Clinical trial NCT05345106: Inter-relationships Among Glucose, Brain, Gut Microbiota and MicroRNAs (IRONmiRNA).
Official title: Inter-relationships Among Iron Stores, the Gut Metagenome, Glucose Levels, and Different Cognitive Domains: the Role of Circulating MicroRNAs (IRONmiRNA Study).
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Obesity
Keywords: Continous Glucose Monitoring
Cognition
Brain Iron content
Lead sponsor: Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta
Sponsor class: OTHER
First posted: 2022-04-25
Enrollment: 120
Sex: ALL
Minimum age (years): 30
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with obesity, without known type 2 diabetes, 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.

Brief summary: The brain is a recognized target of iron deposition. This process is enhanced by the presence of obesity and hyperglycemia and impacts cognitive functions. There is evidence suggesting that the gut microbiota composition modulates this process. It has been proposed that microRNAs are mediators in the dialogue between the composition and functionality of the intestinal microbiota and increased iron deposition in the brain.

The hypothesis is that circulating microRNAs are associated with parameters of cognitive dysfunction, gut microbiota, brain iron content, glucose levels, and physical activity in subjects with and without obesity.

The study includes both a cross-sectional (comparison of subjects with and without obesity) and a longitudinal design (evaluation one year after weight loss induced by bariatric surgery or by diet in patients with obesity) to evaluate the associations between circulating microRNAs, continuous glucose monitoring, brain iron content (by magnetic resonance), cognitive function (by means of cognitive tests), physical activity (measured by activity and sleep tracker device) and the composition of the microbiota, evaluated by metagenomics.

Detailed description: Subjects and methods:

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 inserted for ten days, as well as an activity and sleep tracker device (Fitbit) 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 Food and Drug Administration (Dexcom G6 ®). The sensor will be inserted on day 0 and it 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.

At the end of the week, magnetic resonance imaging will be done to evaluate the iron content in the brain and parameters of "Diffusion Tensor Imaging" in different brain territories.

Cognitive tests will be carried out, and feces and urine will be collected for the study of the microbiota. Additionally, blood samples will be collected for the extraction and purification of circulating RNA and then retrotranscription of circulating miRNAs and preamplification.

The project will be carried out in subjects with obesity (20 men, 20 premenopausal women, and 20 women postmenopausal, Body mass index (BMI) \> = 30kg/m2) and subjects without obesity, similar in age, sex, and menopausal status (20 men, 20 premenopausal women, and 20 postmenopausal women, BMI \<30kg/m2).

B. Longitudinal study:

After one year of follow-up, in which, subjects with obesity will undergo conventional treatment (hypocaloric diet, and physical activity advice) or bariatric surgery for weight loss, a second visit will be carried out.

For comparison, the same protocol of the cross-sectional study will be done again. See the information above.

DATA COLLECTION OF SUBJECTS OF CROSS-SECTIONAL AND LONGITUDINAL STUDIES:

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 15cc of blood (plasma-EDTA) will be extracted for further analyses.
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:

   \*Determination of bacterial DNA and mRNA and study of the LBP binding protein in the blood for the detection of bacterial translocation. LBP binding protein in the blood for the detection of bacterial translocation. Hiseq and Nextseq technology (qPCR and protein analysis (WB, ELISA), OMICS (RNAseq, 16S, Metabolomics, Metagenomics).
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. 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 a 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.
7. 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)), subjective well being, positive and negative affect (Positive and Negative Affect Schedule (PANAS)) will be explored through psychological tests.
8. Profile of circulating miRNAs

   * 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.

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.

Statistical analyses: Firstly, normal distribution and homogeneity of variances will be tested. To determine differences between study groups, it will be used χ2 for categorical variables, unpaired Student's t-test in normal quantitative, and Mann-Whitney U test for non-normal quantitative variables. Nonparametric Spearman analysis will be used to determine the correlation between quantitative variables. The same tests will also be used to study differences before and after follow-up. The significant associations, whether positive or negative, will be explored more in-depth (simple and multivariate linear regression analyses).

The microbiota composition will be analyzed and compared using R "ALDEx2". Bacterial species and functions associated with brain iron and circulating microRNAs will be identified using robust linear regression models as implemented in the R package (Limma R). Moreover, p-values will be adjusted for multiple comparisons using the R package "SGoF".Measures of glycemic variability will be calculated using Matlab software (R2018a).

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 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 behaviour.
5. Persons whose liberty is under 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 drugs abuse.
11. Serum liver enzymes (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).

Primary outcomes:
measure: Concentration of advanced glycation end products (AGE) receptor agonists.
timeFrame: 30 months
measure: Glycemic variability.
timeFrame: 30 months
measure: The percentage of time in glucose target range (glucose level 100mg/dl-125mg/dl)
timeFrame: 30 months
measure: The glycaemic risk measured with low blood glucose index (LBGI)
timeFrame: 30 months
measure: The glycaemic risk measured with high blood glucose index (HBGI)
timeFrame: 30 months
measure: The glycaemic variability measured with mean amplitude of glycaemic excursions (MAGE)
timeFrame: 30 months
measure: Minutes light sleep
timeFrame: 30 months
measure: Minutes deep sleep
timeFrame: 30 months
measure: Minutes rapid eye movement (REM)
timeFrame: 30 months
Secondary outcomes:
measure: Effect on brain structure.
timeFrame: 30 months
measure: Effect on gut microbiota.
timeFrame: 30 months
measure: Changes from baseline in circulating concentration of AGE receptor agonists and glycemic variability one year of follow-up after weight loss in association with changes in brain structure and gut microbiota.
timeFrame: 30 months
measure: Anxiety state
timeFrame: 30 months
measure: Audioverbal memory
timeFrame: 30 months
measure: Visual memory
timeFrame: 30 months
measure: Depressive symptomatology
timeFrame: 30 months
measure: Impulsivity
timeFrame: 30 months
measure: Food Addiction
timeFrame: 30 months
measure: Behavioral inhibition
timeFrame: 30 months
measure: Behavioral activation
timeFrame: 30 months
measure: Visoconstructive function
timeFrame: 30 months
measure: Selective and alternating attention
timeFrame: 30 months
measure: Attention and working memory
timeFrame: 30 months
measure: Inhibition
timeFrame: 30 months
measure: Phonemic verbal fluency
timeFrame: 30 months
measure: Semantic verbal fluency
timeFrame: 30 months
measure: Facial recognition
timeFrame: 30 months
measure: Emotion recognition
timeFrame: 30 months
measure: Diffusion Tensor Imaging brain sequences
timeFrame: 30 months
measure: Brain iron accumulation
timeFrame: 30 months
measure: Resting-state functional brain sequences
timeFrame: 30 months
measure: Insulin resistance
timeFrame: 30 months
measure: Markers of chronic inflammation: C-reactive protein, IL-6, adiponectin and soluble, tumor necrosis factor-α receptor fractions.
timeFrame: 30 months
measure: Glycosylated hemoglobin (HbA1c) value
timeFrame: 30 months
measure: The percentage of time in hyperglycaemia (glucose level above 250 mg/dl)
timeFrame: 30 months
measure: The percentage of time in hypoglycaemia (glucose level below 70mg/dl)
timeFrame: 30 months
measure: The percentage of time in glucose range (glucose level below 100 mg/dl)
timeFrame: 30 months
measure: The percentage of time in glucose range (glucose level between 126-139 mg/dl)
timeFrame: 30 months
measure: The percentage of time in glucose range (glucose level between 140-199 mg/dl)
timeFrame: 30 months
measure: The percentage of time in glucose range (glucose level above 200 mg/dl)
timeFrame: 30 months
measure: Burned calories
timeFrame: 30 months
measure: Steps
timeFrame: 30 months
measure: Distance
timeFrame: 30 months
measure: Minutes null activity
timeFrame: 30 months
measure: Minutes slight activity
timeFrame: 30 months
measure: Minutes mean activity
timeFrame: 30 months
measure: Minutes high activity
timeFrame: 30 months
measure: Calories
timeFrame: 30 months
measure: Minutes asleep
timeFrame: 30 months
measure: Minutes awake
timeFrame: 30 months
measure: Bed time
timeFrame: 30 months
measure: Number time awake
timeFrame: 30 months
Other outcomes:
Locations:
city: Girona
country: Spain
facility: Institut d'Investigació Biomèdica de Girona (IDIBGI)
Study officials:
affiliation: Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta
name: José Manuel Fernández-Real, M.D., Ph.D.
role: PRINCIPAL_INVESTIGATOR
References:
citation: Finch C. Regulators of iron balance in humans. Blood. 1994 Sep 15;84(6):1697-702. No abstract available.
citation: Fernandez-Real JM, Ricart-Engel W, Arroyo E, Balanca R, Casamitjana-Abella R, Cabrero D, Fernandez-Castaner M, Soler J. Serum ferritin as a component of the insulin resistance syndrome. Diabetes Care. 1998 Jan;21(1):62-8. doi: 10.2337/diacare.21.1.62.
citation: Fernandez-Real JM, Lopez-Bermejo A, Ricart W. Cross-talk between iron metabolism and diabetes. Diabetes. 2002 Aug;51(8):2348-54. doi: 10.2337/diabetes.51.8.2348.
citation: Fernandez-Real JM, Manco M. Effects of iron overload on chronic metabolic diseases. Lancet Diabetes Endocrinol. 2014 Jun;2(6):513-26. doi: 10.1016/S2213-8587(13)70174-8. Epub 2013 Dec 30.
citation: Fernandez-Real JM, Blasco G, Puig J, Moreno M, Xifra G, Sanchez-Gonzalez J, Maria Alustiza J, Pedraza S, Ricart W, Maria Moreno-Navarrete J. Adipose tissue R2* signal is increased in subjects with obesity: A preliminary MRI study. Obesity (Silver Spring). 2016 Feb;24(2):352-8. doi: 10.1002/oby.21347. Epub 2015 Dec 26.
citation: Moreno-Navarrete JM, Blasco G, Xifra G, Karczewska-Kupczewska M, Stefanowicz M, Matulewicz N, Puig J, Ortega F, Ricart W, Straczkowski M, Fernandez-Real JM. Obesity Is Associated With Gene Expression and Imaging Markers of Iron Accumulation in Skeletal Muscle. J Clin Endocrinol Metab. 2016 Mar;101(3):1282-9. doi: 10.1210/jc.2015-3303. Epub 2016 Jan 14.
citation: Moreno-Navarrete JM, Moreno M, Puig J, Blasco G, Ortega F, Xifra G, Ricart W, Fernandez-Real JM. Hepatic iron content is independently associated with serum hepcidin levels in subjects with obesity. Clin Nutr. 2017 Oct;36(5):1434-1439. doi: 10.1016/j.clnu.2016.09.022. Epub 2016 Sep 29.
citation: Moreno-Navarrete JM, Rodriguez A, Becerril S, Valenti V, Salvador J, Fruhbeck G, Fernandez-Real JM. Increased Small Intestine Expression of Non-Heme Iron Transporters in Morbidly Obese Patients With Newly Diagnosed Type 2 Diabetes. Mol Nutr Food Res. 2018 Jan;62(2). doi: 10.1002/mnfr.201700301. Epub 2017 Dec 29.
citation: Fernandez Real JM, Moreno-Navarrete JM, Manco M. Iron influences on the Gut-Brain axis and development of type 2 diabetes. Crit Rev Food Sci Nutr. 2019;59(3):443-449. doi: 10.1080/10408398.2017.1376616. Epub 2017 Oct 17.
citation: Geijselaers SLC, Sep SJS, Stehouwer CDA, Biessels GJ. Glucose regulation, cognition, and brain MRI in type 2 diabetes: a systematic review. Lancet Diabetes Endocrinol. 2015 Jan;3(1):75-89. doi: 10.1016/S2213-8587(14)70148-2. Epub 2014 Aug 24.
citation: Kharabian Masouleh S, Beyer F, Lampe L, Loeffler M, Luck T, Riedel-Heller SG, Schroeter ML, Stumvoll M, Villringer A, Witte AV. Gray matter structural networks are associated with cardiovascular risk factors in healthy older adults. J Cereb Blood Flow Metab. 2018 Feb;38(2):360-372. doi: 10.1177/0271678X17729111. Epub 2017 Aug 31.
citation: Ryan CM, Freed MI, Rood JA, Cobitz AR, Waterhouse BR, Strachan MW. Improving metabolic control leads to better working memory in adults with type 2 diabetes. Diabetes Care. 2006 Feb;29(2):345-51. doi: 10.2337/diacare.29.02.06.dc05-1626.
citation: Weinstein G, Maillard P, Himali JJ, Beiser AS, Au R, Wolf PA, Seshadri S, DeCarli C. Glucose indices are associated with cognitive and structural brain measures in young adults. Neurology. 2015 Jun 9;84(23):2329-37. doi: 10.1212/WNL.0000000000001655. Epub 2015 May 6.
citation: Rolandsson O, Backestrom A, Eriksson S, Hallmans G, Nilsson LG. Increased glucose levels are associated with episodic memory in nondiabetic women. Diabetes. 2008 Feb;57(2):440-3. doi: 10.2337/db07-1215. Epub 2007 Oct 31.
citation: Marden JR, Mayeda ER, Tchetgen Tchetgen EJ, Kawachi I, Glymour MM. High Hemoglobin A1c and Diabetes Predict Memory Decline in the Health and Retirement Study. Alzheimer Dis Assoc Disord. 2017 Jan-Mar;31(1):48-54. doi: 10.1097/WAD.0000000000000182.
citation: Blasco G, Puig J, Daunis-I-Estadella J, Molina X, Xifra G, Fernandez-Aranda F, Pedraza S, Ricart W, Portero-Otin M, Fernandez-Real JM. Brain iron overload, insulin resistance, and cognitive performance in obese subjects: a preliminary MRI case-control study. Diabetes Care. 2014 Nov;37(11):3076-83. doi: 10.2337/dc14-0664. Epub 2014 Aug 14.
citation: Blasco G, Moreno-Navarrete JM, Rivero M, Perez-Brocal V, Garre-Olmo J, Puig J, Daunis-I-Estadella P, Biarnes C, Gich J, Fernandez-Aranda F, Alberich-Bayarri A, Moya A, Pedraza S, Ricart W, Lopez M, Portero-Otin M, Fernandez-Real JM. The Gut Metagenome Changes in Parallel to Waist Circumference, Brain Iron Deposition, and Cognitive Function. J Clin Endocrinol Metab. 2017 Aug 1;102(8):2962-2973. doi: 10.1210/jc.2017-00133.
citation: Arnoriaga Rodriguez M, Blasco G, Coll C, Biarnes C, Contreras-Rodriguez O, Garre-Olmo J, Puig J, Gich J, Ricart W, Ramio-Torrenta L, Fernandez-Real JM. Glycated Hemoglobin, but not Insulin Sensitivity, is Associated with Memory in Subjects with Obesity. Obesity (Silver Spring). 2019 Jun;27(6):932-942. doi: 10.1002/oby.22457. Epub 2019 Apr 15.
citation: Kau AL, Ahern PP, Griffin NW, Goodman AL, Gordon JI. Human nutrition, the gut microbiome and the immune system. Nature. 2011 Jun 15;474(7351):327-36. doi: 10.1038/nature10213.
citation: Pedersen HK, Gudmundsdottir V, Nielsen HB, Hyotylainen T, Nielsen T, Jensen BA, Forslund K, Hildebrand F, Prifti E, Falony G, Le Chatelier E, Levenez F, Dore J, Mattila I, Plichta DR, Poho P, Hellgren LI, Arumugam M, Sunagawa S, Vieira-Silva S, Jorgensen T, Holm JB, Trost K; MetaHIT Consortium; Kristiansen K, Brix S, Raes J, Wang J, Hansen T, Bork P, Brunak S, Oresic M, Ehrlich SD, Pedersen O. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature. 2016 Jul 21;535(7612):376-81. doi: 10.1038/nature18646. Epub 2016 Jul 13.
citation: Kang SS, Jeraldo PR, Kurti A, Miller ME, Cook MD, Whitlock K, Goldenfeld N, Woods JA, White BA, Chia N, Fryer JD. Diet and exercise orthogonally alter the gut microbiome and reveal independent associations with anxiety and cognition. Mol Neurodegener. 2014 Sep 13;9:36. doi: 10.1186/1750-1326-9-36.
citation: Moloney GM, Dinan TG, Clarke G, Cryan JF. Microbial regulation of microRNA expression in the brain-gut axis. Curr Opin Pharmacol. 2019 Oct;48:120-126. doi: 10.1016/j.coph.2019.08.005. Epub 2019 Oct 4.
citation: Liu S, da Cunha AP, Rezende RM, Cialic R, Wei Z, Bry L, Comstock LE, Gandhi R, Weiner HL. The Host Shapes the Gut Microbiota via Fecal MicroRNA. Cell Host Microbe. 2016 Jan 13;19(1):32-43. doi: 10.1016/j.chom.2015.12.005.
citation: Horne R, St Pierre J, Odeh S, Surette M, Foster JA. Microbe and host interaction in gastrointestinal homeostasis. Psychopharmacology (Berl). 2019 May;236(5):1623-1640. doi: 10.1007/s00213-019-05218-y. Epub 2019 Mar 21.
citation: Chen JJ, Zeng BH, Li WW, Zhou CJ, Fan SH, Cheng K, Zeng L, Zheng P, Fang L, Wei H, Xie P. Effects of gut microbiota on the microRNA and mRNA expression in the hippocampus of mice. Behav Brain Res. 2017 Mar 30;322(Pt A):34-41. doi: 10.1016/j.bbr.2017.01.021. Epub 2017 Jan 16.
citation: Teng Y, Ren Y, Sayed M, Hu X, Lei C, Kumar A, Hutchins E, Mu J, Deng Z, Luo C, Sundaram K, Sriwastva MK, Zhang L, Hsieh M, Reiman R, Haribabu B, Yan J, Jala VR, Miller DM, Van Keuren-Jensen K, Merchant ML, McClain CJ, Park JW, Egilmez NK, Zhang HG. Plant-Derived Exosomal MicroRNAs Shape the Gut Microbiota. Cell Host Microbe. 2018 Nov 14;24(5):637-652.e8. doi: 10.1016/j.chom.2018.10.001. Epub 2018 Oct 25.
citation: Moloney GM, O'Leary OF, Salvo-Romero E, Desbonnet L, Shanahan F, Dinan TG, Clarke G, Cryan JF. Microbial regulation of hippocampal miRNA expression: Implications for transcription of kynurenine pathway enzymes. Behav Brain Res. 2017 Sep 15;334:50-54. doi: 10.1016/j.bbr.2017.07.026. Epub 2017 Jul 20.
citation: Hoban AE, Stilling RM, M Moloney G, Moloney RD, Shanahan F, Dinan TG, Cryan JF, Clarke G. Microbial regulation of microRNA expression in the amygdala and prefrontal cortex. Microbiome. 2017 Aug 25;5(1):102. doi: 10.1186/s40168-017-0321-3.
citation: Foster JA, Lyte M, Meyer E, Cryan JF. Gut Microbiota and Brain Function: An Evolving Field in Neuroscience. Int J Neuropsychopharmacol. 2016 Apr 29;19(5):pyv114. doi: 10.1093/ijnp/pyv114. Print 2016 May.
citation: Stilling RM, Moloney GM, Ryan FJ, Hoban AE, Bastiaanssen TF, Shanahan F, Clarke G, Claesson MJ, Dinan TG, Cryan JF. Social interaction-induced activation of RNA splicing in the amygdala of microbiome-deficient mice. Elife. 2018 May 29;7:e33070. doi: 10.7554/eLife.33070.
citation: Picao-Osorio J, Johnston J, Landgraf M, Berni J, Alonso CR. MicroRNA-encoded behavior in Drosophila. Science. 2015 Nov 13;350(6262):815-20. doi: 10.1126/science.aad0217. Epub 2015 Oct 22.
citation: Haramati S, Navon I, Issler O, Ezra-Nevo G, Gil S, Zwang R, Hornstein E, Chen A. MicroRNA as repressors of stress-induced anxiety: the case of amygdalar miR-34. J Neurosci. 2011 Oct 5;31(40):14191-203. doi: 10.1523/JNEUROSCI.1673-11.2011.
citation: Griggs EM, Young EJ, Rumbaugh G, Miller CA. MicroRNA-182 regulates amygdala-dependent memory formation. J Neurosci. 2013 Jan 23;33(4):1734-40. doi: 10.1523/JNEUROSCI.2873-12.2013.
citation: Bocchio-Chiavetto L, Maffioletti E, Bettinsoli P, Giovannini C, Bignotti S, Tardito D, Corrada D, Milanesi L, Gennarelli M. Blood microRNA changes in depressed patients during antidepressant treatment. Eur Neuropsychopharmacol. 2013 Jul;23(7):602-11. doi: 10.1016/j.euroneuro.2012.06.013. Epub 2012 Aug 25.
citation: 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.
citation: 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.
citation: Ortega FJ, Mercader JM, Catalan V, Moreno-Navarrete JM, Pueyo N, Sabater M, Gomez-Ambrosi J, Anglada R, Fernandez-Formoso JA, Ricart W, Fruhbeck G, Fernandez-Real JM. Targeting the circulating microRNA signature of obesity. Clin Chem. 2013 May;59(5):781-92. doi: 10.1373/clinchem.2012.195776. Epub 2013 Feb 8.
citation: Ortega FJ, Mercader JM, Moreno-Navarrete JM, Rovira O, Guerra E, Esteve E, Xifra G, Martinez C, Ricart W, Rieusset J, Rome S, Karczewska-Kupczewska M, Straczkowski M, Fernandez-Real JM. Profiling of circulating microRNAs reveals common microRNAs linked to type 2 diabetes that change with insulin sensitization. Diabetes Care. 2014;37(5):1375-83. doi: 10.2337/dc13-1847. Epub 2014 Jan 29.
citation: Lukiw WJ, Pogue AI. Induction of specific micro RNA (miRNA) species by ROS-generating metal sulfates in primary human brain cells. J Inorg Biochem. 2007 Sep;101(9):1265-9. doi: 10.1016/j.jinorgbio.2007.06.004. Epub 2007 Jun 13.