Estimation Of Visceral Adipose Tissue Research Articles

Use of dual-energy x-ray absorptiometry for body composition in chronic disease management

As individuals age or contend with chronic diseases, shifts in body composition often emerge, characterized by a loss of muscle mass and an increase in fat mass, even among those with stable body weight. Both obesity and sarcopenia are key drivers of frailty, disability, and heightened morbidity and mortality. The simultaneous decline in skeletal muscle and accumulation of visceral fat can work synergistically, magnifying their detrimental effects on physical function and metabolic health. Today, dual-energy x-ray absorptiometry (DEXA) is widely recognized as one of the most versatile imaging techniques for assessing not only osteoporosis but also sarcopenia and obesity. Whole-body DEXA facilitates comprehensive analysis, offering detailed insights into fat mass, non-bone lean mass, and bone mineral content at both total and regional levels. DEXA is highly valued for its accuracy, reproducibility, speed, affordability, and low radiation exposure. Furthermore, advancements in DEXA technology and software now allow for precise estimation of visceral adipose tissue. This review underscores the clinical applications of whole-body DEXA, focusing on the use of muscle and fat mass indices in diagnosing low muscle mass, sarcopenia, and sarcopenic obesity, aligned with the latest research and guidelines.

A Validated Estimate of Visceral Adipose Tissue Volume in Relation to Cancer Risk.

Despite the recognized role of visceral adipose tissue (VAT) in carcinogenesis, its independent association with cancer risk beyond traditional obesity measures remains unknown due to limited availability of imaging data. We developed an estimation equation for VAT volume (L) using Elastic Net Regression based on demographic and anthropometric data in a subcohort of participants in the UK Biobank (UKB; N = 23,148) with abdominal MRI scans. This equation was externally validated in 2,713 participants from the 2017-2018 National Health and Nutrition Examination Survey (NHANES) according to sex, age, and race groups. We then applied the equation to the overall UKB cohort of 461,665 participants to evaluate the prospective association between estimated VAT (eVAT) and cancer risk using Cox proportional hazards models. We also calculated the population attributable risk (PAR) of cancer associated with eVAT and BMI. eVAT showed a high correlation with measured VAT in internal and external validations (r = 0.81-0.86). During a median 12-year follow-up in the UKB, we documented 37,397 incident cancer cases; eVAT was significantly associated with elevated risk of obesity-related and individual cancers, independent of BMI and waist circumference. PAR for total cancer associated with high (quartiles 2-4 vs 1) eVAT (9.0-11.6%) was higher than high BMI (Q2-4 vs 1; 5.0-8.2%). eVAT showed robust performance in both UKB and NHANES and was associated with cancer risk independent of BMI and waist circumference. This study provides a potential clinical tool for VAT estimation and underscores that VAT can be an important target for cancer prevention.

Machine learning allows robust classification of visceral fat in women with obesity using common laboratory metrics

The excessive accumulation and malfunctioning of visceral adipose tissue (VAT) is a major determinant of increased risk of obesity-related comorbidities. Thus, risk stratification of people living with obesity according to their amount of VAT is of clinical interest. Currently, the most common VAT measurement methods include mathematical formulae based on anthropometric dimensions, often biased by human measurement errors, bio-impedance, and image techniques such as X-ray absorptiometry (DXA) analysis, which requires specialized equipment. However, previous studies showed the possibility of classifying people living with obesity according to their VAT through blood chemical concentrations by applying machine learning techniques. In addition, most of the efforts were spent on men living with obesity while little was done for women. Therefore, this study aims to compare the performance of the multilinear regression model (MLR) in estimating VAT and six different supervised machine learning classifiers, including logistic regression (LR), support vector machine and decision tree-based models, to categorize 149 women living with obesity. For clustering, the study population was categorized into classes 0, 1, and 2 according to their VAT and the accuracy of each MLR and classification model was evaluated using DXA-data (DXAdata), blood chemical concentrations (BLDdata), and both DXAdata and BLDdata together (ALLdata). Estimation error and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {R}^{2}$$\\end{document} were computed for MLR, while receiver operating characteristic (ROC) and precision-recall curves (PR) area under the curve (AUC) were used to assess the performance of every classification model. MLR models showed a poor ability to estimate VAT with mean absolute error ≥401.40\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge 401.40$$\\end{document} and R2≤0.62\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {R}^{2} \\le 0.62$$\\end{document} in all the datasets. The highest accuracy was found for LR with values of 0.57, 0.63, and 0.53 for ALLdata, DXAdata, and BLDdata, respectively. The ROC AUC showed a poor ability of both ALLdata and DXAdata to distinguish class 1 from classes 0 and 2 (AUC = 0.31, 0.71, and 0.85, respectively) as also confirmed by PR (AUC = 0.24, 0.57, and 0.73, respectively). However, improved performances were obtained when applying LR model to BLDdata (ROC AUC ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge $$\\end{document} 0.61 and PR AUC ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge $$\\end{document} 0.42), especially for class 1. These results seem to suggest that, while a direct and reliable estimation of VAT was not possible in our cohort, blood sample-derived information can robustly classify women living with obesity by machine learning-based classifiers, a fact that could benefit the clinical practice, especially in those health centres where medical imaging devices are not available. Nonetheless, these promising findings should be further validated over a larger population.

Standardization of dual-energy x-ray visceral adipose tissue measures for comparison across clinical imaging systems.

Excess visceral adipose tissue (VAT) is a major risk factor for metabolic syndrome (MetS) and clinical guidelines have been proposed to define VAT levels associated with increased risk. The aim was to standardize VAT measures between two dual-energy x-ray absorptiometry (DXA) manufacturers who provide different VAT estimates to support standardization of measures across imaging modalities. Scans from 114 individuals (ages 18-81 years) on GE HealthCare (GEHC) and Hologic DXA systems were compared via Deming regression to standardize VAT between the two systems, validated in a separate sample (n = 15), with κ statistics to assess agreement of VAT measurements for classifying patients into risk categories. The GEHC and Hologic VAT measures were highly correlated and validated in the separate data set (r2 = 0.97). VAT area measures substantially agreed for metabolic risk classification (weighted κ = 0.76) with no significant differences in the population mean values. VAT measures can be estimated from GEHC and Hologic scans that classify individuals in a substantially similar way into metabolic risk categories, and systematic bias between the measures can be removed using simple regression equations. These findings allow for DXA VAT measures to be used in complement to other imaging modalities, regardless of whether scans used GEHC or Hologic systems.

Towards visceral fat estimation at population scale: correlation of visceral adipose tissue assessment using three-dimensional cross-sectional imaging with BIA, DXA, and single-slice CT.

In terms of assessing obesity-associated risk, quantification of visceral adipose tissue (VAT) has become increasingly important in risk assessment for cardiovascular and metabolic diseases. However, differences exist in the accuracy of various modalities, with a lack of up-to-date comparison with three-dimensional whole volume assessment. Using CT or MRI three-dimensional whole volume VAT as a reference, we evaluated the correlation of various commonly used modalities and techniques namely body impedance analysis (BIA), dual-energy x-ray absorptiometry (DXA) as well as single slice CT to establish how these methods compare. We designed the study in two parts. First, we performed an intra-individual comparison of the 4558 participants from the UK Biobank cohorts with matching data of MRI abdominal body composition, DXA with VAT estimation, and BIA. Second, we evaluated 174 CT scans from the publicly available dataset to assess the correlation of the commonly used single-slice technique compared to three-dimensional VAT volume. Across the UK Biobank cohort, the DXA-derived VAT measurement correlated better (R2 0.94, p<0.0001) than BIA (R2 0.49, p<0.0001) with reference three-dimensional volume on MRI. However, DXA-derived VAT correlation was worse for participants with a BMI of < 20 (R2 = 0.62, p=0.0013). A commonly used single slice method on CT demonstrated a modest correlation (R2 between 0.51 - 0.64), with best values at L3- and L4 (R2 L3 = 0.63, p<0.0001; L4 = 0.64, p<0.0001) compared to reference three-dimensional volume. Combining multiple slices yielded a better correlation, with a strong correlation when L2-L3 levels were combined (R2 = 0.92, p<0.0001). When deployed at scale, DXA-derived VAT volume measurement shows excellent correlation with three-dimensional volume on MRI based on the UK Biobank cohort. Whereas a single slice CT technique demonstrated moderate correlation with three-dimensional volume on CT, with a stronger correlation achieved when multiple levels were combined.

Associations between visceral adipose tissue estimates produced by near-infrared spectroscopy, mobile anthropometrics, and traditional body composition assessments and estimates derived from dual-energy X-ray absorptiometry.

Assessments of visceral adipose tissue (VAT) are critical in preventing metabolic disorders; however, there are limited measurement methods that are accurate and accessible for VAT. The purpose of this cross-sectional study was to evaluate the association between VAT estimates from consumer-grade devices and traditional anthropometrics and VAT and subcutaneous adipose tissue (SAT) from dual-energy X-ray absorptiometry (DXA). Data were collected from 182 participants (female = 114; White = 127; Black/African-American (BAA) = 48) which included anthropometrics and indices of VAT produced by near-infrared reactance spectroscopy (NIRS), visual body composition (VBC) and multifrequency BIA (MFBIA). VAT and SAT were collected using DXA. Bivariate and partial correlations were calculated between DXAVAT and DXASAT and other VAT estimates. All VAT indices had positive moderate-strong correlations with VAT (all P < 0·001) and SAT (all P < 0·001). Only waist:hip (r = 0·69), VATVBC (r = 0·84), and VATMFBIA (r = 0·86) had stronger associations with VAT than SAT (P < 0·001). Partial associations between VATVBC and VATMFBIA were only stronger for VAT than SAT in White participants (r = 0·67, P < 0·001) but not female, male, or BAA participants individually. Partial correlations for waist:hip were stronger for VAT than SAT, but only for male (r = 0·40, P < 0·010) or White participants (r = 0·48, P < 0·001). NIRS was amongst the weakest predictors of VAT which was highest in male participants (r = 0·39, P < 0·010) but non-existent in BAA participants (r = -0·02, P > 0·050) after adjusting for SAT. Both anthropometric and consumer-grade VAT indices are consistently better predictors of SAT than VAT. These data highlight the need for a standardised, but convenient, VAT estimation protocol that can account for the relationship between SAT and VAT that differs by sex/race.

Use of Dual-Energy X-ray Absorptiometry for Body Composition in Chronic Disease Management

Aging and chronic disease are often accompanied by changes in body composition that lead to decreased muscle mass and increased fat mass, even in weight-stable individuals. Both obesity and sarcopenia are important causes of frailty, disability, morbidity, and mortality. Diminished skeletal muscle and expanded visceral fat can act synergistically, which might maximize their effects on physical impairments and metabolic disorders. Dual-energy X-ray absorptiometry (DEXA) was regarded as one of the most versatile imaging techniques for evaluation of sarcopenia and obesity as well as osteoporosis. Whole-body DEXA allows total and regional assessment of the three compartments for fat mass, non-bone lean mass, and bone mineral content. Moreover, DEXA is accurate, reproducible, fast, relatively inexpensive, and involves very low radiation dose to the patient. Developments in DEXA equipment and software allow an accurate and differential estimate of visceral adipose tissue. This review summarizes the clinical practical application of whole-body DEXA values, with use of muscle and fat mass indices in the diagnostic workup of low muscle mass, sarcopenia, and sarcopenic obesity according to the most recent studies and guidelines.

Role of Ultrasonography Compared to Computed Tomography in Measurement of Visceral Adipose Tissue and Subcutaneous Adipose Tissue in Diabetic Overweight and Obese Adolescents

Background: Ultrasound is considered as a suitable, accurate, safe, available technique to measure abdominal adipose tissue of low cost compared to other imaging modalities as CT and MRI. It is superior to BMI as a monitor for diabesity because of it is ability to differentiate between visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in wide epidemiological studies.&#x0D; Results: The correlation between the ultrasound and CT measurements was high with correlation coefficient 0.921 and 0.988 for VAT and SAT respectively. Also there was high significant correlation between the BMI and US and CT measurements of VAT and SAT in all studied groups with correlation coefficient ranging from 0.514 to 0.956.&#x0D; Conclusion: Ultrasound provides reproducible and valid estimates of VAT and SAT and represents a useful method to assess abdominal fat in large scale epidemiological studies.

Validity of dual-energy x-ray absorptiometry for estimation of visceral adipose tissue and visceral adipose tissue change after surgery-induced weight loss in women with severe obesity.

Reliable and simple methods to quantify visceral adipose tissue (VAT) and VAT changes are needed. This study investigated the validity of dual-energy x-ray absorptiometry (DXA) compared with magnetic resonance imaging (MRI) for estimating VAT cross sectionally and longitudinally after surgery-induced weight loss in women with severe obesity. Women with obesity (n = 36; mean age 43 [SD 10] years; 89% White) with DXA and MRI before bariatric surgery (T0) at 12 (T12) and 24 months (T24) post surgery were included. CoreScan (GE Healthcare, Chicago, Illinois) estimated VAT from 20% of the distance between the top of the iliac crest and the base of the skull. MRI VAT (total VAT) was measured from the base of the heart to the sacrum/coccyx on a whole-body scan. Mean DXA VAT was 45% of MRI VAT at T0, 46% at T12, and 68% at T24. DXA underestimated change in MRI VAT between T0 and T12 by 26.1% (0.81 kg, p = 0.03) and by 71.7% (0.43 kg, p < 0.001) between T12 and T24. The relationship between DXA VAT and MRI VAT differed between T12 and T24 (p value for interaction = 0.03). CoreScan lacks validity for comparing VAT across individuals or for estimating the size of changes within individuals; however, within the limits of measurement error, it may provide a useful indicator of whether some VAT change has occurred within an individual.

Evaluation of the Value of Waist Circumference and Metabolomics in the Estimation of Visceral Adipose Tissue.

Visceral adipose tissue (VAT) is a strong prognostic factor for cardiovascular disease and a potential target for cardiovascular risk stratification. Because VAT is difficult to measure in clinical practice, we estimated prediction models with predictors routinely measured in general practice and VAT as outcome using ridge regression in 2,501 middle-aged participants from the Netherlands Epidemiology of Obesity study, 2008–2012. Adding waist circumference and other anthropometric measurements on top of the routinely measured variables improved the optimism-adjusted R2 from 0.50 to 0.58 with a decrease in the root-mean-square error (RMSE) from 45.6 to 41.5 cm2 and with overall good calibration. Further addition of predominantly lipoprotein-related metabolites from the Nightingale platform did not improve the optimism-corrected R2 and RMSE. The models were externally validated in 370 participants from the Prospective Investigation of Vasculature in Uppsala Seniors (PIVUS, 2006–2009) and 1,901 participants from the Multi-Ethnic Study of Atherosclerosis (MESA, 2000–2007). Performance was comparable to the development setting in PIVUS (R2 = 0.63, RMSE = 42.4 cm2, calibration slope = 0.94) but lower in MESA (R2 = 0.44, RMSE = 60.7 cm2, calibration slope = 0.75). Our findings indicate that the estimation of VAT with routine clinical measurements can be substantially improved by incorporating waist circumference but not by metabolite measurements.

The site of waist measurement impacts the estimation of visceral fat: results from three-dimensional photonic body scanning.

Currently, various protocols regarding the site of waist circumference (WC) measurement are in place. This study aimed to analyse the effect of the site of WC measurement on visceral adipose tissue (VAT) estimation. WC was obtained at 7 anatomical sites in 211 German volunteers (103 males) aged 23-81 years using three-dimensional photonic body scanning (PBS). At one site, WC was additionally measured by tape. The quantity of VAT was assessed by MRI. Models to estimate VAT based on WC were developed; the precision of the estimation is represented by R2. The influence of the applied method of WC assessment (tape v. PBS) on the estimations is reported. Results show that the amount of estimated VAT and the precision of VAT estimation were dependent on the site of measurement. VAT was estimated most precisely by WC taken at the level of the lowest rib (WCrib: R2 = 0·75 females; 0·79 males), the minimum circumference (WCmin: R2 = 0·75 females; 0·77 males) and at the narrowest part of the torso (WCnar: R2 = 0·76 females; 0·77 males), and least precisely by WC assessed at the top of iliac crest (WCiliac: R2 = 0·61 females; 0·60 males). VAT estimates based on WC obtained by PBS were smaller and estimations were slightly less precise compared to estimates based on tape measures. Our results indicate that the method and the site of waist measurement should be considered when estimating VAT based on WC. The implementation of a standardised protocol using either WCrib, WCmin or WCnar could improve the precision of VAT estimation.

Precision of Lunar Dual-energy X-ray Absorptiometry (iDXA) in measuring body composition among colorectal cancer patients and healthy subjects.

High quality and precise methods are needed when monitoring changes in body composition among colorectal cancer (CRC) patients and healthy subjects. The aim of this study was to estimate precision of the Dual-energy X-ray absorptiometry (Lunar iDXA, GE Healthcare software enCORE version 16) in measuring body composition in CRC patients and healthy subjects. Precision error of iDXA in measuring body composition was investigated in the current study. Thirty CRC patients and 30 healthy subjects, including both men and women underwent two consecutive whole-body DXA scan with repositioning. Precision estimates of visceral adipose tissue (VAT) and subcutaneousadipose tissue (SAT) in the abdominal region, and total fat mass (FM), fat-free mass (FFM), lean mass (LM), bone mineral density (BMD) and bone mineral content (BMC) were calculated. Precision error expressed as coefficient of variation (% CV) of VAT and SAT were estimated to be 3.56% and 3.28% among CRC patients, and 5.30% and 3.46% among healthy subjects. Estimated precision errors for body masses in the total region ranged between 0.49-1.01% and 0.40-0.88% in CRC patients and healthy subjects, respectively. Least significant change (LSC) in VAT mass, SAT mass, FM and LM were 140.9g, 121.4g, 637.0g and 701.0g, respectively, among CRC patients. Among healthy subjects the LSC in VAT, SAT, FM and LM were 80.93g, 98.90g, 484.0g and 618.0g, respectively. Only minor and non-significant differences between the two consecutive measurements for each body compartment were observed within both populations, and we found no systematic bias in the distribution of the differences. The Lunar iDXA demonstrated high precision in body composition measurements among both CRC patients and healthy subjects. Hence, iDXA is a useful tool in clinical following-up and interventions targeted towards changes in body composition.

MRI Based Validation of Abdominal Adipose Tissue Measurements From DXA in Postmenopausal Women

Visceral adipose tissue (VAT) is a hypothesized driver of chronic disease. Dual-energy X-ray absorptiometry (DXA) potentially offers a lower cost and more available alternative compared to gold-standard magnetic resonance imaging (MRI) for quantification of abdominal fat sub-compartments, VAT and subcutaneous adipose tissue (SAT). We sought to validate VAT and SAT area (cm2) from historical DXA scans against MRI. Participants (n = 69) from the Women's Health Initiative (WHI) completed a 3 T MRI scan and a whole body DXA scan (Hologic QDR2000 or QDR4500; 2004-2005). A subset of 43 participants were scanned on both DXA devices. DXA-derived VAT and SAT at the 4th lumbar vertebrae (5 cm wide) were analyzed using APEX software (v4.0, Hologic, Inc., Marlborough, MA). MRI VAT and SAT areas for the corresponding DXA region of interest were quantified using sliceOmatic software (v5.0, Tomovision, Magog, Canada). Pearson correlations between MRI and DXA-derived VAT and SAT were computed, and a Bland-Altman analysis was performed. Participants were primarily non-Hispanic white (86%) with a mean age of 70.51 ± 5.79 years and a mean BMI of 27.33 ± 5.40 kg/m2. Correlations between MRI and DXA measured VAT and SAT were 0.90 and 0.92, respectively (p ≤ 0.001). Bland-Altman plots showed that DXA-VAT slightly overestimated VAT on the QDR4500 (-3.31 cm2); this bias was greater in the smaller subset measured on the older DXA model (QDR2000; -30.71 cm2). The overestimation of DXA-SAT was large (-85.16 to -118.66 cm2), but differences were relatively uniform for the QDR4500. New software applied to historic Hologic DXA scans provide estimates of VAT and SAT that are well-correlated with criterion MRI among postmenopausal women.

DEVELOPMENT AND VALIDATION OF EQUATIONS TO ESTIMATE VISCERAL ADIPOSE TISSUE IN MILITARY MEN

ABSTRACT Introduction It has been suggested that visceral adipose tissue (VAT) is associated with several non-communicable chronic diseases, but measuring it is difficult. Thus, anthropometry could be used because is easily applied in clinical practice. Objectives The present study aimed to develop and validate VAT estimation equations (Eq) in military men. Methods The sample consisted of 409 (mean age, 36.5 ± 6.7 years) military men in the Brazilian Army (BA) divided into an equation group (EG) ( n = 270; mean age, 37.0 ± 6.3 years) and a validation group (VG) ( n =139; mean age, 36.0 ± 7.2 years). Anthropometric, hemodynamic and DXA body composition evaluations ( GE iLunar ) were performed. The Student’s t test, Pearson’s correlation, and stepwise general linear regression were applied. Bland-Altman graphics were used to assess the concordance between VAT by Eq and by DXA. The level of significance was 95% ( p &lt; 0.05). Results Age, waist circumference (WC), hip circumference and body mass index presented the main significant positive correlations with the VAT-DXA. Four Eq were created Eq1 ( r 2 = 0.793), Eq2 ( r 2 = 0.810), Eq3 ( r 2 = 0.817), and Eq 4 ( r 2 = 0.823) ( p &lt; 0.05). No differences were observed between VAT by DXA and VAT by Eq ( p = 0.982, p = 0.970, p = 0.495 and p = 0.698). Bland-Altman analysis also presented good concordance as the bias was close to zero and was not statistically significant. Conclusion Eq2 (age*13.0 + WC*60.0 - 4975,.5) was more suitable because it is easier to apply, has a higher predictive power (81.0%), less bias (1.86) and validation yielded average VAT values close to those found in DXA. It may still be considered a valuable tool for other extensive epidemiological studies in military men in the BA and can be used in adult men. Evidence Level I: Development of diagnostic criteria on consecutive patients (with universally applied reference ‘‘gold’’ standard).

Associations of DXA-measured abdominal adiposity with cardio-metabolic risk and related markers in early adolescence in Project Viva.

Increased visceral adipose tissue (VAT) precedes development of insulin resistance and dyslipidemia in adults. The associations of abdominal adiposity derived from dual-energy X-ray absorptiometry (DXA), including VAT, subcutaneous abdominal adipose tissue (SAAT) and total abdominal adipose tissue (TAAT) with cardio-metabolic risk in adolescents are understudied. We examined the cross-sectional associations of DXA-measured abdominal adiposity with cardio-metabolic risk and related markers in early adolescence (mean [SD] age 13.0 [0.7] years). We collected data from 740 adolescents (374 girls and 366 boys) in Project Viva, a U.S. pre-birth cohort. We used DXA estimates of VAT, SAAT and TAAT area. We conducted overall and sex-stratified linear regression models, adjusting for age, sex (in overall models), race/ethnicity, puberty score and body mass index (BMI) z-score. Mean BMI z-score was 0.59 (1.28). After adjustment, greater VAT (per 1 SD score) was associated with higher metabolic risk z-score (β 0.14 units; 95% CI 0.08, 0.20), higher log high-sensitivity C-reactive protein (β 0.51 mg/L; 0.36, 0.66) and log leptin (β 0.36 ng/mL; 0.27, 0.44), and lower log adiponectin (β -0.08 ug/mL; -0.13, -0.02). SAAT and TAAT showed similar associations as VAT with comparable or greater effect sizes. In early adolescence, DXA-measured VAT, SAAT and TAAT are associated with cardio-metabolic risk and related markers, independent of current BMI. Among two adolescents with the same BMI, there is an associated higher cardio-metabolic risk in the adolescent with greater DXA-measured abdominal adiposity.

Estimation Of Visceral Adipose Tissue: A Device Comparison

Many body composition devices now provide an estimate of visceral adipose tissue (VAT), a significant risk factor for cardiometabolic disease. PURPOSE: to evaluate the relationship between estimates of VAT from bioelectrical impedance (BIA), ultrasound (US), and dual-energy x-ray absorptiometry (DXA). METHODS: VAT was estimated in 124 adults (66% Female; Mean ± SD: Age: 25.4±8.9 yrs; BMI: 25.4±5.5 kg·m-2; %BF: 29.7±10.5%). VAT area (cm2) was estimated from a BIA system specific algorithm. VAT thickness (cm) was estimated using US, quantified as the distance between the linea alba and aorta. VAT volume (cm3) was estimated from the DXA predefined android region. Linear regression was used to evaluate the relationship between estimates and to identify factors that may contribute to estimate differences in the entire group and by sex. RESULTS: In the full group, VAT estimates from all three methods were significantly correlated [BIA-DXA (R=0.768; R2=0.589); BIA-US (R=0.545; R2=0.297); DXA-US (R=0.785; R2=0.616) (p<0.001)]. In men, stronger relationships were observed with DXA [BIA-DXA (R=0.852; R2=0.727); BIA-US (R=0.774; R2=0.600); DXA-US (R=0.878; R2=0.772) (p<0.001)]; in women, weaker relationships were observed with US [BIA-DXA (R=0.890; R2=0.793); BIA-US (R=0.567; R2=0.321); DXA-US (R=0.690; R2=0.477) (p<0.001)]. In men, total body water (TBW) explained 31.9% and 12.0% of the variance in the difference between BIA-DXA and BIA-US, respectively; %BF explained 13.1% of the variance in the difference between DXA-US (all p<0.05). In women, %BF explained 28.9%, 34.0%, and 15.6% of the variance in the difference between BIA-DXA, BIA-US, and DXA-US, respectively (all p<0.001). CONCLUSIONS: BIA and US are cost-effective alternatives to DXA. BIA may provide a more comparable estimate to DXA, while greater variability may occur when comparing with US, especially in women. Differences between estimates may be influenced by TBW and %BF.

DXA Measured Visceral Adipose Tissue, Total Fat, Anthropometric Indices and its Association With Cardiometabolic Risk Factors in Mother-Daughter Pairs From India

Visceral fat is the pathogenic fat depot associated with diabetes, dyslipidemia, and cardiovascular diseases. Estimation of visceral adipose tissue (VAT) by dual energy-X-ray absorptiometry (DXA) is a newer technique with less radiation exposure, shorter scanning time, and lower cost. In this study, we attempted to look at relationship between cardiometabolic risk factors and VAT, total body fat percent (TBF%) and anthropometry. We also studied the changes in body composition and metabolic parameters with menopause. The familial resemblance of VAT and TBF% in mother-daughter pair was also compared. This was a cross sectional community study of 300 women (150 postmenopausal mothers and 150 premenopausal daughters). Body composition indices by DXA and metabolic parameters were assessed. The association between DXA-VAT, TBF%, anthropometric measures, and cardiometabolic risk factors were studied by correlation, receiver operating characteristics curves, and logistic regression analysis. VAT indices were significantly higher and lean indices lower in postmenopausal women as compared to premenopausal women. One fourth of postmenopausal women were categorized as metabolically obese normal weight. DXA-VAT was a better predictor of cardiometabolic risk factors as compared to waist circumference, body mass index, and TF% in postmenopausal women (AUC:0.68 vs 0.62, 0.60 & 0.5, respectively), whereas body mass index had a better prediction in premenopausal women(AUC:0.68). VAT area >100 cm² had a significant association with the presence of ≥2 cardiometabolic risk factors (p = 0.04, OR: 2.2, CI:1.0–4.7) in the postmenopausal women. Daughters of the mothers with higher TBF% were found to have a higher TBF% compared to daughters of mothers with normal TBF% (36.2 ± 4.2 vs 32.2 ± 4.4, p = 0.03), similar resemblance was not seen for VAT. The study showed that the VAT increases and lean mass decreases with age and menopause. DXA measured VAT is a better predictor of cardiometabolic risk in postmenopausal women but not in premenopausal women. Total body fat may have a familial resemblance, but not the VAT which is determined by age, menopause, and probable life style factors.

Added values of DXA-derived visceral adipose tissue to discriminate cardiometabolic risks in pre-pubertal children.

BackgroundThe new generation of dual energy X-ray absorptiometry (DXA) scanners provide visceral adipose tissue (VAT) estimates by applying different algorithms to the conventional DXA-derived fat parameters such as total fat, trunk fat and android fat for the same image data.ObjectiveThis cross-sectional study aimed to investigate whether VAT estimates from Hologic scanners are better predictors of VAT than conventional DXA parameters in pre-pubertal children, and to explore the discrimination ability of these VAT methods for cardiometabolic risks.MethodsHealthy pre-pubertal children aged 7–10 years were recruited for basic anthropometric, DXA and magnetic resonance imaging (MRI) measurements. Laboratory tests included lipid profile, glycaemic tests and blood pressure.ResultsAll VAT methods had acceptable to excellent performance for the diagnosis of dyslipidaemia (area under the curve [AUC] = 0.753–0.837) and hypertensive risk (AUC = 0.710–0.821) in boys, but suboptimal performance for these risks in girls, except for VAT by MRI in the diagnosis of dyslipidaemia. In both sexes, all VAT methods had no or poor discrimination ability for diabetes risk.ConclusionsDXA-derived VAT estimates are very highly correlated with standard methods but has equivalent discrimination abilities compared to the existing DXA-derived fat estimates.

The combination of insulin resistance and visceral adipose tissue estimation improves the performance of metabolic syndrome as a predictor of type 2 diabetes.

To assess the performance of metabolic syndrome as a predictor of type 2 diabetes in a model that also includes both a measure of insulin resistance and a metabolic score for visceral fat, and to propose a novel metabolic syndrome definition. In a prospective Metabolic Syndrome Cohort (n=6143), we evaluated improvements in type 2 diabetes risk prediction using International Diabetes Federation-defined and Adult Treatment Panel III-defined metabolic syndrome, after inclusion in the model of updated homeostatic model assessment of insulin resistance and a metabolic score for visceral fat. We also developed a modified metabolic syndrome construct, 'MS-METS', which used the metabolic score for visceral fat instead of waist circumference to evaluate improved predictive performance for risk of developing type 2 diabetes. Participants who had metabolic syndrome as defined by both the Adult Treatment Panel III and the International Diabetes Federation criteria had a higher risk of type 2 diabetes compared to participants who did not meet these criteria. Addition of updated homeostatic model assessment of insulin resistance and metabolic score for visceral fat to both metabolic syndrome definitions increased predictive performance for type 2 diabetes risk. Homeostatic model assessment of insulin resistance was the only additional predictor of type 2 diabetes in participants without metabolic syndrome. Conversely, in participants with metabolic syndrome, the use of the metabolic score for visceral fat was the stronger added predictor for type 2 diabetes. When evaluating participants using the MS-METS definition we observed the largest improvement in predictive ability for type 2 diabetes risk and a significant reduction in risk overestimation compared to evaluation using metabolic syndrome defined according to the International Diabetes Federation and Adult Treatment Panel III criteria alone. Inclusion of updated homeostatic model assessment of insulin resistance and metabolic score for visceral fat increases performance of metabolic syndrome in prediction of type 2 diabetes. Assessment of insulin resistance could be more useful than conventional metabolic syndrome and assessment of visceral adipose tissue could be more useful in people with metabolic syndrome. Metabolic syndrome as defined using our modified MS-METS construct improved the accuracy of type 2 diabetes prediction.

Visceral Adiposity Index as an Independent Marker of Subclinical Atherosclerosis in Individuals Prone to Diabetes Mellitus.

Aims: The visceral adiposity index (VAI) has been proposed as an estimate of visceral adipose tissue (VAT) mass and as an indicator of VAT dysfunction. Both parameters are associated with cardiometabolic risk, including insulin resistance. In this study, we investigated whether VAI is associated with subclinical atherosclerosis in subjects who were free of cardiovascular disease but were at risk of developing diabetes mellitus.Methods: A total of 731 adults with a median age of 47 years old without diabetes mellitus were included in this cross-sectional study. The anthropometric data, blood pressure, and lipid profiles of 398 women and 333 men were measured. All subjects underwent an oral glucose tolerance test, and carotid intima–media thickness (cIMT) was evaluated by ultrasound. Insulin resistance was estimated using the homeostatic model assessment of insulin resistance (HOMA-IR).Results: VAI and HOMA-IR (βst = 0.44, p < 0.0001), VAI and cIMT (βst = 0.17, p < 0.0001), and HOMA-IR and cIMT (βst = 0.09, p = 0.0127) were correlated with each other. After adjusting for cofounding variables, VAI is still correlated with HOMA-IR (βst = 0.42, p < 0.0001). Furthermore, VAI (βst = 0.07, p = 0.0392) but not HOMA-IR (βst = 0.03, p = 0.37) was correlated with cIMT independently of other established cardiovascular risk factors.Conclusion: The calculation of VAI may provide a better estimation of subclinical atherosclerosis than the calculation of HOMA-IR.