Adipose tissue MRI for quantitative measurement of central obesity

Poonawalla, Aziz H.; Sjoberg, Brett P.; Rehm, Jennifer L.; Hernando, Diego; Hines, Catherine D. G.; Irarrázaval, Pablo; Reeder, Scott B.

doi:10.1002/jmri.23846

Cited by 43 publications

(36 citation statements)

References 50 publications

(90 reference statements)

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“…MR-PDFF was determined using an investigational version of a chemical shift encoded water-fat separation method (3D-IDEAL-SPGR) 36, 37 . Separated water-only and fat-only images, as well as hepatic MR-PDFF maps 38 were provided using an on-line reconstruction algorithm method that includes spectral modeling of fat 39 , corrects for eddy currents 40 , T1 bias 41 , T2* decay 42 , and noise related bias 41 . Because all known confounders have been addressed, the resulting MR-PDFF map provides an accurate and fundamental measure of the fat concentration in tissue 37 .…”

Section: Methodsmentioning

confidence: 99%

Predicting Hepatic Steatosis in a Racially and Ethnically Diverse Cohort of Adolescent Girls

Rehm¹,

Connor²,

Wolfgram³

et al. 2014

The Journal of Pediatrics

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Objectives To develop a risk assessment model for early detection of hepatic steatosis using common anthropometric and metabolic markers. Study design Cross-sectional study of 134 girls, age 11–22 years (mean 13.3±2), Ethnicity: 27% Hispanic, 73% Non-Hispanic; Race: 64% Caucasian, 31% African-American, 5% Asian, from a middle school and clinics (Madison, WI). Fasting glucose, fasting insulin, alanine aminotransferase (ALT), body mass index (BMI), waist circumference (WC) and other metabolic markers were assessed. Hepatic fat was quantified using magnetic resonance proton density fat fraction (MR-PDFF). Hepatic steatosis was defined as MR-PDFF >5.5%. Outcome measures were sensitivity, specificity, and positive predictive value (PPV) of BMI, WC, ALT, fasting insulin and ethnicity as predictors of hepatic steatosis, individually and combined, in a risk assessment model. Classification and regression tree methodology constructed a decision tree for predicting hepatic steatosis. Results MR-PDFF revealed hepatic steatosis in 16% of subjects (27% overweight, 3% non-overweight). Hispanic ethnicity conferred an odds ratio of 4.26 (CI 1.65–11.04, p=0.003) for hepatic steatosis. BMI and ALT did not independently predict hepatic steatosis. A BMI > 85% combined with ALT > 65 U/L had 9% sensitivity, 100% specificity and 100% PPV. Lowering ALT to 24 U/L increased sensitivity to 68%, but reduced PPV to 47%. A risk assessment model incorporating fasting insulin, total cholesterol, WC, and ethnicity increased sensitivity to 64%, specificity to 99% and PPV to 93%. Conclusions A risk assessment model can increase specificity, sensitivity, and PPV for identifying risk of hepatic steatosis and guide efficient use of biopsy or imaging for early detection and intervention.

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Section: Methodsmentioning

confidence: 99%

Predicting Hepatic Steatosis in a Racially and Ethnically Diverse Cohort of Adolescent Girls

Rehm¹,

Connor²,

Wolfgram³

et al. 2014

The Journal of Pediatrics

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“…In both obese and non-obese children, 2,3 adipose tissue located in ectopic sites (e.g. liver, viscera, muscle) predicts insulin resistance and risk for type 2 diabetes better than BMI.…”

Section: Introductionmentioning

confidence: 99%

Salivary Cortisol does not Correlate with Metabolic Syndrome Markers or Subjective Stress in Overweight Children

et al. 2018

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Objective Being overweight is a risk factor for metabolic syndrome in children, but not all overweight children develop metabolic syndrome. Cortisol excess from chronic psychological stress has been proposed as an independent risk factor for metabolic syndrome in this already at-risk population. The present study assesses the relationship of biochemical and body composition radiographic markers of metabolic syndrome to salivary cortisol and self-report of chronic psychological stress in a cohort of overweight children. Methods This cross-sectional study took place in a multi-disciplinary pediatric obesity clinic at a tertiary care hospital, and involved fifteen children with BMI at or above the 85th percentile for age and sex, 10 of whom provided salivary cortisol samples. The main outcomes measured were salivary bedtime cortisol, first-waking cortisol, and cortisol awakening response (CAR–the rise in cortisol in the first half hour after waking); fasting serum triglycerides, HDL cholesterol, glucose and insulin for HOMA-IR; the ratio of abdominal fat to total body fat by DXA scan; and scores of validated stress and bullying questionnaires (PANAS-C, PSS, and SEC-Q). Results In this pilot study, no correlation was found between salivary cortisol measures and questionnaire scores of subjective stress or bullying, and no correlation was found between any of these measures and markers of metabolic syndrome (dyslipidemia, insulin resistance, increased abdominal fat). Conclusions These results suggest that measures of psychological stress, whether biochemical or subjective, do not appear to predict risk of metabolic syndrome in overweight children. While ease of collection and demonstrated utility both in detection of pediatric Cushing disease and in adult psychological research make salivary cortisol assessment an attractive clinical tool, further investigation into the value of salivary measures in pediatric stress research is needed.

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“…Hence, DXA measurements contain contributions from other tissues, leading to errors in estimation compared to true three-dimensional (3D) distribution of fat mass. Estimation of fat mass and distribution in the true 3D space therefore typically requires cross-sectional imaging, including computed tomography (CT) and magnetic resonance imaging (MRI) [43]. This is mainly because these imaging modalities do not have the conceptual drawbacks associated with measurement via BMI and DXA since both CT and MRI resolve tissue composition at every volume element (voxel) inside the body.…”

Section: Discussionmentioning

confidence: 99%

Chest Fat Quantification via CT Based on Standardized Anatomy Space in Adult Lung Transplant Candidates

et al. 2017

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PurposeOverweight and underweight conditions are considered relative contraindications to lung transplantation due to their association with excess mortality. Yet, recent work suggests that body mass index (BMI) does not accurately reflect adipose tissue mass in adults with advanced lung diseases. Alternative and more accurate measures of adiposity are needed. Chest fat estimation by routine computed tomography (CT) imaging may therefore be important for identifying high-risk lung transplant candidates. In this paper, an approach to chest fat quantification and quality assessment based on a recently formulated concept of standardized anatomic space (SAS) is presented. The goal of the paper is to seek answers to several key questions related to chest fat quantity and quality assessment based on a single slice CT (whether in the chest, abdomen, or thigh) versus a volumetric CT, which have not been addressed in the literature.MethodsUnenhanced chest CT image data sets from 40 adult lung transplant candidates (age 58 ± 12 yrs and BMI 26.4 ± 4.3 kg/m2), 16 with chronic obstructive pulmonary disease (COPD), 16 with idiopathic pulmonary fibrosis (IPF), and the remainder with other conditions were analyzed together with a single slice acquired for each patient at the L5 vertebral level and mid-thigh level. The thoracic body region and the interface between subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in the chest were consistently defined in all patients and delineated using Live Wire tools. The SAT and VAT components of chest were then segmented guided by this interface. The SAS approach was used to identify the corresponding anatomic slices in each chest CT study, and SAT and VAT areas in each slice as well as their whole volumes were quantified. Similarly, the SAT and VAT components were segmented in the abdomen and thigh slices. Key parameters of the attenuation (Hounsfield unit (HU) distributions) were determined from each chest slice and from the whole chest volume separately for SAT and VAT components. The same parameters were also computed from the single abdominal and thigh slices. The ability of the slice at each anatomic location in the chest (and abdomen and thigh) to act as a marker of the measures derived from the whole chest volume was assessed via Pearson correlation coefficient (PCC) analysis.ResultsThe SAS approach correctly identified slice locations in different subjects in terms of vertebral levels. PCC between chest fat volume and chest slice fat area was maximal at the T8 level for SAT (0.97) and at the T7 level for VAT (0.86), and was modest between chest fat volume and abdominal slice fat area for SAT and VAT (0.73 and 0.75, respectively). However, correlation was weak for chest fat volume and thigh slice fat area for SAT and VAT (0.52 and 0.37, respectively), and for chest fat volume for SAT and VAT and BMI (0.65 and 0.28, respectively). These same single slice locations with maximal PCC were found for SAT and VAT within both COPD and IPF groups. Most of the attenuation pr...

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Adipose tissue MRI for quantitative measurement of central obesity

Cited by 43 publications

References 50 publications

Predicting Hepatic Steatosis in a Racially and Ethnically Diverse Cohort of Adolescent Girls

Predicting Hepatic Steatosis in a Racially and Ethnically Diverse Cohort of Adolescent Girls

Salivary Cortisol does not Correlate with Metabolic Syndrome Markers or Subjective Stress in Overweight Children

Chest Fat Quantification via CT Based on Standardized Anatomy Space in Adult Lung Transplant Candidates

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