Diversity in levels of intracellular total creatine and triglycerides in human skeletal muscles observed by 1H-MRS

We used1H-magnetic resonance spectroscopy to noninvasively determine total creatine (TCr), choline-containing compounds (Cho), and intracellular (IT) and extracellular (between-muscle fibers) triglycerides (ET) in three human skeletal muscles. Subjects' (n = 15 men) TCr concentrations in soleus [Sol; 100.2 ± 8.3 (SE) mmol/kg dry wt] were lower (P < 0.05) than those in gastrocnemius (Gast; 125.3 ± 9.2 mmol/kg dry wt) and tibialis anterior (TA; 123.7 ± 8.8 mmol/kg dry wt). The Cho levels in Sol (35.8 ± 3.6 mmol/kg dry wt) and Gast (28.5 ± 3.5 mmol/kg dry wt) were higher (P < 0.001 andP < 0.01, respectively) compared with TA (13.6 ± 2.4 mmol/kg dry wt). The IT values were found to be 44.8 ± 4.6 and 36.5 ± 4.2 mmol/kg dry wt in Sol and Gast, respectively. The IT values of TA (24.5 ± 4.5 mmol/kg dry wt) were lower than those of Sol (P < 0.01) and Gast (P < 0.05). There were no differences in ET [116.0 ± 11.2 (Sol), 119.1 ± 18.5 (Gast), and 91.4 ± 19.2 mmol/kg dry wt (TA)]. It is proposed that the differences in metabolite levels may be due to the differences in fiber-type composition and deposition of metabolites due to the adaptation of different muscles during locomotion.

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

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.

Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies

Introduction Quantitative and accurate measurements of fat and muscle in the body are important for prevention and diagnosis of diseases related to obesity and muscle degeneration. Manually segmenting muscle and fat compartments in MR body-images is laborious and time-consuming, hindering implementation in large cohorts. In the present study, the feasibility and success-rate of a Dixon-based MR scan followed by an intensity-normalised, non-rigid, multi-atlas based segmentation was investigated in a cohort of 3,000 subjects. Materials and Methods 3,000 participants in the in-depth phenotyping arm of the UK Biobank imaging study underwent a comprehensive MR examination. All subjects were scanned using a 1.5 T MR-scanner with the dual-echo Dixon Vibe protocol, covering neck to knees. Subjects were scanned with six slabs in supine position, without localizer. Automated body composition analysis was performed using the AMRA Profiler™ system, to segment and quantify visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT) and thigh muscles. Technical quality assurance was performed and a standard set of acceptance/rejection criteria was established. Descriptive statistics were calculated for all volume measurements and quality assurance metrics. Results Of the 3,000 subjects, 2,995 (99.83%) were analysable for body fat, 2,828 (94.27%) were analysable when body fat and one thigh was included, and 2,775 (92.50%) were fully analysable for body fat and both thigh muscles. Reasons for not being able to analyse datasets were mainly due to missing slabs in the acquisition, or patient positioned so that large parts of the volume was outside of the field-of-view. Discussion and Conclusions In conclusion, this study showed that the rapid UK Biobank MR-protocol was well tolerated by most subjects and sufficiently robust to achieve very high success-rate for body composition analysis. This research has been conducted using the UK Biobank Resource.