Fluid Imbalance Clarifies Differences in Fat Estimated with Bio-Electrical Impedance Analysis Compared to Dual-Energy X-ray Absorptiometry and Single Lateral Standing Digital Image Analy-Sis

2. Materials and Methods

2.1. Participants

We recruited healthy adult Caucasian women and men aged 19 to 64 y using advertisements and word of mouth to participate in this study, which was conducted at the Eubion Medical Center and Tor Vergata University, in Rome, Italy. Prospective participants underwent a clinical examination and completed a health questionnaire to establish the absence of an unhealthy condition prior to participation. Exclusion criteria include metal implants, current treatment for metastatic disease, diabetes, diuretic therapy, or limb loss. This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the University of Tor Vergata. Each participant provided written informed consent prior to participation in any testing.

2.2. Body Composition Assessment

Volunteers, wearing form-fitting clothing without jewelry, came to the laboratory after consuming a light meal and emptying their bladders. Standing height and body weight were determined using standard medical equipment (SECA Stadiometer and SECA 762 scale; Hamburg, Germany).

Body composition estimation included BIA, SLSDI, and DXA administered in random order. Volunteers underwent whole-body BIA testing using a 50 kHz phase-sensitive impedance analyzer that introduced a sinusoidal, constant current (250 µA RMS) (BIVA, EFG 3 Monitor; Akern, Florence, Italy) using a tetrapolar surface electrode placement with paired current-injecting and voltage drop-measuring electrodes (BIAtrodes; Akern, Florence, Italy) separated a minimum of 5 cm and placed on the right wrist and ankle. Volunteers rested supine on a bed without contact with metal for 10 min. This BIA instrument provided direct measurements of resistance (R), reactance (Xc), and phase angle (PhA). The technical accuracy and precision of the BIA instrument were determined with a calibrated precision parallel circuit formed by a 384 Ω (±1%) resistor in parallel with a 780 pF (±2%) capacitor yielding 47 Ω of Xc at 50 kHz.

Body composition was estimated by using the following BI prediction models. Kyle et al. [26] (BIA1):

FFM = 4.104 + 0.518 Ht²/R + 0.231 Wt + 0.130 Xc + 4.229 Sex* [1 for men and 0 for women]

Sun et al. [27] (BIA2):

Males: FFM = -9.88 + 0.65 Ht²/R + 0.26 Wt + 0.02 R

Females: FFM = -11.03 + 0.70 Ht²/R + 0.07 Wt + 0.02 R

Units are FFM (kg), height (Ht)²/R (cm²/Ω), weight (Wt, kg), and Xc (Ω).

We also estimated body composition using the proprietary equations of the BIA instrument manufacturer (Bodygram PLUS; Akern, Florence, Italy) (BIA3). This approach estimates the actual hydration (water content) of the FFM of an individual and uses the measured hydration in lieu of the customary 0.732 steady-state hydration ratio to estimate FFM (Akern Bodygram PRO; Florence, Italy). Fat mass is calculated as the difference between body weight and FFM.

A single SLSDI of each volunteer was obtained using the Fit.Your.Outfit (Pixelcando SL-Spain) smartphone APP implemented with a Cloud-based artificial intelligence (AI). An SLSDI of each volunteer was obtained without regard to background or illumination and transformed into an anonymous silhouette [22]. The individual stood upright with the head positioned in the horizontal plane, arms fully extended alongside the body with feet and legs touching and aligned sagittal to the camera to provide a lateral profile of the body (Figure 1). The smartphone cameras, either iOS- iPhone SE or iPhone 11 with CCD resolutions greater than 50 megapixels, were either pre-positioned on a stable tripod or held by a second individual who directed the handheld smartphone camera with the lens pointed at the middle of the standing height of each study participant. The AI system software automatically scaled all the digital pictures to a single homogeneous resolution of 5 megapixels and removed any background from the subject. The distance from the camera to the individual was 1.8 to 2.1 m with the feedback system of the smartphone APP. The operator downloaded and installed the Fit.Your.Outfit APP, available in iOS and Android from the APP stores, ensures the high technical quality of digital images for analysis to estimate body composition using proprietary software. The adequacy of the technical quality of the photograph is controlled and artifacts are prevented by using built-in sensors of modern smart devices and native libraries to detect specific anatomic nodal points (Figure 1) to identify parallaxes, improper distance from an individual to a camera, and to recognize incorrect arm extension and position, improper alignment of legs and feet, and non-horizonal head position. The quality control protocol provides visual warning instructions to the operator to ensure acquisition of high-quality digital images for analysis.

Precision, determined as coefficients of determination and concordance coefficients for repeated profiles of SLSDI images and FM estimates were 0.996 and 0.997 (p < 0.0001), respectively, with no difference (<0.1 kg) between repeated images.

Reference whole-body composition was determined using DXA, a GE Lunar iDXAncore sn 200278 using software version 14.10.022 (Madison, WI). All participants were positioned supine and scanned within the dimensions of the DXA table. Precision estimates were 1.3% for FM and 0.5% for FFM.

Fluid distribution was evaluated using BIA measurements and bioelectrical impedance vector analysis (BIVA) [28]. This method uses tetrapolar BIA and a 50 kHz phase-sensitive impedance device and provides whole-body, direct serial measurements of resistance (R), reactance (Xc), and phase angle (PhA). One measure was the ratio of ECW to total body water (TBW), ECW%, which was estimated with a proprietary model (Akern BodyGram PLUS; Florence, Italy). We also assessed fluid distribution (ECW/ICW) using PhA, which is inversely related to ECW/ICW [29].

2.3. Statistical Methods

Statistical analyses were performed using SYSTAT version 13 (Systat Corporation; San Jose, CA, USA) and version 19.0.3 (MedCalc Software Bv, Ostend, Belgium). Descriptive data are expressed as mean ± SD. Statistical significance was set at p < 0.05.

Estimates of FM from the smartphone SLSDI and each BIA prediction model were compared with the reference DXA values. Measurement agreement was evaluated with concordance correlation coefficient (CCC) [29], standard error of the estimate (SEE) between DXA and each method. Reference and estimates of body fat values in each sex group were compared separately with a paired t-test. Bland–Altman plot [30,31] was used to determine bias and limits of agreement (LOA) for the SLSDI and BIA methods compared to DXA determinations.

Effects of fluid distribution were evaluated in a sub-group of males with BMI greater than 25 kg/m², the population indicator for overweight and obesity to contrast the effects of differences in adipose tissue with an unpaired t-test. Differences between group vector distributions were determined using Hotelling’s T2 test.

3. Results

Table 1 describes the physical characteristics of the participants and shows the wide ranges of BMI and FM in the groups.

Levels of agreement differed between the reference and candidate methods (Table 2). FM values were similar for SLSDI-estimated and DXA-determined FM for females (24.1 ± 11.3 and 24.3 ± 11.6 kg) and males (18.8 ± 7.2 and 18.8 ± 7.6 kg) with no significant difference between methods (Table 2). All BIA equations underestimated (p<0.0001) FM (1.1 ± 2.4, 3.2 ± 2.4, and 3.6 ± 2.3 kg) in females. One BIA model underestimated FM (2.4 ± 3.7 kg) and two BIA equations overestimated (p<0.0001) FM (1.44 ± 3.9 and 2.9 ± 3.7 kg) in males.

Figure 2 reveals differences between SLSDI and BIA FM estimates compared to DXA. According to McBride [29], SLSDI shows substantial concordance (0.96) in females and moderate concordance in males (0.93) relative to DXA fat mass. Among females, BIA1 showed substantial concordance (0.98) while BIA2 and BIA3 showed moderate concordance (0.94 to 0.93) with DXA. In contrast, all male samples showed poor CCC (< 0.90) between the BIA prediction models and DXA.

For females, BIA, compared to SLSDI, had similar mean absolute error (MAE; 2.2 to 3.6 vs 2.5 kg) and greater MAE in males (3.3 to 3.6 vs 2.3 kg). Mean absolute percent error (MAPE) was less with SLSDI than BIA for females and males (8.6 vs 8.9 to 15% and 11.5 vs 16.9 to 19%, respectively).

The SLSDI predictions of FM for females and males were distributed equivalent with the line of identity with the slopes (0.995 and 0.986) the same as 1, and the intercepts (0.37 and 0.26 kg) not different than 0. The SEE values were 3.0 and 2.9 kg for females and males, respectively. In contrast, the intercepts for the BIA2 and BIA3 (2.3 and 3.4 kg; p<0.0001) prediction models in females and BIA1, BIA2, and BIA3 (2.16, 3.50 and 4.11 kg; p<0.0001) in males were different from 0. The slopes for these lines were variable from 1.0 to 1.08 in females and from 0.82 to 0.99 for males, but not different than 1. The SEE in females (2.3 kg) was less than SEE in males (3.7 kg) (Figure 2).

Figure 3 shows the Bland-Altman plots of SLSDI and BIA compared to DXA. On average, smartphone SLSDI had similar FM values as DXA FM in females and males (0.2 and 0 kg). Compared to DXA, the BIA predictions of FM had significant bias in females (1.1 to 3.6 kg; p<0.0001) and males (2.3 and 2.4 kg; p<0.0001) with one BIA equation overestimating FM (-1.4 kg; p<0.0001). The limits of agreement (LOA) for SLSDI were smaller compared to the BIA FM estimates. The slopes of the lines relating the differences between DXA and individual methods were not different from 0 for SLSDI in the females and males but were different (p<0.0001) than 0 for two of the BIA equations in the females.

We tested the hypothesis that excess fat affected fluid balance in men with BMI>25 kg/m² by comparing male elite male rugby players with men who were non-rugby players (Table 2). Compared to non-rugby men, male rugby players had greater BMI (30.04 ± 3.3 vs 28.7 ± 3.0 kg/m²; p < 0.05) and FFM (84.7 ± 6.6 vs 68.1 ± 10.1 kg; p < 0.0001) with less FM (19.3 ± 6.6 vs 23.3 ± 6.8 kg; p < 0.01), %fat (18.2 ± 4.7 vs 25.4 ± 6.2%; p < 0.0001) and ECW% (36.2 ±2.4 vs 39.54 ± 3.5%; p < 0.001).

Estimates of FM using SLSDI were not different than DXA-determined FM in the rugby and non-rugby men (Table 3). Compared to DXA, one BIA equation overestimated FM (4.1 ± 3.3 kg; p<0.0001) in rugby players and two BIA models underestimated FM (3.7 ± 3.5 and 4.2 ± 3.6 kg; p<0.0001) in non-rugby males. Concordance was moderate between SLSDI and DXA FM and poor for all BIA equations. For both groups, BIA, compared to SLSDI, had a greater MAE (2.6 to 4.8 vs 2.6 kg and 2.7 to 34.3 vs 2.1 kg, respectively) and MAPE (11.3 to 21.4 vs 7.9% and 8.3 to 20.9 vs 10.8%, respectively).

Compared to DXA FM values, the SLSDI FM estimates were distributed with slopes not different than 1 and intercepts not different than 0 whereas BIA predictions differed significantly from the line of identity (Figure 4). CCC values also were greater for SLSDI compared to BIA.

Bland-Altman analysis revealed significant bias and greater variability of data with BIA equations compared to SLSDI (Figure 5 and Table 3). No bias was detected with SLSDI in either group of men. Fat mass was significantly overestimated (4.0 ± 3.3 kg) with one BIA equation in rugby players. One BIA equation underestimated FM (-0.9 ± 3.6 kg) in the rugby males and all BIA equations significantly underestimated FM in the non-rugby players (-3.7 ± 3.5 and -4.2 ± 3.6 kg). The slopes of the lines relating the differences between DXA and individual methods were not different from 0 for SLSDI in both groups but were different (p<0.0001) than 0 for the BIA equations in all male groups. The LOA were greater for all BIA equations compared to SLSDI.

Figure 6 shows the significant group differences (p<0.0001) between the 95% confidence ellipses of men with BMI>25 kg/m². The rugby players had a shorter impedance vector displaced to the left indicating greater conductive mass and structure than the non-rugby males. The larger PhA of the rugby players designates less relative ECW or ECW/ICW compared to the non-athletic men associated with the lesser FM.

4. Discussion

Awareness of the limitations of BMI coupled with interest by clinicians and public health researchers for point-of-contact methods to estimate body adiposity is increasing [15,16,17]. Characteristics such as validity, high reproducibility, mobility, cost-effectiveness, and simplicity for operators focused research on SLSDI and BIA to meet this need. The findings of the present study demonstrate a high level of agreement between SLSDI FM estimates compared to reference DXA FM measurements with significant underestimation of FM with BIA. These results are consistent with other comparative studies of 2DI and BIA relative to reference DXA determinations of body fat [20,21]. However, they provide the first evidence that fluid imbalance contributes to the errors of BIA in estimating body fat.

A few reports speculated that increased body fat could adversely impact the validity of BIA predictions of FFM [24,25,33,34]. The researchers proposed that an expansion of ECW would contribute to an overestimation of FFM but provided no evidence. We evaluated this hypothesis in a subgroup of men with BMI>25 kg/m² with different body composition profiles. Male rugby players, compared to non-rugby playing men, had decreased specific resistivity (R/Ht; 196.98±17.21 vs 237.22±30.97 ohm/m; p<0.0001) and increased FFM (84.7 vs 68.1 kg; p<0.001) with decreased FM (19.3 vs 23.3 kg; p<0.001) and ECW% (36.23±2.4 vs 39.54±3.5%; p<0.001). The decreased specific resistivity, or increased conductivity, reflects greater TBW and FFM in the rugby players. The increased ECW% among the non-rugby men indicates altered fluid distribution in association with the increased body fat and reduced ICW due to less FFM. Increased body fat is associated with an expanded adipose tissue volume that is directly related to an expanded ECW, absolute and relative, previously reported among adults classified as obese using BMI criteria [23,34]. Chemical analyses demonstrated the appreciable average water content of adipose tissue as 15% [35], which contributes to its conductivity but to a lesser degree than muscle. Thus, inter-individual differences in body fat contribute to the variability of ECW in adults.

Raw BIA measurements provide additional support for the finding of increased ECW. The significantly decreased PhA values in the non-rugby men indicate an expansion of ECW based on tracer dilution determinations of fluid volumes and demonstrate an inverse relationship between PhA and ECW/ICW [32]. Also, a decreased PhA has been associated with an expansion of ECW in novice runners with increased risk of acute kidney injury after a marathon [36]. The findings of imbalanced fluid distribution, ECW/ICW, contribute to the concerns regarding the assumption of constant hydration inherent in the application of BIA to estimate FFM and predict body fat [37,38,39].

• The independent variables in the BIA prediction models offer potential sources of error in the prediction of FFM. The common predictors of Ht²/R and R are significantly related to TBW. Thus, an increase of ECW in individuals with excess body fat increases TBW and decreases R, which assuming constant hydration of FFM, can overestimate FFM and thus underestimate FM. The BIA models also include body weight, which is highly correlated with body fat, and can vary depending on environmental, dietary, and physical activity conditions and hence affect hydration. Additionally, the application of BIA prediction equations in groups in whom the original model was not developed can lead to errors. It is well established that body geometry (e.g., limb length, cross-sectional area, and volume) and fluid content (total and distribution) directly affect resistivity and contribute to inter-individual differences in whole body and regional BIA measurements [37,40]. Additionally, regional BIA measurements using different electrode placements, such as foot-to-foot and hand-to-hand, yield discordant estimates of body composition compared to whole-body measurements [41]. These factors contribute to the errors in BIA predictions of body fat using various BIA methods and models and DXA in the present study and other reports [20,21].

Our finding of a high level of agreement between SLSDI and DXA fat measures is consistent with two previous studies that used smartphone digital imaging with machine learning and proprietary prediction models to determine body fat in adults [20,21]. Although these studies utilized standing body positions for 2DI, they differed in body positions in relation to the smartphone camera. Nana et al. [20] used front and side positions with 24 replicate digital images for analysis, and Majmudar et al. [21] employed front and back poses. In contrast, the present study utilized a single lateral pose to obtain body contour silhouettes for analysis. Use of multiple body positions and repetitive digital images resulted in greater variability in body fat estimates (±1.5 %fat) compared to 0 in the present study.

This study has some limitations. The inference of expansion of ECW relies on qualitative assessments using BIA. Future studies should incorporate tracer dilution determinations of ECW and TBW and obtain threshold values at which altered fluid distribution affect errors in estimating body fat using BIA. Additionally, investigators should determine the effect of graded body fat levels on errors relative to criteria reference methods particularly in adults with sarcopenic obesity.

5. Conclusions

Smartphone SLSDI provides highly reliable and accurate estimates of FM comparable to DXA of adults with a wide range of body fat. The accuracy and reproducibility, coupled with the convenience, practicality, and cost-efficiency facilitate an innovative method to assess body composition to enable routine assessment of body fat for health care personnel in many environments and for biomedical researchers outside the laboratory. It surmounts the limitations of BMI as an index of body fat [15,16,17] and provides a novel method to estimate abdominal FM [22]. Moreover, SLSDI image-based FM estimates provide better performances than impedance-based methods seemingly because they are not influenced by variable hydration states.