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

Health and Fitness

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

R. L. Nielsen, M. Helenius, et al.

This groundbreaking research explores how individual features like gut microbiome and genetics can predict weight loss in overweight Danes. With models achieving an impressive accuracy of 84-88%, this study by Nielsen et al. sheds light on personalized weight management strategies.

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Playback language: English
Abstract
This study investigates the prediction of weight loss in overweight, non-diabetic middle-aged Danes participating in two dietary trials. Random forest models integrated gut microbiome, host genetics, urine metabolome, physiology, and anthropometrics to identify individual predisposing features for weight loss. The most predictive models included diet, gut bacterial species, and urine metabolites (ROC-AUC: 0.84–0.88), outperforming a diet-only model (ROC-AUC: 0.62). An ensemble model identified 64% of non-responders with 80% confidence, suggesting potential for personalized weight management strategies.
Publisher
Scientific Reports
Published On
Nov 18, 2020
Authors
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 Warinners, 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
Tags
weight loss
gut microbiome
dietary trials
personalized strategies
predictive modeling
non-diabetic
middle-aged

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