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Improving prevention and prediction of cardiovascular disease
Adam Butterworth
University Lecturer in Cardiovascular EpidemiologyCardiovascular Epidemiology Unit
June 25th, 2014
Research programmes
New bioresources
New bioresources
Screening and risk prediction
Cardiovascular Epidemiology
Unit
Medicines development
Internationalvascular
health
Gene-lifestyle interplay
Integrative genomics
Blood donor health
Research programmes
New bioresources
New bioresources
Screening and risk prediction
Cardiovascular Epidemiology
Unit
Medicines development
Internationalvascular
health
Gene-lifestyle interplay
Integrative genomics
Blood donor health
Quantitative methods
Research programmes
New bioresources
New bioresources
Screening and risk prediction
Cardiovascular Epidemiology
Unit
Medicines development
Internationalvascular
health
Gene-lifestyle interplay
Integrative genomics
Blood donor health
The Emerging Risk Factors Collaboration
ERFC, Eur J Epidemiol 2008ERFC, Int J Epidemiol 2010
2.5M individuals130 prospective studies 60K new-onset
CVD outcomes>10 yrs of follow-up
What is the clinical relevance of cardiovascular risk factors?
13
25
22
10
70916
95198
92504
38532
3271
9560
5152
5519
0.7434 (0.7350, 0.7517)
0.7452 (0.7368, 0.7535)
0.7172 (0.7122, 0.7222)
0.7185 (0.7134, 0.7235)
0.7362 (0.7298, 0.7426)
0.7367 (0.7304, 0.7431)
0.7193 (0.7126, 0.7260)
0.7197 (0.7130, 0.7264)
Reference
0.0018 (0.0003, 0.0033)a
Reference
0.0013 (0.0007, 0.0018)b
Reference
0.0005 (-0.0002, 0.0013)
Reference
0.0004 (-0.0001, 0.0009)
0-.002 0 .002 .004
HbA1c
Plus HbA1c
Conventional risk factors*
Addition of glycemia measures
Fasting glucose
Plus fasting glucose
Conventional risk factors*
Random glucose
Plus random glucose
Conventional risk factors*
Post-load glucose
Plus post-load glucose
Conventional risk factors*
No. of studies
Change in C-index (95% CI)
C-index (95% CI)
No. of participants
No. of cases
Change in C-index (95% CI)
Glycemic markers add little to CVD risk prediction
Emerging Risk Factors Collaboration, JAMA 2014
MI & stroke & d
MI & diabetes
Stroke & diabetes
MI & stroke
MI only
Stroke only
Diabetes only
None
Disease status at baseline
MI & stroke & diabetes
MI & diabetes
Stroke & diabetes
MI & stroke
MI only
Stroke only
Diabetes only
None
Consequences of vascular multi-morbidity
Emerging Risk Factors Collaboration, unpublished
5.6 (4.7, 6.5)
3.1 (2.7, 3.5)
8.4 (6.7, 10.6)
5.6 (4.9, 6.4)
5.6 (4.8, 6.5)
2.7 (2.4, 3.1)
2.2 (2.1, 2.4)
1.0 (Reference)
HR (95% CI)
0 5 10 15 20 25 30 35 40Events per 1000 person-years (95% CI)
Risk of subsequent CVD
5.6 (4.7, 6.5)
3.1 (2.7, 3.5)
8.4 (6.7, 10.6)
5.6 (4.9, 6.4)
5.6 (4.8, 6.5)
2.7 (2.4, 3.1)
2.2 (2.1, 2.4)
1.0 (Reference)
0 5 10 15 20 25 30 35 40Events per 1000 person-years (95% CI)
Research programmes
New bioresources
New bioresources
Screening and risk prediction
Cardiovascular Epidemiology
Unit
Medicines development
Internationalvascular
health
Gene-lifestyle interplay
Integrative genomics
Blood donor health
Functional genetic variant (Asp358Ala) in IL6R
Type Marker
LDL cholesterol
HDL cholesterol
TriglycerideFasting glucoseSystolic blood pressureBody mass indexWaist circumferenceEver vs. never smokers
History of diabetes
Soluble-IL-6RInterleukin-6C-reactive proteinFibrinogen
-20 -10 0 10 20 30 40% change per 358 Ala allele
Co
nve
nti
on
alIn
flam
mat
ion
Risk factors CHD
Ala/AlaAsp/Asp
1.0
0.90
0.92
0.94
0.96
0.98
Asp/Ala
Od
ds
ra
tio
(9
5%
CI)
1.0
0.90
0.92
0.94
0.96
0.98
Coronary disease
Disease
51,441
ncases
OR (95% CI) per minor allele
Other diseases
10.8 0.9 1.1 1.21.3
Atrial fibrillation
AAA
Rheumatoid arthritis
Atopic dermatitis
Asthma
All cancer
Breast cancer
Colorectal cancer
2260
4524
11,475
2890
15,797
5376
14,456
1863
IL6RGC, Lancet 2012Schnabel, Circ Cardiov Genet 2011
Harrison, Eur Heart J 2012Eyre, Nat Genet 2012
Gordillo, ASHG abstract 2012Ferreira, Lancet 2012
IL6RMRC, Lancet 2012
Coronary heart diseaseCombined+
Rheumatoid ArthritisOkada 2014
Abdominal aortic aneurysmAAA genetics consortium
Ischaemic strokeMetastroke
Type 2 diabetesDIAGRAM + InterAct*
Asthma and HayfeverFerreira et al, J Allerg Clin Immunol 2014
TuberculosisNejentsev et al
Breast cancerBreast Cancer Association Consortium
Childhood acute lymphoblastoid lymphomaMigliorini et al, Blood 2013
Chronic lymphocytic leukaemiaSpeedy et al, Nat Genet 2014
Colorectal cancerWhiffin et al, Hum Mol Gen 21014
Lung cancerWang et al, Nature Genetics 2014
MelanomaBishop Melanoma consortium
Multiple myelomaChubb et al, Nat Genet 2013
Renal cell carcinomaHenrion et al, Hum Mol Genet 2013
1.04 (1.02, 1.05)
0.97 (0.95, 0.99)
1.08 (1.04, 1.12)
1.00 (0.98, 1.02)
0.99 (0.97, 1.01)
0.98 (0.95, 1.01)
1.01 (0.97, 1.05)
1.01 (1.00, 1.03)
1.01 (0.96, 1.07)
0.99 (0.93, 1.05)
0.97 (0.94, 1.01)
0.99 (0.96, 1.01)
1.02 (1.00, 1.05)
0.98 (0.93, 1.03)
1.06 (1.01, 1.12)
Odds ratio (95% CI)
2.4x10-8
9.9x10-4
1.7x10-5
0.9
0.5
0.2
0.04
0.7
0.09
0.4
0.1
0.4
0.02
P-value
0.6
0.7
1.9 .95 1 1.05 1.1
Odds ratio (95% Confidence interval) per allele
Potential safety signals for IL-1 related agents
Freitag et al., unpublished
Research programmes
New bioresources
New bioresources
Screening and risk prediction
Cardiovascular Epidemiology
Unit
Medicines development
Internationalvascular
health
Gene-lifestyle interplay
Integrative genomics
Blood donor health
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
Current: 4000 AMI cases, 4000 controls
Planned total: 20,000 participants
Key hypothesis: arsenic and other heavy metals
Local collaboration with icddr,b and NICVD in Dhaka
Arsenic (µmol/l)
Cadmium (nmol/l)
Copper (µmol/l)
Mercury (nmol/l)
Risk factors
1.84 (1.37, 2.47)
1.10 (0.75, 1.62)
2.53 (1.18, 5.42)
1.48 (1.14, 1.92)
OR (95% CI)
10.1 0.5 2.5 5 10
Odds ratios per 1 SD(unless specified otherwise)
0.79 (0.74, 0.84)
1.79 (1.49, 2.16)
1.60 (1.50, 1.70)
HDL-C (mmol/l)
LDL-C (mmol/l)
Total cholesterol (mmol/l)
4.92 (3.65, 6.62) History of hypertension, yes
3.81 (2.40, 6.03) History of diabetes, yes
2.36 (1.29, 4.31) Smoking status, current
Investigating the impact of conventional and local risk factors
Chowdhury et al., unpublished
Research programmes
New bioresources
New bioresources
Screening and risk prediction
Cardiovascular Epidemiology
Unit
Medicines development
Internationalvascular
health
Gene-lifestyle interplay
Integrative genomics
Blood donor health
TootingSheffield
Manchester PGPlymouth
NewcastleStoke-on-Trent
LancasterOxford
GloucesterLiverpool
BristolEdgware
Leeds City
CambridgeWest End, LondonLeedsPooleBirminghamManchester NHBradfordLutonSouthamptonLeicester BrentwoodNottingham
~ 50,000 blood donors from 25 different geographical regions
The INTERVAL study - a large nationwide bioresource
What is the optimum interval between blood donations?
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
Extensive biological measurements for integrative genomics studies
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
Kamstrup, JAMA 2009ERFC, JAMA 2010Triglyceride Studies Coll., Lancet 2010ERFC, JAMA 2009Thompson, JAMA 2008Voight, Lancet 2012
Triglycerides (per 16% higher)
Implications for compounds
Various
CETPi
Higher circulating triglycerides
Genetically higher via Apo-AV
1.10 (1.08, 1.12)
1.18 (1.11, 1.26)
1.10 (1.08, 1.12)
1.18 (1.11, 1.26)
1.5 .7 .9 1 1.1 1.3 1.5Risk ratio (95%CI)
HDL-C (per 15mg/dl [1-SD] higher)
Higher circulating HDL-C
Genetically higher (via CETP)Genetically higher (via several HDL-C loci)
0.71 (0.68, 0.75)
0.93 (0.68, 1.27)
0.71 (0.68, 0.75)
0.93 (0.68, 1.27)
1.5 .7 .9 1 1.1 1.3 1.5Risk ratio (95%CI)
0.72 (0.58, 0.93)
Lp(a) (per 100% higher)
?
Risk ratio (95%CI)
1.5 .7 .9 1 1.1 1.3 1.5
Higher circulating Lp(a)Genetically higher Lp(a)
1.06 (1.04, 1.08)1.22 (1.09, 1.37)
Examples: inflammation markers
CCGC, BMJ 2011FSC, JAMA 2005Keavney, Int J Epidemiol 2006IL6R Genetics Consortium, Lancet 2012
C-reactive protein (per 1-SD higher)
Interleukin-6 receptor (per 34% higher)
Fibrinogen (per 0.14 g/l higher)
Implications for
compounds
anti-CRP
anti-fibrinogen
TocilizumabHigher circulating IL6R
Genetically higher IL6R 0.97 (0.95, 0.98)
?
0.97 (0.95, 0.98)
1.9 1 1.1 1.2 1.3 1.4 1.5
Risk ratio (95%CI)
Higher circulating fibrinogen
Genetically higher fibrinogen
1.13 (1.12, 1.14)
1.02 (0.99, 1.06)
1.9 1 1.1 1.2 1.3 1.4 1.5
Risk ratio (95%CI)
Higher circulating CRP
Genetically higher CRP
1.33 (1.23, 1.43)
1.00 (0.89, 1.12)
1.9 1 1.1 1.2 1.3 1.4 1.5
Risk ratio (95%CI)
Examples of findings
FindingFinding PublicationPublication
Lp(a) is independently associated with CHD riskLp(a) is independently associated with CHD risk JAMAJAMA 2009 2009
Lipid assessment can be done without the need to fastLipid assessment can be done without the need to fast JAMA JAMA 20092009
CRP is associated with vascular and nonvascular CRP is associated with vascular and nonvascular outcomesoutcomes
LancetLancet 20102010
LpPLALpPLA22 is log-linearly associated with CVD risk is log-linearly associated with CVD risk LancetLancet 20112011
Diabetes mellitus is associated with risk of death from Diabetes mellitus is associated with risk of death from CVD, and from several other non-vascular causes CVD, and from several other non-vascular causes
NEJM NEJM 20112011
Diabetes and survival
0
1
2
3
4
5
6
7
40 50 60 70 80 90 40 50 60 70 80 90
Vasculardeaths
Cancerdeaths
Non-cancernon-vasculardeaths
Unknowncauses
Year
s of
life
lost
Age (years)
Men Women
ERFC, NEJM 2011
About 6 years of life lost in middle age due to diabetes
New dimensions
CCGC, BMJ 2011
Greater integration of traditional and genetic epidemiology
Circulating usual levels of CRP
Genetically elevated levels of CRP
SNP analyses
Haplotype analyses
10.8 1.2 1.4 1.6 1.8 2
Risk ratio (95% CI) for CHD per 1-SD higher log CRP (mg/dl)
1.0
2.0
3.0
4 5 6 7 8
HbA1c
Ris
k ra
tio f
or C
HD
)
20 25 30 35 40 45
Ris
k ra
tio f
or C
HD
)
BMI
1.0
2.0
3.0
Integration with CARDIoGRAMplusC4D:
CardioMetabochip, GWAS
60K CHD cases, 120K controls
Gluco-metabolic traits
Drug interventions
RCT
Sample
Randomisation
Intervention Control
Biomarker lower
Biomarker higher
CV eventrate lower
CV eventrate higher
Mendelian randomisation
Population
Random allocation of alleles
Genotype aa Genotype AA
Biomarker lower
Biomarker higher
CV eventrate lower
CV eventrate higher
Genetics
How can genetic epidemiology help identify novel drug targets?
Examples of findings
FindingFinding PublicationPublication
APOE genotypes are log-linearly associated both with APOE genotypes are log-linearly associated both with LDL-C levels and with CHD risk LDL-C levels and with CHD risk
JAMAJAMA 2007 2007
CETP genotypes associated with reduced CETP activity CETP genotypes associated with reduced CETP activity are related with lower CHD riskare related with lower CHD risk
JAMA JAMA 20082008
APOA5 genotypes associated with higher triglycerides APOA5 genotypes associated with higher triglycerides concentration are related with increased risk of CHDconcentration are related with increased risk of CHD
LancetLancet 20102010
A functional IL6R allele is associated with lower levels of A functional IL6R allele is associated with lower levels of acute phase reactants and lower CHD riskacute phase reactants and lower CHD risk
Lancet Lancet 20122012
Testing for concordanceImplications for
compounds
Observational epidemiology
Genetic epidemiology
1.8 .9 1 1.1 1.2 1.3
Statin trials
Lower LDL-C concentration
LDL-C lowering
Higher circulating lipoprotein(a)
Lower circulating Lp-PLA2 activity
Circulating interleukin-6 receptor
?
Odds ratio (95% CI)
Observational epidemiology
Genetic epidemiology
Observational epidemiology
Genetic epidemiology
Observational epidemiology
Genetic epidemiology
1.8 .9 1 1.1 1.2 1.3 1.4
1.8 .9 1 1.1 1.2 1.3 1.4
1.8 .9 1 1.1 1.2 1.3 1.4
X
?
Darapladib
Various
IL-6 inhibitors
LSC, Lancet 2010ERFC, JAMA 2009IL6R Genetics Consortium, Lancet 2012Clarke, NEJM 2009
New dimensions
Feature Example
Novel hybrid chipNovel hybrid chip 350K SNPs for discovery350K SNPs for discovery100K SNPs for evaluation100K SNPs for evaluation
Exceptional powerExceptional power
50K CHD cases, 50K controls50K CHD cases, 50K controls
Phenotype-richPhenotype-rich ≈≈110 vascular phenotypes110 vascular phenotypes
Detailed databaseDetailed database Individual-level informationIndividual-level information
Follow-on studiesFollow-on studies Biomarker assays Biomarker assays ““reverse mendelian randomization”reverse mendelian randomization”
Recall by genotype Recall by genotype functional studiesfunctional studies
Exome+ array CHD consortium
Multiple disease outcomes
15K MI cases
5K T2D cases
5K stroke cases
20K controls
Clinical & lifestyle information
Multiple intermediate phenotypes
Genetic information
Why are South Asians especially susceptible to CVD?
Examples of findings
FindingFinding PublicationPublication
Discovery of :Discovery of :•9 loci in CHD 9 loci in CHD •6 loci in type 2 diabetes 6 loci in type 2 diabetes •several loci for blood pressure several loci for blood pressure
Nat Genet Nat Genet 2011a, 2011a, PLoS Genet PLoS Genet 2011 2011 Nat Genet Nat Genet 2011b2011bNatureNature 2011 2011
Pakistanis have a distinctive genetic architecturePakistanis have a distinctive genetic architecture Circ Cardiov Genet 2010
9p21 is weaker in Pakistanis9p21 is weaker in Pakistanis ATVB 2010
Study of Pakistani and European data has identified
“cosmopolitan” loci for complex diseases
5 novel loci for CHD5 novel loci for CHD
C4D consortium, Nat Genet 2011a
EuropeanS Asian
EuropeanS Asian
EuropeanS Asian
EuropeanS Asian
EuropeanS Asian
Ethnic group
Odds ratio (95% CI)0.9 1.0 1.1 1.25
Gene/locus
LIPA
ADAMTS7-MORF4L1
PDGFD
KIAA1462
7q22
Kooner & Saleheen et al., Nat Genet 2011b
6 novel loci for T2DM6 novel loci for T2DM
GRB14
HMG20A
HNF4A
VPS26A
ST6GAL1
AP3S2
1.9 1 1.1 1.25
S AsianEuropean
S AsianEuropean
S AsianEuropean
S AsianEuropean
S AsianEuropean
S AsianEuropean
Odds ratio (95% CI)
Ethnic groupGene/locus
Research programmes
New bioresources
New bioresources
Collaborative meta-analyses
Cardiovascular Epidemiology
Unit
Epidemiology for therapeutics
CVDin South Asia
Gene-lifestyle interplay
Optimising CVD screening
New bioresources
Do, PLoS Med 2012
Association of 9p21 SNP with CHD may be modified by diet
How exactly do genetic and lifestyle factors interplay in CVD?
INTERHEART Finrisk
High
Medium AG
AAAG
AG
GG
GGLow
AA
GG
9p21 genotype
11 1.5 2OR (95% CI)
compared to reference group
11 1.5 2HR (95% CI)
compared to reference group
AA (Reference)
Prudent dietgroup
New dimensions
Genes Lifestyles
Cardiovascular disease
~25 biomarkers of Intermediate causal
pathways
650K SNPs
EPIC-Heart520K people, 10 countries
>50 objective nutritional biomarkers
25K CVD outcomes
~500 exposures
Examples of findings from the ERFC
FindingFinding PublicationPublication
Assessment of chronic kidney disease provides about half as Assessment of chronic kidney disease provides about half as much predictive gain as does history of diabetesmuch predictive gain as does history of diabetes
BMJBMJ 2010 2010
Measures of adiposity do not enhance CVD risk predictionMeasures of adiposity do not enhance CVD risk prediction Lancet Lancet 20112011
Targeted additional assessment of CRP or fibrinogen improves Targeted additional assessment of CRP or fibrinogen improves CVD prediction modestly CVD prediction modestly
NEJM NEJM 20122012
Replacement of total and HDL-C with apolipoproteins reduces Replacement of total and HDL-C with apolipoproteins reduces the accuracy of CVD risk predictionthe accuracy of CVD risk prediction
JAMA JAMA 20122012
How can CVD screening be improved?
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
New dimensions
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
How can new bioresources complement UK Biobank?
