| 高朋飞.血清Ng、IL-23水平与阿尔茨海默病源性轻度认知功能障碍的相关性及预测风险模型构建△[J].安徽医药,待发表. |
| 血清Ng、IL-23水平与阿尔茨海默病源性轻度认知功能障碍的相关性及预测风险模型构建△ |
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| 投稿时间：2024-06-04 录用日期：2024-07-10 |
| DOI： |
| 中文关键词: 神经颗粒素 白介素-23 阿尔兹海默病 轻度认知功能障碍 Nomogram模型 |
| 英文关键词: |
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| 中文摘要: |
| 【摘要】目的：分析血清神经颗粒素（Ng）、白介素-23（IL-23）水平与阿尔茨海默病（AD）源性轻度认知功能障碍（MCI）的相关性，并构建AD源性MCI的预测风险模型。方法：选择于2022年1月至2024年1月入院治疗的AD源性MCI患者80例纳入MCI组，另选取同期健康体检老人80例纳入对照组。对比两组简易精神状态量表（MMSE）评分、血清Ng、IL-23水平，Pearson分析血清Ng、IL-23水平与AD源性MCI患者认知功能的相关性，分析AD源性MCI发生的危险因素，构建AD源性MCI的预测风险模型。结果：MCI组患者MMSE评分、血清Ng、IL-23水平均明显高于对照组（P＜0.05）。Pearson分析显示，AD源性MCI患者血清Ng、IL-23水平与MMSE评分呈负相关（P＜0.05）。MCI组受教育年限、血清TNF-α、IL-1β、IL-6水平均明显高于对照组（P＜0.05）。Logistic分析显示，低MMSE评分及受教育年限、高水平血清Ng、IL-23、TNF-α、IL-1β、IL-6是发生AD源性MCI的危险因素（P＜0.05）。基于MMSE评分、受教育年限、血清Ng、IL-23、TNF-α、IL-1β、IL-6水平建立的AD源性MCI Nomogram风险预测模型的C-index为0.909、95% CI为0.853~0.977，AUC为0.942，其校正曲线与理想曲线拟合反映良好。结论：血清Ng、IL-23水平与AD源性MCI患者认知功能呈负相关，而基于MMSE评分、受教育年限、血清Ng、IL-23、TNF-α、IL-1β、IL-6水平建立的AD源性MCI风险预测模型可有效预测老年人AD源性MCI发生风险。 |
| 英文摘要: |
| [Abstract] Objective: To analyze the correlation between serum neurogranin (Ng) and interleukin-23 (IL-23) levels and Alzheimer's disease (AD) -derived mild cognitive impairment (MCI), and to construct a predictive risk model for AD-derived MC. Methods: A total of 80 patients with AD-derived MCI who were hospitalized from January 2022 to January 2024 were included in the MCI group, and another 80 healthy elderly patients who underwent physical examination during the same period were included in the control group. The correlation between serum Ng and IL-23 levels and the cognitive function of patients with AD-derived MCI was analyzed by Pearson, and the risk factors for the occurrence of AD-derived MCI were analyzed to construct a predictive risk model for AD-derived MCI. Results: MMSE score, serum Ng and IL-23 levels in MCI group were significantly higher than those in control group (P < 0.05). Pearson analysis showed that serum Ng and IL-23 levels in patients with AD-derived MCI were negatively correlated with MMSE scores (P < 0.05). Years of education, serum levels of TNF-α, IL-1β and IL-6 in MCI group were significantly higher than those in control group (P < 0.05). Logistic analysis showed that low MMSE score and years of education, high levels of serum Ng, IL-23, TNF-α, IL-1β and IL-6 were risk factors for the occurrence of AD-derived MCI (P < 0.05). The C-index, 95%CI, and AUC of an AD-derived MCI Nomogram risk prediction model based on MMSE score, years of schooling, and serum Ng, IL-23, TNF-α, IL-1β, and IL-6 levels were 0.909, 0.853-0.977, and 0.942 respectively. The calibration curve fits well with the ideal curve. Conclusions: Serum Ng and IL-23 levels are negatively correlated with cognitive function in patients with AD-derived MCI, and the risk prediction model of AD-derived MCI based on MMSE score, years of education, serum Ng, IL-23, TNF-α, IL-1β, and IL-6 levels can effectively predict the risk of AD-derived MCI in the elderly. |
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