Cross-lagged analysis in nephrology.

Abstract

Cross-lagged analysis is a statistical method employed to examine directional relationships between variables over time, making it especially valuable for addressing causality challenges in clinical research. This method is essential for comprehending complex bidirectional relationships, such as stress and immunity, dietary habits and metabolic conditions, or medication adherence and health outcomes. By analyzing longitudinal data, cross-lagged analysis establishes temporal precedence, tests reciprocal influences, and controls for confounding variables, thereby enhancing causal inferences. In nephrology, this approach can be beneficial for studying the interaction between acute kidney injury (AKI) and chronic kidney disease (CKD), clarifying whether AKI episodes accelerate CKD progression or if pre-existing CKD increases susceptibility to AKI. It also illuminates the relationship between CKD and cardiovascular diseases, investigating whether CKD exacerbates heart failure or vice versa while considering shared risk factors like hypertension and diabetes. Furthermore, cross-lagged analysis can elucidate the kidney-brain connection by examining whether CKD leads to cognitive decline through mechanisms such as uremic toxin accumulation or if neurological dysfunction worsens kidney outcomes through sympathetic overactivation. Cross-lagged analysis accommodates latent variables and measurement errors, allowing researchers to explore how variables interact over time. This method provides a strong framework for understanding dynamic relationships in nephrology, offering critical insights to guide interventions and advance knowledge of disease progression mechanisms.