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.
Diabetes, depression, chronic kidney disease (CKD), and Cardiovascular disease (CVD) are major health concerns imposing substantial health and economic burden on individuals and society. This study proposes to estimate the effects of depression on CVD events among people with diabetes (F99 phase) and investigate the causal relationship between diabetes, CKD, and CVD mortality using machine learning within the causal inference structure (K00 phase). This project will provide research and training, contributing a much-needed analysis of diabetes and CKD on long-term adverse health outcomes as well as helping me develop into an independent researcher with expertise in diabetic kidney disease epidemiology.
