使用监督集群技术,探索与CKD新发病相关的MASLD亚群
Itaru Hosaka1, Marenao Tanaka2, Tatsuya Sato2,3
1Division of Cardiovascular Surgery, Department of Surgery, Sapporo Medical University School of Medicine, Sapporo, Japan.
概括
机器学习确定了不同的代谢功能障碍相关的脂肪性肝病 (MASLD) 子组. 一个肥胖的MASLD亚组,高脂和尿酸显示出患慢性病 (CKD) 的风险最高.
科学领域:
- 肝病学和病学 肝病学和病学
- 代谢综合征研究 代谢综合征研究
- 机器学习在医学中的应用
背景情况:
- 与代谢功能障碍相关的脂肪性肝病 (MASLD) 与心血管--代谢综合征有关,包括慢性病 (CKD).
- 目前尚不完全了解MASLD的异质性及其对CKD发展的影响.
- 在MASLD患者中,准确的CKD风险分层是至关重要的.
研究的目的:
- 研究MASLD亚组与新发性CKD之间的关系.
- 利用机器学习来识别不同的MASLD表型.
- 改善MASLD中CKD发展的风险分层.
主要方法:
- 雇员监督集群使用SHapley添加式扩展 (SHAP) 和统一的多重近似和投影 (UMAP) 在脂肪性肝病数据上.
- 在10年的随访期间,分析了12168名日本受试者的健康检查数据.
- 利用高斯混合模型来检测MASLD子集群和卡普兰-梅尔分析用于CKD发病率.
主要成果:
- 使用机器学习识别了10个子集群和3个不同的MASLD子集群.
- 在MASLD子组中,累计CKD发病率存在显著差异.
- 一个肥胖的MASLD亚组,其特点是有动脉性脂质样本,高脂肪肝指数和高尿酸,表现出患CKD的风险最高.
结论:
- 监督的MASLD聚类揭示了表型上不同的亚群,增强了CKD风险分层.
- 一个肥胖的MASLD亚组与动脉动脉脂缺血症和高尿血症相关,与显著增加的CKD风险有关.
- 这些发现有助于对MASLD患者进行个性化风险评估和管理,以预防CKD.
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