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现型衰老加速作为良性前列腺增生的一个新型预测因素:一个前性队列研究

Xuwen Li¹, Penghu Lian², Hongyan Chen³

¹Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

|September 10, 2025

概括

现型衰老加速,生物衰老的衡量标准,显著预测良性前列腺增生症 (BPH) 的风险. 整合这些衰老生物标志物的机器学习模型可以提高BPH早期检测和个性化风险评估.

科学领域:

生物遗传学生物遗传学
泌尿器科泌尿器科泌尿器科泌尿器科
计算生物学计算生物学

背景情况:

良性前列腺增生症 (BPH) 是老年男性常见的疾病.
当前的BPH风险预测模型可能无法完全捕捉生物衰老过程.
对于精确的BPH预防和管理,需要新的生物标志物.

研究的目的:

调查BPH的表型年龄和表型年龄加速 (PhenoAgeAccel) 的预测价值.
开发一种机器学习 (ML) 模型,用于使用衰老生物标志物预测BPH风险.
为了告知BPH的精确预防和临床管理战略.

主要方法:

来自美国国家健康和营养检查调查 (NHANES) 的784名男性参与者的分析.
从时间学年龄和9个血清生物标志物的表型年龄计算.
开发一个XGBoost ML模型,包含34个通过递归特征消除 (RFE) 识别的BPH相关特征.
通过SHapley添加式解释 (SHAP) 评估使用后勤回归和特征重要性的PhenoAgeAccel-BPH关联.

主要成果:

在测试组中,XGBoost模型实现了0.833的AUC.
现型年龄加速 (PhenoAgeAccel) 与BPH风险增加显著相关 (p < 0.001).
纳入表型年龄改善了ML模型的预测性能 (AUC = 0.853).
现型年龄被确定为一个关键预测因素,突出其在BPH风险分层中的临床相关性.

结论:

表型年龄和PhenoAgeAccel是BPH风险的有价值预测指标.
将表型年龄与ML整合在一起,为早期BPH检测提供了一个强大的框架.
这种方法支持个性化风险评估和针对老年人群BPH的有针对性的干预措施.

关键词:

良性前列腺增生症良性前列腺增生症生物衰老生物衰老现型年龄现型年龄现象型的年龄加速.在XGBoost上使用.

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