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

• 批准号: 10649563
• 负责人: Meera Nair Harhay
• 金额: $ 27.35万
• 依托单位: DREXEL UNIVERSITY
• 依托单位国家: 美国
• 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/10649563
• 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; Classification; Clinical; Confidence Intervals; Consensus; Data; Data Set; Dialysis patients; Dialysis procedure; Dietitian; Dimensions; 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; Machine Learning; Malnutrition; Measures; Medical; Modeling; Muscle function; Muscular Atrophy; Nephrology; Non obese; Nutritional; Obesity; Outcome; Patient-Centered Care; Patient-Focused Outcomes; Patients; Persons; 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; longitudinal, prospective study; machine learning algorithm; machine learning prediction; meter; morphogens; mortality; mortality risk; muscle form; muscle strength; novel; nutrition; obese patients; obese person; obesity management; patient population; predictive tools; prevent; prospective; reduced muscle strength; sarcopenia; sleep quality; stakeholder perspectives; tool; weight loss intervention

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

项目成果

Obesity Management in Kidney Transplant Candidates: Current Paradigms and Gaps in Knowledge.

• DOI: 10.1053/j.ackd.2021.09.009
• 发表时间: 2021-11
• 期刊: Advances in chronic kidney disease
• 影响因子: 2.9
• 作者: Lee JH;McDonald EO;Harhay MN
• 通讯作者: Harhay MN

Living Organ Donor Health Care Priorities During the COVID-19 Pandemic.

在COVID-19-19大流行期间，活器官捐赠者的卫生保健优先事项。

• DOI: 10.1016/j.ekir.2021.01.031
• 发表时间: 2021-04
• 期刊: Kidney international reports
• 影响因子: 6
• 作者: Zaidi H;Klassen AC;Fleetwood J;Lentine KL;Reese PP;Mittelman M;Bertha R;Harhay MN
• 通讯作者: Harhay MN