INTRODUCTORY PARAGRAPH Variation in body fat distribution contributes to the metabolic sequelae of obesity. The genetic determinants of body fat distribution are poorly understood. The goal of this study was to gain new insights into the underlying genetics of body fat distribution by conducting sample-size weighted fixed-effects genome-wide association meta-analyses in up to 9,594 women and 8,738 men for six ectopic fat traits in European, African, Hispanic, and Chinese ancestry populations, with and without sex stratification. In total, 7 new loci were identified in association with ectopic fat traits (ATXN1, UBE2E2, EBF1, RREB1, GSDMB, GRAMD3 and ENSA; P<5×10−8; FDR<1%). Functional analysis of these genes revealed that loss of function of both ATXN1 and UBE2E2 in primary mouse adipose progenitor cells impaired adipocyte differentiation, suggesting a physiological role for ATXN1 and UBE2E2 in adipogenesis. Future studies are necessary to further explore the mechanisms by which these genes impact adipocyte biology and how their perturbations contribute to systemic metabolic disease.

Objective To investigate multi-echo chemical shift-encoded MRI-based mapping of proton density fat fraction (PDFF) and fat-corrected R2* in bone marrow as biomarkers for osteoporosis assessment. Methods Fifty-one patients (28 female; mean age 69.7 ± 9.0 years) underwent dual energy X-ray absorptiometry (DXA). On the basis of the t score, 173 valid vertebrae bodies were divided into three groups (healthy, osteopenic and osteoporotic). Three echo chemical shift-encoded MRI sequences were acquired at 3 T. PDFF and R2* with correction for multiple-peak fat (R2*MP) were measured for each vertebral body. Kruskal–Wallis test and post hoc analysis were performed to evaluate differences between groups. Further, the area under the curve (AUC) for each technique was calculated using logistic regression analysis. Results On the basis of DXA, 92 samples were normal (53 %), 47 osteopenic (27 %) and 34 osteoporotic (20 %). PDFF was increased in osteoporosis compared with healthy (P=0.007). R2*MP showed significant differences between normal and osteopenia (P=0.004), and between normal and osteoporosis (P<0.001). AUC to differentiate between normal and osteoporosis was 0.698 for R2*MP, 0.656 for PDFF and 0.74 for both combined. Conclusion PDFF and R2*MP are moderate biomarkers for osteoporosis. PDFF and R2*MP combination might improve the prediction in differentiating healthy subjects from those with osteoporosis.

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Fully automatic 3-D segmentation techniques for clinical applications or epidemiological studies have proven to be a very challenging task in the domain of medical image analysis. 3-D organ segmentation on magnetic resonance (MR) datasets requires a well-designed segmentation strategy due to imaging artifacts, partial volume effects, and similar tissue properties of adjacent tissues. We developed a 3-D segmentation framework for fully automatic kidney parenchyma volumetry that uses Bayesian concepts for probability map generation. The probability map quality is improved in a multistep refinement approach. An extended prior shape level set segmentation method is then applied on the refined probability maps. The segmentation quality is improved by incorporating an exterior cortex edge alignment technique using cortex probability maps. In contrast to previous approaches, we combine several relevant kidney parenchyma features in a sequence of segmentation techniques for successful parenchyma delineation on native MR datasets. Furthermore, the proposed method is able to recognize and exclude parenchymal cysts from the parenchymal volume. We analyzed four different quality measures showing better results for right parenchymal tissue than for left parenchymal tissue due to an incorporated liver part removal in the segmentation framework. The results show that the outer cortex edge alignment approach successfully improves the quality measures.

The association of abnormal radiographic values with true clinical hip pathology is tenuous at best. Assuming that a patient with an abnormal radiograph requires treatment is unwise. The clinical picture has to be substantial for therapeutic decisions.