Changes in serum urate and body weight interact to affect cognitive decline rates of community‐dwelling older adults
Bibliographic record
Abstract
Abstract Background Several innate and environmental factors affect the rate and degree of cognitive decline with each factor representing a potentially valuable intervention point to slow progression of this decline. Serum urate, a product of purine metabolism and potent antioxidant, and weight loss have been associated with cognitive decline. The aim of this study was to determine the relation among urate, body weight, and their interaction with cognitive function. Methods In the context of a larger study, 133 adults aged 65‐80 at baseline (mean baseline age 72.7 years, 56.5% male) were recruited to participate in a double‐blind, randomized controlled trial. The Montreal Cognitive Assessment (MoCA) was administered to assess cognitive function. Those with MoCA scores >25 were considered cognitively typical, whereas those with MoCA scores below this threshold were deemed to be experiencing mild cognitive decline (MCD). The cognitively typical group was instructed to maintain their regular diet and served as a reference, whereas MCD groups were randomized to receive either 35mg lyophilized wild blueberries or dextrose (serving as control) daily in addition to their regular diet. MoCA, anthropometric measures, and serum were collected at baseline and repeated at 3 and 6 months. The statistical analysis was performed using MiniTab v.19. Results A regression‐based analysis showed that changes in serum urate were positively associated with changes in MoCA scores (ß = 0.94, SE = 0.47, p < 0.05) at 6 months. Groupwise analyses showed that changes in MoCA scores were not correlated with changes in urate or body weight in individuals with typical cognition or those in the blueberry group. However, in the control group, changes in serum urate (ß = 1.96, SE = 0.92, p<0.05), changes in body weight (ß = ‐0.31, SE = 0.13, p<0.05), and interaction between urate and body weight significantly impacted the change in MoCA scores. (ß = 0.663, SE = 0.20, p<0.05). Individuals with increased urate and decreased body weight showed less cognitive decline as compared to those who had weight gain and decreased urate. Conclusion Our findings show that changes in urate and body weight interact with each other to significantly impact changes in cognitive function.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".