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

Autor: Wang T; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA., Fu Y; School of Life Sciences, Westlake University, Hangzhou, China.; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China., Shuai M; School of Life Sciences, Westlake University, Hangzhou, China.; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.; Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA., Zheng JS; School of Life Sciences, Westlake University, Hangzhou, China.; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China., Zhu L; Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA, 52242, USA., Chan AT; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.; Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA., Sun Q; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA., Hu FB; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA., Weiss ST; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA., Liu YY; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA. yyl@channing.harvard.edu.; Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. yyl@channing.harvard.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2024 Oct 22; Vol. 15 (1), pp. 9112. Date of Electronic Publication: 2024 Oct 22.
DOI: 10.1038/s41467-024-53567-w
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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