Ling Chun Kong1, Pierre-Henri Wuillemin, Jean-Philippe Bastard, Nataliya Sokolovska, Sophie Gougis, Soraya Fellahi, Froogh Darakhshan, Dominique Bonnefont-Rousselot, Randa Bittar, Joël Doré, Jean-Daniel Zucker, Karine Clément, Salwa Rizkalla. 1. 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).
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.
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.
Authors: Kelvin H M Kwok; Mikael Rydén; Daniel P Andersson; Gallic Beauchef; Christelle Guere; Katell Vie; Otto Bergman; Veroniqa Lundbäck; Peter Arner; Ingrid Dahlman Journal: Int J Obes (Lond) Date: 2019-06-04 Impact factor: 5.095
Authors: M Svendstrup; K H Allin; T I A Sørensen; T H Hansen; N Grarup; T Hansen; H Vestergaard Journal: Int J Obes (Lond) Date: 2017-11-16 Impact factor: 5.095
Authors: Francesco Rotella; Lisa Lazzeretti; Valeria Barbaro; Giovanni Castellini; Michela Bigiarini; Barbara Cresci; Valdo Ricca; Carlo Maria Rotella; Edoardo Mannucci Journal: J Endocrinol Invest Date: 2014-07-20 Impact factor: 4.256