419 research outputs found

    Fully Automated Segmentation and Quantification of Abdominal Adipose Tissue Compartments in Mouse MRI

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    Obesity currently affects 25% of Canadians and is strongly associated with many diseases including diabetes, cardiovascular disease, and cancer. However, only Intra-Abdominal Adipose Tissue (IAAT) is an important predictor of obesity associated disease and mortality. Magnetic Resonance Imaging (MRI) is an effective modality for imaging fat and methods have been developed to automate segmentation of IAAT. Currently existing techniques for automated segmentation in mice require acquisition using high magnetic field MRI equipment and use image acquisition techniques with low precision for fat quantification. We demonstrate a new fully automated technique for fat quantification in clinical strength mouse MRI through adaptation of an existing human fat quantification technique. We validated this method using images collected from mice in a 3 T clinical MRI against manual segmentation. Dice correlation coefficients revealed that 84% of voxels agreed for Subcutaneous Adipose Tissue (SAT) and 87% of voxels agreed for IAAT

    Whole-Body Magnetic Resonance Imaging Assessment of the Contributions of Adipose and Nonadipose Tissues to Cardiovascular Remodeling in Adolescents

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    BACKGROUND: Greater body mass index is associated with cardiovascular remodeling in adolescents. However, body mass index cannot differentiate between adipose and nonadipose tissues. We examined how visceral and subcutaneous adipose tissue are linked with markers of early cardiovascular remodeling, independently from nonadipose tissue. METHODS AND RESULTS: Whole‐body magnetic resonance imaging was done in 82 adolescents (39 overweight/obese; 36 female; median age, 16.3 [interquartile range, 14.4–18.1] years) to measure body composition and cardiovascular remodeling markers. Left ventricular diastolic function was assessed by echocardiography. Waist, waist:height ratio, and body mass index z scores were calculated. Residualized nonadipose tissue, subcutaneous adipose tissue, and visceral adipose tissue variables, uncorrelated with each other, were constructed using partial regression modeling to allow comparison of their individual contributions in a 3‐compartment body composition model. Cardiovascular variables mostly related to nonadipose rather than adipose tissue. Nonadipose tissue was correlated positively with left ventricular mass (r=0.81), end‐diastolic volume (r=0.70), stroke volume (r=0.64), left ventricular mass:end‐diastolic volume (r=0.37), and systolic blood pressure (r=0.35), and negatively with heart rate (r=−0.33) (all P<0.01). Subcutaneous adipose tissue was associated with worse left ventricular diastolic function (r=−0.42 to −0.48, P=0.0007–0.02) and higher heart rates (r=0.34, P=0.007) but linked with better systemic vascular resistance (r=−0.35, P=0.006). There were no significant relationships with visceral adipose tissue and no associations of any compartment with pulse wave velocity. CONCLUSIONS: Simple anthropometry does not reflect independent effects of nonadipose tissue and subcutaneous adipose tissue on the adolescent cardiovascular system. This could result in normal cardiovascular adaptations to growth being misinterpreted as pathological sequelae of excess adiposity in studies reliant on such measures

    Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large-scale human studies

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    Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such asMRI has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles to the use of MRI in large-scale studies. In this study we assess the validity of the recently proposed fat–muscle quantitation system (AMRATM Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images. Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using sliceOmatic, the current gold-standard, and the AMRATM Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by the two analysis methods, (Pearson correlation r = 0.97, p < 0.001), with the AMRATM Profiler analysis being significantly faster (~3 min) than the conventional sliceOmatic approach (~40 min). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 versus sliceOmatic 4.73 ± 1.75 l, p = 0.97). For the AMRATM Profiler analysis, the intra-observer coefficient of variation was 1.6% for IAAT and 1.1% for ASAT, the inter-observer coefficient of variationwas 1.4%for IAAT and 1.2%for ASAT, the intra-observer correlationwas 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRATM Profiler, opening up the possibility of large-scale human phenotypic studies

    Relationship between obesity and structural brain abnormality: Accumulated evidence from observational studies

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    Body mass index; Structural brain abnormalitiesÍndex de massa corporal; Anormalitats estructurals del cervellÍndice de masa corporal; Anomalías estructurales del cerebroWe aimed to evaluate the relationship between obesity and structural brain abnormalities assessed by magnetic resonance imaging using data from 45 observational epidemiological studies, where five articles reported prospective longitudinal results. In cross-sectional studies’ analyses, the pooled weighted mean difference for total brain volume (TBV) and gray matter volume (GMV) in obese/overweight participants was -11.59 (95 % CI: -23.17 to -0.02) and -10.98 (95 % CI: -20.78 to -1.18), respectively. TBV was adversely associated with BMI and WC, GMV with BMI, and hippocampal volume with BMI, WC, and WHR. WC/WHR are associated with a risk of lacunar and white matter hyperintensity (WMH). In longitudinal studies’ analyses, BMI was not statistically associated with the overall structural brain abnormalities (for continuous BMI: RR = 1.02, 95 % CI: 0.94–1.12; for categorial BMI: RR = 1.18, 95 % CI: 0.75–1.85). Small sample size of prospective longitudinal studies limited the power of its pooled estimates. A higher BMI is associated with lower brain volume while greater WC/WHR, but not BMI, is related to a risk of lacunar infarct and WMH. Future longitudinal research is needed to further elucidate the specific causal relationships and explore preventive measures.This work was supported by the National Natural Science Foundation of China (No. 82070851, 81870556, 81930019, 81770686, 81970591), Beijing Municipal Administration of Hospital’s Youth Program (QML20170204), Excellent Talents in Dongcheng District of Beijing

    Advanced body composition assessment: from body mass index to body composition profiling

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    This paper gives a brief overview of common non-invasive techniques for body composition analysis and a more in-depth review of a body composition assessment method based on fatreferenced quantitative MRI. Earlier published studies of this method are summarized, and a previously unpublished validation study, based on 4753 subjects from the UK Biobank imaging cohort, comparing the quantitative MRI method with dualenergy X-ray absorptiometry (DXA) is presented. For whole-body measurements of adipose tissue (AT) or fat and lean tissue (LT), DXA and quantitative MRIs show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 and 4.6 per cent for fat (computed from AT) and LT, respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of >20 per cent. The additional ability of MRI to also measure muscle volumes, muscle AT infiltration and ectopic fat, in combination with rapid scanning protocols and efficient image analysis tools, makes quantitative MRI a powerful tool for advanced body composition assessment

    Normalized indices derived from visceral adipose mass assessed by MRI and their correlation with markers for insulin resistance and prediabetes

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    Visceral adipose tissue (VAT) plays an important role in the pathogenesis of insulin resistance (IR), prediabetes and type 2 diabetes. However, VAT volume alone might not be the best marker for insulin resistance and prediabetes or diabetes, as a given VAT volume may differently impact on these metabolic traits based on body height, gender, age and ethnicity. In a cohort of 1295 subjects from the Tübingen Diabetes Family Study (TDFS) and in 9978 subjects from the UK Biobank (UKBB), undergoing magnetic resonance imaging for quantification of VAT volume, total adipose tissue (TAT, in the TDFS), total abdominal adipose tissue (TAAT) in the UKBB, and total lean tissue (TLT), VAT volume and several VAT-indices were investigated for their relationships with insulin resistance and glycemic traits. VAT-related indices were calculated by correcting for body height (VAT/m: VAT/body height; VAT/m²: VAT/(body height)², and VAT/m³: VAT/(body height)³), TAT (%VAT), TLT (VAT/TLT) and weight (VAT/WEI), with closest equivalents used within the UKBB dataset. Prognostic values of VAT and VAT-related indices for insulin sensitivity, HbA1c levels and prediabetes/diabetes were analyzed for males and females. Males had higher VAT volume and VAT-related indices than females in both cohorts (p < 0.0001) and VAT volume has shown to be a stronger determinant for insulin sensitivity than anthropometric variables. Among the parameters uncorrected VAT and derived indices, VAT/m³ most strongly correlated negatively with insulin sensitivity and positively with HbA1c levels and prediabetes/diabetes in the TDFS (R² = 0.375/0.305 for females/males for insulin sensitivity, 0.178/0.148 for HbA1c levels vs. – e.g. – 0.355/0.293 and 0.144/0.133 for VAT, respectively) and positively with HbA1c (R² = 0.046/0.042) in the UKBB for females and males. Furthermore, VAT/m³ was found to be a significantly better determinant of insulin resistance or prediabetes than uncorrected VAT volume (p < 0.001/0.019 for females/males regarding insulin sensitivity, p < 0.001/< 0.001 for females/males regarding HbA1c). Evaluation of several indices derived from VAT volume identified VAT/m³ to most strongly correlate with insulin sensitivity and glucose metabolism. Thus, VAT/m³ appears to provide better indications of metabolic characteristics (insulin sensitivity and pre-diabetes/diabetes) than VAT volume alone

    Segmentation and Evaluation of Adipose Tissue from Whole Body MRI Scans

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    Accurate quantification of total body and the distribution of regional adipose tissue using manual segmentation is a challenging problem due to the high variation between manual delineations. Manual segmentation also requires highly trained experts with knowledge of anatomy. We present a hybrid segmentation method that provides robust delineation results for adipose tissue from whole body MRI scans. A formal evaluation of accuracy of the segmentation method is performed. This semi-automatic segmentation algorithm reduces significantly the time required for quantification of adipose tissue, and the accuracy measurements show that the results are close to the ground truth obtained from manual segmentations
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