在T2DM患者中开发和验证用于区分糖尿病病与非糖尿病病的预测性名录:一项多中心研究
Zishan Lin1,2,3, Tao Hong1,2,3, Wenfeng Wang1,2,3
1Department of Nephrology, Blood Purification Research Center, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Frontiers in nutrition
|June 18, 2025
概括
一个新的非侵入性模型准确地区分了2型糖尿病 (T2DM) 患者的糖尿病病 (DN) 与非糖尿病病 (NDN),为改善治疗决策提供了侵入性活检的实用替代方案.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 内分泌学 在内分泌学.
- 诊断医学 诊断医学 诊断医学
背景情况:
- 2型糖尿病 (T2DM) 是一个主要的全球健康问题,糖尿病病 (DN) 是慢性病的主要原因.
- 区分DN与非糖尿病病 (NDN) 对T2DM患有病的患者至关重要,因为治疗途径不同.
- 脏活检,目前的黄金标准,是侵入性的,通常是不切实际的,强调了对非侵入性诊断方法的需求.
研究的目的:
- 开发和验证一种非侵入性诊断模型,用于在T2DM患者中区分DN和NDN.
- 确定可预测DN的关键临床和实验室参数.
- 创建一个实用的工具,用于在脏活检不可行的环境中临床使用.
主要方法:
- 一项多中心研究招募了292名接受脏活检的T2DM患者.
- 使用后勤回归分析,使用临床和实验室数据构建预测模型.
- 一个视觉预测名录图被构建并在一个独立的队列上验证.
主要成果:
- 该模型确定了糖尿病视网膜病变,糖尿病持续时间,HbA1c,静态血压,中性粒细胞与淋巴细胞的比例,体积,甘油三,估计的淋巴细胞过率和尿红细胞计数作为DN的独立预测因素.
- 开发的诺米图实现了高诊断准确性 (AUC = 0.941),验证证实其性能 (AUC = 0.923).
- 校准和决策曲线分析表明了良好的一致性和临床效用.
结论:
- 开发的非侵入性模型为脏活检提供了可靠的替代方案,用于在T2DM患者中区分DN和NDN.
- 这种工具在临床实践中特别有价值,用于指导适当的治疗策略,当活检是不切实际的.
- 该模型增强了诊断能力,有可能改善T2DM相关病患者的治疗结果.
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