Ignite Creation Date:
2025-12-24 @ 9:44 PM
Ignite Modification Date:
2025-12-25 @ 7:25 PM
Study NCT ID:
NCT03889132
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-12-12
First Post:
2019-02-27
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Glucose, Brain and Microbiota
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D009765', 'term': 'Obesity'}], 'ancestors': [{'id': 'D050177', 'term': 'Overweight'}, {'id': 'D044343', 'term': 'Overnutrition'}, {'id': 'D009748', 'term': 'Nutrition Disorders'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D001835', 'term': 'Body Weight'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D050110', 'term': 'Bariatric Surgery'}], 'ancestors': [{'id': 'D049088', 'term': 'Bariatrics'}, {'id': 'D000073319', 'term': 'Obesity Management'}, {'id': 'D013812', 'term': 'Therapeutics'}, {'id': 'D013514', 'term': 'Surgical Procedures, Operative'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 128}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2019-03-05', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-04', 'completionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-05', 'studyFirstSubmitDate': '2019-02-27', 'studyFirstSubmitQcDate': '2019-03-21', 'lastUpdatePostDateStruct': {'date': '2025-12-12', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2019-03-26', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-01-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Concentration of advanced glycation end products (AGE) receptor agonists.', 'timeFrame': '30 months', 'description': 'Enzyme-linked immunosorbent assay (ELISA).'}, {'measure': 'Glycemic variability.', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of glucose measures in mg/dL using a continuous glucose monitoring during 10 days.'}, {'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', 'description': 'Low blood glucose index (LBGI) is a parameter that quantifies the risk of glycaemic excursions in non-negative numbers.'}, {'measure': 'The glycaemic risk measured with high blood glucose index (HBGI)', 'timeFrame': '30 months', 'description': 'High blood glucose index (HBGI) is a parameter that quantifies the risk of glycaemic excursions in non-negative numbers.'}, {'measure': 'The glycaemic variability measured with mean amplitude of glycaemic excursions (MAGE)', 'timeFrame': '30 months', 'description': 'measured in mg/dl'}, {'measure': 'Minutes light sleep', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of minutes light sleep measures by activity and sleep tracker device.'}, {'measure': 'Minutes deep sleep', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of minutes deep sleep measures by activity and sleep tracker device.'}, {'measure': 'Minutes rapid eye movement (REM)', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of minutes REM measures by activity and sleep tracker device.'}], 'secondaryOutcomes': [{'measure': 'Effect on brain structure.', 'timeFrame': '30 months', 'description': 'Brain structure will be assessed using magnetic resonance imaging.'}, {'measure': 'Effect on gut microbiota.', 'timeFrame': '30 months', 'description': 'Gut microbiota will be analysed by metagenomics and metabolomics.'}, {'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', 'description': 'Subjects with obesity will be undertaken conventional treatment or bariatric surgery for weight loss; controls will not undergo any additional measure.'}, {'measure': 'Cognitive impairment', 'timeFrame': '30 months', 'description': 'It will be measured by Mini-Examen Cognoscitivo (MEC). Minimum/maximum scale values (0-30), where ≥ 27 is a normal score.'}, {'measure': 'Audioverbal memory', 'timeFrame': '30 months', 'description': 'It will be measured by California Verbal Learning Test (CVLT). Minimum/maximum scale values (0-16), where 16 is a better audioverbal memory.'}, {'measure': 'Visual memory', 'timeFrame': '30 months', 'description': 'It will be measured by Rey-Osterrieth Complex Figure. Minimum/maximum scale values (0-36), where 36 is a better visual memory'}, {'measure': 'Depressive symptomatology', 'timeFrame': '30 months', 'description': 'It will be measured by Patient Health Questionnaire-9 (PHQ-9). Minimum/maximum scale values (0-27), where ≥ 20 is severe depression.'}, {'measure': 'Impulsivity', 'timeFrame': '30 months', 'description': 'It will be measured by Impulsive Behavior Scale (UPPS-P). The test evaluates: Negative urgency (tendency to act rashly under extreme negative emotions), Lack of Premeditation (tendency to act without thinking), Lack of Perseverance (inability to remain focused on a task) and Sensation Seeking (tendency to seek out novel and thrilling experiences). All items are rated on a four point scale from 1 (strongly agree) to 4 (strongly disagree).'}, {'measure': 'Food Addiction', 'timeFrame': '30 months', 'description': '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.'}, {'measure': 'Behavioral inhibition', 'timeFrame': '30 months', 'description': '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.'}, {'measure': 'Behavioral activation', 'timeFrame': '30 months', 'description': '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.'}, {'measure': 'Visoconstructive function', 'timeFrame': '30 months', 'description': 'It will be measured by Rey-Osterrieth Complex Figure. Minimum/maximum scale values (0-36), where 36 is a better visoconstructive function.'}, {'measure': 'Visuospatial perception', 'timeFrame': '30 months', 'description': 'It will be measured by Judgment Line Orientation'}, {'measure': 'Naming', 'timeFrame': '30 months', 'description': 'It will be measured by Boston Naming Test.'}, {'measure': 'Selective and alternating attention', 'timeFrame': '30 months', 'description': 'It will be measured by Trail making test (Part A y B).'}, {'measure': 'Attention and working memory', 'timeFrame': '30 months', 'description': 'It will be measured by the Digits subtest of Wechsler Adult Intelligence Scales, Fourth Edition (WAIS-IV).'}, {'measure': 'Inhibition', 'timeFrame': '30 months', 'description': 'It will be measured by Stroop Color-Word Test.'}, {'measure': 'Phonemic verbal fluency', 'timeFrame': '30 months', 'description': 'It will be measured by PMR'}, {'measure': 'Semantic verbal fluency', 'timeFrame': '30 months', 'description': '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.'}, {'measure': 'Diffusion Tensor Imaging brain sequences', 'timeFrame': '30 months', 'description': '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.'}, {'measure': 'Brain iron accumulation', 'timeFrame': '30 months', 'description': 'It will be assessed using magnetic resonance imaging using (R2\\*)'}, {'measure': 'Resting-state functional brain sequences', 'timeFrame': '30 months', 'description': '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.'}, {'measure': 'Insulin resistance', 'timeFrame': '30 months', 'description': 'It will be measured by HOMA'}, {'measure': 'Markers of chronic inflammation: C-reactive protein, IL-6, adiponectin and soluble, tumor necrosis factor-α receptor fractions.', 'timeFrame': '30 months', 'description': 'Enzyme-linked immunosorbent assay (ELISA) and quantitative polymerase chain reaction (qPCR)'}, {'measure': 'Glycosylated hemoglobin (HbA1c) value', 'timeFrame': '30 months', 'description': 'Glycosylated hemoglobin (HbA1c) in % or mmol/mol'}, {'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', 'description': 'Mean and standard deviation of burned calories measures by activity and sleep tracker device.'}, {'measure': 'Steps', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of steps measures by activity and sleep tracker device.'}, {'measure': 'Distance', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of distance measures by activity and sleep tracker device.'}, {'measure': 'Minutes null activity', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of minutes null activity measures by activity and sleep tracker device.'}, {'measure': 'Minutes slight activity', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of minutes slight activity measures by activity and sleep tracker device.'}, {'measure': 'Minutes mean activity', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of minutes mean activity measures by activity and sleep tracker device.'}, {'measure': 'Minutes high activity', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of minutes high activity measures by activity and sleep tracker device.'}, {'measure': 'Calories', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of calories measures by activity and sleep tracker device.'}, {'measure': 'Minutes asleep', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of minutes asleep measures by activity and sleep tracker device.'}, {'measure': 'Minutes awake', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of minutes awake measures by activity and sleep tracker device.'}, {'measure': 'Bed time', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of bed time measures by activity and sleep tracker device.'}, {'measure': 'Number time awake', 'timeFrame': '30 months', 'description': 'Mean and standard deviation of number time awake measures by activity and sleep tracker device.'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Obesity', 'Continous Glucose Monitoring', 'Cognition', 'Brain Iron content'], 'conditions': ['Obesity']}, 'referencesModule': {'references': [{'pmid': '8080980', 'type': 'BACKGROUND', 'citation': 'Finch C. Regulators of iron balance in humans. Blood. 1994 Sep 15;84(6):1697-702. No abstract available.'}, {'pmid': '9580307', 'type': 'BACKGROUND', '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.'}, {'pmid': '12145144', 'type': 'BACKGROUND', '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.'}, {'pmid': '24731656', 'type': 'BACKGROUND', '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.'}, {'pmid': '26813526', 'type': 'BACKGROUND', '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.'}, {'pmid': '26765579', 'type': 'BACKGROUND', '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.'}, {'pmid': '27745814', 'type': 'BACKGROUND', '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.'}, {'pmid': '29082606', 'type': 'BACKGROUND', '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.'}, {'pmid': '27842994', 'type': 'BACKGROUND', 'citation': 'Moreno-Navarrete JM, Lopez-Navarro E, Candenas L, Pinto F, Ortega FJ, Sabater-Masdeu M, Fernandez-Sanchez M, Blasco V, Romero-Ruiz A, Fontan M, Ricart W, Tena-Sempere M, Fernandez-Real JM. Ferroportin mRNA is down-regulated in granulosa and cervical cells from infertile women. Fertil Steril. 2017 Jan;107(1):236-242. doi: 10.1016/j.fertnstert.2016.10.008. Epub 2016 Nov 16.'}, {'pmid': '25163604', 'type': 'BACKGROUND', '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.'}, {'pmid': '28842522', 'type': 'BACKGROUND', 'citation': 'Geijselaers SLC, Sep SJS, Claessens D, Schram MT, van Boxtel MPJ, Henry RMA, Verhey FRJ, Kroon AA, Dagnelie PC, Schalkwijk CG, van der Kallen CJH, Biessels GJ, Stehouwer CDA. The Role of Hyperglycemia, Insulin Resistance, and Blood Pressure in Diabetes-Associated Differences in Cognitive Performance-The Maastricht Study. Diabetes Care. 2017 Nov;40(11):1537-1547. doi: 10.2337/dc17-0330. Epub 2017 Aug 25.'}, {'pmid': '28500216', 'type': 'BACKGROUND', 'citation': 'Luchsinger JA, Ma Y, Christophi CA, Florez H, Golden SH, Hazuda H, Crandall J, Venditti E, Watson K, Jeffries S, Manly JJ, Pi-Sunyer FX; Diabetes Prevention Program Research Group. Metformin, Lifestyle Intervention, and Cognition in the Diabetes Prevention Program Outcomes Study. Diabetes Care. 2017 Jul;40(7):958-965. doi: 10.2337/dc16-2376. Epub 2017 May 12.'}, {'pmid': '28857651', 'type': 'BACKGROUND', '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.'}, {'pmid': '16443885', 'type': 'BACKGROUND', '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.'}, {'pmid': '25948725', 'type': 'BACKGROUND', '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.'}, {'pmid': '17977953', 'type': 'BACKGROUND', '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.'}, {'pmid': '28225507', 'type': 'BACKGROUND', '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.'}, {'pmid': '25459912', 'type': 'BACKGROUND', 'citation': 'Spauwen PJ, van Eupen MG, Kohler S, Stehouwer CD, Verhey FR, van der Kallen CJ, Sep SJ, Koster A, Schaper NC, Dagnelie PC, Schalkwijk CG, Schram MT, van Boxtel MP. Associations of advanced glycation end-products with cognitive functions in individuals with and without type 2 diabetes: the maastricht study. J Clin Endocrinol Metab. 2015 Mar;100(3):951-60. doi: 10.1210/jc.2014-2754. Epub 2014 Dec 2.'}, {'pmid': '22634723', 'type': 'BACKGROUND', 'citation': 'Chavan SS, Huerta PT, Robbiati S, Valdes-Ferrer SI, Ochani M, Dancho M, Frankfurt M, Volpe BT, Tracey KJ, Diamond B. HMGB1 mediates cognitive impairment in sepsis survivors. Mol Med. 2012 Sep 7;18(1):930-7. doi: 10.2119/molmed.2012.00195.'}, {'pmid': '28886251', 'type': 'BACKGROUND', '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.'}, {'pmid': '25125507', 'type': 'BACKGROUND', '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.'}, {'pmid': '28591831', 'type': 'BACKGROUND', '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.'}, {'pmid': '21677749', 'type': 'BACKGROUND', '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.'}, {'pmid': '27409811', 'type': 'BACKGROUND', '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.'}, {'pmid': '12433763', 'type': 'BACKGROUND', 'citation': 'Gera T, Sachdev HP. Effect of iron supplementation on incidence of infectious illness in children: systematic review. BMJ. 2002 Nov 16;325(7373):1142. doi: 10.1136/bmj.325.7373.1142.'}, {'pmid': '25217888', 'type': 'BACKGROUND', '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.'}, {'pmid': '26590418', 'type': 'BACKGROUND', 'citation': 'Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalova L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015 Nov 19;163(5):1079-1094. doi: 10.1016/j.cell.2015.11.001.'}, {'pmid': '29220029', 'type': 'BACKGROUND', 'citation': 'Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Corrigendum: Shotgun metagenomics, from sampling to analysis. Nat Biotechnol. 2017 Dec 8;35(12):1211. doi: 10.1038/nbt1217-1211b.'}]}, 'descriptionModule': {'briefSummary': 'The accumulation of iron is known to affect the functions of the liver, adipose tissue and muscle. The brain is a well-known place of iron deposition, which is associated with cognitive parameters of subjects with obesity.\n\nThe hypothesis is that certain parameters related to glucose metabolism (glycemic variability, the circulating concentration of AGE receptor agonists, pentosidine and HbA1c) are associated with cognitive function, brain iron content and gut microbiota composition in subjects with obesity.\n\nThe 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 patient with obesity) to evaluate the associations between 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.', 'detailedDescription': 'Subjects and methods:\n\nA. Cross-sectional study:\n\nPatients 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.\n\nA blood glucose 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 in on day 0 and will retire on day 10 midmorning. 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.\n\nAt the end of the week an magnetic resonance imaging will be done to evaluate the iron content in the brain and parameters of "Diffusion Tensor Imaging" in different brain territories.\n\nCognitive tests will be carried out and feces will be collected for the study of the microbiota.\n\nThe project will be carried out in subjects with obesity (20 men, 20 premenopausal women and 20 women postmenopausal, 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).\n\nB. Longitudinal study:\n\nAfter one year of follow-up, in which, subjects with obesity will undergo conventional treatment (hypocaloric diet and physical activity advise) or bariatric surgery for weight loss, a second visit will be carried out.\n\nFor comparison, the same protocol of the cross-sectional study will be done again. See information above.\n\nData collection of subjects of cross-sectional and longitudinal studies:\n\n* Subsidiary data: Age, sex and birth date.\n* 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 and registry of antecedent relatives with obesity, diabetes and comorbidities.\n* 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 haemoglobin (HbA1c), ferritin, soluble transferrin receptor, ultrasensitive C reactive protein, erythrocyte sedimentation rate, lipopolysaccharide binding protein, free thyroxine (free T4), thyroid stimulating hormone (TSH) and baseline cortisol). An additional 15cc of blood (plasma-EDTA) will be extracted for further analyses.\n* 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.\n* Magnetic Resonance Imaging: All MRI examinations will be performed on a 1.5-T scanner (Ingenia ®; Philips Medical Systems). First, 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-subjects images will be normalized to a standard space. The normalized image will be compared to normal population using t-test analysis with age and sex as co-variables. As result, a parametric map will show individual differences in 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.\n* Neuropsychological examination: General cognitive functioning will be measured using the Vocabulary and Similarities subtests of the Wechsler Adult Intelligence Scale-III (WAIS-III); attention and working memory by the Forward and Backward Digit Span subtest of the WAIS-II; memory using the California Verbal Learning Test II; executive functions by the Trail Making Test, the Color-Word Stroop Test and the Verbal Fluency; mood using the Patient Health Questionnaire-9 and impulsive behaviors using the Iowa Gambling Task.\n* Microbiota composition: the microbiota composition will be analyzed according to a previous described protocol. 16s rRNA qPCR and LPS-binding protein in blood samples will be used for detection of bacterial translocation.\n\nThe information will remain registered in a notebook and will be computerized in the database of the study.\n\nStatistical methods:\n\nSample size: There are no previous data showing expected differences for sample size estimation regarding glucose variability, physical activity, 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.\n\nStatistical 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).\n\nThe microbiota composition will be analyzed and compared using HeatMaps, Principal Component Analysis (PCA) and PLSDA. For multivariate statistics (PLSDA and hierarchical clustering), variables comprising morphological tissue characteristics, gut microbiota and functional test will be log transformed, filtered using interquartile range estimate and scaled using auto-scaling calculation (mean-centered and divided by the standard deviation of each variable) by using the Metaboanalyst ® platform, the R ® package ropls and MATLAB ® scripts. Alpha and beta biodiversity will be compared according to obesity, insulin resistance and iron status. It will also be used SPSS ® statistical software and Minitab ®.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '65 Years', 'minimumAge': '30 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.\n\nSubjects without obesity will also be recruited through a public announcement.', 'healthyVolunteers': True, 'eligibilityCriteria': "Inclusion Criteria:\n\n1. Men and women aged 30-65 years.\n2. Informed consent for participation in the study.\n\nExclusion Criteria:\n\n1. Serious systemic disease unrelated to obesity such as cancer, severe kidney, or liver disease, known type 1 or type 2 diabetes.\n2. 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.\n3. Pregnancy and lactation.\n4. Patients with severe disorders of eating behaviour.\n5. Persons whose liberty is under legal or administrative requirement.\n6. Clinical symptoms and signs of infection in the previous month.\n7. Antibiotic, antifungal or antiviral treatment in the previous 3 months.\n8. Anti-inflammatory chronic treatment with steroidal and/or non-steroidal anti-inflammatory drugs.\n9. Major psychiatric antecedents.\n10. Excessive alcohol intake, either acute or chronic (alcohol intake greater than 40 g a day (women) or 80 g/day (men)) or drugs abuse.\n11. Serum liver enzymes (AST, ALT) activity over twice the upper limit of normal.\n12. History of disturbances in iron balance (e.g., genetic hemochromatosis, hemosiderosis from any cause, atransferrinemia, paroxysmal nocturnal hemoglobinuria)."}, 'identificationModule': {'nctId': 'NCT03889132', 'acronym': 'IRONMET+CGM', 'briefTitle': 'Glucose, Brain and Microbiota', 'organization': {'class': 'OTHER', 'fullName': "Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta"}, 'officialTitle': 'Integrated Analysis of the Interactions Between Glycemia and Microbiota Composition, and Their Impact on Brain Iron Deposition and Cognition in Subjects With Obesity', 'orgStudyIdInfo': {'id': 'IRONMET+CGM-2017.139'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Premenopausal women with obesity', 'interventionNames': ['Procedure: Bariatric Surgery']}, {'label': 'Postmenopausal women with obesity', 'interventionNames': ['Procedure: Bariatric Surgery']}, {'label': 'Men with obesity', 'interventionNames': ['Procedure: Bariatric Surgery']}, {'label': 'Premenopausal women without obesity'}, {'label': 'Postmenopausal women without obesity'}, {'label': 'Men without obesity'}], 'interventions': [{'name': 'Bariatric Surgery', 'type': 'PROCEDURE', 'description': 'Subjects with obesity (N=60) will be undertaken a hypocaloric diet and a periodic follow up, also 30 of them will undergo bariatric surgery', 'armGroupLabels': ['Men with obesity', 'Postmenopausal women with obesity', 'Premenopausal women with obesity']}]}, 'contactsLocationsModule': {'locations': [{'zip': '17007', 'city': 'Girona', 'state': 'Girona', 'country': 'Spain', 'facility': "Institut d'Investigació Biomèdica de Girona (IDIBGI)", 'geoPoint': {'lat': 41.98311, 'lon': 2.82493}}], 'overallOfficials': [{'name': 'José Manuel Fernández-Real, M.D., Ph.D.', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': "Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta"}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': "Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta", 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal investigator, clinical professor, section chief of Endocrinology and Nutrition Department of Josep Trueta University Hospital', 'investigatorFullName': 'José Manuel Fernández-Real', 'investigatorAffiliation': "Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta"}}}}