Sex Steroids Regulate Liver Fat Content and Body Fat Distribution in Both Men and Women: A Study in Transgender Persons.

CONTEXT: Liver fat content and visceral fat volume are associated with insulin resistance and cardiovascular disease and are higher in men than in women. OBJECTIVE: To determine the effect of estradiol and testosterone treatment on liver fat and visceral fat in transgender persons. DESIGN: Open-label intervention study (SHAMVA) with a 1-year follow-up. SETTING: Gender clinic in a hospital. PATIENTS: 8 trans women and 18 trans men receiving hormone treatment. INTERVENTIONS: Trans women received an antiandrogen and after 6 weeks estradiol was added. Trans men were randomized to receive triptorelin, testosterone, and anastrozole for 12 weeks or triptorelin and testosterone for 12 weeks, followed by only testosterone until week 52. MAIN OUTCOME MEASURES: Liver fat content, visceral and abdominal subcutaneous fat volume, measured by magnetic resonance spectrometry or imaging at baseline, 6, 8, 18, and 58 weeks in transwomen or at baseline; at 6 and 12 weeks in trans men with anastrozole; and at 52 weeks in trans men without anastrozole. RESULTS: In trans women, liver fat content decreased by 1.55% (-2.99 to -0.12) after 58 weeks, compared to week 6. Visceral fat did not change. In trans men with anastrozole, the liver fat content and visceral fat volume did not change. In trans men without anastrozole, after 52 weeks, liver fat content increased by 0.83% (0.14 to 1.52) and visceral fat volume increased by 34% (16 to 51). CONCLUSIONS: Sex hormones regulate liver fat content and visceral fat in men and women.

Self-supervised neural network improves tri-exponential intravoxel incoherent motion model fitting compared to least-squares fitting in non-alcoholic fatty liver disease.

Recent literature suggests that tri-exponential models may provide additional information and fit liver intravoxel incoherent motion (IVIM) data more accurately than conventional bi-exponential models. However, voxel-wise fitting of IVIM results in noisy and unreliable parameter maps. For bi-exponential IVIM, neural networks (NN) were able to produce superior parameter maps than conventional least-squares (LSQ) generated images. Hence, to improve parameter map quality of tri-exponential IVIM, we developed an unsupervised physics-informed deep neural network (IVIM3-NET). We assessed its performance in simulations and in patients with non-alcoholic fatty liver disease (NAFLD) and compared outcomes with bi-exponential LSQ and NN fits and tri-exponential LSQ fits. Scanning was performed using a 3.0T free-breathing multi-slice diffusion-weighted single-shot echo-planar imaging sequence with 18 b-values. Images were analysed for visual quality, comparing the bi- and tri-exponential IVIM models for LSQ fits and NN fits using parameter-map signal-to-noise ratios (SNR) and adjusted R 2. IVIM parameters were compared to histological fibrosis, disease activity and steatosis grades. Parameter map quality improved with bi- and tri-exponential NN approaches, with a significant increase in average parameter-map SNR from 3.38 to 5.59 and 2.45 to 4.01 for bi- and tri-exponential LSQ and NN models respectively. In 33 out of 36 patients, the tri-exponential model exhibited higher adjusted R 2 values than the bi-exponential model. Correlating IVIM data to liver histology showed that the bi- and tri-exponential NN outperformed both LSQ models for the majority of IVIM parameters (10 out of 15 significant correlations). Overall, our results support the use of a tri-exponential IVIM model in NAFLD. We show that the IVIM3-NET can be used to improve image quality compared to a tri-exponential LSQ fit and provides promising correlations with histopathology similar to the bi-exponential neural network fit, while generating potentially complementary additional parameters.

Assessment of Imaging Modalities Against Liver Biopsy in Nonalcoholic Fatty Liver Disease: The Amsterdam NAFLD-NASH Cohort.

BACKGROUND: Noninvasive diagnostic methods are urgently required in disease stratification and monitoring in nonalcoholic fatty liver disease (NAFLD). Multiparametric magnetic resonance imaging (MRI) is a promising technique to assess hepatic steatosis, inflammation, and fibrosis, potentially enabling noninvasive identification of individuals with active and advanced stages of NAFLD. PURPOSE: To examine the diagnostic performance of multiparametric MRI for the assessment of disease severity along the NAFLD disease spectrum with comparison to histological scores. STUDY TYPE: Prospective, cohort. POPULATION: Thirty-seven patients with NAFLD. FIELD STRENGTH/SEQUENCE: Multiparametric MRI at 3.0 T consisted of magnetic resonance (MR) spectroscopy (MRS) with multi-echo stimulated-echo acquisition mode, magnitude-based and three-point Dixon using a two-dimensional multi-echo gradient echo, MR elastography (MRE) using a generalized multishot gradient-recalled echo sequence and intravoxel incoherent motion (IVIM) using a multislice diffusion weighted single-shot echo-planar sequence. ASSESSMENT: Histological steatosis grades were compared to proton density fat fraction measured by MRS (PDFFMRS ), magnitude-based MRI (PDFFMRI-M ), and three-point Dixon (PDFFDixon ), as well as FibroScan® controlled attenuation parameter (CAP). Fibrosis and disease activity were compared to IVIM and MRE. FibroScan® liver stiffness measurements were compared to fibrosis levels. Diagnostic performance of all imaging parameters was determined for distinction between simple steatosis and nonalcoholic steatohepatitis (NASH). STATISTICAL TESTS: Spearman's rank test, Kruskal-Wallis test, Dunn's post-hoc test with Holm-Bonferroni P-value adjustment, receiver operating characteristic curve analysis. A P-value <0.05 was considered statistically significant. RESULTS: Histological steatosis grade correlated significantly with PDFFMRS (rs  = 0.66, P < 0.001), PDFFMRI-M (rs  = 0.68, P < 0.001), and PDFFDixon (rs  = 0.67, P < 0.001), whereas no correlation was found with CAP. MRE and IVIM diffusion and perfusion significantly correlated with disease activity (rs  = 0.55, P < 0.001, rs  = -0.40, P = 0.016, rs  = -0.37, P = 0.027, respectively) and fibrosis (rs  = 0.55, P < 0.001, rs  = -0.46, P = 0.0051; rs  = -0.53, P < 0.001, respectively). MRE and IVIM diffusion had the highest area-under-the-curve for distinction between simple steatosis and NASH (0.79 and 0.73, respectively). DATA CONCLUSION: Multiparametric MRI is a promising method for noninvasive, accurate, and sensitive distinction between simple hepatic steatosis and NASH, as well as for the assessment of steatosis and fibrosis severity. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: 2.

Donor Fecal Microbiota Transplantation Alters Gut Microbiota and Metabolites in Obese Individuals With Steatohepatitis.

The intestinal microbiota has been linked to the development and prevalence of steatohepatitis in humans. Interestingly, steatohepatitis is significantly lower in individuals taking a plant-based, low-animal-protein diet, which is thought to be mediated by gut microbiota. However, data on causality between these observations in humans is scarce. In this regard, fecal microbiota transplantation (FMT) using healthy donors is safe and is capable of changing microbial composition in human disease. We therefore performed a double-blind randomized controlled proof-of-principle study in which individuals with hepatic steatosis on ultrasound were randomized to two study arms: lean vegan donor (allogenic n = 10) or own (autologous n = 11) FMT. Both were performed three times at 8-week intervals. A liver biopsy was performed at baseline and after 24 weeks in every subject to determine histopathology (Nonalcoholic Steatohepatitis Clinical Research Network) classification and changes in hepatic gene expression based on RNA sequencing. Secondary outcome parameters were changes in intestinal microbiota composition and fasting plasma metabolomics. We observed a trend toward improved necro-inflammatory histology, and found significant changes in expression of hepatic genes involved in inflammation and lipid metabolism following allogenic FMT. Intestinal microbial community structure changed following allogenic FMT, which was associated with changes in plasma metabolites as well as markers of . Conclusion: Allogenic FMT using lean vegan donors in individuals with hepatic steatosis shows an effect on intestinal microbiota composition, which is associated with beneficial changes in plasma metabolites and markers of steatohepatitis.

Comparison of clinical MRI liver iron content measurements using signal intensity ratios, R 2 and R 2.

PURPOSE: To compare three types of MRI liver iron content (LIC) measurement performed in daily clinical routine in a single center over a 6-year period. METHODS: Patients undergoing LIC MRI-scans (1.5T) at our center between January 1, 2008 and December 31, 2013 were retrospectively included. LIC was measured routinely with signal intensity ratio (SIR) and MR-relaxometry (R 2 and R 2*) methods. Three observers placed regions-of-interest. The success rate was the number of correctly acquired scans over the total number of scans. Interobserver agreement was assessed with intraclass correlation coefficients (ICC) and Bland-Altman analysis, correlations between LICSIR, R 2, R 2*, and serum values with Spearman's rank correlation coefficient. Diagnostic accuracies of LICSIR, R 2 and serum transferrin, transferrin-saturation, and ferritin compared to increased R 2* (≥44 Hz) as indicator of iron overload were assessed using ROC-analysis. RESULTS: LIC MRI-scans were performed in 114 subjects. SIR, R 2, and R 2* data were successfully acquired in 102/114 (89%), 71/114 (62%), and 112/114 (98%) measurements, with the lowest success rate for R 2. The ICCs of SIR, R 2, and R 2* did not differ at 0.998, 0.997, and 0.999. R 2 and serum ferritin had the highest diagnostic accuracies to detect elevated R 2* as mark of iron overload. CONCLUSIONS: SIR and R 2* are preferable over R 2 in terms of success rates. R 2*'s shorter acquisition time and wide range of measurable LIC values favor R 2* over SIR for MRI-based LIC measurement.