We conducted a study involving 50 obese or overweight individuals to determine if insulin resistance and inflammation markers can predict body weight changes after dietary intervention. We found that these markers, along with numbers of certain microbes in faecal samples, could predict response to the interventions we used at a rate of 75% accuracy. While this suggests other (unidentified) factors are probably involved, we are confident that the clusters we identified are relevant in terms of weight management in these patients. The study was published in the American Journal of Clinical Nutrition, 30th October 2013.

Full title

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.

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. This trial was registered at clinicaltrials.gov as NCT01314690.  

Authors

Kong LC1, Wuillemin PH, Bastard JP, Sokolovska N, Gougis S, Fellahi S, Darakhshan F, Bonnefont-Rousselot D, Bittar R, Doré J, Zucker JD, Clément K, Rizkalla S.

Journal and Citation

American Journal of Clinical Nutrition 98(6): 1385-1394 (30th October 2013)

First published online 30th October 2013

DOI: 10.3945/ajcn.113.058099

Link: http://ajcn.nutrition.org/content/98/6/1385.abstract

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