Bioelectrical impedance techniques are easy to use and portable tools for assessing body composition. While measurements vary according to standing vs supine position in adults, and fasting and ...bladder voiding have been proposed as additional important influences, these have not been assessed in young children. Therefore, the influence of position, fasting, and voiding on bioimpedance measurements was examined in children. Bioimpedance measurements (ImpediMed SFB7) were made in 50 children (3.38 years). Measurements were made when supine and twice when standing (immediately on standing and after four minutes). Impedance and body composition were compared between positions, and the effect of fasting and voiding was assessed. Impedance varied between positions, but body composition parameters other than fat mass (total body water, intra- and extra-cellular water, fat-free mass) differed by less than 5%. There were no differences according to time of last meal or void. Equations were developed to allow standing measurements of fat mass to be combined with supine measurements. In early childhood, it can be difficult to meet requirements for fasting, voiding, and lying supine prior to measurement. This study provides evidence to enable standing and supine bioimpedance measurements to be combined in cohorts of young children.
Summary
Bioelectrical impedance analysis (BIA) is an easy to use, portable tool, but the accuracy of the technique in infants and young children (<24 months) remains unclear. A systematic literature ...review was conducted to identify studies that have developed and validated BIA equations in this age group. MEDLINE, Scopus, EMBASE, and CENTRAL were searched for relevant literature published up until June 30, 2020, using terms related to bioelectrical impedance, body composition, and paediatrics. Two reviewers independently screened studies for eligibility, resulting in 15 studies that had developed and/or validated equations. Forty‐six equations were developed and 34 validations were conducted. Most equations were developed in young infants (≤6 months), whereas only seven were developed among older infants and children (6‐24 months). Most studies were identified as having a high risk of bias, and only a few included predominantly healthy children born at term. Using the best available evidence, BIA appears to predict body composition at least as well as other body composition tools; however, among younger infants BIA may provide little benefit over anthropometry‐based prediction equations. Currently, none of the available equations can be recommended for use in research or in clinical practice.
There has been increasing interest in understanding body composition in early life and factors that may influence its evolution. While several technologies exist to measure body composition in ...infancy, the equipment is typically large, and thus not readily portable, is expensive, and requires a qualified operator. Bioelectrical impedance analysis shows promise as an inexpensive, portable, and easy to use tool. Despite the technique being widely used to assess body composition for over 35 years, it has been seldom used in infancy. This may be related to the evolving nature of the fat-free mass compartment during this period. Nonetheless, a number of factors have been identified that may influence bioelectrical impedance measurements, which, when controlled for, may result in more accurate measurements. Despite this, questions remain in infants regarding the optimal size and placement of electrodes, the standardization of normal hydration, and the influence of body position on the distribution of water throughout the body. The technology requires further evaluation before being considered as a suitable tool to assess body composition in infancy.
This study aimed to cross-calibrate body composition measures from the GE Lunar Prodigy and GE Lunar iDXA in a cohort of young children. 28 children (mean age 3.4 years) were measured on the iDXA ...followed by the Prodigy. Prodigy scans were subsequently reanalysed using enCORE v17 enhanced analysis ("Prodigy enhanced"). Body composition parameters were compared across three evaluation methods (Prodigy, Prodigy enhanced, iDXA), and adjustment equations were developed. There were differences in the three evaluation methods for all body composition parameters. Body fat percentage (%BF) from the iDXA was approximately 1.5-fold greater than the Prodigy, whereas bone mineral density (BMD) was approximately 20% lower. Reanalysis of Prodigy scans with enhanced software attenuated these differences (%BF: - 5.2% 95% CI - 3.5, - 6.8; and BMD: 1.0% 95% CI 0.0, 1.9), although significant differences remained for all parameters except total body less head (TBLH) total mass and TBLH BMD, and some regional estimates. There were large differences between the Prodigy and iDXA, with these differences related both to scan resolution and software. Reanalysis of Prodigy scans with enhanced analysis resulted in body composition values much closer to those obtained on the iDXA, although differences remained. As manufacturers update models and software, researchers and clinicians need to be aware of the impact this may have on the longitudinal assessment of body composition, as results may not be comparable across devices and software versions.
Nutritional intervention preconception and throughout pregnancy has been proposed as an approach to promoting healthy postnatal weight gain in the offspring but few randomised trials have examined ...this.
Measurements of weight and length were obtained at multiple time points from birth to 2 years among 576 offspring of women randomised to receive preconception and antenatally either a supplement containing myo-inositol, probiotics, and additional micronutrients (intervention) or a standard micronutrient supplement (control). We examined the influence on age- and sex-standardised BMI at 2 years (WHO standards, adjusting for study site, sex, maternal parity, smoking and pre-pregnancy BMI, and gestational age), together with the change in weight, length, BMI from birth, and weight gain trajectories using latent class growth analysis.
At 2 years, there was a trend towards lower mean BMI among intervention offspring (adjusted mean difference aMD - 0.14 SD 95% CI 0.30, 0.02, p = 0.09), and fewer had a BMI > 95th percentile (i.e. > 1.65 SD, 9.2% vs 18.0%, adjusted risk ratio aRR 0.51 95% CI 0.31, 0.82, p = 0.006). Longitudinal data revealed that intervention offspring had a 24% reduced risk of experiencing rapid weight gain > 0.67 SD in the first year of life (21.9% vs 31.1%, aRR 0.76 95% CI 0.58, 1.00, p = 0.047). The risk was likewise decreased for sustained weight gain > 1.34 SD in the first 2 years of life (7.7% vs 17.1%, aRR 0.55 95% CI 0.34, 0.88, p = 0.014). From five weight gain trajectories identified, there were more intervention offspring in the "normal" weight gain trajectory characterised by stable weight SDS around 0 SD from birth to 2 years (38.8% vs 30.1%, RR 1.29 95% CI 1.03, 1.62, p = 0.029).
Supplementation with myo-inositol, probiotics, and additional micronutrients preconception and in pregnancy reduced the incidence of rapid weight gain and obesity at 2 years among offspring. Previous reports suggest these effects will likely translate to health benefits, but longer-term follow-up is needed to evaluate this.
ClinicalTrials.gov, NCT02509988 (Universal Trial Number U1111-1171-8056). Registered on 16 July 2015.
Maternal obesity is associated with an increased risk of large-for-gestational-age births and childhood obesity. However, evidence on its potential associations with long-term offspring body ...composition remains limited. This prospective cohort study examined associations between maternal body mass index (BMI) during pregnancy and body composition in the young adult offspring.
Participants were the offspring from a birth cohort in Chiang Mai (Thailand). Maternal BMI was assessed at the first antenatal clinic visit (≤24 weeks of gestation) in 1989-1990. In 2010-2011, we followed up the offspring at approximately 20 years of age, assessing their body composition using whole-body dual-energy X-ray absorptiometry (DXA) scans. Associations between maternal BMI and offspring body composition were explored using unadjusted and adjusted analyses.
We assessed 391 young adults (55% were females). Higher maternal BMI was associated with increased offspring fat mass and lean mass. In adjusted analyses, offspring of mothers with overweight/obesity exhibited total body fat percentages 1.5 (95% CI 0.1, 2.9;
= 0.032) and 2.3 (95% CI 0.2, 4.5;
= 0.036) percentage points higher than offspring of normal-weight and underweight mothers, respectively. Fat mass index was similarly higher: 0.9 kg/m
(95% CI 0.3, 1.5 kg/m
;
= 0.002) and 1.4 kg/m
(95% CI 0.5, 2.3 kg/m
;
= 0.002), respectively. However, no differences in visceral adiposity were detected.
Higher maternal BMI during pregnancy was associated with increased adiposity in young adult offspring. Our findings suggest that the cross-generational transmission of maternal obesity-related traits is associated with increased offspring adiposity in the long term.
Bioelectrical impedance analysis (BIA) is widely used to measure body composition but has not been adequately evaluated in infancy. Prior studies have largely been of poor quality, and few included ...healthy term-born offspring, so it is unclear if BIA can accurately predict body composition at this age.
This study evaluated impedance technology to predict fat-free mass (FFM) among a large multi-ethnic cohort of infants from the United Kingdom, Singapore, and New Zealand at ages 6 weeks and 6 months (
= 292 and 212, respectively).
Using air displacement plethysmography (PEA POD) as the reference, two impedance approaches were evaluated: (1) empirical prediction equations; (2) Cole modeling and mixture theory prediction. Sex-specific equations were developed among ∼70% of the cohort. Equations were validated in the remaining ∼30% and in an independent University of Queensland cohort. Mixture theory estimates of FFM were validated using the entire cohort at both ages.
Sex-specific equations based on weight and length explained 75-81% of FFM variance at 6 weeks but only 48-57% at 6 months. At both ages, the margin of error for these equations was 5-6% of mean FFM, as assessed by the root mean squared errors (RMSE). The stepwise addition of clinically-relevant covariates (i.e., gestational age, birthweight SDS, subscapular skinfold thickness, abdominal circumference) improved model accuracy (i.e., lowered RMSE). However, improvements in model accuracy were not consistently observed when impedance parameters (as the impedance index) were incorporated instead of length. The bioimpedance equations had mean absolute percentage errors (MAPE) < 5% when validated. Limits of agreement analyses showed that biases were low (< 100 g) and limits of agreement were narrower for bioimpedance-based than anthropometry-based equations, with no clear benefit following the addition of clinically-relevant variables. Estimates of FFM from BIS mixture theory prediction were inaccurate (MAPE 11-12%).
The addition of the impedance index improved the accuracy of empirical FFM predictions. However, improvements were modest, so the benefits of using bioimpedance in the field remain unclear and require further investigation. Mixture theory prediction of FFM from BIS is inaccurate in infancy and cannot be recommended.
. Prediction of body composition from bioimpedance spectroscopy (BIS) measurements using mixture theory-based biophysical modelling invokes a factor (
) to account for differing body geometry (or ...proportions) between individuals. To date, a single constant value is commonly used. The aim of this study was to investigate variation in
across individuals and to develop a procedure for estimating an individualized
value.
Publicly available body dimension data, primarily from the garment industry, were used to calculate
values for individuals of varying body sizes across the life-span. The 3D surface relationship between weight, height and
, was determined and used to create look-up tables to enable estimation of
in individuals based on height and weight. The utility of the proposed method was assessed by comparing fat-free mass predictions from BIS using either a constant
value or the individualized value.
Computed
values were well fitted to height and weight by a 3D surface (
= 0.988). Body composition was predicted more accurately compared to reference methods when using individualized
than a constant value in infants and children but improvement in prediction was less in adults particularly those with high body mass index.
Prediction of body composition from BIS and mixture theory is improved by using an individualized body proportion factor in those of small body habitus, e.g. children. Improvement is small in adults or non-existent in those of large body size. Further improvements may be possible by incorporating a factor to account for trunk size, i.e. waist circumference.
Bioimpedance devices are practical for measuring body composition in preschool children, but their application is limited by the lack of validated equations.
To develop and validate fat-free mass ...(FFM) bioimpedance prediction equations among New Zealand 3.5-year olds, with dual-energy X-ray absorptiometry (DXA) as the reference method.
Bioelectrical impedance spectroscopy (SFB7, ImpediMed) and DXA (iDXA, GE Lunar) measurements were conducted on 65 children. An equation incorporating weight, sex, ethnicity, and impedance was developed and validated. Performance was compared with published equations and mixture theory prediction.
The equation developed in ~70% (n = 45) of the population (FFM kg = 1.39 + 0.30 weight kg + 0.39 length
/resistance at 50 kHz cm
/Ω + 0.30 sex M = 1/F = 0 + 0.28 ethnicity 1 = Asian/0 = non-Asian) explained 88% of the variance in FFM and predicted FFM with a root mean squared error of 0.39 kg (3.4% of mean FFM). When internally validated (n = 20), bias was small (40 g, 0.3% of mean FFM), with limits of agreement (LOA) ±7.6% of mean FFM (95% LOA: -0.82, 0.90 kg). Published equations evaluated had similar LOA, but with marked bias (>12.5% of mean FFM) when validated in our cohort, likely due to DXA differences. Of mixture theory methods assessed, the SFB7 inbuilt equation with personalized body geometry values performed best. However, bias and LOA were larger than with the empirical equations (-0.43 kg 95% LOA: -1.65, 0.79, p < 0.001).
We developed and validated a bioimpedance equation that can accurately predict FFM. Further external validation of the equation is required.
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