Nuevos marcadores de lesión subclínica.

649 views

Published on

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.com

Published in: Health & Medicine
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
649
On SlideShare
0
From Embeds
0
Number of Embeds
122
Actions
Shares
0
Downloads
22
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Nuevos marcadores de lesión subclínica.

  1. 1. New  biomarkers  of  subclinical  organ  damage  :  are  they  useful  in  the  assessment  of  global  CV   risk   Anna  F  Dominiczak    M.D.  
  2. 2. New  biomarkers  of  subclinical  organ  damage  :  are  they  useful  in  the  assessment  of  global  CV   risk   Anna  F  Dominiczak    M.D.  
  3. 3. 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  
  4. 4. Biomarkers  –  what  for?   PREDICTION  -­‐  Determine  risk  of  complica>ons     PATHOPHYSIOLOGY  -­‐  Iden>fy  causal  pathways     RESPONSE  -­‐  Guide  therapy  choice     Tradi>onal  vs.  Novel  markers    
  5. 5. 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  
  6. 6. Pathway  of  puta>ve   Biomarker   risk  mechanism   IL-­‐6,  CRP,  Fibrinogen,  Myeloperoxidase,  Neopterin,  Inflamma>on   Osteopon>n,  MCP-­‐1,  ST-­‐2,  MMP-­‐9  Tissue  damage/  ischaemia   Hs  Troponin  I  /T,  NT-­‐proBNP   Insulin,  Proinsulin,  NEFAs,  Adiponec>n,  Lep5n,  HBA1c,  Metabolic   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  
  7. 7. 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  
  8. 8. 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  
  9. 9. BNP  &  Troponin   Zethelius  et  al,  NEJM  2008  Zethelius et al (2008) NEJM
  10. 10. 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  
  11. 11. B-­‐Type  Natriure>c  Pep>de  Ter>les  &  Cardiac  Target  Organ   Damage   Nadir  et  al,  J  Am  Coll  Cardiol    2012;60:960  
  12. 12. High  Sensi>vity  Cardiac  Troponin-­‐T  Ter>les  and  Cardiac   Target  Organ  Damage   Nadir  et  al,  J  AM  Coll  Cardiol    2012;60:960  
  13. 13. Number  of  missed  Cases  of  cTOD  when  cutoff  is  applied     BNP  >15pg/ml  or  hs-­‐cTnT  >  5.93  ng/l  Framingham  +  BNP  adds  0.1777  to  AUC  ;  p<0.001  Framingham  +  BNP  +  c  TnT    adds  0.204  ;  p  <0.001  Prescreening  with  BNP  +/-­‐-­‐  hs-­‐TnT    followed  by  targeted  phenotyping  is   worth  exploring  further  to  improve  primary  preven>on     Nadir  et  al,  J  Am  Coll  Cardiol    2012;60:960  
  14. 14.  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  ?  
  15. 15. Proteomics  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.   Anderson  NL  &  Anderson  NG.  Electrophoresis  1998  
  16. 16. Proteomics
  17. 17. Samples   Tuñòn  J  et  al.  JACC  2010  
  18. 18. Why Urine? Cardiovascular Continuum §  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. De Hortus SanitatisMainz, Germany, 1491
  19. 19. Urinary Proteomics: CE/MS Platform Cardiovascular ContinuumCapillary Electrophoresis coupled to Mass Spectrometry Mass Spectrometry Separation and analysis of proteins and peptides (>1,000) Capillary Electrophoresis Run time ~60 min Ionization CE §  fast §  robust Data Storage and §  inexpensive Evaluation §  reproducible MS Urine §  resolutionSample §  scan speed Disease specific Diagnosis Biomarker pattern Report
  20. 20. Pa>ents   Study cohort Samples CAD Control Primary Usage Secondary UsageBiomarker 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) Discovery   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] Fenofibrate, baseline [13] (N=26) Adjustment  for  Medication markers SVM modeling 16 26 0 0 16 26 drug  treatment   modeling Medication markers SVM Fenofibrate, follow-up 26 0 26 Medication markers SVM modelingBlinded cohort (N=138) Blinded  cohort   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] Effect  of  treatment   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 Delles  C  et  al.  J  Hypertens  2010  
  21. 21. 238  Biomarker  Panel   Delles  C  et  al.  J  Hypertens  2010  
  22. 22. ROC  curve  analyses  of  the  CAD-­‐specific  polypep/de  paAern   AUC  95%   AUC  87%   (CI  93-­‐97)   (CI  81-­‐92)  Training  Set   Test  Set  
  23. 23. 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    
  24. 24. Effect of Drug Therapy 10-week treatment with irbesartan2-year treatment with irbesartan
  25. 25. LV Diastolic DysfunctionCardiovascular Continuum Kuznetsova T et al. Eur Heart J 2012
  26. 26. Chronic Kidney Disease Pattern CKD Controls CKD pattern (n=273 biomarkers): Fragments of •  Various collagens kDa] •  Plasma proteins (serum albumin, transthyretin, alpha-1-antitrypsin, alpha-1B-Mass [ glycoprotein, alpha-2-HS-glycoprotein, antithrombin-III, apolipoprotein A-I, beta-2- microglobulin, fibrinogen alpha) •  Clusterin •  Uromodulin CE migration time [min] CE migration time [min] •  Na/K-transporting ATPase gamma chain •  Psoriasis susceptibility 1 candidate gene 2 •  Prostaglandin-H2 D-isomerase Training set •  Proprotein convertase subtilisin/kexin type 1 inhibitor CASES CONTROLS •  Polymeric-immunoglobulin receptor n = 230 n = 379 •  Osteopontin 30 ANCA, 379 C •  Neurosecretory protein VGF 30 MGN, 22 MCD, •  Membrane associated progesterone 44 IgAN, receptor component 1 25 FSGS, •  CD99 antigen 58 DN, •  Ig lambda chain C regions 21 SLE Good DM et al. Mol Cell Protomics 2010, Jantos-Siwy J et al. J Proteome Res 2009
  27. 27. StrokeCardiovascular Continuum Dawson J et al. PloS One 2012
  28. 28. Stroke Cardiovascular ContinuumDiagnostic accuracy Stroke severity Dawson J et al. PloS One 2012
  29. 29. "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 2007But we need better and fully validatedbiomarkers to stratify patients with earlyand asymptomatic / silent CVD.
  30. 30. Call  Text  
  31. 31. Assessment of Cardiovascular Risk
  32. 32. Cardiovascular Risk ESH/ESC Guidelines. J Hypertens 2007
  33. 33. Left Ventricular Hypertrophy Cardiovascular Continuum Gallego-Delgado J et al. J Proteome Res 2006
  34. 34. Urinary  Proteomics:  CE/MS  Plaporm   a)   CAD s  (kDMas Migra>on  Time  (min)   CAD
  35. 35. Better Discrimination with More Markers CAD Controls CAD Controls 24 Markers 1.0Sensitivity 0.8 238 Markers 50 Markers 0.6 0.4 AUC 0.786 0.2 AUC 0.786 AUC 0.882 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1 - Specificity
  36. 36. SYSTEMS  MEDICINE  STRATEGIES   Dissemina/on  Integrate  &  evaluate   Tools  
  37. 37. Detec/on  and  therapy  evalua/on  of  CAD   CAD   Control   >  600  subjects   CAD   Control   Mul/-­‐center   (Germany,  UK,  USA,   Australia)     Coronary   12  weeks   12  weeks   angiography  as  a   gold  standard  Classifica/on    factor   Low   High   physical   physical   ac/vity   ac/vity  
  38. 38. Combining  omics  datasets  to  molecular  model  of  disease    Mischak  et  al    
  39. 39. Cardiovascular Continuum Cardiovascular Continuum Tissue injury (MI, stroke, renal insufficiency, peripheral arterial insufficiency) Atherothrombosis and progressive CV disease Pathological remodeling Altered gene expression Altered protein expressionEarly tissue dysfunction Target organ damage Oxidative and End-organ failure mechanical stress (CHF, ESRD) Inflammation Genome Risk factors Death Dzau V et al. Circulation 2006

×