Predictive equation for assessing appendicular lean soft tissue mass using bioelectric impedance analysis in older adults: Effect of body fat distribution

Problems and Opportunities in the use of Bioelectrical Impedance Analysis for Assessing Body Composition During Ketogenic Diets: A Scoping Review

PURPOSE OF THE REVIEW

The use of bioelectrical impedance analysis (BIA) for monitoring body composition during the ketogenic diet has experienced a rapid surge. This scoping review aimed to assess the validity of procedures applying BIA in the ketogenic diet and to suggest best practices for optimizing its utilization.

RECENT FINDINGS

We conducted a systematic scoping review of peer-reviewed literature involving BIA for assessing body composition in individuals adhering to a ketogenic diet. Searches of international databases yielded 1609 unique records, 72 of which met the inclusion criteria and were reviewed. Thirty-five studies used foot-to-hand technology, 34 used standing position technology, while 3 did not declare the technology used. Raw bioelectrical parameters were reported in 21 studies. A total of 196 body mass components were estimated, but predictive equations were reported in only four cases. Most research on BIA during ketogenic diets did not report the equations used for predicting body composition, making it impossible to assess the validity of BIA outputs. Furthermore, the exceedingly low percentage of studies reporting and analyzing raw data makes it challenging to replicate methodologies in future studies, highlighting that BIA is not being utilized to its full potential. There is a need for more precise technology and device characteristics descriptions, full report of raw bioelectrical data, and predictive equations utilized. Moreover, evaluating raw data through vectorial analysis is strongly recommended. Eventually, we suggest best practices to enhance BIA outcomes during ketogenic diets.

High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review

The appropriate use of predictive equations in estimating body composition through bioelectrical impedance analysis (BIA) depends on the device used and the subject's age, geographical ancestry, healthy status, physical activity level and sex. However, the presence of many isolated predictive equations in the literature makes the correct choice challenging, since the user may not distinguish its appropriateness. Therefore, the present systematic review aimed to classify each predictive equation in accordance with the independent parameters used. Sixty-four studies published between 1988 and 2023 were identified through a systematic search of international electronic databases. We included studies providing predictive equations derived from criterion methods, such as multi-compartment models for fat, fat-free and lean soft mass, dilution techniques for total-body water and extracellular water, total-body potassium for body cell mass, and magnetic resonance imaging or computerized tomography for skeletal muscle mass. The studies were excluded if non-criterion methods were employed or if the developed predictive equations involved mixed populations without specific codes or variables in the regression model. A total of 106 predictive equations were retrieved; 86 predictive equations were based on foot-to-hand and 20 on segmental technology, with no equations used the hand-to-hand and leg-to-leg. Classifying the subject's characteristics, 19 were for underaged, 26 for adults, 19 for athletes, 26 for elderly and 16 for individuals with diseases, encompassing both sexes. Practitioners now have an updated list of predictive equations for assessing body composition using BIA. Researchers are encouraged to generate novel predictive equations for scenarios not covered by the current literature.Registration code in PROSPERO: CRD42023467894.

New bioelectrical impedance vector references and phase angle centile curves in 4,367 adults: The need for an urgent update after 30 years

BACKGROUND & AIMS

The bioelectrical impedance vector analysis (BIVA) represents a qualitative analysis of body composition. The vector, defined by resistance (R) and reactance (Xc) standardized by stature, can be evaluated compared to the 50%,75%, and 95% tolerance ellipses representative of the reference populations. The tolerance ellipses for healthy adults have been provided in 1995 and were developed by mixing underage, adult, and elderly subjects, possibly misrepresenting the actual adult population. The current multicentric, cross-sectional study aimed to provide new tolerance ellipses specific for the general adult population and as a secondary aim to present centile curves for the bioelectrical phase angle.

METHODS

R, Xc, and phase angle were measured in 2137 and 2230 males and females using phase-sensitive foot-to-hand analyzers at 50 kHz. A minimum of 35 subjects were included for each sex and age category from 18 to 65 years.

RESULTS

The new mean vectors showed a leftward shift on the R-Xc graph with respect to the former reference values (males: F = 75.3; p < 0.001; females: F = 36.6, p < 0.001). The results provided new 3rd, 5th, 10th, 25th, 50th, 75th, 90th, 95th, and 97th percentile curves for phase angle, identifying time point phases of decrement (males: -0.03° per year at 33.0-51.0 years and -0.05° per year after 51 years; females: -0.03° per year from 37.2 to 57.9 years).

CONCLUSIONS

Compared to the original references, the new data are characterized by a different distribution within the R-Xc graph with a higher phase angle. Thirty years after the BIVA invention, the current study presents new tolerance ellipses and phase angle reference values for the adult population.

Development and validation of bioelectrical impedance prediction equations estimating regional lean soft tissue mass in middle-aged adults

BACKGROUND/OBJECTIVES

Bioelectrical impedance (BIA) whole-body and regional raw parameters have been used to develop prediction models to estimate whole-body lean soft tissue (LSTM), with less attention being given to the development of models for regional LSTM. Therefore, we aimed to develop and validate BIA-derived equations predicting regional LSTM against dual x-ray absorptiometry (DXA) in healthy adults.

SUBJECTS/METHODS

149 adults were included in this cross-sectional investigation. Whole-body and regional LSTM were assessed by DXA, and raw bioelectrical parameters of distinct body regions were measured using a 50 kHz phase sensitive BIA analyzer. BIA-derived equations were developed using a stepwise multiple linear regression approach in 2/3 of the sample and cross-validated in the remaining sample.

RESULTS

Slopes and intercepts of predicted LSTM and DXA measured LSTM did not differ from 1 and 0, respectively, for each region (p ≥ 0.05), with the exception for the trunk (p < 0.05). The BIA-derived equations exhibited a strong relationship (p < 0.001) between the predicted and measured LSTM for each of the following body regions: right and left arms (R = 0.94; R = 0.96), right and left legs (R = 0.88; R = 0.88), upper body (R = 0.96), lower body (R = 0.89), right and left sides of the body (R = 0.94; R = 0.94), and trunk (R = 0.90). Agreement analyses revealed no associations between the differences and the means of the predicted and DXA-derived LSTM.

CONCLUSION

The developed BIA-derived equations provide a valid estimate of regional LSTM in middle-aged healthy adults, representing a cost-effective and time-efficient alternative to DXA for the assessment and identification of LSTM imbalances in both clinical and sport-specific contexts.

External validation of BIA equations to estimate appendicular skeletal muscle mass in older adults: Importance of the bias analysis and derivation of correction factors to achieve agreement

There are several equations based on bioelectrical impedance analysis (BIA) to estimate with high precision appendicular skeletal muscle mass (ASM). However, most of the external validation studies have reported that these equations are inaccurate or biased when applied to different populations. Furthermore, none of the published studies has derived correction factors (CFs) in samples of community-dwelling older adults, and none of the published studies have assessed the influence of the dual-energy X-ray absorptiometry (DXA) model on the validation process. This study assessed the agreement between six BIA equations and DXA to estimate ASM in non-Caucasian older adults considering the DXA model and proposed a CF for three of them. This analysis included 547 non-institutionalized subjects over 60 years old from the northwest of Mexico who were physically independent and without cognitive impairment: 192 subjects were measured using DXA Hologic, while 355 were measured by DXA Lunar. The agreement between each of the equations and DXA was tested considering the DXA model used as a reference method for the design of each equation, using the Bland and Altman procedure, a paired t test, and simple linear regression as objective tests. This process was supported by the differences reported in the literature and confirmed in a subsample of 70 subjects measured with both models. Only six published BIA equations were included. The results showed that four equations overestimated ASM_DXA, and two underestimated it (p < 0.001, 95% CI for Kim's equation:−5.86-−5.45, Toselli's:−0.51-−0.15, Kyle's: 1.43–1.84, Rangel-Peniche's: 0.32–0.74, Sergi's: 0.83–1.23, and Yoshida's: 4.16–4.63 kg). However, Toselli's, Kyle's and Rangel-Peniche's equations were the only ones that complied with having a homogeneous bias. This finding allowed the derivation of CFs, which consisted of subtracting or adding the mean of the differences from the original equation. After estimating ASM applying the respective CF, the new ASM estimations showed no significant bias and its distribution remained homogeneously distributed. Therefore, agreement with DXA in the sample of non-Caucasian was achieved. Adding valid CFs to some BIA equations allowed to reduce the bias of some equations, making them valid to estimate the mean values of ASM at group level.

Two-megahertz impedance index prediction equation for appendicular lean mass in Korean older people

Background

Whole-body bioelectrical impedance analysis (BIA) has been accepted as an indirect method to estimate appendicular lean mass (ALM) comparable to dual-energy X-ray absorptiometry (DXA). However, single or limited frequencies currently used for these estimates may over or under-estimate ALM. Accordingly, there is a need to measure the impedance parameter with appendicular lean-specific across multiple frequencies to more accurately estimate ALM. We aimed to validate muscle-specific frequency BIA equation for ALM using multifrequency BIA (MF-BIA) with DXA as the reference.

Methods

195 community-dwelling Korean older people (94 men and 101 women) aged 70 ~ 92y participated in this study. ALM was measured by DXA and bioimpedance measures at frequencies of 5 kHz ~ 3 MHz were assessed for independent predictive variables. Regression analyses were used to find limb-specific frequencies of bioimpedance, to develop the ALM equations and to conduct the internal cross-validation. The six published equations and the final equation of MF-BIA were externally cross-validated.

Results

195 participants completed the measurements of MF-BIA and DXA. Using bivariate regression analysis, the 2 MHz impedance index explained R² = 91.5% of variability (P < 0.001) in ALM and predictive accuracy of standard error of estimate (SEE) was 1.0822 kg ALM (P < 0.001). Multiple stepwise regression analysis obtained in the development group had an adjusted R² of 9.28% (P < 0.001) and a SEE of 0.97 kg ALM. The cross-validation group had no significant difference between the measured ALM and the predicted ALM (17.8 ± 3.9 kg vs. 17.7 ± 3.8 kg, P = .486) with 93.1% of R² (P < 0.001) and 1.00 kg ALM of total error. The final regression equation was as follows: ALM = 0.247ZI_@2 MHz + 1.254SEX_M1F0 + 0.067Xc_@5 kHz + 1.739 with 93% of R² (P < 0.001), 0.97 kg ALM of SEE (Subjective Rating as “excellent” for men and “very good” for women). In the analysis of the diagnostic level for sarcopenia of the final regression, the overall agreement was 94.9% (k = 0.779, P < 0.001) with 71.4% of sensitivity, 98.8% of specificity, 91.3 of positive prediction value and 95.3% of negative prediction value.

Conclusion

The newly developed appendicular lean-specific high-frequency BIA prediction equation has a high predictive accuracy, sensitivity, specificity, and agreement for both individual and group measurements. Thus, the high-frequency BIA prediction equation is suitable not only for epidemiological studies, but also for the diagnosis of sarcopenia in clinical settings.