Digging Deeper Into Cardiovascular Plasma Proteomics: Opportunities and Limitations of Current Platforms
Bibliographic record
Abstract
Coronary artery disease remains the leading cause of death worldwide. One of the greatest developments in preventive cardiology has been the identification and treatment of standard modifiable risk factors associated with coronary artery disease. However, despite advances in the management of standard modifiable risk factors, there is an escalating number of patients who continue to present with acute coronary syndromes, a trend that is particularly concerning given the decreasing age-adjusted incidence rates of these conditions. This persistent clinical challenge underscores the urgency to explore alternative approaches for early detection and improved risk stratification. In recent years, the emergence of proteomics technologies has brought forth promising avenues for the discovery of novel biomarkers that hold the potential to revolutionize the timely detection and management of coronary artery disease. Proteomics enables the high throughput and often unbiased analysis of protein abundance, modifications, and interactions within pathways relevant to cardiovascular disease pathogenesis. Of particular importance is the capability to detect low-abundance proteins including those with currently unknown functions. While the functional assessment of these proteins aligns more with mechanistic studies, their role in biomarker discovery is equally important. Such detection may provide new insights into cardiac pathophysiology, including potential new markers for early disease detection and risk assessment. Although the latest proteomics technology and bioinformatic approaches do provide the opportunity for novel discoveries, understanding the limitations of each technology platform is important. This review provides an updated overview of major proteomic platforms and discusses their methodological strengths, constraints, and applications, using recent coronary artery disease studies as illustrative examples. By integrating proteomics data with clinical information, including advanced noninvasive imaging techniques and other omics disciplines, such as genomics and metabolomics, we can deepen our understanding of disease mechanisms and improve risk stratification. Although the discovery of novel biomarkers represents a significant step forward in the field, their true clinical value is contingent upon their rigorous validation in clinical trials and implementation studies. With our current capabilities and emerging advancements, we are well-positioned to advance proteomics-guided precision medicine in cardiovascular care over the coming decade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".