Improving prevention and prediction of
cardiovascular disease
Adam Butterworth
University Lecturer in Cardiovascular Epidemiology
Cardiovascular Epidemiology Unit
June 25th, 2014
Research programmes
Screening and
risk prediction
Blood donor
health
Medicines
development
Cardiovascular
New
Epidemiology
bioresources
Unit
Gene-lifestyle
interplay
International
vascular
health
Integrative
genomics
Research programmes
Screening and
risk prediction
Blood donor
health
Medicines
development
Cardiovascular
New
Epidemiology
bioresources
Unit
Quantitative methods
Gene-lifestyle
interplay
International
vascular
health
Integrative
genomics
Research programmes
Screening and
risk prediction
Blood donor
health
Medicines
development
Cardiovascular
New
Epidemiology
bioresources
Unit
Gene-lifestyle
interplay
International
vascular
health
Integrative
genomics
What is the clinical relevance of
cardiovascular risk factors?
The Emerging Risk Factors Collaboration
2.5M individuals
130 prospective studies
60K new-onset
CVD outcomes
>10 yrs of follow-up
ERFC, Eur J Epidemiol 2008
ERFC, Int J Epidemiol 2010
Glycemic markers add little to CVD risk prediction
No. of
studies
No. of
No. of
participants cases
C-index
(95% CI)
Change in C-index
(95% CI)
Addition of glycemia measures
HbA1c
Conventional risk factors*
13
70916
3271
Plus HbA1c
0.7434 (0.7350, 0.7517)
Reference
0.7452 (0.7368, 0.7535)
0.0018 (0.0003, 0.0033)a
0.7172 (0.7122, 0.7222)
Reference
0.7185 (0.7134, 0.7235)
0.0013 (0.0007, 0.0018)b
0.7362 (0.7298, 0.7426)
Reference
0.7367 (0.7304, 0.7431)
0.0005 (-0.0002, 0.0013)
0.7193 (0.7126, 0.7260)
Reference
0.7197 (0.7130, 0.7264)
0.0004 (-0.0001, 0.0009)
Fasting glucose
Conventional risk factors*
25
95198
9560
Plus fasting glucose
Random glucose
Conventional risk factors*
22
92504
5152
Plus random glucose
Post-load glucose
Conventional risk factors*
10
38532
5519
Plus post-load glucose
-.002
0
.002
Change in C-index (95% CI)
.004
Emerging Risk Factors Collaboration, JAMA 2014
Consequences of vascular multi-morbidity
Risk of subsequent CVD
Disease status at baseline
HR (95% CI)
MI & stroke & diabetes
d
MI & diabetes
8.4 (6.7, 10.6)
Stroke & diabetes
5.6 (4.9, 6.4)
MI & stroke
5.6 (4.8, 6.5)
MI only
3.1 (2.7, 3.5)
Stroke only
2.7 (2.4, 3.1)
Diabetes only
2.2 (2.1, 2.4)
None
1.0 (Reference)
5.6 (4.7, 6.5)
0 5 10 15 20 25 30 35 40
Events per 1000 person-years (95% CI)
Emerging Risk Factors Collaboration, unpublished
Research programmes
Screening and
risk prediction
Blood donor
health
Medicines
development
Cardiovascular
New
Epidemiology
bioresources
Unit
Gene-lifestyle
interplay
International
vascular
health
Integrative
genomics
Functional genetic variant (Asp358Ala) in IL6R
Other diseases
CHD
Risk factors
Inflammation
Marker
LDL cholesterol
HDL cholesterol
Triglyceride
Fasting glucose
Systolic blood pressure
Body mass index
Waist circumference
Ever vs. never smokers
History of diabetes
Soluble-IL-6R
Interleukin-6
C-reactive protein
Fibrinogen
ncases
Coronary disease
51,441
1.0
Atrial fibrillation
Odds ratio (95% CI)
Conventional
Type
Disease
AAA
2260
4524
0.98
Rheumatoid arthritis
11,475
0.96
Atopic dermatitis
2890
Asthma0.94
15,797
All cancer
0.92
5376
Breast cancer
14,456
0.90 cancer
Colorectal
Asp/Asp
-20 -10 0 10 20 30 40
% change per 358 Ala allele
Asp/Ala
0.8 0.9 1
Ala/Ala
1863
1.1 1.21.3
OR (95% CI) per minor allele
IL6RGC, Lancet 2012
Schnabel, Circ Cardiov Genet 2011
Harrison, Eur Heart J 2012
Eyre, Nat Genet 2012
Gordillo, ASHG abstract 2012
Ferreira, Lancet 2012
IL6RMRC, Lancet 2012
Potential safety signals for IL-1 related agents
Odds ratio (95% CI) P-value
Coronary heart disease
Combined+
1.04 (1.02, 1.05)
2.4x10-8
Rheumatoid Arthritis
Okada 2014
0.97 (0.95, 0.99)
9.9x10-4
Abdominal aortic aneurysm
AAA genetics consortium
1.08 (1.04, 1.12)
1.7x10-5
Ischaemic stroke
Metastroke
1.00 (0.98, 1.02)
0.9
Type 2 diabetes
DIAGRAM + InterAct*
0.99 (0.97, 1.01)
0.5
Asthma and Hayfever
Ferreira et al, J Allerg Clin Immunol 2014
0.98 (0.95, 1.01)
0.2
Tuberculosis
Nejentsev et al
1.01 (0.97, 1.05)
0.6
Breast cancer
Breast Cancer Association Consortium
1.01 (1.00, 1.03)
0.04
Childhood acute lymphoblastoid lymphoma
Migliorini et al, Blood 2013
1.01 (0.96, 1.07)
0.7
Chronic lymphocytic leukaemia
Speedy et al, Nat Genet 2014
0.99 (0.93, 1.05)
0.7
Colorectal cancer
Whiffin et al, Hum Mol Gen 21014
0.97 (0.94, 1.01)
0.09
Lung cancer
Wang et al, Nature Genetics 2014
0.99 (0.96, 1.01)
0.4
Melanoma
Bishop Melanoma consortium
1.02 (1.00, 1.05)
0.1
Multiple myeloma
Chubb et al, Nat Genet 2013
0.98 (0.93, 1.03)
0.4
Renal cell carcinoma
Henrion et al, Hum Mol Genet 2013
1.06 (1.01, 1.12)
0.02
.9
.95
1
1.05
1.1
Odds ratio (95% Confidence interval) per allele
Freitag et al., unpublished
Research programmes
Screening and
risk prediction
Blood donor
health
Medicines
development
Cardiovascular
New
Epidemiology
bioresources
Unit
Gene-lifestyle
interplay
International
vascular
health
Integrative
genomics
Why are South Asians especially susceptible to CVD?
Bangladesh Risk of Acute Vascular Events (BRAVE)
A large-scale case-control study of acute myocardial infarction in Bangladesh
Key hypothesis: arsenic and other heavy metals
Current:
4000 AMI cases, 4000 controls
Planned total:
20,000 participants
Local collaboration with icddr,b and
NICVD in Dhaka
Investigating the impact of conventional
and local risk factors
Risk factors
OR (95% CI)
Smoking status, current
2.36 (1.29, 4.31)
History of hypertension, yes
4.92 (3.65, 6.62)
History of diabetes, yes
3.81 (2.40, 6.03)
Total cholesterol (mmol/l)
1.60 (1.50, 1.70)
LDL-C (mmol/l)
1.79 (1.49, 2.16)
HDL-C (mmol/l)
0.79 (0.74, 0.84)
Copper (µmol/l)
2.53 (1.18, 5.42)
Arsenic (µmol/l)
1.84 (1.37, 2.47)
Mercury (nmol/l)
1.48 (1.14, 1.92)
Cadmium (nmol/l)
1.10 (0.75, 1.62)
0.1
0.5
1
2.5
5
10
Odds ratios per 1 SD
(unless specified otherwise)
Chowdhury et al., unpublished
Research programmes
Screening and
risk prediction
Blood donor
health
Medicines
development
Cardiovascular
New
Epidemiology
bioresources
Unit
Gene-lifestyle
interplay
International
vascular
health
Integrative
genomics
The INTERVAL study - a large nationwide bioresource
~ 50,000 blood donors from 25 different geographical regions
What is the optimum interval between blood donations?
Tooting
Sheffield
Manchester PG
Plymouth
Newcastle
Stoke-on-Trent
Lancaster
Oxford
Gloucester
Liverpool
Bristol
Edgware
Leeds City
Cambridge
West End, London
Leeds
Poole
Birmingham
Manchester NH
Bradford
Luton
Southampton
Leicester
Brentwood
Nottingham
Extensive biological measurements for
integrative genomics studies
Type
| Phenotypes
| Funder
Genetic array: Affy 820k “Biobank”
| ~20M imputed variants
| NIHR
Extended haematology profile
| ~200 blood cell traits
| NHSBT
NMR metabolomics
| ~250 analytes
| EC
Clinical biomarkers
| ~ 40 analytes
| NIHR
Conclusions
Major clinical and scientific questions in CVD can be addressed
through powerful and detailed epidemiological studies
Both population bioresources and post-genomic assay tools have
matured rapidly in recent years
Greater interdisciplinary collaboration should help accelerate
discovery and impact on healthcare
Key external funders
The Cardiovascular Epidemiology Unit
Examples: lipids
Implications for
compounds
Lp(a) (per 100% higher)
Higher circulating Lp(a)
Genetically higher Lp(a)
1.06 (1.04, 1.08)
1.22 (1.09, 1.37)
.5
.7
?
.9 1 1.1 1.3 1.5
Risk ratio (95%CI)
Triglycerides (per 16% higher)
Higher circulating triglycerides
1.10 (1.08, 1.12)
Genetically higher via Apo-AV
1.18 (1.11, 1.26)
.5
Various
.7
.9 1 1.1 1.3 1.5
Risk ratio (95%CI)
HDL-C (per 15mg/dl [1-SD] higher)
Higher circulating HDL-C
0.71 (0.68, 0.75)
Genetically higher
(via CETP)
Genetically higher
(via several HDL-C loci)
0.72 (0.58, 0.93)
CETPi
0.93 (0.68, 1.27)
.5
.7
.9 1 1.1 1.3 1.5
Risk ratio (95%CI)
Kamstrup, JAMA 2009
ERFC, JAMA 2010
Triglyceride Studies Coll., Lancet 2010
ERFC, JAMA 2009
Thompson, JAMA 2008
Voight, Lancet 2012
Examples: inflammation markers
Implications for
C-reactive protein (per 1-SD higher)
compounds
Higher circulating CRP
1.33 (1.23, 1.43)
Genetically higher CRP
1.00 (0.89, 1.12)
anti-CRP
.9
1
1.1
1.2
1.3
1.4 1.5
Risk ratio (95%CI)
Fibrinogen (per 0.14 g/l higher)
Higher circulating fibrinogen
1.13 (1.12, 1.14)
anti-fibrinogen
Genetically higher fibrinogen
1.02 (0.99, 1.06)
.9
1
1.1
1.2
1.3
1.4 1.5
Risk ratio (95%CI)
Interleukin-6 receptor (per 34% higher)
Tocilizumab
Higher circulating IL6R
?
Genetically higher IL6R
0.97 (0.95, 0.98)
.9
1
1.1
1.2
Risk ratio (95%CI)
1.3
1.4 1.5
CCGC, BMJ 2011
FSC, JAMA 2005
Keavney, Int J Epidemiol 2006
IL6R Genetics Consortium, Lancet 2012
Examples of findings
Finding
Publication
Lp(a) is independently associated with CHD risk
JAMA 2009
Lipid assessment can be done without the need to fast
JAMA 2009
CRP is associated with vascular and nonvascular
outcomes
Lancet 2010
LpPLA2 is log-linearly associated with CVD risk
Lancet 2011
Diabetes mellitus is associated with risk of death from CVD,
and from several other non-vascular causes
NEJM 2011
Diabetes and survival
About 6 years of life lost in middle age due to diabetes
Men
7
Women
Years of life lost
6
5
4
3
2
1
0
40
50
60
70
80
90
40
50
60
70
80
90
Age (years)
Vascular
deaths
Cancer
deaths
Non-cancer
non-vascular
deaths
Unknown
causes
ERFC, NEJM 2011
New dimensions
Greater integration of traditional and genetic epidemiology
Circulating usual levels of CRP
Genetically elevated levels of CRP
SNP analyses
Haplotype analyses
0.8
1
1.2
1.4
1.6
1.8
2
Risk ratio (95% CI) for CHD per 1-SD higher log CRP (mg/dl)
CCGC, BMJ 2011
Gluco-metabolic traits
HbA1c
BMI
3.0
Risk ratio for CHD)
Risk ratio for CHD)
3.0
2.0
1.0
Integration with CARDIoGRAMplusC4D:
2.0
CardioMetabochip, GWAS
60K CHD cases, 120K controls
1.0
4
5
6
7
8
20
25
30
35
40
45
How can genetic epidemiology help
identify novel drug targets?
Drug interventions
Genetics
RCT
Mendelian randomisation
Sample
Population
Randomisation
Random allocation of alleles
Intervention
Control
Genotype aa
Genotype AA
Biomarker
lower
Biomarker
higher
Biomarker
lower
Biomarker
higher
CV event
rate lower
CV event
rate higher
CV event
rate lower
CV event
rate higher
Examples of findings
Finding
Publication
APOE genotypes are log-linearly associated both with LDLC levels and with CHD risk
JAMA 2007
CETP genotypes associated with reduced CETP activity
are related with lower CHD risk
JAMA 2008
APOA5 genotypes associated with higher triglycerides
concentration are related with increased risk of CHD
Lancet 2010
A functional IL6R allele is associated with lower levels of
acute phase reactants and lower CHD risk
Lancet 2012
Testing for concordance
Implications for
compounds
Lower LDL-C concentration


Observational epidemiology
Genetic epidemiology
Statin trials
LDL-C lowering

.8
.9
1
1.1 1.2 1.3
Lower circulating Lp-PLA2 activity

Observational epidemiology
Genetic epidemiology
Darapladib
X
.8
.9
1
1.1 1.2 1.3 1.4
Higher circulating lipoprotein(a)


Observational epidemiology
Genetic epidemiology
.8
.9
1
Various
1.1 1.2 1.3 1.4
Circulating interleukin-6 receptor
Observational epidemiology
Genetic epidemiology
?
?

.8
.9
1 1.1 1.2 1.3 1.4
Odds ratio (95% CI)
IL-6 inhibitors
LSC, Lancet 2010
ERFC, JAMA 2009
IL6R Genetics Consortium, Lancet 2012
Clarke, NEJM 2009
New dimensions
Exome+ array CHD consortium
Feature
Example
Novel hybrid chip
350K SNPs for discovery
100K SNPs for evaluation
Exceptional power
50K CHD cases, 50K controls
Phenotype-rich
≈110 vascular phenotypes
Detailed database
Individual-level information
Follow-on studies
Biomarker assays 
“reverse mendelian randomization”
Recall by genotype 
functional studies
Why are South Asians especially
susceptible to CVD?
Genetic
information
Multiple
intermediate
phenotypes
Clinical & lifestyle
information
Multiple disease
outcomes
15K MI
cases
5K T2D
cases
5K stroke
cases
20K
controls
Examples of findings
Finding
Publication
Discovery of :
•9 loci in CHD
•6 loci in type 2 diabetes
•several loci for blood pressure
Nat Genet 2011a, PLoS Genet 2011
Nat Genet 2011b
Nature 2011
Pakistanis have a distinctive genetic architecture
Circ Cardiov Genet 2010
9p21 is weaker in Pakistanis
ATVB 2010
Study of Pakistani and European data has
identified
“cosmopolitan” loci for complex diseases
5 novel loci for CHD
Gene/locus
LIPA
6 novel loci for T2DM
Ethnic group
Gene/locus
S Asian
European
ADAMTS7- S Asian
MORF4L1 European
PDGFD
KIAA1462
7q22
GRB14
S Asian
European
ST6GAL1
S Asian
European
VPS26A
S Asian
European
HMG20A
S Asian
European
AP3S2
S Asian
European
HNF4A
S Asian
European
S Asian
European
S Asian
European
S Asian
European
0.9
1.0
1.1
1.25
Odds ratio (95% CI)
C4D consortium, Nat Genet 2011a
Ethnic group
.9
1
1.1
1.25
Odds ratio (95% CI)
Kooner & Saleheen et al., Nat Genet 2011b
Research programmes
Collaborative
meta-analyses
New
bioresources
Epidemiology
for therapeutics
Cardiovascular
New
Epidemiology
bioresources
Unit
Gene-lifestyle
interplay
CVD
in South Asia
Optimising
CVD screening
How exactly do genetic and lifestyle factors
interplay in CVD?
Association of 9p21 SNP with CHD may be modified by diet
INTERHEART
Prudent diet
group
9p21 genotype
Low
GG
AG
AA
Medium
GG
AG
AA
High
Finrisk
GG
AG
AA (Reference)
1
1.5
2
OR (95% CI)
compared to reference group
1
1.5
2
HR (95% CI)
compared to reference group
Do, PLoS Med 2012
New dimensions
520K people, 10 countries
EPIC-Heart
Genes
Lifestyles
650K SNPs
~500
exposures
~25 biomarkers of
Intermediate causal
pathways
Cardiovascular disease
25K CVD outcomes
>50 objective
nutritional
biomarkers
How can CVD screening be improved?
Examples of findings from the ERFC
Finding
Publication
Assessment of chronic kidney disease provides about half as
much predictive gain as does history of diabetes
BMJ 2010
Measures of adiposity do not enhance CVD risk prediction
Lancet 2011
Targeted additional assessment of CRP or fibrinogen improves
CVD prediction modestly
NEJM 2012
Replacement of total and HDL-C with apolipoproteins reduces the
accuracy of CVD risk prediction
JAMA 2012
New dimensions
EPIC-CVD
520k participants in 10 countries
25k new-onset CVD cases
15k controls
Assays in progress
650k common and uncommon SNPs
75 soluble biomarkers
Comparison of approaches, eg:
genetic vs “modifiable” risk scores
mass vs stepwise screening
aggregated vs disaggregated outcomes
Danesh et al., Eur J Epidemiol 2007
How can new bioresources
complement UK Biobank?
Existing resources:
Donation teams, transport links, 25 donation clinics
across England
1.4 million donors:
Broad population group - >17yrs, 50:50 M/F
Repeat donations:
Baseline and follow-up measurements