Race, Medicine, and the Science behind BiDil: How ACE-Inhibition Took the Fall for the First Ethnic Drug
Bibliographic record
Abstract
A substantial biomedical literature has accumulated around the question of racial differences in the response to treatment with angiotensin converting enzyme (ACE) inhibitors for hypertension and congestive heart failure. African-origin populations are often asserted to be “low renin”, and therefore to have blunted response to agents that interfere with the renin-angiotensin system. Although the US Food and Drug Administration (FDA) rejected a combination of hydralazine and isosorbide dinitrate as a general heart failure treatment in 1997, this literature on Black resistance to ACE-inhibition therapy for heart failure was used to argue for a race-specific approval for this drug. Specifically, a paper by Exner and colleagues published in the New England Journal of Medicine in 2001 reported that ACE-inhibition reduced hospitalization for White patients, but not for Black patients. The Exner et al paper was used to argue for conducting a randomized trial in Black patients only, the successful completion of which led to FDA approval of the hydralazine and isosorbide dinitrate combination (known as BiDil) as a race-specific therapy in 2005. We re-analyze the data in the 2001 Exner et al study, and show that it is not well suited for answering the question of differential response by race. Even so, the published analysis ignored important facets of the data in order to arrive at the stated conclusion of a race-specific response. Black subjects were recruited mostly in a few regions, and were medically distinct from white patients in terms of clinical measures such as hypertension, diabetes and prescription drug history. Overall, Black subjects had a high risk of the outcomes, and the effect of treatment varied widely by clinical center or by region. The stated conclusion by Exner et al of a race-specific response to ACE inhibition is therefore suspect, as is the use of this conclusion to support the notion of race-specific therapies, both in the specific case of BiDil and in general.
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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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 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".