Data integration for prediction of weight loss in randomized controlled dietary trials

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Data integration for prediction of weight loss in randomized controlled dietary trials. / Nielsen, Rikke Linnemann; Helenius, Marianne; Garcia, Sara L; Roager, Henrik Munch; Aytan-Aktug, Derya; Hansen, Lea Benedicte Skov; Lind, Mads Vendelbo; Vogt, Josef Korbinian; Dalgaard, Marlene Danner; Bahl, Martin Iain; Jensen, Cecilia Bang; Muktupavela, Rasa; Warinner, Christina; Aaskov, Vincent; Gøbel, Rikke; Kristensen, Mette Bredal; 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.

In: Scientific Reports, Vol. 10, 20103, 2020.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Nielsen, RL, Helenius, M, Garcia, SL, Roager, HM, Aytan-Aktug, D, Hansen, LBS, Lind, MV, Vogt, JK, Dalgaard, MD, Bahl, MI, Jensen, CB, Muktupavela, R, Warinner, C, Aaskov, V, Gøbel, R, Kristensen, MB, Frøkiær, H, Sparholt, MH, Christensen, AF, Vestergaard, H, Hansen, T, Kristiansen, K, Brix, S, Petersen, TN, Lauritzen, L, Licht, TR, Pedersen, O & Gupta, R 2020, 'Data integration for prediction of weight loss in randomized controlled dietary trials', Scientific Reports, vol. 10, 20103. https://doi.org/10.1038/s41598-020-76097-z

APA

Nielsen, R. L., Helenius, M., Garcia, S. L., Roager, H. M., Aytan-Aktug, D., Hansen, L. B. S., Lind, M. V., Vogt, J. K., Dalgaard, M. D., Bahl, M. I., Jensen, C. B., Muktupavela, R., Warinner, C., Aaskov, V., Gøbel, R., Kristensen, M. B., Frøkiær, H., Sparholt, M. H., Christensen, A. F., ... Gupta, R. (2020). Data integration for prediction of weight loss in randomized controlled dietary trials. Scientific Reports, 10, [20103]. https://doi.org/10.1038/s41598-020-76097-z

Vancouver

Nielsen RL, Helenius M, Garcia SL, Roager HM, Aytan-Aktug D, Hansen LBS et al. Data integration for prediction of weight loss in randomized controlled dietary trials. Scientific Reports. 2020;10. 20103. https://doi.org/10.1038/s41598-020-76097-z

Author

Nielsen, Rikke Linnemann ; Helenius, Marianne ; Garcia, Sara L ; Roager, Henrik Munch ; Aytan-Aktug, Derya ; Hansen, Lea Benedicte Skov ; Lind, Mads Vendelbo ; Vogt, Josef Korbinian ; Dalgaard, Marlene Danner ; Bahl, Martin Iain ; Jensen, Cecilia Bang ; Muktupavela, Rasa ; Warinner, Christina ; Aaskov, Vincent ; Gøbel, Rikke ; Kristensen, Mette Bredal ; 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. In: Scientific Reports. 2020 ; Vol. 10.

Bibtex

@article{5a1b7903ec0f47ca8ebec8fb3b3c4520,
title = "Data integration for prediction of weight loss in randomized controlled dietary trials",
abstract = "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.",
author = "Nielsen, {Rikke Linnemann} and Marianne Helenius and Garcia, {Sara L} and Roager, {Henrik Munch} and Derya Aytan-Aktug and Hansen, {Lea Benedicte Skov} and Lind, {Mads Vendelbo} and Vogt, {Josef Korbinian} and Dalgaard, {Marlene Danner} and Bahl, {Martin Iain} and Jensen, {Cecilia Bang} and Rasa Muktupavela and Christina Warinner and Vincent Aaskov and Rikke G{\o}bel and Kristensen, {Mette Bredal} and Hanne Fr{\o}ki{\ae}r and Sparholt, {Morten H} and Christensen, {Anders F} and Henrik Vestergaard and Torben Hansen and Karsten Kristiansen and Susanne Brix and Petersen, {Thomas Nordahl} and Lotte Lauritzen and Licht, {Tine Rask} and Oluf Pedersen and Ramneek Gupta",
note = "CURIS 2020 NEXS 352",
year = "2020",
doi = "10.1038/s41598-020-76097-z",
language = "English",
volume = "10",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Data integration for prediction of weight loss in randomized controlled dietary trials

AU - Nielsen, Rikke Linnemann

AU - Helenius, Marianne

AU - Garcia, Sara L

AU - Roager, Henrik Munch

AU - Aytan-Aktug, Derya

AU - Hansen, Lea Benedicte Skov

AU - Lind, Mads Vendelbo

AU - Vogt, Josef Korbinian

AU - Dalgaard, Marlene Danner

AU - Bahl, Martin Iain

AU - Jensen, Cecilia Bang

AU - Muktupavela, Rasa

AU - Warinner, Christina

AU - Aaskov, Vincent

AU - Gøbel, Rikke

AU - Kristensen, Mette Bredal

AU - Frøkiær, Hanne

AU - Sparholt, Morten H

AU - Christensen, Anders F

AU - Vestergaard, Henrik

AU - Hansen, Torben

AU - Kristiansen, Karsten

AU - Brix, Susanne

AU - Petersen, Thomas Nordahl

AU - Lauritzen, Lotte

AU - Licht, Tine Rask

AU - Pedersen, Oluf

AU - Gupta, Ramneek

N1 - CURIS 2020 NEXS 352

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

U2 - 10.1038/s41598-020-76097-z

DO - 10.1038/s41598-020-76097-z

M3 - Journal article

C2 - 33208769

VL - 10

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 20103

ER -

ID: 251789444