Application of leukocyte transcriptomes to assess systemic consequences of risk factors for cardiovascular disease
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".