Co-occurrence between C1 esterase inhibitor deficiency and autoimmune disease: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: Hereditary angioedema (HAE) is caused by a SERPING1 gene defect resulting in decreased (Type I) or dysfunctional (Type II) C1 esterase inhibitor (C1-INH). The prevalence of autoimmune diseases (ADs) in patients with HAE appears to be higher than the general population. A systematic literature review was conducted to examine the co-occurrence between HAE and ADs. METHODS: PubMed/EMBASE were searched for English-language reviews, case reports, observational studies, retrospective studies, and randomized controlled trials up to 04/15/2018 (04/15/2015-04/15/2018 for EMBASE) that mentioned patients with HAE Type I or II and comorbid ADs. Non-human or in vitro studies and publications of C1-INH deficiency secondary to lymphoproliferative disorders or angiotensin-converting-enzyme inhibitors were excluded. RESULTS: Of the 2880 records screened, 76 met the eligibility criteria and 155 individual occurrences of co-occurring HAE and AD were mentioned. The most common ADs were systemic lupus erythematosus (30 mentions), thyroid disease (21 mentions), and glomerulonephritis (16 mentions). When ADs were grouped by MedDRA v21.0 High Level Terms, the most common were: Lupus Erythematosus and Associated Conditions, n = 52; Endocrine Autoimmune Disorders, n = 21; Gastrointestinal Inflammatory Conditions, n = 16; Glomerulonephritis and Nephrotic Syndrome, n = 16; Rheumatoid Arthritis and Associated Conditions, n = 11; Eye, Salivary Gland and Connective Tissue Disorders, n = 10; and Immune and Associated Conditions Not Elsewhere Classified, n = 5. CONCLUSIONS: Based on literature reports, systemic lupus erythematosus is the most common AD co-occurring with HAE Type I and II. Cause and effect for co-occurring HAE and AD has not been clinically established but could be related to lack of sufficient C1-INH function.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".