Nutrition and the Prevalence of Dementia in Mainland China, Hong Kong, and Taiwan: An Ecological Study
Bibliographic record
Abstract
BACKGROUND: Western diets are associated with obesity, vascular diseases, and metabolic syndrome and might increase dementia risk in later life. If these associations are causal, those low- and middle-income countries experiencing major changes in diet might also see an increasing prevalence of dementia. OBJECTIVE: To investigate the relationship of dietary supply and the prevalence of dementia in mainland China, Hong Kong, and Taiwan over time using existing data and taking diagnostic criteria into account. METHODS: Estimated total energy supply and animal fat from the United Nations was linked to the 70 prevalence studies in mainland China, Hong Kong, and Taiwan from 1980 to 2012 according to the current, 10 years, and 20 years before starting year of investigation. Studies using newer and older diagnostic criteria were separated into two groups. Spearman's rank correlation was calculated to investigate whether trends in total energy, animal fat supply, and prevalence of dementia were monotonically related. RESULTS: The supply of total energy and animal fat per capita per day in China increased considerably over the last 50 years. The original positive relationship of dietary supply and dementia prevalence disappeared after stratifying by newer and older diagnostic criteria and there was no clear time lag effect. CONCLUSION: Taking diagnostic criteria into account, there is no cross-sectional or time lag relationship between the dietary trends and changes in dementia prevalence. It may be too early to detect any such changes because current cohorts of older people did not experience these dietary changes in their early to mid-life.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".