The accurate assessment of total body and regional body circumferences, volumes, and compositions are critical to monitor physical activity and dietary interventions, as well as accurate disease ...classifications including obesity, metabolic syndrome, sarcopenia, and lymphedema. We assessed body composition and anthropometry estimates provided by a commercial 3-dimensional optical (3DO) imaging system compared to criterion measures.
Participants of the Shape Up! Adults study were recruited for similar sized stratifications by sex, age (18–40, 40–60, >60 years), BMI (under, normal, overweight, obese), and across five ethnicities (non-Hispanic NH Black, NH White, Hispanic, Asian, Native Hawaiian/Pacific Islander). All participants received manual anthropometry assessments, duplicate whole-body 3DO (Styku S100), and dual-energy X-ray absorptiometry (DXA) scans. 3DO estimates provided by the manufacturer for anthropometry and body composition were compared to the criterion measures using concordance correlation coefficient (CCC) and Bland–Altman analysis. Test-retest precision was assessed by root mean square error (RMSE) and coefficient of variation.
A total of 188 (102 female) participants were included. The overall fat free mass (FFM) as measured by DXA (54.1 ± 15.2 kg) and 3DO (55.3 ± 15.0 kg) showed a small mean difference of 1.2 ± 3.4 kg (95% limits of agreement −7.0 to +5.6) and the CCC was 0.97 (95% CI: 0.96–0.98). The CCC for FM was 0.95 (95% CI: 0.94–0.97) and the mean difference of 1.3 ± 3.4 kg (95% CI: −5.5 to +8.1) reflected the difference in FFM measures. 3DO anthropometry and body composition measurements showed high test-retest precision for whole body volume (1.1 L), fat mass (0.41 kg), percent fat (0.60%), arm and leg volumes, (0.11 and 0.21 L, respectively), and waist and hip circumferences (all <0.60 cm). No group differences were observed when stratified by body mass index, sex, or race/ethnicity.
The anthropometric and body composition estimates provided by the 3DO scanner are precise and accurate to criterion methods if offsets are considered. This method offers a rapid, broadly available, and automated method of body composition assessment regardless of body size. Further studies are recommended to examine the relationship between measurements obtained by 3DO scans and metabolic health in healthy and clinical populations.
Novel advancements in wearable technologies include continuous measurement of body composition via smart watches. The accuracy and stability of these devices are unknown.
This study evaluated smart ...watches with integrated bioelectrical impedance analysis (BIA) sensors for their ability to measure and monitor changes in body composition.
Participants recruited across BMIs received duplicate body composition measures using 2 wearable bioelectrical impedance analysis (W-BIA) model smart watches in sitting and standing positions, and multiple versions of each watch were used to evaluate inter- and intramodel precision. Duplicate laboratory-grade octapolar bioelectrical impedance analysis (8-BIA) and criterion DXA scans were acquired to compare estimates between the watches and laboratory methods. Test-retest precision and least significant changes assessed the ability to monitor changes in body composition.
Of 109 participants recruited, 75 subjects completed the full manufacturer-recommended protocol. No significant differences were observed between W-BIA watches in position or between watch models. Significant fat-free mass (FFM) differences (P < 0.05) were observed between both W-BIA and 8-BIA when compared to DXA, though the systematic biases to the criterion were correctable. No significant difference was observed between the W-BIA and the laboratory-grade BIA technology for FFM (55.3 ± 14.5 kg for W-BIA versus 56.0 ± 13.8 kg for 8-BIA; P > 0.05; Lin's concordance correlation coefficient = 0.97). FFM was less precise on the watches than DXA {CV, 0.7% root mean square error (RMSE) = 0.4 kg versus 1.3% (RMSE = 0.7 kg) for W-BIA}, requiring more repeat measures to equal the same confidence in body composition changes over time as DXA.
After systematic correction, smart-watch BIA devices are capable of stable, reliable, and accurate body composition measurements, with precision comparable to but lower than that of laboratory measures. These devices allow for measurement in environments not accessible to laboratory systems, such as homes, training centers, and geographically remote locations.
Deuterium oxide (D2O) dilution is the criterion method for total body water (TBW) measurement, but results may vary depending on the specimen type, analysis method, and analyzing laboratory. ...Bioelectrical impedance (BIA) estimates TBW, but results may vary by device make and model.
We investigated the accuracy and precision of TBW estimates and how measurement conditions affected the accuracy of body composition using multicompartment body composition models.
Eighty collegiate athletes received duplicate TBW measures acquired from 3 BIA devices (S10, SFB7, and SOZO) and from unique D2O combinations of specimen type (saliva, urine), analysis methodology Fourier transform infrared spectrophotometry (FTIR), isotope-ratio mass spectrometry (IRMS), and 3 different laboratories. TBW measures were substituted into 2-compartment (2C) and 5-compartment (5C) body composition models. Criterion measures were compared using Lin's concordance correlation coefficient cutoff of poor (<0.90), moderate (0.90-0.95), substantial (0.95-0.99), and almost perfect (>0.99).
Fifty-one participants (26 female) completed the protocol. Using IRMS saliva as the criterion TBW, all other measures produced a substantial or almost perfect agreement, except for SFB7 (poor) and SOZO (moderate). The 2C body composition measures using D2O and BIA produced poor agreement except for moderate agreement for lab 3 FTIR saliva. The 5C body composition measures using D2O produced a substantial agreement, whereas the BIA device S10 and SOZO had a moderate agreement, while the SFB7 had a poor agreement to the criterion. Test-retest precision varied between techniques from 0.3% to 1.2% for TBW.
Small differences in TBW measurement led to significant differences in 2C models. The 5C models partially mitigate differences seen in 2C models when different TBW measures are used. Interchanging TBW measures in multicompartment models can be problematic and should be performed with these considerations.
Objective
The National Health and Nutrition Examination Survey (NHANES) characterizes body composition representative of the US population using dual‐energy x‐ray absorptiometry (DXA) scans. These ...population‐level trends of abdominal subcutaneous and visceral adipose tissue (SAT and VAT) are useful for identifying measures associated with increased disease risk. Recently, VAT and SAT data collected by Hologic DXA in NHANES were published online; however, there are known differences in the absolute calibration of DXA systems by make. The purpose of this study was to create reference tables suitable for calculating z scores and percentile values for GE HealthCare (GEHC) DXA systems.
Methods
DXA scans were acquired on participants aged 8 to 59 years using Hologic systems. DXA measures were converted to GEHC and described using the least median squares curve fitting method in pediatrics (aged <20 years) and adults (aged 20–59 years).
Results
A total of 11,972 adults and 7298 pediatrics were included for this analysis. Adult and pediatric curves were generated by sex and by ethnicity (non‐Hispanic White, non‐Hispanic Black, Hispanic, Asian, Other) and were derived as a function of age.
Conclusions
These results show the ability to generate VAT and SAT reference data for GEHC systems using Hologic DXA data representative of the US youth and adult population.
Objective
This study examined whether body shape and composition obtained by three‐dimensional optical (3DO) scanning improved the prediction of metabolic syndrome (MetS) prevalence compared with BMI ...and demographics.
Methods
A diverse ambulatory adult population underwent whole‐body 3DO scanning, blood tests, manual anthropometrics, and blood pressure assessment in the Shape Up! Adults study. MetS prevalence was evaluated based on 2005 National Cholesterol Education Program criteria, and prediction of MetS involved logistic regression to assess (1) BMI, (2) demographics‐adjusted BMI, (3) 85 3DO anthropometry and body composition measures, and (4) BMI + 3DO + demographics models. Receiver operating characteristic area under the curve (AUC) values were generated for each predictive model.
Results
A total of 501 participants (280 female) were recruited, with 87 meeting the criteria for MetS. Compared with the BMI model (AUC = 0.819), inclusion of age, sex, and race increased the AUC to 0.861, and inclusion of 3DO measures further increased the AUC to 0.917. The overall integrated discrimination improvement between the 3DO + demographics and the BMI model was 0.290 (p < 0.0001) with a net reclassification improvement of 0.214 (p < 0.0001).
Conclusions
Body shape measures from an accessible 3DO scan, adjusted for demographics, predicted MetS better than demographics and/or BMI alone. Risk classification in this population increased by 29% when using 3DO scanning.
The multicompartment approach to body composition modeling provides a more precise quantification of body compartments in healthy and clinical populations. We sought to develop and validate a ...simplified and accessible multicompartment body composition model using 3-dimensional optical (3DO) imaging and bioelectrical impedance analysis (BIA).
Samples of adults and collegiate-aged student-athletes were recruited for model calibration. For the criterion multicompartment model (Wang-5C), participants received measures of scale weight, body volume (BV) via air displacement, total body water (TBW) via deuterium dilution, and bone mineral content (BMC) via dual energy x-ray absorptiometry. The candidate model (3DO-5C) used stepwise linear regression to derive surrogate measures of BV using 3DO, TBW using BIA, and BMC using demographics. Test-retest precision of the candidate model was assessed via root mean square error (RMSE). The 3DO-5C model was compared to criterion via mean difference, concordance correlation coefficient (CCC), and Bland-Altman analysis. This model was then validated using a separate dataset of 20 adults.
67 (31 female) participants were used to build the 3DO-5C model. Fat-free mass (FFM) estimates from Wang-5C (60.1 ± 13.4 kg) and 3DO-5C (60.3 ± 13.4 kg) showed no significant mean difference (-0.2 ± 2.0 kg; 95 % limits of agreement LOA -4.3 to +3.8) and the CCC was 0.99 with a similar effect in fat mass that reflected the difference in FFM measures. In the validation dataset, the 3DO-5C model showed no significant mean difference (0.0 ± 2.5 kg; 95 % LOA -3.6 to +3.7) for FFM with almost perfect equivalence (CCC = 0.99) compared to the criterion Wang-5C. Test-retest precision (RMSE = 0.73 kg FFM) supports the use of this model for more frequent testing in order to monitor body composition change over time.
Body composition estimates provided by the 3DO-5C model are precise and accurate to criterion methods when correcting for field calibrations. The 3DO-5C approach offers a rapid, cost-effective, and accessible method of body composition assessment that can be used broadly to guide nutrition and exercise recommendations in athletic settings and clinical practice.
Athletes vary in hydration status due to ongoing training regimes, diet demands, and extreme exertion. With water being one of the largest body composition compartments, its variation can cause ...misinterpretation of body composition assessments meant to monitor strength and training progress. In this study, we asked what accessible body composition approach could best quantify body composition in athletes with a variety of hydration levels.
The Da Kine Study recruited collegiate and intramural athletes to undergo a variety of body composition assessments including air-displacement plethysmography (ADP), deuterium-oxide dilution (D2O), dual-energy X-ray absorptiometry (DXA), underwater-weighing (UWW), 3D-optical (3DO) imaging, and bioelectrical impedance (BIA). Each of these methods generated 2- or 3-compartment body composition estimates of fat mass (FM) and fat-free mass (FFM) and was compared to equivalent measures of the criterion 6-compartment model (6CM) that accounts for variance in hydration. Body composition by each method was used to predict abdominal and thigh strength, assessed by isokinetic/isometric dynamometry.
In total, 70 (35 female) athletes with a mean age of 21.8 ± 4.2 years were recruited. Percent hydration (Body Water6CM/FFM6CM) had substantial variation in both males (63–73 %) and females (58–78 %). ADP and DXA FM and FF M had moderate to substantial agreement with the 6C model (Lin’s Concordance Coefficient CCC = 0.90–0.95) whereas the other measures had lesser agreement (CCC <0.90) with one exception of 3DO FFM in females (CCC = 0.91). All measures of FFM produced excellent precision with %CV < 1.0 %. However, FM measures in general had worse precision (% CV < 2.0 %). Increasing quartiles (significant p < 0.001 trend) of 6CM FFM resulted in increasing strength measures in males and females. Moreover, the stronger the agreement between the alternative methods to the 6CM, the more robust their correlation with strength, irrespective of hydration status.
The criterion 6CM showed the best association to strength regardless of the hydration status of the athletes for both males and females. Simpler methods showed high precision for both FM and FFM and those with the strongest agreement to the 6CM had the highest strength associations.
This study compared various body composition analysis methods in 70 athletes with varying states of hydration to the criterion 6-compartment model and assessed their relationship to muscle strength. The results showed that accurate and precise estimates of body composition can be determined in athletes, and a more accurate body composition measurement produces better strength estimates. The best laboratory-based techniques were air displacement plethysmography and dual-energy x-ray absorptiometry, while the commercial methods had moderate-poor agreement. Prioritizing accurate body composition assessment ensures better strength estimates in athletes.
Body shape, an intuitive health indicator, is deterministically driven by body composition. We developed and validated a deep learning model that generates accurate dual-energy X-ray absorptiometry ...(DXA) scans from three-dimensional optical body scans (3DO), enabling compositional analysis of the whole body and specified subregions. Previous works on generative medical imaging models lack quantitative validation and only report quality metrics.
Our model was self-supervised pretrained on two large clinical DXA datasets and fine-tuned using the Shape Up! Adults study dataset. Model-predicted scans from a holdout test set were evaluated using clinical commercial DXA software for compositional accuracy.
Predicted DXA scans achieve R
of 0.73, 0.89, and 0.99 and RMSEs of 5.32, 6.56, and 4.15 kg for total fat mass (FM), fat-free mass (FFM), and total mass, respectively. Custom subregion analysis results in R
s of 0.70-0.89 for left and right thigh composition. We demonstrate the ability of models to produce quantitatively accurate visualizations of soft tissue and bone, confirming a strong relationship between body shape and composition.
This work highlights the potential of generative models in medical imaging and reinforces the importance of quantitative validation for assessing their clinical utility.
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