How to identify the risk factors for advanced kidney disease: the progression of diabetic kidney disease to end-stage renal disease?
It is known that patients with diabetes are at risk for kidney disease. Risk factors for the development of kidney disease in patients with type 1 diabetes have been identified. However, specific risk factors leading not to the development of kidney disease, but the progression of more advanced stages of kidney disease, have not been studied extensively.
The study referenced the Diabetes Control and Complications Trial (DCCT) and the Epidemiology of Diabetes Interventions and Complications (EDIC) to assess which risk factors in patients with type 1 diabetes have the most influence on the incidence of macroalbuminuria (albumin excretion rate [AER] > 300 mg/24 h) and reduced estimated glomerular filtration rate (eGFR < 60 mL/min/1.73 m2). The DCCT was a randomized controlled trial with 1,441 patients who have type 1 diabetes being assigned to either intensive diabetes therapy or conventional therapy. This study grouped the potential risk factors into 11 blocks, listed as, design, physical, behavioral, family history, BP/pulse, medication use, lipid levels, HDL cholesterol, diabetes-specific, microvascular complications, hypoglycemic events, and glycemia.
The researchers modeled their cohorts to achieve a power of 83%. The Kaplan-Meier method was used to determine the event-free probability for both outcomes. To assess the association between fixed and time-dependent covariates and the risk of outcomes, they used Cox proportional hazards (PH). These associations were cased on cause-specific hazard ratios (HRs). Continuous variables were defined by medians and first and third quartiles, and discrete variables via counts and percentages. Lastly, the risk factor variable approach used both a forward-and backward-selection approach to add and then eliminate factors to make a final model.
Two models were identified, the first, nonrenal mechanistic models to assess covariates affecting the risk of nephropathy, and the second, clinical models, reflecting periodic measurements in clinical practice. The first model did not include AER and eGFR as predictors, whereas the second included the mean AER to be a predictor of macroalbuminuria and the mean level of eGFR as a predictor of reduced eGFR.
There were 192 reported cases of macroalbuminuria and 189 reduced eGFR. The event-free probability declined to ~85% after 30 years of follow-up for both outcomes. But after 30 years of follow-up of the patients, ~75% were free from both macroalbuminuria and reduced eGFR. In terms of insulin therapy groups, those in the conventional therapy group were more likely to have incident macroalbuminuria and a higher risk of reduced eGFR. Incorporating risk factors, after adjustment for age and mean HbA1c, risk factors most associated with the risk of macroalbuminuria were sex, blood pressures, lipids, daily insulin dose, and any progression in retinopathy. In terms of risk factors towards reducing eGFR, those most observed were blood pressure lipids, use of antihypertensive, use of lipid-lowering medication, duration of type 1 diabetes, AER, retinopathy, and glycemia.
From the Cox models, the z test value determined the magnitude of association for the incidence of the outcomes; again, two models were considered. The nonrenal mechanistic model for macroalbuminuria found the most significant risk factors to be, a higher mean HbA1c and male sex. Similarly, the clinical model for macroalbuminuria with adjustment for AER, found both higher mean HbA1 and male sex to be significant, but also a higher AER. Looking at reduced eGFR, the nonrenal mechanistic model found the strongest risk factor to be updated mean HbA1c, trailed by triglycerides, age, any use of calcium channel blockers, systolic BP, and hypertension. From the clinical model, after adjustment for eGFR and AER, the most significant risk factors were lower eGFR, higher AER, and higher updated mean HbA1c.
This study was able to consider glycemic exposure with nonglycemic risk factors to accomplish what previous studies have not yet accomplished. The strongest risk factor found, associated with the incidence of both macroalbuminuria and reduced eGFR, was a higher long-term cumulative glycemic exposure. Similarly, but to a lesser extent, higher triglyceride levels and higher blood pressure were also considered statistically significant risk factors. The authors believe, in terms of modifiable risk factors, glycemic exposure has the greatest cause of late-stage kidney disease in patients with type 1 diabetes. The incidence of macroalbuminuria due to glycemic exposure seemed to have both an immediate and compounding effect because of the current level and cumulative glycemic exposure. Regarding the nonmodifiable risk factors for advanced stages of renal disease, the stronger relationships seen were for male sex and age.
- The strongest risk factor found increasing the incidence of macroalbuminuria, and reduced eGFR is a higher cumulative glycemic exposure long-term.
- The frequency of screening for kidney disease in patients with type 1 diabetes should be further assessed.
- To reduce advanced stages of kidney disease, aggressive control of glycemic levels should be perused, along with control of other significant metabolic risk factors.
Reference for “Risk Factors for Advanced Kidney Disease in Patients with Type 1 Diabetes”:
Perkins, Bruce, et al. Risk Factors for Kidney Disease in Type 1 Diabetes. Diabetes Care. 2019 May 1.
Emma Kammerer, L|E|C|O|M Bradenton School of Pharmacy, PharmD Candidate