Improving prevention and prediction of cardiovascular disease

1
Improving prevention and prediction of
cardiovascular disease

• Adam Butterworth
• University Lecturer in Cardiovascular
  Epidemiology
• Cardiovascular Epidemiology Unit
• June 25th, 2014

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Research programmes
New bioresources
Screening and risk prediction
Medicines development
Blood donor health
Cardiovascular Epidemiology Unit
International vascular health
Gene-lifestyle interplay
Integrative genomics
3
Research programmes
New bioresources
Screening and risk prediction
Quantitative methods
Medicines development
Blood donor health
Cardiovascular Epidemiology Unit
International vascular health
Gene-lifestyle interplay
Integrative genomics
4
Research programmes
New bioresources
Screening and risk prediction
Medicines development
Blood donor health
Cardiovascular Epidemiology Unit
International vascular health
Gene-lifestyle interplay
Integrative genomics
5
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
gt10 yrs of follow-up
ERFC, Eur J Epidemiol 2008 ERFC, Int J Epidemiol
2010
6
Glycemic markers add little to CVD risk prediction
No. of studies
Change in C-index (95 CI)
C-index (95 CI)
No. of participants
No. of cases
Addition of glycemia measures
HbA1c
13
70916
3271
0.7434 (0.7350, 0.7517)
Reference
Conventional risk factors
Plus HbA1c
0.7452 (0.7368, 0.7535)
0.0018 (0.0003, 0.0033)a
Fasting glucose
25
95198
9560
0.7172 (0.7122, 0.7222)
Reference
Conventional risk factors
0.7185 (0.7134, 0.7235)
0.0013 (0.0007, 0.0018)b
Plus fasting glucose
Random glucose
Conventional risk factors
22
92504
5152
0.7362 (0.7298, 0.7426)
Reference
Plus random glucose
0.7367 (0.7304, 0.7431)
0.0005 (-0.0002, 0.0013)
Post-load glucose
Conventional risk factors
10
38532
5519
0.7193 (0.7126, 0.7260)
Reference
Plus post-load glucose
0.7197 (0.7130, 0.7264)
0.0004 (-0.0001, 0.0009)
0
-.002
0
.002
.004
Change in C-index (95 CI)
Emerging Risk Factors Collaboration, JAMA 2014
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Consequences of vascular multi-morbidity
Risk of subsequent CVD
Disease status at baseline
HR (95 CI)
MI stroke d
MI stroke diabetes
8.4 (6.7, 10.6)
8.4 (6.7, 10.6)
MI diabetes
MI diabetes
5.6 (4.7, 6.5)
5.6 (4.7, 6.5)
Stroke diabetes
Stroke diabetes
5.6 (4.9, 6.4)
5.6 (4.9, 6.4)
MI stroke
MI stroke
5.6 (4.8, 6.5)
5.6 (4.8, 6.5)
MI only
MI only
3.1 (2.7, 3.5)
3.1 (2.7, 3.5)
Stroke only
Stroke only
2.7 (2.4, 3.1)
2.7 (2.4, 3.1)
Diabetes only
Diabetes only
2.2 (2.1, 2.4)
2.2 (2.1, 2.4)
None
None
1.0 (Reference)
1.0 (Reference)
0
5
10
15
20
25
30
35
40
0
5
10
15
20
25
30
35
40
Events per 1000 person
-
years (95 CI)
Events per 1000 person
-
years (95 CI)
Emerging Risk Factors Collaboration, unpublished
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Research programmes
New bioresources
Screening and risk prediction
Medicines development
Blood donor health
Cardiovascular Epidemiology Unit
International vascular health
Gene-lifestyle interplay
Integrative genomics
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Functional genetic variant (Asp358Ala) in IL6R
Risk factors
Other diseases
ncases
Disease
Coronary disease
51,441
Type 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



Atrial fibrillation
2260
AAA
4524
Conventional
Rheumatoid arthritis
11,475
Atopic dermatitis
2890
Asthma
15,797
All cancer
5376
Breast cancer
14,456
Inflammation
Colorectal cancer
1863

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0.8
0.9
1.1
1.2
1.3
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
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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
0.97 (0.95, 0.99)
9.9x10-4
Okada 2014
Abdominal aortic aneurysm
1.08 (1.04, 1.12)
1.7x10-5
AAA genetics consortium
Ischaemic stroke
1.00 (0.98, 1.02)
0.9
Metastroke
Type 2 diabetes
0.99 (0.97, 1.01)
0.5
DIAGRAM InterAct
Asthma and Hayfever
0.98 (0.95, 1.01)
0.2
Ferreira et al, J Allerg Clin Immunol 2014
Tuberculosis
1.01 (0.97, 1.05)
0.6
Nejentsev et al
Breast cancer
1.01 (1.00, 1.03)
0.04
Breast Cancer Association Consortium
Childhood acute lymphoblastoid lymphoma
1.01 (0.96, 1.07)
0.7
Migliorini et al, Blood 2013
Chronic lymphocytic leukaemia
0.99 (0.93, 1.05)
0.7
Speedy et al, Nat Genet 2014
Colorectal cancer
0.97 (0.94, 1.01)
0.09
Whiffin et al, Hum Mol Gen 21014
Lung cancer
0.99 (0.96, 1.01)
0.4
Wang et al, Nature Genetics 2014
Melanoma
1.02 (1.00, 1.05)
0.1
Bishop Melanoma consortium
Multiple myeloma
0.98 (0.93, 1.03)
0.4
Chubb et al, Nat Genet 2013
Renal cell carcinoma
1.06 (1.01, 1.12)
0.02
Henrion et al, Hum Mol Genet 2013


1
.9
.95
1
1.05
1.1
Freitag et al., unpublished
Odds ratio (95 Confidence interval) per allele
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Research programmes
New bioresources
Screening and risk prediction
Medicines development
Blood donor health
Cardiovascular Epidemiology Unit
International vascular health
Gene-lifestyle interplay
Integrative genomics
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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
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Investigating the impact of conventional and
local risk factors
Risk factors
OR (95 CI)
2.36 (1.29, 4.31)
Smoking status, current
4.92 (3.65, 6.62)
History of hypertension, yes
History of diabetes, yes
3.81 (2.40, 6.03)
1.60 (1.50, 1.70)
Total cholesterol (mmol/l)
1.79 (1.49, 2.16)
LDL-C (mmol/l)
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)

1
0.1
0.5
2.5
5
10
Odds ratios per 1 SD (unless specified otherwise)
Chowdhury et al., unpublished
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Research programmes
New bioresources
Screening and risk prediction
Medicines development
Blood donor health
Cardiovascular Epidemiology Unit
International vascular health
Gene-lifestyle interplay
Integrative genomics
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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 Liverp
ool Bristol Edgware Leeds City
Cambridge West End, London Leeds Poole Birmingham
Manchester NH Bradford Luton Southampton Leicester
Brentwood Nottingham
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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
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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
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Key external funders
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The Cardiovascular Epidemiology Unit
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Examples lipids
Implications for compounds
Lp(a) (per 100 higher)
?
Triglycerides (per 16 higher)
Various
HDL-C (per 15mg/dl 1-SD higher)
CETPi
Kamstrup, JAMA 2009 ERFC, JAMA 2010 Triglyceride
Studies Coll., Lancet 2010 ERFC, JAMA
2009 Thompson, JAMA 2008 Voight, Lancet 2012
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Examples inflammation markers
Implications for compounds
C-reactive protein (per 1-SD higher)
Higher circulating CRP
1.33 (1.23, 1.43)
anti-CRP
Genetically higher CRP
1.00 (0.89, 1.12)


1
.9
1
1.1
1.2
1.3
1.4
1.5
Risk ratio (95CI)
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)


1
.9
1
1.1
1.2
1.3
1.4
1.5
Risk ratio (95CI)
Interleukin-6 receptor (per 34 higher)
Tocilizumab
Higher circulating IL6R
?
Genetically higher IL6R
0.97 (0.95, 0.98)
0.97 (0.95, 0.98)
CCGC, BMJ 2011 FSC, JAMA 2005 Keavney, Int J
Epidemiol 2006 IL6R Genetics Consortium, Lancet
2012


1
.9
1
1.1
1.2
1.3
1.4
1.5
Risk ratio (95CI)
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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
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Diabetes and survival
About 6 years of life lost in middle age due to
diabetes
ERFC, NEJM 2011
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New dimensions
Greater integration of traditional and genetic
epidemiology
Circulating usual levels of CRP
Genetically elevated levels of CRP
SNP analyses
Haplotype analyses
1
0.8
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
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How can genetic epidemiology help identify novel
drug targets?
Drug interventions
RCT
Sample
Randomisation
Intervention
Control
Biomarker lower
Biomarker higher
CV event rate higher
CV event rate lower
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Examples of findings
Finding Publication
APOE genotypes are log-linearly associated both with LDL-C 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
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Testing for concordance
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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
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Why are South Asians especially susceptible to
CVD?
Genetic information
Multiple intermediate phenotypes
Clinical lifestyle information
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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



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Study of Pakistani and European data has
identified cosmopolitan loci for complex
diseases
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Research programmes
New bioresources
Collaborative meta-analyses
Epidemiology for therapeutics
New bioresources
Cardiovascular Epidemiology Unit
CVD in South Asia
Gene-lifestyle interplay
Optimising CVD screening
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How exactly do genetic and lifestyle factors
interplay in CVD?
Association of 9p21 SNP with CHD may be modified
by diet
INTERHEART
Finrisk
Do, PLoS Med 2012
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New dimensions
EPIC-Heart
520K people, 10 countries
Genes
Lifestyles
650K SNPs
500 exposures
25 biomarkers of Intermediate causal pathways
gt50 objective nutritional biomarkers
Cardiovascular disease
25K CVD outcomes
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Examples of findings from the ERFC
How can CVD screening be improved?
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
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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
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How can new bioresources complement UK Biobank?
Existing resources Donation teams, transport
links, 25 donation clinics across England
1.4 million donorsBroad population group -
gt17yrs, 5050 M/F
Repeat donations Baseline and follow-up
measurements