Exploring the Association between Serum BDNF and Attempted Suicide
Bibliographic record
Abstract
Suicide is a leading cause of death and a significant public health concern. Brain-derived neurotrophic factor (BDNF), a protein important to nervous system function, has been implicated in psychiatric disorders and suicidal behaviour. We investigated the association between serum levels of BDNF and attempted suicide in a sample of 281 participants using a case-control study design. Participants were recruited from clinical and community settings between March 2011 and November 2014. Cases (individuals who had attempted suicide) (n = 84) were matched on sex and age (within five years) to both psychiatric controls (n = 104) and community controls (n = 93) with no history of suicide attempts. We collected fasting blood samples, socio-demographic information, physical measurements, and detailed descriptions of suicide attempts. We used linear regression analysis to determine the association between BDNF level (dependent variable) and attempted suicide (key exposure variable), adjusting for age, sex, body mass index, current smoking status, and antidepressant use. 250 participants were included in this analysis. In the linear regression model, attempted suicide was not significantly associated with BDNF level (β = 0.28, SE = 1.20, P = 0.82). Our findings suggest that no significant association exists between attempted suicide and BDNF level. However, the findings need to be replicated in a larger cohort study.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".