VLDL overproduction, a process that is driven by an excess amount of hepatic fat, is a well-documented feature of familial combined hyperlipidemia (FCHL). The aims of this study were to investigate whether fatty liver, measured with ultrasound and as plasma alanine aminotransferase (ALT) levels, develops against a genetic background in FCHL and to identify chromosomal loci that are linked to these traits. In total, 157 FCHL family members and 20 spouses participated in this study. Radiological evidence of fatty liver was more prevalent not only in FCHL probands (40%) but also in their relatives (35%) compared with spouses (15%) (P , 0.05). Heritability calculations revealed that 20-36% of the variability in ALT levels could be attributed to genetic factors. Nonparametric quantitative trait locus (QTL) analysis revealed three significant (P , 0.001) loci with either the ultrasound or the ALT trait in the male sample: 1q42.3, 7p12-21, and 22p13-q11; none was found in the female sample or the entire group. Of these QTLs, the 7p region was consistent over time, because reanalysis with ALT levels that were determined during a visit 5 years earlier yielded similar results. This study shows that fatty liver is a heritable aspect of FCHL. Replication of particularly the 7p region is awaited.-Brouwers,

Overproduction of VLDL (very-low-density lipoprotein) particles is an important cause of FCHL (familial combined hyperlipidaemia). It has been shown recently that VLDL production is driven by the amount of hepatic fat. The present study was conducted to determine the prevalence of fatty liver in relation to the different fat compartments and lipid parameters in FCHL. A total of 68 FCHL patients, 110 normolipidaemic relatives and 66 spouses underwent ultrasound of the abdominal region to estimate the amount of subcutaneous, visceral and hepatic fat. Skinfold callipers were used to measure subcutaneous fat of the biceps, triceps, subscapular and supra-iliacal regions. Fatty liver was observed in 18% of the spouses, 25% of the normolipidaemic relatives and 49% of the FCHL patients. After adjustment for age, gender and body mass index, the prevalence of fatty liver was significantly higher in FCHL patients compared with spouses [OR (odds ratio), 3.1; P=0.03], and also in the normolipidaemic relatives compared with spouses (OR, 4.0; P=0.02), whereas no differences were observed between FCHL patients and normolipidaemic relatives (OR, 0.8; P=0.58). In the normolipidaemic relatives and FCHL patients combined, both visceral fat mass and subcutaneous abdominal fat were independent predictors of fatty liver (P<0.001 for both fat compartments; FCHL status corrected). Of interest, fatty liver stages were correlated with both VLDL-apoB (apolipoprotein B) and VLDL-triacylglycerols (triglycerides) in a representative subset (n=69) of patients and relatives (r(2)=0.12, P=0.006; and r(2)=0.18, P=0.001 respectively). These results show that fatty liver is a central aspect of FCHL, i.e. patients and normolipidaemic relatives. Both visceral and subcutaneous adiposity contribute to its 3-4-fold higher risk in FCHL.

Purpose This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Menière’s disease. Materials and methods A retrospective, multicentric diagnostic case–control study was performed. This study included 120 patients with unilateral or bilateral Menière’s disease and 140 controls from four centers in the Netherlands and Belgium. Multiple radiomic features were extracted from conventional MRI scans and used to train a machine learning-based, multi-layer perceptron classification model to distinguish patients with Menière’s disease from controls. The primary outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the classification model. Results The classification accuracy of the machine learning model on the test set was 82%, with a sensitivity of 83%, and a specificity of 82%. The positive and negative predictive values were 71%, and 90%, respectively. Conclusion The multi-layer perceptron classification model yielded a precise, high-diagnostic performance in identifying patients with Menière’s disease based on radiomic features extracted from conventional T2-weighted MRI scans. In the future, radiomics might serve as a fast and noninvasive decision support system, next to clinical evaluation in the diagnosis of Menière’s disease.

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