Genomic Resources for Enhancing Available Therapies (GREAT1.0) Study
概览
- 阶段
- 不适用
- 干预措施
- 未指定
- 疾病 / 适应症
- Chronic Pancreatitis
- 发起方
- University of Pittsburgh
- 入组人数
- 120000
- 试验地点
- 1
- 主要终点
- Common disease mechanisms and repurposing of medications.
- 状态
- 暂停
- 最后更新
- 3个月前
概览
简要总结
This is a prospective, descriptive, observational research study designed to observe and document the clinical practice by domain experts, and how the knowledge of new findings that are published in the medical literature affect clinical decision making.
The study will evaluate risk factors and co-variants, including genetic variants that are associated with disease progression such as pain, inflammation, organ dysfunction, disability and quality of life.
详细描述
The Genomic Resource to Enhance Available Therapies (GREAT1.0) Study is a research program for personalized medicine. It is a highly annotated genetic and biosample resource for multiple nested observational cohort studies. It is designed to begin to understand the mechanisms underlying complex diseases using clinical information from the UPMC electronic health record (EHR), from case-report forms, and from biological samples. Aim 1. To test the hypothesis that point-of-care electronic health record (EHR)-based phenotyping and clinical measures will be useful for classifying patient by disease risk, subtype, activity, complications, quality of life or using statistical or systems approaches. Aim 2. To test the hypothesis that common diseases can be subtyped using genotype data. Aim 3. To test the hypothesis biological samples will provide additional functional and mechanistic information about subject health, disease or state. The study will be conducted using UPMC patients and population controls. Consent will allow EHR and/or case report form data, plus biological samples to be given a unique code number and transferred to researchers for analysis. Consent will also allow for a secure link to be maintained allowing the research data or samples to be updated, and to contact the clinical team and/or subject to provide them with additional information.
研究者
David Binion, MD
Professor
University of Pittsburgh
入排标准
入选标准
- •Case Subjects
- •Clinical diagnosis of a chronic disease or disorder (ex. pancreatitis, hepatitis or fatty liver, inflammatory bowel disease, irritable bowel syndrome, diarrhea, constipation, chronic pain syndromes, diabetes, hypertension, cardiovascular disease, chronic kidney disease, chronic neurologic disorders, rheumatological disorders, endocrine disorders, chronic pulmonary diseases, sinorespiratory disorders, chronic skin diseases, cancers and related disorders)
- •Ability to read and write in English;
- •Ability to provide informed consent
- •Control Subjects
- •UPMC patients age 12 years without a chronic disorder.
排除标准
- •Chronic infectious disease as the primary medical problem
- •Less than 12 years of age
- •Inability of the subject to understand the protocol
- •Inability to the subject provide informed consent
结局指标
主要结局
Common disease mechanisms and repurposing of medications.
时间窗: through study completion, an average of 1 year
Many chronic diseases, including inflammatory and autoimmune diseases, have similar disease features that arise in different organs. Harmonization of similar disease processes in different organs will be used to increase study power, and to determine if there is evidence that therapeutic interventions for one disease may be effective in another disease, providing evidences to consider drug repurposing and new treatment approaches.
Define the molecular disorders contributing to clinicopathological disease definitions for common complex disorders
时间窗: through study completion, an average of 1 year
Diseases are defined by symptoms and pathologic features in specific tissues. The study uses genetic variants associated with disease to define the underlying genes associated with disease, and uses cell biology methods to understand which mechanisms within the specialized cells lead to disease under specific conditions.
Define risk factors for disease progression, severity, complications and poor quality of life.
时间窗: through study completion, an average of 1 year
Life-style (e.g. alcohol, smoking, diet, exercise), medications, metabolic, genetic and epigenetic factors alter the features of disease. Nested studies, subgroup analysis, stepwise regression, statistical and machine learning will be used to develop disease models where early intervention may alter disease progression and severity.
次要结局
- Pain profile(through study completion, an average of 1 year)
- Patient Reported Global Health Assessment(through study completion, an average of 1 year)