数据驱动的集群分析确定了不同类型的心血管--代谢综合征
Mengge Yang1, Chang Su1, Xiaona Chang1
1Department of Endocrinology, Beijing Chao-yang Hospital, Capital Medical University, Beijing 100020, China.
European journal of preventive cardiology
|November 9, 2025
概括
这项研究使用生物标志物完善了心血管--代谢 (CKM) 综合征的亚型,确定了五个不同的患者群,以改善对CKM综合征的风险评估和个性化干预措施.
科学领域:
- 心血管医学 心血管医学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 代谢障碍 代谢障碍 代谢障碍
- 遗传学 是一个遗传学.
- 蛋白质组学是指蛋白质组学
背景情况:
- 目前对心血管--代谢 (CKM) 综合征的分期缺乏准确性,原因是临床异质性.
- 这种异质性限制了对CKM综合征的有效风险预测和个性化医疗方法.
研究的目的:
- 通过临床生物标志物来完善CKM综合征亚型化.
- 加强风险评估,并为CKM综合征提供个性化的预防策略.
主要方法:
- K-表示基于临床生物标志物的英国生物库参与者的集群 (n=44,200).
- 多变量考克斯回归分析,评估中位数随访时间为15.88年的并发症风险.
- 在已识别的集群中分析遗传风险因素和血蛋白质组特征.
主要成果:
- 确定了五个不同的CKM综合征集群:低风险 (LR),肝脏高风险 (LHR),脑血管高风险 (CBHR),年龄驱动高风险 (ADHR) 和心脏高风险 (CRHR).
- 每个群体都表现出独特的预后和敏感性概况,LHR,CBHR,ADHR和CRHR群体显示出特定疾病的风险增加,如肝病,脑血管事件,中风,CKD,心血管事件和功能障碍.
- 独特的血蛋白质和遗传风险模式与每个群体有关.
结论:
- 已识别的集群提供了对CKM综合征异质性的更细致的理解.
- 这种亚型化有助于识别高风险CKM综合征患者.
- 这些发现支持开发有针对性的干预措施,以改善CKM综合征的管理.
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