,comparedtoweekday,data.Althoughthecorrelationbetween baseline PHQ-9 scores with 2-week GPS features diminished as we moved further from baseline, correlations with the end-of-study scores remained significant regardless of the time point used to calculate the features. Discussion. Our findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of depressive symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict depressive symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression. Subjects Bioinformatics, Psychiatry and Psychology, Public Health, Computational Science Keywords Mobile phone, Depression, Depressive symptoms, Geographic locations, Students INTRODUCTION Depression is common and debilitating, taking an enormous toll in terms of cost, morbidity, and mortality (Ferrari et al., 2013; Greenberg et al., 2015). The 12-month prevalence of major depressive disorder among adults in the US is 6.9% (Kessler et al., 2005), and an additional 2–5% have subsyndromal symptoms that warrant treatment Saeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537
47. The relationship between mobile phone location sensor data and depressive symptom severity Linear correlation coefﬁcients (r) between individual 10-week features and PHQ-9 scores, and their 95% conﬁdence intervals. Features indicated with stars (∗) are replicated from our previous study (Saeb et al., 2015a.). Bold values indicate signiﬁcant correlations. Table 2 Linear correlation coefficients (r) between individual 10-week features and PHQ-9 scores, and their 95% confidence intervals. Features indicated with stars (⇤) are replicated from our previous study (Saeb et al., 2015a.). Bold values indicate significant correlations. Feature Baseline (n = 46) Follow-up (n = 38) Change (n = 38) Location variance⇤ 0.29 ± 0.008 0.43 ± 0.007 0.34 ± 0.008 Circadian movement⇤ 0.34 ± 0.006 0.48 ± 0.006 0.33 ± 0.009 Speed mean 0.03 ± 0.007 0.06 ± 0.005 0.04 ± 0.008 Speed variance 0.07 ± 0.007 0.06 ± 0.005 0.06 ±0.007 Total distance⇤ 0.23 ± 0.004 0.18 ± 0.006 0.03 ± 0.006 Number of clusters⇤ 0.38 ± 0.005 0.44 ± 0.004 0.24 ± 0.007 Entropy⇤ 0.31 ± 0.007 0.46 ± 0.005 0.28 ± 0.008 Normalized entropy⇤ 0.26 ± 0.007 0.44 ± 0.005 0.30 ± 0.009 Raw entropy 0.17 ± 0.009 0.22 ± 0.008 0.15 ± 0.010 Home stay⇤ 0.22 ± 0.008 0.43 ± 0.005 0.30 ± 0.009 Transition time⇤ 0.30 ± 0.006 0.32 ± 0.005 0.12 ± 0.009 Data analysis We evaluated the relationship between each set of features (10-week and 2-week, each for all days, weekends, or weekdays) and depressive symptoms severity as measured by the PHQ-9. We used linear correlation coefficient (r) and considered p < 0.05 as the significance level. In order to reduce the possibility that results were generated by chance, we created 1,000 bootstrap subsamples (Efron & Tibshirani, 1993) to estimate these correlation coefficientsSaeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537
48. Table 3 Linear correlation coefficients (r) between individual weekend and weekday features and PHQ-9 scores, and their 95% confidence in- tervals. Bold values indicate significant correlations (see ‘Data Analysis’). Feature Weekday Weekend Baseline (n = 46) Follow-up (n = 38) Change (n = 38) Baseline (n = 46) Follow-up (n = 38) Change (n = 38) Location variance 0.15 ± 0.008 0.20 ± 0.008 0.22 ± 0.009 0.31 ± 0.008 0.47 ±0.007 0.39 ± 0.008 Circadian movement 0.22 ± 0.007 0.28 ± 0.008 0.25 ± 0.009 0.35 ± 0.007 0.51 ±0.006 0.36 ± 0.008 Speed mean 0.00 ± 0.008 0.06 ± 0.005 0.03 ± 0.008 0.13 ± 0.005 0.06 ± 0.006 0.05 ± 0.009 Speed variance 0.05 ± 0.008 0.07 ± 0.005 0.02 ± 0.007 0.13 ± 0.004 0.05 ± 0.006 0.10 ± 0.008 Total distance 0.20 ± 0.004 0.15 ± 0.005 0.01 ± 0.006 0.25 ± 0.004 0.20 ± 0.005 0.03 ± 0.006 Number of clusters 0.19 ± 0.006 0.25 ± 0.005 0.14 ± 0.008 0.34 ± 0.006 0.46 ±0.004 0.32 ± 0.007 Entropy 0.21 ± 0.007 0.34 ± 0.006 0.20 ± 0.009 0.30 ± 0.008 0.55 ±0.004 0.38 ± 0.008 Normalized entropy 0.21 ± 0.008 0.39 ± 0.006 0.24 ± 0.009 0.28 ± 0.008 0.54 ± 0.004 0.41 ± 0.009 Raw entropy 0.05 ± 0.008 0.04 ± 0.008 0.01 ± 0.010 0.04 ± 0.008 0.01 ± 0.008 0.03 ± 0.009 Home stay 0.19 ± 0.008 0.37 ± 0.006 0.23 ± 0.009 0.23 ± 0.007 0.50 ± 0.004 0.35 ± 0.008 Transition time 0.27 ± 0.006 0.29 ± 0.006 0.14 ± 0.010 0.36 ± 0.006 0.32 ± 0.008 0.06 ± 0.009 only normalized entropy was significantly related to the scores as a weekday feature. The magnitude of the relationship between weekend features and PHQ-9 scores was larger than the magnitude of the relationship between 10-week features and PHQ-9 scores. However, given the small sample size, we were not adequately powered to test if these differences were significant. 2-week features Finally, we examined how 2-week GPS features obtained at different times during the study Linear correlation coefﬁcients (r) between individual weekend and weekday features and PHQ-9 scores, and their 95% conﬁdence intervals. Bold values indicate signiﬁcant correlations.All of those 10-week features that were signiﬁcantly related to PHQ-9 scores (seeTable 2) were also signiﬁcant when calculated from weekends, whereas only normalized entropy was signiﬁcantly related to the scores as a weekday feature Saeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537 The relationship between mobile phone location sensor data and depressive symptom severity
49. Saeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537 Mean temporal correlations between 2-week location features, calculated at different time points during the study, and baseline and follow-up PHQ-9 scores. The relationship between mobile phone location sensor data and depressive symptom severity
50. •디지털 표현형의 대표주자, Ginger.io는 최근 사업 모델의 변화 • 병원 대상의 서비스에서 기업에 B2B2C로 서비스하는 모델로 피보팅 (BM 고민은 미국도 마찬가지) • 페이스북, 스냅챗 등의 주요 기업의 직원에 ‘상담사가’ 정신건강 상담을 제공하는 모델 • 24시간, 수 초 이내에 상담사의 응답이 오며, 디지털 표현형은 내담자 분석의 기저에 깔리게 됨
51. •디지털 표현형의 대표주자, Ginger.io는 최근 사업 모델의 변화 • 이러한 B2B2C 모델은 고용주가 직원에게 건강보험을 제공하는 미국에서만 가능 • 특히 인상 깊었던 것은, 이 기업이 그 자체로 provider(의료기관)으로 발전했다는 것임 • 즉, 경쟁사들과 달리, 정신 상담 서비스를 전문적으로 원격 제공하는 병원을 설립
52. •디지털 표현형을 활용하여 정신 건강 서비스를 제공하는 Mindstrong • 스마트폰의 센서, 키보드, 목소리/스피치 등을 feature 로 이용, • 이를 기반으로 디지털 바이오마커를 추출하여, 머신러닝을 통해 질병 진단 및 모니터링 등에 활용 가능
53. •Mindstrong 에서 현재 진행 중인 연구들 • 스마트폰의 45가지의 키보드 및 스크롤 패턴을 기반 (문자와 스페이스 간의 지연도, 화면 내리는 패턴 등) • 23가지의 signal processing transform을 통해, 총1,035가지의 잠재적 디지털 바이오마커 도출 • 가장 효과적인 바이오마커의 검증, 반복실험을 통해 오버피팅을 줄이기 위해 노력 중
54. •40명 규모의 moderate anxiety & depression 환자의 초기 연구 결과 • 디지털 바이오마커 중 많은 feature가 신경정신학적 수치와의 상관관계를 보임 • 구체적으로 275개의 feature가 어떤 것인지는 공개하지 않는듯
55. •인지와 관련한 gold standard test를 스마트폰의 디지털 바이오마커로 재현할 수 있는가? • 기존의 cognition metric과 가장 상관 관계가 높은 디지털 바이오마커의 퍼포먼스 • P-value를 기준으로 유의미한 상관관계를 보이는 디지털 바이오마커가 있음 • (역시 바이오마커가 구체적으로 어떤 것인지는 공개하지 않음)
56. •인지능력 뿐만 아니라, 우울증의 측정에도 디지털 바이오마커가 효과적인가? • Best biomarker와 best signature (the best set of biomarkers)가     우울증 척도인 PHQ-9의 각 항목과 높은 상관관계를 보임 • 이를 기반으로 PHQ-9 점수를 예측할 수 있는지에 대해서 테스트 중 쾌감상실 무기력 정신운동성
57. Data-driven Healthcare에 대한 두 가지 전략 • top-down: 먼저 가설을 세우고, 그에 맞는 종류의 데이터를 모아서 검증해보자. • bottom-up: 일단 ‘모든’ 데이터를 최대한 많이 모아 놓으면, 뭐라도 큰 게 나오겠지.
58. Data-driven Healthcare에 대한 두 가지 전략 • top-down: 먼저 가설을 세우고, 그에 맞는 종류의 데이터를 모아서 검증해보자. • bottom-up: 일단 ‘모든’ 데이터를 최대한 많이 모아 놓으면, 뭐라도 큰 게 나오겠지.
59. ©2017NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved. NATURE BIOTECHNOLOGY ADVANCE ONLINE PUBLICATION 1 A RT I C L E S In order to understand the basis of wellness and disease, we and others have pursued a global and holistic approach termed ‘systems medicine’1. The defining feature of systems medicine is the collec- tion of diverse longitudinal data for each individual. These data sets can be used to unravel the complexity of human biology and dis- ease by assessing both genetic and environmental determinants of health and their interactions. We refer to such data as personal, dense, dynamic data clouds: personal, because each data cloud is unique to an individual; dense, because of the high number of measurements; and dynamic, because we monitor longitudinally. The convergence of advances in systems medicine, big data analysis, individual meas- urement devices, and consumer-activated social networks has led to a vision of healthcare that is predictive, preventive, personalized, and participatory (P4)2, also known as ‘precision medicine’. Personal, dense, dynamic data clouds are indispensable to realizing this vision3. The US healthcare system invests 97% of its resources on disease care4, with little attention to wellness and disease prevention. Here we investigate scientific wellness, which we define as a quantitative data-informed approach to maintaining and improving health and avoiding disease. Several recent studies have illustrated the utility of multi-omic lon- gitudinal data to look for signs of reversible early disease or disease risk factors in single individuals. The dynamics of human gut and sali- vary microbiota in response to travel abroad and enteric infection was characterized in two individuals using daily stool and saliva samples5. Daily multi-omic data collection from one individual over 14 months identified signatures of respiratory infection and the onset of type 2 diabetes6. Crohn’s disease progression was tracked over many years in one individual using regular blood and stool measurements7. Each of these studies yielded insights into system dynamics even though they had only one or two participants. We report the generation and analysis of personal, dense, dynamic data clouds for 108 individuals over the course of a 9-month study that we call the Pioneer 100 Wellness Project (P100). Our study included whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at 3-month intervals; and frequent activity measure- ments (i.e., wearing a Fitbit). This study takes a different approach from previous studies, in that a broad set of assays were carried out less frequently in a (comparatively) large number of people. Furthermore, we identified ‘actionable possibilities’ for each individual to enhance her/his health. Risk factors that we observed in participants’ clinical markers and genetics were used as a starting point to identify action- able possibilities for behavioral coaching. We report the correlations among different data types and identify population-level changes in clinical markers. This project is the pilot for the 100,000 (100K) person wellness project that we proposed in 2014 (ref. 8). An increased scale of personal, dense, dynamic data clouds in future holds the potential to improve our under- standing of scientific wellness and delineate early warning signs for human diseases. RESULTS The P100 study had four objectives. First, establish cost-efficient procedures for generating, storing, and analyzing multiple sources A wellness study of 108 individuals using personal, dense, dynamic data clouds Nathan D Price1,2,6,7, Andrew T Magis2,6, John C Earls2,6, Gustavo Glusman1 , Roie Levy1, Christopher Lausted1, Daniel T McDonald1,5, Ulrike Kusebauch1, Christopher L Moss1, Yong Zhou1, Shizhen Qin1, Robert L Moritz1 , Kristin Brogaard2, Gilbert S Omenn1,3, Jennifer C Lovejoy1,2 & Leroy Hood1,4,7 Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can improve our understanding of health and disease, including early transitions to disease states. 1Institute for Systems Biology, Seattle, Washington, USA. 2Arivale, Seattle, Washington, USA. 3Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA. 4Providence St. Joseph Health, Seattle, Washington, USA. 5Present address: University of California, San Diego, San Diego, California, USA. 6These authors contributed equally to this work. 7These authors jointly supervised this work. Correspondence should be addressed to N.D.P. (nathan.price@systemsbiology.org) or L.H. (lhood@systemsbiology.org). Received 16 October 2016; accepted 11 April 2017; published online 17 July 2017; doi:10.1038/nbt.3870
60. NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved. Intro a b Round 1 Coaching sessions Round 2 Coaching sessions Round 3 Coaching sessions Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8 Month 9 Clinical labs Cardiovascular HDL/LDL cholesterol, triglycerides, particle profiles, and other markers Blood sample Metabolomics Xenobiotics and metabolism-related small molecules Blood sample Diabetes risk Fasting glucose, HbA1c, insulin, and other markers Blood sample Inflammation IL-6, IL-8, and other markers Blood sample Nutrition and toxins Ferritin, vitamin D, glutathione, mercury, lead, and other markers Blood sample Genetics Whole genome sequence Blood sample Proteomics Inflammation, cardiovascular, liver, brain, and heart-related proteins Blood sample Gut microbiome 16S rRNA sequencing Stool sample Quantified self Daily activity Activity tracker Stress Four-point cortisol Saliva
61. Pioneer 100 Wellness Project • 108 individual • for 9 months, at 3-month interval • whole genome sequences • clinical tests • metabolites • proteomes • microbiomes • frequent measurement • activity (ﬁtbit) actionable possibilities behavioural coaching (pilot of 100K person wellness project)
62. ©2017NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved. Proteomics Genetic traits Microbiome Coriobacteriia Allergic sensitization GH NEMO CD40L REN T PA HSP 27 LEP SIRT2 IL 6 FABP4 IL 1RA EGF VEGF A CSTB BETA NGF PPBP(2) PPBP NCF2 4E BP1 STAM PB SIRT2 CSF 1IL 6 FGF 21 IL 10RA IL 18R1IL8IL7 TNFSF14 CCL20 FLT3L CXCL10CD5HGFAXIN1 VEGFAOPGDNEROSM APCSINHBCCRP(2)CRPCFHR1HGFAC MBL2 SERPINC1 GC PTGDS ACTA2 ACTA2(2) PDGF SUBUNIT B Deletion Cfhr1 Inflammatory Bowel Disease Activated Partial Thromboplastin Time Bladder Cancer Bilirubin Levels Gamma Linolenic Acid Dihomo gamma Linolenic Acid Arachidonic Acid Linoleic Acid Adrenic Acid Deltaproteobacteria Mollicutes Verrucomicrobiae Coriobacteriales Verrucomicrobiales Verrucomicrobia Coriobacteriaceae 91otu13421 91otu4418 91otu1825 M ogibacteriaceae Unclassified Desulfovibrionaceae Pasteurellaceae Peptostreptococcaceae Christensenellaceae Verrucom icrobiaceae Alanine RatioOm6Om3 AlphaAminoN ButyricAcid Interleukinll6 SmallLdlParticle RatioGlnGln Threonine 3Methylhistidine AverageinflammationScore Mercury DocosapentaenoicAcidDocosatetraenoicAcid EicosadienoicAcidHomalrLeucineOmega3indexTyrosine HdlCholesterolCPeptide 1Methylhistidine 3HydroxyisovalericAcid IsovalerylglycineIsoleucine Figlu TotalCholesterolLinoleicDihomoYLinolejc PalmitoleicAcid ArachidonicAcid LdlParticle ArachidonicEicosapentaenoic Pasteurellales Diversity Tenericutes Clinical labs Metabolomics 5Hydroxyhexanoate Tl16:0(palmiticAcid) Tl18:3n6(gLinolenicAcid)Tl15:0(pentadecanoicAcid)Tl14:1n5(myristoleicAcid)Tl20:2n6(eicosadienoicAcid)Tl20:5n3(eicosapentaenoicAcid) Tl18:2n6(linoleicAcid) Tldm16:0(plasmalogenPalmiticAcid) Tl22:6n3(docosahexaenoicAcid) Tl22:4n6(adrenicAcid) Tl18:1n9(oleicAcid) Tldm18:1n9(plasmalogenOleicAcid) Tl20:4n6(arachidonicAcid) Tl14:0(myristicAcid) Arachidate(20:0) StearoylArachidonoylGlycerophosphoethanolamine(1)* 1Linoleoylglycerophosphocholine(18:2n6) StearoylLinoleoylGlycerophosphoethanolamine(1)* 1Palmitoleoylglycerophosphocholine(16:1)* PalmitoylOleoylGlycerophosphoglycerol(2)* PalmitoylLinoleoylGlycerophosphocholine(1)* Tl20:3n6(diHomoGLinoleicAcid) 2Hydroxypalmitate NervonoylSphingomyelin* Titl(totalTotalLipid) Cholesterol D ocosahexaenoate (dha;22;6n3) Eicosapentaenoate (epa; 20:5n3) 3 Carboxy 4 M ethyl 5 Propyl 2 Furanpropanoate (cm pf) 3 M ethyladipate Cholate Phosphoethanolamine 1 Oleoylglycerol (1 Monoolein) Tigloylglycine Valine sobutyrylglycine soleucine eucine P Cresol Glucuronide* Phenylacetylglutamine P Cresol Sulfate Tyrosine S Methylcysteine Cystine 3 Methylhistidine 1 Methylhistidine N Acetyltryptophan 3 Indoxyl Sulfate Serotonin (5ht) Creatinine Glutamate Cysteine Glutathione Disulfide Gamma Glutamylthreonine*Gamma Glutamylalanine Gamma Glutamylglutamate Gamma Glutamylglutamine Bradykinin, Hydroxy Pro(3) Bradykinin, Des Arg(9) BradykininMannoseBilirubin (e,e)* Biliverdin Bilirubin (z,z) L UrobilinNicotinamide Alpha TocopherolHippurate Cinnam oylglycine Ldl Particle N um ber Triglycerides Bilirubin Direct Alkaline Phosphatase EgfrNon AfrAm erican CholesterolTotal LdlSm all LdlM edium BilirubinTotal Ggt EgfrAfricanAmerican Cystine MargaricAcid ElaidicAcid Proinsulin Hba1c Insulin Triglycerides Ldlcholesterol DihomoGammaLinolenicAcid HsCrp GlutamicAcid Height Weight Leptin BodyMasIndex PhenylaceticAcid Valine TotalOmega3 TotalOmega6 HsCrpRelativeRisk DocosahexaenoicAcid AlphaAminoadipicAcid EicosapentaenoicAcid GammaAminobutyricAcid 5 Acetylam ino 6 Form ylam ino 3 M ethyluracil Adenosine 5 Monophosphate (amp) Gamma Glutamyltyrosine Gamma Glutamyl 2 Aminobutyrate N Acetyl 3 Methylhistidine* 3 Phenylpropionate (hydrocinnamate) Figure 2 Top 100 correlations per pair of data types. Subset of top statistically significant Spearman inter-omic cross-sectional correlations between all data sets collected in our cohort. Each line represents one correlation that was significant after adjustment for multiple hypothesis testing using the method of Benjamini and Hochberg10 at padj < 0.05. The mean of all three time points was used to compute the correlations between analytes. Up to 100 correlations per pair of data types are shown in this figure. See Supplementary Figure 1 and Supplementary Table 2 for the complete inter-omic cross-sectional network. Nature Biotechnology 2017 측정한 모든 종류의 데이터들 중에 가장 correlation이 높은 100개의 pair를 선정
63. ©2017NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved. A RT I C L E S edges. The majority of edges involved a metabolite (3,309) or a clini- cal laboratory test (3,366), with an additional 20 edges involving the 130 genetic traits tested, 46 with microbiome taxa or diversity score, and 207 with quantified proteins. The inter-omic delta correlation network contained 822 nodes and 2,406 edges. 375 of the edges in the delta correlation network were also present in the cross-sectional network. The cross-sectional correlation network is provided in Supplementary Table 2 (inter-omic only) and Supplementary Table 3 (full). The delta correlation network is provided in Supplementary Table 4 (inter-omic only) and Supplementary Table 5 (full). We identified clusters of related measurements from the cross- sectional inter-omic correlation network using community analysis, an unsupervised (i.e., using unlabeled data to find hidden structure) approach that iteratively prunes the network (removing the edges with the highest betweenness) to reveal densely inter- connected subgraphs (communities)11. Seventy communities of at least two vertices (mean of 10.9 V and 34.9 E) were identi- fied in the cross-sectional inter-omic network at the cutoff with maximum community modularity12 (Supplementary Fig. 2), and are fully visualized as an interactive graph in Cytoscape13 (Supplementary Dataset 1). 70% of the edges in the cross-sec- tional network remained after community edge pruning. The communities often represented a cluster of physiologically related analytes, as described below. Guanidinosuccinate Alanine IsovalerylcarnitineValine NAcetylleucineNAcetylisoleucine2Methylbutyrylcarnitine(c5) IsoleucineLeucine SAdenosylhomocysteine(sah) CysteineCystineMethionineSulfone NAcetyltryptophan NAcetylkynurenine(2) 3IndoxylSulfate Xanthurenate Kynurenine Kynurenate Tryptophan Phenylalanine N Acetylphenylalanine 4Hydroxyphenylpyruvate Phenylpyruvate Tyrosine N Acetyltyrosine Phenylacetylcarnitine G lutam ine G lutam ate N Acetylglycine G lycine Proline N Delta Acetylornithine N Acetylcitrulline Hom oarginine N2,n5 Diacetylornithine Pro Hydroxy Pro 2 Aminoadipate Lysine Deoxycholate Ursodeoxycholate Arachidate (20:0) Nonadecanoate (19:0) Palmitate (16:0) Erucate (22:1n9) Tl16:0 (palmitic Acid) Tl16:1n7 (palmitoleic Acid) Tl18:1n7 (avaccenic Acid) Tl14:1n5 (myristoleic Acid) Tl24:1n9 (nervonic Acid) Tldm18:1n7 (plasmalogen Vaccenic Acid) Tldm18:0 (plasmalogen Stearic Acid) Tl14:0 (myristic Acid) Tl18:2n6 (linoleic Acid) Tldm16:0 (plasmalogen Palmitic Acid) Tl22:1n9 (erucic Acid) Tl20:3n6 (di Homo G Linoleic Acid) Tl20:4n3 (eicosatetranoic Acid) Tl18:1n9 (oleic Acid) Tl18:3n3 (a Linolenic Acid) Tldm18:1n9 (plasmalogen Oleic Acid) 1 Linoleoylglycerophosphocholine (18:2n6) 1 Linolenoylglycerophosphocholine (18:3n3)* 2 Stearoylglycerophosphocholine*1 Palmitoleoylglycerophosphocholine (16:1)*1 Oleoylglycerophosphocholine (18:1)3 Hydroxylaurate2 Hydroxydecanoate3 Hydroxydecanoate 3 Hydroxyoctanoate 2 Hydroxystearate 3 Hydroxysebacate7 Alpha Hydroxy 3 Oxo 4 Cholestenoate (7 Hoca) CholesterolCarnitinePregnanediol 3 Glucuronide Epiandrosterone Sulfate Stearoylcarnitine Myristoleoylcarnitine* Decanoylcarnitine Laurylcarnitine 2 Oleoylglycerol (2 Monoolein) 1 Linolenoylglycerol 1 Palmitoylglycerol (1 Monopalmitin) 1 Linoleoylglycerol (1 Monolinolein) 1 Dihomo Linolenylglycerol (alpha, Gamma) 1 Oleoylglycerol (1 Monoolein) Caprate (10:0) Laurate (12:0) Caprylate (8:0) 5 Dodecenoate (12:1n7) Palm itoyl Sphingom yelin StearoylSphingom yelin Sphinganine NervonoylSphingom yelin* Sphingosine OleoylSphingom yelin 3 Hydroxybutyrate (bhba) Acetoacetate Butyrylcarnitine Propionylcarnitine DihomoLinolenate(20:3n3OrN6) Hexanoylglycine Glycerophosphoethanolamine Tltl(totalTotalLipid) Eicosanodioate Octadecanedioate 3Methyladipate 2MethylmalonylCarnitine PalmitoylEthanolamide NOleoyltaurine N1Methyl2Pyridone5Carboxamide Nicotinamide AlphaTocopherol GammaTocopherol Threonate Oxalate(ethanedioate) Ergothioneine NAcetylalliin Erythritol Cinnamoylglycine SAllylcysteine 2Pyrrolidinone 2Hydroxyisobutyrate Tartronate(hydroxymalonate) 1,3,7Trimethylurate 4Hydroxycoumarin 2AcetamidophenolSulfate 4AcetylphenolSulfate Mannose Erythronate* Pyruvate Lactate Glucose Glycerate Xylitol GammaGlutamylleucine GammaGlutamylphenylalanine Gam m a Glutam ylisoleucine* Gam m a Glutam ylglutam ine Gam m a Glutam ylhistidine G am m a G lutam ylglutam ate Bradykinin,Hydroxy Pro(3) G lycylleucine Succinylcarnitine Succinate Fum arate M alate Alpha Ketoglutarate Citrate Xanthine LDL Particle hs-CRP Relative Risk ProinsulinHba1cInsulin Gamma Linolenic Acid Triglycerides Manganese Dihomo Gamma Linolenic Acid Glutamic AcidLeptin Body Mass Index Total LC Omega9TryptophanLysineVitamin D 5 Hydroxyindoeacetic AcidWeightLactic Acid Linoleic Dihomo Y LinoleioIsovalerylglycineQuinolinic Acid C-PeptideHDL Cholesterol Indoleacetic Acid Adiponectin Phenylalanine Interleukin IL6 Small LDL Particle Ratio Asn Asp HOMA-IR Lignoceric Acid Succinic Acid Homogentisic Acid Homovanillic Acid Average Inflammation Score FIGLU Ratio Gln Gln Magnesium Pyroglutamic Acid Glucose Gondoic Acid Kynurenic Quinolinic Ratio Alpha Amino N Butyric Acid Tyrosine Alanine HDL Large GGT Triglycerides Bilirubin Direct LDL Medium LDL Pattern Alkaline Phosphatase LDL Peak Size Chloride Glucose LDL Particle Num ber LDL Sm all Ferritin CCL19 H G F IL 10RAIL 6 CXCL10 TNFSF14 CCL20CD5 CD40 VEGF A IL18R1OSM CRPF9(2) APCSINHBCCRP(2) MBL2(2)MBL2GC F9 SERPINC1 TPALEPVEGFAVEGFD IL6FABP4CSTBIL1RA Pasteurellales Pasteurellaceae Omega6FattyAcidLevels(DGLA) FG F 21 hs-CRP G am m a G lutam yltyrosine Amino acid metabolism Olink (CVD) Olink (inflammation) Quest diagnostics Genova diagnostics Nucleotides Energy Peptides Carbohydrates Xenobiotics Vitamins and cofactors Lipid metabolism SRM (liver) Metabolites Clinical labs Microbiome Genetic traits Proteins Figure 3 Cardiometabolic community. All vertices and edges of the cardiometabolic community, with lines indicating significant ( adj < 0.05) correlations. Associations with FGF21 (red lines) and gamma-glutamyltyrosine (purple lines) are highlighted. • inter-omics correlation network 의 분석을 통해서 환자들을 몇가지 cluster로 분류 • 가장 큰 cluster (246 Vertices, 1645 Edges): Cardiometaboic Health • four most connected clinical analyses: C-peptide, insulin, MOMA-IR, triglycerides • four most-connected proteins: leptin, C-reactive protein, FGF21, INHBC gamma-glutamyltyrosine FGF21
64. • inter-omics correlation network 의 분석을 통해서 환자들을 몇가지 cluster로 분류 • 가장 큰 cluster (246 Vertices, 1645 Edges): Cardiometaboic Health • four most connected clinical analyses: C-peptide, insulin, MOMA-IR, triglycerides • four most-connected proteins: leptin, C-reactive protein, FGF21, INHBC atureAmerica,Inc.,partofSpringerNature.Allrightsreserved. A RT I C L E S The largest community (246 V; 1,645 E) contains many clinical analytes associated with cardiometabolic health, such as C-peptide, triglycerides, insulin, homeostatic risk assessment–insulin resistance (HOMA-IR), fasting glucose, high-density lipid (HDL) cholesterol, and small low-density lipid (LDL) particle number (Fig. 3). The four most-connected clinical analytes by degree (the number of edges connecting a particular analyte) were C-peptide (degree 99), insulin (88), HOMA-IR (88), and triglycerides (75). The four most-connected proteins measured using targeted (i.e., selected reaction monitoring analysis) mass spectrometry or Olink proximity extension assays by degree are leptin (18), C-reactive protein (15), fibroblast growth factor 21 (FGF21) (14), and inhibin beta C chain (INHBC) (10). Leptin and C-reactive protein are indicators for cardiovascular risk14,15. FGF21 is positively correlated with the clinical analytes ( = −0.41; padj = 2.1 × 10−3). Hypothyroidism has long been recog- nized clinically as a cause of elevated cholesterol values19. A community formed around plasma serotonin (18 V; 25 E) contain- ing 12 proteins listed in Supplementary Table 6, for which the most significant enrichment identified in a STRING ontology analysis20 was platelet activation (padj = 1.7 × 10−3) (Fig. 4b). Serotonin is known to induce platelet aggregation21; accordingly, selective serotonin reuptake inhibitors (SSRIs) may protect against myocardial infarction22. We identified several communities containing microbiome taxa, suggesting that there are specific microbiome–analyte relationships. Hydrocinnamate, l-urobilin, and 5-hydroxyhexanoate clustered with the bacterial class Mollicutes and family Christensenellaceae (8 V; 8 E). Another community emerged around the Verrucomicrobiaceae and Desulfovibrionaceae families and p-cresol-sulfate (7 V; 6 E). The a c d b e Figure 4 Cholesterol, serotonin, -diversity, IBD, and bladder cancer communities. (a) Cholesterol community. (b) Serotonin community. (c) -diversity community. (d) The polygenic score for inflammatory bowel disease is negatively correlated with cystine. (e) The polygenic score for bladder cancer is positively correlated with 5-acetylamino-6-formylamino-3-methyluracil (AFMU). Cholesterol, serotonin, diversity, IBD, and bladder cancer communities. (a) Cholesterol community. (b) Serotonin community. (c) -diversity community. (d) The polygenic score for inﬂammatory bowel disease is negatively correlated with cystine. (e) The polygenic score for bladder cancer is positively correlated with 5-acetylamino-6-formylamino-3-methyluracil (AFMU).
65. 017NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved. identified with elevated fasting glucose or HbA1c at baseline (pre- diabetes), the coach made recommendations based on the Diabetes Prevention Program36, customized for each person’s lifestyle. These individual recommendations typically fell into one of several major factors (fasting insulin and HOMA-IR), and inflammation (IL-8 and TNF-alpha). Lipoprotein fractionation, performed by both labora- tory companies, produced significant but discordant results for LDL particle number. We observed significant improvements in fasting Table 1 Longitudinal analysis of clinical changes by round Clinical laboratory test Changes in labs in participants out-of-range at baseline Health area Name N per round P-value Nutrition Vitamin D 95 +7.2 ng/mL/round 7.1 × 10−25 Nutrition Mercury 81 −0.002 mcg/g/round 8.9 × 10−9 Diabetes HbA1c 52 −0.085%/round 9.2 × 10−6 Cardiovascular LDL particle number (Quest) 30 +130 nmol/L/round 9.3 × 10−5 Nutrition Methylmalonic acid (Genova) 3 −0.49 mmol/mol creatinine/round 2.1 × 10−4 Cardiovascular LDL pattern (A or B) 28 −0.16 /round 4.8 × 10−4 Inﬂammation Interleukin-8 10 −6.1 pg/mL/round 5.9 × 10−4 Cardiovascular Total cholesterol (Quest) 48 −6.4 mg/dL/round 7.2 × 10−4 Cardiovascular LDL cholesterol 57 −4.8 mg/dL/round 8.8 × 10−4 Cardiovascular LDL particle number (Genova) 70 −69 nmol/L/round 1.2 × 10−3 Cardiovascular Small LDL particle number (Genova) 73 −56 nmol/L/round 3.5 × 10−3 Diabetes Fasting glucose (Quest) 45 −1.9 mg/dL/round 8.2 × 10−3 Cardiovascular Total cholesterol (Genova) 43 −5.4 mg/dL/round 1.2 × 10−2 Diabetes Insulin 16 −2.3 IU/mL/round 1.5 × 10−2 Inﬂammation TNF-alpha 4 −6.6 pg/mL/round 1.8 × 10−2 Diabetes HOMA-IR 19 −0.56 /round 2.0 × 10−2 Cardiovascular HDL cholesterol 5 +4.5 mg/dL/round 2.2 × 10−2 Nutrition Methylmalonic acid (Quest) 7 −42 nmol/L/round 5.2 × 10−2 Cardiovascular Triglycerides (Genova) 14 −18 mg/dL/round 1.4 × 10−1 Diabetes Fasting glucose (Genova) 47 −0.98 mg/dL/round 1.5 × 10−1 Nutrition Arachidonic acid 35 +0.24 wt%/round 1.9 × 10−1 Inﬂammation hs-CRP 51 −0.47 mcg/mL/round 2.1 × 10−1 Cardiovascular Triglycerides (Quest) 17 −14 mg/dL/round 2.4 × 10−1 Nutrition Glutathione 6 +11 micromol/L/round 2.5 × 10−1 Nutrition Zinc 4 −0.82 mcg/g/round 3.0 × 10−1 Nutrition Ferritin 10 −14 ng/mL/round 3.1 × 10−1 Inﬂammation Interleukin-6 4 −1.1 pg/mL/round 3.8 × 10−1 Cardiovascular HDL large particle number 8 +210 nmol/L/round 4.9 × 10−1 Nutrition Copper 10 +0.006 mcg/g/round 6.0 × 10−1 Nutrition Selenium 6 +0.035 mcg/g/round 6.2 × 10−1 Cardiovascular Medium LDL particle number 20 +2.8 nmol/L/round 8.5 × 10−1 Cardiovascular Small LDL particle number (Quest) 14 −2.3 nmol/L/round 8.8 × 10−1 Nutrition Manganese 0 N/A N/A Nutrition EPA 0 N/A N/A Nutrition DHA 0 N/A N/A Generalized estimating equations (GEE) were used to calculate average changes in clinical laboratory tests over time, for those analytes that were actively coached on. The ‘ per round’ column is the average change in the population for that analyte by round adjusted for age, sex, and self-reported ancestry. ‘Out-of-range at baseline’ indicates the average change using only those participants who were out-of-range for that analyte at the beginning of the study. Rows in boldface indicate statistically signiﬁcant improvement, while the italicized row indicates statistically signiﬁcant worsening. N/A values are present where no participants were out-of-range at baseline. For example, the average improvement in vitamin D for the 95 participants that began the study out-of-range was +7.2 ng/mL per round. Several analytes are measured by both Quest and Genova; with the exception of LDL particle number, the direction of effect for signiﬁcantly changed analytes was concordant across the two laboratories. An independence working correlation structure was used in the GEE. See Supplementary Table 10 for the complete results. • 수치가 정상 범위를 벗어나면 코치가 개입하여, 해당 수치를 개선할 수 있는 라이프스타일의 변화 유도 • 예를 들어, 공복혈당 혹은 HbA1c 의 증가: 코치가 당뇨 예방 프로그램(DPP)을 권고 • 몇개의 major category로 나눠짐 • diet, exercise, stress management, dietary supplements, physician referral • 이를 통해서 가장 크게 개선 효과가 있었던 수치들 • vitamin D, mercury, HbA1c • 전반적으로 콜레스테롤 관련 수치나, 당뇨 위험 관련 수치, 염증 수치 등에 개선이 있었음
66. 개인적으로 재미있었던 사례  : hemochromatosis (혈색소증) 관련 • 65살 환자가 등산 하다가 발목에 cartilage damage • Baseline blood collection: ferritin levels of 399 ng/mL • Homozygous for HFE C282Y (risk factor of hemochromatosis) • 혈색소증 (hemochromatosis) • 철에 대한 체내 대사에 이상이 생겨 음식을 통해 섭취한 철이 너무 많이 흡수되는 질환 • 간, 심장 및 췌장 등의 장기를 손상시키고, 간질환, 심장질환 및 악성종양을 유발 • 혈색소증 진단 & therapeutic phlebotomy 처방 (by hematologist) • ferritin levels dropped to 175 ng/mL
67. Inherited Conditions 혈색소증은 유전적 원인으로 철에 대한 체내 대사에 이상이 생겨 음식을 통해 섭취한 철이 너무 많이 흡수되는 질환입니다. 너무 많이 흡수된 철 은 우리 몸의 여러 장기, 특히 간, 심장 및 췌장에 과다하게 축적되며 이 들 장기를 손상시킴으로써 간질환, 심장질환 및 악성종양을 유발합니다.
68. Yoon Sup Choi Hemochromatosis (HFE-related) • Inherited Conditions 1 © 2016 23andMe, Inc. All rights reserved. Hemochromatosis (HFE-related) This report was archived on 2016-08-03. Please note that this report is no longer updated and was developed using scientific criteria that may have since been revised. The following results are based on Established Research for 3 reported markers, updated July 26th, 2012. Introduction Iron, an essential mineral, is absorbed via the intestines from food and is important for many bodily functions including red blood cell formation and proper brain function. The iron absorption process must be tightly regulated or else iron can accumulate in the body, possibly causing organ damage. Inherited forms of iron overload, known as hereditary hemochromatosis (HH), are caused by mutations in genes that normally play important roles in regulating iron levels. This report includes three mutations in the HFE gene that are typically found in people with European ancestry and are responsible for most cases of HH. HFE-related HH is inherited in a recessive manner, meaning that a person must receive a mutated copy of the HFE gene from each parent to have the condition. In Europeans, roughly one in 300 individuals has HFE-related HH and at least one in 10 carries a mutation for the condition. Rates are even higher in certain European populations including Irish, Norwegian and Australian. HFE-related HH is much rarer in Asian and African populations. Yoon Sup's Genetic Result Who Genetic Result Has two copies of the C282Y mutation in the HFE gene. A person with two copies of this mutation is prone to higher levels of iron in the body, which in a small percentage of cases leads to clinical symptoms of hemochromatosis. Has one copy of the C282Y mutation and one copy of either the H63D or S65C mutation in the HFE gene. A person with this combination may be prone to higher levels of iron in the body, which in a very small percentage of cases leads to clinical symptoms of hemochromatosis. Has two copies of either the H63D or S65C mutation, or one of each, in the HFE gene. A person with this combination of mutations is not typically prone to higher levels of iron in the body. Yoon Sup Choi Has one mutation in the HFE gene linked to hemochromatosis. A person with one of these mutations is not typically prone to higher levels of iron in the body, but can pass the mutation to offspring. May have other mutations in the HFE gene (not reported here). Does not have any of the three mutations in the HFE gene linked to hereditary hemochromatosis. May still have other mutations in the HFE gene (not reported here).
69. Data-driven Healthcare에 대한 두 가지 전략 • top-down: 먼저 가설을 세우고, 그에 맞는 종류의 데이터를 모아서 검증해보자. • bottom-up: 일단 ‘모든’ 데이터를 최대한 많이 모아 놓으면, 뭐라도 큰 게 나오겠지.
70. • 버릴리(구글)의 베이스라인 프로젝트 • 건강과 질병을 새롭게 정의하기 위한 프로젝트 • 4년 동안 만 명의 개인의 건강 상태를 면밀하게 추적하여 데이터를 축적 • 심박수와 수면패턴 및 유전 정보, 감정 상태, 진료기록, 가족력, 소변/타액/혈액 검사 등
71. • 버릴리(구글)의 베이스라인 프로젝트 • 건강과 질병을 새롭게 정의하기 위한 프로젝트 • 4년 동안 만 명의 개인의 건강 상태를 면밀하게 추적하여 데이터를 축적 • 심박수와 수면패턴 및 유전 정보, 감정 상태, 진료기록, 가족력, 소변/타액/혈액 검사 등
72. • 버릴리의 ‘Study Watch’ • 2017년 4월 공개한 베이스라인 스터디 용 스마트워치 • 심전도, 심박수, EDA(Electrodermal Activity), 관성움직임(inertial movement) 등 측정 • 장기간 추적연구를 위한 특징들: 배터리 수명(일주일), 데이터 저장 공간, 동기화 (일주일 한 번)
73. • Linda Avey의 Precise.ly • 23andMe의 공동창업자였던 Linda Avey가 2009년 회사를 떠난 이후, 2011년 창업 • ‘We Are Curious’ 라는 이름에서 최근에 Precise.ly로 회사 이름 변경
74. • Linda Avey의 Precise.ly • Genotype + Phenotype + Microbiome + environment 모두 결합하여 의학적인 insight • Genotype: Helix의 플랫폼에서 WES 을 통하여 분석 • Phenotype: 웨어러블, IoT 기기를 이용
75. • ‘Modern diseases’를 주로 타게팅 하겠다고 언급하고 있음 • 예를 들어, autism spectrum syndrome을 다차원적 데이터를 기반으로 분류할 수 있을까? • Helix 플랫폼을 통해서 먼저 Chronic Fatigue 에 대한 앱을 먼저 출시하고, • 향후 autism, PD 등에 대한 앱을 출시할 예정이라고 함.
76. iCarbonX •중국 BGI의 대표였던 준왕이 창업 •'모든 데이터를 측정'하고 이를 정밀 의료에 활용할 계획 •데이터를 측정할 수 있는 역량을 가진 회사에 투자 및 인수 •SomaLogic, HealthTell, PatientsLikMe •향후 5년 동안 100만명-1000만 명의 데이터 모을 계획 •이 데이터의 분석은 인공지능으로
77. • 충분한 수의 (1,000만 명) 데이터를 충분한 기간 (4-5년) 동안 모을 수 있을까? • compliance, 고질적 문제: 5년 동안 모든 종류의 데이터를 측정할 사람이 얼마나 • 현재 단기적으로라도 증명된 방법은 돈을 주는 것 밖에는… (연구 > 사업) • 이 방대한 종류의 데이터를 어떻게 분석할 것인가? • 인공지능도 만능은 아니다. • 일일이 가설을 세우고 분석할 수밖에 없지 않을까. • So What?이 인사이트로 무엇을 할 수 있는가? • 사용자의 건강/질병에 유의미할 정도의 예방/예측/관리/치료가 가능한가 • 발견한 insight에 맞는 바이오마커/치료법/예방법 등을 찾을 수 있나 • 그 결과로 나온 서비스에 대한 고객군의 지불 의사는 얼마나 될까? • 근본적 문제 건강한 사람은 건강에 관심이 없다.
78. • Puretech Health • ‘새로운 개념의 제약회사’를 추구하는 회사 • 기존의 신약 뿐만 아니라, 게임, 앱 등을 이용한 Digital Therapeutics 를 개발 • Digital Therapeutics는 최근 미국 FDA의 de novo 승인을 받기도 함
79. • Puretech Health • 신약 파이프라인 중에는 일반적인 small molecule 등도 있지만, • Akili: ADHD, 우울증, 알츠하이머 등을 위한 인지 능력 개선 목적의 게임 (Project EVO) • Sonde: Voice biomarker 를 이용한 우울증 등 mental health의 진단 및 모니터링 목적
80. • Puretech Health • 신약 파이프라인 중에는 일반적인 small molecule 등도 있지만, • Akili: ADHD, 우울증, 알츠하이머 등을 위한 인지 능력 개선 목적의 게임 (Project EVO) • Sonde: Voice biomarker 를 이용한 우울증 등 mental health의 진단 및 모니터링 목적
81. • Puretech Health • 신약 파이프라인 중에는 일반적인 small molecule 등도 있지만, • Akili: ADHD, 우울증, 알츠하이머 등을 위한 인지 능력 개선 목적의 게임 (Project EVO) • Sonde: Voice biomarker 를 이용한 우울증 등 mental health의 진단 및 모니터링 목적
82. LETTER doi:10.1038/nature12486 Video game training enhances cognitive control in older adults J. A. Anguera1,2,3 , J. Boccanfuso1,3 , J. L. Rintoul1,3 , O. Al-Hashimi1,2,3 , F. Faraji1,3 , J. Janowich1,3 , E. Kong1,3 , Y. Larraburo1,3 , C. Rolle1,3 , E. Johnston1 & A. Gazzaley1,2,3,4 Cognitivecontrolisdefinedbyasetofneuralprocessesthatallowusto interact with our complex environment in a goal-directed manner1 . Humans regularly challenge these control processes when attempting to simultaneously accomplish multiple goals (multitasking), generat- ing interference as the result of fundamental information processing limitations2 . It is clear that multitasking behaviour has become ubi- quitous in today’s technologically dense world3 , and substantial evid- ence has accrued regarding multitasking difficulties and cognitive control deficits in our ageing population4 . Here we show that multi- tasking performance, as assessed with a custom-designed three- dimensional video game (NeuroRacer), exhibits a linear age-related decline from 20 to 79 years of age. By playing an adaptive version of NeuroRacer in multitasking training mode, older adults (60 to 85 years old) reduced multitasking costs compared to both an active control group and a no-contact control group, attaining levels beyond those achieved by untrained 20-year-old participants, with gains persisting for 6 months. Furthermore, age-related deficits in neural signatures of cognitive control, as measured with electroencephalo- graphy,wereremediated by multitasking training (enhanced midline frontal theta power and frontal–posterior theta coherence). Critically, thistrainingresultedinperformancebenefitsthatextendedtountrained cognitive control abilities (enhanced sustained attention and working memory), with an increase in midline frontal theta power predicting the training-induced boost in sustained attention and preservation of multitasking improvement 6 months later. These findings high- light the robust plasticity of the prefrontal cognitive control system in the ageing brain, and provide the first evidence, to our knowledge, ofhowacustom-designedvideogamecanbeusedtoassesscognitive abilities across the lifespan, evaluate underlying neural mechanisms, and serve as a powerful tool for cognitive enhancement. In a first experiment, we evaluated multitasking performance across the adult lifespan. A total of 174 participants spanning six decades of life (ages 20–79; ,30 individuals per decade) played a diagnostic version of NeuroRacertomeasuretheirperceptualdiscriminationability(‘signtask’) withandwithoutaconcurrentvisuomotortrackingtask(‘drivingtask’;see Supplementary Information for details of NeuroRacer). Performance was evaluated using two distinct game conditions: ‘sign only’ (respond as rapidly as possible to the appearance of a sign only when a green circle was present); and ‘sign and drive’ (simultaneously perform the sign task while maintaining a car in the centre of a winding road using a joystick (that is, ‘drive’; see Fig. 1a)). Perceptual discrimination performance was evaluatedusingthesignaldetectionmetricofdiscriminability(d9).A‘cost’ index was used to assess multitasking performance by calculating the percentage change in d9 from ‘sign only’ to ‘sign and drive’, such that greater cost (that is, a more negative percentage cost) indicates increased interference when simultaneously engaging in the two tasks (see Methods Summary). Prior to the assessment of multitasking costs, an adaptive staircase algorithm was used to determine the difficulty levels of the game at which each participant performed the perceptual discrimination and visuomotor tracking tasks in isolation at ,80% accuracy. These levels were then used to set the parameters of the component tasks in the multitasking condition, so that each individual played the game at a customizedchallengelevel.Thisensuredthatcomparisonswouldinform differences in the ability to multitask, and not merely reflect disparities in component skills (see Methods, Supplementary Figs 1 and 2, and Sup- plementary Information for more details). Multitasking performance diminished significantly across the adult lifespan in a linear fashion (that is, increasing cost, see Fig. 2a and Sup- plementaryTable1),withtheonlysignificantdifferenceincostbetween adjacent decades being the increase from the twenties (226.7% cost) to the thirties (238.6% cost). This deterioration in multitasking perform- ance is consistent with the pattern of performance decline across the lifespan observed for fluid cognitive abilities, such as reasoning5 and working memory6 . Thus, using NeuroRacer as a performance assess- ment tool, we replicated previously evidenced age-related multitasking deficits7,8 , and revealed that multitasking performance declines linearly as we advance in age beyond our twenties. In a second experiment, we explored whether older adults who trained by playing NeuroRacer in multitasking mode would exhibit improve- mentsintheirmultitaskingperformanceonthegame9,10 (thatis,diminished NeuroRacer costs). Critically, we also assessed whether this training 1 Department of Neurology, University of California, San Francisco, California 94158, USA. 2 Department of Physiology, University of California, San Francisco, California 94158, USA. 3 Center for Integrative Neuroscience, University of California, San Francisco, California 94158, USA. 4 Department of Psychiatry, University of California, San Francisco, California 94158, USA. 1 month MultitaskingSingle taskNo-contact control Initial visit NeuroRacer EEG and cognitive testing Drive only Sign only Sign and drive and 1 hour × 3 times per week × 1 month or Single task Multitask 6+ months Training intervention NeuroRacer or a b + + Figure 1 | NeuroRacer experimental conditions and training design. a, Screen shot captured during each experimental condition. b, Visualization of training design and measures collected at each time point. 5 S E P T E M B E R 2 0 1 3 | V O L 5 0 1 | N A T U R E | 9 7 Macmillan Publishers Limited. All rights reserved©2013
83. LETTER doi:10.1038/nature12486 Video game training enhances cognitive control in older adults J. A. Anguera1,2,3 , J. Boccanfuso1,3 , J. L. Rintoul1,3 , O. Al-Hashimi1,2,3 , F. Faraji1,3 , J. Janowich1,3 , E. Kong1,3 , Y. Larraburo1,3 , C. Rolle1,3 , E. Johnston1 & A. Gazzaley1,2,3,4 Cognitivecontrolisdefinedbyasetofneuralprocessesthatallowusto interact with our complex environment in a goal-directed manner1 . Humans regularly challenge these control processes when attempting to simultaneously accomplish multiple goals (multitasking), generat- ing interference as the result of fundamental information processing limitations2 . It is clear that multitasking behaviour has become ubi- quitous in today’s technologically dense world3 , and substantial evid- ence has accrued regarding multitasking difficulties and cognitive control deficits in our ageing population4 . Here we show that multi- tasking performance, as assessed with a custom-designed three- dimensional video game (NeuroRacer), exhibits a linear age-related decline from 20 to 79 years of age. By playing an adaptive version of NeuroRacer in multitasking training mode, older adults (60 to 85 years old) reduced multitasking costs compared to both an active control group and a no-contact control group, attaining levels beyond those achieved by untrained 20-year-old participants, with gains persisting for 6 months. Furthermore, age-related deficits in neural signatures of cognitive control, as measured with electroencephalo- graphy,wereremediated by multitasking training (enhanced midline frontal theta power and frontal–posterior theta coherence). Critically, thistrainingresultedinperformancebenefitsthatextendedtountrained cognitive control abilities (enhanced sustained attention and working memory), with an increase in midline frontal theta power predicting the training-induced boost in sustained attention and preservation of multitasking improvement 6 months later. These findings high- light the robust plasticity of the prefrontal cognitive control system in the ageing brain, and provide the first evidence, to our knowledge, ofhowacustom-designedvideogamecanbeusedtoassesscognitive abilities across the lifespan, evaluate underlying neural mechanisms, and serve as a powerful tool for cognitive enhancement. In a first experiment, we evaluated multitasking performance across the adult lifespan. A total of 174 participants spanning six decades of life (ages 20–79; ,30 individuals per decade) played a diagnostic version of NeuroRacertomeasuretheirperceptualdiscriminationability(‘signtask’) withandwithoutaconcurrentvisuomotortrackingtask(‘drivingtask’;see Supplementary Information for details of NeuroRacer). Performance was evaluated using two distinct game conditions: ‘sign only’ (respond as rapidly as possible to the appearance of a sign only when a green circle was present); and ‘sign and drive’ (simultaneously perform the sign task while maintaining a car in the centre of a winding road using a joystick (that is, ‘drive’; see Fig. 1a)). Perceptual discrimination performance was evaluatedusingthesignaldetectionmetricofdiscriminability(d9).A‘cost’ index was used to assess multitasking performance by calculating the percentage change in d9 from ‘sign only’ to ‘sign and drive’, such that greater cost (that is, a more negative percentage cost) indicates increased interference when simultaneously engaging in the two tasks (see Methods Summary). Prior to the assessment of multitasking costs, an adaptive staircase algorithm was used to determine the difficulty levels of the game at which each participant performed the perceptual discrimination and visuomotor tracking tasks in isolation at ,80% accuracy. These levels were then used to set the parameters of the component tasks in the multitasking condition, so that each individual played the game at a customizedchallengelevel.Thisensuredthatcomparisonswouldinform differences in the ability to multitask, and not merely reflect disparities in component skills (see Methods, Supplementary Figs 1 and 2, and Sup- plementary Information for more details). Multitasking performance diminished significantly across the adult lifespan in a linear fashion (that is, increasing cost, see Fig. 2a and Sup- plementaryTable1),withtheonlysignificantdifferenceincostbetween adjacent decades being the increase from the twenties (226.7% cost) to the thirties (238.6% cost). This deterioration in multitasking perform- ance is consistent with the pattern of performance decline across the lifespan observed for fluid cognitive abilities, such as reasoning5 and working memory6 . Thus, using NeuroRacer as a performance assess- ment tool, we replicated previously evidenced age-related multitasking deficits7,8 , and revealed that multitasking performance declines linearly as we advance in age beyond our twenties. In a second experiment, we explored whether older adults who trained by playing NeuroRacer in multitasking mode would exhibit improve- mentsintheirmultitaskingperformanceonthegame9,10 (thatis,diminished NeuroRacer costs). Critically, we also assessed whether this training 1 Department of Neurology, University of California, San Francisco, California 94158, USA. 2 Department of Physiology, University of California, San Francisco, California 94158, USA. 3 Center for Integrative Neuroscience, University of California, San Francisco, California 94158, USA. 4 Department of Psychiatry, University of California, San Francisco, California 94158, USA. 1 month MultitaskingSingle taskNo-contact control Initial visit NeuroRacer EEG and cognitive testing Drive only Sign only Sign and drive and 1 hour × 3 times per week × 1 month or Single task Multitask 6+ months Training intervention NeuroRacer or a b + + Figure 1 | NeuroRacer experimental conditions and training design. a, Screen shot captured during each experimental condition. b, Visualization of training design and measures collected at each time point. 5 S E P T E M B E R 2 0 1 3 | V O L 5 0 1 | N A T U R E | 9 7 Macmillan Publishers Limited. All rights reserved©2013 • 게임을 통해서 노년층의 인지 능력을 개선시킬 수 있음을 증명한 논문 (Nature 2013, UCSF) • ‘뉴로레이서’라는 3차원 비디오 게임 • 왼손으로 꼬불꼬불한 길을 운전하면서 • 화면 상단에 특정 색깔/모양의 표시가 뜨면, 오른손으로 누르는 훈련 sking performance, as assessed with a custom-designed three- mensional video game (NeuroRacer), exhibits a linear age-related ecline from 20 to 79 years of age. By playing an adaptive version of euroRacer in multitasking training mode, older adults (60 to 85 ars old) reduced multitasking costs compared to both an active ontrol group and a no-contact control group, attaining levels beyond ose achieved by untrained 20-year-old participants, with gains ersisting for 6 months. Furthermore, age-related deficits in neural gnatures of cognitive control, as measured with electroencephalo- aphy,wereremediated by multitasking training (enhanced midline ontal theta power and frontal–posterior theta coherence). Critically, istrainingresultedinperformancebenefitsthatextendedtountrained ognitive control abilities (enhanced sustained attention and working emory), with an increase in midline frontal theta power predicting e training-induced boost in sustained attention and preservation multitasking improvement 6 months later. These findings high- ght the robust plasticity of the prefrontal cognitive control system the ageing brain, and provide the first evidence, toour knowledge, howacustom-designedvideogamecanbeusedtoassesscognitive bilities across the lifespan, evaluate underlying neural mechanisms, nd serve as a powerful tool for cognitive enhancement. In a first experiment, we evaluated multitasking performance across e adult lifespan. A total of 174 participants spanning six decades of life ges 20–79; ,30 individuals per decade) played a diagnostic version of euroRacertomeasuretheirperceptualdiscriminationability(‘signtask’) ithandwithoutaconcurrentvisuomotortrackingtask(‘drivingtask’;see upplementary Information for details of NeuroRacer). Performance as evaluated using two distinct game conditions: ‘sign only’ (respond rapidly as possible to the appearance of a sign only when a green circle as present); and ‘sign and drive’ (simultaneously perform the sign task hile maintaining a car in the centre of a winding road using a joystick hat is, ‘drive’; see Fig. 1a)). Perceptual discrimination performance was aluatedusingthesignaldetectionmetricofdiscriminability(d9).A‘cost’ dex was used to assess multitasking performance by calculating the ercentage change in d9 from ‘sign only’ to ‘sign and drive’, such that eater cost (that is, a more negative percentage cost) indicates increased terference when simultaneously engaging in the two tasks (see Methods ummary). Prior to the assessment of multitasking costs, an adaptive staircase gorithm was used to determine the difficulty levels of the game at hich each participant performed the perceptual discrimination and plementaryTable1),withtheonlysignificantdifferenceincostbetween adjacent decades being the increase from the twenties (226.7% cost) to the thirties (238.6% cost). This deterioration in multitasking perform- ance is consistent with the pattern of performance decline across the lifespan observed for fluid cognitive abilities, such as reasoning5 and working memory6 . Thus, using NeuroRacer as a performance assess- ment tool, we replicated previously evidenced age-related multitasking deficits7,8 , and revealed that multitasking performance declines linearly as we advance in age beyond our twenties. In a second experiment, we explored whether older adults who trained by playing NeuroRacer in multitasking mode would exhibit improve- mentsintheirmultitaskingperformanceonthegame9,10 (thatis,diminished NeuroRacer costs). Critically, we also assessed whether this training epartment of Neurology, University of California, San Francisco, California 94158, USA. 2 Department of Physiology, University of California, San Francisco, California 94158, USA. 3 Center for Integrative uroscience, University of California, San Francisco, California 94158, USA. 4 Department of Psychiatry, University of California, San Francisco, California 94158, USA. 1 month MultitaskingSingle taskNo-contact control Initial visit NeuroRacer EEG and cognitive testing Drive only Sign only Sign and drive and 1 hour × 3 times per week × 1 month or Single task Multitask 6+ months Training intervention NeuroRacer or a b + + Figure 1 | NeuroRacer experimental conditions and training design. a, Screen shot captured during each experimental condition. b, Visualization of training design and measures collected at each time point. 5 S E P T E M B E R 2 0 1 3 | V O L 5 0 1 | N A T U R E | 9 7 Macmillan Publishers Limited. All rights reserved©2013
84. Video game training enhances cognitive control in older adults https://www.youtube.com/watch?v=1xPX8F_wl0c
85. transferred to enhancements in their cognitive control abilities11 beyond those attained by participants who trained on the component tasks in isolation. In designing the multitasking training version of NeuroRacer, during game play as a key mechanistic feature of the tr In addition, although cost reduction was observed o group, equivalent improvement in component task sk byboth STTandMTT(seeSupplementary Figs 4 and that enhancedmultitaskingabilitywas notsolelyther component skills, but a function of learning to res generated by the two tasks when performed concurr the d9 cost improvement following training was not th trade-off, as driving performance costs also diminish group from pre- to post-training (see Supplementa Notably in the MTT group, the multitasking pe remained stable 6 months after training without boo 6 months, 221.9% cost). Interestingly, the MTT grou cost improved significantly beyond the cost level attai 20 year olds who played a single session of NeuroRac experiment 3; P , 0.001). Next, we assessed if training with NeuroRacer le enhancementsofcognitivecontrolabilitiesthatareknow in ageing (for example, sustained attention, divided a memory; see Supplementary Table 2)12 . We hypoth immersed in a challenging, adaptive, high-interferen for a prolonged period of time (that is, MTT) would cognitive performance on untrained tasks that also dem control. Consistent with our hypothesis, significant interactions and subsequent follow-up analyses eviden training improvements in both working memory (de task with and without distraction7 ; Fig. 3a, b) and su † –100% –90% –80% –70% –60% –50% –40% –30% –20% –10% Multitaskingcost(d′) † * ba 1 month later 6 months later Experiment 1: lifespan Experiment 2: training Single task training No-contact control Multitasking training 0% 20s 30s 40s 50s 60s 70s Initial Figure 2 | NeuroRacer multitasking costs. a, Costs across the lifespan (n 5 174) increased (that is, a more negative percentage) in a linear fashion when participants were grouped by decade (F(1,5) 5 135.7, P , 0.00001) or analysed individually (F(1,173) 5 42.8, r 5 0.45, P , 0.00001; see Supplementary Fig. 3), with significant increases in cost observed for all age groups versus the 20-year-old group (P , 0.05 for each decade comparison). b, Costs before training, 1 month post-training, and 6 months post-training showed a session X group interaction (F(4,72) 5 7.17, P , 0.0001, Cohen’s d 5 1.10), with follow-up analyses supporting a differential benefit for the MTT group (Cohen’s d for MTT vs STT 5 1.02; MTT vs NCC5 1.20). {P , 0.05 within group improvement from pre to post, *P , 0.05 between groups (n 5 46). Error bars represent s.e.m. –100 0 100 200 Pre–post WM task with distractions (RT) RTdifference(ms) † * a –100 0 100 200 Pre–p without d RTdifference(ms) † b RESEARCH LETTER Video game training enhances cognitive control in older adults Nature 501, 97–101 (2013) • 먼저 나이가 들면서 멀티태스킹 능력이 감소한다는 것을 해당 게임으로 증명 • 20-70대 별로 각각 30명을 대상으로 실험
86. transferred to enhancements in their cognitive control abilities11 beyond those attained by participants who trained on the component tasks in isolation. In designing the multitasking training version of NeuroRacer, during game play as a key mechanistic feature of the tr In addition, although cost reduction was observed o group, equivalent improvement in component task sk byboth STTandMTT(seeSupplementary Figs 4 and that enhancedmultitaskingabilitywas notsolelyther component skills, but a function of learning to res generated by the two tasks when performed concurr the d9 cost improvement following training was not th trade-off, as driving performance costs also diminish group from pre- to post-training (see Supplementa Notably in the MTT group, the multitasking pe remained stable 6 months after training without boo 6 months, 221.9% cost). Interestingly, the MTT grou cost improved significantly beyond the cost level attai 20 year olds who played a single session of NeuroRac experiment 3; P , 0.001). Next, we assessed if training with NeuroRacer le enhancementsofcognitivecontrolabilitiesthatareknow in ageing (for example, sustained attention, divided a memory; see Supplementary Table 2)12 . We hypoth immersed in a challenging, adaptive, high-interferen for a prolonged period of time (that is, MTT) would cognitive performance on untrained tasks that also dem control. Consistent with our hypothesis, significant interactions and subsequent follow-up analyses eviden training improvements in both working memory (de task with and without distraction7 ; Fig. 3a, b) and su † –100% –90% –80% –70% –60% –50% –40% –30% –20% –10% Multitaskingcost(d′) † * ba 1 month later 6 months later Experiment 1: lifespan Experiment 2: training Single task training No-contact control Multitasking training 0% 20s 30s 40s 50s 60s 70s Initial Figure 2 | NeuroRacer multitasking costs. a, Costs across the lifespan (n 5 174) increased (that is, a more negative percentage) in a linear fashion when participants were grouped by decade (F(1,5) 5 135.7, P , 0.00001) or analysed individually (F(1,173) 5 42.8, r 5 0.45, P , 0.00001; see Supplementary Fig. 3), with significant increases in cost observed for all age groups versus the 20-year-old group (P , 0.05 for each decade comparison). b, Costs before training, 1 month post-training, and 6 months post-training showed a session X group interaction (F(4,72) 5 7.17, P , 0.0001, Cohen’s d 5 1.10), with follow-up analyses supporting a differential benefit for the MTT group (Cohen’s d for MTT vs STT 5 1.02; MTT vs NCC5 1.20). {P , 0.05 within group improvement from pre to post, *P , 0.05 between groups (n 5 46). Error bars represent s.e.m. –100 0 100 200 Pre–post WM task with distractions (RT) RTdifference(ms) † * a –100 0 100 200 Pre–p without d RTdifference(ms) † b RESEARCH LETTER Video game training enhances cognitive control in older adults z • 게임을 통한 고령층의 인지 능력 (멀티태스킹 능력) 개선 효과가 있음을 증명 • 60-85세 참가자 46명을 4주간 뉴로레이서를 통해서 훈련 • 그 결과 훈련 받지 않은 20대보다 더 잘 하게 되었으며, • 연습을 하지 않고 6개월이 지나도, 능력은 그대로 남아 있었다. Nature 501, 97–101 (2013)
87. Video game training enhances cognitive control in older adults (vigilance; test of variables of attention (T group (Fig. 3c; see Supplementary Table several statistical trendssuggestive of impro ance on other cognitive controltasks (dual- and changedetectiontask;see analysisofco in Supplementary Table 2). Note that alth and sustained attention improvements w rapid responses to test probes, neither im alternative version of the TOVA) nor accu cant group differences, revealing that traini of a speed/accuracy trade-off. Importantl ments were specific to working memory a cesses, and not theresult ofgeneralized incr as no group X session interactions were fou tasks (a stimulus detection task and the dig see Supplementary Table 2). Finally, only significant correlation between multitaski withNeuroRacer)andimprovementsonan task (delayed-recognition with distraction (Fig. 3d). These important ‘transfer of benefits’ sug lying mechanism of cognitive control was c MTT with NeuroRacer. To assess this furth basis of training effects by quantifying even tions (ERSP) and long-range phase coheren of each sign presented during NeuroRacer Wespecificallyassessedmidlinefrontalthe EEG measure of cognitive control (for exam tained attention15 and interference resolutio prefrontal cortex. In addition, we analysed between frontal and posterior brain region measure also associated with cognitive con memory14 and sustained attention15 ). Se power and coherence each revealed signifi b Long-range theta coherence Older adult post-training PLV (% coherence) 1 5 10 * ) Initial Older adults Younger adults † Midline frontal theta Power(dB) Initial * a Older adults Younger adults Older adult post-training Single task training Multitasking training No-contact control 3.40 3.05 2.70 2.35 1.65 1.30 0.95 0.60 0.25 –0.10 –0.45 –0.80 –1.15 –1.50 2.00 Nature 501, 97–101 (2013) • 인지 능력의 개선은 brain activity 로도 동일하게 관찰되었다. • 노년층 실험군에서 기술이 향상될수록 cognitive control을 관장하는     prefrontal cortex 의 activity가 높아지는 것이 관찰되었다.
88. OPEN ORIGINAL ARTICLE Characterizing cognitive control abilities in children with 16p11.2 deletion using adaptive ‘video game’ technology: a pilot study JA Anguera1,2 , AN Brandes-Aitken1 , CE Rolle1 , SN Skinner1 , SS Desai1 , JD Bower3 , WE Martucci3 , WK Chung4 , EH Sherr1,5 and EJ Marco1,2,5 Assessing cognitive abilities in children is challenging for two primary reasons: lack of testing engagement can lead to low testing sensitivity and inherent performance variability. Here we sought to explore whether an engaging, adaptive digital cognitive platform built to look and feel like a video game would reliably measure attention-based abilities in children with and without neurodevelopmental disabilities related to a known genetic condition, 16p11.2 deletion. We assessed 20 children with 16p11.2 deletion, a genetic variation implicated in attention deﬁcit/hyperactivity disorder and autism, as well as 16 siblings without the deletion and 75 neurotypical age-matched children. Deletion carriers showed signiﬁcantly slower response times and greater response variability when compared with all non-carriers; by comparison, traditional non-adaptive selective attention assessments were unable to discriminate group differences. This phenotypic characterization highlights the potential power of administering tools that integrate adaptive psychophysical mechanics into video-game-style mechanics to achieve robust, reliable measurements. Translational Psychiatry (2016) 6, e893; doi:10.1038/tp.2016.178; published online 20 September 2016 INTRODUCTION Cognition is typically associated with measures of intelligence (for example, intellectual quotient (IQ)1 ), and is a reﬂection of one’s ability to perform higher-level processes by engaging speciﬁc mechanisms associated with learning, memory and reasoning. Such acts require the engagement of a speciﬁc subset of cognitive resources called cognitive control abilities,2–5 which engage the underlying neural mechanisms associated with atten- tion, working memory and goal-management faculties.6 These abilities are often assessed with validated pencil-and-paper approaches or, now more commonly with these same paradigms deployed on either desktop or laptop computers. These approaches are often less than ideal when assessing pediatric populations, as children have highly varied degree of testing engagement, leading to low test sensitivity.7–9 This is especially concerning when characterizing clinical populations, as increased performance variability in these groups often exceeds the range of testing sensitivity,7–9 limiting the ability to characterize cognitive deﬁcits in certain populations. A proper assessment of cognitive control abilities in children is especially important, as these abilities allow children to interact with their complex environment in a goal-directed manner,10 are predictive of academic performance11 and are correlated with overall quality of life.12 For pediatric clinical populations, this characterization is especially critical as they are often assessed in an indirect fashion through intelligence quotients, parent report questionnaires13 and/or behavioral challenges,14 each of which fail to properly characterize these abilities in a direct manner. One approach to make testing more robust and user-friendly is to present material in an optimally engaging manner, a strategy particularly beneﬁcial when assessing children. The rise of digital health technologies facilitates the ability to administer these types of tests on tablet-based technologies (that is, iPad) in a game-like manner.15 For instance, Dundar and Akcayir16 assessed tablet- based reading compared with book reading in school-aged children, and discovered that students preferred tablet-based reading, reporting it to be more enjoyable. Another approach used to optimize the testing experience involves the integration of adaptive staircase algorithms, as the incorporation of such appro- aches lead to more reliable assessments that can be completed in a timely manner. This approach, rooted in psychophysical research,17 has been a powerful way to ensure that individuals perform at their ability level on a given task, mitigating the possi- bility of ﬂoor/ceiling effects. With respect to assessing individual abilities, the incorporation of adaptive mechanics acts as a normalizing agent for each individual in accordance with their underlying cognitive abilities,18 facilitating fair comparisons between groups (for example, neurotypical and study populations). Adaptive mechanics in a consumer-style video game experi- ence could potentially assist in the challenge of interrogating cognitive abilities in a pediatric patient population. This synergistic approach would seemingly raise one’s level of engagement by making the testing experience more enjoyable and with greater sensitivity to individual differences, a key aspect typically missing in both clinical and research settings when testing these populations. Video game approaches have previously been utilized in clinical adult populations (for example, stroke,19,20 1 Department of Neurology, University of California, San Francisco, San Francisco, CA, USA; 2 Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA; 3 Akili Interactive Labs, Boston, MA, USA; 4 Department of Pediatrics, Columbia University Medical Center, New York, NY, USA and 5 Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA. Correspondence: JA Anguera or EJ Marco, University of California, San Francisco, Mission Bay – Sandler Neurosciences Center, UCSF MC 0444, 675 Nelson Rising Lane, Room 502, San Francisco, CA 94158, USA. E-mail: joaquin.anguera@ucsf.edu or elysa.marco@ucsf.edu Received 6 March 2016; revised 13 July 2016; accepted 18 July 2016 Citation: Transl Psychiatry (2016) 6, e893; doi:10.1038/tp.2016.178 www.nature.com/tp Figure 2. Project: EVO selective attention performance. (a) EVO single- and multi-tasking response time performance f non-affected siblings and non-affected control groups). (b) EVO multi-tasking RT. (c) Visual search task performance Characterizing cognitive control abilities in child JA Anguera et al •Project EVO (게임)을 통해서, •아동 집중력 장애(attention disorder) 관련 특정 유전형 carrier 를 골라낼 수 있음 •게임에서의 Response Time을 기준으로 carrier vs. non-carrier 간 유의미한 차이

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