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基于微生物组的纠正,用于从自我报告的饮食评估中得出的营养资料中的随机错误
Tong Wang1, Yuanqing Fu2,3,4, Menglei Shuai2,3,5
1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
bioRxiv : the preprint server for biology
|December 4, 2023
概括
在研究中准确测量饮食是很困难的. 一个新的深度学习工具,METRIC,使用肠道微生物来修复自我报告的饮食数据中的错误,改进营养特征计算.
科学领域:
- 营养流行病学 营养流行病学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 大群体的饮食摄入量评估依赖于自我报告的数据,容易产生重大测量错误.
- 这些错误导致营养资料计算中的不准确性,限制了流行病学研究的有效性.
- 现有的计算方法来纠正这些饮食评估错误很少.
研究的目的:
- 引入一种新的深度学习方法,即基于微生物组的营养特征校正器 (METRIC).
- 利用肠道微生物组成来纠正自我报告的饮食数据中的随机错误.
- 评估METRIC在改善营养特征准确性的表现.
主要方法:
- 开发了一个深度学习模型,METRIC,集成肠道微生物组数据.
- 应用METRIC来纠正24小时回忆和饮食记录中的自我报告的饮食数据.
- 使用合成数据集和三个现实世界队列数据集验证的METRIC.
主要成果:
- METRIC有效地减少了饮食评估中的模拟随机错误.
- 对于肠道细菌代谢的营养物质来说,修正特别重要.
- 在合成和真实世界的数据中表现出强大的性能.
结论:
- METRIC显示出强大的潜力,可以提高从自我报告仪器中获得的饮食摄入数据的准确性.
- 基于微生物组的方法为营养流行病学的持续挑战提供了一个有希望的计算解决方案.
- 需要进一步验证,以评估METRIC在纠正实际测量错误方面的有效性.
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