A role for autoantibodies in atherogenesis
Bibliographic record
Abstract
An increased risk of cardiovascular disease (CVD) has long been recognized amongst people with autoimmune disease. It has been unclear whether this is due mainly to the ensuing treatment, particularly steroids, or whether some of this risk is due to the autoimmune process itself with subsequent inflammation. Indeed, a large body of evidence supports a role for chronic inflammation in atherogenesis, and autoantibodies have been identified as mediators in this complex inflammatory environment. Our aim is to carry out a systematic review of existing literature in order to formally establish the strength of the association between autoantibodies and atherosclerosis, amongst individuals without clinical autoimmune disease. An electronic search of five databases to June 2016 was performed by two independent reviewers. Inclusion criteria were analytical studies of adults, with at least two studies per autoantibody. Quality analysis was carried out using the Newcastle-Ottawa scale and the Cochrane Risk of Bias Quality Assessment Tool where appropriate. Where possible, studies were pooled using random effects models. Raised levels of anti-cardiolipin (odds ratio [OR] = 1.30; 95% CI: 1.15-1.49) and anti-oxidized low-density lipoprotein Immunoglobulin (Ig) G (OR = 1.25; 95% CI: 1.11-1.41), unspecified anti-cyclic citrullinated protein (OR = 3.09; 95% CI: 1.49-6.41) and anti-human heat shock protein 60 IgA (OR = 1.57; 95% CI: 1.15-2.16) were observed to increase the risk of cardiovascular outcomes. Alternatively, Anti-phosphorylcholine IgM (OR = 1.31; 95% CI: 1.14-1.50) conferred protection against CVD. Our results support an important role for autoantibodies in mediating cardiovascular events, independent of therapeutic treatments. Future research may focus on the presence of autoantibodies as markers of immune dysregulation and CVD risk.
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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 | 0.071 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".