Estimating effects of serum vitamin B12 levels on psychiatric disorders and cognitive impairment: a Mendelian randomization study
Bibliographic record
Abstract
BACKGROUND: Vitamin B12 deficiency can lead to pernicious anemia and has been associated with various neuropsychiatric diseases and cognitive decline. However, it is unclear whether increasing serum vitamin B12 levels can help to prevent the onset of psychiatric disorders and cognitive impairment in the general population. METHODS: Leveraging large-scale genome-wide association studies (GWASs), we conducted Mendelian randomization (MR) and sensitivity analyses to estimate the potential effects of serum vitamin B12 levels on eight psychiatric disorders, educational attainment and cognitive performance. We conducted additional MR analyses utilizing within-sibship studies to mitigate potential residual confounding effects. RESULTS: ). In contrast, MR estimates of vitamin B12 effects on all eight psychiatric disorders, educational attainment and cognitive performance largely overlap with the null. For example, a one standard deviation increase in genetically predicted vitamin B12 levels is associated with an OR of 1.02 for depression (95% CI: 1.00 - 1.04; p-value = 0.11), a 0.0077 standard deviation increase in educational attainment (95% CI: -0.010 - 0.025; p-value = 0.39) and a 0.013 standard deviation increase in cognitive performance (95% CI: -0.0088 - 0.035; p-value = 0.24). No significant associations are identified in sensitivity analyses excluding pleiotropic genetic instruments or MR analyses based on within-sibship studies. CONCLUSIONS: Our findings suggest that increasing overall vitamin B12 levels may not meaningfully protect against the investigated psychiatric disorders or cognitive impairment in the general population.
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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 | 0.086 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".