P053 ANGIOTENSIN II RECEPTOR TYPE 1 (AT1) AUTOANTIBODIES IN PATIENTS WITH PRIMARY ALDOSTERONISM
Bibliographic record
Abstract
Background: Primary aldosteronism (PA) is a common cause of hypertension and is characterized by excessive aldosterone secretion. Determination of PA subtype (i.e., unilateral vs. bilateral) is clinically important to inform treatment. Immune mechanisms may have a role in the pathogenesis of PA. The aim of this study was to examine whether angiotensin receptor type 1 (AT1) autoantibodies predict the underlying subtype of PA. Methods: We conducted a cross-sectional study of patients with PA who were referred for adrenal vein sampling (AVS). Patients had AT1 autoantibody titres measured. AT1 antibodies were defined as present when >17 U/mL, negative if <10 U/mL, and indeterminate if between 10-17 U/mL. AVS was used as a standard to determine PA subtype. The frequency of AT1 autoantibodies was determined according to PA subtype and major clinical predictors of lateralization. Results: 54 patients with successful AVS were included (mean age, 52.7 years; 46.3% male). Positive AT1 antibodies were detected in 14 (25.9%), negative antibodies in 32 (59.3%), and indeterminate antibodies in 8 (14.8%) patients. There was no significant association detected between AT1 antibody status and lateralization (p=0.39). There were no associations found between AT1 autoantibody status and any of the major clinical factors traditionally predictive of PA severity or prognosis, including sex (p=0.94), age (p=0.76), hypokalemia (p=0.91), estimated glomerular filtration rate (p=0.46), or aldosterone-to-renin ratio (p=0.58). Conclusion: AT1 autoantibodies are common in patients with PA; over a quarter of patients who underwent AVS in our study. However, AT1 autoantibodies were not predictive of PA subtype.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 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".