Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis

• Published in: Computers in biology and medicine Vol. 75; pp. 90 - 97
• Main Authors: Jelinek, Herbert F., Stranieri, Andrew, Yatsko, Andrew, Venkatraman, Sitalakshmi
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.08.2016; Elsevier Limited
• Subjects: Algorithms; Ankle; Biomarkers; Biomarkers - blood; Blood pressure; Body mass index; Data analytics; Data Mining; Databases, Factual; Datasets; Diabetes; Diabetes Mellitus, Type 2 - blood; Fasting; Female; Glucose; Glycated haemoglobin; Glycated Hemoglobin A - metabolism; Heart rate; Homocysteine; Humans; Internal Medicine; Male; Other; Oxidative stress; Proteins; Type 2 diabetes mellitus; Urine; Glycated haemoglobin; Data analytics; Type 2 diabetes mellitus
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2016.05.005

Glycated haemoglobin (HbA1c) is being more commonly used as an alternative test for the identification of type 2 diabetes mellitus (T2DM) or to add to fasting blood glucose level and oral glucose tolerance test results, because it is easily obtained using point-of-care technology and represents long-term blood sugar levels. HbA1c cut-off values of 6.5% or above have been recommended for clinical use based on the presence of diabetic comorbidities from population studies. However, outcomes of large trials with a HbA1c of 6.5% as a cut-off have been inconsistent for a diagnosis of T2DM. This suggests that a HbA1c cut-off of 6.5% as a single marker may not be sensitive enough or be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied on a large clinical dataset to identify an optimal cut-off value for HbA1c and to identify whether additional biomarkers can be used together with HbA1c to enhance diagnostic accuracy of T2DM. T2DM classification accuracy increased if 8-hydroxy-2-deoxyguanosine (8-OhdG), an oxidative stress marker, was included in the algorithm from 78.71% for HbA1c at 6.5% to 86.64%. A similar result was obtained when interleukin-6 (IL-6) was included (accuracy=85.63%) but with a lower optimal HbA1c range between 5.73 and 6.22%. The application of data analytics to medical records from the Diabetes Screening programme demonstrates that data analytics, combined with large clinical datasets can be used to identify clinically appropriate cut-off values and identify novel biomarkers that when included improve the accuracy of T2DM diagnosis even when HbA1c levels are below or equal to the current cut-off of 6.5%. •We prepared data from a Diabetes and Cardiovascular Disease screening unit for mining.•We used information gain mining to find that the cut-off threshold for HbA1c of 6.2% was the best threshold.•We discovered co-markers that, along with HbA1c could e predict T2DM better than the HbA1c alone.•We advanced a chart that can be used to readily depict HbA1c cut-offs and co-markers.