空腹血糖对无症状颈动脉粥样硬化斑块患者认知功能的影响—— 1项针对非城镇居民的调查 Effect of Fasting Blood Glucose on Cognitive Function in Patients with Asymptomatic Carotid Atherosclerotic Plaque: A Survey of Rural Residents
Bibliographic record
Abstract
目的 探索空腹血糖对无症状颈动脉粥样硬化斑块患者认知功能的影响。 方法 研究人群为2015年9月-2017年9月期间参与中国国家脑卒中高危人群筛查和干预（China national stroke screen survey，CNSSS）项目，北京市昌平区北七家社区卫生服务中心和北京市顺义区空港医院筛查点的40岁以上非城镇居民。入组标准为既往无脑血管疾病及相关神经功能缺损症状和体征，且行超声检查确诊存在颈动脉粥样硬化斑块。采用MoCA量表（北京版）评价认知功能。采用多因素logistic回归分析空腹血糖与认知障碍（MoCA<26分）的关系。 结果 共有1446名居民纳入本项研究，平均年龄为62.0±8.3岁，男性809人（55.9%），受教育年限≤12年者1364人（94.3%），其中936人（64.7%）存在认知障碍。单因素logistic回归分析显示，空腹血糖与认知障碍相关（趋势P=0.003）。校正混杂因素后，空腹血糖升高仍然是认知障碍的独立危险因素（趋势P=0.008），相较于最低四分位数空腹血糖水平，最高四分位数空腹血糖水平患者的认知障碍风险增加了0.49倍（OR 1.49，95%CI 1.10～2.02，P=0.010）。 结论 空腹血糖升高是无症状颈动脉粥样硬化斑块患者认知障碍的独立危险因素。 Abstract: Objective To explore the effect of fasting blood glucose on cognitive function in patients with asymptomatic carotid atherosclerotic plaque. Methods Participants aged ≥40 years who participated in the China National Stroke Screen Survey (CNSSS) program between September 2015 to September 2017, from two rural communities (Beiqijia community, Changping District, Beijing and Konggang community, Shunyi District, Beijing), were enrolled in this study. The included patients had carotid atherosclerotic plaques confirmed by carotid ultrasound, yet no cerebrovascular diseases and neurological deficits. Cognitive function was evaluated by the Montreal cognitive assessment (Beijing version). The multivariable logistic regression analysis was used to determine the association between fasting blood glucose and cognitive impairment (MoCA<26 points). Results Finally, a total of 1446 subjects were included in this study, with a mean age of 62.0±8.3 years and 809 males (55.9%). 1364 (94.3%) had an education level of no more than 12 years, and 936 (64.7%) had cognitive impairment. Univariate logistic regression analysis showed that fasting blood glucose was associated with cognitive impairment (Ptrend=0.003). After adjusting for the confounding factors, fasting blood glucose was still an independent risk factor for cognitive impairment (Ptrend=0.008). The patients with the highest quartile of fasting blood glucose had an increased risk of cognitive impairment by 0.49 times, compared to patients with the lowest quartile (OR 1.49, 95%CI 1.10-2.02, P=0.010). Conclusions High fasting blood glucose was an independent risk factor for cognitive impairment in patients with asymptomatic carotid atherosclerotic plaque.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".