Thyroid Hormone Mediates the Effect of Antidepressants on Cognitive Function in Patients with Depression: A Mediation Analysis in a Longitudinal Study
Bibliographic record
Abstract
Abstract Background: Patients with depression frequently experience cognitive impairment. Our purpose is to determine whether thyroid hormones mediate the effect of depression on cognitive impairment. Methods: A total of 119 depressed patients were enrolled (mean age 32 years, 56.30% female). The Montreal Cognitive Assessment Scale, the 17-item Hamilton Depression Scale, and thyroid hormone levels, including free triiodothyronine (FT3), free thyroxine (FT4), and thyroid-stimulating hormone (TSH), were evaluated at intervals of 8 weeks. In order to describe the temporal relationship between depression and cognitive impairment, we initially used cross-lagged panel analysis. After that, linear regression analysis was utilized to show how depression and thyroid hormones are related to one another. To further investigate the causal role of thyroid hormones in depression and cognitive impairment, a causal mediation model was created. Results: The cross-lagged panel analysis showed that there was a significant cross-lagged path coefficient from baseline depression to follow-up cognition(β=－0.284, P=0.002) . Baseline depression had an impact on FT3 (F = 1.880, P＜0.05) and FT4 (F = 2.466, P＜0.05), according to a linear regression analysis. Baseline depression were affected by baseline FT4 ( = 0.316, t = 2.687, P＜0.05). The link between baseline depression and follow-up cognitive performance was revealed to be partially mediated by serum FT4 levels, according to the causal mediation analysis (a=0.008, se=0.004, p=0.022, CI=0.001/0.016). Conclusion: Serum FT4 levels may be biological markers of cognitive impairment in patients with depression and may mediate the effect of depression on cognitive impairment.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".