Abstract

While low-carbohydrate and low-fat diets can both lead to weight-loss, a substantial variability in achieved long-term outcomes exists among obese but otherwise healthy adults. We examined the hypothesis that structural differences in the gut microbiota explain a portion of variability in weight-loss using two cohorts of obese adults enrolled in the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) study. A total of 161 pre-diet fecal samples were sequenced from a discovery cohort (n = 66) and 106 from a validation cohort (n = 56). An additional 157 fecal samples were sequenced from the discovery cohort after 10 weeks of dietary intervention. We found no specific bacterial signatures associated with weight loss that were consistent across both cohorts. However, the gut microbiota plasticity (i.e. variability), was correlated with long-term (12-month) weight loss in a diet-dependent manner; on the low-fat diet subjects with higher pre-diet daily plasticity had higher sustained weight loss, whereas on the low-carbohydrate diet those with higher plasticity over 10 weeks of dieting had higher 12-month weight loss. Our findings suggest the potential importance of gut microbiota plasticity for sustained weight-loss. We highlight the advantages of evaluating kinetic trends and assessing reproducibility in studies of the gut microbiota.

Details

Title
Gut microbiota plasticity is correlated with sustained weight loss on a low-carb or low-fat dietary intervention
Author
Grembi, Jessica A 1   VIAFID ORCID Logo  ; Nguyen, Lan H 2   VIAFID ORCID Logo  ; Haggerty, Thomas D 3 ; Gardner, Christopher D 4 ; Holmes, Susan P 5   VIAFID ORCID Logo  ; Parsonnet, Julie 6   VIAFID ORCID Logo 

 Stanford University, Department of Civil and Environmental Engineering, Stanford, United States (GRID:grid.168010.e) (ISNI:0000000419368956); Stanford University School of Medicine, Department of Medicine, Stanford, United States (GRID:grid.168010.e) (ISNI:0000000419368956) 
 Stanford University, Institute for Computational and Mathematical Engineering, Stanford, United States (GRID:grid.168010.e) (ISNI:0000000419368956) 
 Stanford University School of Medicine, Department of Medicine, Stanford, United States (GRID:grid.168010.e) (ISNI:0000000419368956) 
 Stanford University School of Medicine, Stanford Prevention Research Center, Department of Medicine, Stanford, United States (GRID:grid.168010.e) (ISNI:0000000419368956) 
 Stanford University, Department of Statistics, Stanford, United States (GRID:grid.168010.e) (ISNI:0000000419368956) 
 Stanford University School of Medicine, Department of Medicine, Stanford, United States (GRID:grid.168010.e) (ISNI:0000000419368956); Stanford University School of Medicine, Department of Health Research and Policy, Stanford, United States (GRID:grid.168010.e) (ISNI:0000000419368956) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2348291275
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.