Examining the Relationship between Trace Lithium in Drinking Water and the Rising Rates of Age-Adjusted Alzheimer’s Disease Mortality in Texas
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) mortality rates have steadily increased over time. Lithium, the current gold standard treatment for bipolar disorder, can exert neuroprotective effects against AD. OBJECTIVE: We examined the relationship between trace levels of lithium in drinking water and changes in AD mortality across several Texas counties. METHODS: 6,180 water samples from public wells since 2007 were obtained and averaged for 234 of 254 Texas counties. Changes in AD mortality rates were calculated by subtracting aggregated age-adjusted mortality rates obtained between 2000-2006 from those obtained between 2009-2015. Using aggregated rates maximized the number of counties with reliable mortality data. Correlational analyses between average lithium concentrations and changes in AD mortality were performed while also adjusting for gender, race, education, rural living, air pollution, physical inactivity, obesity, and type 2 diabetes. RESULTS: Age-adjusted AD mortality rate was significantly increased over time (+27%, p < 0.001). Changes in AD mortality were negatively correlated with trace lithium levels (p = 0.01, r = -0.20), and statistical significance was maintained after controlling for most risk factors except for physical inactivity, obesity, and type 2 diabetes. Furthermore, the prevalence of obesity and type 2 diabetes positively correlated with changes in AD mortality (p = 0.01 and 0.03, respectively), but also negatively correlated with trace lithium in drinking water (p = 0.05 and <0.0001, respectively). CONCLUSION: Trace lithium in water is negatively linked with changes in AD mortality, as well as obesity and type 2 diabetes, which are important risk factors for AD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".