Decoding mechanisms underlying metabolic dysregulation in obesity and digestive cancer risk

肥胖和消化道癌症风险中代谢失调的解码机制

• 批准号: 10504203
• 负责人: EDWARD GIOVANNUCCI
• 金额: $ 134.29万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10504203
• 关键词: Address; Adipose tissue; Adult; Biological; Blood; Blood specimen; Body mass index; C-reactive protein; Chronic; Cocoa Powder; Cohort Studies; Colorectal; Colorectal Cancer; Communities; Data; Derivation procedure; Development; Diagnosis; Dual-Energy X-Ray Absorptiometry; Ensure; Ethnic Origin; Follow-Up Studies; Future; Gene Expression; General Population; Genotype-Tissue Expression Project; Glycosylated hemoglobin A; Goals; Health; Health Professional; Individual; Inflammation; Inflammation Process; Inflammatory; Insulin Resistance; Link; Lipoproteins; Liver; Logistics; Longitudinal cohort; Longterm Follow-up; Machine Learning; Malignant Neoplasms; Malignant neoplasm of liver; Measures; Mediating; Mediator of activation protein; Mendelian randomization; Metabolic; Modeling; Multivitamin; Nurses' Health Study; Obesity; Omega-3 Fatty Acids; Organ; Outcome Study; Pathway interactions; Pattern; Phenotype; Physicians; Play; Population Study; Prevention strategy; Preventive; Prospective cohort; Prospective cohort study; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; Proteins; Proteomics; Public Health; Recording of previous events; Regulator Genes; Regulatory Element; Research Personnel; Resources; Risk; Risk Factors; Role; Techniques; Tissues; Validation; Visceral; Vitamin D; Woman; Women' s Health; Work; base; biobank; cancer risk; case control; cohort; cost; epidemiology study; high risk; innovation; men; novel; novel strategies; prevent; programs; prospective; public database; risk prediction; success; therapeutic target; trait

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.

项目摘要/摘要 肥胖与至少13个癌症的风险增加有关。 结肠癌和肝脏占男性癌症的55％，女性为48％，不包括生殖 癌症。 （BMI），累积结直肠癌和肝癌的证据暗示着内心的肥胖。 密切相关的血糖代谢失调）是可能的直接因果成分。 机械上的易感性是癌症近端器官，这在很大程度上尚不清楚。 在Malignnancies，包含结直肠癌和肝癌的发展和计划中的作用； 一般炎症过程和全身标记（例如C反应蛋白（CRP））的大量证据 关于消化系统癌症的意义不大，可能大多数是非特异性的高风险 代谢上不健康的肥胖本身。 大多数反映代谢上不可分割的肥胖状态具有巨大潜在的Touncover关键机制 和信息 代谢上不毫无疑问的肥胖通常是昂贵且后勤的不可行的 因此，大型基于人群的研究。 （i）得出新型基于蛋白质组学的炎症特征的空白是代谢不健康的肥胖症 （“炎症型”）在通过双能X射线吸收测定法量化的内脏脂肪组织中 （DXA）和血糖代谢功能的特征； 在纵向同伴中，与入射结直肠癌和肝癌风险有关 通过最先进的奥林克蛋白质组学面板（384个炎症相关蛋白）来表征炎症型 描述性不健康的肥胖症（即较高内脏肥胖，具有稳态模型评估 用于胰岛素耐药性[HOMA-IR]，血红蛋白A1C [HBA1C]或脂蛋白胰岛素抵抗评分[LPUR]）。 机器学习分析以识别炎症型将在外部队列中复制 研究蛋白质组学炎症型与长期出现大肠肠风险的关系（1000 病例/1000个对照）和肝癌（500例/500例对照），将纵向队列与存储 基线血液和长期随访（中值范围为6.1-16.7岁）。 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 与循环蛋白的异常谱有关，新型炎症是 长期结直肠癌和肝癌的风险不可预测。 代谢失调和癌症风险计划的目标，包括增强高风险的识别 个人，风险预测以及潜在的预防和治疗剂目标的阐明。