Metabolic Obesity Phenotypes and Obesity-related Cancer Survival

代谢性肥胖表型和肥胖相关的癌症生存

• 批准号: 10752062
• 负责人: Maci Winn
• 金额: $ 3.58万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-01 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10752062
• 关键词: Active Learning; Affect; Biological Markers; Biostatistical Methods; Body mass index; Body measure procedure; Cancer Patient; Cancer Survivorship; Cancer and Nutrition; Caring; Characteristics; Classification; Clinical; Clinical Practice Guideline; Comprehensive Cancer Center; Computerized Medical Record; Cox Proportional Hazards Models; Data; Data Analyses; Deterioration; Diagnosis; Dyslipidemias; Early Diagnosis; Ensure; Epidemiologic Methods; Epidemiology; European; Goals; Health; Huntsman Cancer Institute at the University of Utah; Hyperglycemia; Hyperinsulinism; Hypertension; Incidence; Individual; Intervention; Investigation; Knowledge; Logistic Regressions; Malignant Neoplasms; Measurement; Measures; Medical History; Mentorship; Metabolic; Metabolic dysfunction; Metabolic syndrome; Metabolism; Methods; Modeling; Morbidity - disease rate; Obesity; Obesity associated cancer; Overweight; Participant; Patient Monitoring; Patients; Phenotype; Physicians; Prevention; Process; Public Health; Quality of life; Research; Research Training; Risk; Risk Factors; Role; Scientist; Survival Analysis; Time; Training; Weight; cancer diagnosis; cancer risk; cancer survival; cancer therapy; cardiometabolic risk; career; clinical care; cohort; demographics; design; electronic data; experience; hazard; high risk; improved; large datasets; mortality; obese patients; obesity risk; prospective; survival outcome; survivorship; tumor progression

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.

项目总结。肥胖相关癌症(ORC)的发病率继续快速上升，尽管 总体癌症发病率下降；目前兽人占所有癌症的40%以上，使其成为 严重的公共卫生问题。强有力的证据表明，肥胖和代谢功能障碍(通常 定义为存在高血糖、高血压、血脂异常或肥胖)会增加患兽人疾病的风险。 然而，关于代谢功能障碍与ORC后存活率的关系的研究有限。这个 癌症诊断后癌症和癌症治疗对代谢健康的影响也在很大程度上是未知的。 此外，尽管代谢功能障碍通常与肥胖高度相关，但也出现了 有证据表明，多达三分之一的正常体重个人有不同程度的代谢功能障碍。新陈代谢 肥胖表型，根据存在的代谢综合征标准和肥胖状态来定义 (以BMI衡量)，是一种超越肥胖单独评估代谢功能障碍的新兴方法。的目标是 这个F30项目是为了更好地了解癌症对代谢健康的影响，以及 ORC诊断后存活的代谢功能障碍。首先，我将评估两者之间的联系 ORC诊断时的代谢功能障碍，通过代谢性肥胖表型和ORC特异性和 总体生存(目标1)。为了实现这一目标，我将利用欧洲对 癌症与营养(EPIC)-互动病例队列，包含代谢生物标记物和 几个ORC患者(N~1,777)的癌症诊断。在ORC的参与者中，我将确定 COX比例风险回归分析代谢性肥胖表型与生存期的关系 模型，对相关协变量进行调整。其次，我的目标是确定与以下因素相关的临床人口统计学因素 代谢健康和代谢性肥胖的表型均恶化(目标2)。使用从 在亨斯迈癌症接受治疗的一组兽人患者(N=3,021)的电子病历 我将研究代谢性健康和代谢性肥胖表型的变化 随着时间的推移，使用混合效应模型和多变量Logistic回归模型，并确定预测因素 代谢功能障碍。结果可能有助于为干预措施提供信息，并改变临床实践指南 ORC诊断后代谢功能障碍的处理，并可能改善生活质量和 癌症患者的生存经历。这份提案中概述的目标将为我提供 在癌症和新陈代谢研究方面有丰富的经验，同时也造成了 代谢功能障碍在癌症进展中的作用。此外，结合严谨的研究 高级流行病学和生物统计学方法、经验学习、临床背景和专家方面的培训 导师将确保我过渡到一个从事积极研究的成功的内科医生-科学家。