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Use of Metabolomic Profiling to Understand Variability in Adiposity Changes Following an Intentional Weight Loss Intervention in Older Adults

Inter-individual response to dietary interventions remains a major challenge to successful weight loss among older adults. This study applied metabolomics technology to identify small molecule signatures associated with a loss of fat mass and overall weight in a cohort of older adults on a nutrition...

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Autores principales: Quillen, Ellen E., Beavers, Daniel P., O’Brien Cox, Anderson, Furdui, Cristina M., Lee, Jingyun, Miller, Ryan M., Wu, Hanzhi, Beavers, Kristen M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603124/
https://www.ncbi.nlm.nih.gov/pubmed/33086512
http://dx.doi.org/10.3390/nu12103188
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author Quillen, Ellen E.
Beavers, Daniel P.
O’Brien Cox, Anderson
Furdui, Cristina M.
Lee, Jingyun
Miller, Ryan M.
Wu, Hanzhi
Beavers, Kristen M.
author_facet Quillen, Ellen E.
Beavers, Daniel P.
O’Brien Cox, Anderson
Furdui, Cristina M.
Lee, Jingyun
Miller, Ryan M.
Wu, Hanzhi
Beavers, Kristen M.
author_sort Quillen, Ellen E.
collection PubMed
description Inter-individual response to dietary interventions remains a major challenge to successful weight loss among older adults. This study applied metabolomics technology to identify small molecule signatures associated with a loss of fat mass and overall weight in a cohort of older adults on a nutritionally complete, high-protein diet. A total of 102 unique metabolites were measured using liquid chromatography-mass spectrometry (LC-MS) for 38 adults aged 65–80 years randomized to dietary intervention and 36 controls. Metabolite values were analyzed in both baseline plasma samples and samples collected following the six-month dietary intervention to consider both metabolites that could predict the response to diet and those that changed in response to diet or weight loss.Eight metabolites changed over the intervention at a nominally significant level: D-pantothenic acid, L-methionine, nicotinate, aniline, melatonin, deoxycarnitine, 6-deoxy-L-galactose, and 10-hydroxydecanoate. Within the intervention group, there was broad variation in the achieved weight-loss and dual-energy x-ray absorptiometry (DXA)-defined changes in total fat and visceral adipose tissue (VAT) mass. Change in the VAT mass was significantly associated with the baseline abundance of α-aminoadipate (p = 0.0007) and an additional mass spectrometry peak that may represent D-fructose, myo-inositol, mannose, α-D-glucose, allose, D-galactose, D-tagatose, or L-sorbose (p = 0.0001). This hypothesis-generating study reflects the potential of metabolomic biomarkers for the development of personalized dietary interventions.
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spelling pubmed-76031242020-11-01 Use of Metabolomic Profiling to Understand Variability in Adiposity Changes Following an Intentional Weight Loss Intervention in Older Adults Quillen, Ellen E. Beavers, Daniel P. O’Brien Cox, Anderson Furdui, Cristina M. Lee, Jingyun Miller, Ryan M. Wu, Hanzhi Beavers, Kristen M. Nutrients Article Inter-individual response to dietary interventions remains a major challenge to successful weight loss among older adults. This study applied metabolomics technology to identify small molecule signatures associated with a loss of fat mass and overall weight in a cohort of older adults on a nutritionally complete, high-protein diet. A total of 102 unique metabolites were measured using liquid chromatography-mass spectrometry (LC-MS) for 38 adults aged 65–80 years randomized to dietary intervention and 36 controls. Metabolite values were analyzed in both baseline plasma samples and samples collected following the six-month dietary intervention to consider both metabolites that could predict the response to diet and those that changed in response to diet or weight loss.Eight metabolites changed over the intervention at a nominally significant level: D-pantothenic acid, L-methionine, nicotinate, aniline, melatonin, deoxycarnitine, 6-deoxy-L-galactose, and 10-hydroxydecanoate. Within the intervention group, there was broad variation in the achieved weight-loss and dual-energy x-ray absorptiometry (DXA)-defined changes in total fat and visceral adipose tissue (VAT) mass. Change in the VAT mass was significantly associated with the baseline abundance of α-aminoadipate (p = 0.0007) and an additional mass spectrometry peak that may represent D-fructose, myo-inositol, mannose, α-D-glucose, allose, D-galactose, D-tagatose, or L-sorbose (p = 0.0001). This hypothesis-generating study reflects the potential of metabolomic biomarkers for the development of personalized dietary interventions. MDPI 2020-10-19 /pmc/articles/PMC7603124/ /pubmed/33086512 http://dx.doi.org/10.3390/nu12103188 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Quillen, Ellen E.
Beavers, Daniel P.
O’Brien Cox, Anderson
Furdui, Cristina M.
Lee, Jingyun
Miller, Ryan M.
Wu, Hanzhi
Beavers, Kristen M.
Use of Metabolomic Profiling to Understand Variability in Adiposity Changes Following an Intentional Weight Loss Intervention in Older Adults
title Use of Metabolomic Profiling to Understand Variability in Adiposity Changes Following an Intentional Weight Loss Intervention in Older Adults
title_full Use of Metabolomic Profiling to Understand Variability in Adiposity Changes Following an Intentional Weight Loss Intervention in Older Adults
title_fullStr Use of Metabolomic Profiling to Understand Variability in Adiposity Changes Following an Intentional Weight Loss Intervention in Older Adults
title_full_unstemmed Use of Metabolomic Profiling to Understand Variability in Adiposity Changes Following an Intentional Weight Loss Intervention in Older Adults
title_short Use of Metabolomic Profiling to Understand Variability in Adiposity Changes Following an Intentional Weight Loss Intervention in Older Adults
title_sort use of metabolomic profiling to understand variability in adiposity changes following an intentional weight loss intervention in older adults
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603124/
https://www.ncbi.nlm.nih.gov/pubmed/33086512
http://dx.doi.org/10.3390/nu12103188
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