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Metabolic syndrome is a clustering of metabolic abnormalities that include central obesity, dyslipidemia, insulin resistance, and increased blood pressure. The aim of this study was to evaluate and identify the risk factors associated with metabolic syndrome by using a decision tree algorithm as a data mining tool. A total of 6578 individuals were included in the analysis using a body mass index (BMI) cutoff >= 25 kg/m2 for the definition of overweight, in accordance with International Diabetic Federation (IDF) criteria. Subjects with obesity plus two or more of the criteria for defining metabolic syndrome were included in the metabolic syndrome group. Of the 6578 subjects, 70% (4539 subjects) were selected as a “training” dataset and 30% (2039 cases) were used as the testing dataset to evaluate the performance of decision tree. Two models were evaluated. In model I, age, sex, educational level, marriage and job status, fasted serum triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), uric acid, fasting blood glucose (FBG), high sensitive C-reactive protein (Hs-CRP), systolic (SBP) and diastolic (DBP) blood pressure, and physical activity level were considered as input variables and in model II, age, gender, Hs-CRP, white blood cell (WBC), red blood cell (RBC), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), Platelets (PLT), red cell distribution width (RDW), and platelet distribution width (PDW) were used as input variables. The validation of the model was assessed by constructing a receiver operating characteristic (ROC) curve. The results showed that in model I, serum fasted TG was the most important associated risk factor for metabolic syndrome. In model II, serum Hs-CRP was identified as a risk factor of metabolic syndrome. The sensitivity, specificity, accuracy, and the area under the ROC curve (AUC) values for model I were 99%, 94%, 97% and 0.972 and for model II were 74%, 77%, 76% and 0.812, respectively. Our findings in model I suggest that the IDF criteria are suitable for identifying individuals within the Iranian population into those with, or without MetS. Furthermore, model II showed that serum Hs-CRP concentrations were identified as a risk factor for metabolic syndrome within the Iranian population.

Metabolic syndrome (MetS) is characterized by abdominal obesity, dyslipidemia, hypertension and glucose intolerance, and is associated with an increased risk of developing cardiovascular disease (CVD), diabetes mellitus and related diseases. Circulating levels of inflammatory markers such as C-reactive-protein (CRP) have reported to be associated with CVD. Against this background, the prevalence of MetS is increasing globally, and thus predictive biomarkers are required for identification of MetS patients at an increased risk. Here we explored the value of CRP as a biomarker in 7284 subjects and also investigated which features of MetS have the greatest association with the hs-CRP level.The subjects were recruited from the Mashhad stroke and heart atherosclerotic disorder study. Anthropometric factors and biochemical parameters (for example, high-sensitivity CRP (hs-CRP), high-density lipoprotein/low-density lipoprotein, triglycerides (TGs) and fasting blood glucose (FBG)) were determined. Univariate and multivariate analyses were conducted to evaluate the association of hs-CRP and MetS.Our results illustrated that the concentration of serum hs-CRP increased progressively with the number of MetS components, and subjects who fulfilled the criteria of MetS for waist circumference, TGs, blood pressure and FBG were found to have hs-CRP of 0.53, 0.38, 0.34 and 0.71 mg/l, respectively, higher than matched-subjects. Importantly, FBG had the greatest association with hs-CRP concentration.Our data demonstrate the significant association between MetS components with hs-CRP, indicating that this association was cumulative by increasing the number of the defining features of MetS, supporting further studies to explore the value of emerging marker as a novel method for detecting individuals at high risk of developing MetS.

Objective: The Mashhad stroke and heart atherosclerotic disorder (MASHAD) study is a 10-year cohort study that aims to evaluate the impact of various genetic, environmental, nutritional and psychosocial risk factors on the incidence of cardiovascular events among an urban population in eastern Iran. Methods: The MASHAD study comprises a cohort of 9704 individuals aged 35-65 years using a stratified cluster random sampling design. This cohort will be followed up until 2020, with follow-up examinations being undertaken every 3 years. Ten-year cardiovascular disease (CVD) risk estimation was determined using NCEP ATP III criteria. Results: Overall, 88.4 of women and 79.2 of men (P 20 were observed to be 86.6, 11 and 2.5 , respectively. Predicted risk of CVD > 10 using the Framingham algorithm was considerably higher in men compared to women. Overall, 9.5 95 % confidence interval (CI) 8.9�10.1 % of our subjects had prevalent CAD. Conclusion: The prevalence of CVD risk factors within our population is high compared to Western countries, indicating the necessity for interventional risk modifications. © 2015, Swiss School of Public Health.

To evaluate the effect of a computerized Decision Support System (CDSS) on improving adherence to an anticoagulation guideline for the treatment of atrial fibrillation (AF). This study had an interrupted time series design. The adherence to the guideline was assessed at fortnightly (two weeks) intervals from January 2016 to January 2017, 6 months before and 6 months after intervention. Newly diagnosed patients with AF were included in the offices of ten cardiologists. Stroke and major bleeding risks were calculated by the CDSS which was implemented via a mobile application. Treatment recommendations based on the guideline were shown to cardiologists. The segmented regression model was used to evaluate the effect of CDSS on level and trend of guideline adherence for the treatment of AF. In our analysis, 373 patients were included. The trend of adherence to the anticoagulation guideline for the treatment of AF was stable in the pre-intervention phase. After the CDSS intervention, mean of the adherence to the guideline significantly increased from 48% to 65.5% (P-value

Cold exposure stimulates energy expenditure and glucose disposal. If these factors play a significant role in whole body energy balance, and glucose homeostasis, it is predicted that both obesity and type 2 diabetes prevalence would be lower where it is colder. Previous studies have noted connections between ambient temperature and obesity, but the direction of the effect is confused. No previous studies have explored the link of type 2 diabetes to ambient temperature. We used county level data for obesity and diabetes prevalence across the mainland USA and matched this to county level ambient temperature data. Average ambient temperature explained 5.7% of the spatial variation in obesity and 29.6% of the spatial variation in type 2 diabetes prevalence. Correcting the type 2 diabetes data for the effect of obesity reduced the explained variation to 26.8%. Even when correcting for obesity, poverty and race, ambient temperature explained 12.4% of the variation in the prevalence of type 2 diabetes, and this significant effect remained when latitude was entered into the model as a predictor. When obesity prevalence was corrected for poverty and race the significant effect of temperature disappeared. Enhancing energy expenditure by cold exposure will likely not impact obesity significantly, but may be useful to combat type 2 diabetes.