New  biomarkers  of  subclinical  organ  damage  :  are  they  useful  in  the  assessment  of  global  CV  

risk  

Anna  F  Dominiczak    M.D.  

New  biomarkers  of  subclinical  organ  damage  :  are  they  useful  in  the  assessment  of  global  CV  

risk  

Anna  F  Dominiczak    M.D.  

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  

Biomarkers  –  what  for?  

PREDICTION  -­‐  Determine  risk  of  complica>ons    

PATHOPHYSIOLOGY  -­‐  Iden>fy  causal  pathways    

RESPONSE  -­‐  Guide  therapy  choice    

Tradi>onal  vs.  Novel  markers    

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  

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  

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  

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  

BNP  &  Troponin  

Zethelius et al (2008) NEJM Zethelius  et  al,  NEJM  2008  

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  

B-­‐Type  Natriure>c  Pep>de  Ter>les  &  Cardiac  Target  Organ  Damage  

Nadir  et  al,  J  Am  Coll  Cardiol    2012;60:960  

High  Sensi>vity  Cardiac  Troponin-­‐T  Ter>les  and  Cardiac  Target  Organ  Damage  

Nadir  et  al,  J  AM  Coll  Cardiol    2012;60:960  

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  

 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  ?  

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.  

Proteomics

Samples  

Tuñòn  J  et  al.  JACC  2010  

§  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

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

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  

238  Biomarker  Panel  

Delles  C  et  al.  J  Hypertens  2010  

ROC  curve  analyses  of  the  CAD-­‐specific  polypep/de  paAern  

Training  Set   Test  Set  

AUC  95%  (CI  93-­‐97)  

AUC  87%  (CI  81-­‐92)  

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    

Effect of Drug Therapy

10-week treatment with irbesartan

2-year treatment with irbesartan

Cardiovascular Continuum LV Diastolic Dysfunction

Kuznetsova T et al. Eur Heart J 2012

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

Cardiovascular Continuum Stroke

Dawson J et al. PloS One 2012

Cardiovascular Continuum Stroke

Dawson J et al. PloS One 2012

Diagnostic accuracy Stroke severity

"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.

Call  Text  

Assessment of Cardiovascular Risk

Cardiovascular Risk

ESH/ESC Guidelines. J Hypertens 2007

Cardiovascular Continuum Left Ventricular Hypertrophy

Gallego-Delgado J et al. J Proteome Res 2006

Urinary  Proteomics:  CE/MS  Plaporm  

CAD Control

CAD Control

CAD Control

CAD ControlMigra>on  Time  (min)  

Mass  (kD

a)  

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

SYSTEMS  MEDICINE  STRATEGIES  

Integrate  &  evaluate   Tools  

Dissemina/on  

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  

Combining  omics  datasets  to  molecular  model  of  disease    

Mischak  et  al    

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