Influence of PCDH9 (rs9540720) and narcissistic personality traits on the incidence of major depressive disorder in Chinese first-year university students: findings from a 2-year cohort study
Bibliographic record
Abstract
Objective: The objective of this study was to explore the influence of the polymorphism of the protocadherin 9 ( PCDH9 ) gene and the narcissistic personality trait (NPT) on the risk of major depressive disorder (MDD) in Chinese first-year university students. Methods: A 2-year cohort study was conducted among Chinese first-year university students who were enrolled in 2018 from two universities in Shandong Province, China. The snapshot technique was used to detect the genotypes of PCDH9 (rs9540720). The Chinese version of the Composite International Diagnostic Interview was used for the MDD assessment. The NPTs were measured by 11 items based on DSM-IV. Patient Health Questionnaire-9 and the Beck Anxiety Inventory were used to assess depressive and anxiety symptoms, respectively. Logistic regression modeling was carried out to examine the relationship between rs9540720, NPTs, and the incidence of MDD. Results: A total of 5,327 students participated in the baseline and follow-up studies and provided their blood samples. PCDH9 (rs9540720) (OR GG+GA = 2.33, 95% CI: 1.35–4.02) and NPTs (OR 5–9 = 2.26, 95% CI: 1.40–3.64) increased the risk of MDD onset. There was no multiplicative interaction between NPTs and Rs9540720 (OR = 1.51, 95% CI: 0.30–7.63). Furthermore, there was no additive interaction between them (RERI = 2.40, 95% CI: –0.82–5.62; AP = 0.47, 95% CI: –0.04–0.97; and S = 2.37, 95% CI: 0.54–10.33). Conclusion: PCDH9 (rs9540720) and more NPTs are the risk factors for the incidence of MDD in Chinese first-year university students.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".