ObesityNCT06545435
Predicting Appendicular Lean and Fat Mass With Bioelectrical Impedance Analysis Among Adult Patients With Obesity.
An observational study of Obesity, run by University of Roma La Sapienza at 9 sites in 6 countries.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Obesity
- How many people
- 400 participants
- Where
- 9 sitesin 6 countries
- Study dates
- 13 May 2021to 31 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 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.
Who may be able to join
- Adults with obesity (BMI ≥ 30 kg/m²)
- Age 18 years and older
- Available baseline DXA and BIA measurements
- Provided informed consent for data use
Who cannot join
- 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.
Trial locations and contacts
9 sites in 6 countries.
9 sites in 6 countries
Italy3 sites, 3 recruiting
- Recruiting
University of Cagliari, Department of Life and Environmental Sciences
Cagliari, 09042
Elisabetta Marini
- Recruiting
Sapienza, University of Rome
Roma
Lorenzo M Donini, MD, 00390649690215, lorenzomaria.donini@uniroma1.it
Eleonora Poggiogalle, MD, PhD, 00390649690215, eleonora.poggiogalle@uniroma1.it
- Recruiting
Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
Trieste
Rocco Barazzoni
United States2 sites, 2 recruiting
- Recruiting
Pennington Biomedical Research Center, Louisiana State University
Baton Rouge, Louisiana, 70808
Steven Heymsfield
- Recruiting
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
John A Batsis
Australia1 site, 1 recruiting
- Recruiting
Curtin University, School of Population Health
Perth, 6102
Brazil1 site, 1 recruiting
- Recruiting
Federal University of Pelotas
Pelotas, Rio Grande do Sul, 96010-610
Maria Cristina Gonzalez
Canada1 site, 1 recruiting
- Recruiting
University of Alberta, Department of Agricultural, Food and Nutritional Science
Edmonton, Alberta, T6G 2P5
Carla Prado
Portugal1 site, 1 recruiting
- Recruiting
Universidade de Lisboa, Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana
Lisbon, 1495-751
Analiza Monica Silva
About this study
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.
Read the detailed description
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.
Regrettably, 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.
METHODS:
This study is based on baseline data (anthropometric, BIA, and DXA) collected in pre-existing datasets. In particular
- 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;
- 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
STUDY PARAMETERS:
-Anthropometry: anthropometric parameters should have been measured in accordance with validated and standardized methodologies.
The anthropometric parameters of interest are body mass, stature, waist circumference, calf circumference, arm circumference, and triceps skinfold thickness, limb length.
-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.
Since measurements vary among instruments from different manufacturers, calibration equations will be used to address these issues and improve the agreement between devices.
The 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.
-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.
The electric parameters of interest are resistance (R: restriction of current flow), reactance (Xc: capacitance of cell membranes and tissue interfaces), and phase angle (PhA).
The information about BIA devices will be recorded since raw R and Xc values may not be not comparable.
Due 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.
With 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.
STATISTICS:
Data 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.
Preliminary 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.
Model 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.
The 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.
Additionally, the agreement between ALM_BIA estimated in our sample, ALM_SERGI, ALM_Provide, and ALM_KYLE will be assessed using regression analysis.
Finally, the agreement between the ALM/FM-ratios estimated by DXA and by BIA will be evaluated using Bland and Altman analysis.
- Conditions
- Obesity
- Keywords
- Appendicular Lean Mass, Fat Mass, Body Composition, Bio-Impedance Analysis
- Medical subject headings
- Obesity
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline
Secondary outcomes (4)
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Aug 2024
- Study start13 May 2021
- Primary completion30 Jun 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (21)Papers the sponsor has linked to this study.
- Other trials for ObesityStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Roma La Sapienza
- Sponsor type
- University, hospital or other
- 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
- Responsible party
- Principal investigator, Donini Lorenzo M, University of Roma La Sapienza
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06545435
- Sponsor's protocol ID
- 0606/2021
- Status last verified
- Aug 2024
- First submitted
- 5 Aug 2024
- Last updated
- 26 Aug 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
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.
PubMed 9554417Borga 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.
PubMed 29581385Gortan 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.
PubMed 36947988Donini 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.
PubMed 35227529Guralnik 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.
PubMed 8126356Hamilton-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.
PubMed 34620328Janssen 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.
PubMed 12028177Kyle 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.
PubMed 11312069Kyle 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.
PubMed 14613755Lohman, 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.
PubMed 32279031Prado 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.
PubMed 24760978Salmon-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.
PubMed 36928809Scafoglieri 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.
PubMed 27178302Sergi 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.
PubMed 25103151
Study at a glance
From the registry record, last updated by the sponsor on 26 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Appendicular Lean and Fat Mass With Bioelectrical Impedance Analysis Among Adult Patients With Obesity.
- ClinicalTrials.gov ID
- NCT06545435
- Acronym / sponsor ID
- 0606/2021
- Conditions
- Obesity
- Keywords
- Appendicular Lean Mass, Fat Mass, Body Composition, Bio-Impedance Analysis
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- University of Roma La SapienzaUniversity, hospital or other
- 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
- Study start
- 13 May 2021 (actual)
- Primary completion
- 30 Jun 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 9 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Regrettably, 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.
METHODS:
This study is based on baseline data (anthropometric, BIA, and DXA) collected in pre-existing datasets. In particular
- 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;
- 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
STUDY PARAMETERS:
-Anthropometry: anthropometric parameters should have been measured in accordance with validated and standardized methodologies.
The anthropometric parameters of interest are body mass, stature, waist circumference, calf circumference, arm circumference, and triceps skinfold thickness, limb length.
-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.
Since measurements vary among instruments from different manufacturers, calibration equations will be used to address these issues and improve the agreement between devices.
The 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.
-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.
The electric parameters of interest are resistance (R: restriction of current flow), reactance (Xc: capacitance of cell membranes and tissue interfaces), and phase angle (PhA).
The information about BIA devices will be recorded since raw R and Xc values may not be not comparable.
Due 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.
With 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.
STATISTICS:
Data 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.
Preliminary 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.
Model 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.
The 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.
Additionally, the agreement between ALM_BIA estimated in our sample, ALM_SERGI, ALM_Provide, and ALM_KYLE will be assessed using regression analysis.
Finally, the agreement between the ALM/FM-ratios estimated by DXA and by BIA will be evaluated using Bland and Altman analysis.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (4 listed)
- Adults with obesity (BMI ≥ 30 kg/m²)
- Age 18 years and older
- Available baseline DXA and BIA measurements
- Provided informed consent for data use
Key exclusion criteria (7 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Development and Cross-Validation of BIA Equations for Appendicular Soft Tissue Masses
Time frame: Baseline
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.
4 secondary outcomes
Comparison of New BIA Equations with Existing Models
Time frame: Baseline
Algorithm Development for Conversion Between BIA Devices
Time frame: Baseline
Cross-Validation of New BIA Equations with Different DXA Systems
Time frame: Baseline
Validation of BIA Equations Using Magnetic Resonance Imaging (MRI)
Time frame: Baseline
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Aug 2024.
- Background: PubMed 9554417Baumgartner 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
9 sites in 6 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Obesity, run by University of Roma La Sapienza at 9 sites in 6 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 26 Aug 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 9 sites in Italy, United States, Australia, Brazil, Canada and 1 more country. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Lorenzo M Donini, MD (00390649690215, lorenzomaria.donini@uniroma1.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development and Cross-Validation of BIA Equations for Appendicular Soft Tissue Masses (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06545435, which also lists 21 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Aug 2024; AzTrials last refreshed its copy on 29 Oct 2026.