U-Shaped Association Between Serum Uric Acid Levels and Cognitive Functions in Patients with Type 2 Diabetes: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Serum uric acid (SUA) is a natural antioxidant that may exert neuroprotective effects against neurodegenerative diseases. The relationship between uric acid and cognitive functions has been extensively studied, but results remain conflicting. OBJECTIVE: To investigate potential associations between SUA level and mild cognitive impairment (MCI), and different domains of cognitive performances in patients with type 2 diabetes mellitus (T2DM). METHODS: A total of 352 T2DM subjects (208 males and 144 females) were enrolled. SUA level was determined by using the uricase method. Cognitive performances were assessed using a validated neuropsychological test battery. Generalized additive models and binary logistic regression analysis were fitted to determine the association between SUA and cognitive functions. RESULTS: A total of 157 T2DM patients had MCI, and 195 displayed normal cognition. Compared with the controls, MCI patients exhibited lower SUA level (p = 0.009). Generalized additive models revealed a U-shaped curve relationship among SUA with Montreal Cognitive Assessment, Auditory Verbal Learning Test-immediate recall and Trail Making Test-B scores (all p < 0.05). Further logistic regression analysis showed a significant trend toward decreased MCI risk with increased SUA level among the subjects whose SUA level was below the cut-point (388.63 μmol/L); each unit increment in SUA level reduced the MCI risk by 0.7% (p = 0.003). CONCLUSION: A U-shaped association between SUA level and global cognitive function, especially executive and memory function, existed in T2DM patients. Our findings will provide additional suggestions that an increase of SUA to a certain level may be a novel method to reduce the burden of T2DM-associated cognitive impairment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".