Variability in HbA1c, blood pressure, lipid parameters and serum uric acid, and risk of development of chronic kidney disease in type 2 diabetes

Diabetes Obes Metab. 2017 Nov;19(11):1570-1578. doi: 10.1111/dom.12976. Epub 2017 Jul 10.


Aim: Variability in HbA1c and blood pressure is associated with the risk of diabetic kidney disease (DKD). No evidence exists on the role of variability in lipids or serum uric acid (UA), or the interplay between the variability of different parameters, in renal outcomes.

Methods: Within the AMD Annals database, we identified patients with ≥5 measurements of HbA1c, systolic blood pressure (SBP) and diastolic blood pressure (DBP), total-, high-density lipoprotein (HDL)- and low-density lipoprotein (LDL)-cholesterol, triglycerides, and UA. Patients were followed-up for up to 5 years. The impact of measures of variability on the risk of DKD was investigated by Cox regression analysis and recursive partitioning techniques.

Results: Four-thousand, two-hundred and thirty-one patients were evaluated for development of albuminuria, and 7560 for decreased estimated glomerular filtration rate (eGFR; <60 mL/min/1.73 m2 ). A significantly higher risk of developing albuminuria was associated with variability in HbA1c [upper quartile hazard ratio (HR) = 1.3; 95% confidence interval (CI) 1.1-1.6]. Variability in SBP, DBP, HDL-C, LDL-C and UA predicted the decline in eGFR, the association with UA variability being particularly strong (upper quartile HR = 1.8; 95% CI 1.3-2.4). The concomitance of high variability in HbA1c and HDL-C conferred the highest risk of developing albuminuria (HR = 1.47; 95% CI 1.17-1.84), while a high variability in UA (HR = 1.54; 95% CI 1.19-1.99) or DBP (HR = 1.47; 95% CI 1.11-1.94) conferred the highest risk of decline in eGFR.

Conclusion: The variability of several parameters influences the development of DKD, having a different impact on albuminuria development and on the decline in GFR.

Keywords: albuminuria; diabetes kidney disease; glomerular filtration rate; variability.

MeSH terms

  • Aged
  • Blood Glucose Self-Monitoring / statistics & numerical data
  • Blood Pressure / physiology*
  • Blood Pressure Determination / statistics & numerical data
  • Databases, Factual
  • Diabetes Mellitus, Type 2 / blood
  • Diabetes Mellitus, Type 2 / complications*
  • Diabetes Mellitus, Type 2 / epidemiology
  • Diabetes Mellitus, Type 2 / physiopathology
  • Diabetic Nephropathies / blood
  • Diabetic Nephropathies / epidemiology
  • Diabetic Nephropathies / etiology
  • Diabetic Nephropathies / physiopathology
  • Disease Progression
  • Female
  • Glomerular Filtration Rate
  • Glycated Hemoglobin / analysis
  • Glycated Hemoglobin / metabolism*
  • Humans
  • Lipids / blood*
  • Longitudinal Studies
  • Male
  • Middle Aged
  • Observer Variation
  • Renal Insufficiency, Chronic / blood
  • Renal Insufficiency, Chronic / epidemiology
  • Renal Insufficiency, Chronic / etiology*
  • Renal Insufficiency, Chronic / physiopathology
  • Risk Factors
  • Uric Acid / blood*


  • Glycated Hemoglobin A
  • Lipids
  • Uric Acid