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DESCRIPTION
Bloque: HIPERTENSIÓN ARTERIAL Y RIESGO CARDIOVASCULAR GLOBAL Ponente: Dra. Anna Dominiczak Curso Medicina Cardiovascular que tuvo lugar el 8 y 9 octubre 2012 en Barcelona. Enlace: www.riesgocardiovascular.comTRANSCRIPT
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New biomarkers of subclinical organ damage : are they useful in the assessment of global CV
risk
Anna F Dominiczak M.D.
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New biomarkers of subclinical organ damage : are they useful in the assessment of global CV
risk
Anna F Dominiczak M.D.
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Biomarkers
• Biomarker – • Indicator signaling an event or condi>on in a biological system or sample and giving a measure of exposure, effect, or suscep>bility
• detectable and measurable by a variety of methods including • physical examina/on, • laboratory assays • medical imaging
• Age, Social Class, Ethnicity, etc
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Biomarkers – what for?
PREDICTION -‐ Determine risk of complica>ons
PATHOPHYSIOLOGY -‐ Iden>fy causal pathways
RESPONSE -‐ Guide therapy choice
Tradi>onal vs. Novel markers
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Steps for the evalua>on of novel markers
1. Proof of concept = difference between subjects with & without disease
2. Prospec>ve valida>on = predic>ng in a prospec>ve cohort 3. Incremental value = does it add predic>ve informa>on 4. Clinical u>lity = does it change risk enough to change
recommended therapy 5. Clinical outcome = does it improve outcomes esp. in RTC 6. Cost effec>veness = does it do (5) sufficiently to jus>fy
addi>onal cost of tes>ng
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Pathway of puta>ve risk mechanism Biomarker
Inflamma>on IL-‐6, CRP, Fibrinogen, Myeloperoxidase, Neopterin, Osteopon>n, MCP-‐1, ST-‐2, MMP-‐9
Tissue damage/ ischaemia Hs Troponin I /T, NT-‐proBNP
Metabolic Insulin, Proinsulin, NEFAs, Adiponec>n, Lep5n, HBA1c, glucose, GGT?
Renal eGFR, Cysta>n-‐C
Lipoproteins Apolipoproteins AI, B, LpPLA2, sPLA2, Paroxonase , Lp(a)
Nutri>onal Homocysteine, N-‐3 faby acids, Vitamin D
Endothelial ADMA, t-‐PA , CAMS, VWF
Thrombo>c Fibrin D-‐dimer, Plasma viscosity
Oxida>on Telomeres, oxLDL
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CRP / Inflamma>on summary
• CRP not agreed as useful • CRP not causal for CVD on gene>c basis
• Lawlor PLOS One 2008, • Brunner Plos One 2008 • Emerging Risk factors Collaboration (2011) BMJ • Hingorani et al, European Heart Journal 2012
• IL6 may be causal: IL-6R polymorphism data linked to CVD events Emerging Risk factors Collabora5on, JAMA 2009
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The gene>c test points to IL-‐6 as a poten>al cause for CHD
• IL6R variant : Higher circula>ng IL-‐6 log concentra>on = pabern of IL6R receptor blockade
• Lower CRP, lower fibrinogen higher Albumin
• Overall, protec>ve vs. CVD events ? New drug target
lL-‐6R Mendelian Randomisa5on consor5um, Lancet 2012
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BNP & Troponin
Zethelius et al (2008) NEJM Zethelius et al, NEJM 2008
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Biomarkers to iden>fy silent cardiac target organ damage in a primary preven>on popula>on
• 300 asymptoma>c individuals receiving primary preven>on therapy • Biochemical markers : BNP, hs-‐cTnT, microalbuminuria, eGFR , uric acid, ECG, echocardiography + stress echo, 24hr ABPM • 102 = 34% pa>ents had evidence of cTOD , LVH 30%, LVDD 21%, • The area under the curve (AUC ) for BNP to iden>fy silent cTOD was 0.78 • The AUC for hs-‐TnT was 0.7 • The AUC for BNP + hs-‐TnT was 0.81 • The discrimina>on power of other markers was poor with AUCs of 0.61 for microalbuminuria, 0.49 for uric acid, and 0.58 for eGFR
Nadir et al, JACC 2012;60:960
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B-‐Type Natriure>c Pep>de Ter>les & Cardiac Target Organ Damage
Nadir et al, J Am Coll Cardiol 2012;60:960
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High Sensi>vity Cardiac Troponin-‐T Ter>les and Cardiac Target Organ Damage
Nadir et al, J AM Coll Cardiol 2012;60:960
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Number of missed Cases of cTOD when cutoff is applied BNP >15pg/ml or hs-‐cTnT > 5.93 ng/l
Nadir et al, J Am Coll Cardiol 2012;60:960
Prescreening with BNP +/-‐-‐ hs-‐TnT followed by targeted phenotyping is worth exploring further to improve primary preven>on
Framingham + BNP adds 0.1777 to AUC ; p<0.001 Framingham + BNP + c TnT adds 0.204 ; p <0.001
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Lessons from CVD biomarker research so far ?
New biomarkers of interest: 1. BNP, hsTrop, IL6, others; 2. Embed into very large well phenotyped studies with robustly validated end-‐points
3. Reclassifica>on metrics 4. Cost-‐benefit 5. Should we use omics technologies and try some
uncharted waters ?
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Proteomics
Anderson NL & Anderson NG. Electrophoresis 1998
The goal of proteomics is a comprehensive, quan>ta>ve descrip>on of protein expression and its changes under the influence of biological perturba>ons such as disease or drug treatment.
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Proteomics
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Samples
Tuñòn J et al. JACC 2010
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§ Easily accessible § Non invasive sampling § Available in large
quantities § Urinary polypeptides are
stable, yielding comparable datasets.
§ Urinary polypeptides display the “status” of the kidney, bladder, prostate and vascular architecture, are capable of depicting systemic diseases.
Cardiovascular Continuum Why Urine?
De Hortus Sanitatis Mainz, Germany, 1491
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Cardiovascular Continuum Urinary Proteomics: CE/MS Platform
Capillary Electrophoresis coupled to Mass Spectrometry
Urine Sample
Capillary Electrophoresis
Mass Spectrometry
Ionization
Report
Data Storage and
Evaluation
Diagnosis Disease specific Biomarker pattern
Separation and analysis of proteins and peptides (>1,000) Run time ~60 min CE § fast § robust § inexpensive § reproducible MS § resolution § scan speed
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Pa>ents
Study cohort Samples CAD Control Primary Usage Secondary Usage
Biomarker Discovery 586 204 382
CAD [9,10] (N=120†) 183 151 32 CAD markers SVM modeling UAP [10] (N=59) 59 35 24 SVM modeling n.a. CACTI [11] (N=33) 33 18 15 SVM modeling n.a. Additional controls [14] (N=153) 229 0 229 SVM modeling n.a. TRENDY, baseline [9,12] (N=17†) 14 0 14 Medication markers SVM modeling TRENDY, follow-up [9,12] 16 0 16 Medication markers SVM modeling Fenofibrate, baseline [13] (N=26) 26 0 26 Medication markers SVM modeling Fenofibrate, follow-up 26 0 26 Medication markers SVM modeling
Blinded cohort (N=138) 138 71 67 Validation n.a.
Short-term treatment effects [15] 193 n.a. n.a.
HIB 0 mg (N=55‡) 55 n.a. n.a. Drug interference n.a. HIB 300 mg 48 n.a. n.a. Drug interference n.a. HIB 600 mg 45 n.a. n.a. Drug interference n.a. HIB 900 mg 45 n.a. n.a. Drug interference n.a.
Long-term treatment effects [16] 44 n.a. n.a.
IRMA -2 Irbesartan baseline (N=11†) 11 n.a. n.a. Therapy monitoring n.a. IRMA-2 Irbesartan follow-up 11 n.a. n.a. Therapy monitoring n.a. IRMA-2 Placebo baseline(N=11†) 11 n.a. n.a. Therapy monitoring n.a. IRMA-2 Placebo follow-up 11 n.a. n.a. Therapy monitoring n.a.
Total (N=623) 961
Discovery
Adjustment for drug treatment
Effect of treatment
Blinded cohort
Delles C et al. J Hypertens 2010
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238 Biomarker Panel
Delles C et al. J Hypertens 2010
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ROC curve analyses of the CAD-‐specific polypep/de paAern
Training Set Test Set
AUC 95% (CI 93-‐97)
AUC 87% (CI 81-‐92)
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Iden>fica>on of Proteins
• Collagen type 1 • Collagen type 3 • Alpha-‐1-‐an>trypsin (AAT) • Granin-‐like neuroendocrine pep>de precursor (ProSAAS) • Membrane associated progesterone receptor component 1
• Sodium/potassium-‐transpor>ng ATPase gamma chain • Fibrinogen-‐alpha-‐chain
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Effect of Drug Therapy
10-week treatment with irbesartan
2-year treatment with irbesartan
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Cardiovascular Continuum LV Diastolic Dysfunction
Kuznetsova T et al. Eur Heart J 2012
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Chronic Kidney Disease Pattern
Controls CKD
CE migration time [min] CE migration time [min]
Mas
s [k
Da]
Training set
CASES CONTROLS n = 230
30 ANCA, 30 MGN, 22 MCD, 44 IgAN, 25 FSGS,
58 DN, 21 SLE
n = 379 379 C
CKD pattern (n=273 biomarkers): Fragments of • Various collagens • Plasma proteins (serum albumin,
transthyretin, alpha-1-antitrypsin, alpha-1B-glycoprotein, alpha-2-HS-glycoprotein, antithrombin-III, apolipoprotein A-I, beta-2-microglobulin, fibrinogen alpha)
• Clusterin • Uromodulin • Na/K-transporting ATPase gamma chain • Psoriasis susceptibility 1 candidate gene 2 • Prostaglandin-H2 D-isomerase • Proprotein convertase subtilisin/kexin type
1 inhibitor • Polymeric-immunoglobulin receptor • Osteopontin • Neurosecretory protein VGF • Membrane associated progesterone
receptor component 1 • CD99 antigen • Ig lambda chain C regions
Good DM et al. Mol Cell Protomics 2010, Jantos-Siwy J et al. J Proteome Res 2009
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Cardiovascular Continuum Stroke
Dawson J et al. PloS One 2012
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Cardiovascular Continuum Stroke
Dawson J et al. PloS One 2012
Diagnostic accuracy Stroke severity
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"The time has come to abandon the hypertension/ normotension dichotomy and to focus on global risk reduction."
Franz Messerli, Bryan Williams and Eberhard Ritz Lancet 2007
But we need better and fully validated biomarkers to stratify patients with early and asymptomatic / silent CVD.
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Call Text
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Assessment of Cardiovascular Risk
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Cardiovascular Risk
ESH/ESC Guidelines. J Hypertens 2007
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Cardiovascular Continuum Left Ventricular Hypertrophy
Gallego-Delgado J et al. J Proteome Res 2006
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Urinary Proteomics: CE/MS Plaporm
CAD Control
CAD Control
CAD Control
CAD ControlMigra>on Time (min)
Mass (kD
a)
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1 - Specificity 1.0 0.8 0.6 0.4 0.2 0.0
Sens
itivi
ty 1.0
0.8
0.6
0.4
0.2
0.0
CAD Controls
24 M
arke
rs
AUC 0.786
50 M
arke
rs
AUC 0.786 AUC 0.882
238
Mar
kers
CAD Controls
Better Discrimination with More Markers
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SYSTEMS MEDICINE STRATEGIES
Integrate & evaluate Tools
Dissemina/on
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CAD Control
CAD Control Mul/-‐center (Germany, UK, USA, Australia) Coronary angiography as a gold standard
Classifica/on factor
Detec/on and therapy evalua/on of CAD
Low physical ac/vity
High physical ac/vity
12 weeks 12 weeks
> 600 subjects
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Combining omics datasets to molecular model of disease
Mischak et al
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Cardiovascular Continuum Cardiovascular Continuum
Dzau V et al. Circulation 2006
Risk factors
Oxidative and mechanical stress
Inflammation
Early tissue dysfunction
Atherothrombosis and progressive CV disease
Tissue injury (MI, stroke, renal
insufficiency, peripheral arterial
insufficiency)
Pathological remodeling
Target organ damage
End-organ failure (CHF, ESRD)
Death
Altered gene expression Altered protein expression
Genome