Horizon / Plein textes La base de ressources documentaires de l'IRD

IRD

 

Publications des scientifiques de l'IRD

Kong L. C., Wuillemin P. H., Bastard J. P., Sokolovska N., Gougis S., Fellahi S., Darakhshan F., Bonnefont-Rousselot D., Bittar R., Dore J., Zucker Jean-Daniel, Clement K., Rizkalla S. (2013). 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. American Journal of Clinical Nutrition, 98 (6), 1385-1394. ISSN 0002-9165

Accès réservé (Intranet IRD) Demander le PDF

Lien direct chez l'éditeur doi:10.3945/ajcn.113.058099

Titre
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
Année de publication2013
Type de documentArticle référencé dans le Web of Science WOS:000328002000004
AuteursKong L. C., Wuillemin P. H., Bastard J. P., Sokolovska N., Gougis S., Fellahi S., Darakhshan F., Bonnefont-Rousselot D., Bittar R., Dore J., Zucker Jean-Daniel, Clement K., Rizkalla S.
SourceAmerican Journal of Clinical Nutrition, 2013, 98 (6), p. 1385-1394. ISSN 0002-9165
RésuméBackground: 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.
Plan de classementNutrition, alimentation [054] ; Sciences fondamentales / Techniques d'analyse et de recherche [020]
LocalisationFonds IRD [F B010061354]
Identifiant IRDfdi:010061354
Lien permanenthttp://www.documentation.ird.fr/hor/fdi:010061354

Export des données

Disponibilité des documents

Télechargment fichier PDF téléchargeable

Lien sur le Web lien chez l'éditeur

Accès réservé en accès réservé

HAL en libre accès sur HAL


Accès aux documents originaux :

Le FDI est labellisé CollEx

Accès direct

Bureau du chercheur

Site de la documentation

Espace intranet IST (accès réservé)

Suivi des publications IRD (accès réservé)

Mentions légales

Services Horizon

Poser une question

Consulter l'aide en ligne

Déposer une publication (accès réservé)

S'abonner au flux RSS

Voir les tableaux chronologiques et thématiques

Centres de documentation

Bondy

Montpellier (centre IRD)

Montpellier (MSE)

Cayenne

Nouméa

Papeete

Abidjan

Dakar

Niamey

Ouagadougou

Tunis

La Paz

Quito