Relationship of Anti-60 kDa Heat Shock Protein and Anti-Cholesterol Antibodies to Cardiovascular Events
Bibliographic record
Abstract
BACKGROUND: Several recent studies have indicated an association between key inflammatory mediators and atherosclerotic diseases. We evaluated whether high levels of antibodies against heat shock proteins and cholesterol (ACHA) predicted cardiovascular (CV) events. METHODS AND RESULTS: We used blood samples from the Heart Outcomes Prevention Evaluation (HOPE) study to conduct a nested case-control study of 386 cases with CV events and 386 age- and sex-matched HOPE study controls without events. We explored the relationship between anti-hsp antibodies, ACHA, and subsequent outcomes (incident myocardial infarction, stroke, or CV death) during a mean follow-up of 4.5 years using conditional logistic regression. High levels of anti-hsp65 antibodies (> or =90th percentile) predicted CV events (OR, 2.1; 95% CI, 1.2 to 3.9, P=0.01). Anti-hsp60 antibodies did not predict any event type, whereas incident stroke developed significantly less frequently in patients with high ACHA levels. Anti-hsp antibodies and ACHA did not correlate with inflammatory (fibrinogen, C-reactive protein, interleukin-6, intracellular adhesion molecule-1) or infectious markers (C pneumoniae or cytomegalovirus antibodies). Anti-hsp65 antibodies (> or =90th percentile) and fibrinogen (highest tertile) had a strong joint effect: patients with high concentrations of both had more CV events (OR, 5.5; 95% CI, 1.8 to 17.5, P=0.004) than patients with low levels of both. A similar joint effect (OR, 2.7; 95% CI, 1.3 to 5.7, P=0.01) was found for high levels of anti-hsp65 and presence of cytomegalovirus antibodies. CONCLUSIONS: Serum antibodies to hsp65 were associated with subsequent CV events in this study of high-risk patients, independent of conventional cardiovascular risk factors and other inflammatory markers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".