Blood Uric Acid in Mild Cognitive Impairment: A Cross-Sectional Study of Older Chinese Adults
Bibliographic record
Abstract
Lingling Xue,1 Xifeng Xiao,1 Xin’e Mao,2 Xiaoli Zhang,1 Yongbing Liu,3 Beibei Wu1 1Department of Nursing, Yangzhou Maternal and Child Health Care Hospital Affiliated to Yangzhou University, Yangzhou, People’s Republic of China; 2Geriatric Unit, Northern Jiangsu People’s Hospital, Yangzhou, People’s Republic of China; 3Department of Nursing, School of Nursing and public Health, Yangzhou University, Yangzhou, People’s Republic of ChinaCorrespondence: Beibei Wu, Yangzhou Maternal and Child Health Care Hospital Affiliated to Yangzhou University, Yangzhou, People’s Republic of China, Email 1128866@qq.comIntroduction: Previous studies focused on the association between uric acid (UA) as an antioxidant and cognitive impairment have been limited in scope and obtained contradictory results. Therefore, we investigated whether low blood UA levels were related to mild cognitive impairment (MCI) in a cross-sectional study.Methods: This study included 231 elderly Chinese adults 60 years and over. We used the MMSE and MoCA to evaluate cognitive function, and fasting venous blood to measure UA concentration. The relationship between blood UA and cognitive impairment was analyzed with multivariate analysis of variance, controlling for demographic information, physical exercise, lifestyle and laboratory results.Results: A total of 90 (38.96%) participants were healthy and 141 (61.04%) had MCI. Compared with the healthy group, MCI patients were more likely to have fewer years of education, inactivity and lower UA levels. UA levels were significantly lower in MCI patients than healthy individuals (P < 0.05). After adjusting for these variables, we found that among MCI patients, lower UA levels were associated with worse cognitive function in the MMSE. Multivariate logistic regression models demonstrated that UA was a protective factor for MCI. Multivariate analysis comparing the high and low quartile group, which was the reference group, indicated that differences in cognition among groups were statistically significant.Conclusion: Lower UA levels were associated with worse cognitive function; therefore, controlling UA levels within a suitable range may slow the progression of cognitive disorders.Keywords: uric acid, mild cognition impairment, factors, differences between groups
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".