Is the gut important in multiple joint osteoarthritis? A multimodal investigation in humans and pet dogs

项目摘要 母项目（肠道在多关节骨关节炎中重要吗？人类多模式研究 和宠物狗，R 01 AR 080733）整合了血清生物标志物和放射学测量的负担， 在人类和哺乳动物中， 狗这将大大促进我们对迄今为止被忽视的MJOA驱动病因学作用的理解- 改变肠道通透性-并创建一个新的跨物种模型平台，用于测试治疗。这 拟议的补充有效地协同与母项目，增加了一个重点， 关键维度-多关节骨关节炎（MJOA）相关疼痛。OA相关疼痛 患者关心的是什么。这一补充将产生数据，这将有助于我们更好地了解多 驱动疼痛的因素，并发现新的治疗机会。我们将扩大和加强现有的 创新的工作，包括新的生物标志物的疼痛和疼痛的测定有关的疼痛经验， 人类和狗。这种集成和增强有两个核心特性：1）扩展 分析已收集的样本，包括有希望的肌肉骨骼疼痛血清生物标志物 并增强人类队列中的疼痛表型; 2）将先进的分析技术应用于整个 数据集（母数据和补充数据）使我们能够在以下方面进行优雅和创新的疼痛表型分析： 与肠道通透性、肠道微生物组和OA负荷的关系。为了增进我们对 肌肉骨骼疼痛，我们将使用尖端的机器学习分析方法来了解疼痛是如何产生的。 表型、微生物组肠型和生物标志物内型一起和单独变化，以促进 我们对OA患者复杂的多模式和多维度疼痛体验的理解。这 补充提案不是一个独立的项目，而是丰富了父项目，并扩展了 发现潜在的疼痛治疗策略的机会。两名PI的综合专业知识， 团队（OA，肌肉骨骼疼痛，机器学习的应用）使我们非常适合执行 提议的工作。现阶段整合补充工作，从经济上讲是有效的，从科学上讲是可行的， 加快数据生成，以支持新的调查战略，从而更好地 了解如何减轻肌肉骨骼疼痛。

PROJECT SUMMARY The parent project (Is the gut important in multiple joint osteoarthritis? A multimodal investigation in humans and pet dogs, R01AR080733) integrates serum biomarkers and radiographic measures of the burden of the disease of OA with measures of intestinal permeability and microbiome assessments in both humans and dogs. It will significantly advance our understanding of the role of a hitherto ignored driving etiology in MJOA – altered intestinal permeability – and create a novel transspecies model platform for testing therapeutics. This proposed supplement effectively synergizes with the parent project by adding a focus on a clinically critical dimension – pain associated with multi-joint osteoarthritis (MJOA). Pain associated with OA is what patients care about. This supplement will generate data that will help us better understand the multiple factors that drive pain and uncover novel therapeutic opportunities. We will expand and enhance our existing innovative work to include novel biomarkers of pain and pain assays relevant to the pain experience in humans and in dogs. There are two core features of this integration and enhancement: 1) to extend the analysis of samples in already being collected to include promising serum biomarkers for musculoskeletal pain and enhance pain phenotyping in the human cohort; 2) to apply advanced analytical techniques to the entire data set (parent and supplemental data) allowing us to perform elegant and innovative pain phenotyping in relation to intestinal permeability, gut microbiome and burden of OA. To advance our understanding of musculoskeletal pain, we will use cutting-edge machine learning analytical approaches to understand how pain phenotype(s), microbiome enterotype(s) and biomarker endotype(s) vary together and individually to advance our understanding of the complex multi-modal and multi-dimensional pain experience of OA patients. This supplement proposal is not a stand-alone project, rather it enriches the parent project and expands the opportunities for discovery of potential pain therapeutic strategies. The combined expertise of the two PIs and teams (OA, musculoskeletal pain, application of machine learning) makes us ideally suited to perform the proposed work. Integrating the supplement work at this stage is fiscally efficient, and scientifically it will accelerate the generation of data to support new investigative strategies that will lead to a better understanding of how to mitigate musculoskeletal pain.