Biomedical big data and research clinical application for obesity

Contents
1. What is Biomedical Big Data?
2. Biomedical Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Biomedical Big Data + Artificial Intelligence
4. Research/Clinical Application for Obesity
2

Contents
① Genetic Data
④ Medical Images
3

$215MPrecision Medicine Initiative
2015/1/30

Big Data Dimensions
New Analysis Tool (R, Python, Matlab)
New Approach12

Tipping Point for Big Data Healthcare
2013 McKinsey The big data revolution in healthcare

Hypothesis Driven Science Data Driven Science
Hypothesis
Collect
Data
Data
Generate
Hypothesis
Analyze
Analyze

Candidate Gene
Approach
Genome-wide
Approach
Choose a Gene
from Prior Knowledge
Analyze the Gene
Analyze ALL Genes
Discover Novel Findings

GWAS
(Genome wide association study)
SNP chip
Whole Genome
SNP Profiling (500K~1M SNPs)
Common Variant
Choi HJ, Doctoral Thesis

Estrada et al., Nature Genetics, 2012
+ novel targets
for bone biology
Recent largest GWAS
GEFOS consortium

2010 An Environment-Wide
Association Study (EWAS) on Type
2 Diabetes Mellitus
Environment-Wide Association Study (EWAS)
 다양한 환경인자들 

GWAS  PheWAS
Phenotype-wide Association Study

1000 개의 질병들
Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.
Phenotype-wide Association Study

Genome-Envirome-Phenome-wide
Association Study
Phenome-wide
(Lab,Diagnosis)
Proposal
(Choi)
Genome-wide
Environment-wide
(Life style, diet, exercise, pollution)

Anatome-Phenome-wide
Association Study
2015.2.19. Nature. Genetic and epigenetic fine mapping of causal autoimmune disease variants
Phenome
Anatome

Building the Interactome
2015 Science Uncovering disease-disease relationships through the incomplete interactome29

Network-based Model of
Disease-disease Relationship
2015 Science Uncovering disease-disease relationships through the incomplete interactome30

Machine Learning
2014 Big data bioinformatics

New Role of Biomedical Big Data
Classical Approach
Large scale
(unstructured)
data
Summary
(Modify)
Classical hypothesis driven study
Novel Approach
Hypothesis Generating Study

Contents
① Genetic Data
④ Medical Images
33

Here is my
genetic
information.
What’s your
suggestion?
What?
34

2014 NEJM Genotype–Phenotype Correlation — Promiscuity in the Era of Next-Generation Sequencing

Tissue Specific Expression
Comprehensive Catalogues of Genomic Data
Variation in the human genome
Mendelian (monogenic) diseases
(N=22,432)
Whole genome sequencing (N=1,000)
Four ethnic groups
(CEU, YRI, JPT, CHB, N=270)
GWAS catalog
Complex (multigenic) traits
(1926 publications and 13410 SNPs)
Disease-related variations
Functional elements
2014-06-29
36

Disease genetic susceptibility
Cancer driver
somatic mutation
Pharmacogenomics
Targeted
Cancer Treatment
(EGFR)
Causal
Variant
Targeted Drug
(MODY-SU)
Drug Efficacy/Side Effect
Related Genotype
(CYP, HLA)
Genetic Diagnosis
(Mendelian,
Cystic fibrosis)
Molecular
Classification
- Prognosis
(Leukemia)
Hereditary
Cancer
(BRCA)
Microbiome
(Bacteria,
Virus)
Genomic Medicine
Risk prediction
(Complex disease,
Diabetes)
Germline Variants
Fetal DNA

Cancer Targeted Therapy
39
Targeted TherapyGenetic TestCancer

Cancer Rebiopsy
2013 JCO Genomics-Driven Oncology- Framework for an Emerging Paradigm

2) Common Complex Disease
Susceptibility
43

Promise of Human Genome Project

Influence of Genetics on Human Disease
For any condition the overall balance of g
enetic and environmental determinants ca
n be represented by a point somewhere w
ithin the triangle.
45
Single
Locus /
Mendelian
Multiple
Loci or multi-
chromosomal
Environmental
Cystic Fibrosis
Hemophilia A
Examples:
Alzheimer’s Disease
Type II Diabetes
Cardiovascular Disease
Diet
Carcinogens
Infections
Stress
Radiation
Lifestyle
Gene = F8
Gene= CFTR
F8 = Coagulation Factor VIII
CFTR = Cystic Fibrosis Conductance Transmembrane Regulator
Lung Cancer

2008 HMG Genome-based prediction of common diseases- advances and prospects46

2008 HMG Genome-based prediction of common diseases- advances and prospects47

Diabetes ≠ Genetic Disease?
• Familial aggregation
– Genetic influences?
– Epigenetic influences
• Intrauterine environment
– Shared family environment?
• Socioeconomic status
• Dietary preferences
• Food availability
• Gut microbiome content
• Overestimated heritability
– Phantom heritability
2012. Drong AW, Lindgren CM, McCarthy MI. Clin Pharmacol Ther. The genetic and epigenetic basis of type 2 diabetes and obesity.
2012. PNAS The mystery of missing heritability- Genetic interactions create phantom heritability49

52
Weight Waist
Standard DietGenotype
Guided
Diet
Standard DietGenotype
Guided
Diet
Times
More
Effective
Times
More
Effective

Genetics of eating behavior
2011 Genetics of eating behavior

Gene-Environment Interaction
Gene Environment
Disease
Genetic Predisposition Score Sugar-Sweetened Beverages

Soda School
No-Soda School
Obese Family Lean Family

3) Rare Mendelian Disease
Susceptibility
56

Mendelian (single-gene) genetic
disorder
Known single-gene
candidates testing
Whole Genome or Whole
Exome Sequencing
58

Laboratory Director
강현석
59

2012 European Heart Journal. Personalized medicine: hope or hype?
Herceptin
Glivec
62

63
Pharmacogenomic Biomarkers
in Drug Labeling (N=166)
2015.9.14.
Atorvastatin, Azathioprine,
Carbamazepine, Carvediolol,
Clopidogrel, Codein,
Diazepam…..

Large Effect Size Variant?
Disease susceptibility variant Pharmacogenetic variant
Environmental
Exposure
Drug
Exposure

5) Other Genome
Fetal DNA
Microbiome (Bacteria, Virus DNA)
65

(출처: 금창원 대표님 블로그)
$99
TV CF
70

Pharmacogenetic Tests: Hyung Jin Choi
No
Drug
(N= 10)
Gene
(6 genes=8 bioma
rkers)
Target SNPs
(N=12)
#5
(HJC)
Genotype Interpretation Clinical Interpretation
1 Clopidogrel CYP2C19
rs4244285 (G>A) GG
*1/*1
(EM)
Use standard dosers4986893 (G>A) GG
rs12248560 (C>T) CC
2 Warfarin
VKORC1 rs9923231 (C>T) TT
Low dose
(higher risk of bleeding)
Warfarin dose=0.5~2 mg/day
CYP2C9
rs1799853 (C>T) CC
rs1057910 (A>C) AC
3 Simvastatin SLCO1B1 rs4149056 (T>C) TT Normal
4
Azathioprine (AP),
MP, or TG
TPMT rs1142345 (A>G) AA Normal
5
Carbamazepine
or Phenytoin
HLA-B*1502
rs2844682 (C>T) CT
Normal
rs3909184 (C>G) CC
6 Abacavir HLA-B*5701 rs2395029 (T>G) TT Normal
7 Allopurinol HLA-B*5801 rs9263726 (G>A) GG Normal
Clopidogrel1)
: UM/EM=standard dose, IM/PM= consider alternative antiplatelet agent (eg. prasugrel/ticagrelor)
Warfarin2)
: high dose=5~7 mg/day, medium dose=3~4 mg/day, low dose=0.5~2 mg/day
=0
Hyung Jin Choi
+1,000,000
?

Future of Genomic Medicine?
Test when neededWithout information Know your type
Blood
type
Geno
type
Here is my
sequence

Contents
① Genetic Data
④ Medical Images
73

Electronic Health Records
2012 NRG Mining electronic health records- towards better research applications and clinical care
74

Common EHR Data
Joshua C. Denny Chapter 13: Mining Electronic Health Records in the Genomics Era. PLoS Comput Biol. 2012 December; 8(12):
International Classification of Diseases (ICD)
Current Procedural Terminology (CPT)
75

Big Data Analysis
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1/Creatinine
Kidney Function Decline over 10 years
Glucose records distribution of
1400 patients
80

PCA AnalysisGlucose
Kidney
Function
81

Machine Learning
2014 Big data bioinformatics82

밤동안 저혈당수면 Lt.foot rolling Keep떨림,
식은땀, 현기증, 공복감, 두통, 피로감등의 저혈
당 에 저혈당 이 있을 즉알려주도록 밤사이 특
이호소 수면유지상처와 통증 상처부위 출혈
oozing, severe pain 알리도록 고혈당 처방된 당
뇨식이의 중요성과 간식을 자제하도록 .고혈
당 ,,관리 방법 .당뇨약 이해 잘 하고 수술부위
oozing Rt.foot rolling keep드레싱 상태를 고혈
당 고혈당 의식변화 BST 387 checked.고혈당
으로 인한 구강 내 감염 위해 식후 양치, gargle
등 구강 위생 격려.당뇨환자의 발관리 방법에 .
목표 혈당, 목표 당화혈색소에 .식사를 거르거
나 지연하지 않도록 .식사요법, 운동요법, 약물
요법을 정확히 지키는 것이 중요을 .처방된 당
뇨식이의 중요성과 간식을 자제하도록 .고혈
당 ,,관리 방법 .혈당 정상 범위임rt foot rolling
중으로 pain호소 밤사이 수면양호걱정신경 예
민감정변화 중임감정을 표현하도록 지지하고
경청기분상태 condition 조금 나은 듯 하다고 혈
당 조절과 관련하여 신경쓰는 모습 보이며 혈당
self로 측정하는 모습 보임혈당 조절에 안내하
고 불편감 지속알리도록고혈당 고혈당 의식변
화 고혈당 허약감 지남력 혈당조절 안됨고혈당
으로 인한 구강 내 감염 위해 식후 양치, gargle
등 구강 위생 격려.당뇨환자의 정기점검 내용과
빈도에 .BST 140 으로 저혈당 호소 밤동안 저
혈당수면 Lt.foot rolling Keep떨림, 식은땀, 현
기증, 공복감, 두통, 피로감등의 저혈당 에 저
혈당 이 있을 즉알려주도록 pain 및 불편감 호
소 WA 잘고혈당 고혈당 의식변화 고혈당 허
약감 지남력 혈당조절 안됨식사요법, 운동요법,
약물요법을 정확히 지키는 것이 중요을 .저혈당
/고혈당 과 대처법에 .혈당정상화, 표준체중의
유지, 정상 혈중지질의 유지에 .고혈당 ,,관리
방법 .혈당측정법,인슐린 자가 투여법, 경구투
약,수분 섭취량,대체 탄수화물,의료진의 도움이
필요한 사항에 교혈당 정상 범위임수술부위
oozing Rt.foot rolling keep수술 부위 (출혈, 통
증, 부종)수술부위 출혈 상처부위 oozing
Wound 당겨지지 않도록 적절한 체위 취하기
설명감염 발생 위험 요인 수술부위 출혈 밤동안
간호기록지 Word Cloud
Natural Language Processing (NLP)84

Contents
① Genetic Data
④ Medical Images
87

Number of patients with medical treatments related to
osteoporosis in each calendar year
2005 2006 2007 2008
0
500
1000
1500
Male
Female
Calendar year
Number(thousand)
Choi et. Al., 2011 JBMM Burden of osteoporosis in adults in Korea- a national health insurance database study
92

Korean Society for
Bone and Mineral
Research
Anti-hypertensive
prescriptions
(2008-2011)
N = 8,315,709
New users
N = 2,357,908
Age ≥ 50 yrs
Monotherapy
Compliant user (MPR≥80%)
No previous fracture
N = 528,522
Prevalent users
N = 5,957,801
Excluded
Age <50
Combination therapy
Inadequate compliance
Previous fracture
N = 1,829,386
Final study population
Hypertension Medication
and Fracture
Choi et al., 2015 International Journal of Cardiology93

Compare Fracture Risk
Comparator?Hypertension
CCB
High
Blood Pressure
Fracture
Risk
BB
Non-
user
Healthy
Non-
user
Cohort study (Health Insurance Review & Assessment Service)
New-user design (drug-related toxicity)
Non-user comparator (hypertension without medication)
2007 20112008

Distribution of ARB MPR
(Histogram)
ARB Non-user
20
FrequencyDensity
ARB user
80 120
Medication Possession Ratio (MPR)
Total prescription days
Observation days
350 days (Prescription)
365 days (Observation)
MPR
96%
MPR (%)

Overview of secondary data in
public health by data source
보험청구자료 건강검진
NHIS (National Health Insurance Corporation): 국민건강보험공단 (보험공단)
HIRA (Health Insurance Review and Assessment Service) :건강보험심사평가원 (심평원)
통계청 암센터
J Korean Med Assoc 2014 May; 근거중심 보건의료의 시행을 위한 빅데이터 활용99

Big data platform model by Korea Institute of
Drug Safety and Risk Management

Health Insurance Database
IT-HT Convergence Research Team
101
1. Diagnosis (ICD)
2. Treatment
(1) Medication
(2) Surgery
Health Insurance Database
Classification Algorithm
Clinical
Researcher
Computational
Researcher
National Research, Novel Insight

Contents
① Genetic Data
④ Medical Images
102

Heart
SIMENS: CT Cardio-Vascular Engine

Bone Quality and
TBS (Trabecular Bone Score)
106

Clinical Implication of TBS
2014 Endocrine. Utility of the trabecular bone score (TBS) in secondary osteoporosis107

2013 Science Structural and Functional Brain Networks- From Connections to Cognition

fMRI analysis
2013 Science Functional interactions as big data in the human brain

112
2012 Decoding subject-driven cognitive states with whole-brain connectivity patterns

Objective
Neuromodulation Brain Activity Feeding Behavior

Brain Functional Connectivity
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
-4 -2 0 2 4 6
R=0.648
p=0.043
tDCS effect on
right dlPFC - right thalamus
connectivity
tDCS effect on fullness score
Spearman’s correlation
Results
dlPFC (dorsolateral prefrontal cortex)
Thalamus

2014
Radiomics
2014 Nature Communications. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Computer Whole Slide Image Analysis
Image normalization Image segmentation Feature extraction
2015 Laboratory Investigation. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis
of whole slide images116

Integration of quantitative histology with
multifaceted clinical and genomic data
of whole slide images117

Quantitative nuclear morphometry
of whole slide images

Contents
① Genetic Data
④ Medical Images
119

120
2015 Sci Transl Med. The emerging field of mobile health

Electrocardiogram Telemedicine
122

Smart-phone based
Comprehensive Diabetes Care
Doctor
Ask Educate
Analyze
Education Nurse
Any time
Report Care
2-3 months interval
Discussion
Daily
Diet/
Exercise
Smart-phone
Database Server
Self-care
transfer
Analyze
Glucose
Glucometer

Date Time name foodType calories unit amount
2014-08-09 0 미역국 0 23 1국그릇 (300ml) 105 g
2014-08-09 0 잡곡밥 0 80 1/4공기 (52.5g) 52 g
2014-08-09 0 열무김치 0 3 1/4소접시 (8.75g) 9 g
2014-08-09 0 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 0 토란대무침 0 28 1/2소접시(46.5g) 46 g
2014-08-09 1 복숭아 0 91 1개 (269g) 268 g
2014-08-09 2 마른오징어 2 88 1/4마리 (25g) 25 g
2014-08-09 2 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 2 저지방우유 1 72 1컵 (200ml) 180 g
2014-08-09 2 복숭아 0 183 2개 (538g) 538 g
2014-08-09 3 복숭아 0 91 1개 (269g) 268 g
2014-08-09 3 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 4 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 4 식빵 1 92 1장 (33g) 33 g
2014-08-09 4 삶은옥수수 1 197 1개 반 (150g) 150 g
2014-08-09 4 복숭아 0 91 1개 (269g) 268 g
2014-08-09 4 저지방우유 1 72 1컵 (200ml) 180 g
2014-08-10 0 복숭아 0 91 1개 (269g) 268 g
2014-08-10 0 저지방우유 1 36 1/2컵 (100ml) 90 g
2014-08-10 0 두부 0 20 1/4인분 (25g) 25 g
2014-08-10 0 견과류 2 190 1/4 컵 (50g) 31 g
2014-08-10 0 파프리카 0 11 1개 (66.5g) 65 g
124

Real-time Diet Monitoring
0
100
200
300
400
500
600
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
2014-08-09 2014-08-10 2014-08-11 2014-08-12 2014-08-13 2014-08-14 2014-08-15
Overeat Lunch
Skip Dinner
125

Real-time Exercise Monitoring
0
5000
10000
15000
20000
25000
Walks
No Exercise
126

Social Network and
Obesity Prevalence
2013 PLOS One. Assessing the Online Social Environment for Surveillance of Obesity Prevalence

2013 PLOS CB Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza
Google Flu Trends

Contents
① Genetic Data
④ Medical Images
133

2014 JAMA Finding the Missing Link for Big Biomedical Data

Pros and Cons of Different Data Sources
2014 ASBMR meet the professor handbook

Genome Big Data + Brain Big Data
140

Connectome-wide GWAS
2014 Nature Neuroscience. Whole-genome analyses of whole-brain data- working within an expanded search space141

2015 Nature. Common genetic variants influence human subcortical brain structures
Published
online
21 January
2015
142

insurance claims, blood tests, medical histories
US$10-million pilot study N=30M

145
Government
Hospital
Research
Institute
Personal
Identification
Integration
Storage
Artificial
Inteligence
Clinical
Decision
Support
Data
Sharing
Collect Analyze Apply
Personal
Population
Overview of Healthcare Data

2015 Nature Immunology. A vision and a prescription for big data–enabled medicine

Genome
Epigenome
Transcriptome
Proteome
Metabolome
Anatome
Physiome
EnviromeBehaviorome
Phenome
The Whole Picture
Testome
Treatome
Diagnome
Symptome
Medicome
Surgeome
Radiome

Contents
① Genetic Data
④ Medical Images
148

Healthcare Big Data
+ Artificial Intelligence
149
Healthcare Big Data Machine Learning
Novel Insights and Applications

152
In a scan of 3,000 images, IBM
technology was able to spot
melanoma with an accuracy
of about 95 percent, much
better than the 75 percent to
84 percent average of today's
largely manual methods.
IBM Research will continue to
work with Sloan Kettering to
develop additional
measurements and
approaches to further refine
diagnosis, as well as refine
their approach through larger
sets of data.
Dec 17, 2014

153
Aug. 11, 2015
IBM is betting that the same technology that
recognizes cats can identify tumors and other signs of
diseases.
In the long run, IBM and others in the field hope such
systems can become reliable advisers to
radiologists, dermatologists and other practitioners
who analyze images—especially in parts of the world
where health-care providers are scarce.
While IBM hopes Watson will learn to interpret
Merge’s images, it also expects the combination of
imagery, medical records and other data to reveal
patterns relevant to diagnosis and treatment that a
human physician may miss, ushering in an era of
computer-assisted care. Two other recent IBM
acquisitions, Phytel Inc. and Explorys Inc., yielded 50
million electronic medical records.

Contents
① Genetic Data
④ Medical Images
155

Functional Neuroanatomy of
Metabolism Regulation Lab
Pancreas
Gut MuscleLiver
Fat
Hyung Jin Choi

Whole-Organism Physiology
Food Availability Growth
Fertility

158
Environment
Gene
Eat
Exercise
Metabolism
Brain
Glucose
DM
Blood Pressure
HTN
Cardiovascular
Disease
Cognitive
Hormone
Behavior
Psychotherapy
Behavior Therapy
Policy Making
Genetic Testing
Neuroimaging
Neuromodulation
Drug
Drug
Lab
Survey
Survey
Sensor
DrugDiagnosis
EMR
Government
Data
Mining

Environment Survey
NeuroimagingGenetics Lab/Hormone Hospital
Cognitive
Personalize
Psychotherapy
Dietary
Intervention
Exercise
Intervention
Food
Exercise
Glucose
Mobile
Drug
Neuromodulation
Monitoring

The FUTURE MEDICINE
is already
at PRESENT
160

Biomedical big data and research clinical application for obesity

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