Keywords
Glomerular filtration rate
Creatinine
Diabetes mellitus
Chronic kidney disease
How to Cite
Abstract
Context: The methods currently used to calculate glomerular filtration rate (GFR) underestimate this measurement in the population of diabetic patients, therefore, there is a need to develop diabetes-specific methods for estimating glomerular filtration rate in this specific population.
Objective: This study aims to evaluate a predictive model based on the use of HbA1c to estimate the variability of glomerular filtration rate in diabetic patients with or without chronic kidney disease (CKD).
Methods: We analyzed data from diabetic patients belonging to a prospective follow-up cohort of a renal health surveillance program at a Peruvian hospital. The following factors were included in the multiple linear regression model: age, sex, diastolic blood pressure (DBP), systolic blood pressure (SBP), body mass index (BMI), cholesterol, triglycerides, HDL, LDL, serum creatinine, urinary creatinine, microalbuminuria, hemoglobin, basal glycemia, and HbA1c.
Results: A total of 122 patients were included in the analysis. The final multivariate model, which included variation of HbA1c, age, and creatinine variation, was highly significant ( P < 0.0001), with an adjusted R2 of 80%. The other variables analyzed were not significant predictors of glomerular filtration rate variation, despite showing some correlation.
Conclusions: The study shows that HbA1c, age, and creatinine variation significantly predict the variation of glomerular filtration rate in diabetic patients with or without chronic kidney disease and opens the possibility of using this model as a prognostic tool for this specific population.
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