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The Capital Region of Denmark - a part of Copenhagen University Hospital
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Data integration for prediction of weight loss in randomized controlled dietary trials

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  • Rikke Linnemann Nielsen
  • Marianne Helenius
  • Sara L Garcia
  • Henrik M Roager
  • Derya Aytan-Aktug
  • Lea Benedicte Skov Hansen
  • Mads Vendelbo Lind
  • Josef K 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 Vestergaard
  • Torben Hansen
  • Karsten Kristiansen
  • Susanne Brix
  • Thomas Nordahl Petersen
  • Lotte Lauritzen
  • Tine Rask Licht
  • Oluf Pedersen
  • Ramneek Gupta
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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
JournalScientific Reports
Volume10
Issue number1
Pages (from-to)20103
ISSN2045-2322
DOIs
Publication statusPublished - 18 Nov 2020

    Research areas

  • Biomarkers/blood, Diet Therapy/methods, Female, Gastrointestinal Microbiome, Genome-Wide Association Study, Humans, Machine Learning, Male, Postprandial Period, ROC Curve, Randomized Controlled Trials as Topic, Reproducibility of Results, Treatment Outcome, Weight Loss, Whole Grains

ID: 65388201