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Trial details imported from ClinicalTrials.gov
For full trial details, please see the original record at
https://clinicaltrials.gov/study/NCT06545435
Registration number
NCT06545435
Ethics application status
Date submitted
5/08/2024
Date registered
9/08/2024
Date last updated
26/08/2024
Titles & IDs
Public title
Predicting Appendicular Lean and Fat Mass With Bioelectrical Impedance Analysis Among Adult Patients With Obesity.
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Scientific title
Predicting Appendicular Lean and Fat Mass With Bioelectrical Impedance Analysis Among Adult Patients With Obesity.
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Secondary ID [1]
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0606/2021
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Universal Trial Number (UTN)
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Trial acronym
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Linked study record
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Health condition
Health condition(s) or problem(s) studied:
Obesity
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Condition category
Condition code
Diet and Nutrition
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Obesity
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Metabolic and Endocrine
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Other metabolic disorders
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Intervention/exposure
Study type
Observational
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Patient registry
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Target follow-up duration
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Target follow-up type
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Description of intervention(s) / exposure
Obese Adults Cohort - 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).
MRI Validation Subset - A subset of participants from the Obese Adults Cohort selected for additional validation using Magnetic Resonance Imaging (MRI) to assess muscle size and architecture.
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Comparator / control treatment
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Control group
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Outcomes
Primary outcome [1]
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Development and Cross-Validation of BIA Equations for Appendicular Soft Tissue Masses
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Assessment method [1]
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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.
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Timepoint [1]
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Baseline
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Secondary outcome [1]
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Comparison of New BIA Equations with Existing Models
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Assessment method [1]
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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.
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Timepoint [1]
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Baseline
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Secondary outcome [2]
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Algorithm Development for Conversion Between BIA Devices
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Assessment method [2]
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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.
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Timepoint [2]
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Baseline
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Secondary outcome [3]
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Cross-Validation of New BIA Equations with Different DXA Systems
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Assessment method [3]
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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.
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Timepoint [3]
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Baseline
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Secondary outcome [4]
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Validation of BIA Equations Using Magnetic Resonance Imaging (MRI)
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Assessment method [4]
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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.
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Timepoint [4]
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Baseline
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Eligibility
Key inclusion criteria
* Adults with obesity (BMI = 30 kg/m²)
* Age 18 years and older
* Available baseline DXA and BIA measurements
* Provided informed consent for data use
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Minimum age
18
Years
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Maximum age
No limit
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Sex
Both males and females
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Can healthy volunteers participate?
No
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Key exclusion criteria
* 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]
* cognitive impairment (Mini-Mental State Examination <25)
* 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)
* alcohol intake >140g/wk for Males and 70g/wk for Females
* participation in a weight-reducing program (last 3 months)
* impossibility to perform DXA exam
* pregnancy and breast-feeding.
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Study design
Purpose
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Duration
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Selection
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Timing
Retrospective
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Statistical methods / analysis
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Recruitment
Recruitment status
Recruiting
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Data analysis
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Reason for early stopping/withdrawal
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Other reasons
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Date of first participant enrolment
Anticipated
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Actual
13/05/2021
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Date of last participant enrolment
Anticipated
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Actual
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Date of last data collection
Anticipated
31/12/2025
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Actual
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Sample size
Target
400
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Accrual to date
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Final
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Recruitment in Australia
Recruitment state(s)
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Recruitment hospital [1]
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Curtin University, School of Population Health - Perth
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Recruitment postcode(s) [1]
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6102 - Perth
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Recruitment outside Australia
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United States of America
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State/province [1]
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Louisiana
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United States of America
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State/province [2]
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North Carolina
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Brazil
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State/province [3]
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Rio Grande Do Sul
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Canada
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State/province [4]
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Alberta
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Country [5]
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Italy
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State/province [5]
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Cagliari
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Country [6]
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Italy
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State/province [6]
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Roma
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Country [7]
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Italy
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State/province [7]
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Trieste
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Portugal
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State/province [8]
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Lisboa
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Funding & Sponsors
Primary sponsor type
Other
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Name
University of Roma La Sapienza
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Address
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Other collaborator category [1]
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Other
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Name [1]
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University of Trieste
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Address [1]
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Other collaborator category [2]
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Other
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Name [2]
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University of North Carolina, Chapel Hill
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Address [2]
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Other collaborator category [3]
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Other
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Name [3]
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Federal University of Pelotas
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Address [3]
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Other collaborator category [4]
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Other
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Name [4]
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Louisiana State University Health Sciences Center in New Orleans
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Address [4]
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Other collaborator category [5]
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Other
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University of Cagliari
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Other collaborator category [6]
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Other
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University of Lisbon
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Other collaborator category [7]
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Other
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University of Alberta
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Address [7]
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Other collaborator category [8]
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Other
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Name [8]
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Curtin University
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Ethics approval
Ethics application status
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Summary
Brief summary
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.
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Trial website
https://clinicaltrials.gov/study/NCT06545435
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Trial related presentations / publications
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. Erratum In: Am J Epidemiol 1999 Jun 15;149(12):1161. 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. 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. 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. 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. 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. 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. 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. 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. Lohman, T.G., Roche, A.F. and Martorell, R. (1988) Anthropometric standardization reference manual. Human Kinetics Books, Chicago. 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. 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. Erratum In: Am J Clin Nutr. 2016 Apr;103(4):1190. doi: 10.3945/ajcn.116.130823. 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. 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. 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. 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. 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. 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. Vendrami C, Gatineau G, Rodriguez EG, 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. 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. 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.
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Public notes
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Contacts
Principal investigator
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Contact person for public queries
Name
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Lorenzo M Donini, MD
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Phone
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00390649690215
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[email protected]
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Contact person for scientific queries
No information has been provided regarding IPD availability
What supporting documents are/will be available?
No Supporting Document Provided
Results publications and other study-related documents
No documents have been uploaded by study researchers.
Results not provided in
https://clinicaltrials.gov/study/NCT06545435
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