Journal of Metabolic Syndrome

Type 2 diabetes mellitus (DM) could be associated with cognitive impairment. The spectrum of cognitive impairment ranges from mild deficits that are not clinically detectable to the most severe clinical form, dementia. Some of the potential mechanisms include the effects of brain infarcts, white matter disease, hyperinsulinaemia, advanced glycosylated end products, and Lipoprotein related proteins (LRP). There is limited data on the prevalence of cognitive impairment amongst type 2 DM patients in south –east Nigeria. Therefore, this study was undertaken to determine the prevalence of cognitive impairment in type 2 DM patients attending a diabetic clinic in Abakaliki southeast Nigeria. It is a cross-sectional, descriptive and hospital based study carried out over a three months period (October 2013 to September 2014). Mini mental state examination (MMSE) was used for cognitive functions assessment and interpreted as follows; a score of 25-30 as normal, and ≤ 24 as cognitive impairment. The data was analyzed using Statistical Package for Social Sciences (SPSS) version 19 software. Out of 499 type 2 DM patients that were screened for the study, 450 were eligible for the study with male to female sex ratio of 2 (190):3 (260). The age range was 30-89 years with mean age of 59.43 ± 9.28 years. One hundred and eighty (40%) patients had cognitive impairment with male to female sex distribution of 55 (28.9%) and 125 (48.1%) respectively. Advanced age, low education attainment, unskilled occupation and presence of diabetic complications were the identified risk factors for cognitive impairment. Mini mental state examination should be a frequent tool in routine assessment of diabetic patients as it is simple and sensitive in detecting cognitive impairment. Also, identified modifiable risk factors should be corrected.

Metabolic syndrome is an accelerated systemic atherosclerotic process terminating with obesity, hypertension, diabetes mellitus, peripheric artery disease, chronic renal disease, chronic obstructive pulmonary disease, cirrhosis, coronary heart disease, stroke, and eventually early aging and death. It shows itself with some reversible components including smoking, overweight, hyperbetalipoproteinemia, hypertriglyceridemia, dyslipidemia, impaired fasting glucose, impaired glucose tolerance, and white coat hypertension (WCH). The terminal consequences are probably due to the smoking and excess weight induced chronic inflammatory process on the endothelial system for a long period of time. WCH is a pioneer sign of the accelerated systemic atherosclerotic process that can be detected easily, and treated by preventing weight gain.

Purpose: The purpose of the current investigation was to create, compare, and validate sex-specific decision tree models to classify metabolic syndrome.

Methods: Sex-specific Chi-Squared Automatic Interaction Detection, Exhaustive Chi-Squared Automatic
Interaction Detection, and Classification and Regression Tree algorithms were run in duplicate using metabolic syndrome classification criteria, subject characteristics, and cardiovascular predictor variable from the National Health and Nutrition Examination Survey cohort data. Data from 1999-2012 were used (n=10,639; 1999-2010 cohorts for model creation and 2011-2012 cohort for model validation). Metabolic Syndrome was classified as the presence of 3 of 5 American Heart Association National Heart Lung and Blood Institute Metabolic Syndrome classification criteria. The first run was made with all predictor variables and the second run was made excluding metabolic syndrome classification predictor variables. Given that the included decision tree algorithms are non-parametric procedures, all decision tree models were compared to a logistic regression based model to provide a parametric comparison.

Results: The Classification and Regression Tree algorithm outperformed all other decision tree models and logistic regression with a specificity of 0.908 and 0.952, sensitivity of 0.896 and 0.848, and misclassification error of 0.096 and 0.080 for males and females, respectively. Only one predictor variable outside of the metabolic syndrome classification reached significance in the female model (age). All metabolic syndrome classification predictor variables reached significance in the male model. Waist circumference did not reach significance in the female model. Within each model, 5 female and 3 male pathways built off of <3 American Heart Association National Heart Lung and Blood Institute Metabolic Syndrome classification criteria resulted in an increased likelihood of presenting Metabolic Syndrome.

Conclusion: The proposed pathways show promise over other current metabolic syndrome classification
models in identifying Metabolic Syndrome with <3 predictor variables, before current classification criteria.