Application of leukocyte transcriptomes to assess systemic consequences of risk factors for cardiovascular disease
Diego Ardigo1, Carlo A J M Gaillard, Branko Braam
1Department of Internal Medicine and Biomedical Sciences, University of Parma, Italy.
Insights
High-throughput gene expression profiling of circulating white blood cells (WBCs) offers a promising approach to improve cardiovascular disease (CVD) diagnosis and monitoring. This method provides a comprehensive view of cellular activity, overcoming limitations of traditional risk factors for personalized patient assessment.
Area of Science:
- Genomics and Molecular Biology
- Cardiovascular Disease Research
- Biomarker Discovery
Background:
- Cardiovascular disease (CVD) prevention is a significant global health challenge.
- Current CVD risk factor assessment is effective at a population level but lacks individual predictive power.
- Complex diseases like CVD require more sophisticated markers beyond single parameters.
Purpose of the Study:
- To explore high-throughput gene expression profiling for enhanced CVD diagnosis, prognosis, and monitoring.
- To investigate the utility of circulating white blood cells (WBCs) for patient phenotyping in CVD.
- To review conceptual limitations of current biomarkers and the rationale for using WBC transcriptomics.
Main Methods:
- Review of conceptual limitations in risk factor and biomarker use for CVD.
- Exploration of gene expression profiling using microarray technologies (oligo- and cDNA).
- Analysis of circulating white blood cells (WBCs) as representative cells for monitoring systemic CVD consequences.
Main Results:
- Microarrays provide a comprehensive cell phenotype by measuring thousands of mRNA transcripts.
- Circulating WBCs are exposed to systemic risk factors and involved in CVD-related inflammation.
- Methodological considerations, including cell population choice and reproducibility, are discussed.
Conclusions:
- Circulating WBCs offer potential for improved patient phenotyping in CVD.
- Gene expression profiling of WBCs (transcriptomics) shows promise for personalized CVD management.
- Further exploration of circulating cell transcriptomics in CVD is supported by existing evidence.
Abstract:
Prevention of cardiovascular disease (CVD) remains a major health issue in the Western world. The diagnostic and therapeutic approach is currently based on risk factor assessment and treatment, which adequately predicts CVD at population level, but not at the level of a single individual. This may arise from the fact that the stage and activity of complex disease states are not likely to be captured by a single parameter or a small set of markers and thus may need a more complex representation. The aim of this review is to explore the possibility of pursuing the use of high-throughput gene expression profiling as a way to improve diagnosis, prognosis and monitoring of the disease. Novel chip-based techniques such as oligo- and cDNA microarrays can measure the abundance of thousands of mRNA transcripts in parallel and thus provide a comprehensive picture of the cell phenotype. Circulating white blood cells (WBCs), which are exposed to the systemic environment (including the risk factors) and are directly involved in the low-grade chronic inflammation related to CVD, have the potential to be used in this context to improve phenotyping of the patient. The paper reviews conceptual limitations in the use of risk factors and biomarkers, and shows the rationale beyond the possible use of circulating WBCs or subpopulations as representative cells to monitor systemic consequences of CVD. Methodological issues in performing microarray analysis of WBCs are also addressed, including controversies related to the choice of adequate cell populations and reference samples. Reproducibility and challenges occurring in the definition of a disease-specific gene panel are also discussed. The available proofs of principle from the literature presented in the last section of the review further support exploration of the application of circulating cell transcriptomics in CVD.
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