Impact of baseline adipose tissue characteristics on change in adipose tissue volume during a low calorie diet in people with obesity-results from the LION study.

BACKGROUND/OBJECTIVES: Weight loss outcomes vary individually. Magnetic resonance imaging (MRI)-based evaluation of adipose tissue (AT) might help to identify AT characteristics that predict AT loss. This study aimed to assess the impact of an 8-week low-calorie diet (LCD) on different AT depots and to identify predictors of short-term AT loss using MRI in adults with obesity. METHODS: Eighty-one adults with obesity (mean BMI 34.08 ± 2.75 kg/m², mean age 46.3 ± 10.97 years, 49 females) prospectively underwent baseline MRI (liver dome to femoral head) and anthropometric measurements (BMI, waist-to-hip-ratio, body fat), followed by a post-LCD-examination. Visceral and subcutaneous AT (VAT and SAT) volumes and AT fat fraction were extracted from the MRI data. Apparent lipid volumes based on MRI were calculated as approximation for the lipid contained in the AT. SAT and VAT volumes were subdivided into equidistant thirds along the craniocaudal axis and normalized by length of the segmentation. T-tests compared baseline and follow-up measurements and sex differences. Effect sizes on subdivided AT volumes were compared. Spearman Rank correlation explored associations between baseline parameters and AT loss. Multiple regression analysis identified baseline predictors for AT loss. RESULTS: Following the LCD, participants exhibited significant weight loss (11.61 ± 3.07 kg, p < 0.01) and reductions in all MRI-based AT parameters (p < 0.01). Absolute SAT loss exceeded VAT loss, while relative apparent lipid loss was higher in VAT (both p < 0.01). The lower abdominopelvic third showed the most significant SAT and VAT reduction. The predictor of most AT and apparent lipid losses was the normalized baseline SAT volume in the lower abdominopelvic third, with smaller volumes favoring greater AT loss (p < 0.01 for SAT and VAT loss and SAT apparent lipid volume loss). CONCLUSIONS: The LCD primarily reduces lower abdominopelvic SAT and VAT. Furthermore, lower abdominopelvic SAT volume was detected as a potential predictor for short-term AT loss in persons with obesity.

Evaluating Sex-specific Differences in Abdominal Fat Volume and Proton Density Fat Fraction at MRI Using Automated nnU-Net-based Segmentation.

Sex-specific abdominal organ volume and proton density fat fraction (PDFF) in people with obesity during a weight loss intervention was assessed with automated multiorgan segmentation of quantitative water-fat MRI. An nnU-Net architecture was employed for automatic segmentation of abdominal organs, including visceral and subcutaneous adipose tissue, liver, and psoas and erector spinae muscle, based on quantitative chemical shift-encoded MRI and using ground truth labels generated from participants of the Lifestyle Intervention (LION) study. Each organ's volume and fat content were examined in 127 participants (73 female and 54 male participants; body mass index, 30-39.9 kg/m2) and in 81 (54 female and 32 male participants) of these participants after an 8-week formula-based low-calorie diet. Dice scores ranging from 0.91 to 0.97 were achieved for the automatic segmentation. PDFF was found to be lower in visceral adipose tissue compared with subcutaneous adipose tissue in both male and female participants. Before intervention, female participants exhibited higher PDFF in subcutaneous adipose tissue (90.6% vs 89.7%; P < .001) and lower PDFF in liver (8.6% vs 13.3%; P < .001) and visceral adipose tissue (76.4% vs 81.3%; P < .001) compared with male participants. This relation persisted after intervention. As a response to caloric restriction, male participants lost significantly more visceral adipose tissue volume (1.76 L vs 0.91 L; P < .001) and showed a higher decrease in subcutaneous adipose tissue PDFF (2.7% vs 1.5%; P < .001) than female participants. Automated body composition analysis on quantitative water-fat MRI data provides new insights for understanding sex-specific metabolic response to caloric restriction and weight loss in people with obesity. Keywords: Obesity, Chemical Shift-encoded MRI, Abdominal Fat Volume, Proton Density Fat Fraction, nnU-Net ClinicalTrials.gov registration no. NCT04023942 Supplemental material is available for this article. Published under a CC BY 4.0 license.

Rapid recovery in a child with febrile ulceronecrotic Mucha-Habermann disease following intravenous immunoglobulin administration.

Febrile ulceronecrotic Mucha-Habermann disease (FUMHD), a lymphocyte-mediated inflammatory skin disorder, is considered a severe variant of pityriasis lichenoides et varioliformis acuta that can lead to a fatal outcome if not managed in a timely fashion. Children with FUMHD can have systemic complications involving various organs. The scarcity of reported cases and the absence of well-designed studies or randomized clinical trials to evaluate different therapeutic modalities pose a major challenge in treating this potentially life-threatening disorder. We report a five-year-old child with FUMHD and seizures treated unsuccessfully with a combination of systemic steroids, methotrexate, dapsone, and oral erythromycin, who improved rapidly and achieved disease control with just a single infusion of low-dose intravenous immunoglobulin.

Automated segmentation of the human supraclavicular fat depot via deep neural network in water-fat separated magnetic resonance images.

Background: Human brown adipose tissue (BAT), mostly located in the cervical/supraclavicular region, is a promising target in obesity treatment. Magnetic resonance imaging (MRI) allows for mapping the fat content quantitatively. However, due to the complex heterogeneous distribution of BAT, it has been difficult to establish a standardized segmentation routine based on magnetic resonance (MR) images. Here, we suggest using a multi-modal deep neural network to detect the supraclavicular fat pocket. Methods: A total of 50 healthy subjects [median age/body mass index (BMI) =36 years/24.3 kg/m2] underwent MRI scans of the neck region on a 3 T Ingenia scanner (Philips Healthcare, Best, Netherlands). Manual segmentations following fixed rules for anatomical borders were used as ground truth labels. A deep learning-based method (termed as BAT-Net) was proposed for the segmentation of BAT on MRI scans. It jointly leveraged two-dimensional (2D) and three-dimensional (3D) convolutional neural network (CNN) architectures to efficiently encode the multi-modal and 3D context information from multi-modal MRI scans of the supraclavicular region. We compared the performance of BAT-Net to that of 2D U-Net and 3D U-Net. For 2D U-Net, we analyzed the performance difference of implementing 2D U-Net in three different planes, denoted as 2D U-Net (axial), 2D U-Net (coronal), and 2D U-Net (sagittal). Results: The proposed model achieved an average dice similarity coefficient (DSC) of 0.878 with a standard deviation of 0.020. The volume segmented by the network was smaller compared to the ground truth labels by 9.20 mL on average with a mean absolute increase in proton density fat fraction (PDFF) inside the segmented regions of 1.19 percentage points. The BAT-Net outperformed all implemented 2D U-Nets and the 3D U-Nets with average DSC enhancement ranging from 0.016 to 0.023. Conclusions: The current work integrates a deep neural network-based segmentation into the automated segmentation of supraclavicular fat depot for quantitative evaluation of BAT. Experiments show that the presented multi-modal method benefits from leveraging both 2D and 3D CNN architecture and outperforms the independent use of 2D or 3D networks. Deep learning-based segmentation methods show potential towards a fully automated segmentation of the supraclavicular fat depot.