Effects of Chronic Kidney Disease on Cardiovascular Disease and Dementia Among People with Diabetes: Causal Modeling with Machine Learning Approach

慢性肾脏病对糖尿病患者心血管疾病和痴呆的影响：利用机器学习方法进行因果建模

• 批准号: 10059131
• 负责人: Kosuke Inoue
• 金额: $ 3.44万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2021-06-11
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10059131
• 关键词: Address; Adult; Affect; Aging; Area; Award; Big Data; Biological Markers; Cardiovascular Diseases; Cessation of life; Characteristics; Chronic Kidney Failure; Complex; Data; Dementia; Diabetes Mellitus; Diabetic Nephropathy; Dimensions; Disease; Disease Outcome; Economic Burden; Epidemiologic Methods; Epidemiology; Event; Fostering; Future; General Population; Genetic; Goals; Health; Incidence; Individual; Intervention; Joints; Laboratories; Latino; Link; Literature; Longitudinal cohort; Machine Learning; Mediating; Mediation; Mediator of activation protein; Mental Depression; Mentors; Metabolic Diseases; Methodology; Mexican Americans; Morbidity - disease rate; National Health and Nutrition Examination Survey; Observational Study; Outcome; Pathway interactions; Patients; Pharmaceutical Preparations; Phase; Precision Health; Prevalence; Probability; Public Health; Research; Research Personnel; Research Training; Risk Factors; Scoring Method; Societies; Socioeconomic Status; Statistical Methods; Structure; Sustainable Development; Testing; Time; United Nations; United States; Update; Weight; base; cardiovascular disorder risk; cardiovascular risk factor; causal model; clinical practice; cohort; comorbidity; demographics; depressive symptoms; design; diabetic patient; diet and exercise; effective intervention; epidemiology study; follow-up; health economics; high dimensionality; high risk; high risk population; improved; individual patient; innovation; lifestyle intervention; mortality; predictive modeling; prevent; successful intervention; therapy development

PROJECT SUMMARY/ABSTRACT Diabetes has been the major public health issues imposing substantial health and economic burden on individuals and society. Given the Sustainable Development Goals (SDGs) in which United Nations has resolved to reduce morbidity and mortality from non-communicable diseases by one-third by year 2030, understanding the major risk factors of long-term adverse health outcomes such as cardiovascular disease (CVD) among patients with diabetes are imperative. While chronic kidney disease (CKD) and depression are closely interrelated with both diabetes and CVD, the causal link between these non-communicable diseases have not been sufficiently established. This is possibly due to (1) ill-defined temporality (i.e. unclear time- ordering of disease occurrence) and (2) their complex multifactorial and high-dimensional interaction with potential confounders such as demographic characteristics, socio-economic status, and comorbidities. The overall objective of this application is to investigate the causal relationship between diabetes and its complications including CKD. My specific aims are as follows: Aim 1 (F99 phase) assesses the causal relationship between depression and CVD among people with diabetes. After summarizing the previous literature, I will utilize longitudinal data to examine the joint effect of diabetes and depression on CVD sufficiently considering time-dependent exposure and confounders. Aim 2 (K00 phase) examines the causal pathway from diabetes to CKD, and to CVD mortality. I will develop the machine learning-based prediction model of CKD among people with diabetes, and then estimate the effect of CKD on CVD mortality using the obtained prediction model within causal inference structure. I will also investigate the extent to which CKD mediates the pathway from diabetes to CVD mortality. This study presents a timely opportunity to contribute to growing literature on how these non-communicable diseases (i.e. diabetes, depression, CKD, and CVD) interact with each other. Moreover, applications of machine learning in causal inference structure will contribute to the “precision health” concept by targeting high-risk populations and design effective interventions to prevent future non-communicable diseases and their complications.

项目总结/文摘