Biomarkers to Guide Medical Therapy in Primary Aldosteronism
Bibliographic record
Abstract
Primary aldosteronism (PA) is an endocrinopathy characterized by dysregulated aldosterone production that occurs despite suppression of renin and angiotensin II, and that is non-suppressible by volume and sodium loading. The effectiveness of surgical adrenalectomy for patients with lateralizing PA is characterized by the attenuation of excess aldosterone production leading to blood pressure reduction, correction of hypokalemia, and increases in renin-biomarkers that collectively indicate a reversal of PA pathophysiology and restoration of normal physiology. Even though the vast majority of patients with PA will ultimately be treated medically rather than surgically, there is a lack of guidance on how to optimize medical therapy and on key metrics of success. Herein, we review the evidence justifying approaches to medical management of PA and biomarkers that reflect endocrine principles of restoring normal physiology. We review the current arsenal of medical therapies, including dietary sodium restriction, steroidal and nonsteroidal mineralocorticoid receptor antagonists, epithelial sodium channel inhibitors, and aldosterone synthase inhibitors. It is crucial that clinicians recognize that multimodal medical treatment for PA can be highly effective at reducing the risk for adverse cardiovascular and kidney outcomes when titrated with intention. The key biomarkers reflective of optimized medical therapy are unsurprisingly similar to the physiologic expectations following surgical adrenalectomy: control of blood pressure with the fewest number of antihypertensive agents, normalization of serum potassium without supplementation, and a rise in renin. Pragmatic approaches to achieve these objectives while mitigating adverse effects are reviewed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".