Transcript Cardiology Journal Club

Cardiology Journal
Club
Sanjay Dravid, M.D.
January 17, 2006
MULTIPLE BIOMARKERS FOR
THE PREDICTION OF FIRST
MAJOR CARDIOVASCULAR
EVENTS AND DEATH
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Wang, Thomas J., et al.
Massachusetts General Hospital.
NEJM. Volume 355(25), 21 December 2006,
pp 2631-2639.
Overview
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To evaluate the incremental usefulness of
multiple biomarkers from various pathways.
Established risk factors, including smoking, htn,
DM, and dyslipidemia.
Significant interest in new biomarkers for risk
stratification of ambulatory persons.
Novel Approach
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Many individual biomarkers have been studied.
“Multimarker” Approach
Simultaneous measurement may enhance risk
stratification?
Outcomes Analysis
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1. Death from any cause
2. 1st Major cardiovascular event (MI, coronary
insufficiency, heart failure, and stroke.
Reviewed by a committee of three investigators
Study Sample
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Large, community based cohort study
Participants from the sixth examination cycle
(1995-1998) of the Framingham Offspring
Study
IRB of Boston University Medical Center
approval
Written informed consent was obtained
H & P, PE, and Lab Assessment
Exclusion Criteria
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Serum creatinine levels greater than 2.0 mg/dL
Missing covariates
Prior event when determining outcome of
major cardiovascular event
Triglycerides > 400
Biomarker Selection
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1. Marker of inflammation- hsCRP
2. Markers of neurohormonal activity- BNP,
aldosterone, renin, N-terminal pro-atrial
natriuretic peptide
3. Marker of thrombosis and inflammationfibrinogen
4. Marker of fibrinolytic potential and
endothelial function- plasminogen-activator
Biomarker cont’d
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Inhibitor type 1
5. Marker of thrombosis- D-dimer
6. Marker of endotheial function and oxidant
stress- homocysteine
7. Marker of glomerular endothelial functionurinary albumin-to-creatinine ratio
Lab Protocol
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Fasting blood and urine samples collected in
morning after patient supine for ~10 minutes.
Immediately centrifuged and stored at -70
degreesC.
Standardized Assay Methods
Statistical Analysis
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Multivariable proportional-hazards model (2 sets
of analyses for each outcome due to urine
subgroups)
Logarithmic transformation used to normalize
the distribution of biomarkers
To reduce the number of false positives from
multiple testing:
Statistics cont’d
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1) Multivariable Cox regression model
2) Backward elimination
3) Construction of multimarker score
4) Quintiles categorized
5) Cumulative probability curves constructed by
the Kaplan-Meier method for low, intermediate
and high mulitmarker scores
Statistics cont’d
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Then calculated hazard ratios for death and
major cardiovascular events for the mulitmarker
score groups
Adjusted for age, sex, conventional risk factors
including htn, smoking, dm, etc.
“C statistic”
ROC curves
Statistics cont’d
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Secondary Analysis adjusting for medication use
Repeated a Cox proportional-hazards model for
major cardiovascular events adjusting for
“nonmajor events”  angina, intermittent
claudication, TIA
SAS software, version 8 (SAS Institute)
C Statistic
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Defined as the probability of concordanc
among persons who can be compared.
Estimated as the sum of concordance values
divided by the number of comparable pairs.
Better able to measure discrimination than
relative risk.
Results
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Total of 3532 persons- 21 excluded for serum
creatinine and 302 for missing covariates.
10 year follow-up (median 7.4 years) 3209
available for study.
207 (6%) died, of whom 72 were women
169 (6%, excluding prevalent CV disease at
baseline) had a major cardiovascular event, of
whom 68 were women
Results cont’d
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Biomarker panel for nine: P<0.001 for death
and P=0.005 for cardiovascular events
Biomarker panel for ten (2750 persons):
P<0.001 for death and P=0.04 for
cardiovascular events
Results cont’d
Backward elimination models: final statistical
model included only the following biomarkers:
BNP, homocysteine, urinary albumin-to-creatinine
ratio and renin for death.
BNP and urinary albumin-to-creatinine ratio for
major cardiovascular events.
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Utility of Multimarker Scores
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Backward elimination biomarkers selected as
statistically significant were incorporated into
mulitmarker scores.
Restricted to urine sample patients: 1) death
from any cause, the number of events and
number at risk were 172 and 2750, respectively;
2) major cardiovascular events, 133 and 2598,
respectively.
Utility?
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Persons with high multimarker scores had a risk
of death four times as great and a risk of major
cariovascular events almost two times as great as
persons with low mulitmarker scores.
(P<0.001 and P=0.02, respectively)
Discussion
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~10 year study of biomarkers indicating BNP,
hsCRP, homocysteine, renin, and alb/Cr ratio as
most informative for predicting death, while
BNP and alb/Cr ration as significant for
predicting cardiovascular outcome.
Although high multimarker scores conferred
greater risk for death and major cardiovascular
events…
Conclusion
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Mulitmarker scores (combination of
biomarkers) add only moderately to
conventional risk factors as evidenced by small
changes in C statistic.
Single biomarkers may have correlation with
predicting outcomes
Panel likely will not be useful or cost-effective in
ambulatory setting for further risk stratification
Limitations
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Biomarker selection: omission of lipoproteinassociated phospholipase A2
Each individual marker not independently tested
Not a true cohort study to asses for primary
prevention as “nonmajor” cardiovascular events
adjusted
Adiposity or insulin resistance not taken into
account
Summary
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Biomarkers from multiple, biologically distinct
pathways are associated with the risks of death
and major cardiovascular events.
However, only moderately adds to conventional
risk factors currently.