Understanding the neurobiological basis of co-morbid chronic pain and depression by integrating genomics, peripheral biomarkers and neuroimaging data.

背景：慢性疼痛是一种重大的全球健康负担，估计影响五分之一的总人口(Goldberg和McGee，2011年)。另一个重大的全球健康问题是严重抑郁障碍(MDD)，它是全球残疾的主要原因(世界卫生组织2017年)。先前的研究已经发现这两种情况的表现是重叠的，85%的慢性疼痛患者表现出共病的MDD，超过一半的MDD患者报告疼痛症状。值得注意的是，这种共同发病导致的长期结果比单独发生任何一种情况都要差。最近，有证据表明这两种疾病之间存在遗传相关性，特别是涉及神经发生、突触可塑性和神经元发育的基因(Johnston等人)。2019年)。尽管遗传结构有这种重叠，但很少有研究评估慢性疼痛和MDD之间潜在的神经生物学重叠，也没有在全人群水平上的研究。除了这一知识差距之外，位于抑郁症和慢性疼痛之间因果路径上的生物中介还没有得到充分的探索。解决我们对慢性疼痛和MDD理解中的这些差距具有重要的临床意义，因为目前的治疗方法并不能有效地改变潜在的病理生理机制。此外，除了我们目前对癌症疼痛和抑郁之间的关系的有限了解之外，超过三分之一的癌症患者会发生抑郁，具有显著的治疗潜力。目的：该项目将使用最先进的人口数据集(UK Biobank，N~0.5M，STRADL/Generation Scotland，N=23k)，其中包含广泛的表型数据，包括与慢性疼痛和抑郁相关的变量。这些数据集还包含参与者的子样本，以及可用的成像、基因组、表观遗传学和生物标记物小组数据。这些数据集将提供必要的数据，以全面、多层次地了解慢性疼痛/MDD共病的神经生物学。具体地说，该项目旨在确定：1)在人群水平上慢性疼痛在抑郁症中的流行率，反之亦然(例如，抑郁症中最常见的疼痛类型，例如与癌症相关的疼痛，以及慢性疼痛中报告的抑郁特征类型)2)慢性疼痛和抑郁症患者特有的神经生物学、行为、生活方式和认知特征。3)炎症(如CRP)和应激(皮质醇)等疾病的血液和尿液生物标志物及其表观遗传标志物在共病组中的差异表达，及其与症状和神经影像特征的关系。4)通过深化先前在该领域的研究，更有力地理解这些疾病之间的病因方向性(Johnston等人)。2019年)。这将通过使用多性状孟德尔随机化和中介模型来实现，以确定临床/症状特征是否通过这些生物标记物中介。书目：Goldberg，D.S.和McGee，S.J.2011。将疼痛作为全球公共卫生优先事项。BMC公共卫生11(1)，第770页。DOI：10.1186/1471-2458-11-770 Johnston，K.J.A.等人。2019年。英国生物库中多部位慢性疼痛的全基因组关联研究。《公共科学图书馆·遗传学》15(6)，第e1008164页。DOI：10.1371/Joural.pgen.1008164世界卫生组织。2017年。抑郁症：让我们谈谈吧。可在以下网址获得：http://www.who.int/mediacentre/news/releases/2017/worldhealthday/en/

Background:Chronic pain represents a significant global health burden that is estimated to affect one fifth of the general population (Goldberg and McGee 2011). Another significant global health issue is major depressive disorder (MDD), which is the leading cause of disability worldwide (World Health Organisation 2017). Previous research has identified an overlap in the presentation of both conditions with 85% of chronic pain sufferers presenting with comorbid MDD and over half of MDD patients reporting symptoms of pain. It is important to note this co-morbidity results in a worse long-term outcome than either condition alone. Recently, evidence has emerged suggesting genetic correlation between both conditions, specifically, genes involved in neurogenesis, synaptic plasticity and neuronal development (Johnston et al. 2019). Despite this overlap in genetic architecture, few studies have evaluated the potential neurobiological overlap between chronic pain and MDD, and none at a population-wide level. In addition to this knowledge gap, the biological intermediates lying on causal pathways between depression and chronic pain have not been fully explored. Addressing these gaps in our understanding of chronic pain and MDD has important clinical implications as current treatments do not effectively modify underlying pathophysiological mechanisms. Additionally, adding to our currently limited understanding of the relationship between cancer pain and depression, which occurs in more than a third of cancer patients, holds significant therapeutic potential.Aims:This project will use state-of-the-art population datasets (UK Biobank, N~0.5m, STRADL/Generation Scotland, N=23k), containing extensive phenotyping data, including variables relevant to chronic pain and depression. These datasets also contain subsamples of participants with available imaging, genomic, epigenetic and biomarker panel data. These datasets will provide the necessary data to obtain a comprehensive, multi-level understanding of the neurobiology of chronic pain/MDD comorbidity. Specifically, this project will aim to establish:1) The prevalence of chronic pain in depression at a population level, and vice versa (including e.g. types of pain most commonly reported in depression, eg cancer-related pain, & types of depressive traits reported in chronic pain).2) The neurobiological, behavioural, lifestyle, and cognitive features specific to individuals with both chronic pain and depression. Imaging measures, such as structural brain connectivity (DTI), cortical thickness (sMRI), subcortical brain volumes (hippocampus and striatum) and functional connectivity (rsFMRI) will be included in this analysis.3) The differential expression of blood and urine biomarkers for conditions such as inflammation (e.g. CRP) and stress (cortisol), and their epigenetic markers, in the co-morbid group, and their relation to symptoms and neuroimaging features.4) A more robust understanding of causative directionality between these conditions, by furthering previous research in this area (Johnston et al. 2019). This will be achieved through the use of multi-trait Mendelian randomization and mediation modelling, to determine whether clinical/symptomatic features are mediated through these biomarkers. BibliographyGoldberg, D. S. and McGee, S. J. 2011. Pain as a global public health priority. BMC Public Health 11(1), p. 770. doi: 10.1186/1471-2458-11-770Johnston, K. J. A. et al. 2019. Genome-wide association study of multisite chronic pain in UK Biobank. PLOS Genetics 15(6), p. e1008164. doi: 10.1371/journal.pgen.1008164World Health Organisation. 2017. Depression: Let's Talk. Available at: http://www.who.int/mediacentre/news/releases/2017/worldhealthday/en/