Ratio of trunk to leg volume as a new body shape metric for diabetes and mortality

3D Convolutional Deep Learning for Nonlinear Estimation of Body Composition from Whole-Body Morphology

Total and regional body composition are strongly correlated with metabolic syndrome and have been estimated non-invasively from 3D optical scans using linear parameterizations of body shape and linear regression models. Prior works produced accurate and precise predictions on many, but not all, body composition targets relative to the reference dual X-Ray absorptiometry (DXA) measurement. Here, we report the effects of replacing linear models with nonlinear parameterization and regression models on the precision and accuracy of body composition estimation in a novel application of deep 3D convolutional graph networks to human body composition modeling. We assembled an ensemble dataset of 4286 topologically standardized 3D optical scans from four different human body shape databases, DFAUST, CAESAR, Shape Up! Adults, and Shape Up! Kids and trained a parameterized shape model using a graph convolutional 3D autoencoder (3DAE) in lieu of linear PCA. We trained a nonlinear Gaussian process regression (GPR) on the 3DAE parameter space to predict body composition via correlations to paired DXA reference measurements from the Shape Up! scan subset. We tested our model on a set of 424 randomly withheld test meshes and compared the effects of nonlinear computation against prior linear models. Nonlinear GPR produced up to 20% reduction in prediction error and up to 30% increase in precision over linear regression for both sexes in 10 tested body composition variables. Deep shape features produced 6-8% reduction in prediction error over linear PCA features for males only and a 4-14% reduction in precision error for both sexes. Our best performing nonlinear model predicting body composition from deep features outperformed prior work using linear methods on all tested body composition prediction metrics in both precision and accuracy. All coefficients of determination (R2) for all predicted variables were above 0.86. We show that GPR is a more precise and accurate method for modeling body composition mappings from body shape features than linear regression. Deep 3D features learned by a graph convolutional autoencoder only improved male body composition accuracy but improved precision in both sexes. Our work achieved lower estimation RMSEs than all previous work on 10 metrics of body composition.

The association of dietary inflammatory index (DII) and central obesity with non-alcoholic fatty liver disease (NAFLD) in people with diabetes (T2DM)

Background & Objective

High prevalence of non-alcoholic fatty liver disease (NAFLD) in patients with type 2 diabetes mellitus results in deleterious complications and morbidities related to both diseases. Thus, we aimed to investigate dietary and anthropometric risk factors for progression of Non-alcoholic Fatty Liver Disease (NAFLD) in diabetic people.

Methods

Anthropometric, and dietary intakes, and hepatic steatosis and fibrosis were assessed in two hundred participants with type two diabetes (T2DM). Subjects with CAP score of more than 270 dB/m were considered to have NAFLD. Multivariable-adjusted ORs and 95% CIs were used to investigate the association between NAFLD and dietary inflammatory index (DII) score and anthropometric indices.

Results

Participants in the highest tertile of DII had 2.41 (95% CI:1.16-4.97), 2,53 (95% CI: 1.04-6.16), 2.78 (95% CI: 1.09-7.13) times higher odds of developing NAFLD in comparison to the lowest tertile in crude, adjusted model 1 and 2, respectively. Among those with the highest relative to the lowest tertile of trunk-to-leg fat ratio (TLR), ORs and 95% CI were OR = 1.88, 95% CI = 0.9-3.91, and OR = 7.99, 95% CI = 2.43-26.26 in crude and full-adjusted models. Odds of NAFLD in the third tertile of metabolic score for visceral fat (METS-VF) was higher than the first tertile in crude (OR = 9.5, 95% CI = 4.01-22.46) and full-adjusted models (OR = 4.55, 95% CI = 1.46-14.2).

Conclusions

In conclusion, this study highlighted an association between greater DII (pro-inflammatory diet) and higher NAFLD risk. Moreover, TLR and METS-VF are known as novel estimators of central obesity as a risk factor for NAFLD in diabetes.

A device-agnostic shape model for automated body composition estimates from 3D optical scans

BACKGROUND

Many predictors of morbidity caused by metabolic disease are associated with body shape. 3D optical (3DO) scanning captures body shape and has been shown to accurately and precisely predict body composition variables associated with mortality risk. 3DO is safer, less expensive, and more accessible than criterion body composition assessment methods such as dual-energy X-ray absorptiometry (DXA). However, 3DO scanning has not been standardized across manufacturers for pose, mesh resolution, and post processing methods.

PURPOSE

We introduce a scanner-agnostic algorithm that automatically fits a topologically consistent human mesh to 3DO scanned point clouds and predicts clinically important body metrics using a standardized body shape model. Our models transform raw scans captured by any 3DO scanner into fixed topology meshes with anatomical consistency, standardizing the outputs of 3DO scans across manufacturers and allowing for the use of common prediction models across scanning devices.

METHODS

A fixed-topology body mesh template was automatically registered to 848 training scans from three different 3DO systems. Participants were between 18 and 89 years old with body mass index ranging from 14 to 52 kg/m2 . Scans were registered by first performing a coarse nearest neighbor alignment between the template and the input scan with an anatomically constrained principal component analysis (PCA) domain deformation using a device and gender specific bootstrap basis trained on 70 seed scans each. The template mesh was then optimized to fit the target with a smooth per-vertex surface-to-surface deformation. A combined unified PCA model was created from the superset of all automatically fit training scans including all three devices. Body composition predictions to DXA measurements were learned from the training mesh PCA coefficients using linear regression. Using this final unified model, we tested the accuracy of our body composition models on a withheld sample of 562 scans by fitting a PCA parameterized template mesh to each raw scan and predicting the expected body composition metrics from the principal components using the learned regression model.

RESULTS

We achieved coefficients of determination (R2 ) above 0.8 on all nine fat and lean predictions except female visceral fat (0.77). R2 was as high as 0.94 (total fat and lean, trunk fat), and all root-mean-squared errors were below 3.0 kg. All predicted body composition variables were not significantly different from reference DXA measurements except for visceral fat and female trunk fat. Repeatability precision as measured by the coefficient of variation (%CV) was around 2-3x worse than DXA precision, with visceral fat %CV below 2x DXA %CV and female total fat mass at 5x.

CONCLUSIONS

Our method provides an accurate, automated, and scanner agnostic framework for standardizing 3DO scans and a low cost, radiation-free alternative to criterion radiology imaging for body composition analysis. We published a web-app version of this work at https://shapeup.shepherdresearchlab.org/3do-bodycomp-analyzer/ that accepts mesh file uploads and returns templated meshes with body composition predictions for demo purposes.

The impact of body composition and fat distribution on blood pressure in young and middle-aged adults

Background

The relative contributions of each component of body composition to blood pressure (BP) remain unclear.

Objective

We aimed to comprehensively investigate the impact of body composition and fat distribution on BP and quantify their relative contributions to BP in a large cohort with young and middle-aged adults.

Methods

14,412 participants with available data on whole-body DXA measurement from the National Health and Nutrition Examination Survey were included. Multiple stepwise linear regressions of BP on components of body composition and fat distribution were built. Then, relative importance analysis was performed to quantify the contributions of each component to BP.

Results

The median age of participants was 36 years and there were 50.7% women. Linear regression with mutual adjustment showed that total fat mass, total muscle mass, and trunk fat mass significantly and positively associated with BP; however, arm and leg fat mass significantly and negatively associated with BP. In men, after further adjusted for potential covariates, SBP were significantly determined by trunk fat mass (β = 0.33, P < 0.001), leg fat mass (β = − 0.12, P < 0.001), and total muscle mass (β = 0.10, P < 0.001); and DBP were significantly determined by trunk fat mass (β = 0.52, P < 0.001), leg fat mass (β = −0.15, P < 0.001), arm fat mass (β = −0.23, P < 0.001), and total muscle mass (β = 0.06, P < 0.001). Similar results were observed in women. Relative importance analysis showed that trunk fat mass was the major contributor (38–61%) to both SBP and DBP; meanwhile, total muscle mass also made relatively great contribution (35–43%) to SBP.

Conclusion

Both fat mass and muscle mass independently associated with and substantially contributed to SBP in both men and women. After full adjustment, trunk fat mass positively associated with both SBP and DBP, and was the most dominant contributor to BP; however, leg fat mass negatively associated with both SBP and DBP.

Three-dimensional optical body shape and features improve prediction of metabolic disease risk in a diverse sample of adults

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.

Next generation smartwatches to estimate whole body composition using bioimpedance analysis: accuracy and precision in a diverse multiethnic sample

BACKGROUND

Novel advancements in wearable technologies include continuous measurement of body composition via smart watches. The accuracy and stability of devices are unknown.

OBJECTIVES

This study evaluated smart watches with integrated bioimpedance (BIA) sensors for their ability to measure and monitor change in body composition.

DESIGN

Participants recruited across body mass indexes received duplicate body composition measures using two wearable smart watch (W-BIA) models in sitting and standing positions and multiple versions of each watch were used to evaluate inter- and intra-model precision. Duplicate laboratory-grade octapolar bioimpedance (8-BIA) and criterion dual-energy X-ray absorptiometry (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 change in body composition.

RESULTS

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, CCC = 0.97). FFM was less precise on the watches than DXA (CV = 0.7%, RMSE = 0.4 kg versus CV = 1.3%, RMSE = 0.7 kg for W-BIA), requiring more repeat measures to equal the same confidence in body composition change over time as DXA.

CONCLUSIONS

After systematic correction, smart watch BIA devices are capable of stable, reliable and accurate body composition with precision comparable but lower than laboratory measures. These devices allow for measurement in environments not accessible to laboratory systems such as the home, training centers, and geographically remote locations.

Regional Lean Soft Tissue and Intracellular Water Are Associated with Changes in Lower-Body Neuromuscular Performance: A Pilot Study in Elite Soccer Players

The assessment of body composition over a competitive season provides valuable information that can help sports professionals to evaluate the efficacy of training and nutritional strategies, as well as monitoring athletes’ health status. The purpose of this study was to examine the association of changes in body composition and hydration status with changes in lower-body neuromuscular performance in soccer. Twenty-two male professional soccer players (mean ± SD; age: 26.4 ± 4.8 years; height: 184.3 ± 5.7 cm; body mass: 81.1 ± 6.5 kg; body fat: 11.6 ± 1.5%) took part in the study, for which they were tested at the initial and final stage of the competitive season. Total (whole body) and regional (arms and legs) lean soft tissue (LST) were estimated to obtain the body composition profile. Total body water (TBW) content, including extracellular (ECW) and intracellular (ICW) water, was obtained to monitor players’ hydration status. Countermovement jump (CMJ) height, power, and strength were used to derive players’ lower-body neuromuscular performance. The results showed that changes in legs LST and ICW significantly (p < 0.01) explained (r2 = 0.39) the improvements in CMJ height, power, and strength from the initial to the final stage of the season. Given the high demand imposed on the lower limbs during a soccer season, being more susceptible to change compared to whole-body LST, assessing regional LST and ICW would be more appropriate to provide extended information on players’ readiness.

Exenatide, Dapagliflozin, or Phentermine/Topiramate Differentially Affect Metabolic Profiles in Polycystic Ovary Syndrome

CONTEXT

Glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors reduce weight and improve insulin sensitivity via different mechanisms.

OBJECTIVE

The efficacy of once-weekly exenatide (EQW) and dapagliflozin (DAPA) alone and coadministered (EQW/DAPA), DAPA/extended-release (ER) metformin (DAPA/MET), and phentermine topiramate extended release (PHEN/TPM) on metabolic parameters, body composition, and sex hormones were examined in obese women with PCOS.

METHODS

Nondiabetic women (n = 119; aged 18-45 years) with a body mass index (BMI) greater than 30 and less than 45 and polycystic ovary syndrome (National Institutes of Health criteria) were randomly assigned in a single-blinded fashion to EQW (2 mg weekly); DAPA (10 mg daily), EQW/DAPA (2 mg weekly/10 mg daily), DAPA (10 mg)/MET (2000 mg XR daily), or PHEN (7.5 mg)/TPM (46 mg ER daily) treatment for 24 weeks. Study visits at baseline and 24 weeks included weight, blood pressure (BP), waist (WC) measures, and body composition evaluated by dual-energy x-ray absorptiometry (DXA). Oral glucose tolerance tests were conducted to assess glycemia and mean blood glucose (MBG), and compute insulin sensitivity (SI) and secretion (IS) measures. Sex steroids, free androgen index (FAI), and lipid profiles were measured in the fasting sample.

RESULTS

EQW/DAPA and PHEN/TPM resulted in the most loss of weight and total body fat by DXA, and WC. Despite equivalent reductions in BMI and WC with PHEN/TPM, only EQW/DAPA and EQW resulted in significant improvements in MBG, SI, and IS. Reductions in fasting glucose, testosterone, FAI, and BP were seen with all drugs.

CONCLUSION

Dual therapy with EQW/DAPA was superior to either alone, DAPA/MET and PHEN/TPM in terms of clinical and metabolic benefits in this patient population.

Association of X-ray Absorptiometry Body Composition Measurements with Basic Anthropometrics and Mortality Hazard

Dual-energy X-ray absorptiometry (DEXA) is a non-invasive imaging modality that can estimate whole-body and regional composition in terms of fat, lean, and bone mass. We examined the ability of DEXA body composition measures (whole-body, trunk, and limb fat mass and fat-free mass) to predict mortality in conjunction with basic body measures (anthropometrics), expressed using body mass index (BMI) and a body shape index (ABSI). We used data from the 1999-2006 United States National Health and Nutrition Examination Survey (NHANES), with mortality follow-up to 2015. We found that all DEXA-measured masses were highly correlated with each other and with ABSI and that adjustment for BMI and ABSI reduced these dependencies. Whole-body composition did not substantially improve mortality prediction compared to basic anthropometrics alone, but regional composition did, with high trunk fat-free mass and low limb fat-free mass both associated with elevated mortality risk. These findings illustrate how DEXA body composition could guide health assessment in conjunction with the more widely employed simple anthropometrics.

Explaining Discrepancies Between Total and Segmental DXA and BIA Body Composition Estimates Using Bayesian Regression

INTRODUCTION/BACKGROUND

Few investigations have sought to explain discrepancies between dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) body composition estimates. The purpose of this analysis was to explore physiological and anthropometric predictors of discrepancies between DXA and BIA total and segmental body composition estimates.

METHODOLOGY

Assessments via DXA (GE Lunar Prodigy) and single-frequency BIA (RJL Systems Quantum V) were performed in 179 adults (103 F, 76 M, age: 33.6 ± 15.3 yr; BMI: 24.9 ± 4.3 kg/m²). Potential predictor variables for differences between DXA and BIA total and segmental fat mass (FM) and lean soft tissue (LST) estimates were obtained from demographics and laboratory techniques, including DXA, BIA, bioimpedance spectroscopy, air displacement plethysmography, and 3-dimensional optical scanning. To determine meaningful predictors, Bayesian robust regression models were fit using a t-distribution and regularized hierarchical shrinkage "horseshoe" prior. Standardized model coefficients (β) were generated, and leave-one-out cross validation was used to assess model predictive performance.

RESULTS

LST hydration (i.e., total body water:LST) was a predictor of discrepancies in all FM and LST variables (|β|: 0.20-0.82). Additionally, extracellular fluid percentage was a predictor for nearly all outcomes (|β|: 0.19-0.40). Height influenced the agreement between whole-body estimates (|β|: 0.74-0.77), while the mass, length, and composition of body segments were predictors for segmental LST estimates (|β|: 0.23-3.04). Predictors of segmental FM errors were less consistent. Select sex-, race-, or age-based differences between methods were observed. The accuracy of whole-body models was superior to segmental models (leave-one-out cross-validation-adjusted R² of 0.83-0.85 for FM_TOTAL and LST_TOTAL vs. 0.20-0.76 for segmental estimates). For segmental models, predictive performance decreased in the order of: appendicular lean soft tissue, LST_LEGS, LST_TRUNK and FM_LEGS, FM_ARMS, FM_TRUNK, and LST_ARMS.

CONCLUSIONS

These findings indicate the importance of LST hydration, extracellular fluid content, and height for explaining discrepancies between DXA and BIA body composition estimates. These general findings and quantitative interpretation based on the presented data allow for a better understanding of sources of error between 2 popular segmental body composition techniques and facilitate interpretation of estimates from these technologies.

Reference values of body composition parameters for Vietnamese men and women

BACKGROUND

Body composition parameters are linked to cardio-metabolic risk. However, high-quality reference values of body composition are scarce, particularly in Asian population. The aim of study was to construct sex- and age-specific normative reference values of body composition for the Vietnamese population.

METHODS

This study was designed as a cross-sectional investigation that involved 2700 women and 1459 men aged between 20 and 90 (average 48, SD 15) who were participants in the population-based Vietnam Osteoporosis Study. Whole-body composition parameters (e.g., fat mass and lean mass) and site-specific (head, arms, trunk, and legs) parameters were measured by dual-energy X-ray absorptiometry (Hologic Horizon). Reference curves for each parameter and anatomical site were constructed using the Generalized Additive Model for Location Scale and Shape modeling technique.

RESULTS

Overall, 8% of women and 11% of men were classified as obese (body mass index ≥ 27.5 kg/m²). Most fat mass was deposited at the trunk (~50%), followed by the leg (~33%). Women had ~10% more body fat (relative to body weight) than men. However, whole-body lean mass was higher in men than women, with the average difference being ~13 kg. Moreover, men had more bone mineral content than women (2110 vs. 1600 g). We also provided a comparison of age-related changes in body composition parameters between Vietnamese and US Whites.

CONCLUSION

These data provide gender- and age-specific reference values of body composition parameters for Vietnamese population. These normative values provide health professionals and the public with a resource for interpretation of body composition data.

Long-Term Effects of Laparoscopic Sleeve Gastrectomy and Roux-en-Y Gastric Bypass on Body Composition and Bone Mass Density

BACKGROUND

Currently, the two most common bariatric procedures are laparoscopic sleeve gastrectomy (LSG) and laparoscopic Roux-en-Y gastric bypass (LRYGB). Long-term data comparing the two interventions in terms of their effect on body composition and bone mass density (BMD) are scarce.

OBJECTIVE

The aim of this study was to assess body composition and BMD at least 5 years after LSG and LRYGB.

SETTING

Department of Endocrinology and Nutrition, St. Claraspital Basel and St. Clara Research Ltd., Basel, Switzerland.

METHODS

Bariatric patients at least 5 years after surgery (LSG or LRYGB) were recruited, and body composition and BMD were measured by means of dual-energy X-ray absorptiometry. Data from body composition before surgery were included in the analysis. Blood samples were taken for determination of plasma calcium, parathyroid hormone, vitamin D3, alkaline phosphatase, and C-terminal telopeptide, and the individual risk for osteoporotic fracture assessed by the Fracture Risk Assessment Tool score was calculated. After surgery, all patients received multivitamins, vitamin D3, and zinc. In addition, LRYGB patients were prescribed calcium.

RESULTS

A total of 142 patients were included, 72 LSG and 70 LRYGB, before surgery: median body mass index 43.1, median age 45.5 years, 62.7% females. Follow-up after a median of 6.7 years. For LRYGB, the percentage total weight loss at follow-up was 26.3% and for LSG 24.1% (p = 0.243). LRYGB led to a slightly lower fat percentage in body composition. At follow-up, 45% of both groups had a T score at the femoral neck below -1, indicating osteopenia. No clinically relevant difference in BMD was found between the groups.

CONCLUSIONS

At 6.7 years after surgery, no difference in body composition and BMD between LRYGB and LSG was found. Deficiencies and bone loss remain an issue after both interventions and should be monitored.

Hole Filling in 3D Scans for Digital Anthropometric Applications

Anthropometric measurements have been used to assess an individual's body composition, disease risk, and nutritional status. Three-dimensional (3D) optical devices can rapidly acquire body surface scans in the form of a triangular mesh which can then be used to obtain anthropometric measurements such as body volume, limb lengths, and circumferences; however, the meshes provided by some scanners may include missing data patches known as holes. These need to be repaired in order to obtain correct landmark detection and automatic calculation of anthropometric measurements-especially body volume. In this study, we present ScReAM (Scan Reconstruction for Anthropometric Measurements) which is a fully automated geometrical 3D reconstruction approach to find and fill these holes. We compare ScReAM with Alias and MeshFix which are well-known software used for triangular meshing. Evaluations are derived from a sample size of 47 subjects that were scanned by two different 3D optical scanners. Our results validate the accuracy of ScReAM for reconstructing a mesh for volume calculation.

Truncal-to-leg fat ratio and cardiometabolic disease risk factors in US adolescents: NHANES 2003-2006

BACKGROUND

This study aims to describe patterns of truncal versus peripheral fat deposition measured by truncal-to-leg fat ratio (TLR) in adolescents and examine associations of TLR with cardiometabolic (CMD) risk factors.

METHODS

Data were from 3810 adolescents (12-19 years old) in the National Health and Examination Survey (NHANES) 2003-2006. Body fat was assessed by dual-energy X-ray absorptiometry, and CMD risk factors were determined by blood samples and physical examination. Linear and logistic regressions adjusted for BMI z-score and other covariates were used to examine associations of TLR with CMD risk factors as continuous and dichotomized outcomes, respectively.

RESULTS

Adolescents who were Mexican American, who have lower income, and with obesity had the highest mean TLR (all p < 0.05). In linear regression, increasing TLR was associated positively with homeostasis model assessment of insulin resistance (HOMA-IR), triglycerides, total cholesterol, systolic blood pressure (BP), c-reactive protein, and alanine aminotransferase (ALT), and negatively with high-density lipoprotein (HDL) cholesterol in both sexes (p < 0.05). TLR was also associated with diastolic BP in boys and low-density lipoprotein cholesterol in girls (p < 0.05). A similar pattern of findings resulted from logistic regression. When further stratified by race/ethnicity, TLR was positively associated with high triglycerides, total cholesterol, and ALT for White and/or Mexican American (p < 0.05), but not Black adolescents, while associations with HOMA-IR and HDL were significant for all race/ethnicities.

CONCLUSIONS

In this cohort of adolescents, TLR was associated with several risk factors independent of BMI z-score, although some findings were sex or race/ethnicity specific. Body fat distribution may be an important determinant of future CMD.

Association of Body Fat and Risk of Breast Cancer in Postmenopausal Women With Normal Body Mass Index: A Secondary Analysis of a Randomized Clinical Trial and Observational Study

Importance

Obesity is associated with an increased risk of breast cancer, including the estrogen receptor (ER)-positive subtype in postmenopausal women. Whether excess adiposity is associated with increased risk in women with a normal body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) is unknown.

Objective

To investigate the association between body fat and breast cancer risk in women with normal BMI.

Design, Setting, and Participants

This ad hoc secondary analysis of the Women's Health Initiative (WHI) clinical trial and observational study cohorts was restricted to postmenopausal participants with a BMI ranging from 18.5 to 24.9. Women aged 50 to 79 years were enrolled from October 1, 1993, through December 31, 1998. Of these, 3460 participants underwent body fat measurement with dual-energy x-ray absorptiometry (DXA) at 3 US designated centers with follow-up. At a median follow-up of 16 years (range, 9-20 years), 182 incident breast cancers had been ascertained, and 146 were ER positive. Follow-up was complete on September 30, 2016, and data from October 1, 1993, through September 30, 2016, was analyzed August 2, 2017, through August 21, 2018.

Main Outcomes and Measures

Body fat levels were measured at baseline and years 1, 3, 6, and 9 using DXA. Information on demographic data, medical history, and lifestyle factors was collected at baseline. Invasive breast cancers were confirmed via central review of medical records by physician adjudicators. Blood analyte levels were measured in subsets of participants.

Results

Among the 3460 women included in the analysis (mean [SD] age, 63.6 [7.6] years), multivariable-adjusted hazard ratios for the risk of invasive breast cancer were 1.89 (95% CI, 1.21-2.95) for the highest quartile of whole-body fat and 1.88 (95% CI, 1.18-2.98) for the highest quartile of trunk fat mass. The corresponding adjusted hazard ratios for ER-positive breast cancer were 2.21 (95% CI, 1.23-3.67) and 1.98 (95% CI, 1.18-3.31), respectively. Similar positive associations were observed for serial DXA measurements in time-dependent covariate analyses. Circulating levels of insulin, C-reactive protein, interleukin 6, leptin, and triglycerides were higher, whereas levels of high-density lipoprotein cholesterol and sex hormone-binding globulin were lower in those in the uppermost vs lowest quartiles of trunk fat mass.

Conclusions and Relevance

In postmenopausal women with normal BMI, relatively high body fat levels were associated with an elevated risk of invasive breast cancer and altered levels of circulating metabolic and inflammatory factors. Normal BMI categorization may be an inadequate proxy for the risk of breast cancer in postmenopausal women.

Trial Registration

ClinicalTrials.gov identifier: NCT00000611.

Racial and Ethnic Differences in Anthropometric Measures as Risk Factors for Diabetes

OBJECTIVE

The study objective was to examine the impact of race/ethnicity on associations between anthropometric measures and diabetes risk.

RESEARCH DESIGN AND METHODS

A total of 136,112 postmenopausal women aged 50-79 years participating in the Women's Health Initiative without baseline cancer or diabetes were followed for 14.6 years. BMI, waist circumference (WC), and waist-to-hip ratio (WHR) were measured in all participants, and a subset of 9,695 had assessment of whole-body fat mass, whole-body percent fat, trunk fat mass, and leg fat mass by DXA. Incident diabetes was assessed via self-report. Multivariate Cox proportional hazards regression models were used to assess associations between anthropometrics and diabetes incidence.

RESULTS

During follow-up, 18,706 cases of incident diabetes were identified. BMI, WC, and WHR were all positively associated with diabetes risk in each racial and ethnic group. WC had the strongest association with risk of diabetes across all racial and ethnic groups. Compared with non-Hispanic whites, associations with WC were weaker in black women (P < 0.0001) and stronger in Asian women (P < 0.0001). Among women with DXA determinations, black women had a weaker association with whole-body fat (P = 0.02) but a stronger association with trunk-to-leg fat ratio (P = 0.03) compared with white women.

CONCLUSIONS

In postmenopausal women across all racial/ethnic groups, WC was a better predictor of diabetes risk, especially for Asian women. Better anthropometric measures that reflect trunk-to-leg fat ratio may improve diabetes risk assessment for black women.

Modeling the shape and composition of the human body using dual energy X-ray absorptiometry images

There is growing evidence that body shape and regional body composition are strong indicators of metabolic health. The purpose of this study was to develop statistical models that accurately describe holistic body shape, thickness, and leanness. We hypothesized that there are unique body shape features that are predictive of mortality beyond standard clinical measures. We developed algorithms to process whole-body dual-energy X-ray absorptiometry (DXA) scans into body thickness and leanness images. We performed statistical appearance modeling (SAM) and principal component analysis (PCA) to efficiently encode the variance of body shape, leanness, and thickness across sample of 400 older Americans from the Health ABC study. The sample included 200 cases and 200 controls based on 6-year mortality status, matched on sex, race and BMI. The final model contained 52 points outlining the torso, upper arms, thighs, and bony landmarks. Correlation analyses were performed on the PCA parameters to identify body shape features that vary across groups and with metabolic risk. Stepwise logistic regression was performed to identify sex and race, and predict mortality risk as a function of body shape parameters. These parameters are novel body composition features that uniquely identify body phenotypes of different groups and predict mortality risk. Three parameters from a SAM of body leanness and thickness accurately identified sex (training AUC = 0.99) and six accurately identified race (training AUC = 0.91) in the sample dataset. Three parameters from a SAM of only body thickness predicted mortality (training AUC = 0.66, validation AUC = 0.62). Further study is warranted to identify specific shape/composition features that predict other health outcomes.

Proceedings of the 2016 Santa Fe Bone Symposium: New Concepts in the Management of Osteoporosis and Metabolic Bone Diseases

The Santa Fe Bone Symposium is an annual meeting of healthcare professionals and clinical researchers that details the clinical relevance of advances in knowledge of skeletal diseases. The 17th Santa Fe Bone Symposium was held in Santa Fe, New Mexico, USA, on August 5-6, 2016. The program included plenary lectures, oral presentations by endocrinology fellows, meet-the-professor sessions, and panel discussions, all aimed to provide ample opportunity for interactive discussions among all participants. Symposium topics included recent developments in the translation of basic bone science to patient care, new clinical practice guidelines for postmenopausal osteoporosis, management of patients with disorders of phosphate metabolism, new and emerging treatments for rare bone diseases, strategies to enhance fracture healing, and an update on Bone Health Extension for Community Healthcare Outcomes, using a teleconferencing platform to elevate the level of knowledge of healthcare professionals in underserved communities to deliver best practice care for skeletal diseases. The highlights and important clinical messages of the 2016 Santa Fe Bone Symposium are provided herein by each of the faculty presenters.

Dual energy X-ray absorptiometry body composition reference values of limbs and trunk from NHANES 1999-2004 with additional visualization methods

Body Mass Index has traditionally been used as a measure of health, but Fat Mass Index (FMI) and Lean Mass Index (LMI) have been shown to be more predictive of mortality and health risk. Total body FMI and LMI reference curves have particularly been useful in quantifying sarcopenia and sarcopenic obesity. Research has shown regional composition has significant associations to health outcomes. We derived FMI and LMI reference curves of the regions of the body (leg, arm, and trunk) for 15,908 individuals in the 1999-2004 National Health and Nutrition Examination Survey data for each sex and ethnicity using the Lambda-Mu-Sigma (LMS) method and developed software to visualize this regional composition. These reference curves displayed differentiation between males and females during puberty and sharper limb LMI declines during late adulthood for males. For adults ages 30-50, females had 39%, 83%, and 47% larger arm, leg, and trunk FMI values than males, respectively. Males had 49%, 20%, and 15% higher regional LMI values than females for the arms, legs, and trunk respectively. The leg FMI and LMI of black females were 14% and 15% higher respectively than those of Hispanic and white females. White and Hispanic males had 37% higher trunk FMI values than black males. Hispanic females had 20% higher trunk FMI than white and black females. These data underscore the importance of accounting for sex and ethnicity in studies of regional composition. This study is the first to produce regional LMI and FMI reference tables and curves from the NHANES dataset. These reference curves provide a framework useful in studies and research involving sarcopenia, obesity, sarcopenic obesity, and other studies of compositional phenotypes. Further, the software tool we provide for visualizing regional composition will prove useful in monitoring progress in physical therapy, diets, or other attempts to attain healthier compositions.

Higher dietary flavonoid intakes are associated with lower objectively measured body composition in women: evidence from discordant monozygotic twins

Background: Although dietary flavonoid intake has been associated with less weight gain, there are limited data on its impact on fat mass, and to our knowledge, the contribution of genetic factors to this relation has not previously been assessed.Objective: We examined the associations between flavonoid intakes and fat mass.Design: In a study of 2734 healthy, female twins aged 18-83 y from the TwinsUK registry, intakes of total flavonoids and 7 subclasses (flavanones, anthocyanins, flavan-3-ols, flavonols, flavones, polymers, and proanthocyanidins) were calculated with the use of food-frequency questionnaires. Measures of dual-energy X-ray absorptiometry-derived fat mass included the limb-to-trunk fat mass ratio (FMR), fat mass index, and central fat mass index.Results: In cross-sectional multivariable analyses, higher intake of anthocyanins, flavonols, and proanthocyanidins were associated with a lower FMR with mean ± SE differences between extreme quintiles of -0.03 ± 0.02 (P-trend = 0.02), -0.03 ± 0.02 (P-trend = 0.03), and -0.05 ± 0.02 (P-trend < 0.01), respectively. These associations were not markedly changed after further adjustment for fiber and total fruit and vegetable intakes. In monozygotic, intake-discordant twin pairs, twins with higher intakes of flavan-3-ols (n = 154, P = 0.03), flavonols (n = 173, P = 0.03), and proanthocyanidins (n = 172, P < 0.01) had a significantly lower FMR than that of their co-twins with within-pair differences of 3-4%. Furthermore, in confirmatory food-based analyses, twins with higher intakes of flavonol-rich foods (onions, tea, and pears; P = 0.01) and proanthocyanidin-rich foods (apples and cocoa drinks; P = 0.04) and, in younger participants (aged <50 y) only, of anthocyanin-rich foods (berries, pears, grapes, and wine; P = 0.01) had a 3-9% lower FMR than that of their co-twins.Conclusions: These data suggest that higher habitual intake of a number of flavonoids, including anthocyanins, flavan-3-ols, flavonols, and proanthocyanidins, are associated with lower fat mass independent of shared genetic and common environmental factors. Intervention trials are needed to further examine the effect of flavonoid-rich foods on body composition.

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.

An Anthropometric Risk Index Based on Combining Height, Weight, Waist, and Hip Measurements

Body mass index (BMI) can be considered an application of a power law model to express body weight independently of height. Based on the same power law principle, we previously introduced a body shape index (ABSI) to be independent of BMI and height. Here, we develop a new hip index (HI) whose normalized value is independent of height, BMI, and ABSI. Similar to BMI, HI demonstrates a U-shaped relationship to mortality in the Third National Health and Nutrition Examination Survey (NHANES III) population. We further develop a new anthropometric risk index (ARI) by adding log hazard ratios from separate nonlinear regressions of the four indicators, height, BMI, ABSI, and HI, against NHANES III mortality hazard. ARI far outperforms any of the individual indicators as a linear mortality predictor in NHANES III. The superior performance of ARI also holds for predicting mortality hazard in the independent Atherosclerosis Risk in Communities (ARIC) cohort. Thus, HI, along with BMI and ABSI, can capture the risk profile associated with body size and shape. These can be combined in a risk indicator that utilizes complementary information from height, weight, and waist and hip circumference. The combined ARI is promising for further research and clinical applications.

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.