Predicting Appendicular Lean and Fat Mass With Bioelectrical Impedance Analysis Among Adult Patients With Obesity.
NCT ID: NCT06545435
Last Updated: 2024-08-26
Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
Recruitment Status
RECRUITING
Total Enrollment
400 participants
Study Classification
OBSERVATIONAL
Study Start Date
2021-05-13
Study Completion Date
2025-12-31
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
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.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Analyze the Relationship Between the Density of Fat Tissue and Cellulite With the Lumbar Lordosis Degree
NCT02903459
Lordosis
COMPLETED
Fat Mass Estimation in Overweight and Obesity
NCT01806493
Obesity
COMPLETED
Generation of Prediction Equations to Analyze Body Composition of Obese Adults Based on BIA
NCT03779932
Bioelectrical Impedance Measurement of Healthy Adults
COMPLETED
Validation of Methods to Estimate Body Fat
NCT00205218
Healthy
COMPLETED
Generation of Prediction Equations for BIA of the Elderly
NCT04028648
Healthy Elderly
COMPLETED
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
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.
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
See the medical conditions and disease areas that this research is targeting or investigating.
Obesity
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
Observational Model Type
COHORT
Study Time Perspective
RETROSPECTIVE
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
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).
No interventions assigned to this group
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.
No interventions assigned to this group
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
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
* Age 18 years and older
* Available baseline DXA and BIA measurements
* Provided informed consent for data use
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.
* 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.
Minimum Eligible Age
18 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
University of Trieste
OTHER
Sponsor Role
collaborator
University of North Carolina, Chapel Hill
OTHER
Sponsor Role
collaborator
Federal University of Pelotas
OTHER
Sponsor Role
collaborator
Louisiana State University Health Sciences Center in New Orleans
OTHER
Sponsor Role
collaborator
University of Cagliari
OTHER
Sponsor Role
collaborator
University of Lisbon
OTHER
Sponsor Role
collaborator
University of Alberta
OTHER
Sponsor Role
collaborator
Curtin University
OTHER
Sponsor Role
collaborator
University of Roma La Sapienza
OTHER
Sponsor Role
lead
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Donini Lorenzo M
Full Professor
Responsibility Role
PRINCIPAL_INVESTIGATOR
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Pennington Biomedical Research Center, Louisiana State University
Baton Rouge, Louisiana, United States
Site Status
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, United States
Site Status
RECRUITING
Curtin University, School of Population Health
Perth, , Australia
Site Status
RECRUITING
Federal University of Pelotas
Pelotas, Rio Grande do Sul, Brazil
Site Status
RECRUITING
University of Alberta, Department of Agricultural, Food and Nutritional Science
Edmonton, Alberta, Canada
Site Status
RECRUITING
University of Cagliari, Department of Life and Environmental Sciences
Cagliari, , Italy
Site Status
RECRUITING
Sapienza, University of Rome
Roma, , Italy
Site Status
RECRUITING
Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
Trieste, , Italy
Site Status
RECRUITING
Universidade de Lisboa, Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana
Lisbon, , Portugal
Site Status
RECRUITING
Countries
Review the countries where the study has at least one active or historical site.
United States
Australia
Brazil
Canada
Italy
Portugal
Central Contacts
Reach out to these primary contacts for questions about participation or study logistics.
Facility Contacts
Find local site contact details for specific facilities participating in the trial.
Steven Heymsfield
Role: primary
John A Batsis
Role: primary
Maria Cristina Gonzalez
Role: primary
Carla Prado
Role: primary
Elisabetta Marini
Role: primary
Rocco Barazzoni
Role: primary
Analiza Monica Silva
Role: primary
References
Explore related publications, articles, or registry entries linked to this study.
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
Lohman, T.G., Roche, A.F. and Martorell, R. (1988) Anthropometric standardization reference manual. Human Kinetics Books, Chicago.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
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.
Reference Type
BACKGROUND
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
0606/2021
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.
Sarcopenic Obesity in Liver Transplanted Patients
NCT05029713
COMPLETED
Generation of Prediction Equations to Analyze Body Composition of Adults Based on Bioelectrical Impedance Analysis (BIA)
NCT01368640
COMPLETED
Differences in Fat Mass Estimation Formulas in Physically Active Adult Population and Relation With Skinfold Sums.
NCT04429581
COMPLETED
Sarcopenic Obesity: Estimation of Prevalence and Identification of Clinical and Biological Determinants in a Population of Adult Obese Patients
NCT03394469
UNKNOWN
NA
The Investigation of Mechanical Properties in Obesity.
NCT04721431
COMPLETED
NA
Generation of Normal Ranges to Analyze Body Composition of Adults Based on Bioelectrical Impedance Analysis (BIA)
NCT01481285
COMPLETED
Predicting Obesity Consequences Using Body Measure and Urine Metabolomics
NCT04989062
UNKNOWN
Relationship Between Skeletal Muscle Mass and Interventricular Septum Thickness in Apparently Healthy Overweight and Obese Subjects
NCT04264091
COMPLETED
Study of White Adipose Tissue Remodeling by Cryolipolysis in Humans
NCT04819321
UNKNOWN
NA
Effect of Obesity on Extracellular Matrix Composition
NCT06304506
RECRUITING
NA
Development of a Clinical Screening, Diagnostic and Evaluation Tool for Patients With Lower Limb Lymphedema: Aim 1
NCT05269264
RECRUITING
Measurement of Body Fat in Infants
NCT03490227
COMPLETED
Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 1
NCT06646120
WITHDRAWN
Application and Adaption of Device Specific Body Composition Formulas to Various Ethnic Groups
NCT01471938
COMPLETED
Mechanisms Defending Fat Mass in Humans After Lipectomy
NCT00993213
COMPLETED
NA
The Impact of Lower Limb Fractures on Body Mass Index (BMI)
NCT05162521
UNKNOWN
Age-related Post-lockdown BMI Variations
NCT05430542
COMPLETED
Development and Validation of Equations to Evaluate Body Composition in Colombian Population - F20 Project
NCT05450588
UNKNOWN
Effect of Weight Loss on Body Composition and Metabolic Function in Women With Lipedema
NCT03271034
COMPLETED
NA
Body Composition and Fatty Liver Disease
NCT05695118
UNKNOWN
NA
Energy Metabolism and Nutrient Absorption in Lean and Obese Individuals
NCT00414063
COMPLETED
Anthropometric Assessment of Abdominal Obesity and Health Risk in Children and Adolescents
NCT01595100
COMPLETED
Sarcopenic Obesity: Estimation of Prevalence and Identification of Clinical and Biological Determinants in a Cohort of Adult Obese Patients and Longitudinal Follow-up
NCT06880432
NOT_YET_RECRUITING
NA
The Relationship Betweensarcopenia And Myosteatosis With The Natural History Of Liver Cirrhosis
NCT04466709
UNKNOWN
Characterization of Bile Acid Pathway in Obesity
NCT03341052
COMPLETED
NA