Diabetes and Trajectories of Estimated Glomerular Filtration Rate: A Prospective Cohort Analysis of the Atherosclerosis Risk in Communities Study

Progressive Decline in Estimated Glomerular Filtration Rate in Patients With Diabetes After Moderate Loss in Kidney Function-Even Without Albuminuria

OBJECTIVE

Persons with diabetes but no chronic kidney disease (CKD) and without albuminuria have the same age-related decline in kidney function as the background population. Whether this also applies following moderate loss in kidney function is unknown. We quantified the impact of albuminuria status on the development of estimated glomerular filtration rate (eGFR) trajectories following CKD stage 3 (CKD3) and assessed potential heterogeneous development patterns among the subgroup with normoalbuminuria.

RESEARCH DESIGN AND METHODS

We used repeated clinical measures during up to 16 years of follow-up in 935 persons with type 1 diabetes and 1,984 with type 2 diabetes. Trajectories of eGFR by diabetes type and albuminuria status following CKD3 were estimated with spline mixed-effects models with adjustment for relevant confounders. Latent class trajectory modeling was used to find distinct patterns of eGFR development in the subgroups with normoalbuminuria.

RESULTS

Mean annual declines in eGFR for normo-, micro- and macroalbuminuria the first 10 years following CKD3 were 1.9, 2.3, and 3.3 mL/min/1.73 m² in type 1 diabetes and 1.9, 2.1, and 3.0 in type 2 diabetes, respectively. For normoalbuminuria, two distinct eGFR patterns were found, one with accelerated declining eGFR levels. This specific progression pattern was associated with less use of lipid-lowering treatment, renin-angiotensin system blockers, and other antihypertensive treatment.

CONCLUSIONS

Our results support a diabetes-dependent decline in kidney function without albuminuria following CKD3, with a subgroup showing a progressive decline. Furthermore, this group seems to be undertreated in terms of cardioprotective and renal protective treatment and suggests that increased attention should be drawn to normoalbuminuric diabetic kidney disease.

Development and Validation of PREDICT-DM: A New Microsimulation Model to Project and Evaluate Complications and Treatments of Type 2 Diabetes Mellitus

Background: Type 2 diabetes mellitus (T2DM) affects ∼30 million people in the United States and ∼400 million people worldwide, numbers likely to increase due to the rising prevalence of obesity. We sought to design, develop, and validate PREDICT-DM (PRojection and Evaluation of Disease Interventions, Complications, and Treatments-Diabetes Mellitus), a state-transition microsimulation model of T2DM, incorporating recent data. Methods: PREDICT-DM is populated with natural history, risk factor, and outcome data from large-scale cohort studies and randomized clinical trials. The model projects diabetes-relevant outcomes, including cardiovascular and renal disease outcomes, and 5/10-year survival. We assessed the model validity against 62 endpoints from ACCORD (Action to Control Cardiovascular Risk in Diabetes), VADT (Veterans Affairs Diabetes Trial), and Look AHEAD trials via several comparative statistical methods, including mean absolute percentage error (MAPE), Bland-Altman graphs, and Kaplan-Meier curves. Results: For the comparison between simulated and observed outcomes of the intervention/control arms of the trial, the MAPE was 19%/25% (ACCORD), 29%/20% (VADT), and 42%/10% (Look AHEAD). The Bland-Altman's 95% limit of agreement was 0.02 (ACCORD), 0.03 (VADT), and 0.01 (Look AHEAD), and the mean difference (95% confidence interval) for the comparison between PREDICT-DM and trial endpoints was 0.0025 (-0.0018 to 0.0070) for ACCORD, -0.0067 (-0.0137 to 0.0002) for VADT, and -0.0033 (-0.0067 to 0.00002) for Look AHEAD, indicating an adequate model fit to the data. The model-driven Kaplan-Meier curves were similarly close to those previously published. Conclusions: PREDICT-DM can reasonably predict clinical outcomes from ACCORD and other clinical trials of U.S. patients with T2DM. This model may be leveraged to inform clinical strategy questions related to the management and care of T2DM in the United States.