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.
Nature communications
|October 22, 2024
概括
这项研究介绍了METRIC,这是一种深度学习工具,用于改进饮食评估. 它使用肠道微生物组数据来纠正自我报告的营养摄入量的错误,提高营养流行病学的准确性.
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.
- 营养特征计算中的不准确性来自于这些错误,限制了流行病学研究的可靠性.
- 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),用于纠正自我报告的饮食评估中的随机错误.
- 评估METRIC在最小化营养特征错误方面的表现,特别是那些受肠道微生物代谢影响的营养特征.
- 评估肠道微生物组成对METRIC错误纠正能力的影响.
主要方法:
- 开发了一个深度学习模型 (METRIC),集成肠道微生物数据以纠正饮食评估错误.
- 利用24小时的回忆和饮食记录作为对饮食评估纠正的输入.
- 使用合成数据集和三个现实数据集验证的METRIC.
主要成果:
- METRIC在最小化营养资料中模拟的随机错误方面表现出色.
- 该模型对肠道细菌代谢的营养物质特别有效.
- 即使排除了肠道微生物组成数据,METRIC也保持了显著的错误校正能力.
结论:
- 在营养流行病学中,METRIC提供了一个有前途的计算解决方案,用于提高自我报告的饮食评估的准确性.
- 该方法显示了改善营养特征计算的潜力,即使没有直接的微生物群数据.
- 需要进一步的研究来证实METRIC在纠正饮食评估仪器中实际测量错误方面的有效性.
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