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.
  
  
  METHODS
  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%.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Institute of Cardiometabolisme and Nutrition (ICAN), Assistance-Publique-Hôpitaux de Paris, ICAN, Heart and Metabolism Department, Hôpital Pitié-Salpêtrière, Paris, France; Human Nutrition Research Center-Ile de France, Paris, France (LCK, J-PB, NS, SG, FD, DB-R, J-DZ, KC, and SR); the Institut National de la Santé et de la Recherche Médicale (INSERM), Unité (U) 872, Nutriomique, Équipe 7, Paris, France (LCK, J-PB, NS, SG, FD, DB-R, J-DZ, KC, and SR); Centre de Recherche des Cordeliers, the Université Pierre et Marie Curie-Paris 6, Paris, France (LCK, J-PB, NS, SG, FD, DB-R, J-DZ, KC, and SR); the Laboratoire d'Informatique de Paris-6, Département Demonstration of Satellites enabling the Insertion of RPAS in Europe Équipe Décision, Université Pierre et Marie Curie-Paris 6, Campus Jussieu, Paris, France (P-HW); the Assistance Publique-Hôpitaux de Paris, Service de Biochimie et Hormonologie, Hôpital Tenon, Paris, France (J-PB and SF); Service de Biochimie Métabolique, Groupe Hospitalier Pitié-Salpêtrière, Paris, France (DB-R and RB); EA 4466, Département de Biologie Expérimentale, Métabolique et Clinique, Faculté de Pharmacie, the Université Paris Descartes, Paris, France (DB-R and RB); U910, Unité d'Ecologie et de Physiologie du Système Digestif, INRA, Jouy-en-Josas, France (JD); and Unité de modélisation mathématique et informatique des systèmes complexes, the Institut de Recherche pour le Développement, Unité Mixte de Recherche 209, France Nord, Bondy, France (J-DZ).
authors.forename: Ling Chun
authors.initials: LC
authors.lastname: Kong
authors.forename: Pierre-Henri
authors.initials: PH
authors.lastname: Wuillemin
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Bastard
authors.forename: Nataliya
authors.initials: N
authors.lastname: Sokolovska
authors.forename: Sophie
authors.initials: S
authors.lastname: Gougis
authors.forename: Soraya
authors.initials: S
authors.lastname: Fellahi
authors.forename: Froogh
authors.initials: F
authors.lastname: Darakhshan
authors.forename: Dominique
authors.initials: D
authors.lastname: Bonnefont-Rousselot
authors.forename: Randa
authors.initials: R
authors.lastname: Bittar
authors.forename: Joël
authors.initials: J
authors.lastname: Doré
authors.forename: Jean-Daniel
authors.initials: JD
authors.lastname: Zucker
authors.forename: Karine
authors.initials: K
authors.lastname: Clément
authors.forename: Salwa
authors.initials: S
authors.lastname: Rizkalla
mesh_terms: D000328:Adult; D000911:Antibodies, Monoclonal / Q000378:metabolism; D001499:Bayes Theorem; D015415:Biomarkers / Q000097:blood / Q000378:metabolism; D015992:Body Mass Index; D031204:Caloric Restriction; D005260:Female; D006801:Humans; D007328:Insulin / Q000097:blood; D007333:Insulin Resistance; D015850:Interleukin-6 / Q000097:blood; D007700:Kinetics; D007962:Leukocytes / Q000276:immunology*; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000178:diet therapy* / Q000276:immunology / Q000378:metabolism / Q000517:prevention & control; D050177:Overweight / Q000178:diet therapy* / Q000276:immunology / Q000378:metabolism / Q000517:prevention & control; D012372:ROC Curve; D055502:Secondary Prevention; D050507:Subcutaneous Fat, Abdominal / Q000276:immunology* / Q000378:metabolism; D015430:Weight Gain; D015431:Weight Loss
chemical_list: D000911:Antibodies, Monoclonal; D015415:Biomarkers; C096893:HAM-56 antibody; C508600:IL6 protein, human; D007328:Insulin; D015850:Interleukin-6