Melatonin as a Possible Stimulus to Unmask an Oxytocin-Deficient State in Hypopituitarism and Hypothalamic Damage
Bibliographic record
Abstract
CONTEXT: Increasing evidence supports the presence of oxytocin deficiency (OXT-D) in hypopituitarism and hypothalamic damage (HHD). Identifying an applicable and reliable test to diagnose OXT-D is an unmet need. Melatonin (MEL) might be a candidate for such a test as it regulates OXT release in animals. OBJECTIVE: This work aimed to examine the effects of melatonin on OXT release in HHD compared to healthy controls (HCs) and to describe the psychopathology, sexual function, and quality of life (QoL) and their associations with OXT. METHODS: This proof-of-concept study (NCT05319301) included 20 participants with HHD (11 women) and 20 HCs (11 women). Blood samples were collected over 120 minutes to assess plasma OXT. A linear mixed-effects regression model was used to evaluate the change in OXT in response to MEL in HHD compared to HCs. RESULTS: MEL significantly increased OXT at T90 vs T0 in HCs compared to the HHD group (difference 14.57 pg/mL 26% increase; 95% CI, 1.90-27.23; P = .02). The HHD group had more depression symptoms, alexithymia, impaired sexual function, and worse QoL compared to HCs. The mean percentage change in OXT from T0 to T90 was negatively associated with depressive and alexithymia symptoms in the HHD group and anxiety in both groups. CONCLUSION: The reduced OXT response after MEL in HHD supports the existence of an impaired OXT response at least in a subset of patients with HHD. The associations between OXT changes and psychopathology suggest its role in mood and QoL. These findings support further investigation into MEL's role as a diagnostic tool to address OXT-D.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".