A Novel Enzymatic Hydrolysis Method for Urine Aldosterone Quantification: A Case for Reassessing Clinical Cut-Offs of Primary Aldosteronism
Bibliographic record
Abstract
BACKGROUND: Primary aldosteronism (PA) is a common endocrine cause of secondary hypertension. The aldosterone/renin ratio is an important tool for PA screening, and dynamic testing in serum or urine is used to confirm the diagnosis. While LC-MS/MS is considered the gold standard for testing, there is significant interlaboratory variability between the extraction procedures, which can impact diagnostic interpretation. To help overcome this, we present a simple and accurate LC-MS/MS method for the quantification of both serum and urine aldosterone using a novel enzymatic hydrolysis procedure. METHODS: Serum and urine aldosterone was extracted and measured by LC-MS/MS. Urine-conjugated aldosterone glucuronide was hydrolyzed using a genetically modified glucuronidase enzyme. The assay precision, accuracy, limit of quantification, recovery, and carryover were evaluated and the new assay cut-offs were proposed. RESULTS: The liquid chromatography method allowed for adequate separation of the aldosterone peak from closely eluting peaks. Significant in vitro aldosterone loss was observed during acid-catalyzed hydrolysis of urine, which was corrected with the addition of the internal standard to the urine before the hydrolysis step. Glucuronidase catalyzed hydrolysis of urine aldosterone glucuronide displays good correlation with the corrected acid-catalyzed hydrolysis. Serum aldosterone showed good agreement with reference values and the consensus range reported for external quality assessment specimens. CONCLUSION: A simple, fast, and highly accurate method for the detection of serum and urine aldosterone has been developed. The proposed novel enzymatic procedure allows for short hydrolysis time and compensates for urine aldosterone loss during the hydrolysis step.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".