Inference and Prediction Using Functional Principal Components Analysis: Application to Diabetic Kidney Disease Progression in the Chronic Renal Insufficiency Cohort (CRIC) Study

TL;DR

This study applies functional principal components analysis to model and compare long-term kidney disease progression trajectories in diabetic patients, revealing novel patterns and assessing modeling approaches for better disease understanding.

Contribution

The paper introduces the use of functional principal components analysis for modeling irregular and sparse longitudinal biomarker data in disease progression, with a focus on diabetic kidney disease.

Findings

Identified new dominant modes of variation in eGFR trajectories.

Compared cohort-wide and subgroup-specific modeling approaches.

Demonstrated the method's applicability to other biomarker-based disease studies.

Abstract

Repeated longitudinal measurements are commonly used to model long-term disease progression, and timing and number of assessments per patient may vary, leading to irregularly spaced and sparse data. Longitudinal trajectories may exhibit curvilinear patterns, in which mixed linear regression methods may fail to capture true trends in the data. We applied functional principal components analysis to model kidney disease progression via estimated glomerular filtration rate (eGFR) trajectories. In a cohort of 2641 participants with diabetes and up to 15 years of annual follow-up from the Chronic Renal Insufficiency Cohort (CRIC) study, we detected novel dominant modes of variation and patterns of diabetic kidney disease (DKD) progression among subgroups defined by the presence of albuminuria. We conducted inferential permutation tests to assess differences in longitudinal eGFR patterns between groups. To determine whether fitting a full cohort model or separate group-specific models is more optimal for modeling long-term trajectories, we evaluated model fit, using our goodness-of-fit procedure, and future prediction accuracy. Our findings indicated advantages for both modeling approaches in accomplishing different objectives. Beyond DKD, the methods described are applicable to other settings with longitudinally assessed biomarkers as indicators of disease progression. Supplementary materials for this article are available online.