Relationship Between Ginger Consumption and Dementia/Mild Cognitive Impairment: A Cross‐Sectional Study in Shanghai
Bibliographic record
Abstract
ABSTRACT Previous studies have found some cognitive benefits from ginger consumption, but there are little data on this among older Chinese. To explore the relationship between ginger consumption and dementia and explore the possible mechanism of ginger consumption on cognitive decline. A total of 410 elderly patients with dementia and 2426 non‐dementia individuals were analyzed using data from the Shanghai Brain Health Foundation. Each participant's cognitive diagnosis was made by an attending psychiatrist, and their overall cognitive function was assessed by Montreal Cognitive Assessment (MoCA). The Food Frequency Questionnaire (FFQ) was used to investigate their consumption of ginger. To explore the possible mechanisms of ginger prevention of dementia, 408 non‐dementia patients (331 ginger consumers and 77 non‐ginger consumers) completed head MRI and plasma Alzheimer's disease (AD) biomarkers such as amyloid‐beta peptides (Aβ) 42, Aβ40, total tau (t‐tau), phosphorylated tau‐181 (p‐tau‐181), and neurofilament light chain (NfL). The incidence of dementia was found to be reduced by ginger consumption through multiple logistic regression analysis. Compared to non‐ginger consumers, ginger consumers had higher MoCA scores and lower plasma NfL and Aβ40 levels. Regression analysis and mediated models then showed that ginger consumption reduced plasma NfL concentrations, affecting overall MoCA scores. Ginger consumption may be a protective factor against dementia in elderly Chinese and may prevent cognitive decline by affecting plasma NfL concentration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".