Steroid responsiveness in alcohol-associated hepatitis is linked to glucocorticoid metabolism, mitochondrial repair, and heat shock proteins.

BACKGROUND: Alcohol-associated hepatitis (AH) is one of the clinical presentations of alcohol-associated liver disease. AH has poor prognosis, and corticosteroids remain the mainstay of drug therapy. However, ~40% of patients do not respond to this treatment, and the mechanisms underlying the altered response to corticosteroids are not understood. The current study aimed to identify changes in hepatic protein expression associated with responsiveness to corticosteroids and prognosis in patients with AH. METHODS: Patients with AH were enrolled based on the National Institute on Alcohol Abuse and Alcoholism inclusion criteria for acute AH and further confirmed by a diagnostic liver biopsy. Proteomic analysis was conducted on liver samples acquired from patients with AH grouped as nonresponders (AH-NR, n = 7) and responders (AH-R, n = 14) to corticosteroids, and nonalcohol-associated liver disease controls (n = 10). The definition of responders was based on the clinical prognostic model, the Lille Score, where a score < 0.45 classified patients as AH-R and a score > 0.45 as AH-NR. Primary outcomes used to assess steroid response were Lille Score (eg, improved liver function) and survival at 24 weeks. RESULTS: Reduced levels of the glucocorticoid receptor and its transcriptional co-activator, glucocorticoid modulatory element-binding protein 2, were observed in the hepatic proteome of AH-NR versus AH-R. The corticosteroid metabolizing enzyme, 11-beta-hydroxysteroid dehydrogenase 1, was increased in AH-NR versus AH-R along with elevated mitochondrial DNA repair enzymes, while several proteins of the heat shock pathway were reduced. Analysis of differentially expressed proteins in AH-NR who survived 24 weeks relative to AH-NR nonsurvivors revealed several protein expression changes, including increased levels of acute phase proteins, elevated coagulation factors, and reduced mast cell markers. CONCLUSIONS: This study identified hepatic proteomic changes that may predict responsiveness to corticosteroids and mortality in patients with AH.

Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning.

Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcriptomics and proteomics data. The liver tissue models were validated in independent liver tissue datasets. Next, we built integrated gene and protein expression models that allowed us to identify combined gene-protein biomarker panels. For liver tissue, we attained 90% nested-cross validation accuracy in our dataset and 82% accuracy in the independent validation dataset using transcriptomic data. We attained 100% nested-cross validation accuracy in our dataset and 61% accuracy in the independent validation dataset using proteomic data. For PBMCs, we attained 83% and 89% accuracy with transcriptomic and proteomic data, respectively. The integration of the two data types resulted in improved classification accuracy for PBMCs, but not liver tissue. We also identified the following gene-protein matches within the gene-protein biomarker panels: CLEC4M-CLC4M, GSTA1-GSTA2 for liver tissue and SELENBP1-SBP1 for PBMCs. In this study, machine learning models had high classification accuracy for both transcriptomics and proteomics data, across liver tissue and PBMCs. The integration of transcriptomics and proteomics into a multi-omics model yielded improvement in classification accuracy for the PBMC data. The set of integrated gene-protein biomarkers for PBMCs show promise toward developing a liquid biopsy for alcohol-associated liver disease.

Clinical trial facilitators: A novel approach to support the execution of clinical research at the study site level.

In the summer of 2020, multiple efforts were undertaken to establish safe and effective vaccines to combat the spread of the coronavirus disease (COVID-19). In the United States (U.S.), Operation Warp Speed (OWS) was the program designated to coordinate such efforts. OWS was a partnership between the Department of Health and Human Services (HHS), the Department of Defense (DOD), and the private sector, that aimed to help accelerate control of the COVID-19 pandemic by advancing development, manufacturing, and distribution of vaccines, therapeutics, and diagnostics. The U.S. Department of Veterans Affairs' (VA) was identified as a potential collaborator in several large-scale OWS Phase III clinical trial efforts designed to evaluate the safety and efficacy of various vaccines that were in development. Given the global importance of these trials, it was recognized that there would be a need for a coordinated, centralized effort within VA to ensure that its medical centers (sites) would be ready and able to efficiently initiate, recruit, and enroll into these trials. The manuscript outlines the partnership and start-up activities led by two key divisions of the VA's Office of Research and Development's clinical research enterprise. These efforts focused on site and enterprise-level requirements for multiple trials, with one trial serving as the most prominently featured of these studies within the VA. As a result, several best practices arose that included designating clinical trial facilitators to study sites to support study initiation activities and successful study enrollment at these locations in an efficient and timely fashion.

A phase II, multicenter, open-label, randomized trial of pegfilgrastim for patients with alcohol-associated hepatitis.

Background: In trials conducted in India, recombinant granulocyte colony stimulating factor (GCSF) improved survival in alcohol-associated hepatitis (AH). The aim of this trial was to determine the safety and efficacy of pegfilgrastim, a long-acting recombinant GCSF, in patients with AH in the United States. Methods: This prospective, randomized, open label trial conducted between March 2017 and March 2020 randomized patients with a clinical diagnosis of AH and a Maddrey discriminant function score ≥32 to standard of care (SOC) or SOC+pegfilgrastim (0.6 mg subcutaneously) on Day 1 and Day 8 (clinicaltrials.gov NCT02776059). SOC was 28 days of either pentoxifylline or prednisolone, as determined by the patient's primary physician. The second injection of pegfilgrastim was not administered if the white blood cell count exceeded 30,000/mm3 on Day 8. Primary outcome was survival at Day 90. Secondary outcomes included the incidence of acute kidney injury (AKI), hepatorenal syndrome (HRS), hepatic encephalopathy, or infections. Findings: The study was terminated early due to COVID19 pandemic. Eighteen patients were randomized to SOC and 16 to SOC+pegfilgrastim. All patients received prednisolone as SOC. Nine patients failed to receive a second dose of pegfilgrastin due to WBC > 30,000/mm3 on Day 8. Survival at 90 days was similar in both groups (SOC: 0.83 [95% confidence interval [CI]: 0.57-0.94] vs. pegfilgrastim: 0.73 [95% CI: 0.44-0.89]; p > 0.05; CI for difference: -0.18-0.38). The incidences of AKI, HRS, hepatic encephalopathy, and infections were similar in both treatment arms and there were no serious adverse events attributed to pegfilgrastim. Interpretation: This phase II trial found no survival benefit at 90 days among subjects with AH who received pegfilgrastim+prednisolone compared with subjects receiving prednisolone alone. Funding: was provided by the United States National Institutes of Health and National Institute on Alcohol Abuse and Alcoholism U01-AA021886 and U01-AA021884.

Integrated Transcriptomic and Proteomic Analysis Identifies Plasma Biomarkers of Hepatocellular Failure in Alcohol-Associated Hepatitis.

Alcohol-associated hepatitis (AH) is a form of liver failure with high short-term mortality. Recent studies have shown that defective function of hepatocyte nuclear factor 4 alpha (HNF4a) and systemic inflammation are major disease drivers of AH. Plasma biomarkers of hepatocyte function could be useful for diagnostic and prognostic purposes. Herein, an integrative analysis of hepatic RNA sequencing and liquid chromatography-tandem mass spectrometry was performed to identify plasma protein signatures for patients with mild and severe AH. Alcohol-related liver disease cirrhosis, nonalcoholic fatty liver disease, and healthy subjects were used as comparator groups. Levels of identified proteins primarily involved in hepatocellular function were decreased in patients with AH, which included hepatokines, clotting factors, complement cascade components, and hepatocyte growth activators. A protein signature of AH disease severity was identified, including thrombin, hepatocyte growth factor α, clusterin, human serum factor H-related protein, and kallistatin, which exhibited large abundance shifts between severe and nonsevere AH. The combination of thrombin and hepatocyte growth factor α discriminated between severe and nonsevere AH with high sensitivity and specificity. These findings were correlated with the liver expression of genes encoding secreted proteins in a similar cohort, finding a highly consistent plasma protein signature reflecting HNF4A and HNF1A functions. This unbiased proteomic-transcriptome analysis identified plasma protein signatures and pathways associated with disease severity, reflecting HNF4A/1A activity useful for diagnostic assessment in AH.

Differentiating between liver diseases by applying multiclass machine learning approaches to transcriptomics of liver tissue or blood-based samples.

Background & Aims: Liver disease carries significant healthcare burden and frequently requires a combination of blood tests, imaging, and invasive liver biopsy to diagnose. Distinguishing between inflammatory liver diseases, which may have similar clinical presentations, is particularly challenging. In this study, we implemented a machine learning pipeline for the identification of diagnostic gene expression biomarkers across several alcohol-associated and non-alcohol-associated liver diseases, using either liver tissue or blood-based samples. Methods: We collected peripheral blood mononuclear cells (PBMCs) and liver tissue samples from participants with alcohol-associated hepatitis (AH), alcohol-associated cirrhosis (AC), non-alcohol-associated fatty liver disease, chronic HCV infection, and healthy controls. We performed RNA sequencing (RNA-seq) on 137 PBMC samples and 67 liver tissue samples. Using gene expression data, we implemented a machine learning feature selection and classification pipeline to identify diagnostic biomarkers which distinguish between the liver disease groups. The liver tissue results were validated using a public independent RNA-seq dataset. The biomarkers were computationally validated for biological relevance using pathway analysis tools. Results: Utilizing liver tissue RNA-seq data, we distinguished between AH, AC, and healthy conditions with overall accuracies of 90% in our dataset, and 82% in the independent dataset, with 33 genes. Distinguishing 4 liver conditions and healthy controls yielded 91% overall accuracy in our liver tissue dataset with 39 genes, and 75% overall accuracy in our PBMC dataset with 75 genes. Conclusions: Our machine learning pipeline was effective at identifying a small set of diagnostic gene biomarkers and classifying several liver diseases using RNA-seq data from liver tissue and PBMCs. The methodologies implemented and genes identified in this study may facilitate future efforts toward a liquid biopsy diagnostic for liver diseases. Lay summary: Distinguishing between inflammatory liver diseases without multiple tests can be challenging due to their clinically similar characteristics. To lay the groundwork for the development of a non-invasive blood-based diagnostic across a range of liver diseases, we compared samples from participants with alcohol-associated hepatitis, alcohol-associated cirrhosis, chronic hepatitis C infection, and non-alcohol-associated fatty liver disease. We used a machine learning computational approach to demonstrate that gene expression data generated from either liver tissue or blood samples can be used to discover a small set of gene biomarkers for effective diagnosis of these liver diseases.

Hepatic Protein and Phosphoprotein Signatures of Alcohol-Associated Cirrhosis and Hepatitis.

Alcohol-associated liver disease is a global health care burden, with alcohol-associated cirrhosis (AC) and alcohol-associated hepatitis (AH) being two clinical manifestations with poor prognosis. The limited efficacy of standard of care for AC and AH highlights a need for therapeutic targets and strategies. The current study aimed to address this need through the identification of hepatic proteome and phosphoproteome signatures of AC and AH. Proteomic and phosphoproteomic analyses were conducted on explant liver tissue (test cohort) and liver biopsies (validation cohort) from patients with AH. Changes in protein expression across AH severity and similarities and differences in AH and AC hepatic proteome were analyzed. Significant alterations in multiple proteins involved in various biological processes were observed in both AC and AH, including elevated expression of transcription factors involved in fibrogenesis (eg, Yes1-associated transcriptional regulator). Another finding was elevated levels of hepatic albumin (ALBU) concomitant with diminished ALBU phosphorylation, which may prevent ALBU release, leading to hypoalbuminemia. Furthermore, altered expression of proteins related to neutrophil function and chemotaxis, including elevated myeloperoxidase, cathelicidin antimicrobial peptide, complement C3, and complement C5 were observed in early AH, which declined at later stages. Finally, a loss in expression of mitochondria proteins, including enzymes responsible for the synthesis of cardiolipin was observed. The current study identified hepatic protein signatures of AC and AH as well as AH severity, which may facilitate the development of therapeutic strategies.

Development and implementation of standardized study performance metrics for a VA healthcare system clinical research consortium.

The cost of conducting clinical trials is continuously increasing and is driven in large part by the time and resources required to activate trials and reach accrual targets. The impact of low enrollment in a clinical trial can negatively affect the validity of study results and delay its generalizability to the broader population. Quality is a multidimensional concept which could relate to the design, conduct, and analysis of a trial, its clinical relevance, protection/safety of study participants, or quality of reporting. Furthermore, the quality of controlled trials is of obvious relevance to systematic reviews and if the "raw material" or "data" is flawed then the conclusions of systematic reviews cannot be trusted. To date, the literature surrounding the establishment of standardized study enrollment and quality metrics to assess site performance in clinical trial consortiums is scarce. The lack of these metrics presents challenges to study site teams, sponsors, and other clinical research enterprise key stakeholders for adequately monitoring and evaluating study site performance as it relates to fulfilling trial enrollment and quality goals. The Department of Veterans Affairs (VA) Cooperative Studies Program (CSP) Network of Dedicated Enrollment Sites (NODES) undertook an effort to determine the feasibility of establishing and implementing standardized study enrollment and quality metrics for a clinical research consortium (NODES) as a tool to evaluate its performance. In this manuscript, we describe the development and implementation of standardized study enrollment and quality metrics to assess site performance across studies in our clinical research consortium.

Increasing access to clinical research using an innovative mobile recruitment approach: The (MoRe) concept.

BACKGROUND: Access to healthcare delivery programs and systems is a primary correlate to the overall health and well-being of Veterans and the general population. Participation in clinical research is a gateway to novel therapies that are intended to address current global health issues. Meeting or exceeding recruitment goals in clinical research is one of the key determinants of the timely and successful completion of a study. The travel and time burdens experienced by study participants are often considered barriers to their enrollment into clinical research. The Department of Veterans Affairs (VA) Cooperative Studies Program (CSP) established a consortium of nine VA medical centers (VAMCs) called the Network of Dedicated Enrollment Sites (NODES). The NODES program provides study site-level expertise and innovative approaches that address challenges to clinical research execution. In alignment with our mission, our program developed an approach to increase study participant access to clinical research through implementing "Mobile Recruitment (MoRe)" units. This manuscript describes the utility and challenges associated with employing this strategy to address three common barriers to clinical research participation: 1) research participant travel burden, 2) participant access to study opportunities, and 3) low participant enrollment. METHODS: A plan to introduce the Mobile Recruitment (MoRe) unit as a recruitment strategy was piloted for a high-volume, observational cohort study and mega biobank in the VA health care system, the "Million Veteran Program (MVP)". MoRe is a recruitment strategy for CSP research integrating mobile technology and atypical research recruitment locations. Recruitment locations include primary or main VA hospitals and their assigned VA Community-Based Outpatient Clinics (CBOCs). Each Node site (n = 9) received components of the MoRe unit including a laptop, printer, portable cart with storage space, cooler/ice packs for specimen storage and transport. Each site's usage of these components varied based on its respective needs. Activities focused on both VA main facilities and CBOC facilities for recruitment. RESULTS: Seven of the nine Node sites compared the effectiveness of the MoRe unit on MVP study enrollment outcomes over three-time points: pre-intervention period, intervention period, and post-intervention period. The utilization of MoRe in the intervention period demonstrated a 36.9% increase in enrollment compared to the previous six months (pre-intervention period). There was a 2% enrollment increase at the six-month post-intervention period as compared to the intervention period. When comparing the pre-intervention period to the post-intervention period (duration of eighteen months), enrollment increased by 38.9%. CONCLUSION: Five of the seven sites experienced an increase in enrollment during the intervention and post-intervention periods. The two sites without an increase in enrollment experienced various extenuating factors. Characteristics of sites using MoRe included the ability to utilize a smaller, unconventional space, i.e. not a traditional clinical research exam space for recruitment. MoRe was utilized in hospital laboratory space, CBOCs, primary care clinics, and other subspecialty clinics that allowed recruitment activities but did not have dedicated space to offer the research teams for that purpose. This initiative successfully demonstrated the benefit of deploying the unit, proving its utility in cases in where there was a lack of space or alternative workstations for research activities. The implementation of MoRe by NODES as a recruitment strategy for MVP may be transferable to other VA clinical research studies, as well as to other healthcare settings executing similar clinical research activities.

Research site mentoring: A novel approach to improving study recruitment.

BACKGROUND/AIMS: The VA Cooperative Studies Program's (CSP) Network of Dedicated Enrollment Sites (NODES) is a consortium of nine VA medical centers (VAMCs) with teams (nodes) dedicated to enhance performance, compliance, and management of CSP multi-site clinical trials. The West Haven CSP Coordinating Center (WH-CSPCC), study coordinating center for CSP #577, Colonoscopy Versus Fecal Immunochemical Test (FIT) in Reducing Mortality from Colorectal Cancer (CONFIRM) trial, and NODES piloted a "site mentoring" (hub-and-spoke) model. In this model, a node site would work one-on-one with a low enrolling CONFIRM site to identify and overcome barriers to recruitment. The aim was to determine the impact of a research site mentoring model on study recruitment and examine site-level characteristics that facilitate or impede it. RESULTS: Sites in the mentorship pilot had an average improvement of 5 ±â€¯4 participants randomized per month (min -2.6; max 11.6; SD 4.3). Four of ten sites (40%) demonstrated continuous improvement in the average number of randomized participants per month after the pilot intervention and at three-month follow-up (post-intervention), as compared to the five-month period preceding the intervention. An additional two sites (20%) demonstrated improvement in the average number of randomized participants per month after the pilot intervention, and sustained that level of improvement at three-month follow-up (post-intervention). Additionally, six of ten sites (60%) demonstrated an increased number of participants screened for eligibility immediately following the intervention and at three-month follow-up (post-intervention). Only one site showed a decreased monthly average of randomized participants shortly after the intervention and through the three-month follow-up period. CONCLUSIONS: The site mentoring model was successful in improving recruitment at low enrolling CONFIRM sites. An additional feasibility assessment is needed to determine if this mentoring model will be effective with other CSP trials.

A cross-cutting approach to enhancing clinical trial site success: The Department of Veterans Affairs' Network of Dedicated Enrollment Sites (NODES) model.

BACKGROUND: Recruitment into clinical trials remains a key determinant to study completion and success. While various strategies have been proposed, it is unclear how they apply across different populations, diseases, and/or study goals. The ability to effectively overcome challenges may require different approaches that more broadly focus on addressing obstacles among sites that cannot be overcome by individual studies. METHODS: The Department of Veterans Affairs (VA) Cooperative Studies Program (CSP) established the Network of Dedicated Enrollment Sites (NODES) as a consortium of sites to generate systematic site-level solutions to more efficiently recruit in CSP studies. Initial activities identified priorities and developed approaches through team-based efforts. Metrics were also developed to assess overall network performance. RESULTS: Network efforts produced several new strategies and best practices for common problems in CSP research. Recruitment strategies included bringing studies to patients and developing data programs using algorithms for finding eligible patients. Efficiency efforts focused on cross-training and standardizing performance reports. CONCLUSION: NODES addressed site challenges in clinical trial recruitment and management by taking an overall approach that looked at the system rather than individual studies. Practices and operational changes were implemented for CSP research related to recruitment, staff training and research methodology. The network activities suggest that team-based development of tools and insights may help better identify targets and increase efficiencies for clinical trials recruitment.

Vulnerability to somatic symptoms of depression during interferon-alpha therapy for hepatitis C: a 16-week prospective study.

OBJECTIVE: This study evaluated the distinctive clinical and biological manifestations of depressive symptom subtypes (i.e., cognitive-affective and somatic) in Veterans with hepatitis C viral infection (HCV) before and during interferon-alpha (IFN) based antiviral therapy. METHODS: Thirty-two Veterans with HCV and no prior history of IFN therapy were followed prospectively during the first 16weeks of therapy to evaluate depressive symptoms and to determine if baseline cytokine and serotonin levels predicted subsequent changes in depressive scores. RESULTS: IFN therapy resulted in a significant increase in total depressive symptoms from baseline (week 0) to week 16, with neurovegetative and somatic symptoms of depression including loss of appetite, fatigue and irritability increasing within the first two weeks of therapy and continuing to increase throughout IFN therapy. When depressive symptoms were evaluated using a two-factor (i.e., Cognitive-Affective and Somatic) model, the Cognitive-Affective factor score did not change significantly following IFN therapy initiation, while the Somatic factor score showed a significant increase from week 0 to week 16. Veterans with the largest increases in somatic symptoms from week 0 to week 2 had significantly higher levels of tumor necrosis factor-alpha (TNF-α) and lower levels of serotonin at baseline, as compared to Veterans with minimal or no increase in somatic symptoms. CONCLUSION: Somatic symptoms of depression can be significantly exacerbated during IFN therapy and may be predicted by higher TNF-α levels and lower serotonin levels at baseline.