Lithium Therapy’s Potential to Lower Dementia Risk and the Prevalence of Alzheimer’s Disease: A Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Dementia is a neurodegenerative disease with insidious onset and progressive progression, of which the most common type is Alzheimer's disease (AD). Lithium, a trace element in the body, has neuroprotective properties. However, whether lithium can treat dementia or AD remains a highly controversial topic. Therefore, we conducted a meta-analysis. METHODS: A systematic literature review was conducted on PubMed, Embase, and Web of Science. Comparison of the effects of lithium on AD or dementia in terms of use, duration, and dosage, and meta-analysis to test whether lithium therapy is beneficial in ameliorating the onset of dementia or AD. Sensitivity analyses were performed using a stepwise exclusion method. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of included studies. We determined the relative risk (RR) between patient groups using a random-effects model. RESULTS: A total of seven studies were included. The forest plot results showed that taking lithium therapy reduced the risk of AD (RR 0.59, 95% confidence interval [CI]: 0.44-0.78) and is also protective in reducing the risk of dementia (RR 0.66, 95% CI: 0.56-0.77). The duration of lithium therapy was able to affect dementia incidence (RR 0.70, 95% CI: 0.55-0.88); however, it is unclear how this effect might manifest in AD. It is also uncertain how many prescriptions for lithium treatment lower the chance of dementia development. CONCLUSION: The duration of treatment and the usage of lithium therapy seem to lower the risk of AD and postpone the onset of dementia.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.061 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".