Antagonism of the prostaglandin D <sub>2</sub> receptor 1 suppresses nicotinic acid-induced vasodilation in mice and humans
Bibliographic record
Abstract
Nicotinic acid (NA) is commonly used to treat dyslipidemia, but it elicits an adverse effect, termed flushing, which consists of cutaneous vasodilation with associated discomfort. An animal model of NA-induced flushing has been established in mice. As in humans, NA stimulated vasodilation in a dose-dependent manner, was associated with an increase of the vasodilatory prostaglandin (PG) D2 in plasma and could be blocked by pretreatment with aspirin. Two PGD2 receptors have been identified: PGD2 receptor 1 (DP1, also called DP) and PGD2 receptor 2 (DP2, sometimes termed CRTH2). DP2 does not mediate NA-induced vasodilation; the DP2-specific agonist DK-PGD2 (13,14-dihydro-15-keto-PGD2) did not induce cutaneous vasodilation, and DP2-/- mice had a normal vasodilatory response to NA. By contrast, BW245C, a DP1-selective agonist, induced vasodilation in mice, and MK-0524, a DP1-selective antagonist, blocked both PGD2- and NA-induced vasodilation. NA-induced vasodilation was also studied in DP1+/+, DP1+/-, and DP1-/- mice; although NA-induced vasodilation depended almost completely on DP1 in female mice, it depended only partially on DP1 in male mice. The residual NA-induced vasodilation in male DP-/- mice was aspirin-sensitive. Thus, in the mouse, DP1 appears to be an important component involved in NA-induced vasodilation, but other cyclooxygenase-dependent mechanisms also may be involved. A clinical study in healthy men and women demonstrated that treatment with MK-0524 reduced the symptoms of flushing and the increase in skin perfusion after the administration of NA. These studies suggest that DP1 receptor antagonism may be an effective means to suppress NA-induced flushing in humans.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".