Lower Central Serotonergic Responsivity Is Associated With Preclinical Carotid Artery Atherosclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Central nervous system serotonergic neurotransmission appears to play a role in mood disorders, eating habits, and sleep, and also modulates blood pressure and metabolism. This investigation tested a hypothesized association between central serotonergic functioning and preclinical atherosclerosis. METHODS: Subjects were 244 adults 30 to 55 years of age and free of clinically evident vascular disease (52% men, 84% white). Central serotonergic responsivity was measured as the rise in serum prolactin concentration (area under the curve) over 2.5 hours, adjusted for baseline prolactin, after citalopram administered intravenously at 0.33 mg/kg lean body weight. Carotid artery morphology served as a marker of preclinical atherosclerosis, and carotid artery intima-media thickness and plaque occurrence were determined by B-mode ultrasonography. RESULTS: In linear regression models including age, gender, race, and citalopram concentration, a 1 SD lower prolactin response was associated with greater maximum intima-media thickness (+0.016 mm; P=0.006) and with greater mean intima-media thickness (+0.009 mm; P=0.03). The odds ratio for carotid artery plaque corresponding to a 1 SD decrease in prolactin response, adjusted for age, race, sex, and citalopram concentration, was 1.47 (95% CI, 0.98 to 2.19; P=0.06). The metabolic syndrome mediated (P<0.01), but did not fully account for, the association between lower prolactin response and greater maximum intima-media thickness. CONCLUSIONS: In this young and relatively healthy sample, blunted prolactin response to citalopram was associated with carotid artery thickening, suggesting that individual differences in central serotonergic responsivity are inversely related to preclinical vascular disease.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".