Impact of Age, Sex, and Race on Primary Aldosteronism Test Interpretations
Bibliographic record
Abstract
BACKGROUND: New guidelines recommend testing for primary aldosteronism (PA) in all people with hypertension. Interpreting population-based results for PA will require understanding the influence of demographic characteristics. METHODS: 858 adults from across the US meeting traditional guideline criteria for PA testing underwent testing. The influence of demographic factors on test result interpretations was assessed after multivariable adjustment. RESULTS: Mean age was 62±11 years, with 54.6% women, 14% Black, and 22.9% Hispanic. Independent of antihypertensive medications and clinical comorbidities, participants aged ≥70 years had 24% lower aldosterone ( P <0.05), 48% lower renin activity ( P <0.001), and trended toward higher aldosterone-to-renin ratio (52% higher; P =0.14), compared with those <50 years. Aldosterone levels were higher in women compared with men across the lifespan. Black participants had 81% higher aldosterone-to-renin ratio than White participants ( P <0.001), primarily due to lower renin ( P <0.005). These demographic factors influenced the ultimate PA testing interpretation: participants ≥70 years old had 19% higher odds of a positive test than those <50 years old ( P =0.002), and Black participants had 22% higher odds of a positive test than White participants ( P <0.001). CONCLUSIONS: In this large, diverse, nationwide sample, older and Black participants had higher aldosterone-to-renin ratio and higher odds of testing positive for PA, and women had higher aldosterone levels than men across the lifespan. With newly expanded indications for PA testing, a greater appreciation of the influence of demographic characteristics on PA test results will be required.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".