The American Heart Association’s Cardio-Kidney-Metabolic (CKM) syndrome framework integrates cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic dysfunction, using staging (0-4) for precise risk stratification. Patients with autoimmune diseases (ADs), such as rheumatoid arthritis, represent a crucial, high-risk subgroup for accelerated CKM progression. Chronic systemic inflammation and corticosteroid exposure hasten this process, but the precise longitudinal trajectories remain poorly characterized, limiting targeted prevention.
This study leverages the UK Biobank (UKB), a large, longitudinally followed cohort, to quantify the dynamic transitions between CKM stages, identify determinants of progression, and estimate time spent in each state. We will use continuous-time multi-state Markov modeling to achieve this. Our primary objective is to construct the Markov model to determine transition probabilities across CKM stages in the AD population and specific AD subtypes. Key questions address whether ADs independently accelerate progression to advanced CKM stages (Stage 3-4) and what the projected 5-10 year probabilities of progression are under various risk profiles.
We will estimate transition intensities (hazard rates) using maximum likelihood methods, incorporating key covariates (age, sex, ethnicity, socioeconomic status). Mean sojourn times and transition probability matrices over 5- and 10-year intervals will be computed, stratified by AD subtypes. This project will produce the first longitudinal model of CKM progression in AD patients. The results will improve risk stratification, support targeted prevention, and inform interventions to optimize CKM management and outcomes in this high-risk population.
