Genotype-first analysis of a generally healthy population cohort supports genetic testing for diagnosis of hereditary angioedema of unknown cause
Bibliographic record
Abstract
BACKGROUND: Hereditary angioedema (HAE) is a potentially life-threatening group of conditions that is often underdiagnosed or misdiagnosed. As HAE is typically diagnosed by detecting C1 inhibitor deficiency, there is a critical need for methods that can identify affected individuals with normal C1 inhibitor. The recent discovery of associations between PLG K330E and ANGPT1 A119S and HAE of unknown genetic cause (HAE-U), has raised the possibility that genetic evaluation could be used to diagnose HAE-U in patients with unexplained angioedema or non-confirmatory laboratory testing. CASE PRESENTATION: We analyzed genome sequences from a generally healthy population cohort of 2820 adults and identified PLG K330E in one individual. Subsequent review of this participant's medical history revealed symptoms clinically attributed to allergy of unknown etiology but that are consistent with published descriptions of HAE patients carrying the PLG K330E variant. The participant, a 31 year old female, reported lip and tongue angioedema, without wheals, which did not respond to treatment with steroids or antihistamines. CONCLUSIONS: The genotype-first approach demonstrated that detection of PLG K330E in undiagnosed or misdiagnosed individuals can identify patients actually affected with HAE-U. The genetic diagnosis will facilitate selection of appropriate treatment, discontinuation of therapies ineffective for this condition, and timely diagnosis of affected family members. The results support a role of PLG K330E in the pathogenesis of HAE and suggest that genetic testing be considered as an approach to diagnose patients with unexplained angioedema.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".