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halt-RONIN: Discovering chronic inflammation biomarkers that define key stages in the Healthy-to-NASH (non-alcoholic steatohepatitis) transition to inform early prevention and treatment strategies

halt-RONIN: Discovering chronic inflammation biomarkers that define key stages in the Healthy-to-NASH (non-alcoholic steatohepatitis) transition to inform early prevention and treatment strategies
halt-RONIN:发现定义从健康到 NASH(非酒精性脂肪性肝炎)过渡的关键阶段的慢性炎症生物标志物,为早期预防和治疗策略提供信息
批准号:
10067052
负责人:
金额:
$172.48万
依托单位:
依托单位国家:
英国
项目类别:
EU-Funded
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --

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中文摘要
翻译
非酒精性脂肪性肝病(NAFLD)是一种多因素慢性炎症性疾病,在四分之一的个体中流行,具有显著的个人,社会经济和医疗保健负担,特别是在疾病的晚期,更严重的炎症阶段-非酒精性脂肪性肝炎(NASH)。尽管该疾病对社会造成严重的负面影响,但NAFLD仍然难以诊断和治疗。此外,由于缺乏忠实反映人类疾病复杂性的模型,从健康到脂肪肝到NASH转变的分子机制仍然知之甚少。因此,Halt-RONIN旨在通过实施具有综合疾病建模的系统生物学方法来揭示疾病启动的早期触发因素和疾病进展的复杂机制驱动因素,从而为改善现有检测方法提供机会,为NAFLD的个性化干预策略和药物发现提供蓝图。为了实现这一目标,Halt-RONIN将联合收割机将来自先进的体外和体内模型的实验数据与来自广泛的人类NAFLD队列和生物库的多模态数据相结合,并使用计算机机器学习方法,以发现新的生物标志物和特定于健康到疾病过渡的每个阶段的分子靶点。通过验证无项目摘要
英文摘要
Non-alcoholic fatty liver disease (NAFLD) is a multifactorial chronic inflammatory disease that is prevalent in 1 of 4 individuals with a significant personal, socioeconomic and healthcare burden, especially at the later, more severe inflammatory stage of disease - non alcoholic steatohepatitis (NASH). Despite the severe negative impact of the disease on society, NAFLD remains difficult to diagnose and treat. Additionally, the molecular mechanisms underlying the transition from health to fatty liver to NASH remain poorly understood due to the lack of models that faithfully reflect the complexity of human disease. Hence, Halt-RONIN aims to uncover the early triggers of disease initiation and complex mechanistic drivers of disease progression by implementing a systems biology approach with integrative disease modelling resulting in opportunities for the improvement of the existing detection methods, providing a blueprint to inform personalized intervention strategies and drug discovery for NAFLD. To achieve this goal, Halt-RONIN will combine experimental data from advanced in vitro and in vivo models with multimodal data from extensive human NAFLD cohorts and biobanks and use in silico machine learning approaches, to discover new biomarkers and molecular targets specific to each stage of the health-to-disease transition. By validating no project summary
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