title: Predictors of estimated GFR decline in patients with type 2 diabetes and preserved kidney function.
abstract: OBJECTIVE
  This study examined predictors of the annual decline in estimated GFR (eGFR) in patients with type 2 diabetes and preserved kidney function.
  
  
  METHODS
  In a prospective, observational cohort study, 1682 individuals with type 2 diabetes and baseline eGFR ≥60 ml/min per 1.73 m(2) (as estimated by the Chronic Kidney Disease Epidemiology Collaboration equation) were followed for 10 years. Linear regression was used to estimate participants' changes in eGFR over time.
  
  
  RESULTS
  During follow-up, 263 (15.6%) individuals had a rapid eGFR decline defined as >4.0% per year. Average eGFR decline was -5.8 ± 3 and -0.6 ± 2 ml/min per 1.73 m(2) per year in rapid decliners and nondecliners, respectively. Compared with normotensive, normoalbuminuric patients (-0.2 ± 0.2 ml/min per 1.73 m(2) per year), those with hypertension (-1.0 ± 0.1 ml/min per 1.73 m(2) per year), hemoglobin A(1c)≥7% (-1.0 ± 0.1 ml/min per 1.73 m(2) per year), longer diabetes duration (-1.0 ± 0.1 ml/min per 1.73 m(2) per year), obesity (-1.2 ± 0.1 ml/min per 1.73 m(2) per year), insulin treatment (-1.5 ± 0.1 ml/min per 1.73 m(2) per year), microalbuminuria (-1.3 ± 0.2 ml/min per 1.73 m(2) per year), or macroalbuminuria (-2.7 ± 0.4 ml/min per 1.73 m(2) per year) had significantly faster age-adjusted annual eGFR declines. Multivariable linear regression analyses revealed that albuminuria (P<0.001) was the strongest predictor of annual eGFR decline. Other independent predictors of annual eGFR decline were older age, hypertension, insulin treatment, and lower baseline eGFR.
  
  
  CONCLUSIONS
  Annual eGFR decline is predicted by multiple modifiable risk factors in patients with type 2 diabetes and preserved kidney function.
authors.affiliation: Section of Endocrinology and Metabolism, Department of Medicine, University of Verona, Italy.
authors.forename: Giacomo
authors.initials: G
authors.lastname: Zoppini
authors.forename: Giovanni
authors.initials: G
authors.lastname: Targher
authors.forename: Michel
authors.initials: M
authors.lastname: Chonchol
authors.forename: Vittorio
authors.initials: V
authors.lastname: Ortalda
authors.forename: Carlo
authors.initials: C
authors.lastname: Negri
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Stoico
authors.forename: Enzo
authors.initials: E
authors.lastname: Bonora
mesh_terms: D000367:Age Factors; D000368:Aged; D000419:Albuminuria / Q000453:epidemiology / Q000503:physiopathology; D001794:Blood Pressure; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000188:drug therapy / Q000453:epidemiology / Q000503:physiopathology*; D003928:Diabetic Nephropathies / Q000097:blood / Q000453:epidemiology / Q000503:physiopathology*; D018450:Disease Progression; D005260:Female; D005500:Follow-Up Studies; D005919:Glomerular Filtration Rate; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D006973:Hypertension / Q000453:epidemiology / Q000503:physiopathology; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D007328:Insulin / Q000627:therapeutic use; D007558:Italy / Q000453:epidemiology; D007668:Kidney / Q000503:physiopathology*; D016018:Least-Squares Analysis; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors
chemical_list: D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents; D007328:Insulin; C517652:hemoglobin A1c protein, human