开发和验证基于Galectin-3和CVAI的模型,用于预测2型糖尿病患者的认知障碍
Xueling Zhou1,2, Ning Dai3, Dandan Yu1,2
1School of Medicine, Southeast University, Nanjing, China.
Journal of endocrinological investigation
|November 20, 2024
概括
这项研究开发了一种名录模型,用于预测2型糖尿病患者的轻度认知障碍 (MCI). 该模型整合了年龄,性别,教育,高血压,Galectin-3和CVAI,用于早期风险评估.
科学领域:
- 内分泌学 在内分泌学.
- 神经学 神经学
- 老年病的医生 老年病的医生
背景情况:
- 2型糖尿病 (T2DM) 与认知能力下降的风险增加有关.
- 轻度认知障碍 (MCI) 是T2DM患者常见的并发症,需要早期检测和干预.
- 在T2DM中,MCI风险评估的现有方法缺乏全面的预测能力.
研究的目的:
- 开发和验证一个用于量化T2DM患者MCI风险的预测模型.
- 在这个人群中确定与MCI发展相关的关键临床指标.
- 在T2DM中创建一个用户友好的工具,用于早期MCI风险分层.
主要方法:
- 招募了一组中国T2DM患者进行数据收集,包括人口统计,病史和生物化学测试.
- 用神经心理测试评估认知功能,通过蒙特利尔认知评估 (MoCA) 评分诊断MCI.
- 在训练组上使用逻辑回归构建了一个名图模型,并在单独的组上进行验证,包括年龄,性别,教育,高血压,CVAI和Galectin-3等因素.
主要成果:
- 诺莫格拉姆模型整合了年龄,性别,教育水平,高血压,CVAI和Galectin-3来预测MCI风险.
- 该模型表现出高预测性能,C指数为0.816,AUC为0.816 (培训) 和0.858 (验证).
- 盖莱克-3和CVAI被确定为认知功能的显著负面预测因素,而教育显示出保护作用.
结论:
- 一种新型的诺莫格拉姆模型有效地预测T2DM患者的MCI风险.
- 该模型的准确性和易用性有助于早期识别患MCI高风险的个体.
- 结合加列克-3,CVAI和常见的临床因素可以提高T2DM中MCI风险预测.
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