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Identifying Healthy and High-Risk Weight Loss Phenotypes to Optimize Obesity Management in End Stage Kidney Disease

识别健康和高风险的减肥表型以优化终末期肾病的肥胖管理

基本信息

批准号：
10179372
负责人：
Meera Nair Harhay
金额：
$ 29.19万
依托单位：
DREXEL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10179372
关键词：
Benefits and Risks Biological Markers Blood Pressure Body Composition Body Weight Body Weight decreased Body mass index Caring Cessation of life Characteristics Chronic Chronic Disease Clinical Confidence Intervals Consensus Data Data Set Dialysis patients Dialysis procedure Dietitian End stage renal failure Enrollment Ensure Foundations Goals Guidelines Health Health Status Hemodialysis Hospitalization Individual Inflammation Inflammatory Intake Interview Joints Kidney Kidney Diseases Kidney Transplantation Kilogram Knowledge Link Longitudinal Studies Machine Learning Malnutrition Measures Medical Modeling Muscle function Muscular Atrophy Nephrology Non obese Nutritional Obesity Outcome Patient-Centered Care Patients Phenotype Physicians Physiological Population Process Prospective cohort study Provider Quality of life Reporting Research Research Proposals Risk Risk Factors Science Services Structure Techniques Transplant Recipients United States Vulnerable Populations Weight adverse outcome clinical care clinical predictors cohort data registry experience hazard health related quality of life healthy weight hemodynamics high risk improved improved mobility inflammatory marker machine learning algorithm meter morphogens mortality mortality risk muscle form muscle strength novel nutrition obese patients obese person obesity management patient oriented patient population predictive modeling predictive tools prevent prospective reduced muscle strength sarcopenia sleep quality stakeholder perspectives tool weight loss intervention

项目摘要

Project Summary/Abstract In 2016, 47,000 individuals who initiated dialysis in the United States (~42% of all incident dialysis patients that year) had obesity, with a body mass index (BMI) of ≥ 30 kilograms per meters squared. Across the BMI spectrum, individuals with kidney disease commonly lose weight after initiating dialysis treatment. However, whereas body weight typically stabilizes after the first several months of dialysis among patients without obesity, those with obesity often continue to lose weight. Both people with and without obesity who are on dialysis may lose weight due to muscle wasting and malnutrition, and recent studies have identified weight loss as a risk factor for death among people on dialysis, independent of BMI. Yet, some of the weight loss observed among obese dialysis patients may also reflect deliberate attempts to improve health, mobility, or access to kidney transplantation. Currently, there are no guidelines to help clinicians to differentiate between healthy and high-risk weight loss among people with obesity on dialysis. Further, typical obesity management paradigms are not easily transferrable to obese people with end-stage kidney disease, given factors such as chronic malnutrition, inflammation, and sarcopenia in this population that may modify the risks and benefits of different weight loss strategies. Therefore, the overarching goal of this five-year research proposal is to define healthy and high-risk weight loss phenotypes among people with obesity who are on dialysis, and to provide clinically feasible tools to improve obesity management in the setting of end-stage kidney disease. We will accomplish this goal by conducting three distinct but interrelated studies. In the first study, we will qualitatively determine patient-prioritized endpoints of weight loss, in addition to patient, physician and other stakeholder perspectives on the key factors that differentiate healthy from high-risk weight loss on dialysis. In the second study, we will leverage a national dataset of 23,000 obese dialysis patients and apply constructs of high and low physiologic reserve to derive healthy and high-risk weight loss phenotypes. We will then develop a weight- loss risk calculator tool that predicts the risks of hospitalization and death that are associated with each weight loss phenotype, using dynamic predictive joint models and machine learning techniques. In the third study, we will enroll 250 obese dialysis patients in a prospective, longitudinal study across five regions in the United States to evaluate the association between nutritional, inflammatory, and hemodynamic biomarkers and measures of health trajectory that are not typically captured in registry data, such as sarcopenia, dynapenia, body composition, and patient-prioritized endpoints such as quality of life. In accomplishing its aims, this research will provide urgently needed knowledge and tools that will improve the medical management of tens of thousands of people with end-stage kidney disease and obesity, ensuring that clinicians will be better able to incorporate patient-prioritized outcomes into assessments of weight loss interventions, and recognize and mitigate the effects of high-risk weight loss.

项目总结/文摘