Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D011236', 'term': 'Prediabetic State'}, {'id': 'D006973', 'term': 'Hypertension'}, {'id': 'D009765', 'term': 'Obesity'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}], 'ancestors': [{'id': 'D003920', 'term': 'Diabetes Mellitus'}, {'id': 'D044882', 'term': 'Glucose Metabolism Disorders'}, {'id': 'D008659', 'term': 'Metabolic Diseases'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D014652', 'term': 'Vascular Diseases'}, {'id': 'D050177', 'term': 'Overweight'}, {'id': 'D044343', 'term': 'Overnutrition'}, {'id': 'D009748', 'term': 'Nutrition Disorders'}, {'id': 'D001835', 'term': 'Body Weight'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'Whole blood and serum'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2013-08'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2015-02', 'completionDateStruct': {'date': '2018-08', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2015-02-12', 'studyFirstSubmitDate': '2013-11-25', 'studyFirstSubmitQcDate': '2013-11-25', 'lastUpdatePostDateStruct': {'date': '2015-02-13', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2013-12-02', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2018-08', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Identify fasting biomarkers and associated algorithms to predict parameters', 'timeFrame': 'Baseline', 'description': 'To assess relations between baseline factors and patterns of change over time due to feeding treatments.'}]}, 'oversightModule': {'oversightHasDmc': False}, 'conditionsModule': {'conditions': ['Prediabetes', 'Hypertension', 'Obesity', 'Cardiovascular Disease']}, 'referencesModule': {'references': [{'pmid': '23283859', 'type': 'BACKGROUND', 'citation': 'Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Borden WB, Bravata DM, Dai S, Ford ES, Fox CS, Franco S, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Huffman MD, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Magid D, Marcus GM, Marelli A, Matchar DB, McGuire DK, Mohler ER, Moy CS, Mussolino ME, Nichol G, Paynter NP, Schreiner PJ, Sorlie PD, Stein J, Turan TN, Virani SS, Wong ND, Woo D, Turner MB; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Executive summary: heart disease and stroke statistics--2013 update: a report from the American Heart Association. Circulation. 2013 Jan 1;127(1):143-52. doi: 10.1161/CIR.0b013e318282ab8f. No abstract available.'}, {'pmid': '22215894', 'type': 'BACKGROUND', 'citation': 'Roger VL, Go AS, Lloyd-Jones DM, Benjamin EJ, Berry JD, Borden WB, Bravata DM, Dai S, Ford ES, Fox CS, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Makuc DM, Marcus GM, Marelli A, Matchar DB, Moy CS, Mozaffarian D, Mussolino ME, Nichol G, Paynter NP, Soliman EZ, Sorlie PD, Sotoodehnia N, Turan TN, Virani SS, Wong ND, Woo D, Turner MB; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Executive summary: heart disease and stroke statistics--2012 update: a report from the American Heart Association. Circulation. 2012 Jan 3;125(1):188-97. doi: 10.1161/CIR.0b013e3182456d46. No abstract available.'}, {'pmid': '21525456', 'type': 'BACKGROUND', 'citation': 'DeFronzo RA, Abdul-Ghani M. Type 2 diabetes can be prevented with early pharmacological intervention. Diabetes Care. 2011 May;34 Suppl 2(Suppl 2):S202-9. doi: 10.2337/dc11-s221.'}, {'pmid': '17327355', 'type': 'BACKGROUND', 'citation': 'Nathan DM, Davidson MB, DeFronzo RA, Heine RJ, Henry RR, Pratley R, Zinman B; American Diabetes Association. Impaired fasting glucose and impaired glucose tolerance: implications for care. Diabetes Care. 2007 Mar;30(3):753-9. doi: 10.2337/dc07-9920. No abstract available.'}]}, 'descriptionModule': {'briefSummary': '1. To develop a database containing matched information from dynamic tests of postprandial glycemic control (OGTT or MMTT), results of a broad panel of fasting biomarkers, and clinical information related to diabetes risk obtain through subject interview.\n2. To use the database to identify fasting biomarkers and associated algorithms to best predict parameters derived from dynamic tests (OGTT or MMTT) such as Insulin Glucose Tolerance (IGT), impaired first phase insulin response, etc., controlling for clinical information such as current medication use.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Representative of the ethnic population of Richmond, VA', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Weight \\>95\n\nExclusion Criteria:\n\n* Prior diagnosis of Diabetes'}, 'identificationModule': {'nctId': 'NCT01998867', 'briefTitle': 'Fasting Predictors of OGTT and MMTT Response', 'organization': {'class': 'INDUSTRY', 'fullName': 'Health Diagnostic Laboratory, Inc.'}, 'officialTitle': 'Identification of Fasting Biomarkers That Predict Responses to the Oral Glucose Tolerance Test (OGTT) and Mixed Meal Tolerance Test (MMTT)', 'orgStudyIdInfo': {'id': 'R2013-1003'}}, 'contactsLocationsModule': {'locations': [{'zip': '23224', 'city': 'Richmond', 'state': 'Virginia', 'country': 'United States', 'facility': 'Health Diagnostic Laboratory, Inc.', 'geoPoint': {'lat': 37.55376, 'lon': -77.46026}}], 'overallOfficials': [{'name': 'Szilard Voros, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Health Diagnostic Laboratory, Inc.'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Health Diagnostic Laboratory, Inc.', 'class': 'INDUSTRY'}, 'responsibleParty': {'type': 'SPONSOR'}}}}