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: Improving the training of physicians about communication skills and patient health literacy (HL) is a major priority that remains an open question. We aimed to examine the effectiveness of communication skills training for physicians on the hypertension outcomes and the health literacy skills, self-efficacy and medication adherence in patients with uncontrolled blood pressure (BP). Methods: A randomized, controlled trial method was conducted on 240 hypertensive patients and 35 physicians presenting to healthcare clinics in the Mashhad, Iran, from 2013 to 2014. Using stratified blocking with block sizes of 4 and 6, eligible patients with uncontrolled blood pressure were randomly allocated to the intervention and control groups. Physicians in the intervention group received educational training over 3 sessions of Focus –Group Discussion and 2 workshops. The control group received the routine care. The primary outcome was a reduction in systolic and diastolic BP from baseline to 6 months. The secondary outcome was promoting HL skills in hypertensive patients. Data were analyzed using the regression model and bivariate tests. Results: After the physician communication training, there was a significant improvement in physicians-patient communication skills, hypertension outcomes, medication adherence, and self-efficacy among the patients being managed by the physicians receiving training, compared to the control group. Conclusion: The educational intervention leads to better BP control; it may have been sufficient training of physicians change to impact counseling, HL and self-efficacy and adherence. The quality of physician-patient communication is an important modifiable element of medical communication that may influences health outcomes in hypertensive Iranian patients. Trial registration: Iranian Registry of Clinical Trials (IRCT), IRCT20160710028863N24. Registered April 4, 2018 [retrospectively registered].

Background. Obesity is a disorder often accompanied by a heightened state of systemic inflammation and immunoactivation. The present randomized crossover trial aimed to investigate the efficacy of curcumin, a bioactive polyphenol with established anti-inflammatory and immunomodulatory effects, on the serum levels of a panel of cytokines and mediators in obese individuals.Methods. Thirty obese individuals were randomized to receive curcumin at a daily dose of 1 g or a matched placebo for 4 weeks. Following a 2-week wash-out period, each group was assigned to the alternate treatment regimen for another 4 weeks. Serum samples were collected at the start and end of each study period. Serum levels of IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, VEGF, IFNγ, EGF, MCP-1, and TNFαwere measured using a multiplex Biochip Array Technology based method.Results. Mean serum IL-1β(P=0.042), IL-4 (P=0.008), and VEGF (P=0.01) were found to be significantly reduced by curcumin therapy. In contrast, no significant difference was observed in the concentrations of IL-2, IL-6, IL-8, IL-10, IFNγ, EGF, and MCP-1.Conclusions. The findings of the present trial suggested that curcumin may exert immunomodulatory effects via altering the circulating concentrations of IL-1β, IL-4, and VEGF.

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.