The risk for major depression conferred by childhood maltreatment is multiplied by <i>BDNF</i> and <i>SERT</i> genetic vulnerability: a replication study
Bibliographic record
Abstract
BACKGROUND: There is limited evidence for a moderating role of both serotonin transporter (SERT) and brain-derived neurotrophic factor (BDNF) genes on the risk for major depression (MD) developing after childhood maltreatment. However, research on this topic remains inconclusive, and there is a lack of data from longitudinal studies with large and representative population samples. Our study aimed to clarify whether, in the presence of previous childhood maltreatment, individuals carrying low functional alleles for both SERT 5-HTTLPR and BDNF Val66Met polymorphisms had a higher risk for MD. METHODS: We explored 2- and 3-way gene (SERT and BDNF) × environment (childhood maltreatment) interactions in a large sample of Spanish adults who were followed up over a 3-year period and assessed in person for both DSM-IV MD and exposure to childhood maltreatment. RESULTS: Our study included 2679 participants. Those with both the 5-HTTLPR s allele and the BDNF Met allele showed the highest risk of MD if they had previously experienced emotional (z = 2.08, p = 0.037), sexual (z = 2.19, p = 0.029) or any kind of childhood abuse (z = 2.37, p = 0.018). These 3-way interactions remained significant regardless of whether the 5-HTTLPR triallelic or the 5-HTTLPR biallelic polymorphisms were included in the analyses. LIMITATIONS: Retrospective assessment of childhood maltreatment may have resulted in a moderate degree of recall bias. CONCLUSION: Our results confirm that the risk of depression conferred by childhood maltreatment is modified by variation at both SERT and BDNF genes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".