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Data integration for prediction of weight loss in randomized controlled dietary trials
Diet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674420/ https://www.ncbi.nlm.nih.gov/pubmed/33208769 http://dx.doi.org/10.1038/s41598-020-76097-z |
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author | Nielsen, Rikke Linnemann Helenius, Marianne Garcia, Sara L. Roager, Henrik M. Aytan-Aktug, Derya Hansen, Lea Benedicte Skov Lind, Mads Vendelbo Vogt, Josef K. Dalgaard, Marlene Danner Bahl, Martin I. Jensen, Cecilia Bang Muktupavela, Rasa Warinner, Christina Aaskov, Vincent Gøbel, Rikke Kristensen, Mette Frøkiær, Hanne Sparholt, Morten H. Christensen, Anders F. Vestergaard, Henrik Hansen, Torben Kristiansen, Karsten Brix, Susanne Petersen, Thomas Nordahl Lauritzen, Lotte Licht, Tine Rask Pedersen, Oluf Gupta, Ramneek |
author_facet | Nielsen, Rikke Linnemann Helenius, Marianne Garcia, Sara L. Roager, Henrik M. Aytan-Aktug, Derya Hansen, Lea Benedicte Skov Lind, Mads Vendelbo Vogt, Josef K. Dalgaard, Marlene Danner Bahl, Martin I. Jensen, Cecilia Bang Muktupavela, Rasa Warinner, Christina Aaskov, Vincent Gøbel, Rikke Kristensen, Mette Frøkiær, Hanne Sparholt, Morten H. Christensen, Anders F. Vestergaard, Henrik Hansen, Torben Kristiansen, Karsten Brix, Susanne Petersen, Thomas Nordahl Lauritzen, Lotte Licht, Tine Rask Pedersen, Oluf Gupta, Ramneek |
author_sort | Nielsen, Rikke Linnemann |
collection | PubMed |
description | Diet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks. Random forest models integrated gut microbiome, host genetics, urine metabolome, measures of physiology and anthropometrics measured prior to any dietary intervention to identify individual predisposing features of weight loss in combination with diet. The most predictive models for weight loss included features of diet, gut bacterial species and urine metabolites (ROC-AUC: 0.84–0.88) compared to a diet-only model (ROC-AUC: 0.62). A model ensemble integrating multi-omics identified 64% of the non-responders with 80% confidence. Such models will be useful to assist in selecting appropriate weight management strategies, as individual predisposition to diet response varies. |
format | Online Article Text |
id | pubmed-7674420 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76744202020-11-19 Data integration for prediction of weight loss in randomized controlled dietary trials Nielsen, Rikke Linnemann Helenius, Marianne Garcia, Sara L. Roager, Henrik M. Aytan-Aktug, Derya Hansen, Lea Benedicte Skov Lind, Mads Vendelbo Vogt, Josef K. Dalgaard, Marlene Danner Bahl, Martin I. Jensen, Cecilia Bang Muktupavela, Rasa Warinner, Christina Aaskov, Vincent Gøbel, Rikke Kristensen, Mette Frøkiær, Hanne Sparholt, Morten H. Christensen, Anders F. Vestergaard, Henrik Hansen, Torben Kristiansen, Karsten Brix, Susanne Petersen, Thomas Nordahl Lauritzen, Lotte Licht, Tine Rask Pedersen, Oluf Gupta, Ramneek Sci Rep Article Diet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks. Random forest models integrated gut microbiome, host genetics, urine metabolome, measures of physiology and anthropometrics measured prior to any dietary intervention to identify individual predisposing features of weight loss in combination with diet. The most predictive models for weight loss included features of diet, gut bacterial species and urine metabolites (ROC-AUC: 0.84–0.88) compared to a diet-only model (ROC-AUC: 0.62). A model ensemble integrating multi-omics identified 64% of the non-responders with 80% confidence. Such models will be useful to assist in selecting appropriate weight management strategies, as individual predisposition to diet response varies. Nature Publishing Group UK 2020-11-18 /pmc/articles/PMC7674420/ /pubmed/33208769 http://dx.doi.org/10.1038/s41598-020-76097-z Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Nielsen, Rikke Linnemann Helenius, Marianne Garcia, Sara L. Roager, Henrik M. Aytan-Aktug, Derya Hansen, Lea Benedicte Skov Lind, Mads Vendelbo Vogt, Josef K. Dalgaard, Marlene Danner Bahl, Martin I. Jensen, Cecilia Bang Muktupavela, Rasa Warinner, Christina Aaskov, Vincent Gøbel, Rikke Kristensen, Mette Frøkiær, Hanne Sparholt, Morten H. Christensen, Anders F. Vestergaard, Henrik Hansen, Torben Kristiansen, Karsten Brix, Susanne Petersen, Thomas Nordahl Lauritzen, Lotte Licht, Tine Rask Pedersen, Oluf Gupta, Ramneek Data integration for prediction of weight loss in randomized controlled dietary trials |
title | Data integration for prediction of weight loss in randomized controlled dietary trials |
title_full | Data integration for prediction of weight loss in randomized controlled dietary trials |
title_fullStr | Data integration for prediction of weight loss in randomized controlled dietary trials |
title_full_unstemmed | Data integration for prediction of weight loss in randomized controlled dietary trials |
title_short | Data integration for prediction of weight loss in randomized controlled dietary trials |
title_sort | data integration for prediction of weight loss in randomized controlled dietary trials |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674420/ https://www.ncbi.nlm.nih.gov/pubmed/33208769 http://dx.doi.org/10.1038/s41598-020-76097-z |
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