Measurement of Visceral Adipose Tissue in Persons With Spinal Cord Injury by Magnetic Resonance Imaging and Dual X-Ray Absorptiometry: Generation and Application of a Predictive Equation.

PURPOSE: Dual energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI) permits quantification of visceral adipose tissue (VAT). However, DXA has not been validated against MRI in persons with chronic spinal cord injury (SCI). A predictive equation was generated from the measurement of VAT by MRI, a "gold" standard to quantitate VAT, compared to that of DXA, a method with several practical advantages. METHOD: DXA and MRI scans were performed in 27 participants with SCI. MRI multiaxial images were captured for VAT analysis. DXA-VAT was quantified at the android region (DXA-VATANDROID-VOL) using enCore software. Android regions of DXA and MRI were matched using android height. Volumes of multiaxial MRI-VAT and subcutaneous adipose tissue (SAT) were quantified for the android region (MRI-VATANDROID-VOL, MRI-SATANDROID-VOL) and total trunk (MRI-VATANDROID-VOL). Linear regression analysis was used to establish the proposed predication equations. The prediction equations were then applied to an independent sample that consisted of 98 participants with SCI. Bland-Altman analysis was used to determine the limits of agreement. RESULTS: DXA-VATANDROID-VOL predicted 92% of the variance in MRI-VATANDROID-VOL (SEE = 252.5, p < 0.0005) and 85% of the variance in MRI-VATTRUNK-VOL (SEE = 1526.9, p < 0.0005). DXA-SATANDROID-VOL predicted 81.5% of the variance in MRI-SATANDROID-VOL (SEE = 458.2, p < 0.0005). Bland-Altman analysis revealed a high level of agreement between MRI-VATANDROID-VOL and DXA-VATANDROID-VOL (mean bias = 58.45 cm3). A predicted mean DXA-VATANDROID-VOL of 995.2 cm3 was estimated as the population-specific cut-off point for high levels of VAT. CONCLUSION: DXA-VATANDROID-VOL may accurately predict MRI-VATANDROID-VOL in persons with SCI. The ability of DXA to detect VAT changes in longitudinal studies in persons with SCI should be performed.

Anthropometric cutoffs and associations with visceral adiposity and metabolic biomarkers after spinal cord injury.

BACKGROUND/OBJECTIVES: To examine associations of different anthropometric measurements of central adiposity to visceral adipose tissue (measured via multi-axial magnetic resonance imaging; MRI) and cardiometabolic disease risk factors in men with spinal cord injury (SCI). Additionally, to determine population-specific seated/supine waist and abdominal circumference cutoffs, which may identify men at increased risk of cardiometabolic disease. PARTICIPANTS/METHODS: Twenty-two men with chronic SCI underwent MRI scans, anthropometric measurements along with assessments of various cardiometabolic risk biomarkers. Pearson/part (accounting for age as a covariate) correlation coefficients were calculated to determine the associations between study variables. Abdominal and waist circumference cutoffs were extrapolated using the slope of linear regression equations. RESULTS: Seated/supine abdominal and waist circumferences were (P < 0.01) associated with MRI visceral fat cross-sectional area (VATCSA), VAT volume and CSA:TotalCSA. Low density lipoprotein, non-high-density lipoprotein and total cholesterol were positively associated with seated/supine abdominal and waist circumferences after controlling for age; r = 0.50-0.61, r = 0.46-0.58, r = 0.52-0.58, P < 0.05, respectively. Tumor necrosis factor alpha was associated with seated/supine abdominal and waist circumferences after accounting for age; r = 0.49-0.51 and r = 0.48-0.56, P < 0.05 respectively. The population-specific cutoffs were 86.5cm and 88.3cm for supine waist and abdominal circumferences, respectively, as well as 89cm and 101cm for seated waist and abdominal circumferences, respectively. After dichotomizing VATCSA (< or ≥ 100cm2), peak oxygen uptake, triglycerides, insulin sensitivity and glycated hemoglobin were different (P < 0.05) between groups. After dichotomizing (< or ≥ 86.5cm) supine waist circumference, VATCSA, triglycerides and insulin sensitivity were different (P < 0.05) between groups. CONCLUSIONS: Seated/supine circumferences are associated with both central adiposity and biomarkers of cardiometabolic disease risk in persons with SCI. Population-specific cutoffs are proposed herein to identify central adiposity and potential cardiometabolic disease risk after SCI.

Anthropometric Prediction of Visceral Adipose Tissue in Persons With Motor Complete Spinal Cord Injury.

BACKGROUND: Spinal cord injury (SCI) results in increased accumulation of visceral adipose tissue (VAT). Anthropometrics may provide an alternative to estimate VAT cross-section area (CSA) compared to magnetic resonance imaging (MRI). OBJECTIVE: To validate the use of anthropometrics, including abdominal circumference and skinfold thickness (SFT) measurements against MRI to predict subcutaneous adipose tissue (SAT) and VAT cross-sectional areas in persons with SCI. DESIGN: Cross-sectional. SETTING: Clinical research center PARTICIPANT: Twenty-two men with motor complete SCI METHODS: Anthropometric measurements and MRI were taken during a single visit. Abdominal circumference and SFT were used to derive prediction equations for subcutaneous adipose tissue (SATAnthro-CSA) and VAT (VATAnthro-CSA). Three-axial MRI at the level of umbilicus was used to establish the prediction equations. VATAnthro-CSA was compared against body mass index (BMI), waist circumference, and SFT. Bland-Altman plots were used to determine limits of agreement between prediction equations and MRI. MAIN OUTCOME MEASUREMENTS: SAT and VAT cross-sectional areas. RESULTS: SATAnthro-CSA explained 76% of the variance in SAT cross-sectional area (r2 = 0.76, standard error of the estimate [SEE] = 49.5 cm2, P <.001). VATAnthro-CSA explained 72% of VAT cross-sectional area (r2 = 0.72, SEE = 45.8 cm2, P <.001). Compared to VATAnthro-CSA, BMI, waist circumference, and SFT explained only 37%, 63%, and 31%, respectively, in the variance of VAT MRI. CONCLUSION: Abdominal circumference and SFT demonstrated an alternative way to predict VAT CSA. VATAnthro-CSA estimated VATMRI more accurately than BMI, waist circumference, and SFT in individuals with chronic SCI. LEVEL OF EVIDENCE: I.