Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments

Tong Wang, Yuanqing Fu, Menglei Shuai, Ju-Sheng Zheng, Lu Zhu, Andrew T. Chan, Qi Sun, Frank B. Hu, Scott T. Weiss and Yang-Yu Liu ()
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Tong Wang: Harvard Medical School
Yuanqing Fu: Westlake University
Menglei Shuai: Westlake University
Ju-Sheng Zheng: Westlake University
Lu Zhu: University of Iowa College of Public Health
Andrew T. Chan: Harvard Medical School
Qi Sun: Harvard Medical School
Frank B. Hu: Harvard Medical School
Scott T. Weiss: Harvard Medical School
Yang-Yu Liu: Harvard Medical School

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach—Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Additionally, we find that METRIC can still correct the random errors well even without including gut microbial compositions. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.

Date: 2024
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DOI: 10.1038/s41467-024-53567-w

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