ACS Recent and Evolving Aspects Definition and Diagnostics...
Transcript of ACS Recent and Evolving Aspects Definition and Diagnostics...
ACS – Recent and Evolving Aspects Definition and Diagnostics,
Acquired and Genetic Factors, Ischemic and Reperfusion Injury
Jason Kovacic MD, PhD
ACS – Recent and Evolving Aspects Definition and Diagnostics,
Acquired and Genetic Factors, Ischemic and Reperfusion Injury
Spectrum of symptomatic CAD and Acute
Coronary Syndrome (ACS) Definition
Kovacic and Fuster, Circ Res, 2014
STEMI NSTEMI
Ischemic
Sudden
Death
Unstable
Angina
Stable
Angina
True “Textbook” Definition of ACS
Evolving/practical use
of the term ACS
Positive troponin
Spectrum of symptomatic CAD and Acute Coronary
Syndrome (ACS) Definition
Kovacic and Fuster, Circ Res, 2014 / Fog Bentzon et al. Circ Res 2014
STEMI NSTEMI
Ischemic
Sudden
Death
Unstable
Angina
Stable
Angina
True “Textbook” Definition of ACS
Evolving/practical use
of the term ACS
Positive troponin
ACS - Diagnostics
• History – Risk factors; chest pain
• Exam
• EKG
• Labs – Troponin, other
• Imaging – CTA/stress test
• Invasive angiography
• Other
ACS – Recent and Evolving Aspects Definition and Diagnostics,
Acquired and Genetic Factors, Ischemic and Reperfusion Injury
Contemporary interpretations suggest heritable factors account for 30–60% of the inter-individual variation in the risk of CAD
Schunkert et al. Nature Genetics; 2011
These factors account for only ~50% of the risk of developing CAD:
• Smoking • Hypertension • Diabetes • Dyslipidemia
• LDL, HDL, triglycerides, Lp(a)
• Obesity • Sedentary Lifestyle • Diet
Genetic factors thought to account
for the remainder of risk
These factors account for only ~50% of the risk of developing CAD:
• Smoking • Hypertension • Diabetes • Dyslipidemia
• LDL, HDL, triglycerides, Lp(a)
• Obesity • Sedentary Lifestyle • Diet
Genetic factors thought to account
for the remainder of risk
63,746 CAD cases 130,681 controls without clinically manifest CAD
Identified and validated 46 risk loci for CAD
Identified a further 104 CAD-related variants that are yet to be validated
Nature Genetics 2013; 45:25-33
The nature of CAD heritability identified to date
• 46 validated loci, and potentially > 100-150 variants identified that contribute to CAD risk
• Each has minimal-modest effect
– Odds Ratio of 1.04 – 1.2 (4 – 20% increased risk)
• 50% of these variants occur in over half the population
• At least 25% of these variants occur in 75% of the population
Bjorkegren, Kovacic et al. JACC 2015
63,746 CAD cases + 130,681 controls = 194,427 subjects 46 validated risk loci for CAD A further 104 CAD-related loci identified that are yet to be validated
Collectively, all these variants explain 10-11% of the heritability of CAD
Nature Genetics 2013; 45:25-33
Kovacic Lab
Mount Sinai
Fog Bentzon et al. Circ Res 2014
Atherosclerosis Lesion Types and their Development
Inflammation in Atherosclerosis Atherosclerosis-prone ApoE-/- mouse after 18 weeks of high-fat diet
50 µm
CD31 – endothelial cells
Auto-fluorescence
CD45 – leukocytes & inflammatory cells
DAPI
Breakdown of elastic laminae via inflammatory release of MMPs, collagenases and elastases
Known genetic risk variants and CAD
Nearby gene (allele)
Chromosome location
SNP Odd’s Ratio
Associated with LDL cholesterol (n = 7) LPA 6q25.3 rs3798220 1.92 (1.48–2.49) APOB 2p24.1 rs515135 1.03 SORT1 1p13.3 rs599839 1.29 (1.18–1.40) LDLR 19p13.2 rs1122608 1.14 (1.09–1.19) APOE 19q13.32 rs2075650 1.14 (1.09–1.19) ABCG5-ABCG8 2p21 rs6544713 1.07 (1.04–1.11) PCSK9 1p32.3 rs11206510 1.15 (1.10–1.21)
Associated with HDL cholesterol (n = 1) ANKS1A 6p21.31 rs12205331 1.04
Associated with Triglycerides (n = 2) TRIB1 8q24.13 rs10808546 1.08 (1.04–1.12) ZNF259, APOA5-A4-C3-A1
11q23.3 rs964184 1.13 (1.10–1.16)
Known genetic risk variants and CAD
Nearby gene (allele)
Chromosome location
SNP Odd’s Ratio
Associated with Hypertension (n = 4) SH2B3 12q24.12 rs3184504 1.13 (1.08–1.18)
CYP127A1, CNNM2, NT5C2
10q24.32 rs12413409 1.12 (1.08–1.16)
GUCYA3 4q31.1 rs7692387 1.13
FURIN-FES 15q26.1 rs17514846 1.04
Associated with Myocardial Infarction (n = 1) ABO 9q34.2 rs579459 1.10 (1.07–1.13)
Known genetic risk variants and CAD Nearby gene (allele)
Chromosome location
SNP Odd’s Ratio
Mechanism of Risk Unknown (n = 32) PHACTR1 6p24.1 rs12526453 1.13 (1.09–1.17)
MRPS6 21q22.11 rs9982601 1.19 (1.13–1.27)
MRAS 3q22.3 rs2306374 1.15 (1.11–1.19)
WDR12 2q33.1 rs6725887 1.16 (1.10–1.22)
CDKN2A, CDKN2B 9p21.3 rs4977574 1.25 (1.18–1.31) to 1.37 (1.26–1.48)
MIA3 1q41 rs17465637 1.20 (1.12–1.30)
KIAA1462 10p11.23 rs2505083 1.07 (1.04–1.09)
PPAP2B 1p32.2 rs17114036 1.17 (1.13–1.22)
TCF21 6q23.2 rs12190287 1.08 (1.06–1.10)
BCAP29 7q22.3 rs10953541 1.08 (1.05–1.11)
ZC3HC1 7q32.2 rs11556924 1.09 (1.07–1.12)
LIPA 10q23.31 rs1412444 1.09 (1.07–1.12)
PDGF 11q22.3 rs974819 1.07 (1.04–1.09)
COL4A1, COL4A2 13q34 rs4773144 1.07 (1.05–1.09)
HHIPL1 14q32.2 rs2895811 1.07 (1.05–1.10)
ADAMTS7 15q25.1 rs3825807 1.08 (1.06–1.10)
SMG6, SRR 17p13.3 rs216172 1.07 (1.05–1.09)
RASD1, SMCR3, PEMT 17p11.2 rs12936587 1.07 (1.05–1.09)
UBE2Z, GIP, ATP5G1, SNF8 17q21.32 rs46522 1.06 (1.04–1.08)
IRX1, ADAMTS16 5p13.3 rs11748327 1.25 (1.18–1.33)
BTN2A1 6p22.1 rs6929846 1.51 (1.28–1.77)
C6orf105 6p24.1 rs6903956 1.65 (1.44–1.90)
HCG27 and HLA-C 6p21.3 rs3869109 1.15
EDNRA Chr4 rs1878406 1.09
HDAC9 7p21.1 rs2023938 1.13
VAMP5-VAMP8 2p11.2 rs1561198 1.07
ZEB2-AC074093.1 Chr2 rs2252641 1
SLC22A4-SLC22A5 Chr5 rs273909 1.11
KCNK5 6p21 rs10947789 1.01
PLG 6q26 rs4252120 1.07
LPL 8p22 rs264 1.06
FLT1 13q12 rs9319428 1.1
Known genetic risk variants and CAD
Nearby gene (allele)
Chromosome location
SNP Odd’s Ratio
Presumed associated with Inflammation (n = 3) CXCL12 10q11.21 rs1746048 1.33 (1.20–1.48)
IL5 5q31.1 rs2706399 1.02 (1.01–1.03)
IL6R 1q21 rs4845625 1.09
→→The known CAD risk variants are overwhelmingly associated with events important in the early
development of atherosclerosis (dyslipidemia and hypertension), rather than late events with clinical
consequences like diabetes and inflammation
Bjorkegren, Kovacic et al. JACC 2015
GWAS favors the identification of alleles that exert their effect during the development of atherosclerosis and which are independent of environmental influences (smoking, diet, sedentary lifestyle etc.)
DNA
RNA
Protein
Atherosclerosis
and CAD
Genomic activity measures (e.g., RNA,
proteins, metabolites) to define disease
driving molecular of CAD
Systems Biology and Systems Genetics
Systems genetics can be summarized as using genomic activity measures (e.g., RNA, proteins, metabolites) to define disease driving molecular processes,
thereby permitting their contribution to complex disease heritability to be
understood.
HEART
VASCULATURE
KIDNEY
IMMUNE SYSTEM
transcriptional network
protein network
metabolite network
Non-coding RNA network
GI TRACT
BRAIN
ENVIRONMENT
EN
VIR
ON
ME
NT
ENVIRONMENT
EN
VIR
ON
ME
NT
Systems Genetics Captures Environmental Influences (smoking, diet, pollution, exercise, obesity, stress etc.)
Tissues of Interest Related to Atherosclerosis
Atherosclerotic Arterial Wall
Atherosclerosis- Free Artery Wall
(LIMA)
Liver
Abdominal Fat
Skeletal Muscle
Subcutaneous Fat
Whole blood
Systems Genetics CAD cohorts
• The STAGE (the STockholm Atherosclerosis Gene Expression) cohort: – 156 CAD patients with full clinical characterization,
– DNA and 8 RNA tissue samples:
• atherosclerotic and non-atherosclerotic arterial wall, blood monocytes (macrophages), blood, liver, abdominal and s.c. fat and skeletal muscle) collected during CABG
Systems Genetics and CAD findings from STAGE
• With 156 subjects, STAGE independently identified 22 of 46 accepted CAD risk loci, which originally required ~200,000 subjects to identify and validate using DNA-based GWAS
• Loci acting across multiple tissues, rather than 1-2 tissues, increase the risk of having CAD
• Identified a causal inflammatory CAD network that is enriched with genes in the trans-endothelial migration of leukocytes (TEML) pathway and with Lim-domain binding 2 (LDB2) as a key driver - now validated as a novel CAD network target for TEML
Systems Genetics CAD cohorts
• The STAGE (the STockholm Atherosclerosis Gene Expression) cohort: – 156 CAD patients with full clinical characterization,
– DNA and 8 RNA tissue samples:
• atherosclerotic and non-atherosclerotic arterial wall, blood monocytes (macrophages), blood, liver, abdominal and s.c. fat and skeletal muscle) collected during CABG
• The STARNET (the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Team) cohort: – 1000 CAD cases
– 100 non-CAD controls having non-CABG open-thorax surgery
– DNA and 8 RNA tissues sampled as for STAGE.
*RNAseq (25 million read depth, 50 pb, single strand) and 700 DNA samples (OmniExpressExome Chip; 700k genome-wide+250k exome) with IlluminaHighSeq2000 platform (2013)
STARNET has defined tissue of action of know CAD loci
Franzen, Giannarelli, Kovacic, et al, Schadt, Bjorkegren. In press
A gene-regulatory network of 21/46 risk loci identified by meta-analysis in the CARDIOGRAM GWAS of CAD
Franzen, Giannarelli, Kovacic, et al, Schadt, Bjorkegren. In press
Summary
→ inflammation → cross-tissue disease regulation → other late events in athero development → interaction among proteins, genes and loci → environmental effects (smoking etc) → therapeutic targets
ACS – Recent and Evolving Aspects Definition and Diagnostics,
Acquired and Genetic Factors, Ischemic and Reperfusion Injury
Systems Genetics Is Expected To Make MAJOR Inroads On Understanding The Basis of CAD
→ Especially ACS and Late Events With Clinical Consequences
Acknowledgements
Kovacic Lab:
• Solene Evrard
• Aya Kitabayashi
• Valentina d‘Escamard
• Katherine Michelis
• Dongkwon Yang
• Meera Purushothaman
• Maria Paola Santini
• Jonathan Lee
• Laura Lecce
Dept. of Genetics:
• Johan Bjorkegren
• Ke Hao
• Shamus Peng
• Oscar Franzen
• Eric Schadt
• Andrew Kasarskis
CVRC:
• Roger Hajjar
• Valentin Fuster