Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis

Background A non-invasive method to grade the severity of steatohepatitis and liver fibrosis is magnetic resonance imaging (MRI) based corrected T1 (cT1). We aimed to identify genetic variants influencing liver cT1 and use genetics to understand mechanisms underlying liver fibroinflammatory disease and its link with other metabolic traits and diseases. Methods First, we performed a genome-wide association study (GWAS) in 14,440 Europeans in UK Biobank with liver cT1 measures. Second, we explored the effects of the cT1 variants on liver blood tests, and a range of metabolic traits and diseases. Third, we used Mendelian randomisation to test the causal effects of 24 predominantly metabolic traits on liver cT1 measures. Results We identified six independent genetic variants associated with liver cT1 that reached GWAS significance threshold (p<5x10-8). Four of the variants (rs75935921 in SLC30A10, rs13107325 in SLC39A8, rs58542926 in TM6SF2, rs738409 in PNPLA3) were also associated with elevated transaminases and had variable effects on liver fat and other metabolic traits. Insulin resistance, type 2 diabetes, non-alcoholic fatty liver and BMI were causally associated with elevated cT1 whilst favourable adiposity (instrumented by variants associated with higher adiposity but lower risk of cardiometabolic disease and lower liver fat) was found to be protective. Conclusion The association between two metal ion transporters and cT1 indicates an important new mechanism in steatohepatitis. Future studies are needed to determine whether interventions targeting the identified transporters might prevent liver disease in at risk individuals

Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations

There is no denying how machine learning and computer vision have grown in the recent years. Their highest advantages lie within their automation, suitability, and ability to generate astounding results in a matter of seconds in a reproducible manner. This is aided by the ubiquitous advancements reached in the computing capabilities of current graphical processing units and the highly efficient implementation of such techniques. Hence, in this paper, we survey the key studies that are published between 2014 and 2020, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic-tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and further partitioned if the amount of works that fall under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites, containing masks of the aforementioned tissues, are thoroughly discussed, highlighting the organizers original contributions, and those of other researchers. Also, the metrics that are used excessively in literature are mentioned in our review stressing their relevancy to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing such as the scarcity of many studies on the vessels segmentation challenge, and why their absence needs to be dealt with in an accelerated manner.Comment: 41 pages, 4 figures, 13 equations, 1 table. A review paper on liver tissues segmentation based on automated ML-based technique

Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio

Investigating genetic determinants of liver disease and its associations with cardiovascular diseases

Background Dramatic modifications in lifestyle have given rise to an epidemic in chronic liver diseases, predominantly driven by non-alcoholic fatty liver disease (NAFLD). The more severe NAFLD phenotypes are associated with elevated liver iron, inflammation (steatohepatitis), scarring and liver failure (fibrosis, cirrhosis), and possibly with cardiovascular diseases (CVDs); genetic and population studies of these phenotypes and their links to CVDs have been limited. Aims 1) Investigate the genetic susceptibility underlying liver MRI phenotypes (iron and corrected T1 (cT1), a steatohepatitis proxy) and explore associations with other cardiometabolic traits. 2) Investigate whether liver fibrosis is an independent risk factor for CVDs. Methods We carried out genome-wide association studies (GWASs) of liver MRI phenotypes (iron (N = 8,289), and corrected T1 (a steatohepatitis proxy, N = 14,440)) in UK Biobank. We used genetics to investigate causality with other traits. We calculated a FIB-4 score (a validated non-invasive scoring system that predicts liver fibrosis) in 44,956 individuals in the UK and investigated its association with the incidence of five CVDs (ischaemic stroke, myocardial infarction, heart failure, peripheral arterial disease, atrial fibrillation (AF)). Results Three genetic variants known to influence hepcidin regulation (rs1800562 (C282Y) and rs1799945 (H63D) in HFE, rs855791 (V736A) in TMPRSS6) were associated with liver iron (p < 5 x 10-8). Mendelian randomisation provided evidence that central obesity causes higher liver iron. Four variants (rs75935921 in SLC30A10, rs13107325 in SLC39A8, rs58542926 in TM6SF2, rs738409 in PNPLA3) were associated with elevated cT1 (p < 5 x 10-8). Insulin resistance, type 2 diabetes, fatty liver, and BMI were causally associated with elevated cT1 whilst favourable adiposity was protective. In 44,956 individuals over a median of 5.4 years, adjusted models demonstrated strong associations of “suspected liver fibrosis” (FIB-4 1.3) with cirrhosis (Hazard ratio (HR 13.64 [10.79 – 17.26], p < 2 x 10-16)) and hepatocellular carcinoma (HR 11.64 [5.15 – 26.31], p = 3.5 x 10-9), but no association with the incidence of most CVDs, albeit a modest increase in AF risk (HR 1.18 [1.01 – 1.37]), when compared to individuals with a FIB-4 < 1.3. Conclusions This thesis provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific. The association between two metal ion transporters and cT1 indicates a new mechanism in steatohepatitis. There is little evidence to suggest that liver fibrosis is an independent risk factor for most CVDs, except possibly a small increase risk in incident AF risk. This thesis’ findings can be used to investigate causality, generate hypotheses for drug development and inform health policies