Longitudinal trajectories of dietary quality and cognitive functions in Chinese older adults: The Taiwan Initiative for Geriatric Epidemiological Research (TIGER) cohort study
Bibliographic record
Abstract
Abstract Background Dietary factors influence cognitive functions, but most previous studies were conducted in Western settings and assessed diet only once. Trajectory analysis of diet measured at multiple timepoints can track changes in diet and identify subpopulations requiring more intervention efforts. We thus assessed associations between dietary trajectories and cognitive functions in an understudied Asian elderly population. Method The TIGER study involves non‐demented community‐dwelling participants aged ≥65 years recruited between 2011‐2013. Dietary intakes were assessed using a food frequency questionnaire during baseline and 4th‐ and 6th‐year follow‐ups. Modified Alternative Healthy Eating Index (mAHEI) scores (energy‐adjusted using the residual method) were generated at each timepoint to reflect diet quality. Longitudinal trajectories of dietary quality were derived using latent growth mixture modelling. The Montreal Cognitive Assessment—Taiwanese version was used to assess global cognition for outcomes. Cognitive functions (baseline‐normalized z‐scores) of four cognitive domains, i.e., memory, attention, executive function, and verbal fluency, were also assessed. Associations between trajectories of dietary quality and cognitive functions were assessed using linear regressions, adjusting for important covariates including sex, apolipoprotein (APOE) e4 status, baseline age, body mass index (BMI), and depressive symptoms. Result Included participants (n = 356) were 54.2% female, had a mean age of 71.6 years, and a mean BMI of 23.8 kg/m2 at baseline. The overall mean mAHEI scores were close to 37 (out of 70 possible) at all three time points. Three trajectories of dietary quality, namely 1) ‘increasing’, 2) ‘decreasing’, and 3) ‘stable‐high’ were identified and deemed optimal considering fit indices and interpretability. Compared with ‘stable‐high’ dietary quality trajectory, the ‘increasing’ trajectory was associated with worse memory performance (adjusted‐β = ‐0.424; 95% CI: ‐0.679, ‐0.169; P = 0.001) whereas the ‘decreasing’ trajectory was associated with worse performance for the verbal fluency domain (adjusted‐β = ‐0.342; 95% CI: ‐0.653, ‐0.030; P = 0.031). No other significant associations were observed. Conclusion Maintaining a stable‐high dietary quality trajectory over time is associated with better cognitive functions among Taiwanese older adults. Since there is much room for dietary improvement in this Taiwanese population, interventions to promote and sustain better dietary quality over time can have substantial impacts on their cognitive functions.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".