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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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