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Blood signature of pre-heart failure: a microarrays study
Fatima Smih1, Franck Desmoulin, Matthieu Berry
1INSERM/Universite Paul Sabatier UMR 1048, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Toulouse, France. fatima.smith-rouet@inserm.fr
Insights
Researchers identified a 7-gene blood signature to detect asymptomatic left ventricular dysfunction (ALVD), a precursor to heart failure. This discovery could enable early screening and treatment for individuals at risk.
Area of Science:
- Cardiology
- Molecular Biology
- Genomics
Background:
- Asymptomatic left ventricular dysfunction (ALVD) precedes severe heart failure (HF) and is currently diagnosed solely by echocardiography.
- ALVD is prevalent and poses a high risk for HF development, necessitating scalable screening methods.
- Identifying novel biomarkers for ALVD is a critical unmet clinical need.
Purpose of the Study:
- To identify blood-based gene expression biomarkers for the early detection of asymptomatic left ventricular dysfunction (ALVD).
- To develop a molecular signature capable of discriminating ALVD cases from healthy individuals.
Main Methods:
- White blood cell gene expression profiling using pangenomic microarrays in 294 individuals.
- Analysis of gene expression data using principal component analysis (PCA) and Significant Analysis of Microarrays (SAM).
- Development of an ALVD classifier model using nearest centroid classification method (NCCM) with ClaNC software and leave-one-out cross-validation.
Main Results:
- A specific molecular signature comprising 7 genes was identified for ALVD.
- The 7-gene model demonstrated high diagnostic accuracy (87%) and precision (100%) in a validation group.
- Receiver Operating Characteristic (ROC) curve analysis confirmed that 6 of the 7 genes effectively discriminate left ventricular dysfunction.
Conclusions:
- The identified 7-gene signature serves as a potential blood biomarker for efficient ALVD detection.
- This biomarker could empower general care practitioners to identify at-risk individuals.
- Early detection facilitates preemptive medical treatment, potentially preventing the progression to heart failure.
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.
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