Predicting weight loss success on a new Nordic diet: an untargeted multi-platform metabolomics and machine learning approach

Frontiers in Nutrition

Nutrition

Samuel Demharter¹  Valdemar Stentoft-Larsen¹  Bekzod Khakimov²  Alessia Trimigno²  Søren Balling Engelsen²  Faidon Magkos³  Lars Ove Dragsted³  Kristina Pigsborg³  Mona Adnan Aldubayan⁴  Arne Astrup⁵  Mads Fiil Hjorth⁵ 

[1] Abzu ApS, Copenhagen, Denmark;Department of Food Science, University of Copenhagen, Frederiksberg, Denmark;Department of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg, Denmark;Department of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg, Denmark;King Saud bin Abdulaziz University for Health Sciences, College of Applied Medical Sciences, Riyadh, Saudi Arabia;Obesity and Nutritional Sciences, Novo Nordisk Foundation, Hellerup, Denmark;

关键词: precision nutrition;    metabolomics;    obesity;    new Nordic diet;    machine learning;   

DOI  :  10.3389/fnut.2023.1191944

 received in 2023-03-22, accepted in 2023-07-12,  发布年份 2023

来源: Frontiers

【 摘 要 】

Background and aimResults from randomized controlled trials indicate that no single diet performs better than other for all people living with obesity. Regardless of the diet plan, there is always large inter-individual variability in weight changes, with some individuals losing weight and some not losing or even gaining weight. This raises the possibility that, for different individuals, the optimal diet for successful weight loss may differ. The current study utilized machine learning to build a predictive model for successful weight loss in subjects with overweight or obesity on a New Nordic Diet (NND).MethodsNinety-one subjects consumed an NND ad libitum for 26 weeks. Based on their weight loss, individuals were classified as responders (weight loss ≥5%, n = 46) or non-responders (weight loss <2%, n = 24). We used clinical baseline data combined with baseline urine and plasma untargeted metabolomics data from two different analytical platforms, resulting in a data set including 2,766 features, and employed symbolic regression (QLattice) to develop a predictive model for weight loss success.ResultsThere were no differences in clinical parameters at baseline between responders and non-responders, except age (47 ± 13 vs. 39 ± 11 years, respectively, p = 0.009). The final predictive model for weight loss contained adipic acid and argininic acid from urine (both metabolites were found at lower levels in responders) and generalized from the training (AUC 0.88) to the test set (AUC 0.81). Responders were also able to maintain a weight loss of 4.3% in a 12 month follow-up period.ConclusionWe identified a model containing two metabolites that were able to predict the likelihood of achieving a clinically significant weight loss on an ad libitum NND. This work demonstrates that models based on an untargeted multi-platform metabolomics approach can be used to optimize precision dietary treatment for obesity.

【 授权许可】

Unknown   
Copyright © 2023 Pigsborg, Stentoft-Larsen, Demharter, Aldubayan, Trimigno, Khakimov, Engelsen, Astrup, Hjorth, Dragsted and Magkos.