机器学习方法来评估人体测量,代谢和营养状况以及精液参数之间的关联
Guillaume Bachelot1,2,3, Antonin Lamaziere1,3, Sebastien Czernichow4
1Sorbonne University School of Medicine, Saint-Antoine Research Center, INSERM UMR 938, 27 rue Chaligny, Paris 75012, France.
Asian journal of andrology
|April 16, 2024
概括
肥胖和营养不良等生活方式因素会影响男性生育能力. 结合健康标志物的机器学习得分表明,有利的模式对生殖健康产生积极影响,并减少精子DNA损伤.
科学领域:
- 生殖医学 生殖医学
- 生物统计学 生物统计学
- 男人的健康 男人的健康
背景情况:
- 包括营养不平衡,肥胖和代谢障碍在内的生活方式因素与男性不孕症有关.
- 评估男性生育能力往往侧重于精液参数,可能会忽视其他有助于健康的方面.
研究的目的:
- 调查精液参数与异常不孕症男性的人类测量,代谢和营养因素之间的关系.
- 通过验证的机器学习得分来评估这些因素的综合影响.
主要方法:
- 从75名患有异常不孕症的男性中收集了人体测量,代谢,抗氧化剂,微量营养素和精子参数.
- 进行了相关性分析,并应用了机器学习模型来生成整体健康评分 (0-1).
主要成果:
- 在某些人体测量,代谢和营养障碍与精子特征之间发现了显著的相关性.
- 一个不利的机器学习得分与精子DNA碎片化增加有关.
- 良好的健康模式对生殖功能产生了积极的影响.
结论:
- 除了传统的精液分析之外,包括生活方式和代谢因素在内的全面评估对于评估男性生育能力至关重要.
- 人工智能技术为整合个性化男性生殖护理的各种参数提供了一个有希望的方法.
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