Proteomic Profiling of IGFBP2: Modulation and Biomarker Potential in Atherosclerotic Cardiovascular Disease Prevention
Bibliographic record
Abstract
The impact of population growth and aging has contributed to a continued increase in the absolute number of people living with atherosclerotic cardiovascular disease (ASCVD).1 In Canada specifically, the 5-year prevalence rates for all subtypes of ASCVD have increased from 43.7 per 1000 individuals between 2004 and 2008 to 69.1 per 1000 individuals between 2013 and 2017, concurring with the global trend.2 With the increasing burden of ASCVD in Canada, medical professionals cannot afford to misidentify high-risk individuals. Using existing research on proteomics, we have highlighted a potential novel biomarker for ASCVD development: Insulin-like Growth Factor Binding Protein 2 (IGFBP2). Previous proteomic research focused on four groups to determine disease progression: (1) extracellular matrix proteins, (2) lipid-binding proteins and proteins associated with metabolism, (3) proteins associated with inflammation, and (4) phagocytic ligands and receptors of apoptotic cells.3 IGFBP2 impacts the vascular smooth muscle cells (VSMC) of arteries by modulating the bioavailability of the insulin-like growth factor 1 (IGF-1), which causes VSMC hypertrophy.4 Individuals with pre-existing cardiomyopathies have displayed higher levels of IGFBP2 and subsequent higher rates of mortality.5 In healthy individuals, higher IGFBP2 levels correlate with decreased arterial stiffness and lower low-density lipoprotein (LDL) cholesterol levels, which are used to determine ASCVD severity.6 As such, the uncertainty surrounding the relationship between IGFBP2 and ASCVD remains unknown; however, further investigation is pertinent as IGFBP2 presents as a promising biomarker due to its association with ASCVD-related effects and bioavailability. We propose further exploration of IGFBP2’s role in different stages of ASCVD and its potential as a therapeutic target through proteomics technology.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".