BIFI-OBESE: Clinical Trial in Paediatric Obesity
Study Details
Study Description
Brief Summary
Obesity is a major, public health concern that affects at least 400 million individuals and is associated with severe disorders including diabetes and cancers. Worldwide, the prevalence of overweight and obesity combined in children, adolescents and youth, between 1980 and 2013, increased to 47.1%, with alarming data also in developing countries. Obesity is often caused by imbalance between excessive caloric intake and reduced physical activity.
Recently, microbial changes in the human gut was proposed to be another possible cause of obesity and it was found that the gut microbes from fecal samples contained 3.3 million non-redundant microbial genes. However, it is still poorly understood how the dynamics and composition of the intestinal microbiota are affected by diet or other lifestyle factors. Moreover it has been difficult to characterize the composition of the human gut microbiota due to large variations between individuals.
The role of the digestive microbiota in the human body is still largely unknown, but the bacteria of the gut flora do contribute enzymes that are absent in humans for food digestion. Moreover, the link between obesity and the microbiota is likely to be more sophisticated than the simple phylum-level Bacteroidetes: Firmicutes ratio that was initially identified, and it is likely to involve a microbiota-diet interaction.
Obese and lean subjects presented increased levels of different bacterial populations. It is hypothesized that the obese microbiome is set up to extract more calories from the daily intake when compared to the microbiome of lean counterparts. In addition, a caloric diet restriction impacted the composition of the gut microbiota in obese/overweight individuals and weight loss.
In lean subjects there are Coriobacteriaceae, Lactobacillus, Enterococcus, Faecalibacterium prausnitzii, Prevotella, Clostridium Eubacterium, E. coli and Staphilococcus. By contrast, Bifidobacterium, Methanobrevibacter, Xylanibacter, Bacteroides characterize the composition of lean gut microbiota.
For this reason, in a cohort of obese paediatric subjects with visceral adiposity, the aim of the study is to assess the efficacy of a supplementation with probiotic bifidobacteria with respect to a conventional treatment on weight loss and improvement of cardio-metabolic risk factors.
Condition or Disease | Intervention/Treatment | Phase |
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Phase 4 |
Detailed Description
Study design: A single-center pilot open-label randomized control trial. Population: The study will comprise a total of 100 subjects of both sexes, between 6 and 18 years of age, obese, according to the IOTF criteria and with visceral adiposity, as waist circumference ≥ 90th percentile, pubertal stage ≥ 2 according to the Tanner stage, HOMA-IR > 2,5 or insulin > 15 µU/ml, diet naïve or with failure of weight loss (defined as -1 kg/m2 BMI in 1 year).
Inclusion/ Exclusion criteria (see Eligibility Criteria). Intervention: In the first part of the study (Study 1, V0-V1) patients will be randomized in a open-label, into two groups homogeneous for number and sex of the subjects. One group will receive a supplementation of probiotic containing Bifidobacterium breve B632 and Bifidobacterium breve BR03, 15 gtt/die (3x108 CFU/die) and one group will receive a placebo for a total of 2 months of treatment. Both group receives a Standard Diet according to routine care and practice. For patients who wants to continue the study there will be a cross-over study (study 2, V2-V3) after one month of wash-out.
Dietary restriction: The standard diet will be distributed with 55-60% of carbohydrates (45-50% complex and no more than 10% refined and processed sugars), 25-30% lipids and 15% proteins, and will be performed in accordance with the calories of an isocaloric balanced diet calculated throughout the Italian LARN Guidelines for age and gender.
Physical activity: all subjects will receive general recommendations about performing physical activity. Exercise will be conducted daily and will consist of 30 minutes of aerobic physical activity.
Randomization: Participants will be randomly assigned in a 1:1 to probiotic intervention group or placebo group.
Timing: Patients will be evaluated firstly at time of enrollment (V0) and, at the end of the first part of study (Study 1, V1), biochemical evaluations will be completed. Next there will be one month of wash-out when the patients don't take any probiotic or placebo. In the second part of the study 2, patients will be evaluated at V2 and, after 2 months of treatment (Study 2, V3). The following anthropometric measures, biochemical and ultrasound evaluations and questionnaires will be obtained:
- Anthropometric measures:
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height (V0, V1, V2, V3);
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weight (V0, V1, V2, V3);
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body mass index (BMI; Kg/m2) (V0, V1, V2, V3);
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waist and hip circumferences (V0, V1, V2, V3) for the calculation of the following ratios: waist/hip, waist/height;
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Tanner stage (V0, V1, V2, V3);
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blood pressure and heart rate (V0, V1, V2, V3). Biochemical evaluations (after a 12-h overnight fast): CBC with formula, serum insulin-like growth factor 1 (IGF1, ng/mL), 25-hydroxy (OH) vitamin D (ng/mL), uric acid (mg/dL), alkaline phosphatase (U/L), ACTH (pg/mL), cortisol (microg/dL), TSH (uuI/mL), fT4 (ng/dL) (V0, V1, V2, V3); aspartate aminotransferase (AST, IU/L), alanine aminotransferase (ALT, IU/L); AST-to-ALT ratio will be calculated as the ratio of AST (IU/L) and ALT(IU/L) (V0, V1, V2, V3); serum creatinine concentration (mg/dL) will be measured with the enzymatic method; according to the NKF-K/DOQI Guidelines for CKD in children and adolescents, the eGFR will be calculated using updated Schwartz's formula: eGFR (mL/min/1.73 m2) = [0.413 x patient's height (cm)] / serum creatinine (mg/dL) (V0, V1, V2, V3); glucose (mg/dL), insulin (μUI/mL); insulin-resistance (IR) will be calculated using the formula of Homeostasis Model Assessment (HOMA)-IR: (insulin [mU/L] x glucose [mmol/lL) / 22.5) (V0, V1, V2, V3); lipid profile: total cholesterol (mg/dL), High-Density Lipoprotein (HDL)-cholesterol (mg/dL), triglycerides (mg/dL); Low-Density Lipoprotein (LDL)-cholesterol will be calculated by the Friedwald formula and non-HDL (nHDL)-cholesterol will be also calculated(V0, V1, V2, V3); oral glucose tolerance test (OGTT: 1.75 g of glucose solution per kg, maximum 75 g) and samples willbe collected for the determination of glucose and insulin every 30 min. The area under the curve (AUC) for parameters after OGTT will be calculated according to the trapezoidal rule. Insulin sensitivity at fasting and during OGTT will be calculated as the formula of the Quantitative Insulin-Sensitivity Check Index (QUICKI) and Matsuda index (ISI). The stimulus for insulin secretion in the increment in plasma glucose as insulinogenic index will be calculated as the ratio of the changes in insulin and glucose concentration from 0 to 30 min (InsI). Βeta-cell compensatory capacity will be evaluated by the disposition index defined as the product of the ISI and InsI (DI) (V0, V1, V2, V3); a collection at rest of first-morning urine sample. Physical and chemical urinalysis; urine albumin (mg/L) will be determined by an advanced immunoturbidimetric assay and urine creatinine (mg/dL) will be measured using the enzymatic method. Urine albumin to creatinine ratio (u-ACR - mg/g), will be calculated using the following formula: [urine albumin (mg/dL) / urine creatinine (mg/dL)] x 1000. For these calculations both albumin and creatinine will be in the same unit. The subjects whose urine will be found positive, they will undergo a collection of two more samples and will be considered the u-ACR mean value of these (V0, V1, V2, V3). A sample of feces will be taken for microbic count (V0, V1, V2, V3). LPS (V0, V1, V2, V3). LPS will be measured with commercial kits (Limulus amoebocyte lysate assay) with standard procedures. Citokines IL1, IL1β, IL6, IL10, TNFα will be evaluated (V0, V1, V2, V3) (ELISA kit).
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A health diary will be taken during the 2 months of treatment: each patient will complete the diary with collateral effects or antibiotic treatment ecc.
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NGS (Next Generation Sequencing) will be analized for fecal analysis (V0, V1, V2, V3)
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Metabolomic analysis will be taken with mass spectrometry on fecal samples (V0, V1, V2, V3)
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SCFA analysis on fecal samples (V0, V1, V2, V3).
Outcomes (see Outcome Measures). Information retrieval: A case report form (CRF) will be completed for each subject included in the study. The source documents will be the hospital's or the physician's chart.
Statistical e sample size: A sample of 16 individuals has been estimated to be sufficient to demonstrate a difference of 10 mg/dl in the basal glucose concentration with 90% power and a significance level of 95% and a drop-out rate of 10% at the 8th weeks of treatment. A sample of 34 individuals in each group has been estimated to be sufficient to demonstrate a difference of 1,4 point in the HOMA-IR index with 90% power and a significance level of 95% and a drop-out rate of 10% at the 8th weeks of treatment. Statistical significance will be assumed at P< 0.05. The statistical analysis will be performed with SPSS for Windows version 17.0 (SPSS Inc., Chicago, IL, USA).
Organization characteristics: The study will be conducted at the Pediatric Endocrine Service of Division of Pediatrics.
All blood samples will be measured evaluated using standardized methods in the Hospital's Chemistry Laboratory, in Maggiore della Carità hospital, in Novara, previously described. Fecal analysis will be measured in the Department of Sciences and Technologies, University of Bologna, in Bologna.
Good Clinical Practice: The protocol will be conducted in accordance with the declaration of Helsinki. Informed consent will be obtained from all parents prior to the evaluations after careful explanations to each patient.
Study Design
Arms and Interventions
Arm | Intervention/Treatment |
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Active Comparator: Active group Bifidobacterium breve BR03 and B632 This arm will receive a supplementation of probiotic containing Bifidobacterium breve B632 and Bifidobacterium breve BR03, 15 gtt/die (3x108 CFU/die) once a day. |
Drug: Bifidobacterium breve BR03 and Bifidobacterium breve B632
Other Names:
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Placebo Comparator: Placebo group This arm will receive a supplementation with a same product equal to the active product but without bifidobacterium inside. |
Drug: Placebos
Other Names:
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Outcome Measures
Primary Outcome Measures
- Change in glucose level during oral glucose tolerance test (OGTT) [Change from Baseline OGTT (V0) at 2 months (V1), 3 months (V2) and 5 months (V3)]
Evaluate if after the treatment with probiotic there is a reduction of glucose values during the OGTT at time 0' e 120' after oral glucose tolerance test.
- Change in HOMA-IR index [Change from baseline HOMA-IR (V0) at 2 months (V1), 3 months (V2) and 5 months (V3)]
Evaluate if after the treatment with probiotic there is a variation of HOMA-IR index.
Secondary Outcome Measures
- Metabolic control: Improvement of metabolic risk factors [Change from baseline lipid profile, insulin, leptin, adiponectin, GLP1 (V0) at 2 months (V1), 3 months (V2) and 5 months (V3)]
Evaluate any variation of serum lipids, leptin, adiponectin, GLP1 and insulin during OGTT.
- Change in fecal microbiome [Change from Baseline fecal microbiome (V0) at 2 months (V1), 3 months (V2) and 5 months (V3)]
Evaluate any variation of fecal microbiome
- Change in SCFA (short-chain fatty acids) in fecal samples [Change from Baseline fecal SCFA (V0) at 2 months (V1), 3 months (V2) and 5 months (V3)]
Evaluate any variation of short-chain fatty acids in fecal samples
Other Outcome Measures
- Change in inflammatory cytokines [Change from Baseline cytokines and metabolites (V0) at 2 months (V1), 3 months (V2) and 5 months (V3)]
Evaluate new citokines and metabolites that regulates hormone metabolism.
Eligibility Criteria
Criteria
Inclusion Criteria:
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both sexes
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between 6 and 18 years of age
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obese, according to the IOTF criteria (Cole TJ et al., 2000)
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pubertal stage ≥ 2 according to the Tanner stage (Tanner et al., 1961)
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HOMA-IR > 2,5 or insulin > 15 µU/ml
Exclusion Criteria:
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Adverse reactions to the product or component of the product (allergies…)
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Genetic obesity (Prader Willi syndrome, Down syndrome), Metabolic obesity (Laurence-biedl syndrome…), endocrinological obesity (Cushinch syndrome, hypotiroidism)
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Chronic diseases, hepatic or gastroenterological diseases
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Medical treatment for chronic diseases
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Probiotic or prebiotic therapies and antibiotic treatment
Contacts and Locations
Locations
Site | City | State | Country | Postal Code | |
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1 | AOU Maggiore della Carità - Clinica Pediatrica - Ambulatorio di Auxologia ed Endocrinologia Pediatrica | Novara | Italy | 28100 |
Sponsors and Collaborators
- Azienda Ospedaliero Universitaria Maggiore della Carita
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
- Angelakis E, Armougom F, Million M, Raoult D. The relationship between gut microbiota and weight gain in humans. Future Microbiol. 2012 Jan;7(1):91-109. doi: 10.2217/fmb.11.142. Review.
- Cacciari E, Milani S, Balsamo A, Spada E, Bona G, Cavallo L, Cerutti F, Gargantini L, Greggio N, Tonini G, Cicognani A. Italian cross-sectional growth charts for height, weight and BMI (2 to 20 yr). J Endocrinol Invest. 2006 Jul-Aug;29(7):581-93.
- Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity. Pediatr Obes. 2012 Aug;7(4):284-94. doi: 10.1111/j.2047-6310.2012.00064.x. Epub 2012 Jun 19.
- Cook S, Weitzman M, Auinger P, Nguyen M, Dietz WH. Prevalence of a metabolic syndrome phenotype in adolescents: findings from the third National Health and Nutrition Examination Survey, 1988-1994. Arch Pediatr Adolesc Med. 2003 Aug;157(8):821-7.
- Cruz ML, Goran MI. The metabolic syndrome in children and adolescents. Curr Diab Rep. 2004 Feb;4(1):53-62. Review.
- de Ferranti SD, Gauvreau K, Ludwig DS, Neufeld EJ, Newburger JW, Rifai N. Prevalence of the metabolic syndrome in American adolescents: findings from the Third National Health and Nutrition Examination Survey. Circulation. 2004 Oct 19;110(16):2494-7. Epub 2004 Oct 11.
- Fukuda S, Ohno H. Gut microbiome and metabolic diseases. Semin Immunopathol. 2014 Jan;36(1):103-14. doi: 10.1007/s00281-013-0399-z. Epub 2013 Nov 6. Review.
- Hogg RJ, Furth S, Lemley KV, Portman R, Schwartz GJ, Coresh J, Balk E, Lau J, Levin A, Kausz AT, Eknoyan G, Levey AS; National Kidney Foundation's Kidney Disease Outcomes Quality Initiative. National Kidney Foundation's Kidney Disease Outcomes Quality Initiative clinical practice guidelines for chronic kidney disease in children and adolescents: evaluation, classification, and stratification. Pediatrics. 2003 Jun;111(6 Pt 1):1416-21.
- Kahn SE, Prigeon RL, McCulloch DK, Boyko EJ, Bergman RN, Schwartz MW, Neifing JL, Ward WK, Beard JC, Palmer JP, et al. Quantification of the relationship between insulin sensitivity and beta-cell function in human subjects. Evidence for a hyperbolic function. Diabetes. 1993 Nov;42(11):1663-72.
- Ley RE, Bäckhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proc Natl Acad Sci U S A. 2005 Aug 2;102(31):11070-5. Epub 2005 Jul 20.
- Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes associated with obesity. Nature. 2006 Dec 21;444(7122):1022-3.
- Ley RE. Obesity and the human microbiome. Curr Opin Gastroenterol. 2010 Jan;26(1):5-11. doi: 10.1097/MOG.0b013e328333d751. Review.
- Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, Mullany EC, Biryukov S, Abbafati C, Abera SF, Abraham JP, Abu-Rmeileh NM, Achoki T, AlBuhairan FS, Alemu ZA, Alfonso R, Ali MK, Ali R, Guzman NA, Ammar W, Anwari P, Banerjee A, Barquera S, Basu S, Bennett DA, Bhutta Z, Blore J, Cabral N, Nonato IC, Chang JC, Chowdhury R, Courville KJ, Criqui MH, Cundiff DK, Dabhadkar KC, Dandona L, Davis A, Dayama A, Dharmaratne SD, Ding EL, Durrani AM, Esteghamati A, Farzadfar F, Fay DF, Feigin VL, Flaxman A, Forouzanfar MH, Goto A, Green MA, Gupta R, Hafezi-Nejad N, Hankey GJ, Harewood HC, Havmoeller R, Hay S, Hernandez L, Husseini A, Idrisov BT, Ikeda N, Islami F, Jahangir E, Jassal SK, Jee SH, Jeffreys M, Jonas JB, Kabagambe EK, Khalifa SE, Kengne AP, Khader YS, Khang YH, Kim D, Kimokoti RW, Kinge JM, Kokubo Y, Kosen S, Kwan G, Lai T, Leinsalu M, Li Y, Liang X, Liu S, Logroscino G, Lotufo PA, Lu Y, Ma J, Mainoo NK, Mensah GA, Merriman TR, Mokdad AH, Moschandreas J, Naghavi M, Naheed A, Nand D, Narayan KM, Nelson EL, Neuhouser ML, Nisar MI, Ohkubo T, Oti SO, Pedroza A, Prabhakaran D, Roy N, Sampson U, Seo H, Sepanlou SG, Shibuya K, Shiri R, Shiue I, Singh GM, Singh JA, Skirbekk V, Stapelberg NJ, Sturua L, Sykes BL, Tobias M, Tran BX, Trasande L, Toyoshima H, van de Vijver S, Vasankari TJ, Veerman JL, Velasquez-Melendez G, Vlassov VV, Vollset SE, Vos T, Wang C, Wang X, Weiderpass E, Werdecker A, Wright JL, Yang YC, Yatsuya H, Yoon J, Yoon SJ, Zhao Y, Zhou M, Zhu S, Lopez AD, Murray CJ, Gakidou E. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014 Aug 30;384(9945):766-81. doi: 10.1016/S0140-6736(14)60460-8. Epub 2014 May 29. Erratum in: Lancet. 2014 Aug 30;384(9945):746.
- Okeke F, Roland BC, Mullin GE. The role of the gut microbiome in the pathogenesis and treatment of obesity. Glob Adv Health Med. 2014 May;3(3):44-57. doi: 10.7453/gahmj.2014.018. Review.
- Prodam F, Ricotti R, Agarla V, Parlamento S, Genoni G, Balossini C, Walker GE, Aimaretti G, Bona G, Bellone S. High-end normal adrenocorticotropic hormone and cortisol levels are associated with specific cardiovascular risk factors in pediatric obesity: a cross-sectional study. BMC Med. 2013 Feb 20;11:44. doi: 10.1186/1741-7015-11-44.
- Prodam F, Savastio S, Genoni G, Babu D, Giordano M, Ricotti R, Aimaretti G, Bona G, Bellone S. Effects of growth hormone (GH) therapy withdrawal on glucose metabolism in not confirmed GH deficient adolescents at final height. PLoS One. 2014 Jan 30;9(1):e87157. doi: 10.1371/journal.pone.0087157. eCollection 2014.
- Prodam F, Zanetta S, Ricotti R, Marolda A, Giglione E, Monzani A, Walker GE, Rampone S, Castagno M, Bellone S, Petri A, Aimaretti G, Bona G. Influence of Ultraviolet Radiation on the Association between 25-Hydroxy Vitamin D Levels and Cardiovascular Risk Factors in Obesity. J Pediatr. 2016 Apr;171:83-9.e1. doi: 10.1016/j.jpeds.2015.12.032. Epub 2016 Jan 12.
- Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz-Ponten T, Turner K, Zhu H, Yu C, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, Wang J, Brunak S, Doré J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J; MetaHIT Consortium, Bork P, Ehrlich SD, Wang J. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010 Mar 4;464(7285):59-65. doi: 10.1038/nature08821.
- Raoult D. Obesity pandemics and the modification of digestive bacterial flora. Eur J Clin Microbiol Infect Dis. 2008 Aug;27(8):631-4. doi: 10.1007/s10096-008-0490-x. Epub 2008 Mar 6.
- Schwiertz A, Taras D, Schäfer K, Beijer S, Bos NA, Donus C, Hardt PD. Microbiota and SCFA in lean and overweight healthy subjects. Obesity (Silver Spring). 2010 Jan;18(1):190-5. doi: 10.1038/oby.2009.167. Epub 2009 Jun 4.
- Società Italiana di Nutrizione Umana.(2014).Livelli di assunzione raccomandati di energia e nutrienti per la popolazione italiana (LARN). Milan, Italy: S.I.N.U.
- Tanner JM. (1961). Growth at adolescence. 2 edn. Oxford: Blackwell Scientific Publications.
- Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, Sogin ML, Jones WJ, Roe BA, Affourtit JP, Egholm M, Henrissat B, Heath AC, Knight R, Gordon JI. A core gut microbiome in obese and lean twins. Nature. 2009 Jan 22;457(7228):480-4. doi: 10.1038/nature07540. Epub 2008 Nov 30.
- Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006 Dec 21;444(7122):1027-31.
- Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med. 2009 Nov 11;1(6):6ra14. doi: 10.1126/scitranslmed.3000322.
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