Decoding mechanisms underlying metabolic dysregulation in obesity and digestive cancer risk

点击翻译按钮获取中文摘要

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.