Data-analytically derived flexible HbA1c thresholds for type 2 diabetes mellitus diagnostic

Glycated haemoglobin (HbA1c) is now more commonly used as an alternative test to the fasting plasma glucose and oral glucose tolerance tests for the identification of Type 2 Diabetes Mellitus (T2DM) because it is easily obtained using the point-of-care technology and represents long-term blood sugar levels. According to WHO guidelines, HbA1c values of 6.5% or above are required for a diagnosis of T2DM. However outcomes of a large number of trials with HbA1c have been inconsistent across the clinical spectrum and further research is required to determine the efficacy of HbA1c testing in identification of T2DM. Medical records from a diabetes screening program in Australia illustrate that many patients could be classified as diabetics if other clinical indicators are included, even though the HbA1c result does not exceed 6.5%. This suggests that a cutoff for the general population of 6.5% may be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied to identify markers that can be used with HbA1c. The results indicate that T2DM is best classified by HbA1c at 6.2% - a cutoff level lower than the currently recommended one, which can be even less, having assumed the threshold flexibility, if additionally to HbA1c being high the rule is conditioned on oxidative stress or inflammation being present, atherogenicity or adiposity being high, or hypertension being diagnosed, etc .

[1]  John H Fuller,et al.  Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial , 2004, The Lancet.

[2]  Kyu Yeon Hur,et al.  The association between glycemic variability and diabetic cardiovascular autonomic neuropathy in patients with type 2 diabetes , 2015, Cardiovascular Diabetology.

[3]  Herbert F. Jelinek,et al.  Cardiac Autonomic Dysfunction in Type 2 Diabetes – Effect of Hyperglycemia and Disease Duration , 2014, Front. Endocrinol..

[4]  G. Bray,et al.  Reduction in Weight and Cardiovascular Disease Risk Factors in Individuals With Type 2 Diabetes , 2007, Diabetes Care.

[5]  Herbert F. Jelinek,et al.  Glutathione: Glutathione Sulfide Redox Imbalance in Early Impaired Fasting Glucose , 2014 .

[6]  C. Rolland,et al.  Effect of weight loss on adipokine levels in obese patients , 2011, Diabetes, metabolic syndrome and obesity : targets and therapy.

[7]  Masaru Harada,et al.  Retrospective study on the efficacy of a low-carbohydrate diet for impaired glucose tolerance , 2014, Diabetes, metabolic syndrome and obesity : targets and therapy.

[8]  Jaakko Tuomilehto,et al.  The Pros and Cons of Diagnosing Diabetes With A1C , 2011, Diabetes Care.

[9]  Andrew Stranieri,et al.  An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy , 2013, Artif. Intell. Medicine.

[10]  Julie Wagner,et al.  Development of a questionnaire to measure heart disease risk knowledge in people with diabetes: the Heart Disease Fact Questionnaire. , 2005, Patient education and counseling.

[11]  John E Hall,et al.  The kidney, hypertension, and obesity. , 2003, Hypertension.

[12]  Y. Jang,et al.  Standards of Medical Care in Diabetes-2010 by the American Diabetes Association: Prevention and Management of Cardiovascular Disease , 2010 .

[13]  Kamlesh Khunti,et al.  Effect of early intensive multifactorial therapy on 5-year cardiovascular outcomes in individuals with type 2 diabetes detected by screening (ADDITION-Europe): a cluster-randomised trial , 2011, The Lancet.

[14]  Oluf Pedersen,et al.  Multifactorial intervention and cardiovascular disease in patients with type 2 diabetes. , 2003, The New England journal of medicine.

[15]  F. Schlösser,et al.  The Semmes Weinstein monofilament examination as a screening tool for diabetic peripheral neuropathy. , 2009, Journal of vascular surgery.

[16]  Anthony Firek,et al.  Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy , 2009 .

[17]  Andrew Stranieri,et al.  Multivariate Data-Driven Decision Guidance for clinical scientists , 2013, 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW).

[18]  R. Bucala,et al.  Advanced glycation end products and endothelial dysfunction in type 2 diabetes. , 2002, Diabetes care.

[19]  Xuehui Meng,et al.  Comparison of three data mining models for predicting diabetes or prediabetes by risk factors , 2013, The Kaohsiung journal of medical sciences.

[20]  S. Twigg,et al.  The imperative to prevent diabetes complications: a broadening spectrum and an increasing burden despite improved outcomes , 2015, The Medical journal of Australia.

[21]  S. Barold,et al.  Significance of QRS complex duration in patients with heart failure. , 2005, Journal of the American College of Cardiology.

[22]  N. Stettler,et al.  Systematic review of clinical studies related to pork intake and metabolic syndrome or its components , 2013, Diabetes, metabolic syndrome and obesity : targets and therapy.

[23]  Andrew Stranieri,et al.  Diagnostic with incomplete nominal/discrete data , 2015, Artif. Intell. Res..

[24]  Thomas G. Dietterich Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[25]  Herbert F. Jelinek,et al.  An innovative Multi-disciplinary Diabetes Complications Screening Program in a Rural Community: A Description and Preliminary Results of the Screening , 2006 .

[26]  Kumar,et al.  Relationship among HbA1c and Lipid Profile in Punajbi Type 2 Diabetic Population , 2012 .

[27]  Edward T H Yeh,et al.  Coming of age of C-reactive protein: using inflammation markers in cardiology. , 2003, Circulation.

[28]  Carol Chen-Scarabelli,et al.  Suboptimal Glycemic Control, Independently of QT Interval Duration, Is Associated with Increased Risk of Ventricular Arrhythmias in a High‐Risk Population , 2006, Pacing and clinical electrophysiology : PACE.

[29]  G. Deed,et al.  General Practice Management of Type 2 Diabetes: 2014-15 , 2014 .

[30]  Herbert F. Jelinek,et al.  Impaired fasting glucose & 8-iso-prostaglandin F2α in diabetes disease progression. , 2014 .

[31]  A. Bhansali,et al.  Prevalence and risk factors of development of peripheral diabetic neuropathy in type 2 diabetes mellitus in a tertiary care setting , 2014, Journal of diabetes investigation.

[32]  N. Ruderman,et al.  AMPK activation: a therapeutic target for type 2 diabetes? , 2014, Diabetes, metabolic syndrome and obesity : targets and therapy.

[33]  Demetris Lamnisos,et al.  Predicting cardiometabolic risk: waist-to-height ratio or BMI. A meta-analysis , 2013, Diabetes, metabolic syndrome and obesity : targets and therapy.

[34]  Herbert F Jelinek,et al.  Oxidative stress and triglycerides as predictors of subclinical atherosclerosis in prediabetes , 2014, Redox report : communications in free radical research.

[35]  M. Goulart,et al.  Oxidative Stress as an Underlying Contributor in the Development of Chronic Complications in Diabetes Mellitus , 2013, International journal of molecular sciences.

[36]  Self-reported prediabetes and risk-reduction activities--United States, 2006. , 2008, MMWR. Morbidity and mortality weekly report.

[37]  David B Sacks,et al.  A new look at screening and diagnosing diabetes mellitus. , 2008, The Journal of clinical endocrinology and metabolism.

[38]  J. Dubernard,et al.  [The kidney]. , 2011, Bulletin de l'Academie nationale de medecine.

[39]  Katharina Eckert,et al.  Impact of physical activity and bodyweight on health-related quality of life in people with type 2 diabetes , 2012, Diabetes, metabolic syndrome and obesity : targets and therapy.

[40]  M. Ashwell,et al.  Waist‐to‐height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta‐analysis , 2012, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[41]  Herbert F. Jelinek,et al.  A Comparison of Nonlinear Measures for the Detection of Cardiac Autonomic Neuropathy from Heart Rate Variability , 2015, Entropy.

[42]  E. Barrett-Connor,et al.  Total, LDL, and HDL cholesterol decrease with age in older men and women. The Rancho Bernardo Study 1984-1994. , 1997, Circulation.

[43]  Marimuthu Palaniswami,et al.  Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis. , 2009, Biomedical engineering online.

[44]  Mohammad Khubeb Siddiqui,et al.  Application of data mining: Diabetes health care in young and old patients , 2013, J. King Saud Univ. Comput. Inf. Sci..

[45]  J. Shaw,et al.  Current controversies in the use of haemoglobin A1c , 2012, Journal of internal medicine.

[46]  Ali Akbar Moosavi-Movahedi,et al.  Glycated albumin: an overview of the In Vitro models of an In Vivo potential disease marker , 2014, Journal of Diabetes & Metabolic Disorders.

[47]  Barbara Thorand,et al.  Elevated Markers of Endothelial Dysfunction Predict Type 2 Diabetes Mellitus in Middle-Aged Men and Women From the General Population , 2005, Arteriosclerosis, thrombosis, and vascular biology.

[48]  Andrew Stranieri,et al.  Feature Selection using Misclassification Counts , 2011, AusDM.

[49]  Xiao-hua Wang,et al.  The relationship between glycemic variability and diabetic peripheral neuropathy in type 2 diabetes with well-controlled HbA1c , 2014, Diabetology & Metabolic Syndrome.

[50]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[51]  Haseeb Ahmad Khan,et al.  Clinical significance of HbA1c as a marker of circulating lipids in male and female type 2 diabetic patients , 2007, Acta Diabetologica.

[52]  Chao-Yung Wang,et al.  Pleiotropic effects of statin therapy: molecular mechanisms and clinical results. , 2008, Trends in molecular medicine.

[53]  Barry Robson,et al.  Data mining and clinical data repositories: Insights from a 667, 000 patient data set , 2006, Comput. Biol. Medicine.

[54]  Witold Pedrycz,et al.  Data Mining: A Knowledge Discovery Approach , 2007 .

[55]  Beverley Balkau,et al.  Glycemic Thresholds for Diabetes-Specific Retinopathy , 2010, Diabetes Care.

[56]  Stephen Colagiuri,et al.  The role of HbA1c in the diagnosis of diabetes mellitus in Australia , 2012, The Medical journal of Australia.

[57]  Gilles Plourde,et al.  Reversal of type 2 diabetes mellitus in an obese man: Role of dietary modification and physical activity , 2013 .

[58]  Farshad Sharifi,et al.  Association of cardiac autonomic neuropathy with arterial stiffness in type 2 diabetes mellitus patients , 2013, Journal of Diabetes & Metabolic Disorders.

[59]  M. Dobiášová Atherogenic index of plasma [log(triglycerides/HDL-cholesterol)]: theoretical and practical implications. , 2004, Clinical chemistry.

[60]  David E. Booth,et al.  Analysis of Incomplete Multivariate Data , 2000, Technometrics.

[61]  A. Thanopoulou,et al.  Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. , 2010, The New England journal of medicine.

[62]  D. Ewing,et al.  The Value of Cardiovascular Autonomic Function Tests: 10 Years Experience in Diabetes , 1985, Diabetes Care.

[63]  Kristine Faerch,et al.  Heterogeneity of Pre-diabetes and Type 2 Diabetes: Implications for Prediction, Prevention and Treatment Responsiveness. , 2015, Current diabetes reviews.

[64]  D. Lloyd‐Jones,et al.  Cardiovascular risk prediction: basic concepts, current status, and future directions. , 2010, Circulation.

[65]  Mohamed Karmali,et al.  Type 2 diabetes mellitus and inflammation: Prospects for biomarkers of risk and nutritional intervention , 2010, Diabetes, metabolic syndrome and obesity : targets and therapy.

[66]  Chintamani Dilip Bodhe,et al.  HbA1c: Predictor of Dyslipidemia and Atherogenicity in Diabetes Mellitus , 2012 .

[67]  C. Apovian,et al.  Management of diabetes across the course of disease: minimizing obesity-associated complications , 2011, Diabetes, metabolic syndrome and obesity : targets and therapy.

[68]  H F Jelinek,et al.  Inflammation, coagulation, endothelial dysfunction and oxidative stress in prediabetes--Biomarkers as a possible tool for early disease detection for rural screening. , 2015, Clinical biochemistry.

[69]  針田 伸子,et al.  Lower serum creatinine is a new risk factor of type 2 diabetes : the Kansai Healthcare Study , 2010 .

[70]  Stephen Colagiuri,et al.  Screening for type 2 diabetes and impaired glucose metabolism: the Australian experience. , 2004, Diabetes care.

[71]  Dhiren P. Shah,et al.  ON OXIDATIVE STRESS AND DIABETIC COMPLICATIONS , 2013 .