BioSpace Collaborative

Academic/Biomedical Research
News & Jobs
Biotechnology and Pharmaceutical Channel Medical Device and Diagnostics Channel Clinical Research Channel BioSpace Collaborative    Job Seekers:  Register | Login          Employers:  Register | Login  

NEWSLETTERS
Free Newsletters
Archive
My Subscriptions

NEWS
News by Subject
News by Disease
News by Date
PLoS
Search News
Post Your News
JoVE

CAREER NETWORK
Job Seeker Login
Most Recent Jobs
Search Jobs
Post Resume
Career Fairs
Career Resources
For Employers

HOTBEDS
Regional News
US & Canada
  Biotech Bay
  Biotech Beach
  Genetown
  Pharm Country
  BioCapital
  BioMidwest
  Bio NC
  BioForest
  Southern Pharm
  BioCanada East
  US Device
Europe
Asia

DIVERSITY

PROFILES
Company Profiles

INTELLIGENCE
Research Store

INDUSTRY EVENTS
Research Events
Post an Event
RESOURCES
Real Estate
Business Opportunities

PLoS By Category | Recent PLoS Articles
Pathology - Radiology and Medical Imaging

Blood Signature of Pre-Heart Failure: A Microarrays Study
Published: Friday, June 24, 2011
Author: Fatima Smih et al.

by Fatima Smih, Franck Desmoulin, Matthieu Berry, Annie Turkieh, Romain Harmancey, Jason Iacovoni, Charlotte Trouillet, Clement Delmas, Atul Pathak, Olivier Lairez, François Koukoui, Pierre Massabuau, Jean Ferrieres, Michel Galinier, Philippe Rouet

Background

The preclinical stage of systolic heart failure (HF), known as asymptomatic left ventricular dysfunction (ALVD), is diagnosed only by echocardiography, frequent in the general population and leads to a high risk of developing severe HF. Large scale screening for ALVD is a difficult task and represents a major unmet clinical challenge that requires the determination of ALVD biomarkers.

Methodology/Principal Findings

294 individuals were screened by echocardiography. We identified 9 ALVD cases out of 128 subjects with cardiovascular risk factors. White blood cell gene expression profiling was performed using pangenomic microarrays. Data were analyzed using principal component analysis (PCA) and Significant Analysis of Microarrays (SAM). To build an ALVD classifier model, we used the nearest centroid classification method (NCCM) with the ClaNC software package. Classification performance was determined using the leave-one-out cross-validation method. Blood transcriptome analysis provided a specific molecular signature for ALVD which defined a model based on 7 genes capable of discriminating ALVD cases. Analysis of an ALVD patients validation group demonstrated that these genes are accurate diagnostic predictors for ALVD with 87% accuracy and 100% precision. Furthermore, Receiver Operating Characteristic curves of expression levels confirmed that 6 out of 7 genes discriminate for left ventricular dysfunction classification.

Conclusions/Significance

These targets could serve to enhance the ability to efficiently detect ALVD by general care practitioners to facilitate preemptive initiation of medical treatment preventing the development of HF.

  More...

 

//-->