- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05345106
Inter-relationships Among Glucose, Brain, Gut Microbiota and MicroRNAs (IRONmiRNA). (IRONmiRNA)
Inter-relationships Among Iron Stores, the Gut Metagenome, Glucose Levels, and Different Cognitive Domains: the Role of Circulating MicroRNAs (IRONmiRNA Study).
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.
Study Overview
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:
- Subsidiary data: Age, sex, and birth date.
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.
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.
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).
- 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.
- 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.
- 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.
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).
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: José Manuel Fernández-Real, M.D., Ph.D.
- Phone Number: +34 972 94 02 00
- Email: jmfreal@idibgi.org
Study Contact Backup
- Name: Marisel Rosell Díaz, M.D., MSc.
- Phone Number: 2325 +34 972 94 02 00
- Email: mrosell@idibgi.org
Study Locations
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-
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Girona, Spain, 17007
- Recruiting
- Institut d'Investigació Biomèdica de Girona (IDIBGI)
-
Contact:
- Yenny Leal
- Phone Number: 2325 0034 972940200
- Email: yleal@idibgi.org
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
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.
Description
Inclusion Criteria:
- Men and women aged 30-65 years.
- Informed consent for participation in the study.
Exclusion Criteria:
- Serious systemic disease unrelated to obesity such as cancer, severe kidney, or liver disease, known type 1 or type 2 diabetes.
- 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.
- Pregnancy and lactation.
- Patients with severe disorders of eating behaviour.
- Persons whose liberty is under legal or administrative requirement.
- Clinical symptoms and signs of infection in the previous month.
- Antibiotic, antifungal or antiviral treatment in the previous 3 months.
- Anti-inflammatory chronic treatment with steroidal and/or non-steroidal anti-inflammatory drugs.
- Major psychiatric antecedents.
- Excessive alcohol intake, either acute or chronic (alcohol intake greater than 40 g a day (women) or 80 g/day (men)) or drugs abuse.
- Serum liver enzymes (AST, ALT) activity over twice the upper limit of normal.
- History of disturbances in iron balance (e.g., genetic hemochromatosis, hemosiderosis from any cause, atransferrinemia, paroxysmal nocturnal hemoglobinuria).
Study Plan
How is the study designed?
Design Details
- Observational Models: Case-Control
- Time Perspectives: Prospective
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
Premenopausal women with obesity
|
Subjects with obesity (N=60) will be undertaken a hypocaloric diet and a periodic follow up, also 30 of them will undergo bariatric surgery
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Postmenopausal women with obesity
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Subjects with obesity (N=60) will be undertaken a hypocaloric diet and a periodic follow up, also 30 of them will undergo bariatric surgery
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Men with obesity
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Subjects with obesity (N=60) will be undertaken a hypocaloric diet and a periodic follow up, also 30 of them will undergo bariatric surgery
|
Premenopausal women without obesity
|
|
Postmenopausal women without obesity
|
|
Men without obesity
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Concentration of advanced glycation end products (AGE) receptor agonists.
Time Frame: 30 months
|
Enzyme-linked immunosorbent assay (ELISA).
|
30 months
|
Glycemic variability.
Time Frame: 30 months
|
Mean and standard deviation of glucose measures in mg/dL using a continuous glucose monitoring during 10 days.
|
30 months
|
The percentage of time in glucose target range (glucose level 100mg/dl-125mg/dl)
Time Frame: 30 months
|
30 months
|
|
The glycaemic risk measured with low blood glucose index (LBGI)
Time Frame: 30 months
|
Low blood glucose index (LBGI) is a parameter that quantifies the risk of glycaemic
|
30 months
|
The glycaemic risk measured with high blood glucose index (HBGI)
Time Frame: 30 months
|
High blood glucose index (HBGI) is a parameter that quantifies the risk of glycaemic
|
30 months
|
The glycaemic variability measured with mean amplitude of glycaemic excursions (MAGE)
Time Frame: 30 months
|
measured in mg/dl
|
30 months
|
Minutes light sleep
Time Frame: 30 months
|
Mean and standard deviation of minutes light sleep measures by activity and sleep tracker device.
|
30 months
|
Minutes deep sleep
Time Frame: 30 months
|
Mean and standard deviation of minutes deep sleep measures by activity and sleep tracker device.
|
30 months
|
Minutes rapid eye movement (REM)
Time Frame: 30 months
|
Mean and standard deviation of minutes REM measures by activity and sleep tracker device.
|
30 months
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Effect on brain structure.
Time Frame: 30 months
|
Brain structure will be assessed using magnetic resonance imaging.
|
30 months
|
Effect on gut microbiota.
Time Frame: 30 months
|
Gut microbiota will be analysed by metagenomics and metabolomics.
|
30 months
|
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.
Time Frame: 30 months
|
Subjects with obesity will be undertaken conventional treatment or bariatric surgery for weight loss; controls will not undergo any additional measure.
|
30 months
|
Anxiety state
Time Frame: 30 months
|
It will be measured by State-Trate Anxiety Inventory (STAI).
|
30 months
|
Audioverbal memory
Time Frame: 30 months
|
It will be measured by California Verbal Learning Test (CVLT).
Minimum/maximum scale values (0-16), where 16 is a better audioverbal memory.
|
30 months
|
Visual memory
Time Frame: 30 months
|
It will be measured by Rey-Osterrieth Complex Figure.
Minimum/maximum scale values (0-36), where 36 is a better visual memory
|
30 months
|
Depressive symptomatology
Time Frame: 30 months
|
It will be measured by Patient Health Questionnaire-9 (PHQ-9).
Minimum/maximum scale values (0-27), where ≥ 20 is severe depression.
|
30 months
|
Impulsivity
Time Frame: 30 months
|
It will be measured by Impulsive Behavior Scale (UPPS-P).
The test evaluates: Negative
|
30 months
|
Food Addiction
Time Frame: 30 months
|
It will be measured by Yale Food Addiction Scale.It is a symptom score from 0-11, based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria, for substance dependence.
Food addiction is diagnosed if ≥3 symptoms are reported.
|
30 months
|
Behavioral inhibition
Time Frame: 30 months
|
It will be measured by Sensitivity to Punishment and Sensitivity to Reward (SPSRQ).
The scale of sensitivity to punishment is related to the behavioral inhibition system.
It is made up of two subscales of 24 items each, where the higher the score, the greater the sensitivity to punishment.
|
30 months
|
Behavioral activation
Time Frame: 30 months
|
It will be measured by Sensitivity to Punishment and Sensitivity to Reward (SPSRQ).
The reward sensitivity scale is related to the behavioral activation system.
It is made up of two subscales of 24 items each, where the higher the score, the greater the sensitivity to reward.
|
30 months
|
Visoconstructive function
Time Frame: 30 months
|
It will be measured by Rey-Osterrieth Complex Figure.
Minimum/maximum scale values (0-36), where 36 is a better visoconstructive function.
|
30 months
|
Selective and alternating attention
Time Frame: 30 months
|
It will be measured by Trail making test (Part A y B).
|
30 months
|
Attention and working memory
Time Frame: 30 months
|
It will be measured by the Digits subtest of Wechsler Adult Intelligence Scales, Fourth Edition (WAIS-IV).
|
30 months
|
Inhibition
Time Frame: 30 months
|
It will be measured by Stroop Color-Word Test.
|
30 months
|
Phonemic verbal fluency
Time Frame: 30 months
|
It will be measured by PMR.
|
30 months
|
Semantic verbal fluency
Time Frame: 30 months
|
It will be measured by Animals test.
The person must name as many animals as possible in 1 minute.
The result is corrected by standard scores, according to age and level of education.
|
30 months
|
Facial recognition
Time Frame: 30 months
|
It will be measured by Picture of Facial Recognition Test (POFA).
|
30 months
|
Emotion recognition
Time Frame: 30 months
|
It will be measured by Benton Facial Recognition Test (BFRT).This test evaluates the recognition of the five basic emotions: happiness, sadness, surprise, disgust, and anger.
|
30 months
|
Diffusion Tensor Imaging brain sequences
Time Frame: 30 months
|
Diffusion Tensor Imaging was acquired at 1.5 T (Philips ingenia) using a single-shot spin echo sequence with echo-planar imaging (EPI), 50 contiguous slices, voxel size 2x2x2.5 mm3, TE/TR of 72/3581 ms/ms, a diffusion-weighting factor b = 800 s/mm2 and diffusion encoding along 32 directions.
|
30 months
|
Brain iron accumulation
Time Frame: 30 months
|
It will be assessed using magnetic resonance imaging using (R2*)
|
30 months
|
Resting-state functional brain sequences
Time Frame: 30 months
|
It will be assessed using magnetic resonance imaging (T2*-weighted echo-planar imaging).
T2 * relaxation data will be acquired with a multi-echo gradient sequence with 10 equidistant echoes (first echo = 4.6ms; echo spacing = 4.6ms; repetition time = 1300ms).
The value value of T2 * will be calculated by adjusting the simple exponential terms for the signal decay of the respective echo time values.
|
30 months
|
Insulin resistance
Time Frame: 30 months
|
It will be measured by HOMA
|
30 months
|
Markers of chronic inflammation: C-reactive protein, IL-6, adiponectin and soluble, tumor necrosis factor-α receptor fractions.
Time Frame: 30 months
|
Enzyme-linked immunosorbent assay (ELISA) and quantitative polymerase chain reaction (qPCR)
|
30 months
|
Glycosylated hemoglobin (HbA1c) value
Time Frame: 30 months
|
Glycosylated hemoglobin (HbA1c) in % or mmol/mol
|
30 months
|
The percentage of time in hyperglycaemia (glucose level above 250 mg/dl)
Time Frame: 30 months
|
30 months
|
|
The percentage of time in hypoglycaemia (glucose level below 70mg/dl)
Time Frame: 30 months
|
30 months
|
|
The percentage of time in glucose range (glucose level below 100 mg/dl)
Time Frame: 30 months
|
30 months
|
|
The percentage of time in glucose range (glucose level between 126-139 mg/dl)
Time Frame: 30 months
|
30 months
|
|
The percentage of time in glucose range (glucose level between 140-199 mg/dl)
Time Frame: 30 months
|
30 months
|
|
The percentage of time in glucose range (glucose level above 200 mg/dl)
Time Frame: 30 months
|
30 months
|
|
Burned calories
Time Frame: 30 months
|
Mean and standard deviation of burned calories measures by activity and sleep tracker device.
|
30 months
|
Steps
Time Frame: 30 months
|
Mean and standard deviation of steps measures by activity and sleep tracker device.
|
30 months
|
Distance
Time Frame: 30 months
|
Mean and standard deviation of distance measures by activity and sleep tracker device.
|
30 months
|
Minutes null activity
Time Frame: 30 months
|
Mean and standard deviation of minutes null activity measures by activity and sleep
|
30 months
|
Minutes slight activity
Time Frame: 30 months
|
Mean and standard deviation of minutes slight activity measures by activity and sleep
|
30 months
|
Minutes mean activity
Time Frame: 30 months
|
Mean and standard deviation of minutes mean activity measures by activity and sleep tracker device.
|
30 months
|
Minutes high activity
Time Frame: 30 months
|
Mean and standard deviation of minutes high activity measures by activity and sleep tracker device.
|
30 months
|
Calories
Time Frame: 30 months
|
Mean and standard deviation of calories measures by activity and sleep tracker device.
|
30 months
|
Minutes asleep
Time Frame: 30 months
|
Mean and standard deviation of minutes asleep measures by activity and sleep tracker
|
30 months
|
Minutes awake
Time Frame: 30 months
|
Mean and standard deviation of minutes awake measures by activity and sleep tracker
|
30 months
|
Bed time
Time Frame: 30 months
|
Mean and standard deviation of bed time measures by activity and sleep tracker device.
|
30 months
|
Number time awake
Time Frame: 30 months
|
Mean and standard deviation of number time awake measures by activity and sleep
|
30 months
|
Collaborators and Investigators
Investigators
- Principal Investigator: José Manuel Fernández-Real, M.D., Ph.D., Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta
Publications and helpful links
General Publications
- Finch C. Regulators of iron balance in humans. Blood. 1994 Sep 15;84(6):1697-702. No abstract available.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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. Erratum In: Lancet Diabetes Endocrinol. 2015 Jan;3(1):e1.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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Study record dates
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Other Study ID Numbers
- IRONmiRNA-2022.043
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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