Introduction: Dyslipidemia may be defined as increased levels of serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), or a decreased serum high-density lipoprotein cholesterol (HDL-C) concentration. Dyslipidemia is an established risk factor for cardiovascular disease (CVD). We aimed to investigate the association of dyslipidemia and CVD events among a population sample from Mashhad, in northeastern Iran. Material and methods: This prospective cohort study comprised a population of 8698 men and women aged 35–65 years who were recruited from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study. Socioeconomic and demographic status, anthropometric parameters, laboratory evaluations, lifestyle factors, and medical history were gathered through a comprehensive questionnaire and laboratory and clinical assessment for all participants. Cox regression model and 95% confidence interval (CI) were used to evaluate the association of dyslipidemia and its components with CVD incidence. Results: After 6 years of follow-up, 233 cases of CVD (including 119 cases of unstable angina [US], 74 cases of stable angina [SA], and 40 cases of myocardial infarction [MI]) were identified in the study population. Unadjusted baseline serum LDL-C, TC, and TG levels were positively associated with the risk of total CVD events among the entire population (HR: 1.54, 95% CI: 1.19–2; P-value< 0.01; HR: 1.53; 95% CI: 1.18–1.98; P < 0.01; HR: 1.57; 95% CI: 1.27–2.03; P < 0.01, respectively). However, after adjusting for confounding factors (age, body mass index [BMI], family history of CVD, smoking status [non-smoker, ex-smoker and current smoker], lipid lowering drug treatment, anti-hypertensive drug treatment, hypertension, healthy eating index [HEI], total energy intake, and presence of diabetes mellitus), a significant direct association only remained between TC and MI risk in men (HR: 2.71; 95%CI: 1.12–6.57; P-value< 0.05). Conclusion: In the present study, TC baseline level was significantly associated with the risk of MI among men.

Background: Previous research has shown that Ramadan fasting has beneficial effects on cardiovascular risk factors, however there are controversies. In the present study, the effect of Ramadan fasting on cardiovascular risk factors has been investigated. Method: This is a prospective observational study that was carried out in a group of patients with at least one cardiovascular risk factor (including history of documented previous history of either coronary artery disease (CAD), metabolic syndrome or cerebro-vascular disease in past 10 y). Eighty two volunteers including 38 male and 44 female, aged 29–70 y, mean 54.0 ± 10 y, with a previous history of either coronary artery disease, metabolic syndrome or cerebro-vascular disease were recruited. Subjects attended the metabolic unit after at least 10 h fasting, before and after Ramadan who were been fasting for at least 10 days. A fasting blood sample was obtained, blood pressure was measured and body mass index (BMI) was calculated. Lipids profile, fasting blood sugar (FBS) and insulin, homocysteine (hcy), high-sensitivity C-reactive protein (hs-CRP) and complete blood count (CBC) were analyzed on all blood samples. Results: A significant improvement in 10 years coronary heart disease risk (based on Framingham risk score) was found (13.0 ± 8 before Ramadan and 10.8 ±7 after Ramadan, P <0.001, t test).There was a significant higher HDL-c, WBC, RBC and platelet count (PLT), and lower plasma cholesterol, triglycerides, LDL-c, VLDL-c, systolic blood pressure, body mass index and waist circumference after Ramadan (P <0.05, t test). The changes in FBS, insulin,Homeostasis Model Assessment Insulin Resistance (HOMA-IR), hcy, hs-CRP and diastolic blood pressure before and after Ramadan were not significant (P >0.05, t test). Conclusions: This study shows a significant improvement in 10 years coronary heart disease risk score and other cardiovascular risk factors such as lipids profile, systolic blood pressure, weight, BMI and waist circumference in subjects with a previous history of cardiovascular disease.

Coronary heart disease (CHD) is an important public health problem globally. Algorithms incorporating the assessment of clinical biomarkers together with several established traditional risk factors can help clinicians to predict CHD and support clinical decision making with respect to interventions. Decision tree (DT) is a data mining model for extracting hidden knowledge from large databases. We aimed to establish a predictive model for coronary heart disease using a decision tree algorithm. Here we used a dataset of 2346 individuals including 1159 healthy participants and 1187 participant who had undergone coronary angiography (405 participants with negative angiography and 782 participants with positive angiography). We entered 10 variables of a total 12 variables into the DT algorithm (including age, sex, FBG, TG, hs-CRP, TC, HDL, LDL, SBP and DBP). Our model could identify the associated risk factors of CHD with sensitivity, specificity, accuracy of 96%, 87%, 94% and respectively. Serum hs-CRP levels was at top of the tree in our model, following by FBG, gender and age. Our model appears to be an accurate, specific and sensitive model for identifying the presence of CHD, but will require validation in prospective studies.

(1) Background: Coronary angiography is considered to be the most reliable method for the diagnosis of cardiovascular disease. However, angiography is an invasive procedure that carries a risk of complications; hence, it would be preferable for an appropriate method to be applied to determine the necessity for angiography. The objective of this study was to compare support vector machine, naïve Bayes and logistic regressions to determine the diagnostic factors that can predict the need for coronary angiography. These models are machine learning algorithms. Machine learning is considered to be a branch of artificial intelligence. Its aims are to design and develop algorithms that allow computers to improve their performance on data analysis and decision making. The process involves the analysis of past experiences to find practical and helpful regularities and patterns, which may also be overlooked by a human. (2) Materials and Methods: This cross-sectional study was performed on 1187 candidates for angiography referred to Ghaem Hospital, Mashhad, Iran from 2011 to 2012. A logistic regression, naive Bayes and support vector machine were applied to determine whether they could predict the results of angiography. Afterwards, the sensitivity, specificity, positive and negative predictive values, AUC (area under the curve) and accuracy of all three models were computed in order to compare them. All analyses were performed using R 3.4.3 software (R Core Team; Auckland, New Zealand) with the help of other software packages including receiver operating characteristic (ROC), caret, e1071 and rminer. (3) Results: The area under the curve for logistic regression, naïve Bayes and support vector machine were similar—0.76, 0.74 and 0.75, respectively. Thus, in terms of the model parsimony and simplicity of application, the naïve Bayes model with three variables had the best performance in comparison with the logistic regression model with seven variables and support vector machine with six variables. (4) Conclusions: Gender, age and fasting blood glucose (FBG) were found to be the most important factors to predict the result of coronary angiography. The naïve Bayes model performed well using these three variables alone, and they are considered important variables for the other two models as well. According to an acceptable prediction of the models, they can be used as pragmatic, cost-effective and valuable methods that support physicians in decision making.

Background: Systemic inflammation is emerging as an important factor in the etiology of psychiatric disorders such as depression and anxiety. Therefore, the inflammatory potential of the diet may also be an etiological factor for these conditions, and this may be estimated by calculating the dietary inflammatory index (DII®) score. We aimed to investigate the association between DII score and incidence of depression and anxiety among a representative sample in northeastern Iran. Methods: This cross-sectional study undertook in a sub-sample of 7083 adults aged 35 to 65 years recruited as part of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study population, and after excluding subjects with incomplete data. All participants completed the Beck Anxiety Inventory (BAI), the Beck Depression Inventory II (BDI-II), and a validated 65-item food frequency questionnaire (FFQ). Logistic regression was used to evaluate the association between DII score and depression/anxiety score. Results: Of the study participants, 37.1% (n = 2631) were found to have mild to severe depression, and 50.5% (n = 3580) were affected by mild to severe anxiety. After adjusting for confounding factors, in women, the third (OR: 1.41, 95% CI: 1.06–1.88, p-values< 0.05) and fourth quartiles (OR: 1.37, 95% CI: 1.03–1.83, p-values< 0.05) of DII score were associated with increased risk of a high depression score compared to the first quartile of DII score. Conclusion: There was a significant association between DII score and severe depression among women but not men in this Iranian population. In order to confirm the association between DII food score, depression, and anxiety, further research is required in different populations, and perhaps an intervention study.