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Background. Liver allografts protect renal allografts from the same donor from some, but not all, preformed donor specific alloantibodies (DSA). However, the precise mechanisms of protection and the potential for more subtle alterations/injuries within the grafts resulting from DSA interactions require further study. Methods. We reevaluated allograft biopsies from simultaneous liver-kidney transplant recipients who had both allografts biopsied within 60 d of one another and within 30 d of DSA being positive in serum (positive: mean florescence intensity ≥5000). Routine histology, C4d staining, and specialized immunohistochemistry for Kupffer cells (KCs; CD163) and a C4d receptor immunoglobulin-like transcript-4 were carried out in 4 patients with 6 paired biopsies. Results. Overt antibody-mediated rejection was found in 3 of 4 renal and liver allografts. One patient had biopsy-confirmed renal and liver allograft antibody-mediated rejection despite serum clearance of DSA. All biopsies showed KC hypertrophy (minimal: 1; mild: 2; moderate: 1; severe: 2) and cytoplasmic C4d KC staining was easily detected in 2 biopsies from 2 patients; minimal and negative in 2 biopsies each. Implications of which are discussed. Control 1-y protocol liver allograft biopsies from DSA– recipients showed neither KC hypertrophy nor KC C4d staining (n = 6). Conclusions. Partial renal allograft protection by a liver allograft from the same donor may be partially mediated by phagocytosis/elimination of antibody and complement split products by KCs, as shown decades ago in controlled sensitized experimental animal experiments.

Background Polygenic risk scores (PRS) hold the promise to refine prognostication in hepatocellular cancer (HCC). The few available HCC PRS include germline risk variants identified among individuals of mostly European ancestry, but data are lacking on the transportability of these PRS in multiethnic U.S patients with cirrhosis from multiple etiologies. Methods We used data from 1644 patients with cirrhosis enrolled in two prospective cohort studies in the U.S. Patients were followed until HCC diagnosis, death, liver transplantation, or last study visit through June 30, 2021. The high-risk variants in PNPLA3-MBOAT7-TM6SF2-GCKR were combined in a PRS and we evaluated its association with HCC. Discriminatory accuracy was assessed using the C-statistic. Results During 4,759 person-years of follow-up, 93 patients developed HCC. Mean age was 59.8 years, 68.6% were male, 27.2% Hispanic, 25.1% non-Hispanic Black, 25.7% had NAFLD, 42.1% had heavy alcohol use, and 19.5% had active HCV. HCC risk increased by 134% per unit increase in PRS (HR = 2.30; 95% CI, 1.35–3.92). Compared to cirrhosis patients in the lowest tertile of the PRS, those in the highest tertile had 2-fold higher risk of HCC (HR = 2.05; 95% CI, 1.22–3.44). The PRS alone had modest discriminatory ability (C-statistic = 0.58; 95% CI, 0.52–0.63); however, adding PRS to a predictive model with traditional HCC risk factors had a C-statistic of 0.70 (95% CI, 0.64–0.76), increasing from 0.68 without the PRS (p = 0.0012). Conclusions Our findings suggest that PRS may enhance risk prediction for HCC in contemporary U.S. cirrhosis patients.

Texas has the highest age-adjusted incidence rate of hepatocellular carcinoma (HCC) in the United States. To address cancer prevention and early detection through research, Cancer Prevention and Research Institute of Texas (CPRIT) has funded the Texas Collaborative Center for Hepatocellular Cancer (TeCH) to facilitate liver cancer research, education and advocacy activities. This paper describes the organizational structure, program measures, the actions completed and future plans of TeCH. This center is comprised of several cores and committees including the Administrative Core, Steering Committee, Data and Biospecimen Core, Scientific Committee, Clinical Network Committee, and the Community Outreach Committee. Each core and committee provide its own level of connectivity and necessary research support. We have developed and published a TeCH Framework, a conceptual model designed for improving primary and secondary prevention of HCC. TeCH and its committees facilitate connections and collaborations among HCC researchers and clinicians, healthcare leaders, biotechnology companies and the public to reduce liver cancer mortality in Texas by 2030.

Introduction and objectives: Cirrhosis-related mortality is underestimated and is increasing; extrahepatic factors may contribute. We examined trends in cirrhosis mortality from 1999-2017 in the United States attributed to liver-related (varices, peritonitis, hepatorenal syndrome, hepatic encephalopathy, hepatocellular carcinoma, sepsis) or extrahepatic (cardiovascular disease, influenza and pneumonia, diabetes, malignancy) causes, and compared mortality trends with congestive heart failure (CHF) and chronic obstructive pulmonary disease (COPD) populations. Materials and methods: A national mortality database was used. Changes in age-standardized mortality over time were determined by joinpoint analysis. Average annual percentage change (AAPC) was estimated. Results: Cirrhosis cohort: From 1999-2017, both liver-related (AAPC 1.3%; 95% confidence interval [CI] 0.7-1.9) and extrahepatic mortality (AAPC 1.0%; 95% CI 0.7-1.2) increased. Cirrhosis vs other chronic disease cohorts: changes in all-cause mortality were higher in cirrhosis (AAPC 1.0%; 95% CI 0.7-1.4) than CHF (AAPC 0.1%; 95% CI -0.5- 0.8) or COPD (AAPC -0.4%; 95% CI -0.6- -0.2). Sepsis mortality was highest in cirrhosis (AAPC 3.6%, 95% 3.2- 4.1) compared to CHF (AAPC 0.6%, 95% CI -0.5- 1.7) or COPD (AAPC 0.8%, 95% CI 0.5- 1.2). Cardiovascular mortality increased in cirrhosis (AAPC 1.3%, 95% CI 1.1- 1.5), declined in CHF (AAPC -2.0%, 95% CI -5.3- 1.3) and remained unchanged in COPD (AAPC 0.1%, 95% CI -0.2- 0.4). Extrahepatic mortality was higher among women, rural populations, and individuals >65 years with cirrhosis. Conclusions: Extrahepatic causes of death are important drivers of mortality and differentially impact cirrhosis compared to other chronic diseases.

A missense variant in the cytoplasmic domain of the insulin receptor (INSR) was identified by exome sequencing in affected members of a four-generation family with fatty liver disease (FLD). The variant (rs766457461, c.4063T>C, p.Y1355H) results in the substitution of histidine for a tyrosine that undergoes autophosphorylation in response to insulin stimulation in vitro. Because insulin promotes lipogenesis in hepatocytes, we hypothesized that the variant was causally linked to FLD in the family. To test this hypothesis, we used CRISPR/Cas9 technology to replace the corresponding tyrosine in mouse INSR with histidine (Y1345H). No significant differences were found in hepatic insulin signaling, as assessed by phosphorylation of INSR or AKT levels or in activation of the insulin-responsive transcription factor SREBP-1c. Glucose tolerance and hepatic triglyceride (TG) content in Insr1345H/H mice fed a chow diet or diets rich in fat, sucrose or fructose did not differ significantly from WT littermates. Thus, our studies in mice failed to support the notion that INSR (Y1355H) is causally related to FLD in the family or that phosphorylation of this residue alters hepatic TG metabolism.

Background and aims. We conducted an individual participant data (IPD) pooled analysis on the diagnostic accuracy of magnetic resonance elastography (MRE) to detect fibrosis stage in liver transplant recipients.Material and methods. Through a systematic literature search, we identified studies on diagnostic performance of MRE for staging liver fibrosis, using liver biopsy as gold standard. We contacted study authors for published and unpublished IPD on age, sex, body mass index, liver stiffness, fibrosis stage, degree of inflammation and interval between MRE and biopsy; from these we limited analysis to patients who had undergone liver transplantation. Through pooled analysis using nonparametric two-stage receiver-operating curve (ROC) regression models, we calculated the cluster-adjusted AUROC, sensitivity and specificity of MRE for any (≥ stage 1), significant (≥ stage 2) and advanced fibrosis (≥ stage 3) and cirrhosis (stage 4).Results. We included 6 cohorts (4 published and 2 unpublished series) reporting on 141 liver transplant recipients (mean age, 57 years; 75.2% male; mean BMI, 27.1 kg/m2). Fibrosis stage distribution stage 0, 1, 2, 3, or 4, was 37.6%, 23.4%, 24.8%, 12% and 2.2%, respectively. Mean AUROC values (and 95% confidence intervals) for diagnosis of any (≥ stage 1), significant (≥ stage 2), or advanced fibrosis (≥ stage 3) and cirrhosis were 0.73 (0.66-0.81), 0.69 (0.62-0.74), 0.83 (0.61-0.88) and 0.96 (0.93-0.98), respectively. Similar diagnostic performance was observed in stratified analysis based on sex, obesity and inflammation grade.Conclusions. In conclusion, MRE has high diagnostic accuracy for detection of advanced fibrosis and cirrhosis in liver transplant recipients, independent of BMI and degree of inflammation.

BackgroundPolygenic risk scores (PRS) hold the promise to refine prognostication in hepatocellular cancer (HCC). The few available HCC PRS include germline risk variants identified among individuals of mostly European ancestry, but data are lacking on the transportability of these PRS in multiethnic U.S patients with cirrhosis from multiple etiologies.MethodsWe used data from 1644 patients with cirrhosis enrolled in two prospective cohort studies in the U.S. Patients were followed until HCC diagnosis, death, liver transplantation, or last study visit through June 30, 2021. The high-risk variants in PNPLA3-MBOAT7-TM6SF2-GCKR were combined in a PRS and we evaluated its association with HCC. Discriminatory accuracy was assessed using the C-statistic.ResultsDuring 4,759 person-years of follow-up, 93 patients developed HCC. Mean age was 59.8 years, 68.6% were male, 27.2% Hispanic, 25.1% non-Hispanic Black, 25.7% had NAFLD, 42.1% had heavy alcohol use, and 19.5% had active HCV. HCC risk increased by 134% per unit increase in PRS (HR = 2.30; 95% CI, 1.35-3.92). Compared to cirrhosis patients in the lowest tertile of the PRS, those in the highest tertile had 2-fold higher risk of HCC (HR = 2.05; 95% CI, 1.22-3.44). The PRS alone had modest discriminatory ability (C-statistic = 0.58; 95% CI, 0.52-0.63); however, adding PRS to a predictive model with traditional HCC risk factors had a C-statistic of 0.70 (95% CI, 0.64-0.76), increasing from 0.68 without the PRS (p = 0.0012).ConclusionsOur findings suggest that PRS may enhance risk prediction for HCC in contemporary U.S. cirrhosis patients.

Summary: Background: Survival of liver transplant recipients beyond 1 year since transplantation is compromised by an increased risk of cancer, cardiovascular events, infection, and graft failure. Few clinical tools are available to identify patients at risk of these complications, which would flag them for screening tests and potentially life-saving interventions. In this retrospective analysis, we aimed to assess the ability of deep learning algorithms of longitudinal data from two prospective cohorts to predict complications resulting in death after liver transplantation over multiple timeframes, compared with logistic regression models. Methods: In this machine learning analysis, model development was done on a set of 42 146 liver transplant recipients (mean age 48·6 years [SD 17·3]; 17 196 [40·8%] women) from the Scientific Registry of Transplant Recipients (SRTR) in the USA. Transferability of the model was further evaluated by fine-tuning on a dataset from the University Health Network (UHN) in Canada (n=3269; mean age 52·5 years [11·1]; 1079 [33·0%] women). The primary outcome was cause of death, as recorded in the databases, due to cardiovascular causes, infection, graft failure, or cancer, within 1 year and 5 years of each follow-up examination after transplantation. We compared the performance of four deep learning models against logistic regression, assessing performance using the area under the receiver operating characteristic curve (AUROC). Findings: In both datasets, deep learning models outperformed logistic regression, with the Transformer model achieving the highest AUROCs in both datasets (p