Metabolic Obesity Phenotypes and Obesity-related Cancer Survival

PROJECT SUMMARY. The incidence of obesity-related cancer (ORC) continues to increase rapidly despite a decrease in overall cancer incidence; currently ORCs constitute over 40% of all cancers making them a significant public health concern. Strong evidence suggests that both obesity and metabolic dysfunction (often

defined as the presence of hyperglycemia, hypertension, dyslipidemia, or adiposity) increase the risk of ORCs. However, there is limited research on the association of metabolic dysfunction with survival after ORC. The impact of cancer and cancer treatment on metabolic health after cancer diagnosis is also largely unknown.

Additionally, although metabolic dysfunction is usually highly correlated with obesity, there is emerging evidence that up to a third of normal weight individuals have some degree of metabolic dysfunction. Metabolic obesity phenotypes, defined according to both presence of metabolic syndrome criteria and obesity status

(measured by BMI), is an emerging way to assess metabolic dysfunction beyond obesity alone. The goal of this F30 project is to better understand the impact of cancer on metabolic health, and the relationship of metabolic dysfunction with survival after ORC diagnosis. Firstly, I will evaluate the association between

metabolic dysfunction at ORC diagnosis, measured by metabolic obesity phenotypes, and ORC-specific and overall survival (Aim 1). To accomplish this aim, I will leverage the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort, which comprises detailed data on metabolic biomarkers and

cancer diagnoses in several ORC patients (N~1,777). Among participants with ORC, I will ascertain associations between metabolic obesity phenotypes and survival using Cox proportional hazards regression models, adjusting for relevant covariates. Secondly, I aim to identify clinicodemographic factors associated with

both metabolic health and metabolic obesity phenotype worsening (Aim 2). Using data extracted from electronic medical records on a cohort of ORC patients (N=3,021) receiving treatment at the Huntsman Cancer Institute at the University of Utah, I will examine changes to metabolic health and metabolic obesity phenotype

over time, using both mixed effects models and multivariable logistic regression models, and identify predictors of metabolic dysfunction. Results may help inform interventions and transform clinical practice guidelines for the management of metabolic dysfunction following ORC diagnosis, and may improve the quality of life and the

survivorship experience of cancer patients. The objectives outlined in this proposal will provide me with extensive experience in cancer and metabolism research, while contributing to the large knowledge gap of the role of metabolic dysfunction on cancer progression. Furthermore, the combination of rigorous research

training in advanced epidemiologic and biostatistical methods, experiential learning, clinical context, and expert mentorship will ensure my transition to a successful physician-scientist engaged in active research.