A Novel Multimodal Approach to Characterize NAFLD Severity and Prognosis

表征 NAFLD 严重程度和预后的新型多模式方法

• 批准号: 10426202
• 负责人: Veeral Haresh Ajmera
• 金额: $ 20.46万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-16 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10426202
• 关键词: Adult; Affect; Biological Markers; Biometry; Biopsy; Caring; Cell Death; Childhood; Cirrhosis; Clinical; Clinical Research; Collaborations; Cross-Sectional Studies; Data; Data Set; Development; Development Plans; Diagnosis; Dietary Factors; Disease; Disease Progression; Exhibits; Fatty Liver; Fibrosis; Functional disorder; Funding; Future; Gastroenterology; Genes; Genetic; Genetic Markers; Goals; Histologic; Histology; Individual; Inflammation; Inflammatory; International; Intervention; Laboratories; Ligands; Lilium; Liver; Liver diseases; Longitudinal cohort; Mentors; Mentorship; Modality; Modeling; Morbidity - disease rate; National Institute of Diabetes and Digestive and Kidney Diseases; Natural Immunity; Participant; Pathogenicity; Patients; Peripheral; Phenotype; Plasma; Population; Prevalence; Procedures; Prognosis; Progressive Disease; Prospective cohort; Publishing; Research; Research Institute; Resources; Risk; Serum; Severities; Severity of illness; Statistical Data Interpretation; Statistical Methods; Subgroup; Techniques; Testing; Translating; Translational Research; United States; United States National Institutes of Health; Work; advanced analytics; base; biobank; biomarker development; biomarker panel; career development; chronic liver disease; clinically significant; cohort; cytokine; dietary; disease phenotype; disease prognosis; disorder risk; disorder subtype; genetic analysis; hepatocyte injury; high risk; improved; innovation; liver biopsy; liver transplantation; mortality; multidimensional data; multimodality; non-alcoholic fatty liver; non-alcoholic fatty liver disease; nonalcoholic steatohepatitis; novel; patient subsets; personalized management; predict clinical outcome; prevent; professor; research data dissemination; risk stratification; therapeutic target; tool; treatment response

PROJECT SUMMARY Nonalcoholic fatty liver disease (NAFLD) is an increasingly common cause of cirrhosis and on pace to be the leading indication for liver transplantation in the United States.(1, 2) NAFLD presents as a spectrum of disease ranging from isolated steatosis, which portends little risk of significant morbidity, to nonalcoholic steatohepatitis (NASH), which is characterized by inflammation and cell death and has substantial risk of progression to cirrhosis and liver-related mortality.(3) Unfortunately, liver biopsy remains the only way to accurately discriminate between isolated steatosis and NASH; however the procedure is invasive and remains impractical to scale to the estimated affected population of 60 million adults in the United States. Attempts to use individual or small combinations of biomarkers to characterize risk in NAFLD have been largely unsuccessful leaving a tremendous need for non-invasive risk stratification. My central hypothesis is that distinct subtypes of NAFLD can be identified by combining multiple non-invasive biomarkers, genetic and clinical factors using advanced analytic techniques for high dimensional data. Through my collaboration with the NIH-funded, multicenter NASH Clinical Research Network (NASH CRN) I explored the association between 28 putative plasma biomarkers and NAFLD histology and found that small sets of biomarkers were limited in discriminating between clinically significant stages of histologic severity. However, by applying a novel statistical technique, latent class analysis (LCA), we generated preliminary data identifying distinct subgroups of patients with NAFLD that are strongly associated with histologic severity. The research goal of this application is to (1) combine clinical and dietary factors, genetic markers and an expanded set of plasma biomarkers to refine distinct phenotypes of NAFLD using LCA, (2) validate the association between LCA defined phenotypes and histologic severity in an independent cohort with biopsy proven NAFLD, (3) build on an existing longitudinal cohort and test the ability of these phenotypes to predict progression of fibrosis and inflammation. My long-term goal is to combine expertise in multimodal, non-invasive biomarkers of NAFLD with advanced analytic techniques to personalize the management and treatment of patients with NAFLD. In order to accomplish this goal, I have assembled an exceptional mentorship team including my primary mentor, Dr. Rohit Loomba, who is an internationally renowned expert in NAFLD and Director of the UCSD NAFLD Research Center. In addition, Dr. Ariel Feldstein, Chief of the Division of Pediatric Gastroenterology, and an expert in translating NAFLD pathophysiology into biomarker development will serve as a co-mentor. Professor Lily Xu, biostatistical director of the UCSD Clinical and Translational Research Institute, will serve as my biostatistical mentor. Together, we formed a four-fold career development plan to gain expertise in (1) cohort development, biobanking and advanced NAFLD phenotyping, (2) statistical analysis of genetic and high dimensional data, (3) NAFLD pathobiology and biomarker development, and (4) research dissemination and the development of national recognition in the non-invasive assessment of NAFLD.

项目摘要 非酒精性脂肪性肝病（NAFLD）是肝硬化的一个越来越常见的原因，并有望成为肝硬化的主要病因。 在美国肝移植的主要适应症。（1，2）NAFLD表现为疾病谱 从孤立性脂肪变性（预示着显著发病风险很小）到非酒精性脂肪性肝炎 （NASH），其特征在于炎症和细胞死亡，并且具有进展为肝硬化的实质性风险 和肝脏相关的死亡率。(3)不幸的是，肝活检仍然是唯一能准确区分 孤立的脂肪变性和NASH;然而，该程序是侵入性的，并且仍然不切实际地扩展到 据估计，美国有6000万成年人受到影响。尝试使用单个或小 用于表征NAFLD风险的生物标志物组合在很大程度上是不成功的， 需要进行无创风险分层。我的中心假设是，NAFLD的不同亚型可以被识别出来， 通过使用先进的分析技术结合多种非侵入性生物标志物、遗传和临床因素， 对于高维数据。通过与NIH资助的多中心NASH临床研究合作， 网络（NASH CRN）I探索了28种推定的血浆生物标志物与NAFLD组织学之间的关联 并发现小的生物标志物集在区分临床上重要的阶段方面是有限的， 组织学严重程度。然而，通过应用一种新的统计技术，潜在类别分析（LCA），我们生成了 初步数据确定了NAFLD患者的不同亚组，这些亚组与组织学 严重性。本申请的研究目标是（1）联合收割机临床和饮食因素、遗传标记和 一组扩展的血浆生物标志物，以使用LCA细化NAFLD的不同表型，（2）验证 LCA定义的表型与活检独立队列中组织学严重程度之间的相关性 证实NAFLD，（3）建立在现有的纵向队列，并测试这些表型预测的能力， 纤维化和炎症的进展。我的长期目标是将联合收割机的专业知识与多模式、非侵入性 NAFLD的生物标志物与先进的分析技术，以个性化的管理和治疗， NAFLD患者为了实现这一目标，我组建了一支出色的导师团队， 包括我的主要导师Rohit Loomba博士，他是国际知名的NAFLD专家， 加州大学圣地亚哥分校NAFLD研究中心主任。此外，儿科主任Ariel Feldstein博士 胃肠病学，以及将NAFLD病理生理学转化为生物标志物开发的专家将为 作为共同导师Lily Xu教授，UCSD临床和转化研究生物统计主任 研究所，将担任我的生物统计导师。我们共同制定了四重职业发展计划， 在以下方面的专业知识：（1）队列开发、生物库和晚期NAFLD表型分析，（2） 遗传和高维数据，（3）NAFLD病理生物学和生物标志物开发，以及（4）研究 传播和发展国家对NAFLD非侵入性评估的认可。