- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06279780
Gut Microbiota, Mitochondrial Function and Metabolic Health in Obesity
February 25, 2025 updated by: Celia Bañuls
Effect of a Very Low-calorie Diet on Microbiota, Oxidative Stress, Inflammatory and Metabolomic Profile in Metabolically Healthy and Unhealthy Obese Subjects
It has been suggested that individuals with the condition known as metabolically healthy obesity (MHO) may not have the same increased risk of developing metabolic abnormalities as their non-metabolically healthy counterparts.
In addition, to date, the identification of metabolic biomarkers and microbiota underlying the MHO state is limited.
In this study, our goal is to provide insight into the underlying metabolic pathways affected by obesity.
To achieve this, we will compare the metabolic profile, inflammatory parameters and mitochondrial function, as well as metabolomic analysis and differential expression of microbiota in obese patients categorized as metabolically healthy vs. non healthy.
In parallel, the effect of a hypocaloric diet on obese subjects' metabolism and microbiota will be assessed to approve their use in the treatment of said disorder.
Specifically, we propose an observational, clinical-basic, comparative and interventional study in a population of 80 obese (BMI>35 kg/m2) patients clustered in two groups according to the presence or absence of altered metabolism (altered fasting glycemia, hypertension, atherogenic dyslipidemia).
Anthropometric and clinical variables and biological samples (serum, plasma, peripheral blood cells and feces) will be collected for the determination of biochemical parameters (glucose, lipid and hormonal profile by enzymatic techniques) and protein-based peripheral biomarkers of mitochondrial function [total and mitochondrial reactive oxygen species (ROS) production, mitochondrial membrane potential, glutathione levels by static cytometry], markers of mitochondrial dynamics [Mitofusin 1 (MFN1), Mitofusin 2 (MFN2), Mitochondrial fision protein 1 (FIS1) and Dynamin-related protein 1 (DRP1) by RT-PCR and Western Blot], markers of inflammation [Interleukin 6 (IL6), Tumoral necrosis factor alpha (TNFα), IL1b, adiponectin, resistin, plasminogen activator inhibitor 1 (PAI-1), Monocyte chemoattractant protein-1 (MCP-1), caspase 1 and NLRP3 by Western Blot and technology XMAP), metabolomic assay (NMR spectroscopy and PLS-DA), as well as gut microbiota content and diversity (16S rRNA, MiSeq sequencing).
Finally, we will evaluate the effect of a dietary weight loss intervention on these biomarkers.
Study Overview
Status
Completed
Conditions
Intervention / Treatment
Study Type
Interventional
Enrollment (Actual)
109
Phase
- Not Applicable
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
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Valencia, Spain, 46020
- FISABIO
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Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Adult
Accepts Healthy Volunteers
No
Description
Inclusion Criteria:
- Patients with BMI≥30kg/m2, with at least 5 years of diagnosed obesity evolution.
- Patients have had stable body weight (±2 kg) during the 3 months prior to the study.
Exclusion Criteria:
- All patients with acute or chronic inflammatory diseases, neoplasic disease, secondary causes of obesity (uncontrolled hypothyroidism, Cushing's syndrome), and established liver and kidney failure (according to transaminase levels ±2 SD of the mean and estimated glomerular filtration rate using the CKD-EPI formula >60) will be excluded.
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Primary Purpose: Treatment
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Experimental: Very low-calorie diet Intervention
|
Subjects undergo two cycles of a very-low-calorie diet (VLCD) for 6 weeks each, alternating with a hypocaloric diet (12 weeks).
The dietetic intervention consists of a VLCD using a liquid formula (Optisource Plus, Nestlé S.A., Vevey, Switzerland), providing 52.8 g protein, 75.0 g carbohydrates, 13.5 g fat, 11.4 g fiber, and essential vitamins and minerals based on Recommended Dietary Allowances (RDA).
This formula supplies 2738 kJ/day (654 kcal/day), replacing the participants' three daily meals.
Following this and before the second VLCD cycle, a dietician performs an individualized nutritional assessment to calculate the resting energy expenditure, and personalized hypocaloric diets were prepared, reducing 500 kcal for each individual on their daily caloric expenditure, maintaining the recommended intake of each of the macronutrients (55% carbohydrates, 30% fats and 15% proteins) for 12-weeks.
Other Names:
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Analyze the changes in the diversity of the intestinal microbiota after dietetic intervention.
Time Frame: 5 years
|
To assess the alpha-diversity of the intestinal microbiota, defined as the average diversity of species in an ecosystem, the Shannon index will be used.
The results are interpreted as follows: values less than 2 are considered low in diversity and values greater than 3 are high in species diversity.
|
5 years
|
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Evaluate the differences in the diversity of the intestinal microbiota depending on whether patients present metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUHO).
Time Frame: 5 years
|
To asses the differences in alpha-diversity of the intestinal microbiota in both groups, it will be evaluated whether there are significant differences between the Shannon indices of the two groups.
The classification of patients between MHO and MUHO will be carried out using the following criteria: MUHO will be considered when patients with obesity present ≥2 metabolic abnormalities, and MHO with ≤1 metabolic abnormalities; the following cardiovascular risk factors are considered metabolic abnormalities: elevated blood pressure (defined as either SBP ≥130 mm Hg, DBP ≥85 mm Hg, or treatment with antihypertensive medications), elevated triglycerides (as fasting triglyceride concentration ≥1.7 mmol/l), low HDL-C levels (defined as HDL-C <1.04 mmol/l, in men, <1.29 mmol/l/l in women, or treatment with lipid-lowering medications), dysglycemia (fasting plasma glucose 5.6 to 6.9 mmol/l, and/or and insulin resistance as HOMA-IR >3.8).
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5 years
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Evaluate significant changes in body fat mass percentage after the dietetic intervention.
Time Frame: 2 years
|
Percentage of body fat mass will be measured by bioelectrical impedance.
It is considered to be high when ≥25% in men and ≥30% in women.
A significant improvement will be considered when notable differences are observed in the mean values between groups measured through p-value (<0.05) with a 95% confidence interval.
|
2 years
|
|
Assess significant changes in high-sensitivity C-reactive protein (hs-CRP) as an inflammatory parameter after the dietetic intervention.
Time Frame: 2 years
|
Participants will be considered to have achieved an improvement in high-sensitivity C-reactive protein levels if they normalize its value (normality values defined between 0 and 1.69mg/dl).
|
2 years
|
|
Evaluate significant changes in C3 protein as an inflammatory parameter after the dietetic intervention.
Time Frame: 2 years
|
Participants will be considered to have achieved an improvement in C3 protein if they normalize its value (normality values defined between 81 and 157mg/dl).
|
2 years
|
|
Assess significant changes in plasmatic homocysteine as an inflammatory parameter after the dietetic intervention.
Time Frame: 2 years
|
Participants will be considered to have achieved an improvement in plasmatic homocysteine if they normalize its value (normality values defined between 5 and 15µmol/L).
|
2 years
|
|
Evaluate significant changes in interleukin 1-beta (IL-1B) levels as a pro-inflammatory molecule after the dietetic intervention.
Time Frame: 2 years
|
IL-1B levels will be measured using the Luminex® 200 analyzer system.
A significant improvement will be considered when notable differences are observed in the mean values between groups measured through p-value (<0.05) with a 95% confidence interval.
|
2 years
|
|
Evaluate significant changes in interleukin 6 (IL-6) levels as a pro-inflammatory molecule after the dietetic intervention.
Time Frame: 2 years
|
IL-6 levels will be measured using the Luminex® 200 analyzer system.
A significant improvement will be considered when notable differences are observed in the mean values between groups measured through p-value (<0.05) with a 95% confidence interval.
|
2 years
|
|
Evaluate significant changes in tumor necrosis factor alpha (TNF-alpha) levels as a pro-inflammatory molecule after the dietetic intervention.
Time Frame: 2 years
|
TNF-alpha levels will be measured using the Luminex® 200 analyzer system.
A significant improvement will be considered when notable differences are observed in the mean values between groups measured through p-value (<0.05) with a 95% confidence interval.
|
2 years
|
|
Assess significant changes in superoxide dismutase (SOD) levels after the dietetic intervention.
Time Frame: 2 years
|
Superoxide dismutase levels will be measured using the Luminex® 200 analyzer system.
A significant improvement will be considered when notable differences are observed in the mean values between groups measured through p-value (<0.05) with a 95% confidence interval.
|
2 years
|
|
Analyze the significant differences between metabolomic profile before and after the dietetic intervention.
Time Frame: 2 years
|
NMR spectra will be used to obtain spectra from serum samples from the cohort.
In order to evaluate if there will be significant differences after the dietetic intervention, a PLS-DA model for discrimination between basal and post intervention levels will be performed.
Scores plots will be calculated with a 95% confidence interval.
|
2 years
|
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Evaluate if there is a significant reduction after the dietetic intervention in total ROS levels.
Time Frame: 2 years
|
Total ROS levels will be assessed by a flow cytometry assay.
A significant improvement will be considered when notable differences are observed in the mean values between groups measured through p-value (<0.05) with a 95% confidence interval.
|
2 years
|
|
Assess if there is a significant reduction after the dietetic intervention in glutathione levels.
Time Frame: 2 years
|
Total glutathione levels will be assessed by a flow cytometry assay.
A significant improvement will be considered when notable differences are observed in the mean values between groups measured through p-value (<0.05) with a 95% confidence interval.
|
2 years
|
|
Analyze if there is a significant change after the dietetic intervention in total free radicals and superoxide levels.
Time Frame: 2 years
|
Total free radicals and superoxide content will be assessed by a flow cytometry assay.
A significant improvement will be considered when notable differences are observed in the mean values between groups measured through p-value (<0.05) with a 95% confidence interval.
|
2 years
|
|
Analyze if there is a significant reduction after the dietetic intervention in mitochondrial ROS production.
Time Frame: 2 years
|
Mitochondrial ROS production will be assessed by a flow cytometry assay.
A significant improvement will be considered when notable differences are observed in the mean values between groups measured through p-value (<0.05) with a 95% confidence interval.
|
2 years
|
|
Evaluate if there is a significant improvement after the dietetic intervention in mitochondrial membrane potential.
Time Frame: 2 years
|
Mitochondrial membrane potential will be assessed by a flow cytometry assay.
A significant improvement will be considered when notable differences are observed in the mean values between groups measured through p-value (<0.05) with a 95% confidence interval.
|
2 years
|
|
Analyze the proportion of subjects achieving at least 10% reduction in weight compared with baseline.
Time Frame: 5 years
|
Proportion of subjects achieving at least 10% reduction in weight after the dietetic intervention (6 months).
|
5 years
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Collaborators
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C, Chang D, Milburn MV, Gall WE, Weinberger KM, Mewes HW, Hrabe de Angelis M, Wichmann HE, Kronenberg F, Adamski J, Illig T. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One. 2010 Nov 11;5(11):e13953. doi: 10.1371/journal.pone.0013953.
- Fiehn O, Garvey WT, Newman JW, Lok KH, Hoppel CL, Adams SH. Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS One. 2010 Dec 10;5(12):e15234. doi: 10.1371/journal.pone.0015234.
- Olefsky JM, Glass CK. Macrophages, inflammation, and insulin resistance. Annu Rev Physiol. 2010;72:219-46. doi: 10.1146/annurev-physiol-021909-135846.
- Tchernof A, Despres JP. Pathophysiology of human visceral obesity: an update. Physiol Rev. 2013 Jan;93(1):359-404. doi: 10.1152/physrev.00033.2011.
- Mangge H, Zelzer S, Puerstner P, Schnedl WJ, Reeves G, Postolache TT, Weghuber D. Uric acid best predicts metabolically unhealthy obesity with increased cardiovascular risk in youth and adults. Obesity (Silver Spring). 2013 Jan;21(1):E71-7. doi: 10.1002/oby.20061. Epub 2013 Jan 29.
- Stefan N, Haring HU, Hu FB, Schulze MB. Metabolically healthy obesity: epidemiology, mechanisms, and clinical implications. Lancet Diabetes Endocrinol. 2013 Oct;1(2):152-62. doi: 10.1016/S2213-8587(13)70062-7. Epub 2013 Aug 30.
- Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, Haqq AM, Shah SH, Arlotto M, Slentz CA, Rochon J, Gallup D, Ilkayeva O, Wenner BR, Yancy WS Jr, Eisenson H, Musante G, Surwit RS, Millington DS, Butler MD, Svetkey LP. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009 Apr;9(4):311-26. doi: 10.1016/j.cmet.2009.02.002. Erratum In: Cell Metab. 2009 Jun;9(6):565-6.
- Smith KB, Smith MS. Obesity Statistics. Prim Care. 2016 Mar;43(1):121-35, ix. doi: 10.1016/j.pop.2015.10.001. Epub 2016 Jan 12.
- Lagerros YT, Rossner S. Obesity management: what brings success? Therap Adv Gastroenterol. 2013 Jan;6(1):77-88. doi: 10.1177/1756283X12459413.
- Gomez-Ambrosi J, Silva C, Galofre JC, Escalada J, Santos S, Millan D, Vila N, Ibanez P, Gil MJ, Valenti V, Rotellar F, Ramirez B, Salvador J, Fruhbeck G. Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. Int J Obes (Lond). 2012 Feb;36(2):286-94. doi: 10.1038/ijo.2011.100. Epub 2011 May 17.
- Phillips CM. Metabolically healthy obesity across the life course: epidemiology, determinants, and implications. Ann N Y Acad Sci. 2017 Mar;1391(1):85-100. doi: 10.1111/nyas.13230. Epub 2016 Oct 10.
- Primeau V, Coderre L, Karelis AD, Brochu M, Lavoie ME, Messier V, Sladek R, Rabasa-Lhoret R. Characterizing the profile of obese patients who are metabolically healthy. Int J Obes (Lond). 2011 Jul;35(7):971-81. doi: 10.1038/ijo.2010.216. Epub 2010 Oct 26.
- Naukkarinen J, Heinonen S, Hakkarainen A, Lundbom J, Vuolteenaho K, Saarinen L, Hautaniemi S, Rodriguez A, Fruhbeck G, Pajunen P, Hyotylainen T, Oresic M, Moilanen E, Suomalainen A, Lundbom N, Kaprio J, Rissanen A, Pietilainen KH. Characterising metabolically healthy obesity in weight-discordant monozygotic twins. Diabetologia. 2014 Jan;57(1):167-76. doi: 10.1007/s00125-013-3066-y. Epub 2013 Oct 8.
- Plourde G, Karelis AD. Current issues in the identification and treatment of metabolically healthy but obese individuals. Nutr Metab Cardiovasc Dis. 2014 May;24(5):455-9. doi: 10.1016/j.numecd.2013.12.002. Epub 2014 Jan 12.
- Velho S, Paccaud F, Waeber G, Vollenweider P, Marques-Vidal P. Metabolically healthy obesity: different prevalences using different criteria. Eur J Clin Nutr. 2010 Oct;64(10):1043-51. doi: 10.1038/ejcn.2010.114. Epub 2010 Jul 14.
- Fruhbeck G, Gomez-Ambrosi J. Rationale for the existence of additional adipostatic hormones. FASEB J. 2001 Sep;15(11):1996-2006. doi: 10.1096/fj.00-0829hyp.
- Messier V, Karelis AD, Robillard ME, Bellefeuille P, Brochu M, Lavoie JM, Rabasa-Lhoret R. Metabolically healthy but obese individuals: relationship with hepatic enzymes. Metabolism. 2010 Jan;59(1):20-4. doi: 10.1016/j.metabol.2009.06.020. Epub 2009 Aug 25.
- Gaye A, Doumatey AP, Davis SK, Rotimi CN, Gibbons GH. Whole-genome transcriptomic insights into protective molecular mechanisms in metabolically healthy obese African Americans. NPJ Genom Med. 2018 Jan 29;3:4. doi: 10.1038/s41525-018-0043-x. eCollection 2018.
- Hotamisligil GS. Inflammation and metabolic disorders. Nature. 2006 Dec 14;444(7121):860-7. doi: 10.1038/nature05485.
- Gregor MF, Hotamisligil GS. Thematic review series: Adipocyte Biology. Adipocyte stress: the endoplasmic reticulum and metabolic disease. J Lipid Res. 2007 Sep;48(9):1905-14. doi: 10.1194/jlr.R700007-JLR200. Epub 2007 May 9.
- Hotamisligil GS. Endoplasmic reticulum stress and the inflammatory basis of metabolic disease. Cell. 2010 Mar 19;140(6):900-17. doi: 10.1016/j.cell.2010.02.034.
- Howard JK, Flier JS. Attenuation of leptin and insulin signaling by SOCS proteins. Trends Endocrinol Metab. 2006 Nov;17(9):365-71. doi: 10.1016/j.tem.2006.09.007. Epub 2006 Sep 28.
- Lebrun P, Van Obberghen E. SOCS proteins causing trouble in insulin action. Acta Physiol (Oxf). 2008 Jan;192(1):29-36. doi: 10.1111/j.1748-1716.2007.01782.x.
- Cai D, Yuan M, Frantz DF, Melendez PA, Hansen L, Lee J, Shoelson SE. Local and systemic insulin resistance resulting from hepatic activation of IKK-beta and NF-kappaB. Nat Med. 2005 Feb;11(2):183-90. doi: 10.1038/nm1166. Epub 2005 Jan 30.
- Kogelman LJ, Fu J, Franke L, Greve JW, Hofker M, Rensen SS, Kadarmideen HN. Inter-Tissue Gene Co-Expression Networks between Metabolically Healthy and Unhealthy Obese Individuals. PLoS One. 2016 Dec 1;11(12):e0167519. doi: 10.1371/journal.pone.0167519. eCollection 2016.
- Esser N, L'homme L, De Roover A, Kohnen L, Scheen AJ, Moutschen M, Piette J, Legrand-Poels S, Paquot N. Obesity phenotype is related to NLRP3 inflammasome activity and immunological profile of visceral adipose tissue. Diabetologia. 2013 Nov;56(11):2487-97. doi: 10.1007/s00125-013-3023-9. Epub 2013 Sep 7.
- Zorzano A, Claret M. Implications of mitochondrial dynamics on neurodegeneration and on hypothalamic dysfunction. Front Aging Neurosci. 2015 Jun 10;7:101. doi: 10.3389/fnagi.2015.00101. eCollection 2015.
- Westermann B. Mitochondrial fusion and fission in cell life and death. Nat Rev Mol Cell Biol. 2010 Dec;11(12):872-84. doi: 10.1038/nrm3013.
- Armitage EG, Rupérez FJ, Barbas C. Metabolomics of diet-related diseases using mass spectrometry. Trends Analyt Chem. 2013; 52: 61-73.
- Wiklund PK, Pekkala S, Autio R, Munukka E, Xu L, Saltevo J, Cheng S, Kujala UM, Alen M, Cheng S. Serum metabolic profiles in overweight and obese women with and without metabolic syndrome. Diabetol Metab Syndr. 2014 Mar 20;6(1):40. doi: 10.1186/1758-5996-6-40.
- Chen HH, Tseng YJ, Wang SY, Tsai YS, Chang CS, Kuo TC, Yao WJ, Shieh CC, Wu CH, Kuo PH. The metabolome profiling and pathway analysis in metabolic healthy and abnormal obesity. Int J Obes (Lond). 2015 Aug;39(8):1241-8. doi: 10.1038/ijo.2015.65. Epub 2015 Apr 24.
- Gao X, Zhang W, Wang Y, Pedram P, Cahill F, Zhai G, Randell E, Gulliver W, Sun G. Serum metabolic biomarkers distinguish metabolically healthy peripherally obese from unhealthy centrally obese individuals. Nutr Metab (Lond). 2016 May 12;13:33. doi: 10.1186/s12986-016-0095-9. eCollection 2016.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
January 1, 2019
Primary Completion (Actual)
December 31, 2023
Study Completion (Actual)
August 31, 2024
Study Registration Dates
First Submitted
February 2, 2024
First Submitted That Met QC Criteria
February 19, 2024
First Posted (Actual)
February 28, 2024
Study Record Updates
Last Update Posted (Actual)
March 25, 2025
Last Update Submitted That Met QC Criteria
February 25, 2025
Last Verified
February 1, 2024
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- PI18/00932
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
NO
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
product manufactured in and exported from the U.S.
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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