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BACKGROUND: Guidelines recommend dual-energy x-ray absorptiometry (DXA) screening to assess fracture risk and benefit from antiresorptive therapy in men with metastatic hormone-sensitive prostate cancer (mHSPC) on androgen deprivation therapy (ADT). However,

BackgroundNew recommendations for the assessment of malnutrition and sarcopenia include body composition, specifically reduced muscle mass. Three-dimensional optical imaging (3DO) is a validated, accessible, and affordable alternative to dual X-ray absorptiometry (DXA).ObjectiveIdentify strengths and weaknesses of 3DO for identification of malnutrition in participants with low body mass index (BMI) and eating disorders.DesignParticipants were enrolled in the cross-sectional Shape Up! Adults and Kids studies of body shape, metabolic risk, and functional assessment and had BMI of

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.

BackgroundThere is evidence that human immunodeficiency virus (HIV) infection and antiretroviral therapy (ART) are independent risk factors for osteoporosis and fracture which is not solely explained by changes in bone mineral density. Thus, we hypothesized that the assessment of trabecular microstructure might play an important role for bone quality in this population and might explain the increased fracture risk. In this study, we have assessed bone microstructure in the proximal femur using high-resolution magnetic resonance imaging (MRI) as well as in the extremities using high resolution peripheral quantitative computed tomography (HR-pQCT) in HIV-infected men and healthy controls and compared these findings to those based on areal bone mineral density (aBMD) derived from dual X-ray absorptiometry (DXA) which is the standard clinical parameter for the diagnosis of osteoporosis.MethodsEight HIV-infected men and 11 healthy age-matched controls were recruited and informed consent was obtained before each scan. High-resolution MRI of the proximal femur was performed using fully balanced steady state free precession (bSSFP) on a 3T system. Three volumes of interest at corresponding anatomic locations across all subjects were defined based on registrations of a common template. Four MR-based trabecular microstructural parameters were analyzed at each region: fuzzy bone volume fraction (f-BVF), trabecular number (Tb.N), thickness (Tb.Th), and spacing (Tb.Sp). In addition, the distal radius and distal tibia were imaged with HR-pQCT. Four HR-pQCT-based microstructural parameters were analyzed: trabecular bone volume fraction (BV/TV), Tb.N, Tb.Th, and Tb.Sp. Total hip and spine aBMD were determined from DXA.ResultsMicrostructural bone parameters derived from MRI at the proximal femur and from HR-pQCT at the distal tibia showed significantly lower bone quality in HIV-infected patients compared to healthy controls. In contrast, DXA aBMD data showed no significant differences between HIV-infected patients and healthy controls.ConclusionsOur results suggest that high-resolution imaging is a powerful tool to assess trabecular bone microstructure and can be used to assess bone health in HIV-infected men who show no differences to healthy males by DXA aBMD. Advances in MRI technology have made microstructural imaging at the proximal femur possible. Further studies in larger patient cohorts are clearly warranted.

We investigate the use of different trabecular bone descriptors and advanced machine learning tech niques to complement standard bone mineral density (BMD) measures derived from dual-energy x-ray absorptiometry (DXA) for improving clinical assessment of osteoporotic fracture risk. For this purpose, volumes of interest were extracted from the head, neck, and trochanter of 146 ex vivo proximal femur specimens on multidetector computer tomography. The trabecular bone captured was characterized with (1) statistical moments of the BMD distribution, (2) geometrical features derived from the scaling index method (SIM), and (3) morphometric parameters, such as bone fraction, trabecular thickness, etc. Feature sets comprising DXA BMD and such supplemental features were used to predict the failure load (FL) of the specimens, previously determined through biomechanical testing, with multiregression and support vector regression. Prediction performance was measured by the root mean square error (RMSE); correlation with measured FL was evaluated using the coefficient of determination R2. The best prediction performance was achieved by a combination of DXA BMD and SIM-derived geometric features derived from the femoral head (RMSE: 0.869 ± 0.121, R2: 0.68 ± 0.079), which was significantly better than DXA BMD alone (RMSE: 0.948 ± 0.119, R2: 0.61 ± 0.101) (p < 10-4). For multivariate feature sets, SVR outperformed multiregression (p < 0.05). These results suggest that supplementing standard DXA BMD measurements with sophisticated femoral trabecular bone characterization and supervised learning techniques can significantly improve biomechanical strength prediction in proximal femur specimens.

In spite of a high prevalence of vitamin D inadequacy in pregnant women and neonates, relationships among vitamin D status (25(OH)D), parathyroid hormone (PTH), bone specific alkaline phosphatase (BALP), and whole body bone mineral content (WBBMC) in the newborn are poorly characterized. The purpose of the present study was to investigate the relationships between maternal and cord 25(OH)D, PTH, BALP, and WBBMC in newborns in a multiethnic population in Oakland, California and to evaluate the predictive value of the biochemical indices as indicators of WBBMC. Maternal and cord blood were collected from 80 mother-infant pairs and infant WBBMC was measured by dual energy X-ray absorptiometry 8-21 days post-birth. Cord PTH and BALP were each inversely correlated with infant WBBMC (r = -0.28, p = 0.01 and r = -0.26, p = 0.02) and with cord 25(OH)D (r = -0.24, p = 0.03 and r = -0.34, p = 0.002), while cord 25(OH)D and unadjusted or weight-adjusted WBBMC were not significantly correlated with one other. In multivariate regression modeling, infant WBBMC was most strongly predicted by infant weight (p < 0.0001), while either PTH or BALP contributed modestly but significantly to the model (p = 0.006 and p = 0.03 respectively). Cord 25(OH)D was not a significant predictor of infant WBBMC. This study provides evidence of associations between feto-maternal 25(OH)D, cord PTH and BALP, and early infant WBBMC, though neither feto-maternal 25(OH)D nor the measured biochemical indices were suitable indicators of WBBMC.

In spite of a high prevalence of vitamin D inadequacy in pregnant women and neonates, relationships among vitamin D status (25(OH)D), parathyroid hormone (PTH), bone specific alkaline phosphatase (BALP), and whole body bone mineral content (WBBMC) in the newborn are poorly characterized. The purpose of the present study was to investigate the relationships between maternal and cord 25(OH)D, PTH, BALP, and WBBMC in newborns in a multiethnic population in Oakland, California and to evaluate the predictive value of the biochemical indices as indicators of WBBMC. Maternal and cord blood were collected from 80 mother-infant pairs and infant WBBMC was measured by dual energy X-ray absorptiometry 8-21 days post-birth. Cord PTH and BALP were each inversely correlated with infant WBBMC (r = -0.28, p = 0.01 and r = -0.26, p = 0.02) and with cord 25(OH)D (r = -0.24, p = 0.03 and r = -0.34, p = 0.002), while cord 25(OH)D and unadjusted or weight-adjusted WBBMC were not significantly correlated with one other. In multivariate regression modeling, infant WBBMC was most strongly predicted by infant weight (p < 0.0001), while either PTH or BALP contributed modestly but significantly to the model (p = 0.006 and p = 0.03 respectively). Cord 25(OH)D was not a significant predictor of infant WBBMC. This study provides evidence of associations between feto-maternal 25(OH)D, cord PTH and BALP, and early infant WBBMC, though neither feto-maternal 25(OH)D nor the measured biochemical indices were suitable indicators of WBBMC.