Clinical trial NCT01314690: Human Intestinal Microbiota in Obesity and Nutritional Transition
Official title: Human Intestinal Microbiota in Obesity and Nutritional Transition
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Obesity
Keywords: obesity
low calorie diet
intestinal microbiota
inflammation (systemic and in adipose tissue)
fat mass
Impact of nutritional transition on intestinal microbiota
relation between food intake, clinical parameters and intestinal microbiota
Lead sponsor: Ceprodi S.A. Kot
Sponsor class: INDUSTRY
Collaborators: name: Institut National de la Santé Et de la Recherche Médicale, France
name: Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement
name: Pierre and Marie Curie University
name: Assistance Publique - Hôpitaux de Paris
First posted: 2011-03-14
Enrollment: 49
Sex: ALL
Minimum age (years): 25
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NON_RANDOMIZED
Intervention model: SINGLE_GROUP
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief summary: The objectives of this study are: 1-To qualify the relationship between the gut microbiota and the host nutritional and metabolic status during dietary transition.2-To define the impact of nutritional transition on the intestinal microbiota and identify metagenomics signatures of nutritional transition.

Detailed description: French overweight (BMI \> 27) and obese people (BMI \> 30 kg/m2, N=50) will be submitted to a low calorie diet (1200 Kcal) for 8 weeks. This type of intervention is know to modify the metabolic phenotype and expected to change the gut microbiota. Nutritional interventions are planned for 6 months, each subject being studied before (base line), after the intervention (8 weeks) and 2 months after the low calorie diet has stopped and a diet of weight maintenance will be provided. This protocol will be implemented in the nutrition department of the Pitié Salpétrière Hospital in Paris, within the Centre de Recherche en Nutrition Humaine d'Ile-de-France, where patients will be followed on the clinical, biological and histological levels. Evaluation will include medical history, physical, and nutritional evaluations. They will be excluded of the protocol if they had evidence of acute or chronic inflammatory disease, infectious diseases, cancer and/or known alcohol consumption (\> 20g per day), as well as other causes of liver, kidney or heart diseases. Physical evaluation will include weight, height, measures of waist/hip ratio, measure of blood pressure. At each time point, body composition will be measured by dual-energy X-ray absorptiometry performed with a total body scanner (Hologic QDR 2500 densitometer). Resting metabolic rate will be evaluated after a one-hour resting period in supine position. Oxygen consumption (VO2) and carbon dioxide production (VCO2), monitored over 30 min by using an open-circuit ventilated-canopy system (Deltatrac II monitor, Datex Instrumentarium Corp., Helsinki, Finland) calibrated with a reference gas. Blood samples will be obtained systematically for biological parameters including lipids (cholesterol, HDL-Chol, TG), insulin and glucose values and OGTT (enabling the determination of insulin sensitivity parameters, adipokines and inflammatory parameters such as leptin, adiponectin, IL-6, TNF alpha, SAA, hsCRP. Serum will be kept for all supplementary measures. The histopathological evaluation of subcutaneous adipose tissue will be performed to evaluate the adipocyte size and the degree of tissue inflammation. Stools samples will be collected for metagenomic studies.

In addition, blood, urine and fecal water samples will be collected and frozen for subsequent metabonomic analysis, and fecal samples for metaproteomic assessment of the microbiota.

Eligibility criteria: Inclusion Criteria:

1. BMI 27 to 38 kg/m2
2. age: 25 to 65 years.
3. non diabetic subjects
4. fasting glycemia \< 1,26 g/l

Exclusion Criteria:

1. SGOT ou SGPT \> 2.5x normale
2. Glycemia \> 1,26 g/l
3. any other health problem or chronic treatment

Primary outcomes:
measure: weight loss
measure: intestinal microbiota genes
measure: Body fat mass
measure: Food intake
Secondary outcomes:
measure: plasma glucose
measure: adipose tissue genes
Other outcomes:
Locations:
city: Paris
country: France
facility: Assistance publique-hopitaux de Paris
Study officials:
affiliation: Assistance Publique - Hôpitaux de Paris
name: Karine Clement, MD, PhD, Prof
role: PRINCIPAL_INVESTIGATOR
References:
citation: Dao MC, Sokolovska N, Brazeilles R, Affeldt S, Pelloux V, Prifti E, Chilloux J, Verger EO, Kayser BD, Aron-Wisnewsky J, Ichou F, Pujos-Guillot E, Hoyles L, Juste C, Dore J, Dumas ME, Rizkalla SW, Holmes BA, Zucker JD, Clement K; MICRO-Obes Consortium. A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity. Front Physiol. 2019 Feb 5;9:1958. doi: 10.3389/fphys.2018.01958. eCollection 2018.
citation: Dao MC, Everard A, Aron-Wisnewsky J, Sokolovska N, Prifti E, Verger EO, Kayser BD, Levenez F, Chilloux J, Hoyles L; MICRO-Obes Consortium; Dumas ME, Rizkalla SW, Dore J, Cani PD, Clement K. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut. 2016 Mar;65(3):426-36. doi: 10.1136/gutjnl-2014-308778. Epub 2015 Jun 22.
citation: Kong LC, Holmes BA, Cotillard A, Habi-Rachedi F, Brazeilles R, Gougis S, Gausseres N, Cani PD, Fellahi S, Bastard JP, Kennedy SP, Dore J, Ehrlich SD, Zucker JD, Rizkalla SW, Clement K. Dietary patterns differently associate with inflammation and gut microbiota in overweight and obese subjects. PLoS One. 2014 Oct 20;9(10):e109434. doi: 10.1371/journal.pone.0109434. eCollection 2014.
citation: Kong LC, Wuillemin PH, Bastard JP, Sokolovska N, Gougis S, Fellahi S, Darakhshan F, Bonnefont-Rousselot D, Bittar R, Dore J, Zucker JD, Clement K, Rizkalla S. 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. Am J Clin Nutr. 2013 Dec;98(6):1385-94. doi: 10.3945/ajcn.113.058099. Epub 2013 Oct 30.