Longitudinal Estimated GFR Trajectories in Patients With and Without Type 2 Diabetes and Nephropathy

Background: In clinical practice and clinical trials, changes in serum creatinine concentrations are used to evaluate changes in kidney function. It has been assumed that these changes follow a linear pattern when serum creatinine concentration is converted to estimated glomerular filtration rate (eGFR). However, the paradigm that kidney function declines linearly over time has been questioned by studies showing either linear or nonlinear patterns. To verify how this impacts on kidney end points in intervention trials, we analyzed eGFR trajectories in multiple clinical trials of patients with and without diabetes. Study Design: Longitudinal observational study. Setting & Participants: 6 clinical trials with repeated measurements of serum creatinine. Predictor: Patient demographic and clinical parameters. Outcomes: Probability of nonlinear eGFR function trajectory calculated for each patient from a Bayesian model of individual eGFR trajectories. Results: The median probability of a nonlinear eGFR decline in all trials was 0.26 (interquartile range, 0.13-0.48). The median probability was 0.28 in diabetes versus 0.09 in nondiabetes trials (P < 0.01). The percentage of patients with a > 50% probability of nonlinear eGFR decline was generally low, ranging from 19.3% to 31.7% in the diabetes trials and from 15.1% to 21.2% in the nondiabetes trials. In the pooled data set, multivariable linear regression showed that higher baseline eGFR, male sex, diabetes status, steeper eGFR slope, and non-renin-angiotensin-aldosterone-system antihypertensives were independently associated with a greater probability of a nonlinear eGFR trajectory. Limitations: Relatively short follow-up and no measured GFR. Conclusions: In both diabetes and nondiabetes trials, the majority of patients show a more or less linear eGFR decline. These data support the paradigm that in diabetic and nondiabetic kidney disease, eGFR decline progresses linearly over time during a clinical trial period. However, in diabetes, one should take the nonlinearity proportion into account in the design of a clinical trial.