Ignite Creation Date:
2025-12-24 @ 10:33 PM
Ignite Modification Date:
2026-02-24 @ 1:15 AM
Study NCT ID:
NCT06545435
Status:
RECRUITING
Last Update Posted:
2024-08-26
First Post:
2024-08-05
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Predicting Appendicular Lean and Fat Mass With Bioelectrical Impedance Analysis Among Adult Patients With Obesity.
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'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 400}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2021-05-13', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-08', 'completionDateStruct': {'date': '2025-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-08-22', 'studyFirstSubmitDate': '2024-08-05', 'studyFirstSubmitQcDate': '2024-08-05', 'lastUpdatePostDateStruct': {'date': '2024-08-26', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-08-09', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-06-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Development and Cross-Validation of BIA Equations for Appendicular Soft Tissue Masses', 'timeFrame': 'Baseline', 'description': 'This primary outcome measures the accuracy and cross-validation of newly developed bioelectrical impedance analysis (BIA) equations in predicting appendicular soft tissue masses, including fat mass (FM) and appendicular lean mass (ALM), in Caucasian adults with obesity. The aim is to validate these equations against dual-energy X-ray absorptiometry (DXA) measurements.'}], 'secondaryOutcomes': [{'measure': 'Comparison of New BIA Equations with Existing Models', 'timeFrame': 'Baseline', 'description': 'This secondary outcome evaluates the performance of newly developed BIA equations against existing BIA-derived prediction models, specifically those by Kyle et al. (2003), Sergi et al. (2015), and the PROVIDE study (2017). The comparison will focus on differences in prediction accuracy for appendicular soft tissue masses.(FM) compared to measurements obtained from DXA.'}, {'measure': 'Algorithm Development for Conversion Between BIA Devices', 'timeFrame': 'Baseline', 'description': 'This outcome involves developing algorithms to facilitate the conversion of raw BIA data (resistance and reactance) between different devices and populations. This aims to standardize BIA measurements and improve compatibility across different settings and demographic groups.'}, {'measure': 'Cross-Validation of New BIA Equations with Different DXA Systems', 'timeFrame': 'Baseline', 'description': 'This outcome assesses the cross-validation of the new BIA equations using different DXA systems as reference standards. The objective is to ensure the robustness and reliability of BIA predictions across various DXA technologies.measurements for muscle size and architecture in a sub-sample of participants.'}, {'measure': 'Validation of BIA Equations Using Magnetic Resonance Imaging (MRI)', 'timeFrame': 'Baseline', 'description': 'This outcome evaluates the validation of the BIA equations in a subset of subjects using magnetic resonance imaging (MRI) to assess muscle size and architecture. The goal is to further validate the accuracy of BIA predictions for soft tissue composition compared to MRI data.'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Appendicular Lean Mass', 'Fat Mass', 'Body Composition', 'Bio-Impedance Analysis'], 'conditions': ['Obesity']}, 'referencesModule': {'references': [{'pmid': '9554417', 'type': 'BACKGROUND', 'citation': 'Baumgartner RN, Koehler KM, Gallagher D, Romero L, Heymsfield SB, Ross RR, Garry PJ, Lindeman RD. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol. 1998 Apr 15;147(8):755-63. doi: 10.1093/oxfordjournals.aje.a009520.'}, {'pmid': '29581385', 'type': 'BACKGROUND', 'citation': 'Borga M, West J, Bell JD, Harvey NC, Romu T, Heymsfield SB, Dahlqvist Leinhard O. Advanced body composition assessment: from body mass index to body composition profiling. J Investig Med. 2018 Jun;66(5):1-9. doi: 10.1136/jim-2018-000722. Epub 2018 Mar 25.'}, {'pmid': '36947988', 'type': 'BACKGROUND', 'citation': 'Gortan Cappellari G, Guillet C, Poggiogalle E, Ballesteros Pomar MD, Batsis JA, Boirie Y, Breton I, Frara S, Genton L, Gepner Y, Gonzalez MC, Heymsfield SB, Kiesswetter E, Laviano A, Prado CM, Santini F, Serlie MJ, Siervo M, Villareal DT, Volkert D, Voortman T, Weijs PJ, Zamboni M, Bischoff SC, Busetto L, Cederholm T, Barazzoni R, Donini LM; SOGLI Expert Panel. Sarcopenic obesity research perspectives outlined by the sarcopenic obesity global leadership initiative (SOGLI) - Proceedings from the SOGLI consortium meeting in rome November 2022. Clin Nutr. 2023 May;42(5):687-699. doi: 10.1016/j.clnu.2023.02.018. Epub 2023 Feb 24.'}, {'pmid': '35227529', 'type': 'BACKGROUND', 'citation': 'Donini LM, Busetto L, Bischoff SC, Cederholm T, Ballesteros-Pomar MD, Batsis JA, Bauer JM, Boirie Y, Cruz-Jentoft AJ, Dicker D, Frara S, Fruhbeck G, Genton L, Gepner Y, Giustina A, Gonzalez MC, Han HS, Heymsfield SB, Higashiguchi T, Laviano A, Lenzi A, Nyulasi I, Parrinello E, Poggiogalle E, Prado CM, Salvador J, Rolland Y, Santini F, Serlie MJ, Shi H, Sieber CC, Siervo M, Vettor R, Villareal DT, Volkert D, Yu J, Zamboni M, Barazzoni R. Definition and diagnostic criteria for sarcopenic obesity: ESPEN and EASO consensus statement. Clin Nutr. 2022 Apr;41(4):990-1000. doi: 10.1016/j.clnu.2021.11.014. Epub 2022 Feb 22.'}, {'pmid': '8126356', 'type': 'BACKGROUND', 'citation': 'Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, Scherr PA, Wallace RB. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994 Mar;49(2):M85-94. doi: 10.1093/geronj/49.2.m85.'}, {'pmid': '34620328', 'type': 'BACKGROUND', 'citation': 'Hamilton-James K, Collet TH, Pichard C, Genton L, Dupertuis YM. Precision and accuracy of bioelectrical impedance analysis devices in supine versus standing position with or without retractable handle in Caucasian subjects. Clin Nutr ESPEN. 2021 Oct;45:267-274. doi: 10.1016/j.clnesp.2021.08.010. Epub 2021 Sep 6.'}, {'pmid': '12028177', 'type': 'BACKGROUND', 'citation': 'Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc. 2002 May;50(5):889-96. doi: 10.1046/j.1532-5415.2002.50216.x.'}, {'pmid': '11312069', 'type': 'BACKGROUND', 'citation': 'Kyle UG, Genton L, Karsegard L, Slosman DO, Pichard C. Single prediction equation for bioelectrical impedance analysis in adults aged 20--94 years. Nutrition. 2001 Mar;17(3):248-53. doi: 10.1016/s0899-9007(00)00553-0.'}, {'pmid': '14613755', 'type': 'BACKGROUND', 'citation': 'Kyle UG, Genton L, Hans D, Pichard C. Validation of a bioelectrical impedance analysis equation to predict appendicular skeletal muscle mass (ASMM). Clin Nutr. 2003 Dec;22(6):537-43. doi: 10.1016/s0261-5614(03)00048-7.'}, {'type': 'BACKGROUND', 'citation': 'Lohman, T.G., Roche, A.F. and Martorell, R. (1988) Anthropometric standardization reference manual. Human Kinetics Books, Chicago.'}, {'pmid': '32279031', 'type': 'BACKGROUND', 'citation': 'Poggiogalle E, Mendes I, Ong B, Prado CM, Mocciaro G, Mazidi M, Lubrano C, Lenzi A, Donini LM, Siervo M. Sarcopenic obesity and insulin resistance: Application of novel body composition models. Nutrition. 2020 Jul-Aug;75-76:110765. doi: 10.1016/j.nut.2020.110765. Epub 2020 Feb 13.'}, {'pmid': '24760978', 'type': 'BACKGROUND', 'citation': 'Prado CM, Siervo M, Mire E, Heymsfield SB, Stephan BC, Broyles S, Smith SR, Wells JC, Katzmarzyk PT. A population-based approach to define body-composition phenotypes. Am J Clin Nutr. 2014 Jun;99(6):1369-77. doi: 10.3945/ajcn.113.078576. Epub 2014 Apr 23.'}, {'pmid': '36928809', 'type': 'BACKGROUND', 'citation': 'Salmon-Gomez L, Catalan V, Fruhbeck G, Gomez-Ambrosi J. Relevance of body composition in phenotyping the obesities. Rev Endocr Metab Disord. 2023 Oct;24(5):809-823. doi: 10.1007/s11154-023-09796-3. Epub 2023 Mar 17.'}, {'pmid': '27178302', 'type': 'BACKGROUND', 'citation': 'Scafoglieri A, Clarys JP, Bauer JM, Verlaan S, Van Malderen L, Vantieghem S, Cederholm T, Sieber CC, Mets T, Bautmans I; Provide Study Group. Predicting appendicular lean and fat mass with bioelectrical impedance analysis in older adults with physical function decline - The PROVIDE study. Clin Nutr. 2017 Jun;36(3):869-875. doi: 10.1016/j.clnu.2016.04.026. Epub 2016 Apr 28.'}, {'pmid': '25103151', 'type': 'BACKGROUND', 'citation': 'Sergi G, De Rui M, Veronese N, Bolzetta F, Berton L, Carraro S, Bano G, Coin A, Manzato E, Perissinotto E. Assessing appendicular skeletal muscle mass with bioelectrical impedance analysis in free-living Caucasian older adults. Clin Nutr. 2015 Aug;34(4):667-73. doi: 10.1016/j.clnu.2014.07.010. Epub 2014 Jul 24.'}, {'pmid': '22623101', 'type': 'BACKGROUND', 'citation': 'Shepherd JA, Fan B, Lu Y, Wu XP, Wacker WK, Ergun DL, Levine MA. A multinational study to develop universal standardization of whole-body bone density and composition using GE Healthcare Lunar and Hologic DXA systems. J Bone Miner Res. 2012 Oct;27(10):2208-16. doi: 10.1002/jbmr.1654.'}, {'pmid': '28625918', 'type': 'BACKGROUND', 'citation': 'Shepherd JA, Ng BK, Sommer MJ, Heymsfield SB. Body composition by DXA. Bone. 2017 Nov;104:101-105. doi: 10.1016/j.bone.2017.06.010. Epub 2017 Jun 16.'}, {'pmid': '21760631', 'type': 'BACKGROUND', 'citation': 'Toombs RJ, Ducher G, Shepherd JA, De Souza MJ. The impact of recent technological advances on the trueness and precision of DXA to assess body composition. Obesity (Silver Spring). 2012 Jan;20(1):30-9. doi: 10.1038/oby.2011.211. Epub 2011 Jul 14.'}, {'pmid': '39108357', 'type': 'BACKGROUND', 'citation': 'Vendrami C, Gatineau G, Gonzalez Rodriguez E, Lamy O, Hans D, Shevroja E. Standardization of body composition parameters between GE Lunar iDXA and Hologic Horizon A and their clinical impact. JBMR Plus. 2024 Jul 10;8(9):ziae088. doi: 10.1093/jbmrpl/ziae088. eCollection 2024 Sep.'}, {'pmid': '30297760', 'type': 'BACKGROUND', 'citation': 'Ward LC. Bioelectrical impedance analysis for body composition assessment: reflections on accuracy, clinical utility, and standardisation. Eur J Clin Nutr. 2019 Feb;73(2):194-199. doi: 10.1038/s41430-018-0335-3. Epub 2018 Oct 8.'}, {'pmid': '32653450', 'type': 'BACKGROUND', 'citation': 'Zambone MA, Liberman S, Garcia MLB. Anthropometry, bioimpedance and densitometry: Comparative methods for lean mass body analysis in elderly outpatients from a tertiary hospital. Exp Gerontol. 2020 Sep;138:111020. doi: 10.1016/j.exger.2020.111020. Epub 2020 Jul 9.'}]}, 'descriptionModule': {'briefSummary': 'This study aims to develop and cross-validate novel bioelectrical impedance analysis (BIA) equations for predicting appendicular soft tissue masses, specifically fat mass (FM) and appendicular lean mass (ALM), in a sample of Caucasian adult subjects affected by obesity. The research will compare these new BIA equations with three established BIA-derived prediction models and validate them using dual-energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI) data. This study utilizes existing datasets to enhance the accuracy and applicability of BIA in assessing body composition and supports the development of standardized algorithms for converting raw BIA data across different devices and populations.', 'detailedDescription': 'Assessing body composition in persons with obesity, and in particular, the excess of fat mass and the possible reduction of muscle mass, is important to define the phenotypic manifestation of obesity (estimating the risk of dysmetabolic, cardiovascular, and functional complications), and to determine a better treatment approach. Dual X-ray absorptiometry (DXA) is a mature technology for assessing body composition with major advances in the technology over the past three decades. DXA is a validated tool to investigate body composition phenotypes, as it reliably assesses whole-body and regional bone mineral content, fat mass and lean mass. Unfortunately, it is not always available in all settings where instead Bio-Impedance Analysis (BIA) (which has lower costs and greater convenience of use) is commonly used to estimate body composition starting from electrical resistance and reactance data.\n\nRegrettably, the two methods often give non-superimposable results and studies have been carried out to predict, from BIA, values commonly obtainable only with DXA. In particular, different studies estimated the appendicular lean mass from BIA, which represents an important parameter for the evaluation of sarcopenia and is correlated with its functional limitations. For example, a post hoc analysis of the PROVIDE study was aimed in particular at assessing the level of agreement between BIA- and DXA-derived soft tissue ratios as indicators of limb tissue quality and at developing and cross-validating new BIA equations for predicting appendicular soft tissue \\[fat mass (FM) and appendicular lean mass (ALM)\\] in older Caucasian adults with physical function decline using both the Hologic Horizon and GE Lunar DXA systems as reference methods.\n\nMETHODS:\n\nThis study is based on baseline data (anthropometric, BIA, and DXA) collected in pre-existing datasets. In particular\n\n* the Sapienza dataset which derived from a study aimed at investigating the association between markers of insulin sensitivity and SO defined by three novel body composition models will be used to develop BIA equations predicting appendicular soft tissue masses;\n* datasets from different studies and in particular from the BIA International Dataset Project will be used to validate the BIA equations assessing the agreement between BIA- and DXA-derived soft tissue estimation\n\nSTUDY PARAMETERS:\n\n-Anthropometry: anthropometric parameters should have been measured in accordance with validated and standardized methodologies.\n\nThe anthropometric parameters of interest are body mass, stature, waist circumference, calf circumference, arm circumference, and triceps skinfold thickness, limb length.\n\n-Dual energy X-ray absorptiometry: all participants should have been scanned using a fan beam whole body DXA device (Hologic Bedford, Massachusetts, USA; Lunar Prodigy, GE Healthcare). Daily calibration of the densitometers should have been performed following the instructions provided by the manufacturer.\n\nSince measurements vary among instruments from different manufacturers, calibration equations will be used to address these issues and improve the agreement between devices.\n\nThe body components of interest are total fat mass (FM), total lean mass (LM), ALM (sum of the lean mass in the limbs), FM (sum of the fat mass in the limbs), and the ratio of ALM to FM.\n\n-Bioelectrical impedance analysis: After overnight fasting and bladder voiding, bioelectrical impedance analysis should have been performed with participants lying supine (with their limbs slightly away from their body; active electrodes should have been placed on the right side on conventional metacarpal and metatarsal lines, recording electrodes in standard positions at the right wrist and ankle) or in vertical position (barefoot, stepping onto the electrodes embedded into the scale and grasping the electrode-embedded handles). At each location, a whole-body tetrapolar BIA device operating at a weak alternating electrical current of 500 µA to 1 mA and a single frequency of 50 kHz should have been used to measure the voltage drop across body tissues.\n\nThe electric parameters of interest are resistance (R: restriction of current flow), reactance (Xc: capacitance of cell membranes and tissue interfaces), and phase angle (PhA).\n\nThe information about BIA devices will be recorded since raw R and Xc values may not be not comparable.\n\nDue to the significant differences found in different studies when comparing vertical to supine position, the results obtained with the two methodologies will be analysed separately.\n\nWith reference to the limitation reported by the PROVIDE study authors (i.e. the absence of a direct measurement of extracellular water), the raw data detected through multifrequency bioimpedance devices will also be used, where available. Specifically, the values of impedance and resistance measured at a frequency of 5 kHz will be included; furthermore, where available, it would be optimal to analyze data measured at the following frequencies; 1, 2, 5, 10, 50, 100, 200, 250 and 500 kHz.\n\nSTATISTICS:\n\nData will be analyzed by using IBM® SPSS® Statistics version 25. The data will be presented as frequency (percent) and mean ± SD for qualitative and quantitative variables, respectively. The Shapiro-Wilk test will be used to evaluate if the data are normally distributed. Comparison of continuous variables will be performed using parametric or non-parametric tests depending on whether the distribution is normal or not. The chi-square test will be used to check whether the frequencies occurring in the sample differ significantly from the expected frequencies. The cut-off for statistical significance will be set at p\\<0.05.\n\nPreliminary equations, using DXA-derived appendicular lean and fat mass as the dependent variables, and age, gender, BMI, weight, impedance index, and reactance as independent variables, will be developed using a stepwise multiple linear regression approach. Only significant regressors of appendicular soft tissue masses will be considered in the equations.\n\nModel performance fit will be assessed using multiple correlations (R2) and standard errors of the estimate (SEE). For each of the appendicular soft tissue components, the model with the lowest standard error of the estimate will be used in the cross-validation analysis.\n\nThe individual and body composition data from the cross-validation samples will be imputed into the developed equations to assess their accuracy. The statistics for cross-validation includes mean difference, limits of agreement, and root mean squared error.\n\nAdditionally, the agreement between ALM\\_BIA estimated in our sample, ALM\\_SERGI, ALM\\_Provide, and ALM\\_KYLE will be assessed using regression analysis.\n\nFinally, the agreement between the ALM/FM-ratios estimated by DXA and by BIA will be evaluated using Bland and Altman analysis.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Cohort of Caucasian adult subjects with obesity, specifically those who have undergone baseline body composition assessments using Dual X-ray Absorptiometry (DXA) and Bioelectrical Impedance Analysis (BIA). Participants are selected from existing datasets, including individuals with a Body Mass Index (BMI) of 30 kg/m² or higher. Eligible participants are aged 18 years and older and have provided informed consent for their data to be used in research. The population excludes those with conditions that may significantly affect body composition.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Adults with obesity (BMI ≥ 30 kg/m²)\n* Age 18 years and older\n* Available baseline DXA and BIA measurements\n* Provided informed consent for data use\n\nExclusion Criteria:\n\n* any chronic disease or medication that can significantly affect body composition \\[eg. malignant diseases in the last 5 years, organ failure, acute inflammation (C-reactive protein\\>10 mg/L) autoimmune diseases, neurological diseases, syndromic obesity\\]\n* cognitive impairment (Mini-Mental State Examination \\<25)\n* subjects that are considered physically active (athletes or very active subjects i.e., performing at least 150 minutes of moderate to vigorous physical activity per week)\n* alcohol intake \\>140g/wk for Males and 70g/wk for Females\n* participation in a weight-reducing program (last 3 months)\n* impossibility to perform DXA exam\n* pregnancy and breast-feeding.'}, 'identificationModule': {'nctId': 'NCT06545435', 'briefTitle': 'Predicting Appendicular Lean and Fat Mass With Bioelectrical Impedance Analysis Among Adult Patients With Obesity.', 'organization': {'class': 'OTHER', 'fullName': 'University of Roma La Sapienza'}, 'officialTitle': 'Predicting Appendicular Lean and Fat Mass With Bioelectrical Impedance Analysis Among Adult Patients With Obesity.', 'orgStudyIdInfo': {'id': '0606/2021'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Obese Adults Cohort', 'description': 'This cohort includes Caucasian adult subjects with obesity (BMI ≥ 30 kg/m²). Participants have undergone baseline assessments using both Dual X-ray Absorptiometry (DXA) and Bioelectrical Impedance Analysis (BIA).'}, {'label': 'MRI Validation Subset', 'description': 'A subset of participants from the Obese Adults Cohort selected for additional validation using Magnetic Resonance Imaging (MRI) to assess muscle size and architecture.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '70808', 'city': 'Baton Rouge', 'state': 'Louisiana', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Steven Heymsfield', 'role': 'CONTACT'}], 'facility': 'Pennington Biomedical Research Center, Louisiana State University', 'geoPoint': {'lat': 30.44332, 'lon': -91.18747}}, {'zip': '27514', 'city': 'Chapel Hill', 'state': 'North Carolina', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'John A Batsis', 'role': 'CONTACT'}], 'facility': 'Division of Geriatric Medicine, School of Medicine, and Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill', 'geoPoint': {'lat': 35.9132, 'lon': -79.05584}}, {'zip': '6102', 'city': 'Perth', 'status': 'RECRUITING', 'country': 'Australia', 'facility': 'Curtin University, School of Population Health', 'geoPoint': {'lat': -31.95224, 'lon': 115.8614}}, {'zip': '96010-610', 'city': 'Pelotas', 'state': 'Rio Grande do Sul', 'status': 'RECRUITING', 'country': 'Brazil', 'contacts': [{'name': 'Maria Cristina Gonzalez', 'role': 'CONTACT'}], 'facility': 'Federal University of Pelotas', 'geoPoint': {'lat': -31.76997, 'lon': -52.34101}}, {'zip': 'T6G 2P5', 'city': 'Edmonton', 'state': 'Alberta', 'status': 'RECRUITING', 'country': 'Canada', 'contacts': [{'name': 'Carla Prado', 'role': 'CONTACT'}], 'facility': 'University of Alberta, Department of Agricultural, Food and Nutritional Science', 'geoPoint': {'lat': 53.55014, 'lon': -113.46871}}, {'zip': '09042', 'city': 'Cagliari', 'status': 'RECRUITING', 'country': 'Italy', 'contacts': [{'name': 'Elisabetta Marini', 'role': 'CONTACT'}], 'facility': 'University of Cagliari, Department of Life and Environmental Sciences', 'geoPoint': {'lat': 39.23054, 'lon': 9.11917}}, {'city': 'Roma', 'status': 'RECRUITING', 'country': 'Italy', 'contacts': [{'name': 'Lorenzo M Donini, MD', 'role': 'CONTACT', 'email': 'lorenzomaria.donini@uniroma1.it', 'phone': '00390649690215'}, {'name': 'Eleonora Poggiogalle, MD, PhD', 'role': 'CONTACT', 'email': 'eleonora.poggiogalle@uniroma1.it', 'phone': '00390649690215'}, {'name': 'Francesco Frigerio, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Alessia Vitozzi', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Alessandro Pinto', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Marianna Minnetti', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Zaira Spinello', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'Sapienza, University of Rome', 'geoPoint': {'lat': 44.99364, 'lon': 11.10642}}, {'city': 'Trieste', 'status': 'RECRUITING', 'country': 'Italy', 'contacts': [{'name': 'Rocco Barazzoni', 'role': 'CONTACT'}], 'facility': 'Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy', 'geoPoint': {'lat': 45.64953, 'lon': 13.77678}}, {'zip': '1495-751', 'city': 'Lisbon', 'status': 'RECRUITING', 'country': 'Portugal', 'contacts': [{'name': 'Analiza Monica Silva', 'role': 'CONTACT'}], 'facility': 'Universidade de Lisboa, Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana', 'geoPoint': {'lat': 38.72509, 'lon': -9.1498}}], 'centralContacts': [{'name': 'Lorenzo M Donini, MD', 'role': 'CONTACT', 'email': 'lorenzomaria.donini@uniroma1.it', 'phone': '00390649690215'}, {'name': 'Eleonora Poggiogalle, Md, PhD', 'role': 'CONTACT', 'email': 'eleonora.poggiogalle@uniroma1.it', 'phone': '00390649690215'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Roma La Sapienza', 'class': 'OTHER'}, 'collaborators': [{'name': 'University of Trieste', 'class': 'OTHER'}, {'name': 'University of North Carolina, Chapel Hill', 'class': 'OTHER'}, {'name': 'Federal University of Pelotas', 'class': 'OTHER'}, {'name': 'Louisiana State University Health Sciences Center in New Orleans', 'class': 'OTHER'}, {'name': 'University of Cagliari', 'class': 'OTHER'}, {'name': 'University of Lisbon', 'class': 'OTHER'}, {'name': 'University of Alberta', 'class': 'OTHER'}, {'name': 'Curtin University', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Full Professor', 'investigatorFullName': 'Donini Lorenzo M', 'investigatorAffiliation': 'University of Roma La Sapienza'}}}}