The Role of Growth Hormone in Depression: A Human Model
Bibliographic record
Abstract
Background Although the relationship between acromegaly and depression has been ascribed to the effects of chronic disease, the role of growth hormone (GH), and insulin like growth factor-1 (IGF-1) is not clear. Objective To determine whether related hormones levels in acromegalics are correlated with depressive symptoms and whether these symptoms are ameliorated following surgery. Materials and Methods A prospective cohort study was conducted on patients diagnosed with acromegaly (n = 15) or non-functioning pituitary adenomas (NFPA; n = 20, as controls) and undergoing first-time surgery, who completed the Center for Epidemiological Studies Depression (CES-D) questionnaire both pre-surgery and post-surgery. The primary outcome was the patient’s severity of depression symptomatology using the CES-D score; GH, IGF-1 levels, and tumor characteristics were also measured. Results Hormone levels (GH and IGF-1) and depression scores in acromegaly patients showed significant reductions following surgery (p < 0.05). The average change in CES-D score was 5.73 ± 2.58 (mean ± SE). A moderate correlation was found between GH levels and CES-D scores (r = 0.52, p < 0.01). The depressed affect subscale accounted for the most improvement in CES-D scores postoperatively and correlated most highly with GH levels. We did not find similar declines in the matched cohort of NFPA patients. Conclusion and Relevance Surgical resection of the pituitary tumor in acromegaly patients leads to reduction in GH levels that is correlated with reduction in CES-D scores. The results suggest a role for GH in depression and provide a stronger foundation on which to build the hypothesis that GH impacts affect. The study also suggests that hormones should be factored into the matrix that entails the neuro-biological underpinnings of depressive disorders. Future work could explore the mechanisms involved, further brain and neuropeptide interactions, and, novel potential therapeutic targets in depressive and other mental health disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".