Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Clinical Trial
. 2013 Dec;98(6):1385-94.
doi: 10.3945/ajcn.113.058099. Epub 2013 Oct 30.

Insulin Resistance and Inflammation Predict Kinetic Body Weight Changes in Response to Dietary Weight Loss and Maintenance in Overweight and Obese Subjects by Using a Bayesian Network Approach

Affiliations
Clinical Trial

Insulin Resistance and Inflammation Predict Kinetic Body Weight Changes in Response to Dietary Weight Loss and Maintenance in Overweight and Obese Subjects by Using a Bayesian Network Approach

Ling Chun Kong et al. Am J Clin Nutr. .

Abstract

Background: The ability to identify obese subjects who will lose weight in response to energy restriction is an important strategy in obesity treatment.

Objective: We aimed to identify obese subjects who would lose weight and maintain weight loss through 6 wk of energy restriction and 6 wk of weight maintenance.

Design: Fifty obese or overweight subjects underwent a 6-wk energy-restricted, high-protein diet followed by another 6 wk of weight maintenance. Network modeling by using combined biological, gut microbiota, and environmental factors was performed to identify predictors of weight trajectories.

Results: On the basis of body weight trajectories, 3 subject clusters were identified. Clusters A and B lost more weight during energy restriction. During the stabilization phase, cluster A continued to lose weight, whereas cluster B remained stable. Cluster C lost less and rapidly regained weight during the stabilization period. At baseline, cluster C had the highest plasma insulin, interleukin (IL)-6, adipose tissue inflammation (HAM56+ cells), and Lactobacillus/Leuconostoc/Pediococcus numbers in fecal samples. Weight regain after energy restriction correlated positively with insulin resistance (homeostasis model assessment of insulin resistance: r = 0.5, P = 0.0002) and inflammatory markers (IL-6; r = 0.43, P = 0.002) at baseline. The Bayesian network identified plasma insulin, IL-6, leukocyte number, and adipose tissue (HAM56) at baseline as predictors that were sufficient to characterize the 3 clusters. The prediction accuracy reached 75.5%.

Conclusion: The resistance to weight loss and proneness to weight regain could be predicted by the combination of high plasma insulin and inflammatory markers before dietary intervention.

Trial registration: ClinicalTrials.gov NCT01314690.

Similar articles

See all similar articles

Cited by 20 articles

See all "Cited by" articles

Publication types

MeSH terms

Associated data

Feedback