%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Kong, L. C.
%A Wuillemin, P. H.
%A Bastard, J. P.
%A Sokolovska, N.
%A Gougis, S.
%A Fellahi, S.
%A Darakhshan, F.
%A Bonnefont-Rousselot, D.
%A Bittar, R.
%A Dore, J.
%A Zucker, Jean-Daniel
%A Clement, K.
%A Rizkalla, S.
%T 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
%D 2013
%L fdi:010061354
%G ENG
%J American Journal of Clinical Nutrition
%@ 0002-9165
%M ISI:000328002000004
%N 6
%P 1385-1394
%R 10.3945/ajcn.113.058099
%U https://www.documentation.ird.fr/hor/fdi:010061354
%> https://www.documentation.ird.fr/intranet/publi/2014/01/010061354.pdf
%V 98
%W Horizon (IRD)
%X 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.
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