从深度学习的发现到临床验证:一种新的复合标志物预测了2型糖尿病的死亡率
Jianjun Wu1, Dawei Yang2, Youqi Zhang1
1Department of Cardiology, the Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Cardiovascular diabetology. Endocrinology reports
|September 27, 2025
概括
一种新的复合生物标志物, ln[ALP × sCr],有效预测了美国成人糖尿病患者的死亡风险. 这种心功能障碍指标的较高水平与全因,心血管和糖尿病相关死亡率的增加有关.
科学领域:
- 生物标志物和健康结果
- 流行病学和公共卫生.
- 人工智能在医学中的应用
背景情况:
- 性酸酶 (ALP) 和血清肌素 (sCr) 独立地与糖尿病患者的不良结果有关.
- 糖尿病是一种主要的公共卫生问题,与显著的死亡率有关.
- 现有的生物标志物可能无法完全捕捉心脏和功能障碍的综合风险.
研究的目的:
- 调查新型复合指标ln[ALP × sCr]用于预测所有原因和心血管疾病 (CVD) 死亡风险.
- 评估这种综合生物标志物与美国糖尿病成年人死亡率之间的关联.
- 探索人工智能在识别关键死亡率相关生物标志物的作用.
主要方法:
- 从NHANES (1999-2014) 分析了82,091名美国成年人,并对死亡率进行了随访.
- 深度学习模型确定ALP,sCr和维生素D是最重要的死亡率预测因素.
- 导出复合指数ln[ALP × sCr],并使用Cox比例危险模型评估其与死亡率的关联.
主要成果:
- ln[ALP × sCr]的最高四分位数与所有原因 (HR 1.47),心血管 (HR 1.44) 和糖尿病相关死亡率 (HR 2.50) 的风险显著增加有关.
- 在ln[ALP × sCr]和全因死亡率之间观察到J形关联.
- 血清维生素D介导了复合生物标志物与全因死亡率之间的联系的24.3%.
结论:
- 复合指数ln[ALP × sCr]显示了作为一种简单,非侵入性的生物标志物,用于糖尿病患者的死亡风险评估的潜力.
- 将人工智能驱动的生物标志物发现与传统的流行病学方法相结合,可以提高死亡风险分层.
- 这些发现支持ln[ALP × sCr]的实用性,用于为与糖尿病护理相关的公共卫生战略提供信息.
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