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A multivariant recall‐by‐genotype study of the metabolomic signature of BMI

OBJECTIVE: This study estimated the effect of BMI on circulating metabolites in young adults using a recall‐by‐genotype study design. METHODS: A recall‐by‐genotype study was implemented in the Avon Longitudinal Study of Parents and Children. Samples from 756 participants were selected for untargeted...

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Autores principales: Fang, Si, Wade, Kaitlin H., Hughes, David A., Fitzgibbon, Sophie, Yip, Vikki, Timpson, Nicholas J., Corbin, Laura J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324973/
https://www.ncbi.nlm.nih.gov/pubmed/35598895
http://dx.doi.org/10.1002/oby.23441
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author Fang, Si
Wade, Kaitlin H.
Hughes, David A.
Fitzgibbon, Sophie
Yip, Vikki
Timpson, Nicholas J.
Corbin, Laura J.
author_facet Fang, Si
Wade, Kaitlin H.
Hughes, David A.
Fitzgibbon, Sophie
Yip, Vikki
Timpson, Nicholas J.
Corbin, Laura J.
author_sort Fang, Si
collection PubMed
description OBJECTIVE: This study estimated the effect of BMI on circulating metabolites in young adults using a recall‐by‐genotype study design. METHODS: A recall‐by‐genotype study was implemented in the Avon Longitudinal Study of Parents and Children. Samples from 756 participants were selected for untargeted metabolomics analysis based on low versus high genetic liability for higher BMI defined by a genetic risk score (GRS). Regression analyses were performed to investigate associations between BMI GRS group and relative abundance of 973 metabolites. RESULTS: After correction for multiple testing, 29 metabolites were associated with BMI GRS group. Bilirubin was among the most strongly associated metabolites, with reduced levels measured in individuals in the high‐BMI GRS group (β = −0.32, 95% CI: −0.46 to −0.18, Benjamini‐Hochberg adjusted p = 0.005). This study observed associations between BMI GRS group and the levels of several potentially diet‐related metabolites, including hippurate, which had lower mean abundance in individuals in the high‐BMI GRS group (β = −0.29, 95% CI: −0.44 to −0.15, Benjamini‐Hochberg adjusted p = 0.008). CONCLUSIONS: Together with existing literature, these results suggest that a genetic predisposition to higher BMI captures differences in metabolism leading to adiposity gain. In the absence of prospective data, separating these effects from the downstream consequences of weight gain is challenging.
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spelling pubmed-93249732022-07-30 A multivariant recall‐by‐genotype study of the metabolomic signature of BMI Fang, Si Wade, Kaitlin H. Hughes, David A. Fitzgibbon, Sophie Yip, Vikki Timpson, Nicholas J. Corbin, Laura J. Obesity (Silver Spring) ORIGINAL ARTICLES OBJECTIVE: This study estimated the effect of BMI on circulating metabolites in young adults using a recall‐by‐genotype study design. METHODS: A recall‐by‐genotype study was implemented in the Avon Longitudinal Study of Parents and Children. Samples from 756 participants were selected for untargeted metabolomics analysis based on low versus high genetic liability for higher BMI defined by a genetic risk score (GRS). Regression analyses were performed to investigate associations between BMI GRS group and relative abundance of 973 metabolites. RESULTS: After correction for multiple testing, 29 metabolites were associated with BMI GRS group. Bilirubin was among the most strongly associated metabolites, with reduced levels measured in individuals in the high‐BMI GRS group (β = −0.32, 95% CI: −0.46 to −0.18, Benjamini‐Hochberg adjusted p = 0.005). This study observed associations between BMI GRS group and the levels of several potentially diet‐related metabolites, including hippurate, which had lower mean abundance in individuals in the high‐BMI GRS group (β = −0.29, 95% CI: −0.44 to −0.15, Benjamini‐Hochberg adjusted p = 0.008). CONCLUSIONS: Together with existing literature, these results suggest that a genetic predisposition to higher BMI captures differences in metabolism leading to adiposity gain. In the absence of prospective data, separating these effects from the downstream consequences of weight gain is challenging. John Wiley and Sons Inc. 2022-05-22 2022-06 /pmc/articles/PMC9324973/ /pubmed/35598895 http://dx.doi.org/10.1002/oby.23441 Text en © 2022 The Authors. Obesity published by Wiley Periodicals LLC on behalf of The Obesity Society (TOS). https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle ORIGINAL ARTICLES
Fang, Si
Wade, Kaitlin H.
Hughes, David A.
Fitzgibbon, Sophie
Yip, Vikki
Timpson, Nicholas J.
Corbin, Laura J.
A multivariant recall‐by‐genotype study of the metabolomic signature of BMI
title A multivariant recall‐by‐genotype study of the metabolomic signature of BMI
title_full A multivariant recall‐by‐genotype study of the metabolomic signature of BMI
title_fullStr A multivariant recall‐by‐genotype study of the metabolomic signature of BMI
title_full_unstemmed A multivariant recall‐by‐genotype study of the metabolomic signature of BMI
title_short A multivariant recall‐by‐genotype study of the metabolomic signature of BMI
title_sort multivariant recall‐by‐genotype study of the metabolomic signature of bmi
topic ORIGINAL ARTICLES
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324973/
https://www.ncbi.nlm.nih.gov/pubmed/35598895
http://dx.doi.org/10.1002/oby.23441
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