Increased Prevalence of Obesity/Type 2 Diabetes and Lower Levels of Lithium in Rural Texas Counties May Explain Greater Alzheimer’s Disease Risk
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: To compare Alzheimer's disease (AD) mortality rates and coinciding risk factors in rural and urban Texas populations. METHODS: 155 Texas counties were divided into 73 rural and 82 urban areas using the U.S. Census Bureau definition of rurality. Changes in age-adjusted AD mortality across these counties were calculated using a 7-year aggregation model from 2000-2006 and 2009-2015. Data pertaining to gender, race, education, obesity, diabetes, physical inactivity, and lithium concentrations in tap water were also collected from readily available databases. RESULTS: Change in age-adjusted AD mortality was higher in rural counties (9.5±1.4) versus urban (5.9±1.1) over the time period examined. Similarly, obesity (30.2±0.2% ), diabetes (11.0±0.1% ), and physical inactivity (29.4±0.2% ) levels were significantly higher in rural populations compared to urban (29.1±0.2%, 9.7±0.1%, and 26.7±0.3, respectively). In contrast, the percent of population with some college education (40.1±0.7% ) was lower compared to urban (29.4±0.2% and 44.4±0.9%, respectively). Lithium concentrations in tap water was significantly lower in rural counties compared to urban (63.3±8.2 and 33.4±4.7μg/L, respectively). No significant differences were observed among females and however, we did find significant differences in the percent of African American and Hispanics. Correlational analysis uncovered a negative association between education status and AD mortality over time (r = -0.17). Further analysis controlling for physical inactivity, education, and trace lithium concentrations results in a loss of statistical significance. CONCLUSIONS: AD mortality rates are higher in rural counties when compared to urban counties, and this may be linked to greater physical inactivity, obesity, and diabetes, as well as lower trace lithium levels in tap water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".