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'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 96}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2018-09-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2018-12', 'completionDateStruct': {'date': '2018-12-06', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2018-12-06', 'studyFirstSubmitDate': '2018-09-10', 'studyFirstSubmitQcDate': '2018-09-10', 'lastUpdatePostDateStruct': {'date': '2018-12-07', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2018-09-11', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2018-12-06', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Body Mass Index', 'timeFrame': 'September - November 2018'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Obesity']}, 'descriptionModule': {'briefSummary': 'Childhood obesity leads to adulthood obesity, demonstrated in many retrospective and longitudinal studies. Genetics as a predictor of obesity is less established. Morandi et al, (2012) assessed whether lifestyle and genetic factors can be used to predict childhood obesity, concluding that genetics had minimal predictive effect. More recently Seyednasrollah, (2017) demonstrated that genetic information, when alongside clinical factors for cardiovascular disease, increased the predictive accuracy of obesity risk in adults. This study aims to investigate if known lifestyle and genetic risk factors are associated with BMI and if they can be used as predictors of overweight/obesity in adults.'}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '65 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'European Citizen Aged between 18-65 years Female. Caucasian', 'eligibilityCriteria': 'Inclusion Criteria:\n\n* European Citizen.\n* Aged between 18-65 years.\n* Female.\n* Caucasian\n\nExclusion Criteria:\n\n* Currently following a diet or weight loss plan or have not been for over 6 months of the previous year.\n* Suffering from diabetes (type I or II).\n* Cancer, or have had cancer in the past.'}, 'identificationModule': {'nctId': 'NCT03666156', 'briefTitle': 'Adult Weight, Genetics and Lifestyle Factors', 'organization': {'class': 'OTHER', 'fullName': "St Mary's University College"}, 'officialTitle': 'The Influence of Nutrition, Childhood and Adult Lifestyle Factors and Genetic Predisposition on Body Mass Index', 'orgStudyIdInfo': {'id': 'SMEC_2017-18_141'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Whole Sample', 'description': 'Female, caucasians, aged 18-65 years', 'interventionNames': ['Other: No intervention']}], 'interventions': [{'name': 'No intervention', 'type': 'OTHER', 'description': 'No Intervention', 'armGroupLabels': ['Whole Sample']}]}, 'contactsLocationsModule': {'locations': [{'zip': 'TW14SX', 'city': 'London', 'country': 'United Kingdom', 'facility': "St Mary's University", 'geoPoint': {'lat': 51.50853, 'lon': -0.12574}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': "St Mary's University College", 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}