alexa Risk Assessment Models for the Development of Complicat
ISSN: 2155-6156

Journal of Diabetes & Metabolism
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Research Article

Risk Assessment Models for the Development of Complications in Maltese Type 2 Diabetic Patients

Sarah Baldacchino*, Liberato Camilleri, Stephen Fava, Anthony Serracino-Inglott and Lilian M Azzopardi

University of Malta, Malta

*Corresponding Author:
Sarah Baldacchino
University of Malta, Malta
E-mail: [email protected]

Received date: October 17, 2012; Accepted date: January 07, 2013; Published date: January 11, 2013

Citation: Baldacchino S, Camilleri L, Fava S, Serracino-Inglott A, Azzopardi LM (2013) Risk Assessment Models for the Development of Complications in Maltese Type 2 Diabetic Patients. J Diabetes Metab 4:242. doi:10.4172/2155-6156.1000242

Copyright: © 2013 Baldacchino S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Introduction: With the IDF Diabetes Atlas 2006 predicting a Type 2 diabetes incidence rate of 11.6% among the Maltese population by 2025, treatment differentiation between high risk and low risk patients is necessary to ensure the sustainability of such a diabetes management program.

Objectives: To identify significant predictors and develop local diabetic neuropathy (DNeurM), retinopathy (DRM), nephropathy (DNephrM) and macrovascular (MVM) models which determine complication risk in Maltese diabetic patients.

Methods: A cross-sectional retrospective study involving 120 randomly selected patients aged 25-70 years, diagnosed with type 2 diabetes ≤ 1 year and taking metformin 500 mg, perindopril 5 mg and simvastatin 40 mg was carried out at the Endocrine and Diabetes Centre at Mater Dei General Hospital in Malta to collect data for 20 predictors. Complication risk scores were assigned to participants using a developed risk scale. SPSS® 17.0 ANCOVA regression model analyses and backward elimination variable selection method (p<0.05) were used to derive parsimonious models.

Results: 12 significant predictors were retained in the models; DNeurM includes body mass index (BMI; p=1×10-4), glycated haemoglobin (HbA1c) level (p=0.00019), serum fasting triglycerides (p=0.002), alcohol abuse (No; p=0.022), systolic blood pressure (BP; p=0.041) and age (p=0.070); DRM includes systolic BP (p=4×10-4), serum fasting triglycerides (p=0.001), HbA1c level (p=0.010), albumin-creatinine ratio (ACR; p=0.040) and waist circumference (p=0.095); DNephrM includes systolic BP (p=3×10-7), urinary glucose (p=3.86×10-4), ACR (p=0.0009), waist circumference (p=0.0012), age (p=0.006), genetic predisposition (No; p=0.026), serum urea (p=0.050) and serum fasting triglycerides (p=0.062); MVM includes waist circumference (p=1×10-6), systolic BP (p=0.0003), total serum cholesterol (p=0.011) and HbA1c level (p=0.060).

Conclusion: Twelve significant predictors featured in the parsimonious models: age, genetic predisposition, alcohol abuse, BMI, waist circumference, systolic BP, HbA1c level, serum total cholesterol level, serum fasting triglyceride level, serum urea level, urinary glucose level and ACR.

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