从6,497个双重标记的水测量中得出的预测方程可以检测错误的自我报告的能量摄入量
Rania Bajunaid1,2, Chaoqun Niu3,4, Catherine Hambly1
1School of Biological Sciences, University of Aberdeen, Aberdeen, UK.
Nature food
|January 13, 2025
概括
营养流行病学面临着不准确的饮食摄入数据的挑战. 这项研究开发了一个新的方程来识别不可靠的能源支出报告,揭示了大数据集中超过50%的错误报告.
科学领域:
- 营养流行病学 营养流行病学
- 能量代谢 能量代谢
- 生物统计学 生物统计学
背景情况:
- 营养流行病学中的饮食评估工具往往产生不准确的数据.
- 识别和量化报告错误对于可靠的研究结果至关重要.
- 将估计的饮食摄入量与总能耗 (TEE) 进行比较是一种检测数据不可靠性的方法.
研究的目的:
- 使用现有数据来导出总能耗 (TEE) 的预测方程.
- 建立一种查饮食摄入研究中错误报告的方法.
- 评估大规模饮食调查中错误报告的流行情况.
主要方法:
- 使用了国际原子能机构的双标记水数据库,其中包含6,497个TEE测量.
- 开发了一个回归方程,根据体重,年龄和性别预测TEE.
- 将预测方程应用于国家饮食和营养调查和国家健康和营养检查调查数据集.
主要成果:
- 导出的回归方程为预期的TEE提供了95%的预测极限.
- 分析显示,在检查的大数据集中,超过50%的错误报告.
- 宏观营养素成分报告中的系统偏差随着错误报告的水平而增加.
结论:
- 开发的TEE预测方程可以作为一个有价值的工具来选饮食错误报告.
- 在主要的饮食调查中,高水平的错误报告可能会导致饮食和健康结果之间的虚假关联.
- 解决数据不准确性对于推进营养流行病学研究至关重要.
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