Melatonin rhythm disorder is more pronounced in major depressive disorder with non-suicidal self-injury
Bibliographic record
Abstract
Introduction Disruptions in melatonin (MT) rhythms have been linked to major depressive disorder (MDD) and may be further associated with non-suicidal self-injury (NSSI). This study investigates whether MDD patients with NSSI exhibit more pronounced MT rhythm disturbances and whether these disruptions correlate with NSSI-related thoughts and the motivation to cease self-injury. Methods The study included 100 participants aged 14–24 years, including 30 healthy controls (HC) and 70 inpatients diagnosed with MDD. The MDD group was further divided into those with NSSI (NSSI group, n = 35) and those without NSSI (Non-NSSI group, n = 35). Salivary MT levels were measured at six intervals (12 a.m., 8 a.m., 11 a.m., 1 p.m., 4 p.m., and 10 p.m.) using enzyme-linked immunosorbent assay (ELISA). The Ottawa Self-Injury Inventory (OSI) assessed NSSI perception and motivation in the NSSI group. Results Melatonin levels were significantly lower across all six time points in the NSSI group compared to both the Non-NSSI and HC groups (P < 0.05), and MT circadian rhythms were notably absent in the NSSI group. Correlational analysis revealed specific associations between MT levels and NSSI behavior, with MT levels at 1 PM positively correlated with invasive self-harm impulses (r = 0.487, P = 0.003, 95% CI: 0.141 to 0.834) and MT levels at 8 a.m. and 11 a.m. inversely correlated with the desire to stop self-injury (r = −0.427, P = 0.010, 95% CI: −0.774 to −0.081; r = 0.348, P = 0.040, 95% CI: 0.002 to 0.695, respectively). Conclusion Lower MT levels and disrupted circadian rhythms are associated with NSSI in MDD patients, highlighting a potential link between circadian dysfunction and self-injurious behaviors. Further research is needed to clarify the underlying mechanisms of this association.
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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 | 0.001 |
| 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 | 0.000 |
| 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".