Predictive equations for central obesity via anthropometrics, stereovision imaging and MRI in adults

Strengths and Limitations of BMI in the Diagnosis of Obesity: What is the Path Forward?

PURPOSE OF REVIEW

This review aims to discuss strengths and limitations of body mass index (BMI) in diagnosing obesity, the use of alternative anthropometric measurements, and potential new technology that may change the future of obesity diagnosis and management.

RECENT FINDINGS

The diagnosis of obesity requires the anthropometric assessment of adiposity. In clinical settings, this should include BMI with confirmation that elevated BMI represents excess adiposity and a measure of fat distribution (i.e., waist circumference (WC), waist to height ratio (WHtR), or WC divided by height0.5 (WHR.5R). Digital anthropometry and bioelectric impedance (BIA) can estimate fat distribution and be feasibly employed in the clinic. In addition, the diagnosis should include a clinical component assessing the presence and severity of weight-related complications. As anthropometric measures used in the diagnosis of obesity, BMI is generally sufficient if confirmed to represent excess adiposity, and there are advantages to the use of WHtR over WC to assess fat distribution. BIA and digital anthropometry have the potential to provide accurate measures of fat mass and distribution in clinical settings. There should also be a clinical evaluation for the presence and severity of obesity complications that can be used to stage the disease.

Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study

BACKGROUND

3D laser-based photonic scanners are increasingly used in health studies to estimate body composition. However, too little is known about whether various 3D body scan measures estimate body composition better than single standard anthropometric measures, and which body scans best estimate it. Furthermore, little is known about differences by sex and age.

METHODS

105 men and 96 women aged between 18 and 90 years were analysed. Bioelectrical Impedance Analysis was used to estimate whole relative fat mass (RFM), visceral adipose tissue (VAT) and skeletal muscle mass index (SMI). An Anthroscan VITUSbodyscan was used to obtain 3D body scans (e.g. volumes, circumferences, lengths). To reduce the number of possible predictors that could predict RFM, VAT and SMI backward elimination was performed. With these selected predictors linear regression on the respective body compositions was performed and the explained variations were compared with models using standard anthropometric measurements (Body Mass Index (BMI), waist circumference (WC) and waist-to-height-ratio (WHtR)).

RESULTS

Among the models based on standard anthropometric measures, WC performed better than BMI and WHtR in estimating body composition in men and women. The explained variations in models including body scan variables are consistently higher than those from standard anthropometrics models, with an increase in explained variations between 5% (RFM for men) and 10% (SMI for men). Furthermore, the explained variation of body composition was additionally increased when age and lifestyle variables were added. For each of the body composition variables, the number of predictors differed between men and women, but included mostly volumes and circumferences in the central waist/chest/hip area and the thighs.

CONCLUSIONS

3D scan models performed better than standard anthropometric measures models to predict body composition. Therefore, it is an advantage for larger health studies to look at body composition more holistically using 3D full body surface scans.

Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies

BACKGROUND

Three-dimensional optical (3DO) body scanning has been proposed for automatic anthropometry. However, conventional measurements fail to capture detailed body shape. More sophisticated shape features could better indicate health status.

OBJECTIVES

The objectives were to predict DXA total and regional body composition, serum lipid and diabetes markers, and functional strength from 3DO body scans using statistical shape modeling.

METHODS

Healthy adults underwent whole-body 3DO and DXA scans, blood tests, and strength assessments in the Shape Up! Adults cross-sectional observational study. Principal component analysis was performed on registered 3DO scans. Stepwise linear regressions were performed to estimate body composition, serum biomarkers, and strength using 3DO principal components (PCs). 3DO model accuracy was compared with simple anthropometric models and precision was compared with DXA.

RESULTS

This analysis included 407 subjects. Eleven PCs for each sex captured 95% of body shape variance. 3DO body composition accuracy to DXA was: fat mass R2 = 0.88 male, 0.93 female; visceral fat mass R2 = 0.67 male, 0.75 female. 3DO body fat test-retest precision was: root mean squared error = 0.81 kg male, 0.66 kg female. 3DO visceral fat was as precise (%CV = 7.4 for males, 6.8 for females) as DXA (%CV = 6.8 for males, 7.4 for females). Multiple 3DO PCs were significantly correlated with serum HDL cholesterol, triglycerides, glucose, insulin, and HOMA-IR, independent of simple anthropometrics. 3DO PCs improved prediction of isometric knee strength (combined model R2 = 0.67 male, 0.59 female; anthropometrics-only model R2 = 0.34 male, 0.24 female).

CONCLUSIONS

3DO body shape PCs predict body composition with good accuracy and precision comparable to existing methods. 3DO PCs improve prediction of serum lipid and diabetes markers, and functional strength measurements. The safety and accessibility of 3DO scanning make it appropriate for monitoring individual body composition, and metabolic health and functional strength in epidemiological settings.This trial was registered at clinicaltrials.gov as NCT03637855.

Anthropometric indices as surrogates for estimating abdominal visceral and subcutaneous adipose tissue: A meta-analysis with 16,129 participants

AIM

To seek anthropometric indices that estimate visceral and subcutaneous adipose tissue (VAT and SAT) by meta-analysis and comparing the predictive efficacy based on different characteristics of participants.

METHODS

PubMed, PubMed Central, Web of Science, China National Knowledge Infrastructure and Wanfang databases were searched for publications containing correlation coefficients of VAT and/or SAT with waist circumference (WC) and/or body mass index (BMI). The overall or subgroup pooled results were analyzed by meta and metafor packages of R with random effects model. MedCalc software was used to compare the correlation coefficients between groups.

RESULTS

Twenty-nine publications were included in this meta-analysis. The correlation coefficients of VAT-WC, VAT-BMI, SAT-WC and SAT-BMI for total studies were between 0.640 and 0.785. The correlation of VAT with WC was larger than that with BMI (Z = 11.664, P < 0.001). Meanwhile, the correlation coefficients of VAT-WC were statistically different among different age groups, areas, ethnicities, body shapes, scanning levels, units and instruments of measuring VAT (P < 0.05). The overall correlation of SAT with BMI was larger than that with WC (Z = 3.805, P < 0.001). The subgroups' correlation coefficients of SAT-BMI showed statistical differences between genders, age groups, areas, ethnicities, body shapes, scanning levels, units (cm² and cm³) and instruments of measuring SAT (P < 0.05).

CONCLUSIONS

WC may be a common and simple surrogate for estimating VAT, and BMI for SAT, especially in Europeans, but not in the aged people.

The use of consumer depth cameras for 3D surface imaging of people with obesity: A feasibility study

OBJECTIVE

Three dimensional (3D) surface imaging is a viable alternative to traditional body morphology measures, but the feasibility of using this technique with people with obesity has not been fully established. Therefore, the aim of this study was to investigate the validity, repeatability and acceptability of a consumer depth camera 3D surface imaging system in imaging people with obesity.

METHODS

The concurrent validity of the depth camera based system was investigated by comparing measures of mid-trunk volume to a gold-standard. The repeatability and acceptability of the depth camera system was assessed in people with obesity at a clinic.

RESULTS

There was evidence of a fixed systematic difference between the depth camera system and the gold standard but excellent correlation between volume estimates (r²=0.997), with little evidence of proportional bias. The depth camera system was highly repeatable - low typical error (0.192L), high intraclass correlation coefficient (>0.999) and low technical error of measurement (0.64%). Depth camera based 3D surface imaging was also acceptable to people with obesity.

CONCLUSION

It is feasible (valid, repeatable and acceptable) to use a low cost, flexible 3D surface imaging system to monitor the body size and shape of people with obesity in a clinical setting.

Equation to estimate visceral adipose tissue volume based on anthropometry for workplace health checkup in Japanese abdominally obese men

The purpose of this study was to develop a new equation model for predicting abdominal visceral adipose tissue (VAT) volume using anthropometric values for workplace health checkup and to clarify the association between metabolic risk factors and measured and predicted VAT volumes. Two hundred sixty male workers (200 for derivation group and 60 for validation group) participated in the cross-sectional study. The anthropometric variables and VAT volume were measured with 24 consecutive magnetic resonance images. Measurements in the validation group also included metabolic risk factors, i.e. blood pressure, HDL cholesterol, triglyceride, fasting glucose and HbA1c. Using multiple regression analyses for the derivation group, we determined the best prediction equation for abdominal VAT volume with a variance of 47% as follows: 47.03 age+117.79 BMI+74.18 waist circumference -8,792.7. In our validation group, the correlation coefficient between the measured and predicted VAT volumes was 0.74 (p<0.01). Furthermore, blood pressure, fasting glucose and HbA1c correlated with both measured and predicted VAT volumes. This study suggests that the equation model has potential to assess VAT accumulation levels in workers health checkup where CT and MRI are not available.

Novel Body Shape Descriptors for Abdominal Adiposity Prediction Using Magnetic Resonance Images and Stereovision Body Images

OBJECTIVE

The purpose of this study was to design novel shape descriptors based on three-dimensional (3D) body images and to use these parameters to establish prediction models for abdominal adiposity.

METHODS

Sixty-six men and fifty-five women were recruited for abdominal magnetic resonance imaging (MRI) and 3D whole-body imaging. Volumes of abdominal visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were measured from MRI sequences by using a fully automated algorithm. The shape descriptors were measured on the 3D body images by using the software developed in this study. Multiple regression analysis was employed on the training data set (70% of the total participants) to develop predictive models for VAT and SAT, with potential predictors selected from age, BMI, and the body shape descriptors. The validation data set (30%) was used for the validation of the predictive models.

RESULTS

Thirteen body shape descriptors exhibited high correlations (P < 0.01) with abdominal adiposity. The optimal predictive equations for VAT and SAT were determined separately for men and women.

CONCLUSIONS

Novel body shape descriptors defined on 3D body images can effectively predict abdominal adiposity quantified by MRI.

Anthropometric dimensions provide reliable estimates of abdominal adiposity: A validation study

Abdominal fat accumulation is a major risk factor for cardiometabolic morbidity and mortality. The purpose of the study is to assess the possibility of developing accurate estimation equations based on body measurements to determine total abdominal (TFA), subcutaneous (SFA) and visceral fat area (VFA). Hungarian volunteers (n=198) aged between 20 and 81 years were enrolled in the study, which was conducted between July and November 2014. All persons underwent anthropometric measurements and computer tomographic (CT) scanning. Sex-specific multiple linear regression analyses were conducted in a subgroup of 98 participants to generate estimation models, then Bland-Altman's analyses were applied in the cross-validation group to compare their predictive efficiency. The variables best predicting VFA were hip circumference, calf circumference and waist-to-hip ratio (WHR) for males (R²=0.713; SEE=5602.1mm²) and sagittal abdominal diameter (SAD), WHR, thigh circumference and triceps skinfold for females (R²=0.845; SEE=3835.6mm²). The SFA prediction equation included SAD, thigh circumference and abdominal skinfold for males (R²=0.848; SEE=4124.1mm²), body mass index and thigh circumference for females (R²=0.861; SEE=5049.7mm²). Prediction accuracy was the highest in the case of TFA: hip circumference and WHR for males (R²=0.910; SEE=5637.2mm²), SAD, thigh circumference and abdominal skinfold for females (R²=0.915; SEE=6197.5mm²) were used in the equations. The results suggested that deviations in the predictions were independent of the amount of adipose tissue. Estimation of abdominal fat depots based on anthropometric traits could provide a cheap, reliable method in epidemiologic research and public health screening to evaluate the risk of cardiometabolic events.

A Smartphone Application for Personal Assessments of Body Composition and Phenotyping

Personal assessments of body phenotype can enhance success in weight management but are limited by the lack of availability of practical methods. We describe a novel smart phone application of digital photography (DP) and determine its validity to estimate fat mass (FM). This approach utilizes the percent (%) occupancy of an individual lateral whole-body digital image and regions indicative of adipose accumulation associated with increased risk of cardio-metabolic disease. We measured 117 healthy adults (63 females and 54 males aged 19 to 65 years) with DP and dual X-ray absorptiometry (DXA) and report here the development and validation of this application. Inter-observer variability of the determination of % occupancy was 0.02%. Predicted and reference FM values were significantly related in females (R² = 0.949, SEE = 2.83) and males (R² = 0.907, SEE = 2.71). Differences between predicted and measured FM values were small (0.02 kg, p = 0.96 and 0.07 kg, p = 0.96) for females and males, respectively. No significant bias was found; limits of agreement ranged from 5.6 to −5.4 kg for females and from 5.6 to −5.7 kg for males. These promising results indicate that DP is a practical and valid method for personal body composition assessments.

Clinical anthropometrics and body composition from 3D whole-body surface scans

BACKGROUND/OBJECTIVES

Obesity is a significant worldwide epidemic that necessitates accessible tools for robust body composition analysis. We investigated whether widely available 3D body surface scanners can provide clinically relevant direct anthropometrics (circumferences, areas and volumes) and body composition estimates (regional fat/lean masses).

SUBJECTS/METHODS

Thirty-nine healthy adults stratified by age, sex and body mass index (BMI) underwent whole-body 3D scans, dual energy X-ray absorptiometry (DXA), air displacement plethysmography and tape measurements. Linear regressions were performed to assess agreement between 3D measurements and criterion methods. Linear models were derived to predict DXA body composition from 3D scan measurements. Thirty-seven external fitness center users underwent 3D scans and bioelectrical impedance analysis for model validation.

RESULTS

3D body scan measurements correlated strongly to criterion methods: waist circumference R2=0.95, hip circumference R2=0.92, surface area R2=0.97 and volume R2=0.99. However, systematic differences were observed for each measure due to discrepancies in landmark positioning. Predictive body composition equations showed strong agreement for whole body (fat mass R2=0.95, root mean square error (RMSE)=2.4 kg; fat-free mass R2=0.96, RMSE=2.2 kg) and arms, legs and trunk (R2=0.79-0.94, RMSE=0.5-1.7 kg). Visceral fat prediction showed moderate agreement (R2=0.75, RMSE=0.11 kg).

CONCLUSIONS

3D surface scanners offer precise and stable automated measurements of body shape and composition. Software updates may be needed to resolve measurement biases resulting from landmark positioning discrepancies. Further studies are justified to elucidate relationships between body shape, composition and metabolic health across sex, age, BMI and ethnicity groups, as well as in those with metabolic disorders.

The importance of prediction model validation and assessment in obesity and nutrition research

Deriving statistical models to predict one variable from one or more other variables, or predictive modeling, is an important activity in obesity and nutrition research. To determine the quality of the model, it is necessary to quantify and report the predictive validity of the derived models. Conducting validation of the predictive measures provides essential information to the research community about the model. Unfortunately, many articles fail to account for the nearly inevitable reduction in predictive ability that occurs when a model derived on one data set is applied to a new data set. Under some circumstances, the predictive validity can be reduced to nearly zero. In this overview, we explain why reductions in predictive validity occur, define the metrics commonly used to estimate the predictive validity of a model (for example, coefficient of determination (R(2)), mean squared error, sensitivity, specificity, receiver operating characteristic and concordance index) and describe methods to estimate the predictive validity (for example, cross-validation, bootstrap, and adjusted and shrunken R(2)). We emphasize that methods for estimating the expected reduction in predictive ability of a model in new samples are available and this expected reduction should always be reported when new predictive models are introduced.

Efficacy of thigh volume ratios assessed via stereovision body imaging as a predictor of visceral adipose tissue measured by magnetic resonance imaging

OBJECTIVES

The research examined the efficacy of regional volumes of thigh ratios assessed by stereovision body imaging (SBI) as a predictor of visceral adipose tissue measured by magnetic resonance imaging (MRI). Body measurements obtained via SBI also were utilized to explore disparities of body size and shape in men and women.

METHOD

One hundred twenty-one participants were measured for total/regional body volumes and ratios via SBI and abdominal subcutaneous and visceral adipose tissue areas by MRI.

RESULTS

Thigh to torso and thigh to abdomen-hip volume ratios were the most reliable parameters to predict the accumulation of visceral adipose tissue depots compared to other body measurements. Thigh volume in relation to torso [odds ratios (OR) 0.44] and abdomen-hip (OR 0.41) volumes were negatively associated with increased risks of greater visceral adipose tissue depots, even after controlling for age, gender, and body mass index (BMI). Irrespective of BMI classification, men exhibited greater total body (80.95L vs. 72.41L), torso (39.26L vs. 34.13L), and abdomen-hip (29.01L vs. 25.85L) volumes than women. Women had higher thigh volumes (4.93L vs. 3.99L) and lower-body volume ratios [thigh to total body (0.07 vs. 0.05), thigh to torso (0.15 vs. 0.11), and thigh to abdomen-hip (0.20 vs. 0.15); P < 0.05].

CONCLUSIONS

The unique parameters of the volumes of thigh in relation to torso and abdomen-hip, by SBI were highly effective in predicting visceral adipose tissue deposition. The SBI provided an efficient method for determining body size and shape in men and women via total and regional body volumes and ratios. Am. J. Hum. Biol. 27:445-457, 2015. © 2015 Wiley Periodicals, Inc.

Prediction of Android and Gynoid Body Adiposity via a Three-dimensional Stereovision Body Imaging System and Dual-Energy X-ray Absorptiometry

OBJECTIVE

Current methods for measuring regional body fat are expensive and inconvenient compared to the relative cost-effectiveness and ease of use of a stereovision body imaging (SBI) system. The primary goal of this research is to develop prediction models for android and gynoid fat by body measurements assessed via SBI and dual-energy x-ray absorptiometry (DXA). Subsequently, mathematical equations for prediction of total and regional (trunk, leg) body adiposity were established via parameters measured by SBI and DXA.

METHODS

A total of 121 participants were randomly assigned into primary and cross-validation groups. Body measurements were obtained via traditional anthropometrics, SBI, and DXA. Multiple regression analysis was conducted to develop mathematical equations by demographics and SBI assessed body measurements as independent variables and body adiposity (fat mass and percentage fat) as dependent variables. The validity of the prediction models was evaluated by a split sample method and Bland-Altman analysis.

RESULTS

The R(2) of the prediction equations for fat mass and percentage body fat were 93.2% and 76.4% for android and 91.4% and 66.5% for gynoid, respectively. The limits of agreement for the fat mass and percentage fat were -0.06 ± 0.87 kg and -0.11% ± 1.97% for android and -0.04 ± 1.58 kg and -0.19% ± 4.27% for gynoid. Prediction values for fat mass and percentage fat were 94.6% and 88.9% for total body, 93.9% and 71.0% for trunk, and 92.4% and 64.1% for leg, respectively.

CONCLUSIONS

The three-dimensional (3D) SBI produces reliable parameters that can predict android and gynoid as well as total and regional (trunk, leg) fat mass.