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、社会和经济代价 个人和社会。负面情绪和相关障碍--包括抑郁、焦虑、创伤后应激障碍、 神经质、灾难性和多感官敏感性--增加术后慢性疼痛发生的风险 手术或受伤。这些相关性已经在流行病学、遗传学和大脑结构层面上得到了证明。 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。基于负值的定量预测模型 情感和相关风险因素可以帮助确定哪些患者可能在受伤或手术后出现疼痛 以及那些对心理、行为、药物或多模式治疗有更好反应的人。 这样的模型可以很容易地在临床筛查中采用，但要以有用和公平的方式实现这一点， 必须克服几个障碍。首先，负面情绪通常被认为是生理上多余的。 驱动疼痛的机制。其次，将它们联系在一起的现有证据仅限于统计上的关联 在综合衡量指标之间，而不是精确的预测模型之间，效果大小适中，但低于 临床实用所需的水平。第三，相关的心理社会风险因素及其影响程度是 可能会因血统、性别和文化而有所不同。这项研究利用了我们小组在过去两年中的工作 在大样本遗传、表型和神经影像数据分析方面取得进展 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500,000人中的多种慢性疼痛联系起来- 个人英国生物库样本，将心理社会风险概况链接到(A)&>400,000中的基因组广泛关联数据 欧洲人后裔和20,000名非洲人和亚洲人后裔样本，以及(B)磁共振成像测量 60,000名受试者的大脑结构和功能。在目标2中，我们调查了这些模型如何应用于 在我们所有人的47.7万人研究中，如何为不同的美国人口定制它们，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)和美国急性到慢性疼痛信号研究，该研究跟踪了2800名患者 手术前后(膝关节成形术或胸部手术)的纵向心理社会，功能， 成像和多组学措施。总而言之，这项工作将提供新的、量化的心理社会模型 手术后疼痛的风险和更高的风险都可以很容易地扩展到临床使用，并基于 和神经生物学框架。

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.