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Sources of Variation in Food-Related Metabolites during Pregnancy
The extent to which variation in food-related metabolites are attributable to non-dietary factors remains unclear, which may explain inconsistent food-metabolite associations observed in population studies. This study examined the association between non-dietary factors and the serum concentrations...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227758/ https://www.ncbi.nlm.nih.gov/pubmed/35745237 http://dx.doi.org/10.3390/nu14122503 |
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author | Rafiq, Talha Azab, Sandi M. Anand, Sonia S. Thabane, Lehana Shanmuganathan, Meera Morrison, Katherine M. Atkinson, Stephanie A. Stearns, Jennifer C. Teo, Koon K. Britz-McKibbin, Philip de Souza, Russell J. |
author_facet | Rafiq, Talha Azab, Sandi M. Anand, Sonia S. Thabane, Lehana Shanmuganathan, Meera Morrison, Katherine M. Atkinson, Stephanie A. Stearns, Jennifer C. Teo, Koon K. Britz-McKibbin, Philip de Souza, Russell J. |
author_sort | Rafiq, Talha |
collection | PubMed |
description | The extent to which variation in food-related metabolites are attributable to non-dietary factors remains unclear, which may explain inconsistent food-metabolite associations observed in population studies. This study examined the association between non-dietary factors and the serum concentrations of food-related biomarkers and quantified the amount of variability in metabolite concentrations explained by non-dietary factors. Pregnant women (n = 600) from two Canadian birth cohorts completed a validated semi-quantitative food frequency questionnaire, and serum metabolites were measured by multisegment injection-capillary electrophoresis-mass spectrometry. Hierarchical linear modelling and principal component partial R-square (PC-PR2) were used for data analysis. For proline betaine and DHA (mainly exogenous), citrus foods and fish/fish oil intake, respectively, explained the highest proportion of variability relative to non-dietary factors. The unique contribution of dietary factors was similar (15:0, 17:0, hippuric acid, TMAO) or lower (14:0, tryptophan betaine, 3-methylhistidine, carnitine) compared to non-dietary factors (i.e., ethnicity, maternal age, gestational age, pre-pregnancy BMI, physical activity, and smoking) for metabolites that can either be produced endogenously, biotransformed by gut microbiota, and/or derived from multiple food sources. The results emphasize the importance of adjusting for non-dietary factors in future analyses to improve the accuracy and precision of the measures of food intake and their associations with health and disease. |
format | Online Article Text |
id | pubmed-9227758 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92277582022-06-25 Sources of Variation in Food-Related Metabolites during Pregnancy Rafiq, Talha Azab, Sandi M. Anand, Sonia S. Thabane, Lehana Shanmuganathan, Meera Morrison, Katherine M. Atkinson, Stephanie A. Stearns, Jennifer C. Teo, Koon K. Britz-McKibbin, Philip de Souza, Russell J. Nutrients Article The extent to which variation in food-related metabolites are attributable to non-dietary factors remains unclear, which may explain inconsistent food-metabolite associations observed in population studies. This study examined the association between non-dietary factors and the serum concentrations of food-related biomarkers and quantified the amount of variability in metabolite concentrations explained by non-dietary factors. Pregnant women (n = 600) from two Canadian birth cohorts completed a validated semi-quantitative food frequency questionnaire, and serum metabolites were measured by multisegment injection-capillary electrophoresis-mass spectrometry. Hierarchical linear modelling and principal component partial R-square (PC-PR2) were used for data analysis. For proline betaine and DHA (mainly exogenous), citrus foods and fish/fish oil intake, respectively, explained the highest proportion of variability relative to non-dietary factors. The unique contribution of dietary factors was similar (15:0, 17:0, hippuric acid, TMAO) or lower (14:0, tryptophan betaine, 3-methylhistidine, carnitine) compared to non-dietary factors (i.e., ethnicity, maternal age, gestational age, pre-pregnancy BMI, physical activity, and smoking) for metabolites that can either be produced endogenously, biotransformed by gut microbiota, and/or derived from multiple food sources. The results emphasize the importance of adjusting for non-dietary factors in future analyses to improve the accuracy and precision of the measures of food intake and their associations with health and disease. MDPI 2022-06-16 /pmc/articles/PMC9227758/ /pubmed/35745237 http://dx.doi.org/10.3390/nu14122503 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Rafiq, Talha Azab, Sandi M. Anand, Sonia S. Thabane, Lehana Shanmuganathan, Meera Morrison, Katherine M. Atkinson, Stephanie A. Stearns, Jennifer C. Teo, Koon K. Britz-McKibbin, Philip de Souza, Russell J. Sources of Variation in Food-Related Metabolites during Pregnancy |
title | Sources of Variation in Food-Related Metabolites during Pregnancy |
title_full | Sources of Variation in Food-Related Metabolites during Pregnancy |
title_fullStr | Sources of Variation in Food-Related Metabolites during Pregnancy |
title_full_unstemmed | Sources of Variation in Food-Related Metabolites during Pregnancy |
title_short | Sources of Variation in Food-Related Metabolites during Pregnancy |
title_sort | sources of variation in food-related metabolites during pregnancy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227758/ https://www.ncbi.nlm.nih.gov/pubmed/35745237 http://dx.doi.org/10.3390/nu14122503 |
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