Exploring the causal relationship between BMI and psychiatric disorders using two-sample Mendelian randomization
Bibliographic record
Abstract
Abstract Background The study aimed to assess the causal relationship using two-sample Mendelian randomization analyses of BMI and five classic psychiatric disorders (depression, bipolar disorder, schizophrenia, autism, and anxiety disorder) in sequence. Methods Data related to BMI, depression, bipolar disorder, schizophrenia, autism, and anxiety disorders were downloaded from the GWAS database, and based on the genetic variation associated with each data were analyzed by utilizing five methods: MR Egger, Weighted median, Inverse variance weighted, Simple mode, Weighted mode, and five methods, while Mendelian randomization analysis between two samples was performed. Results Under IVW analysis, a positive causal relationship was found between BMI and depression (OR: 1.009, 95% CI: 1.002–1.016, P = 0.009) as well as bipolar disorder (OR: 1.001, 95% CI: 1.001–1.002, P = 0.002). Additionally, a negative causal relationship was found between BMI and schizophrenia (OR: 0.702, 95% CI: 0.560–0.881, P = 0.002). However, no causal relationship was found between BMI and autism (OR: 1.114, 95% CI: 0.972–1.278, P = 0.120) or anxiety disorders (OR: 1.000, 95% CI: 0.998–1.001, P = 0.630). Conclusion A causal relationship between BMI in depression, bipolar disorder, and schizophrenia. Maintaining BMI within the range of normal indicators is important not only for physical health care, but also for the prevention of psychiatric disorders.
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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 | 0.028 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".