Predicting Appendicular Lean and Fat Mass With Bioelectrical Impedance Analysis Among Adult Patients With Obesity.
NCT Number: NCT06545435
Status: RECRUITING
Study Type: OBSERVATIONAL
Conditions: Obesity
Sponsor: University of Roma La Sapienza
Collaborators: University of Trieste|University of North Carolina, Chapel Hill|Federal University of Pelotas|Louisiana State University Health Sciences Center in New Orleans|University of Cagliari|University of Lisbon|University of Alberta|Curtin University
Sex: ALL
Age: ADULT, OLDER_ADULT
Enrollment: 400
Study Design: Observational Model: |Time Perspective: p
Start Date: 2021-05-13
Completion Date: 2025-12-31
Locations: Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, 70808, United States|Division of Geriatric Medicine, School of Medicine, and Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27514, United States|Curtin University, School of Population Health, Perth, 6102, Australia|Federal University of Pelotas, Pelotas, Rio Grande Do Sul, 96010-610, Brazil|University of Alberta, Department of Agricultural, Food and Nutritional Science, Edmonton, Alberta, T6G 2P5, Canada|University of Cagliari, Department of Life and Environmental Sciences, Cagliari, 09042, Italy|Sapienza, University of Rome, Roma, Italy|Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy, Trieste, Italy|Universidade de Lisboa, Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, Lisboa, 1495-751, Portugal

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.

Primary Outcome Measures: Development and Cross-Validation of BIA Equations for Appendicular Soft Tissue Masses, 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., Baseline
Secondary Outcome Measures: Comparison of New BIA Equations with Existing Models, 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., Baseline|Algorithm Development for Conversion Between BIA Devices, 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., Baseline|Cross-Validation of New BIA Equations with Different DXA Systems, 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., Baseline|Validation of BIA Equations Using Magnetic Resonance Imaging (MRI), 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., Baseline