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2. Combinatorial, additive and dose-dependent drug-microbiome associations

Author

Forslund, SK, Chakaroun, R, Zimmermann-Kogadeeva, M, Markó, L, Aron-Wisnewsky, J, Nielsen, T, Moitinho-Silva, L, Schmidt, TSB, Falony, G, Vieira-Silva, S, Adriouch, S, Alves, RJ, Assmann, K, Bastard, J-P, Birkner, T, Caesar, R, Chilloux, J, Coelho, LP, Fezeu, L, Galleron, N, Helft, G, Isnard, R, Ji, B, Kuhn, M, Le Chatelier, E, Myridakis, A, Olsson, L, Pons, N, Prifti, E, Quinquis, B, Roume, H, Salem, J-E, Sokolovska, N, Tremaroli, V, Valles-Colomer, M, Lewinter, C, Søndertoft, NB, Pedersen, HK, Hansen, TH, Amouyal, C, Andersson Galijatovic, EA, Andreelli, F, Barthelemy, O, Batisse, J-P, Belda, E, Berland, M, Bittar, R, Blottière, H, Bosquet, F, Boubrit, R, Bourron, O, Camus, M, Cassuto, D, Ciangura, C, Collet, J-P, Dao, M-C, Djebbar, M, Doré, A, Engelbrechtsen, L, Fellahi, S, Fromentin, S, Galan, P, Gauguier, D, Giral, P, Hartemann, A, Hartmann, B, Holst, JJ, Hornbak, M, Hoyles, L, Hulot, J-S, Jaqueminet, S, Jørgensen, NR, Julienne, H, Justesen, J, Kammer, J, Krarup, N, Kerneis, M, Khemis, J, Kozlowski, R, Lejard, V, Levenez, F, Lucas-Martini, L, Massey, R, Martinez-Gili, L, Maziers, N, Medina-Stamminger, J, Montalescot, G, Moute, S, Neves, AL, Olanipekun, M, Le Pavin, LP, Poitou, C, Pousset, F, Pouzoulet, L, Rodriguez-Martinez, A, Rouault, C, Silvain, J, Svendstrup, M, Swartz, T, Vanduyvenboden, T, Vatier, C, Walther, S, Gøtze, JP, Køber, L, Vestergaard, H, Hansen, T, Zucker, J-D, Hercberg, S, Oppert, J-M, Letunic, I, Nielsen, J, Bäckhed, F, Ehrlich, SD, Dumas, M-E, Raes, J, Pedersen, O, Clément, K, Stumvoll, M, Bork, P, The MetaCardis Consortium (Hoyles, L.), European Molecular Biology Laboratory [Heidelberg] (EMBL), Max Delbrück Center for Molecular Medicine [Berlin] (MDC), Helmholtz-Gemeinschaft = Helmholtz Association, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Berlin Institute of Health (BIH), German Center for Cardiovascular Research (DZHK), Universität Leipzig, Nutrition et obésités: approches systémiques (UMR-S 1269) (Nutriomics), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), University of Copenhagen = Københavns Universitet (UCPH), University of New South Wales [Sydney] (UNSW), Paul Scherrer Institute (PSI), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Heidelberg University, Equipe 3: EREN- Equipe de Recherche en Epidémiologie Nutritionnelle (CRESS - U1153), Université Sorbonne Paris Nord-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre de Recherche Saint-Antoine (CRSA), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), CHU Henri Mondor [Créteil], Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases (ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut de Cardiométabolisme et Nutrition = Institute of Cardiometabolism and Nutrition [CHU Pitié Salpêtrière] (IHU ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-CHU Pitié-Salpêtrière [AP-HP], University of Gothenburg (GU), Imperial College London, MetaGenoPolis (MGP (US 1367)), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Paris-Saclay, Institut de cardiologie [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Chalmers University of Technology [Gothenburg, Sweden], Unité de modélisation mathématique et informatique des systèmes complexes [Bondy] (UMMISCO), Université de Yaoundé I-Institut de la francophonie pour l'informatique-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Université Gaston Bergé (Saint-Louis, Sénégal)-Université Cadi Ayyad [Marrakech] (UCA)-Sorbonne Université (SU)-Institut de Recherche pour le Développement (IRD [France-Nord]), Centre d'investigation clinique Paris Est [CHU Pitié Salpêtrière] (CIC Paris-Est), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Biobyte Solutions [Heidelberg, Germany] (BS), IT University of Copenhagen (ITU), Sahlgrenska University Hospital [Gothenburg], Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283 (EGENODIA (GI3M)), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), National Heart and Lung Institute [London] (NHLI), Imperial College London-Royal Brompton and Harefield NHS Foundation Trust, McGill University and Genome Quebec Innovation Centre, Helmholtz Institute Ulm (HIU), Helmholtz Zentrum München = German Research Center for Environmental Health, University of Würzburg = Universität Würzburg, Yonsei University, MetaCardis Consortium*: Chloe Amouyal, Ehm Astrid Andersson Galijatovic, Fabrizio Andreelli, Olivier Barthelemy, Jean-Paul Batisse, Eugeni Belda, Magalie Berland, Randa Bittar, Hervé Blottière, Frederic Bosquet, Rachid Boubrit, Olivier Bourron, Mickael Camus, Dominique Cassuto, Cecile Ciangura, Jean-Philippe Collet, Maria-Carlota Dao, Morad Djebbar, Angélique Doré, Line Engelbrechtsen, Soraya Fellahi, Sebastien Fromentin, Pilar Galan, Dominique Gauguier, Philippe Giral, Agnes Hartemann, Bolette Hartmann, Jens Juul Holst, Malene Hornbak, Lesley Hoyles, Jean-Sebastien Hulot, Sophie Jaqueminet, Niklas Rye Jørgensen, Hanna Julienne, Johanne Justesen, Judith Kammer, Nikolaj Krarup, Mathieu Kerneis, Jean Khemis, Ruby Kozlowski, Véronique Lejard, Florence Levenez, Lea Lucas-Martini, Robin Massey, Laura Martinez-Gili, Nicolas Maziers, Jonathan Medina-Stamminger, Gilles Montalescot, Sandrine Moute, Ana Luisa Neves, Michael Olanipekun, Laetitia Pasero Le Pavin, Christine Poitou, Francoise Pousset, Laurence Pouzoulet, Andrea Rodriguez-Martinez, Christine Rouault, Johanne Silvain, Mathilde Svendstrup, Timothy Swartz, Thierry Vanduyvenboden, Camille Vatier, Stefanie Walther., ANR-16-IDEX-0004,ULNE,ULNE(2016), ANR-18-IBHU-0001,PreciDIAB,PreciDIAB Institute, the holistic approach of personal diabets care(2018), Dumas, Marc-Emmanuel, Universität Leipzig [Leipzig], Service de Nutrition [CHU Pitié-Salpétrière], Institut E3M [CHU Pitié-Salpêtrière], CHU Henri Mondor, Centre d'investigation clinique pluridisciplinaire [CHU Pitié Salpêtrière] (CIC-1901(ex CIC-1421)), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Imperial College London - National Heart and Lung Institute, and Division of Computational and Systems Medicine, Imperial College London, London, SW7 2AZ, UK

Subjects
Clostridiales, Science & Technology, Multidisciplinary, ANTIBIOTIC USE, IMPACT, Microbiota, [SDV]Life Sciences [q-bio], HUMAN GUT MICROBIOME, Atherosclerosis, Gastrointestinal Microbiome, [SDV] Life Sciences [q-bio], Multidisciplinary Sciences, PROTON PUMP INHIBITORS, Cardiovascular and Metabolic Diseases, GUIDELINE, Metabolome, MANAGEMENT, Humans, Science & Technology - Other Topics, ALTERS
Abstract

During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery1-5. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease. ispartof: NATURE vol:600 issue:7889 pages:500-+ ispartof: location:England status: published

Published
2021
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4. Gut microbiota profile of obese diabetic women submitted to roux-en-y gastric bypass and its association with food intake and postoperative diabetes remission

Author

Al Assal, K., Prifti, E., Belda, E., Sala, P., Clement, K., Dao, M. C., Dore, J., Levenez, F., Taddei, C. R., Fonseca, D. C., Rocha, I. M., Balmant, B. D., Thomas, A. M., Santo, M. A., Dias-Neto, E., Setubal, J. C., Zucker, Jean-Daniel, Belarmino, G., Torrinhas, R. S., and Waitzberg, D. L.

Subjects
gut microbiota, type 2 diabetes mellitus, bariatric surgery, digestive system
Abstract

Gut microbiota composition is influenced by environmental factors and has been shown to impact body metabolism. Objective: To assess the gut microbiota profile before and after Roux-en-Y gastric bypass (RYGB) and the correlation with food intake and postoperative type 2 diabetes remission (T2Dr). Design: Gut microbiota profile from obese diabetic women was evaluated before (n = 25) and 3 (n = 20) and 12 months (n = 14) after RYGB, using MiSeq Illumina-based V4 bacterial 16S rRNA gene profiling. Data on food intake (7-day record) and T2Dr (American Diabetes Association (ADA) criteria) were recorded. Results: Preoperatively, the abundance of five bacteria genera differed between patients with (57%) and without T2Dr (p < 0.050). Preoperative gut bacteria genus signature was able to predict the T2Dr status with 0.94 accuracy ROC curve (receiver operating characteristic curve). Postoperatively (vs. preoperative), the relative abundance of some gut bacteria genera changed, the gut microbial richness increased, and the Firmicutes to Bacteroidetes ratio (rFB) decreased (p < 0.05) regardless of T2Dr. Richness levels was correlated with dietary profile pre and postoperatively, mainly displaying positive and inverse correlations with fiber and lipid intakes, respectively (p < 0.05). Conclusions: Gut microbiota profile was influenced by RYGB and correlated with diet and T2Dr preoperatively, suggesting the possibility to assess its composition to predict postoperative T2Dr.

Published
2020

5. A data integration multi-omics approach to study calorie restriction-induced changes in insulin sensitivity

Author

Dao, M. C., Sokolovska, N., Brazeilles, R., Affeldt, S., Pelloux, V., Prifti, E., Chilloux, J., Verger, E., Kayser, B. D., Aron-Wisnewsky, J., Ichou, F., Pujos-Guillot, E., Hoyles, L., Juste, C., Dore, J., Dumas, M. E., Rizkalla, S. W., Holmes, B. A., Zucker, Jean-Daniel, Clement, K., and Micro-Obes Consortium

Subjects
microbiota, insulin sensitivity, lifestyle factors, data integration, omics
Abstract

Background: The mechanisms responsible for calorie restriction (CR)-induced improvement in insulin sensitivity (IS) have not been fully elucidated. Greater insight can be achieved through deep biological phenotyping of subjects undergoing CR, and integration of big data. Materials and Methods: An integrative approach was applied to investigate associations between change in IS and factors from host, microbiota, and lifestyle after a 6-week CR period in 27 overweight or obese adults (ClinicalTrials.gov: NCT01314690). Partial least squares regression was used to determine associations of change (week 6 - baseline) between IS markers and lifestyle factors (diet and physical activity), subcutaneous adipose tissue (sAT) gene expression, metabolomics of serum, urine and feces, and gut microbiota composition. ScaleNet, a network learning approach based on spectral consensus strategy (SCS, developed by us) was used for reconstruction of biological networks. Results: A spectrum of variables from lifestyle factors (10 nutrients), gut microbiota (10 metagenomics species), and host multi-omics (metabolic features: 84 from serum, 73 from urine, and 131 from feces; and 257 sAT gene probes) most associated with IS were identified. Biological network reconstruction using SCS, highlighted links between changes in IS, serum branched chain amino acids, sAT genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species (MGS). Linear regression analysis to model how changes of select variables over the CR period contribute to changes in IS, showed greatest contributions from gut MGS and fiber intake. Conclusion: This work has enhanced previous knowledge on links between host glucose homeostasis, lifestyle factors and the gut microbiota, and has identified potential biomarkers that may be used in future studies to predict and improve individual response to weight-loss interventions. Furthermore, this is the first study showing integration of the wide range of data presented herein, identifying 115 variables of interest with respect to IS from the initial input, consisting of 9,986 variables.

Published
2019

6. Elevated serum ceramides are linked with obesity-associated gut dysbiosis and impaired glucose metabolism

Author

Kayser, B. D., Prifti, E., Lhomme, M., Belda, E., Dao, M. C., Aron-Wisnewsky, J., Kontush, A., Zucker, Jean-Daniel, Rizkalla, S. W., Dugail, I., Clement, K., Kennedy, S. P., Pons, N., Le Chatelier, E., Almeida, M., Quinquis, B., Galleron, N., Batto, J. M., Renault, P., Ehrlich, S. D., Blottiere, H., Leclerc, M., de Wouters, T., Lepage, P., Dore, J., and MICRO-Obes Consortium

Subjects
Sphingolipids, Glucose metabolism, Endotoxin, Microbiome, Ceramides
Abstract

Introduction Low gut microbiome richness is associated with dyslipidemia and insulin resistance, and ceramides and other sphingolipids are implicated in the development of diabetes. Objectives Determine whether circulating sphingolipids, particularly ceramides, are associated with alterations in the gut microbiome among obese patients with increased diabetes risk. Methods This was a cross-sectional and longitudinal retrospective analysis of a dietary/weight loss intervention. Fasted serum was collected from 49 participants (41 women) and analyzed by HPLC-MS/MS to quantify 45 sphingolipids. Shotgun metagenomic sequencing of stool was performed to profile the gut microbiome. Results Confirming the link to deteriorated glucose homeostasis, serum ceramides were positively correlated with fasting glucose, but inversely correlated with fasting and OGTT-derived measures of insulin sensitivity and beta-cell function. Significant associations with gut dysbiosis were demonstrated, with SM and ceramides being inversely correlated with gene richness. Ceramides with fatty acid chain lengths of 20-24 carbons were the most associated with low richness. Diet-induced weight loss, which improved gene richness, decreased most sphingolipids. Thirty-one MGS, mostly corresponding to unidentified bacteria species, were inversely correlated with ceramides, including a number of Bifidobacterium and Methanobrevibacter smithii. Higher ceramide levels were also associated with increased metagenomic modules for lipopolysaccharide synthesis and flagellan synthesis, two pathogen-associated molecular patterns, and decreased enrichment of genes involved in methanogenesis and bile acid metabolism. Conclusion This study identifies an association between gut microbiota richness, ceramides, and diabetes risk in overweight/obese humans, and suggests that the gut microbiota may contribute to dysregulation of lipid metabolism in metabolic disorders.

Published
2019

9. A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity

Author

Maria Carlota Dao, Nataliya Sokolovska, Rémi Brazeilles, Séverine Affeldt, Véronique Pelloux, Edi Prifti, Julien Chilloux, Eric O. Verger, Brandon D. Kayser, Judith Aron-Wisnewsky, Farid Ichou, Estelle Pujos-Guillot, Lesley Hoyles, Catherine Juste, Joël Doré, Marc-Emmanuel Dumas, Salwa W. Rizkalla, Bridget A. Holmes, Jean-Daniel Zucker, Karine Clément, The MICRO-Obes Consortium, Aurélie Cotillard, Sean P. Kennedy, Nicolas Pons, Emmanuelle Le Chatelier, Mathieu Almeida, Benoit Quinquis, Nathalie Galleron, Jean-Michel Batto, Pierre Renault, Stanislav Dusko Ehrlich, Hervé Blottière, Marion Leclerc, Tomas de Wouters, Patricia Lepage, Gestionnaire, Hal Sorbonne Université, Instituts Hospitalo-Universitaires - Institut de Cardiologie-Métabolisme-Nutrition - - ICAN2010 - ANR-10-IAHU-0005 - IAHU - VALID, Génomique microbienne - Microbiome intestinal humain dans l'obésité et la transition nutritionnelle – initiative Franco-Chinoise - - MICRO-Obes2007 - ANR-07-GMGE-0002 - GMGE - VALID, Metagenomics in Cardiometabolic Diseases - METACARDIS - - EC:FP7:HEALTH2012-11-01 - 2017-10-31 - 305312 - VALID, Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases (ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut de Cardiométabolisme et Nutrition = Institute of Cardiometabolism and Nutrition [CHU Pitié Salpêtrière] (IHU ICAN), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Nutrition et obésités: approches systémiques (UMR-S 1269) (Nutriomics), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Danone Nutricia Research [Palaiseau, France], Centre Daniel Carasso [Palaiseau, France], Unité de modélisation mathématique et informatique des systèmes complexes [Bondy] (UMMISCO), Université de Yaoundé I-Institut de la francophonie pour l'informatique-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Université Gaston Bergé (Saint-Louis, Sénégal)-Université Cadi Ayyad [Marrakech] (UCA)-Sorbonne Université (SU)-Institut de Recherche pour le Développement (IRD [France-Nord]), Imperial College London, Service de Nutrition [CHU Pitié-Salpétrière], Institut E3M [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Nottingham Trent University, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, AgroParisTech, This work was supported by Agence Nationale de la Recherche (ANR MICRO-Obes and ANR-10-IAHU-05) and the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement HEALTH-F4-2012-305312 (METACARDIS) and Fondation Leducq (to KC’s team). The clinical work received support from KOT-Ceprodi, Danone Nutricia Research and the Foundation Coeur et Artère. LH was in receipt of an MRC Intermediate Research Fellowship in Data Science (Grant No. MR/L01632X/1) and Heart and Stroke Foundation of Canada., MICRO-Obes Consortium : Aurélie Cotillard, Sean P. Kennedy, Nicolas Pons, Emmanuelle Le Chatelier, Mathieu Almeida, Benoit Quinquis, Nathalie Galleron, Jean-Michel Batto, Pierre Renault, Stanislav Dusko Ehrlich, Hervé Blottière, Marion Leclerc, Tomas de Wouters, Patricia Lepage., ANR-10-IAHU-0005,ICAN,Institut de Cardiologie-Métabolisme-Nutrition(2010), ANR-07-GMGE-0002,MICRO-Obes,Microbiome intestinal humain dans l'obésité et la transition nutritionnelle – initiative Franco-Chinoise(2007), European Project: 305312,EC:FP7:HEALTH,FP7-HEALTH-2012-INNOVATION-1,METACARDIS(2012), Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Institute of cardiometabolism and nutrition (ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU), Institut de la francophonie pour l'informatique-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Université Gaston Bergé (Saint-Louis, Sénégal)-Universtié Yaoundé 1 [Cameroun]-Université Cadi Ayyad [Marrakech] (UCA)-Sorbonne Université (SU)-Institut de Recherche pour le Développement (IRD [France-Nord]), Service de nutrition [CHU Pitié-Salpétrière], Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Pitié-Salpêtrière [APHP], Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), ANR-10-IAHU-0005/10-IAHU-0005,ICAN,ICAN(2010), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut de Recherche pour le Développement (IRD [France-Nord])-Institut de la francophonie pour l'informatique-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Université Gaston Bergé (Saint-Louis, Sénégal)-Université Cadi Ayyad [Marrakech] (UCA)-Université de Yaoundé I-Sorbonne Université (SU), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Dao, M. C., and Clement, K.

Subjects
0301 basic medicine, data integration, insulin sensitivity, lifestyle factors, microbiota, omics, IMPACT, [SDV.MHEP.PHY] Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], Physiology, Calorie restriction, WEIGHT-LOSS, Adipose tissue, Computational biology, Gut flora, lcsh:Physiology, ACYLCARNITINES, 03 medical and health sciences, 0302 clinical medicine, Metabolomics, DIETARY, MICRO-Obes Consortium, Weight loss, Physiology (medical), medicine, [SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], Glucose homeostasis, OBESE, Physiologie, 2. Zero hunger, [SDV.MHEP.EM] Life Sciences [q-bio]/Human health and pathology/Endocrinology and metabolism, Science & Technology, PLASMA, biology, lcsh:QP1-981, GUT MICROBIOTA, [SDV.MHEP.EM]Life Sciences [q-bio]/Human health and pathology/Endocrinology and metabolism, biology.organism_classification, Omics, Clinical Trial, ADIPOSE-TISSUE, 030104 developmental biology, SPECTROMETRY, Metagenomics, 030220 oncology & carcinogenesis, medicine.symptom, Life Sciences & Biomedicine, RESISTANCE
Abstract

Background: The mechanisms responsible for calorie restriction-induced improvement in insulin sensitivity have not been fully elucidated. Greater insight can be achieved through deep biological phenotyping of subjects undergoing calorie restriction, and integration of big data.\ud \ud Materials and Methods: An integrative approach was applied to investigate associations between change in insulin sensitivity and factors from host, microbiota and lifestyle after a 6-week calorie restriction period in 27 overweight or obese adults (ClinicalTrials.gov: NCT01314690). Partial least squares regression was used to determine associations of change (week 6 – baseline) between insulin sensitivity markers and lifestyle factors (diet and physical activity), subcutaneous adipose tissue (sAT) gene expression, metabolomics in serum, urine and feces, and gut microbiota composition. ScaleNet, a network learning approach based on spectral consensus strategy (SCS, developed by us) was used for reconstruction of biological networks.\ud \ud Results: A spectrum of variables from lifestyle factors (10 nutrients), gut microbiota (10 metagenomics species) and host multi-omics (metabolic features: 84 from serum, 73 from urine, and 131 from feces; and 257 subcutaneous adipose tissue gene probes) most associated with insulin sensitivity were identified. Biological network reconstruction using SCS, highlighted links between changes in insulin sensitivity, serum branched chain amino acids, sAT genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species. Linear regression analysis to model how changes of select variables over the calorie restriction period contribute to changes in insulin sensitivity, showed greatest contributions from gut metagenomic species and fiber intake.\ud \ud Conclusions: This work has enhanced previous knowledge on links between host glucose homeostasis, lifestyle factors and microbiota, and has identified potential biomarkers that may be used in future studies to predict and improve individual response to weight-loss interventions. Furthermore, this is the first study showing integration of the wide range of data presented herein, identifying 115 variables of interest with respect to insulin sensitivity from the initial input, consisting of 9,986 variables.

Published
2019
Full Text
View/download PDF

10. Effect of a financial incentive on the acceptance of a smoking cessation programme with service charge: a cluster-controlled trial.

Author

Wong KS, Fu SN, Cheung KL, Dao MC, and Sy WM

Subjects
Adult, Aged, Female, Humans, Logistic Models, Male, Middle Aged, Reward, Motivation, Smoking Cessation economics
Abstract

Introduction: Frontline health care professionals in Hong Kong may encounter high refusal rates for the Hospital Authority's Smoking Counselling and Cessation Programme (SCCP) when smokers know it is subject to a service charge. We compared SCCP booking and attendance rates among smokers with or without a financial incentive., Methods: In this multicentre non-randomised cluster-controlled trial, adult smokers who attended one of six general out-patient clinics between November 2015 and April 2016 were invited to join an SCCP. Attendees in the three intervention-group centres but not the three control-group centres received a supermarket coupon to offset the service charge., Results: A total of 173 smokers aged 18 years or older (92 in the intervention group and 81 in the control group) were recruited into the study. In the intervention group, 47 smokers (51%) agreed via a questionnaire that they would join the SCCP, compared with only 23 smokers in the control group (28%). The booking rates were 83% (n=39) in the intervention group and 83% (n=19) in the control group. Among those who had booked a place, 19 (49%) intervention-group participants and 11 (58%) control-group participants attended an SCCP session. Multivariable logistic regression revealed that offering a coupon was associated with agreeing to join an SCCP (odds ratio=4.963, 95% confidence interval=2.173-11.334; P<0.001) and booking an SCCP place (odds ratio=4.244, 95% confidence interval=1.838-9.799; P<0.001)., Conclusion: Provision of a financial incentive was positively associated with agreement to join an SCCP and booking an SCCP place. Budget holders should consider providing the SCCP free of charge to increase smokers' access to the service.

Published
2018
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