Energy-adjusted dietary inflammatory index and cognitive function in Chinese older adults: a population-based cross-sectional study
Bibliographic record
Abstract
Diet can regulate systemic inflammation, which may play an important role in the development and progression of cognitive impairment and dementia. To explore the relationship between the dietary inflammatory potential and cognitive ability. A total of 2307 adults aged 60 years or older were recruited from the Fujian Provincial Hospital (Fujian, China). Dietary inflammatory properties were analyzed using the energy-adjusted dietary inflammatory index (E-DII). The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to assess cognitive function. Logistic regression and restricted cubic spline (RCS) were fit to assess the associations between variables. The MCI subjects with the highest E-DII scores had a higher risk of AD compared to subjects with the lowest E-DII scores (OR = 1.98, 95%CI = 1.49–2.64, P for trend < 0.001). Subjects with the highest E-DII levels were at increased risk of cognitive impairment compared to those with the lowest E-DII levels (OR = 1.56, 95%CI = 1.25–1.93, P for trend < 0.001). The link between E-DII and cognitive impairment was significant in a nonlinear dose response analysis (P for nonlinear = 0.001). Higher E-DII scores were associated with an increased risk of developing AD or cognitive impairment. These findings may contribute to the effective prevention of cognitive impairment by constructing a multidisciplinary synergistic prevention strategy and controlling dietary inflammation levels.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".