Association of autoantibodies to heat‐shock protein 60 with arterial vascular events in patients with antiphospholipid antibodies
Bibliographic record
Abstract
OBJECTIVE: Anti-heat shock protein 60 autoantibodies (anti-Hsp60) are associated with cardiovascular disease and are known to affect endothelial cells in vitro, and we have recently shown that anti-Hsp60 promote thrombosis in a murine model of arterial injury. Based on those findings, we undertook the present study to investigate the hypothesis that the presence of anti-Hsp60, alone or in combination with other thrombogenic risk factors, is associated with an elevated risk of vascular events. METHODS: The study population was derived from 3 ongoing cohort studies: 2 independent systemic lupus erythematosus (SLE) registries and 1 cohort comprising SLE patients and non-SLE patients. Data from a total of 402 participants were captured; 199 of these participants had had confirmed vascular events (arterial vascular events in 102, venous vascular events in 76, and both arterial and venous vascular events in 21). Anti-Hsp60 were detected by enzyme-linked immunoassay, and association with vascular events was assessed by regression analysis. RESULTS: Multiple regression analysis revealed that arterial vascular events were associated with male sex, age, and hypertension. Analyses of the vascular events according to their origin showed an association of anti-Hsp60 with arterial vascular events (odds ratio 2.26 [95% confidence interval 1.13-4.52]), but not with venous vascular events. Anti-Hsp60 increased the risk of arterial vascular events (odds ratio 5.54 [95% confidence interval 1.89-16.25]) in antiphospholipid antibody (aPL)-positive, but not aPL-negative, individuals. CONCLUSION: We demonstrate that anti-Hsp60 are associated with an increased risk of arterial vascular events, but not venous vascular events, in aPL-positive individuals. These data suggest that anti-Hsp60 may serve as a useful biomarker to distinguish risk of arterial and venous vascular events in patients with aPL.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".