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

Rikke Linnemann Nielsen, Marianne Helenius, Sara L. Garcia, Henrik M. Roager, Derya Aytan-Aktug, Lea Benedicte Skov Hansen, Mads Vendelbo Lind, Josef Korbinian Vogt, Marlene Danner Dalgaard, Martin I. Bahl, Cecilia Bang Jensen, Rasa Muktupavela, Christina Warinner, Vincent Aaskov, Rikke Gøbel, Mette Kristensen, Hanne Frøkiær, Morten H. Sparholt, Anders F. Christensen, Henrik VestergaardTorben Hansen, Karsten Kristiansen, Susanne Brix, Thomas Nordahl Petersen, Lotte Lauritzen, Tine Rask Licht, Oluf Pedersen, Ramneek Gupta*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
Article number20103
JournalScientific Reports
Volume10
Issue number1
Number of pages15
ISSN2045-2322
DOIs
Publication statusPublished - 2020

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