SFEBES2016 Poster Presentations Diabetes and Cardiovascular (30 abstracts)
1Diabetes and Endocrine Centre, Mater Dei Hospital, Malta, Malta; 2University of Malta, Malta, Malta.
Background: Diabetes is the commonest cause of end-stage renal disease in the western world. However not all type 2 diabetic subjects develop renal disease, and of those who do, not all progress. At present it is not possible to identify patients who will progress.
Aim: The aim of the study was to identify baseline risk factors for the development and progression of renal disease in a cohort with type 2 diabetes and use this data to generate risk equations.
Patients and methods: Type 2 diabetic patients who had albumin:creatinine ratio (ACR) measurement in 20072008 were recruited, baseline characteristics were recorded and followed up for 8 years.
Results: Two hundred and sixty patients were included in the study. Of all the normo and microalbuminuric patients, 24.3% progressed and of all the micro and macroalbuminuric patients 22.1% regressed.
Baseline HbA1c, white cell count (WCC), smoking and duration of diabetes were associated with progression of renal disease in univariate analysis. Smoking (P=0.064) and duration of diabetes (P=0.034) were independently associated with progression in binary logistic regression.
Spearman correlation showed baseline HbA1c (P=0.0016), age (P=0.0064), serum creatinine (P=0.0178), serum potassium (P=0.0414), WCC (P=0.0226), serum triglycerides (P=0.0156), systolic blood pressure (P=0.0164) and duration of diabetes (P=0.003) to be positively correlated with % change in ACR, whilst baseline eGFR (P=0.0278), serum sodium (P=0.039), haemoglobin (P=0.0006) and haematocrit (P=0.0002) were negatively correlated. Duration of diabetes (P=0.025) and baseline HbA1c (P=0.018) was independently associated with % change in ACR in multivariate analysis.
Based on these results risk equations were generated.
Conclusions: We have identified baseline characteristics associated with progression of renal disease in type 2 diabetic subjects and generated equations to estimate risk of progression. If validated in other populations, these equations might be useful in predicting risk of progression in clinical practice.