Involvement of the Gut Microbiota-brain Cross-talk in the Loss of Eating Control

NCT ID: NCT05646901

Last Updated: 2025-12-12

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 108 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-12-12
• Study Completion Date: 2026-08-31

Brief Summary

Overweight and obesity are increasingly prevalent worldwide. These bodyweight disorders are closely related to deficiencies in the control of food intake. A potential yet unexplored mechanism to explain the loss of eating control is the interaction between the gut microbiota and the brain. The mechanisms underlying the communication between the gut microbiome and the host remain largely unexplored. These mechanisms could occur in part through small non-coding RNAs, called microRNAs (miRNAs). miRNAs regulate epigenetic mechanisms to control gene expression.

Two hypotheses have been proposed:

I. The interaction between the gut microbiota and the brain and its associated epigenetic changes play an important role in the overweight-related loss of eating control and metabolic imbalance.

II.The composition and functionality of the gut microbiota are associated with circulating microRNAs and glycemic variability and modify the effect of physical activity on cognitive parameters and brain microstructure (R2*).

The study includes a cross-sectional design (comparison of subjects with and without obesity) to evaluate parameters associated with food addiction through validated questionnaires. The metabolic and behavioral profiles of the cohort will be characterized. The medial prefrontal cortex connectivity will be studied using functional magnetic resonance imaging (fMRI). The composition and functionality of the gut metagenome of the subjects will be analyzed in association with metabolic and behavioral parameters and imaging data. miRNAs can act as mediators of epigenomics of the effects of the metagenome that impact the brain, therefore it will be analyzed a broad profile of miRNAs circulating in plasma.

Detailed Description

The study includes a cross-sectional design (comparison of subjects with and without obesity) to assess parameters associated with food addiction through validated questionnaires. The metabolic and behavioral profile of the cohort and medial prefrontal cortex (mPFC) connectivity using fMRI will be characterized. The composition and functionality of the gut metagenome of these subjects will be analyzed in terms of its links to metabolic and behavioral parameters and imaging data. Since miRNAs may act as epigenomic mediators of metagenome effects impacting the brain, a broad profile of miRNAs circulating in plasma will also be analyzed.

Subjects and methods:

A cohort of subjects (n=100, 50% with obesity) will be recruited in whom parameters of food addiction (reward sensitivity, punishment sensitivity, and Yale Food Addiction Scale (YFAS 2.0 score) will be collected. The project will be carried out in subjects with obesity (25 men, 25 women, Body mass index (BMI) > = 30kg/m2) and subjects without obesity, similar in age and sex (25 men, 25 women, BMI <30kg/m2). A comprehensive metabolic profile (body weight, glucose and lipid profile, insulin resistance, blood pressure, and plasma and fecal metabolomics) will be analyzed.

A. Cross-sectional study:

Patients with obesity previously scheduled at the Service of Endocrinology, Diabetes, and Nutrition (UDEN) of the Hospital "Dr. Josep Trueta" of Girona (Spain) will be recruited and studied. Subjects without obesity will also be recruited through a public announcement.

A glycemia sensor will be implanted for ten days, as well as an activity and sleep tracker device to record physical activity during this period of time. Interstitial subcutaneous glucose concentrations will be monitored on an outpatient basis for a period of time of 10 consecutive days using a glucose sensor validated by the FDA (Dexcom G6 ®). The sensor will be implanted on day 0 and will retire on day 10 mid-morning. Glucose records will preferably be evaluated on days 2 to 9 to avoid the bias caused by the insertion and removal of the sensor, which prevents a sufficient stabilization of the monitoring system. The characteristic glycemic pattern of each patient will be calculated on average from the profiles obtained on days 2 to 9.

After 10 days, urine and feces will be collected for the study of the gut microbiota. Subjects will undergo a fasting blood test and after eating, neuropsychological testing will be performed. Subsequently, the sensor and the device for monitoring physical activity/sleep will be removed. Lastly, fMRI will be done to evaluate the iron content in the brain (R2*) and the parameters of "Diffusion Tensor Imaging" in different brain territories. We will characterize mPFC connectivity in subjects in this cohort by resting-state functional MRI and structural connectivity by MRI.

The gut metagenomic composition and functionality associated with these cognitive traits, miRNA, and metabolites in plasma and brain imaging data will be studied.

Visit planning:

Visit 1(day 1): Physical examination, Nutritional survey, Bioimpedance, Densitometry, glycemia sensor, and activity and sleep tracker device. Consent form.

Visit 2 (day 10): Sample: blood, urine, and feces. Diet questionnaire, Neuropsychological assessment, Glycemia sensor withdrawal. Activity and sleep tracker device withdrawal, fMRI.

DATA COLLECTION OF SUBJECTS OF CROSS-SECTIONAL STUDY:

1. Subsidiary data: Age, sex, and birth date.

2. Clinical variables:

• Weight
• height,
• body mass index
• waist and hip perimeters
• waist-to-hip ratio
• blood pressure (systolic and diastolic)
• fat mass and fat free-mass (bioelectric impedance and DEXA)
• smoking status
• alcohol intake
• registry of usual medicines
• personal history of blood transfusion and/or donation
• a record of family history of obesity, cardiovascular events, and diabetes
• psychiatric and eating disorder history.

3. Laboratory variables: 15cc of blood will be extracted from fasted subjects to determine the following variables using the usual routine techniques of the clinical laboratory:

• hemogram
• glucose
• bilirubin
• aspartate aminotransferase (AST/GOT)
• alanine aminotransferase (ALT/GPT)
• gamma-glutamyl transpeptidase (GGT)
• urea
• creatinine
• uric acid
• total proteins,
• albumin
• total cholesterol | HDL cholesterol | LDL cholesterol
• triglycerides,
• glycated hemoglobin (HbA1c)
• ferritin | soluble transferrin receptor
• ultrasensitive C reactive protein
• erythrocyte sedimentation rate
• lipopolysaccharide binding protein
• free thyroxine (free T4) | thyroid stimulating hormone (TSH) | baseline cortisol -plasma insulin
• inflammation markers | interleukin 6 (IL-6). An additional 20cc of blood (plasma-EDTA), 18cc of serum, and 20cc of plasma with heparin will be extracted for further analysis.

4. Stool samples collection: A stool sample will be provided from each patient. The sample should be collected at home or in the hospital, sent to the laboratory within 4 hours from the collection, fragmented, and stored at -80ºC.

Analysis of gut microbiota in stool:

*Fecal genomic DNA extraction and whole-genome sequencing. Total DNA will be extracted from frozen human stool using the QIAamp DNA mini stool kit (Qiagen, Courtaboeuf, France). DNA quantification will be performed with a Qubit 3.0 fluorometer (Thermo Fisher Scientific, Carlsbad, CA, USA). Subsequently, 1 ng of each sample (0.2 ng/μl) will be used for the preparation of shotgun libraries for high-throughput sequencing, using the Nextera DNA Flex Library Prep kit (Illumina, Inc., San Diego, CA, USA) according to the manufacturer's protocol. Sequencing will be performed on a NextSeq 500 sequencing system (Illumina) with 2 X 150-bp paired-end chemistry, at the facilities of the Sequencing and Bioinformatics Service of the FISABIO (Valencia, Spain).

5. Urine sample collection: Necessary to determine alterations in the metabolic pathways involved in tryptophan metabolism, and to determine the role of the intestinal microbiota in these metabolic changes.

6. Metabolomics in plasma and feces: In addition to metabolomic analyses in urine samples, metabolomic analyses will be performed using techniques such as 1H-NMR and HPLC-MS/MS in plasma and stool samples.

7. Magnetic Resonance Imaging: All MRI examinations will be performed on a 1.5-T scanner (Ingenia ®; Philips Medical Systems). First, a fluid-attenuated inversion recovery (FLAIR) sequence will be used to exclude subjects with preexisting brain lesions. Brain iron load will be assessed by means of R2* values. T2* relaxation data will be acquired with a multi-echo gradient-echo sequence with 10 equally spaced echoes (first echo=4.6ms; inter echo spacing=4.6ms; repetition time=1300ms). T2* will be calculated by fitting the single exponential terms to the signal decay curves of the respective multi-echo data.R2* values will be calculated as R2*=1/T2* and expressed as Hz. In addition, R2* values will be converted to μmol Fe/g units as previously validated on phantom tests. Brain iron images from control subjects will be normalized to a standard space using a template image for this purpose (EPI MNI template). Subsequently, all normalized images will be averaged for the determination of normal iron content. Normal values (mean and SD) will be also calculated for anatomical regions of interest using different atlas masks, addressing possible differences between gender and age. The brain iron comparison between control and obese subjects will be performed using voxel-based analysis. Obese-subject images will be normalized to a standard space. The normalized image will be compared to the normal population using t-test analysis with age and sex as co-variables. As result, a parametric map will show individual differences in the iron deposition. Based on previous observational studies showing increased brain iron load at some specific regions and the evidence suggesting hippocampal and hypothalamic changes in association with obesity and insulin resistance, the statistical and image analyses will be focused on iron differences at the caudate, lenticular, thalamus, hypothalamus, hippocampus, and amygdala.

8. Neuropsychological examination: Different domains of cognition will be explored: memory (Test aprendizaje verbal-TAVEC, Rey-Osterrieth Complex Figure) attention, and executive function(WAIS-IV, Trail making test (Part A y B), Stroop test), social cognition(POFA and BFRT), language (animals). Furthermore, depression (PHQ9), anxiety (State-Trait Anxiety Inventory (STAI)), impulsivity (Impulsive Behavior Scale (UPPS-P)), sensitivity to punishment and reward (Sensitivity to Punishment and Sensitivity to Reward (SRSPQ)), food addiction (Yale Food Addiction Scale (YFAS II)), binge eating disorder (Binge Eating scale), subjective well being, positive and negative affect (Positive and Negative Affect Schedule (PANAS)), emotion recognition (Pictures of Facial Affect and Benton Facial Recognition Test) will be explored through psychological tests.

9. Profile of circulating miRNAs: Additionally, to metabolomic analyses in urine samples, metabolomic analyses will be performed using techniques such as 1H-NMR and HPLC-MS/MS in plasma and stool samples.

• Circulating RNA extraction and purification: Plasma will be obtained by standard venipuncture and centrifugation using EDTA-coated Vacutainer tubes (Becton-Dickinson, Franklin Lakes, NJ). Plasma separation will be performed by double centrifugation using a laboratory centrifuge (Beckman J-6M Induction Drive Centrifuge, Beckman Instruments Inc). RNA extraction will be performed from an initial volume of 300 μL of plasma using the mirVana PARIS isolation kit (Applied Biosystems, Darmstadt, Germany).
• Retrotranscription of circulating miRNAs and preamplification: A fixed volume of 3 μL of RNA solution from the 40 mL, eluate of the RNA isolate will be used as input for retrotranscription using the TaqMan miRNA reverse transcription kit (Life Technology, Darmstadt, Germany). Preamplification will be carried out using the TaqMan PreAmp Master Mix (Life Technology, Darmstadt, Germany).
• Analysis of individual miRNAs by TaqMan hydrolysis probes: Gene expression will be assessed by real-time PCR using the LightCycler 480 real-time PCR system (Roche Diagnostics, Barcelona, Spain), using the appropriate TaqMan technology for the quantification of relative gene expression.

10. Drosophila

The relevant gut microbiota identified in the human cohort will first be tested in Drosophila. High-throughput screening in Drosophila of the metabolic and behavioral effects of the gut microbiota identified in mice with loss of feeding control will be performed. Microbial strains obtained from the storage facilities will be cultured in high yield under conditions suitable for selecting aerotolerant bacteria to associate with flies. These bacteria will be used to generate mono-associated gnotobiotic flies, which will be analyzed for alterations in feeding behavior using the high-throughput quantitative flyPAD feeding assay. We will test both fully-fed flies and flies deprived of amino acids. Bacterial strains identified as modifiers of the drive to eat will be evaluated for their effects on fly metabolism using standard metabolomic approaches. This task will identify specific bacterial strains capable of modifying the feeding drive, and behavioral and metabolic responses of Drosophila.

The information will remain registered in a notebook and will be computerized in the database of the study.

STATICAL METHODS:

Sample size: There are no previous data showing expected differences for sample size estimation regarding glucose variability, physical activity, the composition of gut microbiota, and cognitive function. In a previous study, differences in brain iron content were observed in 20 obese vs. 20 nonobese subjects. Thus, the proposed sample size is at least 20 individuals per group, with balanced age and gender (pre-and postmenopausal women) representation.

Student's t-test for independent samples will be used to compare the variables of subjects with obesity vs subjects without obesity. Prior to statistical analysis, the data will be normalized using specific normalization procedures. Next, the normal distribution and homogeneity of variances will be tested. Parameters that do not meet these requirements will be logarithmically transformed (log10). Student's t-test for paired samples will be used to study differences before and after follow-up. Significant associations, whether positive or negative, will be studied further (simple linear and multivariate regression analysis).

Metagenomic analysis.

Raw counts will be transformed using a centered logarithmic transformation (clr) as implemented in the R package "ALDEx2". Bacterial species and functions associated with brain iron and circulating microRNAs will be identified using robust linear regression models such as those implemented in the R package. Limma R, adjusting for age, body mass index, sex, and years of education. Taxa and bacterial functions will be previously filtered so that only those with more than 10 reads in at least five samples will be selected. The p-values will be adjusted for multiple comparisons using sequential goodness of fit as implemented in the R package "SGoF". SGoF has been shown to perform particularly better than FDR methods with a high number of tests and low sample size, which is the case for large omics data sets. Statistical significance will be set at p adjusted <0.1.

Continuos glucose monitoring

The glycemic risks and measures of variability to be assessed are standard deviation (SD), coefficient of variation (CV), mean amplitude of glycemic excursions (MAGE), risk index (RI), low blood glucose index (LBGI), and high blood glucose index (HBGI). In addition, percent (%) time in range (70-180mg/dL), % time in euglycemia (70 - 140 mg/dL), hypoglycemia (<70mg/dL), and hyperglycemia (>180 mg/dL). Measures of glycemia variability were calculated using Matlab software (R2018a).

Conditions

Obesity

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: PROSPECTIVE

Study Groups

Men with obesity

No interventions assigned to this group

Premenopausal women without obesity

No interventions assigned to this group

Postmenopausal women without obesity

No interventions assigned to this group

Men without obesity

No interventions assigned to this group

Premenopausal women with obesity

No interventions assigned to this group

Postmenopausal women with obesity

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

1. Men and women aged 30-65 years.

2. Informed consent for participation in the study.

Exclusion Criteria

1. Serious systemic disease unrelated to obesity such as cancer, severe kidney, or liver disease, known as type 1 or type 2 diabetes.

2. Systemic diseases with intrinsic inflammatory activity such as rheumatoid arthritis, Crohn's disease, asthma, chronic infection (e.g., HIV, active tuberculosis), or any type of infectious disease.

3. Pregnancy and lactation.

4. Patients with severe disorders of eating behavior.

5. Persons whose liberty is under the legal or administrative requirement.

6. Clinical symptoms and signs of infection in the previous month.

7. Antibiotic, antifungal or antiviral treatment in the previous 3 months.

8. Anti-inflammatory chronic treatment with steroidal and/or non-steroidal anti-inflammatory drugs.

9. Major psychiatric antecedents.

10. Excessive alcohol intake, either acute or chronic (alcohol intake greater than 40 g a day (women) or 80 g/day (men)) or drug abuse.

11. Serum liver enzyme (AST, ALT) activity over twice the upper limit of normal.

12. History of disturbances in iron balance (e.g., genetic hemochromatosis, hemosiderosis from any cause, atransferrinemia, paroxysmal nocturnal hemoglobinuria).

13. Creatinine greater than 1.2 and glomerular filtration rate less than 40.

14. Immunosuppressants treatment.

15. Chronic constipation (depositional habit ≥ 7 days)

16. Kidney failure, history of a kidney transplant, or current treatment with dialysis.

17. Treatment with a slimming product during the previous two months.

18. Class III or IV heart failure (according to the New York Heart Association), medical records of ischemic cardiovascular disease.

19. Current treatment for malignant neoplasm.

• Minimum Eligible Age: 30 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta

OTHER

Sponsor Role: lead

Responsible Party

José Manuel Fernández-Real

Principal investigator, clinical professor, section chief of Endocrinology and Nutrition Department of Josep Trueta University Hospital

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

José Manuel Fernández-Real, M.D., Ph.D.

Role: PRINCIPAL_INVESTIGATOR

Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta

Locations

Institut d'Investigació Biomèdica de Girona (IDIBGI)

Girona, Girona, Spain

Site Status: RECRUITING

Countries

Spain

Central Contacts

José Manuel Fernández-Real, M.D., Ph.D.

Role: CONTACT

jmfreal@idibgi.org

+34 972 94 02 00

Marisel Rosell Díaz, M.D., MSc.

Role: CONTACT

mrosell@idibgi.org

+34 972 94 02 00 ext. 2325

Facility Contacts

Yenny Leal

Role: primary

yleal@idibgi.org

0034 972940200 ext. 2325

References

Ramirez V, Wiers CE, Wang GJ, Volkow ND. Personality traits in substance use disorders and obesity when compared to healthy controls. Addiction. 2020 Nov;115(11):2130-2139. doi: 10.1111/add.15062. Epub 2020 Apr 29.

Reference Type: BACKGROUND

PMID: 32350970 (View on PubMed)

Volkow ND, Wang GJ, Tomasi D, Baler RD. The addictive dimensionality of obesity. Biol Psychiatry. 2013 May 1;73(9):811-8. doi: 10.1016/j.biopsych.2012.12.020. Epub 2013 Jan 29.

Reference Type: BACKGROUND

PMID: 23374642 (View on PubMed)

Volkow ND, Wise RA. How can drug addiction help us understand obesity? Nat Neurosci. 2005 May;8(5):555-60. doi: 10.1038/nn1452. No abstract available.

Reference Type: BACKGROUND

PMID: 15856062 (View on PubMed)

Koob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry. 2016 Aug;3(8):760-773. doi: 10.1016/S2215-0366(16)00104-8.

Reference Type: BACKGROUND

PMID: 27475769 (View on PubMed)

Gearhardt AN, Corbin WR, Brownell KD. Development of the Yale Food Addiction Scale Version 2.0. Psychol Addict Behav. 2016 Feb;30(1):113-21. doi: 10.1037/adb0000136.

Reference Type: BACKGROUND

PMID: 26866783 (View on PubMed)

Kalon E, Hong JY, Tobin C, Schulte T. Psychological and Neurobiological Correlates of Food Addiction. Int Rev Neurobiol. 2016;129:85-110. doi: 10.1016/bs.irn.2016.06.003. Epub 2016 Jul 22.

Reference Type: BACKGROUND

PMID: 27503449 (View on PubMed)

Gupta A, Osadchiy V, Mayer EA. Brain-gut-microbiome interactions in obesity and food addiction. Nat Rev Gastroenterol Hepatol. 2020 Nov;17(11):655-672. doi: 10.1038/s41575-020-0341-5. Epub 2020 Aug 27.

Reference Type: BACKGROUND

PMID: 32855515 (View on PubMed)

Gearhardt AN, Boswell RG, White MA. The association of "food addiction" with disordered eating and body mass index. Eat Behav. 2014 Aug;15(3):427-33. doi: 10.1016/j.eatbeh.2014.05.001. Epub 2014 May 27.

Reference Type: BACKGROUND

PMID: 25064294 (View on PubMed)

Arnoriaga-Rodriguez M, Mayneris-Perxachs J, Burokas A, Contreras-Rodriguez O, Blasco G, Coll C, Biarnes C, Miranda-Olivos R, Latorre J, Moreno-Navarrete JM, Castells-Nobau A, Sabater M, Palomo-Buitrago ME, Puig J, Pedraza S, Gich J, Perez-Brocal V, Ricart W, Moya A, Fernandez-Real X, Ramio-Torrenta L, Pamplona R, Sol J, Jove M, Portero-Otin M, Maldonado R, Fernandez-Real JM. Obesity Impairs Short-Term and Working Memory through Gut Microbial Metabolism of Aromatic Amino Acids. Cell Metab. 2020 Oct 6;32(4):548-560.e7. doi: 10.1016/j.cmet.2020.09.002.

Reference Type: BACKGROUND

PMID: 33027674 (View on PubMed)

Arnoriaga-Rodriguez M, Mayneris-Perxachs J, Contreras-Rodriguez O, Burokas A, Ortega-Sanchez JA, Blasco G, Coll C, Biarnes C, Castells-Nobau A, Puig J, Garre-Olmo J, Ramos R, Pedraza S, Brugada R, Vilanova JC, Serena J, Barretina J, Gich J, Perez-Brocal V, Moya A, Fernandez-Real X, Ramio-Torrenta L, Pamplona R, Sol J, Jove M, Ricart W, Portero-Otin M, Maldonado R, Fernandez-Real JM. Obesity-associated deficits in inhibitory control are phenocopied to mice through gut microbiota changes in one-carbon and aromatic amino acids metabolic pathways. Gut. 2021 Dec;70(12):2283-2296. doi: 10.1136/gutjnl-2020-323371. Epub 2021 Jan 29.

Reference Type: BACKGROUND

PMID: 33514598 (View on PubMed)

Williams MR, Stedtfeld RD, Tiedje JM, Hashsham SA. MicroRNAs-Based Inter-Domain Communication between the Host and Members of the Gut Microbiome. Front Microbiol. 2017 Sep 27;8:1896. doi: 10.3389/fmicb.2017.01896. eCollection 2017.

Reference Type: BACKGROUND

PMID: 29021788 (View on PubMed)

Arnoriaga-Rodriguez M, Mayneris-Perxachs J, Burokas A, Perez-Brocal V, Moya A, Portero-Otin M, Ricart W, Maldonado R, Fernandez-Real JM. Gut bacterial ClpB-like gene function is associated with decreased body weight and a characteristic microbiota profile. Microbiome. 2020 Apr 30;8(1):59. doi: 10.1186/s40168-020-00837-6.

Reference Type: BACKGROUND

PMID: 32354351 (View on PubMed)

Mayneris-Perxachs J, Arnoriaga-Rodriguez M, Luque-Cordoba D, Priego-Capote F, Perez-Brocal V, Moya A, Burokas A, Maldonado R, Fernandez-Real JM. Gut microbiota steroid sexual dimorphism and its impact on gonadal steroids: influences of obesity and menopausal status. Microbiome. 2020 Sep 20;8(1):136. doi: 10.1186/s40168-020-00913-x.

Reference Type: BACKGROUND

PMID: 32951609 (View on PubMed)

Other Identifiers

GMBCrossTalk-Food-2022.166

Identifier Type: -

Identifier Source: org_study_id