Correlation between gabapentin and depression: A study from the NHANES and FAERS databases
Bibliographic record
Abstract
Post-marketing surveillance has indicated an association between gabapentin use and an increased risk of depression. However, observational findings on this relationship have been inconsistent. This study aims to investigate the correlation between gabapentin exposure and depression. We analyzed data from the National Health and Nutrition Examination Survey and the Food and Drug Administration Adverse Event Reporting System in the United States from 2011 to 2018. Descriptive statistical analysis, multivariate logistic regression, and linear regression were employed to explore the association between gabapentin use and depression. Our analysis revealed that gabapentin users had a higher risk of depression. In a multivariate logistic regression model, the odds ratio was 1.8 (95% confidence interval: 1.3-2.4; P < .001), indicating a significant association when accounting for demographics and lifestyle factors. Similarly, in a linear regression model, the depression score was significantly higher (β = 4.0; 95% confidence interval: 3.0-5.0; P < .001) among gabapentin users. This risk was notably greater in women and individuals who slept <7 hours. The Food and Drug Administration Adverse Event Reporting System database included 9951 adverse reactions, with 1165 reports of psychiatric-related adverse events, including depression, constituting 11.71% of the total reports. Gabapentin use is associated with an increased risk of depression. It is crucial for clinicians to monitor patients' mental health closely when prescribing gabapentin and to provide timely intervention if needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".