Psychosocial risk factors for chronic pain: Characterizing brain and genetic pathways and variation across understudied populations

慢性疼痛是一种非常普遍的健康问题，给患者带来巨大的认知、社会和经济成本。 个人和社会。负面影响和相关疾病--包括抑郁、焦虑、创伤后应激障碍， 神经质、灾难化和多感官敏感性-- 手术或受伤。这些相关性已经在流行病学、遗传学和大脑结构水平上得到了证实， 这表明负面情绪可能是多种类型慢性疼痛的一致风险因素， 因此是预防和治疗的有效靶点。基于阴性的定量预测模型 情感和相关的风险因素可以帮助识别受伤或手术后可能出现疼痛的患者 以及那些对心理、行为、药物或多模式治疗反应更好的患者。 这种模型可以很容易地在临床筛选中采用，但要以一种有用和公平的方式进行， 必须克服若干障碍。首先，负面影响通常被视为生物学上的多余 驱动疼痛的机制。其次，现有的证据将它们联系起来，仅限于统计关联 之间的综合措施，而不是精确的预测模型，和效果大小是中等，但低于 临床应用所需的水平。第三，相关的心理社会危险因素及其影响程度是 可能因血统、性别和文化而异。这项研究利用了我们小组过去两年的工作 进行大样本遗传、表型和神经影像数据分析，以在这些方面取得进展 挑战在目标1中，我们将使用现代统计学习技术来开发定量预测模型 将负面情绪和相关的心理社会风险因素与50万人中的多种类型的慢性疼痛联系起来， 英国生物库样本，将心理社会风险概况与（a）> 400，000的全基因组协会数据联系起来 欧洲血统的个体和> 20，000个非洲和亚洲血统的样品，和（B） 大脑的结构和功能超过60，000人。在目标2中，我们研究了这些模型如何应用于， 如何在477，000人的All Of Us研究中为不同的美国人群定制，包括 针对种族/民族、性别和社会经济特征的定制。在目标3（探索性）中，我们将评估 这些风险特征是否预测了我们所有人的术后疼痛（在大约40，000名患有 接受手术）和美国急性至慢性疼痛特征研究，该研究跟踪了2，800名患者 术前和术后（膝关节置换术或胸外科手术）的纵向社会心理，功能， 成像和多组学测量。总之，这项工作将提供新的，定量模型的心理社会 手术后疼痛的风险以及其他风险都很容易扩展到临床使用，并以遗传学为基础。 神经生物学框架。

Chronic pain is a highly prevalent health problem with tremendous cognitive, social, and economic costs to the individual and society. Negative affect and associated disorders–including depression, anxiety, PTSD, neuroticism, catastrophizing, and multisensory sensitivity–confer risk for the development of chronic pain after surgery or injury. These correlations have been shown at the epidemiological, genetic, and brain structural levels, which suggests that negative affect may be a consistent risk factor for multiple types of chronic pain, and therefore an effective target for prevention and treatment. Quantitative predictive models based on negative affect and related risk factors could help identify patients who are likely to develop pain after injury or surgery and those who would respond better to psychological, behavioral, pharmacological, or multimodal treatment. Such models could be readily adopted in clinical screening, but for this to happen in a useful and equitable way, several barriers must be overcome. First, negative affect is often seen as superfluous to the biological mechanisms that drive pain. Second, existing evidence linking them is restricted to statistical associations between aggregate measures, rather than precise predictive models, and effect sizes are moderate but below the level needed for clinical utility. Third, the relevant psychosocial risk factors and the extent of their effects is likely to vary across ancestry, sex, and culture. This study capitalizes on our group’s work over the past 2 years on large-sample genetic, phenotypic, and neuroimaging data analysis to make forward progress on these challenges. In Aim 1, we will use modern statistical learning techniques to develop quantitative predictive models linking negative affect and related psychosocial risk factors to multiple types of chronic pain in the 500,000- person UK Biobank sample, linking psychosocial risk profiles to (a) Genome Wide Association data in >400,000 individuals of European descent and >20,000 samples of African and Asian descent, and (b) MRI measures of brain structure and function on >60,000 individuals. In Aim 2, we investigate how these models apply to, and how they can be customized for, diverse U.S. populations in the 477,000-person All Of Us study, including customizations for racial/ethnic, sex, and socioeconomic characteristics. In Aim 3 (exploratory), we will assess whether these risk profiles predict post-surgical pain in All Of Us (in approximately 40,000 individuals who have undergone surgery) and in the U.S. Acute to Chronic Pain Signatures study, which tracks 2,800 patients longitudinally pre- and post-surgery (knee arthroplasty or thoracic surgery) with psychosocial, functional, imaging, and multi-omics measures. Together, this work will provide new, quantitative models of psychosocial risks for post-surgical pain and beyond that are both readily scalable for clinical use and grounded in a genetic and neurobiological framework.