Letter to the Editor From Spence: [Prevalence and Characteristics of Low-Renin Hypertension in a Primary Care Population]
Bibliographic record
Abstract
There is an important conceptual omission in the review of low-renin hypertension by Shah et al [1]. In focusing on mineralocorticoid receptor antagonists as the appropriate treatment for low-renin hypertension, they ignore the important existence of 2 streams of low-renin hypertension: low renin/high aldosterone and low renin/low aldosterone. The former represents a primary aldosteronism (PA) phenotype, the latter a Liddle syndrome phenotype, with excess activity of the renal epithelial sodium channel. The reason this is important is that the treatment is different: mineralocorticoid antagonists are the appropriate treatment for the PA phenotype, whereas amiloride is the appropriate treatment for the Liddle phenotype [2]. (Triamterene, a possible alternative, is probably nephrotoxic [3]). A PA phenotype is common, as variants of at least 6 genes contribute to hyperaldosteronism. (CYP11B2, KCNJ5, ATP1A1, ATP2B3, CACNA1D, and ARMC5) [4]. Although true Liddle syndrome due to variants of SCNN1B/ENaC is rare, there are polymorphisms of at least 5 other genes that result in the Liddle phenotype: GRK, NEDD4L, CYP4A11, NPPA, UMOD, and perhaps others [4]. A Liddle phenotype is far more common than most physicians suppose. In a study in China [5], “Among patients with systolic pressure ≥180 mmHg ± diastolic pressure ≥ 100 mmHg stimulated only by diuretics, the phenotypes were 25% Liddle, 38% Primary Aldosteronism, 8.7% renal, and 28.3% mixed.” [5]. In the Jackson Heart Study in Mississippi, “15.9% had the Liddle phenotype and 9.3% had the PA phenotype. Amiloride would therefore have been the appropriate therapy for 1.68-fold more patients than an aldosterone antagonist. This is why simply adding an aldosterone antagonist in patients with uncontrolled hypertension, as recommended in guidelines, would miss an important opportunity for better BP control” [6]. primary aldosteronism
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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.021 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".