[Association between mild cognitive impairment and serum uric acid levels among people aged 55 and above in 4 provinces of China].
Bibliographic record
Abstract
OBJECTIVE: To explore the association between mild cognitive impairment and hyperuricemia among people aged 55 and above. METHODS: Based on the baseline survey data of "community cohort study on neurological diseases" from 2018 to 2019, 4272 residents aged 55 and above with complete data of individual socioeconomic status, lifestyle, mild cognitive impairment and serum uric acid level were selected as the research objects. The Montreal cognitive assessment(MoCA) was used to evaluate the mild cognitive impairment of the research objects. The relationship between serum uric acid level and MoCA score was analyzed. Multivariate Logistic regression model was used to analyze the association of serum uric acid level and mild cognitive impairment. RESULTS: The prevalence of cognitive impairment in the normal and high serum uric acid groups were 38. 6% and 38. 4%, respectively. In the normal serum uric acid level group, the serum uric acid level of the non-cognitive impairment group was significantly higher(291. 4 μmol/L)than that of the cognitive impairment group(283. 7 μmol/L)(F=16. 12, P<0. 05), and the MoCA score of the subjects in this group was significantly positively correlated with the serum uric acid level(r=0. 103, P<0. 05). In the hyperuricemia group, no significant difference was found in serum uric acid level between non-cognitive impairment group(450. 9 μmol/L) and cognitive impairment group(442. 4 μmol/L)(F=2. 44, P>0. 05), and there was no correlation between MoCA score and serum uric acid level(r=0. 064, P>0. 05). Logistic regression analysis showed that hyperuricemia was not a risk factor for cognitive impairment in people aged 55 and above(OR=1. 04, 95% CI 0. 87-1. 25, P=0. 630). CONCLUSION: Within the normal range of serum uric acid, appropriate increase of serum uric acid may play a protective role in cognitive impairment. Hyperuricemia has not been found to increase the risk of cognitive impairment in people 55 years and older.
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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 | 0.001 |
| 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".