Waistline to thigh circumference ratio as a predictor of MAFLD: a health care worker study with 2-year follow-up.

BACKGROUND: This study aimed to determine whether the waist-to-thigh ratio (WTTR) is associated with the incidence of metabolic-associated fatty liver disease (MAFLD) in health care workers. METHODS: There were 4517 health care workers with baseline data and results from 2 follow-up examinations. We divided the subjects into 3 groups according to baseline WTTR and used the Cox hazard regression model to estimate MAFLD risk. RESULTS: The WTTRs were categorized by tertiles at baseline using the values 1.58 and 1.66. Patients with higher WTTR tended to have significantly greater values for the following factors, body mass index (BMI), fasting blood glucose (FPG), systolic blood pressure, diastolic blood pressure, total cholesterol (TC), triglycerides (TG), low-density lipoprotein-cholesterol (LDL-C) and neck circumference. The incidence of MAFLD significantly increased with increasing WTTR tertiles (5.74%, 12.75% and 22.25% for the first, second and third tertiles, respectively, P < 0.05 for trend). Kaplan-Meier(K-M) survival analysis revealed a significant tendency towards increased MAFLD risk with increasing WTTR tertile. In the fully adjusted model, the hazard ratios (95% CIs) for MAFLD in the second, third WTTR tertiles compared with the first quartile were 2.17(1.58,2.98), 3.63(2.70,4.89), respectively, third neck circumference tertiles compared with the first quartile were 2.84(1.89,4.25), 8.95(6.00,13.35), respectively. Compared with those of individuals with a BMI > 23 kg/m2, the associations between WTTR and MAFLD incidence were more pronounced in subjects with a BMI < 23 kg/m2. Similarly, the difference in neck circumference was more pronounced in these patients with a BMI < 23 kg/m2. CONCLUSIONS: Our results revealed that the WTTR is an independent risk factor for MAFLD, and there was a dose‒response relationship between the WTTR and MAFLD risk. The neck circumference was significantly different in subjects with a BMI < 23 kg/m2. This approach provides a new way to predict the incidence rate of MAFLD.

Crosstalk between skeletal muscle ratio and cholesterol metabolism disorders: a cross-section study.

BACKGROUND: Dysfunction of cholesterol metabolism may be associated with low skeletal muscle mass.  This study aimed to explore the relationship between skeletal muscle mass and cholesterol metabolic disorders in adults. METHODS: The data of a total of 5949 people with complete medical history data, biochemical data and body composition analysis were recruited. According to the serum cholesterol, low density lipoprotein (LDL), high density lipoprotein (HDL) and nonHDL, the population was divided into a disorder group and a normal group. Independent sample t tests, chi-square tests, Pearson's correlation analyses and binary logistic regression analyses were used to study the effect of body composition on abnormal cholesterol metabolism. According to BMI and sex, the population was divided into different subgroups, and binary logistic regression analysis was used to study the effect of the skeletal mass ratio on cholesteral metabolic disorders in different subgroups. RESULTS: There were significant differences in sex, alcohol consumption, body weight, BMI, skeletal muscle mass index (SMI) [total skeletal muscle mass (kg)/height 2 (m2)] and skeletal muscle mass ratio (SMR) [total skeletal muscle mass (kg)/weight (kg) *100] between the disorder group (hypercholesterolemia, hyper-LDL, lower-HDL and hyper-nonHDL) and the normal group. Pearson correlation analysis revealed that the SMR was negatively correlated, while the SMI was positively correlated with cholesterol metabolic disorders in both sexes. The overweight group was older and had a greater SMI, abnormal cholesteral metabolism ratio and lower SMR than the normal-weight group. In the normal-weight group, the SMR was an independent protective factor against different kinds of cholesteral metabolic disorders in both sexes, while the SMI was a risk factor. In the overweight subgroup, the protective effect on HDL and nonHDL metabolism remained in the male subgroup but disappeared in the female subgroup. However, the SMI was an independent risk factor for different kinds of cholesteral metabolic disorders in both sexes. CONCLUSIONS: SMR was an independent protective factor against cholesterol metabolic disorders in both males and females, especially in the normal weight group. SMI was an independent risk factor, especially in the overweight group.

Explore the effect of pressure and time of compression on the risk of intraoperatively acquired pressure injury based on theoretical framework: A prospective study.

Surgery is a high risk factor for the occurrence of pressure injury (PI). On the basis of theoretical research, pressure and duration of pressure are key factors affecting PI. Pressure is affected by the individual pressure redistribution capacity. So our study aims to explore how the surgery time and pressure intensity affect the occurrence of PI and what are the risk factors. A prospective study. A total of 250 patients who underwent elective surgery in a grade-A general hospital from November 2021 to February 2023 were selected and divided into a group of 77 patients with IAPI (intraoperatively acquired pressure injury) and a group of 173 patients with no IAPI. Visual pressure inductive feedback system and body composition analysis technology were used to record the local pressure value and change of patients before and after anaesthesia. Relevant data of the patients were collected to explore the influencing factors. The maximum pressure and average pressure at the pressure site of the same patient changed before and after anaesthesia, and the pressure after anaesthesia was significantly higher than that before anaesthesia. There was no statistical difference in the average pressure after anaesthesia (p > 0.05), but the maximum pressure in the IAPI group was higher than that in the non-occurrence group (p < 0.05). The average pressure multiplied by the operation time in IAPI group is significantly higher than that in the non-IAPI group (p < 0.01). Multiple linear regression analysis (stepwise regression) showed that fat-free weight, age, waist circumference, body mass index (BMI) and gender were taken as independent variables into the regression model, affecting the maximum pressure. In addition, operation time ≥4 h may be a high risk factor for IAPI. In future studies, more objective research tools can be applied to improve the accuracy of predicting the risk of IAPI. In addition to gender and BMI, follow-up studies may consider including measures such as waist circumference and fat-free body weight in IAPI risk assessment to guide the clinical nursing work more scientifically.

Retrospective Cross-Sectional Study of the Relationship of Thyroid Volume and Function with Anthropometric Measurements, Body Composition Analysis Parameters, and the Diagnosis of Metabolic Syndrome in Euthyroid People Aged 18-65.

Background and Objectives: The thyroid is a key endocrine gland for the regulation of metabolic processes. A body composition analysis (BCA) is a valuable complement to the assessment of body mass index, which is derived only from body weight and height. This cross-sectional retrospective study aimed to investigate the relationships between thyroid volume (TV) and thyroid function parameters, anthropometric measurements, BCA parameters, and the presence of metabolic syndrome (MetS) in adults without clinically overt thyroid disease. Material and Methods: This study involved 45 people (females: 57.8%; MetS: 28.9%) hospitalized for planned diagnostics without signs of acute illness or a deterioration of their health and without thyroid disease, who underwent thyroid ultrasound scans, biochemical tests to assess their thyroid function, MetS assessments, anthropometric measurements, and BCAs using the bioelectrical impedance method. Results: The TV was significantly larger in people with MetS compared to people without MetS. The TV was significantly higher and the serum thyrotropin (TSH) concentration was significantly lower in overweight and obese people than in normal and underweight people. The free triiodothyronine (FT3) serum concentration and TV were correlated with waist circumference and some parameters of the BCA, and the FT3 concentration was also correlated with the body mass index, waist-hip ratio, and waist-height ratio. No significant correlations were found between the FT4 and TSH and the results of the anthropometric and BCA measurements. Conclusions: Even in a population of euthyroid patients without clinically overt thyroid disease, there were some significant relationships between the volume and function of the thyroid gland and the results of their anthropometric parameters, BCAs, and the presence of MetS features.

[Comparison and application of obesity screening indicators in children and adolescents aged 6-18 years in Hangzhou in 2023].

OBJECTIVE: To determine the percentile reference values and reference curves of total body fat percentage in children and adolescents by sex and age, and to evaluate the reliability of different anthropometric indicators in screening for obesity in children and adolescents in order to explore the validity and practicality of a large-scale screening tool for childhood obesity and its desirable thresholds. METHODS: A total of 5983 children and adolescents aged 6-18 years from six primary and secondary schools were selected by stratified randomised cluster sampling in Hangzhou City in 2023, and their weight, height, waist and hip circumferences were measured, and the children's total body fat content was measured using the bioelectrical resistance-antibody composition analyser. A generalised additive model(GAMLSS) was used to construct a percentile reference curve for children's body fat percentage, and the performance of each anthropometric measure in correctly classifying children and adolescents as obese was assessed using receiver operating characteristic(ROC) curves. RESULTS: Over the period 6-18 years, body fat percentage in girls tended to increase with age from year to year, whereas in boys it tended to increase and then decrease. Fat mass index was the main factor contributing to the difference in BMI at the same age and sex(the optimal cutoff values were 26.82 and 24.52, with AUC values of 0.988 and 0.992, respectively), and the contribution of fat mass index was greater in those with a higher BMI. BMI, which had the largest area under the curve for both boys and girls, was the best indicator for assessing obesity in children and adolescents; the waist-to-height ratio(WHtR), with small variability and equally large area under curve values(0.980 for boys and 0.970 for girls), could simultaneously meet the requirements of accuracy and practicality for obesity assessment in mass screening of children and adolescents. CONCLUSION: This study establish the percentile reference value and reference curve of body fat percentage of children and adolescents aged 6-18 years in Hangzhou based on bioelectrical impedance method, and found that WHtR is a simple, effective and practical screening tool for childhood obesity with low variability, which can provide a reference basis for evaluating growth and development and identifying obesity in children aged 6-18 years in this region.

A differentiated approach to the diagnosis of overweight and obesity in children based on bioimpedance analysis of body composition.

OBJECTIVE: Aim: The current study introduces a novel diagnostic algorithm employing bioimpedance analysis to comprehensively evaluate body composition in children, assessing fat content, skeletal muscle content, and fat distribution. PATIENTS AND METHODS: Materials and Methods: Bioelectrical impedance measurements were obtained using the TANITA MC-780 MA analyzer. Indicators such as body weight, BMI, total fat content, absolute limb muscle mass, skeletal muscle strength, and waist-to-hip ratio (WHR) were assessed. A sample of 101 children aged 9 to 14 were studied using the proposed algorithm, refining BMI-based classifications. RESULTS: Results: The algorithm comprises three steps, categorizing children based on fat content, presence of sarcopenia, and central fat distribution. It identified diverse somatotypes within the groups classified by BMI. Notably, it revealed prognostically unfavorable somatotypes, such as sarcopenic obesity with central fat distribution, highlighting potential health risks. Current BMI-centric diagnoses may misclassify cardiometabolic risks, making early detection challenging. The algorithm enables a detailed evaluation, unmasking metabolically unfavorable conditions like sarcopenic obesity. The incorporation of functional tests, such as a standardized hand-grip test, enhances diagnostic accuracy. The proposed WHR indicator for characterizing fat distribution provides a practical method for determining somatotypes in children. CONCLUSION: Conclusions: This comprehensive algorithm offers an alternative to BMI-based classifications, enabling early detection of obesity and associated risks. Further validation through large-scale epidemiological studies is essential to establish correlations between somatotypes and cardiometabolic risks, fostering a more nuanced and individualized approach to pediatric obesity management.

The relationship between computed tomography-derived body composition, systemic inflammation, and survival in patients with abdominal aortic aneurysm.

OBJECTIVE: Patient selection and risk stratification for elective repair of abdominal aortic aneurysm (AAA), either by open surgical repair or by endovascular aneurysm repair, remain challenging. Computed tomography (CT)-derived body composition analysis (CT-BC) and systemic inflammation-based scoring systems such as the systemic inflammatory grade (SIG) appear to offer prognostic value in patients with AAA undergoing endovascular aneurysm repair. The relationship between CT-BC, systemic inflammation, and prognosis has been explored in patients with cancer, but data in noncancer populations are lacking. The present study aimed to examine the relationship between CT-BC, SIG, and survival in patients undergoing elective intervention for AAA. METHODS: A total of 611 consecutive patients who underwent elective intervention for AAA at three large tertiary referral centers were retrospectively recruited for inclusion into the study. CT-BC was performed and analyzed using the CT-derived sarcopenia score (CT-SS). Subcutaneous and visceral fat indices were also recorded. SIG was calculated from preoperative blood tests. The outcomes of interest were overall and 5-year mortality. RESULTS: Median (interquartile range) follow-up was 67.0 (32) months, and there were 194 (32%) deaths during the follow-up period. There were 122 (20%) open surgical repair cases, 558 (91%) patients were male, and the median (interquartile range) age was 73.0 (11.0) years. Age (hazard ratio [HR]: 1.66, 95% confidence interval [CI]: 1.28-2.14, P < .001), elevated CT-SS (HR: 1.58, 95% CI: 1.28-1.94, P < .001), and elevated SIG (HR: 1.29, 95% CI: 1.07-1.55, P < .01) were independently associated with increased hazard of mortality. Mean (95% CI) survival in the CT-SS 0 and SIG 0 subgroup was 92.6 (84.8-100.4) months compared with 44.9 (30.6-59.2) months in the CT-SS 2 and SIG ≥2 subgroup (P < .001). Patients with CT-SS 0 and SIG 0 had 90% (standard error: 4%) 5-year survival compared with 34% (standard error: 9%) in patients with CT-SS 2 and SIG ≥2 (P < .001). CONCLUSIONS: Combining measures of radiological sarcopenia and the systemic inflammatory response offers prognostic value in patients undergoing elective intervention for AAA and may contribute to future clinical risk predication strategies.

QT Prolongation in Dialysis Patients: An Epidemiological Study with a Focus on Malnutrition.

BACKGROUND/AIMS: QT prolongation is a known risk factor for ventricular fibrillation and ventricular tachycardia. Therefore, more refined management is necessary to reduce sudden cardiac death secondary to such arrhythmias. METHODS: Electrocardiographic findings were reviewed in 224 patients, and the associations of QT prolongation with various clinical parameters were examined, including the nutritional state. Correlations were also examined between QT prolongation and body composition measurements determined by multifrequency bioelectrical impedance analysis. RESULTS: Prolongation of the corrected QT (QTc) interval over 0.44 s was seen in 140 patients (62.5%). QT prolongation was independent of age and dialysis therapy duration and was more frequent in diabetics (70.1%) than in nondiabetics (54.2%, p = 0.014) and more frequent in women (78.8%) than in men (53.5%, p < 0.001). Serum levels of albumin (p < 0.001) and Cr (p < 0.001) and the Geriatric Nutritional Risk Index (GNRI, p < 0.001) were negatively correlated with QTc interval; no significant correlation was noted with total protein, urea nitrogen, or uric acid. Negative correlations with QTc interval were found for BMI(p < 0.01), percent total body water (%TBW; p < 0.05), and percent intracellular water (%ICW; p < 0.01) but not with the percent extracellular water/TBW ratio or edema ratio. The longer the QTc interval, the lower the fat-free mass (FFM; p < 0.01) and muscle mass (MM; p < 0.01), but there was no significant correlation with percent fat. CONCLUSION: These results suggest that QT prolongation is a common complication and is more frequent in women and diabetic patients. The decreases in serum albumin and Cr levels, GNRI, BMI, %TBW, %ICW, FFM, and MM together coincided with malnutrition and thus suggest a close relationship of QT prolongation with malnutrition. Management of QT prolongation may be achieved better in the future by understanding these biochemical and biophysical changes, particularly those regarding malnutrition.

Usefulness of skeletal muscle measurement by computed tomography in patients with esophageal cancer: changes in skeletal muscle mass due to neoadjuvant therapy and the effect on the prognosis.

PURPOSE: This analysis was performed to clarify the usefulness of skeletal muscle measurements using computed tomography (CT) in patients with esophageal cancer and the effect of treatment-induced changes in the skeletal muscle mass on the prognosis. METHODS: Ninety-seven male patients who underwent thoracoscopic esophagectomy for esophageal squamous cell carcinoma were included in the study. The preoperative CT images were analyzed retrospectively. RESULTS: In a survival analysis performed according to the preoperative data of skeletal muscle, the low-skeletal muscle index (l-SMI) group had a poorer outcome than the normal skeletal muscle index (n-SMI) group in terms of both the overall survival (OS) and the relapse-free survival (RFS) (OS: P < 0.01, RFS: P = 0.01). In the multivariate analysis for the OS, preoperative l-SMI was an independent predictor (hazard ratio: 3.68, 95% confidence interval 1.32-10.2, P = 0.01). In patients who underwent neoadjuvant therapy (NAT), the SMI was significantly reduced after NAT (P < 0.01). The preoperative skeletal muscle area on CT was strongly correlated with the results of a bioelectrical impedance analysis (BIA) (ρ = 0.77, P < 0.01). CONCLUSIONS: A decreased preoperative skeletal muscle mass was associated with a poor outcome. In patients who underwent NAT, the SMI was significantly reduced after NAT. An analysis of the skeletal muscle mass using CT images was found to be useful for providing data that corresponded with BIA data.

[Construction of a diagnostic model for fatty liver using human body composition analysis].

Objective: To construct a diagnostic model for fatty liver using body composition analysis and further evaluate the diagnostic effect of the model on fatty liver. Methods: 726 cases with chronic liver disease who visited Tianjin Second People's Hospital from April 2019 to June 2022 and had body composition analysis tests were retrospectively enrolled and were divided into a fatty liver group (551 cases with fatty liver) and a control group (175 cases without fatty liver) according to the measured values of abdominal ultrasound and controlled attenuation parameter. An independent sample t-test and a non-parametric rank sum test were used for statistical processing. Logistic regression was used to construct a diagnostic model. Hosmer-Lemeshow was used to validate the fit of model. Receiver operating characteristic curve was used to confirm the diagnostic efficiency of the model. In addition, 341 cases of chronic liver disease who visited Tianjin Second People's Hospital were included to further verify the application effect of the model between July 2022 and February 2023. Results: Compared with the control group, the differences in various indicators of body composition analysis in the fatty liver group were statistically significant (P < 0.05). Basal metabolic rate (X1), visceral fat area (X2), and body fat (X3) were eventually included in the diagnostic model for BCA-FL (body composition analysis-fatty liver)= -7.771+0.002X1-0.035X2+0.456X3 with the Hosmer-Lemeshow test (P=0.059). The measured area under the receiver operating characteristic curve, the sensitivity, and the specificity were 0.888, 0.889, and 0.726, respectively, when the diagnostic threshold value was 0.615 with the Youden index and the receiver operating characteristic curve. In the validated model group, the area under the receiver operating characteristic curve, Youden index, sensitivity, and specificity were 0.875, 0.624, 0.799, and 0.825, respectively. Conclusion: The diagnostic model BCA-FL for fatty liver constructed using human body composition analysis has good diagnostic efficacy and is suitable for screening fatty liver in different basic liver disease populations.

Patterns of body composition and alteration after treatment in patients with newly diagnosed idiopathic inflammatory myopathies.

OBJECTIVES: To define the pattern of body composition and alteration after treatment of patients with newly diagnosed idiopathic inflammatory myopathies (IIMs) using DXA. METHODS: DXA was used to obtain regional and whole-body measurements of fat mass and lean tissue mass (LTM) in 50 patients with newly diagnosed IIM and matched controls. The DXA indices of fat mass and LTM were calculated. The analyses included correlations between DXA indices and clinical parameters [manual muscle test (MMT), Myositis Damage Index (MDI), Myositis Intention-to-Treat Activities Index (MITAX), handgrip, percentage forced vital capacity (%FVC) and creatine kinase level], comparison between patients with IIM and controls, comparison between IIM subgroups, receiver operating characteristic (ROC) analysis, and comparison of body composition before and after treatment. RESULTS: DXA LTM measurements were significantly correlated with MMT, MDI-muscle, handgrip strength, and %FVC. Patients with IIM had decreased LTM of the upper limbs and appendicular region. Male patients with IIM had significantly decreased LTM in the upper and lower limbs, whereas female patients with IIM had significantly decreased LTM in the upper limbs. Patients with IIM with anti-SRP seropositivity had lower LTM than patients with anti-SRP seronegativity. In ROC analysis, the DXA LTM indices presented good diagnostic values for distinguishing patients with newly diagnosed IIM from healthy controls. After treatment, the LTM of the upper limbs and appendicular region significantly increased. CONCLUSION: DXA is an attractive method for the evaluation of patients with newly diagnosed IIM as well as a new way of monitoring disease conditions.

Musculoskeletal trauma and artificial intelligence: current trends and projections.

Musculoskeletal trauma accounts for a significant fraction of emergency department visits and patients seeking urgent care, with a high financial cost to society. Diagnostic imaging is indispensable in the workup and management of trauma patients. However, diagnostic imaging represents a complex multifaceted system, with many aspects of its workflow prone to inefficiencies or human error. Recent technological innovations in artificial intelligence and machine learning have shown promise to revolutionize our systems for providing medical care to patients. This review will provide a general overview of the current state of artificial intelligence and machine learning applications in different aspects of trauma imaging and provide a vision for how such applications could be leveraged to enhance our diagnostic imaging systems and optimize patient outcomes.

Significance of Acquisition Parameters for Adipose Tissue Segmentation on CT Images.

OBJECTIVE. CT-based body composition analysis quantifies skeletal muscle and adipose tissue. However, acquisition parameters and quality can vary between CT images obtained for clinical care, which may lead to unreliable measurements and systematic error. The purpose of this study was to estimate the influence of IV contrast medium, tube current-exposure time product, tube potential, and slice thickness on cross-sectional area (CSA) and mean attenuation of subcutaneous (SAT), visceral (VAT), and inter-muscular adipose tissue (IMAT). MATERIALS AND METHODS. We retrospectively analyzed 244 images from 105 patients. We applied semiautomated threshold-based segmentation to CTA, dual-energy CT, and CT images acquired as part of PET examinations. An axial image at the level of the third lumbar vertebral body was extracted from each examination to generate 139 image pairs. Images from each pair were obtained with the same scanner, from the same patient, and during the same examination. Each image pair varied in only one acquisition parameter, which allowed us to estimate the effect of the parameter using one-sample t or median tests and Bland-Altman plots. RESULTS. IV contrast medium application reduced CSA in each adipose tissue compartment, with percentage change ranging from -0.4% (p = .03) to -9.3% (p < .001). Higher tube potential reduced SAT CSA (median percentage change, -4.2%; p < .001) and VAT CSA (median percentage change, -2.8%; p = .001) and increased IMAT CSA (median percentage change, -5.4%; p = .001). Thinner slices increased CSA in the VAT (mean percentage change, 3.0%; p = .005) and IMAT (median percentage change, 17.3%; p < .001) compartments. Lower tube current-exposure time product had a variable effect on CSA (median percentage change, -3.2% for SAT [p < .001], -12.6% for VAT [p = .001], and 58.8% for IMAT [p < .001]). IV contrast medium and higher tube potential increased mean attenuation, with percentage change ranging from 0.8% to 1.7% (p < .05) and from 6.2% to 20.8% (p < .001), respectively. Conversely, thinner slice and lower tube current-exposure time product reduced mean attenuation, with percentage change ranging from -5.4% to -1.0% (p < .001) and from -8.7% to -1.8% (p < .001), respectively. CONCLUSION. Acquisition parameters significantly affect CSA and mean attenuation of adipose tissue. Details of acquisition parameters used for CT-based body composition analysis need to be scrutinized and reported to facilitate interpretation of research studies.

Pixel-wise body composition prediction with a multi-task conditional generative adversarial network.

The analysis of human body composition plays a critical role in health management and disease prevention. However, current medical technologies to accurately assess body composition such as dual energy X-ray absorptiometry, computed tomography, and magnetic resonance imaging have the disadvantages of prohibitive cost or ionizing radiation. Recently, body shape based techniques using body scanners and depth cameras, have brought new opportunities for improving body composition estimation by intelligently analyzing body shape descriptors. In this paper, we present a multi-task deep neural network method utilizing a conditional generative adversarial network to predict the pixel level body composition using only 3D body surfaces. The proposed method can predict 2D subcutaneous and visceral fat maps in a single network with a high accuracy. We further introduce an interpreted patch discriminator which optimizes the texture accuracy of the 2D fat maps. The validity and effectiveness of our new method are demonstrated experimentally on TCIA and LiTS datasets. Our proposed approach outperforms competitive methods by at least 41.3% for the whole body fat percentage, 33.1% for the subcutaneous and visceral fat percentage, and 4.1% for the regional fat predictions.

Reproducibility and repeatability of MRI-based body composition analysis.

PURPOSE: There is an absence of reproducibility studies on MRI-based body composition analysis in current literature. Therefore, the aim of this study was to investigate the between-scanner reproducibility and the repeatability of a method for MRI-based body composition analysis. METHODS: Eighteen healthy volunteers of varying body mass index and adiposity were each scanned twice on five different 1.5T and 3T scanners from three different vendors. Two-point Dixon neck-to knee images and two additional liver scans were acquired with similar protocols. Visceral adipose tissue (VAT) volume, abdominal subcutaneous adipose tissue (ASAT) volume, thigh muscle volume, and muscle fat infiltration (MFI) in the thigh muscle were measured. Liver proton density fat fraction (PDFF) was assessed using two different methods, the scanner vendor's 6-point method and an in-house 2-point method. Within-scanner test-retest repeatability and between-scanner reproducibility were calculated using analysis of variance. RESULTS: Repeatability coefficients were 13 centiliters (cl) (VAT), 24 cl (ASAT), 17 cl (total thigh muscle volume), 0.53% (MFI), and 1.27-1.37% for liver PDFF. Reproducibility coefficients were 24 cl (VAT), 42 cl (ASAT), 31 cl (total thigh muscle volume), 1.44% (MFI), and 2.37-2.40% for liver PDFF. CONCLUSION: For all measures except MFI, the within-scanner repeatability explained much of the overall reproducibility. The two methods for measuring liver fat had similar reproducibility. This study showed that the investigated method eliminates effects due to scanner differences. The results can be used for power calculations in clinical studies or to better understand the scanner-induced variability in clinical applications.

CoreSlicer: a web toolkit for analytic morphomics.

BACKGROUND: Analytic morphomics, or more simply, "morphomics," refers to the measurement of specific biomarkers of body composition from medical imaging, most commonly computed tomography (CT) images. An emerging body of literature supports the use of morphomic markers measured on single-slice CT images for risk prediction in a range of clinical populations. However, uptake by healthcare providers been limited due to the lack of clinician-friendly software to facilitate measurements. The objectives of this study were to describe the interface and functionality of CoreSlicer- a free and open-source web-based interface aiming to facilitate measurement of analytic morphomics by clinicians - and to validate muscle and fat measurements performed in CoreSlicer against reference software. RESULTS: Measurements of muscle and fat obtained in CoreSlicer show high agreement with established reference software. CoreSlicer features a full set of DICOM viewing tools and extensible plugin interface to facilitate rapid prototyping and validation of new morphomic markers by researchers. We present published studies illustrating the use of CoreSlicer by clinicians with no prior knowledge of medical image segmentation techniques and no formal training in radiology, where CoreSlicer was successfully used to predict operative risk in three distinct populations of cardiovascular patients. CONCLUSIONS: CoreSlicer enables extraction of morphomic markers from CT images by non-technically skilled clinicians. Measurements were reproducible and accurate in relation to reference software.

Variations in fat mass contribution to bone mineral density by gender, age, and body mass index: the Korea National Health and Nutrition Examination Survey (KNHANES) 2008-2011.

UNLABELLED: The relationship of body composition and bone mineral density is complex and controversial. When classifying Korean population based on gender, age, and body mass index, fat mass had varying contributions to bone mineral density. INTRODUCTION: The relationship between body composition and bone mineral density (BMD) is complex, and it is uncertain how components of body mass variably affect BMD. METHODS: This cross-sectional observational study was performed in subjects ≥20 years based on the Korea National Health and Nutrition Examination Survey (KNHANES) 2008 to 2011. Among 17,583 subjects, the mean ages were 49.1 ± 16.0 years (M, n = 7495) and 49.3 ± 16.3 years (F, n = 10,088). Subjects were divided into age groups, either <50 or ≥50 years for males, or menopausal state, either premenopausal or postmenopausal, for females. A further classification used BMI, either <25 or ≥25 kg/m(2). Anthropometric and body composition parameters were compared and evaluated to look for correlations with BMD. Further, appendicular lean mass (ALM), fat mass (FM), fat percentage (FP), and waist circumference (WC) were included for multivariate analysis with BMD, controlling for covariates in each age group and BMI subgroup. RESULTS: Anthropometric and body composition parameters significantly correlated with BMD in all age groups for both genders. After adjusting for covariates, ALM strongly affected BMD in all age groups for both genders. FM, FP, and WC significantly affected BMD in both age groups of women and in older men, but they did not affect BMD in younger men. Fat indices positively affected BMD of all sites in all non-obese women and in non-obese older men. However, little contribution was found in obese subgroups of both genders and in non-obese younger men. CONCLUSION: Considering different weights of covariates, ALM strongly contributed to BMD in all gender, age, and BMI groups. On the other hand, fat indices positively affected BMD of both age groups in women and older men with normal BMI, but they showed little contribution to BMD within the same age groups with high BMI or any BMI subgroups of younger men.

Smartphone-Based Bioelectrical Impedance Analysis Devices for Daily Obesity Management.

Current bioelectric impedance analysis (BIA) systems are often large, cumbersome devices which require strict electrode placement on the user, thus inhibiting mobile capabilities. In this work, we developed a handheld BIA device that measures impedance from multiple frequencies (5 kHz~200 kHz) with four contact electrodes and evaluated the BIA device against standard body composition analysis systems: a dual-energy X-ray absorptiometry (DXA) system (GE Lunar Prodigy, GE Healthcare, Buckinghamshire, UK) and a whole-body BIA system (InBody S10, InBody, Co. Ltd, Seoul, Korea). In the study, 568 healthy participants, varying widely in body mass index, age, and gender, were recruited at two research centers: the Samsung Medical Center (SMC) in South Korea and the Pennington Biomedical Research Center (PBRC) in the United States. From the measured impedance data, we analyzed individual body fat and skeletal muscle mass by applying linear regression analysis against target reference data. Results indicated strong correlations of impedance measurements between the prototype pathways and corresponding InBody S10 electrical pathways (R = 0.93, p < 0.0001). Additionally, body fat estimates from DXA did not yield significant differences (p > 0.728 (paired t-test), DXA mean body fat 29.45 ± 10.77 kg, estimated body fat 29.52 ± 12.53 kg). Thus, this portable BIA system shows a promising ability to estimate an individual's body composition that is comparable to large stationary BIA systems.

Body composition analysis: A snapshot across the perimenopause.

During the perimenopause, estrogen concentrations gradually decrease, and this is associated with changes to women's energy expenditure and intake. These changes result in weight gain and altered body fat distribution, with increased abdominal fat deposition and cardiometabolic risk via insulin resistance. Body composition analysis is a useful clinical tool in outpatient settings, as it is simple, not expensive and provides information on body mass index, skeletal mass, fat mass, fat percentage and basal metabolic rate. This review discusses body composition analysis as part of a health assessment for healthy women during the perimenopause and investigates the associations between body composition and cardiometabolic profile.

Exploring influencing factors and predictive analysis of sarcopenia in the Chinese population using the body composition analyzer.

OBJECTIVE: Employing body composition analysis, this study aims to examine the influencing factors and conduct predictive analysis regarding sarcopenia incidence in the middle-aged and elderly population in China. METHODS: This study recruited inpatients from the General Medicine Department of Tongji Medical College Affiliated Union Hospital, Huazhong University of Science and Technology, as the subjects for a single-center retrospective study. Diagnosis was conducted according to the 2019 criteria from the Asian Working Group for Sarcopenia. Binary logistic regression analysis was utilized to identify factors influencing sarcopenia, and predictive modeling for sarcopenia occurrence was performed based on the area under the ROC curve (AUC). RESULTS: This study comprised 1258 hospitalized patients, of whom 340 were diagnosed with sarcopenia and 918 were not, resulting in a prevalence of 27%. The baseline characteristics showed statistically significant differences between the two groups. Binary logistic regression analysis revealed that low protein, low total body water, low minerals, low basal metabolic rate, and age were risk factors for sarcopenia (OR > 1, P < 0.05). Conversely, being male, having a higher BMI, greater fat-free mass index, and a higher InBody score were identified as protective factors against sarcopenia (OR < 1, P < 0.05). The AUC values for predicting sarcopenia occurrence based on low protein, low total body water, low minerals, low basal metabolic rate, and age were 0.871, 0.846, 0.757, 0.645, and 0.649, respectively, indicating their significance as predictive indicators. Combining these five indicators into a new predictive model for sarcopenia yielded an area under the curve (AUC) value of 0.932, demonstrating excellent sensitivity and specificity concurrently. CONCLUSION: The results of body composition analysis indicate that sarcopenia occurrence in the middle-aged and elderly population in China is associated with factors such as low protein, low total body water, low minerals, low basal metabolic rate, age, gender, BMI, fat-free mass index, and InBody score. The combination of specific body composition indicators facilitates the effective prediction of sarcopenia. Clinical practitioners should proactively identify the risk factors influencing sarcopenia, accurately predict.