Evaluation of Pain in Chronic Pancreatitis using the NAPS2 cohorts

慢性胰腺炎(CP)是一种进行性和破坏性的胰腺炎症性疾病。其中一个 最令人痛苦的特征是疼痛，大约90%的患者会出现疼痛，其中大约一半的人会持续疼痛， 这与住院人数增加和生活质量下降有关。因为有多重疼痛 由于没有标准化的方法来评估CP患者的疼痛，因此新的方法是 需要更好地了解CP疼痛的机制以及如何适当地治疗。北方 美国胰腺炎研究2(NAPS2)从最大的美国招募、表型和获得生物标本 我们已经完成了一项全基因组关联研究，其中包括1,171个 有详细表型疼痛数据的NAPS2 CP病例。我们建议分析NAPS2数据 SET和生物素(DNA、血清、胰腺组织)以确定遗传风险和潜在的机制 持续疼痛，确定炎症性疼痛的标志，并描述临床疼痛复合体，以指导 病人管理。我们已经通过完整的和嵌套的GWAS8候选基因确定了 “持续”的痛苦。目标1将确定这些变异是否与基因的功能变化有关 与持续性疼痛表型有关。将进行有针对性的SNP基因分型和基因表达研究 包括在人类样本中进行的mRNA研究和免疫组织化学染色。目标2将 确定C反应蛋白(CRP)或细胞因子生物标志物是否与持续性疼痛相关。疼痛是一种 活动性炎症的指标，促炎症细胞因子增加疼痛和抗炎 细胞因子可以减轻疼痛。我们将检测500个NAPS2样本中升高的CRP和10个Th1/Th2细胞因子 炎症的生物标志物。作为阳性对照，我们将包括40例急性胰腺炎患者的血液样本。 因疼痛或炎症而住院4天的患者。AIM 3将应用机器学习 基因型别、生物标记物和形态(梗阻)相关性的检验方法 疼痛模式、严重程度和特征的测量以及SF12 v2生活质量评分(心理和身体) 同时控制性行为、吸烟和饮酒。我们预计，我们的机器学习方法将 为疼痛的正确分类提供决策规则，根据值得正式测试的病因 临床试验。此外，机器学习的使用有望通过以下方式提供对疼痛机制的洞察 最佳地将症状特征、生物标记物、影像研究和遗传学与复杂的机制联系起来 这些都见于疼痛的CP患者。这项研究的目标是利用现有的NAPS2数据和生物谱 为未来的临床研究构建一个框架，以测试个性化疼痛管理的有效性 基于我们的机器学习-预测病因，而不仅仅是症状。

Chronic pancreatitis (CP) is a progressive and destructive inflammatory disorder of the pancreas. One of the most distressing features is pain, which occurs in ~90% of patients, about half of whom have constant pain, which is associated with increased hospitalization and lower quality of life. Since there are multiple pain etiologies and since no standardized method exists to assess pain in patients with CP, novel approaches are needed to better understand the mechanisms of pain in CP and how it can be appropriately treated. The North American Pancreatitis Study 2 (NAPS2) recruited, phenotyped, and obtained biospecimens from the largest US cohort of pancreatitis, and we have completed a genome-wide association study (GWAS) that includes 1,171 NAPS2 CP cases for whom detailed phenotypic pain data are available. We propose to analyze the NAPS2 data set and biospecimens (DNA, serum, pancreas tissue) to define genetic risks and potential mechanisms of constant pain, identify markers of inflammatory pain, and characterize clinical pain complexes that can guide patient management. We have already identified through the full and a nested GWAS 8 candidate genes for "constant" pain. Aim 1 will determine whether these variants are associated with functional changes in genes associated with the constant pain phenotype. Targeted SNP genotyping and gene expression studies will be conducted, including mRNA studies and immunohistochemical staining in human samples. Aim 2 will determine whether c-reactive protein (CRP) or cytokine biomarkers correlate with constant pain. Pain is an indicator of active inflammation, with pro-inflammatory cytokines increasing pain, and anti-inflammatory cytokines diminishing pain. We will test 500 NAPS2 samples for elevated CRP and 10 Th1/Th2 cytokines as biomarkers of inflammation. For positive controls we will include blood samples from 40 acute pancreatitis patients who remain hospitalized for > 4 days for pain or inflammation. Aim 3 will apply machine-learning approaches to test for correlation of genotype, biomarkers, and morphology (obstruction) with quantitative measures of pain pattern, severity, and character as well as SF12 v2 quality of life scores (mental and physical) while controlling for sex, smoking, and alcohol. We anticipate that our machine learning approaches will provide decision rules for the proper classification of pain according to etiology worthy of formal testing in clinical trials. In addition, use of machine learning is anticipated to provide insight into pain mechanism by optimally linking the symptoms signatures, biomarkers, imaging studies, and genetics to complex mechanisms that are seen in patients with painful CP. The goal of this study is to use existing NAPS2 data and biospecimens to construct a framework for future clinical studies that test the effectiveness of personalized pain management based on our machine-learning-predicted etiology rather than symptoms alone.