Decoding mechanisms underlying metabolic dysregulation in obesity and digestive cancer risk

项目总结/摘要 肥胖与至少13种癌症的风险增加有关。在所有可归因于过度肥胖的癌症中， 结肠直肠和肝脏占男性癌症的55%，女性癌症的48%，不包括生殖系统癌症。 癌的虽然大多数肥胖作为癌症危险因素的流行病学研究评估了体重指数 (BMI)结直肠癌和肝癌的证据越来越多地表明内脏肥胖（及其 密切相关的血糖代谢失调）作为可能的直接因果成分。内脏性肥胖 在机制上使其近端器官易患癌症在很大程度上是未知的。毫无疑问，炎症 在恶性肿瘤（包括结肠直肠癌和肝癌）的发展和进展中发挥作用;然而， 一般炎症过程和全身标志物（如C反应蛋白（CRP））的大量证据 与消化道癌症有关的研究并不令人印象深刻，可能是因为大多数研究都是非特异性的， 代谢不健康的肥胖症因此，提取炎症途径和标记物来识别那些 最能反映代谢不健康的肥胖状态的是， 并为强有力的广谱预防战略提供信息。获得精确测量的技术， 典型的代谢不健康的肥胖症通常是昂贵的，并且在后勤上是不可行的。 基于大量人群的研究。因此，我们提出了一种创新的方法来解决这些问题， 通过（i）推导出代谢不健康肥胖症的新的基于蛋白质组学的炎症特征， 在具有通过双能X射线吸收测定法定量的内脏脂肪组织的队列中， (DXA)和特征的血糖代谢功能;然后（ii）前瞻性地研究这些新的炎症类型 在纵向队列中，储存与结肠直肠癌和肝癌风险相关的血液样本。我们将 通过最先进的Olink蛋白质组学面板（384种炎症相关蛋白）表征炎症类型， 描述代谢不健康的肥胖（即，内脏脂肪过多，稳态模型评估 胰岛素抵抗[HOMA-IR]、血红蛋白A1 c [HbA 1c]或脂蛋白胰岛素抵抗评分[LPIR]）。 用于识别炎症类型的机器学习分析将在外部队列中重复。然后我们将 研究蛋白质组学炎症类型与结直肠癌长期发病风险之间的关系（1000 例/1000对照）和肝癌（500例/500对照），将纵向队列与储存的 基线血液和长期随访（中位数范围6.1-16.7年）。根据令人信服的初步 数据，我们假设更大的内脏脂肪组织和血糖代谢失调的组合 与循环蛋白质的异常谱相关，并且这些新的炎症型是 独立预测长期结直肠癌和肝癌风险。这些目标与 代谢失调和癌症风险计划的目标，包括加强高风险的识别 个体、风险预测以及潜在预防和治疗靶点的阐明。

PROJECT SUMMARY/ABSTRACT Obesity is associated with increased risk of at least 13 cancers. Of all cancers attributable to excess adiposity, colorectum and liver account for 55% of cancer among men and 48% among women, excluding reproductive cancers. Although most epidemiologic studies of obesity as a cancer risk factor evaluated body mass index (BMI), accumulating evidence for colorectal and liver cancers implicates viscerally located adiposity (and its closely related glycemic metabolic dysregulation) as the likely direct causal component. How visceral adiposity mechanistically predisposes its proximal organs to cancer is largely unknown. Inflammation undoubtedly plays a role in development and progression of malignancies, including colorectal and liver cancers; however, the large body of evidence for general inflammation processes and systemic markers like C-reactive protein (CRP) in relation to digestive cancers are underwhelming, possibly because most are non-specific to high-risk metabolically unhealthy obesity per se. Thus, distilling the inflammatory pathways and markers to identify those most reflective of the metabolically unhealthy obese state has immense potential to uncover key mechanisms and inform powerful broad-spectrum strategies for prevention. Techniques to obtain precise measures to characterize metabolically unhealthy obesity are often prohibitively costly and logistically infeasible in the context of large population-based studies. Therefore, we propose an innovative approach to address these gaps by (i) deriving novel proteomic-based inflammation signatures of metabolically unhealthy obesity (“Inflammotypes”) in cohorts with visceral adipose tissue quantified via dual-energy X-ray absorptiometry (DXA) and traits of glycemic metabolic function; then (ii) prospectively investigating these novel Inflammotypes in longitudinal cohorts with stored blood samples in relation to incident colorectal and liver cancer risk. We will characterize Inflammotypes via state-of-the-art Olink proteomic panel (384 inflammation-related proteins) to describe metabolically unhealthy obesity (i.e., higher visceral adiposity, with homeostatic model assessment for insulin resistance [HOMA-IR], hemoglobin A1c [HbA1c], or lipoprotein insulin resistance score [LPIR]). Machine learning analyses to identify the Inflammotypes will be replicated in an external cohort. We will then investigate the relationship between proteomic Inflammotypes with long-term risk of incident colorectal (1000 cases/1000 controls) and liver cancer (500 cases/500 controls), combining longitudinal cohorts with stored baseline bloods and long-term follow-up (median ranges 6.1-16.7 years). Based on compelling preliminary data, we hypothesize the combination of greater visceral adipose tissue and glycemic metabolic dysregulation are associated with abnormal profiles of circulating proteins, and that these novel Inflammotypes are independently predictive of long-term colorectal and liver cancer risks. These aims are closely aligned with the goals of the Metabolic Dysregulation and Cancer Risk Program, including enhancing identification of high-risk individuals, risk prediction, and elucidation of potential preventive and therapeutic targets.