Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments
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 meas...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-53567-w |
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| author | Tong Wang Yuanqing Fu Menglei Shuai Ju-Sheng Zheng Lu Zhu Andrew T. Chan Qi Sun Frank B. Hu Scott T. Weiss Yang-Yu Liu |
| author_facet | Tong Wang Yuanqing Fu Menglei Shuai Ju-Sheng Zheng Lu Zhu Andrew T. Chan Qi Sun Frank B. Hu Scott T. Weiss Yang-Yu Liu |
| author_sort | Tong Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8984f295b45547c8b54831fc0c7daa23 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-8984f295b45547c8b54831fc0c7daa232025-08-20T02:11:29ZengNature PortfolioNature Communications2041-17232024-10-0115111210.1038/s41467-024-53567-wMicrobiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessmentsTong Wang0Yuanqing Fu1Menglei Shuai2Ju-Sheng Zheng3Lu Zhu4Andrew T. Chan5Qi Sun6Frank B. Hu7Scott T. Weiss8Yang-Yu Liu9Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolSchool of Life Sciences, Westlake UniversitySchool of Life Sciences, Westlake UniversitySchool of Life Sciences, Westlake UniversityDepartment of Epidemiology, University of Iowa College of Public HealthChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolAbstract 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.https://doi.org/10.1038/s41467-024-53567-w |
| spellingShingle | Tong Wang Yuanqing Fu Menglei Shuai Ju-Sheng Zheng Lu Zhu Andrew T. Chan Qi Sun Frank B. Hu Scott T. Weiss Yang-Yu Liu Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments Nature Communications |
| title | Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments |
| title_full | Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments |
| title_fullStr | Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments |
| title_full_unstemmed | Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments |
| title_short | Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments |
| title_sort | microbiome based correction for random errors in nutrient profiles derived from self reported dietary assessments |
| url | https://doi.org/10.1038/s41467-024-53567-w |
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