在心力衰竭事件中基于微生物组的风险预测:一个社区挑战
Pande Putu Erawijantari1, Ece Kartal2, José Liñares-Blanco2,3,4
1Department of Computing, Faculty of Technology, University of Turku, Turku, Finland.
medRxiv : the preprint server for health sciences
|October 24, 2023
概括
肠道微生物组可以预测心力衰竭 (HF) 风险. 使用便微生物组数据的机器学习模型识别了与炎症相关的细菌物种,改善了芬兰大型队列中的HF风险预测.
科学领域:
- 微生物组研究的研究.
- 心血管疾病流行病学
- 计算生物学是一种计算生物学.
背景情况:
- 心力衰竭 (HF) 构成了重大的公共卫生挑战,需要及早识别风险以进行有效的干预.
- 预期人口研究对于理解疾病的发病因子和开发预测模型至关重要.
结论:
- 将肠道微生物组数据纳入风险模型提供了对HF病原性有价值的生物学见解.
- 应用于微生物组数据的机器学习方法有望提高心血管疾病风险预测.
- 这项研究强调了肠道微生物组在理解和潜在地减轻心力衰竭风险方面的重要性.
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