MICA: Partnership for Assessment and Investigation of Neuropathic Pain: Studies Tracking Outcomes, Risks and Mechanisms (PAINSTORM).

MICA：神经病理性疼痛评估和调查伙伴关系：跟踪结果、风险和机制的研究 (PAINSTORM)。

• 批准号: MR/W002388/1
• 负责人: David Bennett
• 金额: $ 461.28万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FW002388%2F1
• 关键词: MICA; Partnership; Assessment; Investigation; Neuropathic

This consortium brings together experts in Neuropathic pain (NeuP). NeuP affects 8% of the population and is caused by damage to the sensory nervous system (through conditions such as diabetes, chemotherapy and HIV). It is increasingly common as a consequence of the ageing population, increasing levels of diabetes and enhanced cancer survival. NeuP has a major negative impact on quality of life. Unfortunately current management options are inadequate as they are only effective in a small subgroup of patients. Additionally, whilst NeuP impact is multidimensional, most research and clinical management in this area is separate rather than being interdisciplinary. They over emphasise pharmacological approaches, often associated with side effects, rather than taking a more holistic approach addressing the complex social and psychological aspects of NeuP. To rectify this situation we need to understand the mechanisms driving NeuP in patients. In order to do so, PAINSTORM will use a broad range of approaches cutting across traditional disciplinary boundaries, to uncover the causes of NeuP and understand how they interact. This inter-disciplinary collaboration will include people living with NeuP (embedding patient and public involvement), scientists from diverse clinical and scientific backgrounds, and industry expertise to help translate the research into effective, multifaceted interventions.Our focus will be on studying people at risk of NeuP and following their progress over time. We will use a number of established cohorts, as well as recruiting new participants, and harmonise outcomes with national scale community studies. A key question is understanding why some people are severely impacted by NeuP whilst others with a similar pattern of nerve damage are not. Hence we will identify the personal characteristics (such as age, gender and ethnicity), environmental/social and clinical factors which determine NeuP risk. We will identify and validate novel genetic risk factors for NeuP. Tissue samples and patient-derived cells will be used to validate molecular pathways contributing to chronic NeuP and help develop blood biomarkers. These samples will be stored and made available to other researchers via a biobank. We will optimise measures to assess NeuP, including sensory profiling, application of remote monitoring and assessment of psychosocial factors to understand the impact of pain on daily activities (from self-care to work) and important conditions that are often associated with chronic pain such as depression, anxiety and poor sleep. We will use innovative technologies, including brain, spinal cord and nerve imaging and electrophysiology, to directly assess the factors that drive NeuP. We will integrate this multi-dimensional dataset to understand the interaction between risk and protective factors. We will develop biomarkers, as a means to measure pain and how it changes over time, which can be applied to clinical practice and drug trials. We aim to improve targeting of existing therapies, as well as identifying and prioritising novel treatment targets. We will engage key stakeholder groups including health professionals, people living with NeuP and industry at the outset and throughout PAINSTORM. Results will be widely disseminated through development of accessible databases, lay summaries, an accessible biobank and ongoing training of scientists and clinicians both within and external to our consortium to enhance impact. Our aim is that PAINSTORM should transform lives through our understanding and future interdisciplinary management of NeuP.

该联盟汇集了神经性疼痛（NeuP）的专家。NeuP影响8%的人口，由感觉神经系统损伤引起（通过糖尿病，化疗和HIV等疾病）。随着人口老龄化、糖尿病水平的提高和癌症存活率的提高，这种情况越来越普遍。NeuP对生活质量有重大负面影响。不幸的是，目前的管理选择是不够的，因为它们只在一小部分患者中有效。此外，虽然NeuP的影响是多方面的，但该领域的大多数研究和临床管理是独立的，而不是跨学科的。他们过度强调药理学方法，通常与副作用有关，而不是采取更全面的方法来解决NeuP复杂的社会和心理方面。为了纠正这种情况，我们需要了解患者中NeuP的驱动机制。为了做到这一点，PANORM将使用广泛的方法跨越传统的学科界限，揭示NeuP的原因，并了解它们如何相互作用。这种跨学科的合作将包括NeuP患者（嵌入患者和公众参与），来自不同临床和科学背景的科学家，以及行业专业知识，以帮助将研究转化为有效的，多方面的干预措施。我们的重点将是研究NeuP风险人群，并随着时间的推移跟踪他们的进展。我们将使用一些已建立的队列，以及招募新的参与者，并与全国规模的社区研究协调结果。一个关键问题是理解为什么有些人受到NeuP的严重影响，而其他具有类似神经损伤模式的人则没有。因此，我们将确定确定NeuP风险的个人特征（如年龄、性别和种族）、环境/社会和临床因素。我们将识别和验证NeuP的新遗传风险因素。组织样本和患者源性细胞将用于验证导致慢性NeuP的分子途径，并帮助开发血液生物标志物。这些样本将被储存，并通过生物库提供给其他研究人员。我们将优化评估NeuP的措施，包括感觉特征分析、远程监测的应用和心理社会因素的评估，以了解疼痛对日常活动（从自我护理到工作）的影响，以及通常与慢性疼痛相关的重要状况，如抑郁、焦虑和睡眠不良。我们将使用创新技术，包括脑、脊髓和神经成像以及电生理学，直接评估驱动NeuP的因素。我们将整合这个多维数据集，以了解风险和保护因素之间的相互作用。我们将开发生物标志物，作为测量疼痛及其随时间变化的手段，可应用于临床实践和药物试验。我们的目标是改善现有疗法的靶向，以及确定和优先考虑新的治疗靶点。我们将从一开始就在整个PANGORM期间与关键利益相关者群体（包括卫生专业人员、NeuP患者和行业）进行接触。将通过开发可访问的数据库、非专业摘要、可访问的生物库以及对我们联盟内外的科学家和临床医生进行持续培训来广泛传播结果，以提高影响力。我们的目标是，PAYPORM应该通过我们的理解和未来的跨学科管理NeuP改变生活。

项目成果

Classification of Painful or Painless Diabetic Peripheral Neuropathy and Identification of the Most Powerful Predictors Using Machine Learning Models in Large Cross-Sectional Cohorts

在大横截面队列中使用机器学习模型对疼痛或无痛糖尿病周围神经病变进行分类并识别最强大的预测因子

• DOI: 10.21203/rs.3.rs-1074596/v1
• 发表时间: 2021
• 作者: Baskozos G

Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts.

• DOI: 10.1186/s12911-022-01890-x
• 发表时间: 2022-05-29
• 期刊: BMC MEDICAL INFORMATICS AND DECISION MAKING
• 影响因子: 3.5
• 作者: Baskozos, Georgios;Themistocleous, Andreas C.;Hebert, Harry L.;Pascal, Mathilde M., V;John, Jishi;Callaghan, Brian C.;Laycock, Helen;Granovsky, Yelena;Crombez, Geert;Yarnitsky, David;Rice, Andrew S. C.;Smith, Blair H.;Bennett, David L. H.
• 通讯作者: Bennett, David L. H.

The epidemiology of neuropathic pain: an analysis of prevalence and associated factors in UK Biobank

神经性疼痛的流行病学：英国生物银行的患病率和相关因素分析

• DOI: 10.1101/2022.07.26.22278063
• 发表时间: 2022
• 作者: Baskozos G

Introduction to a special issue on big data and pain.

• DOI: 10.1097/pr9.0000000000001115
• 发表时间: 2023-12
• 期刊: Pain reports
• 影响因子: 4.8

Additional file 1 of Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts

附加文件 1：疼痛或无痛糖尿病周围神经病变的分类以及在大横截面队列中使用机器学习模型识别最强大的预测因子

• DOI: 10.6084/m9.figshare.19923357
• 发表时间: 2022
• 作者: Baskozos G