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Estimates of body composition have been derived using 3-dimensional optical imaging (3DO), but no equations to date have been calibrated using a 4-component (4C) model criterion. This investigation reports the development of a novel body fat prediction formula using anthropometric data from 3DO imaging and a 4C model. Anthropometric characteristics and body composition of 179 participants were measured via 3DO (Size Stream.sup.® SS20) and a 4C model. Machine learning was used to identify significant anthropometric predictors of body fat (BF%), and stepwise/lasso regression analyses were employed to develop new 3DO-derived BF% prediction equations. The combined equation was externally cross-validated using paired 3DO and DXA assessments (n = 158), producing a R.sup.2 value of 0.78 and a constant error of (X [plus or minus] SD) 0.8 [plus or minus] 4.5%. 3DO BF% estimates demonstrated equivalence with DXA based on equivalence testing with no proportional bias in the Bland-Altman analysis. Machine learning methods may hold potential for enhancing 3DO-derived BF% estimates., Author(s): Patrick S. Harty [sup.1], Breck Sieglinger [sup.2], Steven B. Heymsfield [sup.3], John A. Shepherd [sup.4], David Bruner [sup.2], Matthew T. Stratton [sup.1], Grant M. Tinsley [sup.1] Author Affiliations: (1) [...]

Background: We aimed to explore the associations between thigh muscle fat density and vascular events. Methods: A total of 3,595 adults (mean age, 57.2 years; women, 1,715 [47.7%]) without baseline cardiovascular events from the Korean Atherosclerosis Study-2 were included. Muscle and fat area at the mid-thigh level were measured by computed tomography (CT) using the following Hounsfield Unit range: 0–30 for low density muscle (LDM); 31–100 for normal density muscle (NDM); and − 250 to − 50 for fat. Results: During a median follow-up period of 11.8 (4.3–13.9) years, vascular events occurred in 11.6% of men and 5.9% of women. Individuals with vascular events had a larger LDM area (men: 48.8 ± 15.5 cm2 vs. 44.6 ± 14.5 cm2; women: 39.4 ± 13.2 cm2 vs. 35.0 ± 11.8 cm2, both P < 0.001) compared with those who did not have vascular events during the follow-up of at least 5 years. The LDM/NDM ratio was also independently associated with vascular events after adjusting for cardiometabolic risk factors. Moreover, the LDM/NDM ratio improved the prognostic value for vascular events when added to conventional risk factors. Conclusions: The current study suggests that a higher thigh muscle fat infiltration is associated with an increased risk of developing vascular events among Korean adults. [ABSTRACT FROM AUTHOR]

Objective: This study's objective was to develop models predicting the relative reduction in skeletal muscle (SM) mass during periods of voluntary calorie restriction (CR) and to validate model predictions in longitudinally monitored samples. Methods: The model development group included healthy nonexercising adults (n = 897) who had whole‐body SM mass measured with magnetic resonance imaging. Model predictions of relative SM changes with CR were evaluated in two longitudinal studies, one 12 to 14 weeks in duration (n = 74) and the other 12 months in duration (n = 26). Results: A series of SM prediction models were developed in a sample of 415 males and 482 females. Model‐predicted changes in SM mass relative to changes in body weight (i.e., ΔSM/Δbody weight) with a representative model were (mean ± SE) 0.26 ± 0.013 in males and 0.14 ± 0.007 in females (sex difference, p < 0.001). The actual mean proportions of weight loss as SM in the longitudinal studies were 0.23 ± 0.02/0.20 ± 0.06 in males and 0.10 ± 0.02/0.17 ± 0.03 in females, similar to model‐predicted values. Conclusions: Nonelderly males and females with overweight and obesity experience respective reductions in SM mass with voluntary CR in the absence of a structured exercise program of about 2 to 2.5 kg and 1 to 1.5 kg per 10‐kg weight loss, respectively. These estimates are predicted to be influenced by interactions between age and body mass index in males, a hypothesis that needs future testing. [ABSTRACT FROM AUTHOR]

Author(s): Steven B. Heymsfield [sup.1] Author Affiliations: (1) Pennington Biomedical Research Center, LSU System, Baton Rouge, USA Early exposure to nature What do Galileo and Lavoisier have in common? Both [...]

A long-standing question in nutrition and obesity research involves quantifying the relationship between body fat and anthropometry. To date, the mathematical formulation of these relationships has relied on pairing easily obtained anthropometric measurements such as the body mass index (BMI), waist circumference, or hip circumference to body fat. Recent advances in 3D body shape imaging technology provides a new opportunity for quickly and accurately obtaining hundreds of anthropometric measurements within seconds, however, there does not yet exist a large diverse database that pairs these measurements to body fat. Herein, we leverage 3D scanned anthropometry obtained from a population of United States Army basic training recruits to derive four subpopulations of homogenous body shape archetypes using a combined principal components and cluster analysis. While the Army database was large and diverse, it did not have body composition measurements. Therefore, these body shape archetypes were paired to an alternate smaller sample of participants from the Pennington Biomedical Research Center in Baton Rouge, LA that were not only similarly imaged by the same 3D scanning machine, but also had concomitant measures of body composition by dual-energy X-ray absorptiometry body composition. With this enhanced ability to obtain anthropometry through 3D scanning quickly of large populations, our machine learning approach for pairing body shapes from large datasets to smaller datasets that also contain state-of-the-art body composition measurements can be extended to pair other health outcomes to 3D body shape anthropometry., Author(s): James D. Pleuss [sup.1] , Kevin Talty [sup.1] , Steven Morse [sup.1] , Patrick Kuiper [sup.1] , Michael Scioletti [sup.1] , Steven B. Heymsfield [sup.2] , Diana M. Thomas [...]

Author(s): Steven B. Heymsfield [sup.1] Author Affiliations: (1) Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, USA Introduction What do the following three equations for predicting resting energy expenditure [...]

ObjectiveChanges in body fat distribution and abnormal glucose metabolism are common in HIV-infected patients. We hypothesized that HIV-infected participants would have a higher prevalence of impaired glucose tolerance (IGT) compared with control subjects.Research design and methodsA total of 491 HIV-infected and 187 control participants from the second examination of the Study of Fat Redistribution and Metabolic Change in HIV Infection (FRAM) underwent glucose tolerance testing (GTT). Multivariable regression was used to identify factors associated with GTT parameters.ResultsThe prevalence of impaired fasting glucose (IFG) (>110 mg/dL) was similar in HIV-infected and control participants (21 vs. 25%, P = 0.23). In those without IFG, the prevalence of IGT was slightly higher in HIV-infected participants compared with control subjects (13.1 vs. 8.2%, P = 0.14) and in HIV+ participants with lipoatrophy versus without (18.1 vs. 11.5%, P = 0.084). Diabetes detected by GTT was rare (HIV subjects 1.3% and control subjects 0%, P = 0.65). Mean 2-h glucose levels were 7.6 mg/dL higher in the HIV-infected participants (P = 0.012). Increased upper trunk subcutaneous adipose tissue (SAT) and decreased leg SAT were associated with 2-h glucose and IGT in both HIV-infected and control participants. Adjusting for adipose tissue reduced the estimated effects of HIV. Exercise, alcohol use, and current tenofovir use were associated with lower 2-h glucose levels in HIV-infected participants.ConclusionsIn HIV infection, increased upper trunk SAT and decreased leg SAT are associated with higher 2-h glucose. These body fat characteristics may identify HIV-infected patients with normal fasting glucose but nonetheless at increased risk for diabetes.

ObjectiveIncreased body mass index (BMI) is associated with reduced all-cause and cardiovascular (CV) mortality in hemodialysis (HD) patients, whereas CV risk increases with BMI in the general population. In the general population, obesity is associated with inflammation, decreased high-density lipoprotein (HDL) cholesterol, increased low-density lipoprotein (LDL) cholesterol, and triglycerides (TGs), all risk factors for CV disease. Low-density lipoprotein cholesterol does not predict CV risk in HD, whereas increased C-reactive protein and interleukin-6 (IL-6), low HDL and apolipoprotein (apo) AI, and increased fasting TGs do predict risk. Renal failure is associated with dyslipidemia and inflammation in normal-weight patients. We hypothesized that the effects of obesity may be obscured by renal failure in HD.MethodsWe explored the relationship between adipose tissue pools and distribution, i.e., subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) (measured by magnetic resonance imaging) and measures of inflammation (C-reactive protein, IL-6, ceruloplasmin, and alpha1 acid glycoprotein), HDL and LDL cholesterol, total TGs, apo AI, apo B, apo CII (an activator of lipoprotein lipase), apo CIII (an inhibitor of lipoprotein lipase), and the adipokines, leptin and adiponectin, in 48 patients with prevalent HD.Results and conclusionsTotal TG concentrations were positively correlated with VAT controlled for age, sex, and weight. Both apo CII and apo CIII were correlated only with VAT. Adiponectin was inversely correlated with VAT, and leptin was positively associated with SAT. C-reactive protein and alpha1 acid glycoprotein were weakly associated with SAT, whereas ceruloplasmin was strongly associated with VAT according to multiple regression analysis. In contrast, apo B, LDL, apo AI, HDL, and IL-6 were not correlated with any measure of body composition, potentially mitigating the effects of obesity in HD.

Assessing skeletal muscle mass (SMM) is critical in studying and detecting sarcopenia. Direct measurements by MRI or computerized tomography are expensive or high in radiation exposure. Dual-energy X-ray absorptiometry (DXA) is promising for body composition assessments, but the validity of DXA for predicting SMM in the elderly is still under investigation. The objective of this study was to assess the relationship between DXA-derived measurements of lean soft tissue mass (LSTM) and SMM in older women. Study participants were postmenopausal women (n = 101) recruited in southern Arizona. Total and regional body composition was measured using MRI and DXA (QDR4500w). The participants' mean age was 70.7 +/- 6.4 y and their mean BMI was 27.4 +/- 5.1 kg/m2. DXA-derived LSTM was highly correlated with MRI-derived SMM for the whole body (r = 0.94; P < 0.001) and leg region (r = 0.91; P < 0.001). In multivariate models, adjusting for age and DXA-derived percent fat slightly increased the amount of variance in SMM that can be explained by the DXA-derived LSTM assessments for the leg region but not for the total body. In conclusion, although the relationships between DXA measures and MRI-derived SMM vary by region of interest, the overall prediction of SMM by DXA is excellent. We conclude that DXA is a reliable method for cross-sectional assessments of SMM in older women.

Lack of efficacy of weight loss(WL) interventions is attributed in-part to low adherence to dietary/physical activity(PA) recommendations. However, some compensation may occur in PA as a response to energy restriction such as a decrease in non-exercise PA(NEPA) or non-exercise activity thermogenesis(NEAT). The current study aim was (1) to investigate whether adaptive thermogenesis(AT) in NEAT occurs after WL, and (2) to understand the associations of these compensations with WL. Ninety-four former athletes [mean±SD, age: 43.0±9.4y, BMI: 31.1±4.3 kg/m2, 34.0% female] were recruited and randomly assigned to intervention or control groups (IG, CG). The IG underwent a one-year lifestyle WL-intervention; no treatments were administered to the CG. PA was measured using accelerometery and NEAT was predicted with a model including sample baseline characteristics. AT was calculated as measuredNEAT4mo/12mo(kcal/d)–predictedNEAT4mo/12mo(kcal/d)-measuredNEATbaseline(kcal/d)–predictedNEATbaseline(kcal/d). Dual-energy x-ray absorptiometry was used to assess fat-free mass and fat mass. No differences were found in the IG for NEAT or NEPA after WL. Considering mean values, AT was not found for either group. The SD of individual response (SDIR) for AT was −2(4-months) and 24(12-months) (smallest worthwhile change = 87kcal/d), suggesting that the interindividual variability regarding AT in NEAT is not relevant and the variability in this outcome might reflect a large within-subject variability and/or a large degree of random measurement error. No associations were found between AT in NEAT and changes in body composition. Further studies are needed to clarify the mechanisms behind the large variability in AT observed in NEAT and related changes in NEPA to better implement lifestyle-induced WL interventions. Highlights No significant differences were found for non-exercise activity thermogenesis (NEAT) or non-exercise physical activity (NEPA) after the weight loss (WL) intervention; Although a large variability was found for NEAT and NEPA, the interindividual variability regarding these outcomes is not relevant. The variability in these outcomes might reflect a large within-subject variability and/or a large degree of random measurement error; Although no energy conservation was observed in NEAT after moderate WL (mean values), further studies are needed to clarify the mechanisms behind the large variability in adaptive thermogenesis observed in NEAT and related changes in NEPA to better implement lifestyle-induced WL interventions. Trial registration: ClinicalTrials.gov identifier: NCT03031951. [ABSTRACT FROM AUTHOR]

Anthropometry, Greek for human measurement, is a tool widely used across many scientific disciplines. Clinical nutrition applications include phenotyping subjects across the lifespan for assessing growth, body composition, response to treatments, and predicting health risks. The simple anthropometric tools such as flexible measuring tapes and calipers are now being supplanted by rapidly developing digital technology devices. These systems take many forms, but excitement today surrounds the introduction of relatively low cost three-dimensional optical imaging methods that can be used in research, clinical, and even home settings. This review examines this transformative technology, providing an overview of device operational details, early validation studies, and potential applications. Digital anthropometry is rapidly transforming dormant and static areas of clinical nutrition science with many new applications and research opportunities., Author(s): Steven B. Heymsfield [sup.1] , Brianna Bourgeois [sup.1] , Bennett K. Ng [sup.2] [sup.3] , Markus J. Sommer [sup.2] , Xin Li [sup.4] , John A. Shepherd [sup.2] [sup.3] [...]