A Deficit in Peripheral Serotonin Levels in Major Depressive Disorder but Not in Chronic Widespread Pain
Bibliographic record
Abstract
OBJECTIVES: It has been proposed that serotonin dysfunctions underlie the pathophysiology of various mood disorders (including major depressive disorder, MDD) and chronic pain conditions characterized by deficient pain inhibition, such as fibromyalgia (FM). There is reliable data showing that serotonin disturbances are involved in the pathophysiology of MDD. However, in the case of FM, results published so far are less consistent. Therefore, the current cross-sectional study sought to measure plasma serotonin levels in FM patients, MDD patients, and healthy controls (HC). METHODS: Twenty-nine FM patients, 17 MDD patients, and 57 HC were recruited who did not differ in terms of age, sex, and the presence or absence of a regular menstrual cycle. Plasma samples were analysed with mass spectrometry. RESULTS: Serotonin levels were decreased in MDD patients, relative to FM patients and HC. Post hoc analyses showed that serotonin levels were decreased in FM patients taking antidepressants, relative to HC, but not in drug-free FM patients. Moreover, serotonin levels were negatively correlated with mood symptoms across groups. DISCUSSION: Our results further confirm that MDD is associated with decreased serotonin levels, but that serotonin levels are not altered in FM per se, and suggest that 5-Hydroxytryptamine is related to mood symptoms in these patient groups. Our results also suggest that the taking of antidepressant is a major confound to consider when studying serotonin functioning in FM. The long-term use of antidepressants in FM may lead to serotonin depletion. Conversely, serotonin depletion may be before the taking of antidepressants in FM.
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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".