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.