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

Research output: Contribution to journalJournal articleResearchpeer-review


  • Rikke Linnemann Nielsen
  • Marianne Helenius
  • Sara L Garcia
  • Derya Aytan-Aktug
  • Lea Benedicte Skov Hansen
  • Mads Vendelbo Lind
  • Josef Korbinian Vogt
  • Marlene Danner Dalgaard
  • Martin Iain Bahl
  • Cecilia Bang Jensen
  • Christina Warinner
  • Vincent Aaskov
  • Rikke Gøbel
  • Mette Bredal Kristensen
  • Morten H Sparholt
  • Anders F Christensen
  • Henrik Vestergaard
  • Thomas Nordahl Petersen
  • Tine Rask Licht
  • Ramneek Gupta

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
Number of pages15
Publication statusPublished - 2020

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