Early diagnosis of candidemia with explainable machine learning on automatically extracted laboratory and microbiological data: results of the AUTO-CAND project.

BACKGROUND: Candidemia is associated with a heavy burden of morbidity and mortality in hospitalized patients. The availability of blood culture results could require up to 48-72 h after blood draw; thus, early treatment decisions are made in the absence of a definite diagnosis. METHODS: In this retrospective study, we assessed the performance of different supervised machine learning algorithms for the early differential diagnosis of candidemia and bacteremia in adult patients on a large dataset automatically extracted within the AUTO-CAND project. RESULTS: Overall, 12,483 episodes of candidemia (1275; 10%) or bacteremia (11,208; 90%) were included in the analysis. A random forest classifier achieved the best diagnostic performance for candidemia, with sensitivity 0.98 and specificity 0.65 on the training set (true skill statistic [TSS] = 0.63) and sensitivity 0.74 and specificity 0.57 on the test set (TSS = 0.31). Then, the random classifier was trained in the subgroup of patients with available serum ß-D-glucan (BDG) and procalcitonin (PCT) values by exploiting the feature ranking learned in the entire dataset. Although no statistically significant differences were observed from the performance measures obtained by employing BDG and PCT alone, the performance measures of the classifier that included the features selected in the entire dataset, plus BDG and PCT, were the highest in most cases. CONCLUSIONS: Random forest classifiers trained on large datasets of automatically extracted data have the potential to improve current diagnostic algorithms for candidemia. However, further development through implementation of automatically extracted clinical features may be necessary to achieve crucial improvements.

Investigating the inflammatory status in the patients candidate for second angiography after coronary stent implantation.

BACKGROUND: Inflammation has been shown to be an important feature of atherosclerosis. We aimed to assess a profile of inflammatory cytokines and growth factors in patients with established coronary artery disease (CAD), 12 months after stent implantation. METHODS: A total of 193 patients with CAD, who were candidates for angiography, 12 months after stent implantation (cases), were compared with 107 patients with CAD, who were candidates for their first angiography (controls). Fasting blood glucose (FBG), triglycerides (TGs), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and high-sensitive C-reactive protein (hs-CRP) were measured using routine methods. The serum concentrations of IL-1α, IL-1ß, IL-2, IL-4, IL-6, IL-8, IL-10, TNF-α, IFN-γ, MCP-1, EGF and VEGF were determined using competitive chemiluminescence immunoassays. RESULTS: Serum levels of FBG (p = .002), TG (p = .029) and hs-CRP (p = .005) were significantly lower in cases than controls. The cytokines and growth factor profiles in cases were significantly different from controls. After multivariate analysis, serum levels of IL-2 (p < .001), IL-4 (p = .028) were significantly lower in cases compared with the controls while serum levels of IL-8, TNF-α, MCP-1, EGF and VEGF were significantly higher in the cases (p < .001). CONCLUSIONS: In patients with CAD and higher consumption of drug used (statins, aspirin and glucose lowering agents) to mitigate the risk of a secondary event, the level of hs-CRP one year after stent implantation decreased despite of significant higher serum levels of pro- and anti-inflammatory cytokines and growth factors.

Potential value and impact of data mining and machine learning in clinical diagnostics.

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.

Dietary Inflammatory Index is associated with Healthy Eating Index, Alternative Healthy Eating Index, and dietary patterns among Iranian adults.

BACKGROUND: Recent investigations have evaluated the effect of the inflammatory potential of diet in several populations by calculating the Dietary Inflammatory Index (DII) score. We aimed to evaluate the association of the DII with the Healthy Eating Index (HEI), the Alternative Healthy Eating Index (AHEI), and dietary pattern (DP) among healthy Iranian adults. METHODS: A cross-sectional study was conducted among 4365 middle-aged adults. Major DPs and DII score were identified using a validated semi-quantitative food frequency questionnaire (FFQ). Poisson regression was used to evaluate the association of DPs, HEI, and AHEI across tertiles of DII. RESULTS: After adjustment for confounding variables, a low HEI (HEI < 55) and AHEI (AHEI < 56.5) were more prevalent among the participants in the highest tertile of DII compared to the first tertile (PR: 1.13, P-value <.05; PR: 1.10, P-value <.05; respectively). Adherence to a balanced healthy dietary pattern was significantly lower in subjects with a diet that was more pro-inflammatory compared to those with anti-inflammatory diet (PR: 0.85, P-value P < .01). No significant association was found between the DII and a western DP. High levels of HDL and hip and waist circumference were observed in the highest tertile of DII, and high levels of dietary intake of protein and fiber, minerals, fasting blood glucose, and monounsaturated fat were reported in the lowest tertile of DII. CONCLUSION: The highest tertile of the DII (a pro-inflammatory diet) was associated with a lower HEI, AHEI, and lower adherence to balanced DP in a representative sample of adults in Iran.

A comparison of dietary intake between personnel of a gas processing company and a sample population of public employees from Mashhad.

INTRODUCTION: An imbalanced dietary pattern may result in a number of non-communicable disorders that include: obesity, diabetes, metabolic syndrome, cardiovascular diseases, and subsequently impaired quality of life (QOL). Micro- and macro-nutrients play some important roles in maintaining human health. The objective of this study was to compare dietary intake of employees of a gas processing company, who were provided with meals by their employee, with staff from other public employers from Mashhad, in northeastern Iran. METHODS: Twenty-four-hour food recall and a food frequency questionnaire (FFQ) were used to evaluate dietary intakes of 654 male employees of Shahid Hasheminejad Gas Processing (SHGP) Company (case group), and 681 randomly selected male public employees from Mashhad as control subjects in this observational, analytic, longitudinal, prospective cohort study. The analysis of the dietary intakes was performed using Dietplan6 software. RESULTS: Dietary macro and micro-nutrients were evaluated: including total energy, fat, MUFA, PUFA, trans fatty acids, cholesterol, starch, fiber, protein, total nitrogen, zinc, phosphor, potassium, calcium, manganese, selenium, iodine, sodium, copper, retinol, carotene, thiamin, riboflavin, niacin, pantothen, vitamin B6, biotin, folate, vitamin B12, vitamin C and vitamin D. The intake of macro-nutrients, including: energy, fat, monounsaturated fatty acids, polyunsaturated fatty acids, carbohydrates, starch, and protein; minerals including zinc, iron, selenium; and vitamins including niacin and vitamin D were higher in the SHGP group, compared to the control group (P < 0.05). The dietary intake of the SHGP group contained lower amounts of several mineral and vitamins, which included: iodine, sodium, carotene, thiamin, pantothenic acid, vitamin B6, biotin, folic acid, and vitamin C, in comparison with the control group (P < 0.05). CONCLUSION: In conclusion, we suggest it will be important to evaluate the composition of meals provided by companies and organizations, especially those that provide their employees with the majority of their food intake.

Association of a genetic variant in the AKT gene locus and cardiovascular risk factors.

Cardiovascular disease (CVDs) is the leading cause of morbidity and death worldwide. Most genetic variants could be identified by several genome-wide-association-studies (GWAS), including within genes encoding proteins involved in the AKT/PI3K pathways that are related with an increased risk of metabolic syndrome and CVDs. Therefore, due to the importance of genetic variants in the prognosis of diseases, we examined the genetic polymorphism of AKT-rs1130233 located on chromosome 14 with cardiovascular risk factors. In this cross-sectional study, 721 subjects recruited from the Mashhad-Stroke and Heart-Atherosclerotic-Disorders (MASHAD) cohort study. The participants including 257 subjects with metabolic syndrome, 144 subjects with cardiovascular disease and 320 subjects as a control group. Anthropometric, biochemical and demographic information measures were prepared. Dietary assessment was managed by 24h dietary recall. DNA extraction and genotyping were carried out by using the TaqMan real-time-PCR based method. The association of AKT rs1130233 locus with dietary intakes, metabolic syndrome and cardiovascular risk factors were assessed. Data were analyzed by using SPSS 21 software. Frequencies of genotypes AA, AG and GG of the AKT rs1130233 polymorphism were 12.6%, 44.5% and 42.9% in subjects with metabolic syndrome and 9.7%, 39.6% and 50.7% in subjects with cardiovascular disease, respectively. The frequency of allele A and G in cardiovascular disease and metabolic syndrome population were 29.5%, 70.5% and 34.8%, 65.2%, respectively. We have found no significant association between the AKT rs1130233 polymorphism with cardiovascular risk factors and metabolic syndrome. The results of dietary intake showed that the levels of phosphorus intake (p=0.008), calcium intake (p=0.007) and iodine intake (p=0.04) were different in subjects with and without metabolic syndrome. And also, energy intake was significantly different in subjects with cardiovascular disease (p=0.01) compared to the control group. Our findings suggest that AKT rs1130233 was not associated with the risk of metabolic syndrome and cardiovascular disease in the Iranian population. More studies are needed to validate our results. We did functional analysis, due to certify our investigation about value of this genetic biomarker for CVD risk.

Association of body mass index with serum calcium and phosphate levels.

OBJECTIVE: It has been shown that several environmental and physiological factors can affect on the serum levels of calcium and phosphate. The objective of the present study was explored the relationship between serum calcium and phosphate levels with anthropometric and hematological markers. METHODS: 908 subjects were recruited from the Mashhad stroke and heart atherosclerosis disorder (MASHHAD) program. Anthropometric parameters, liver/kidney function tests (e.g., Urea nitrogen, creatinine, urea and uric acid, creatinine, AST, ALT) were determined in all participants. Serum concentrations of calcium and phosphate were measured using Autoanalyzer BT3000P (Pars Azmoon kit, Tehran, Iran). SPSS software was used for statistical analyses. RESULTS: We observed that obese subjects had a lower level of serum calcium (p˂0.05). Moreover, a relationship was detected between serum phosphate level and different menopausal status (p˂0.05). Serum calcium and phosphate did not change by increasing age in the population. Additionally, there was a correlation between lymphocyte count with serum phosphate level (p˂0.05). No statistically different were detected for the levels of calcium/phosphate with respect to smoking status, physical activity, lipid profile, liver and renal function markers. CONCLUSION: We found an association between serum calcium and BMI as well as with serum phosphate and menopausal status.

Rare P376L variant in the SR-BI gene associates with HDL dysfunction and risk of cardiovascular disease.

BACKGROUND: Scavenger receptor class B type 1 (SR-BI) encoded by SCARB1 gene serves as a multifunctional HDL receptor, facilitating the uptake of cholesteryl esters from HDL to the liver. Recent studies have identified the association between the P376L missense mutation of the SCARB1 gene with increased serum HDL-Cholesterol level. However, the contribution of this variant to the development of cardiovascular disease (CVD) remains unclear. OBJECTIVE: We have investigated the association between the P376L polymorphism with the properties of HDL and CVD outcomes in a population sample recruited as part of the Mashhad-Stroke and Heart-Atherosclerotic-Disorders (MASHAD) cohort. METHODS: Six hundred and fifteen individuals who had a median follow-up period of 7 years were recruited as part of the MASHAD cohort. Anthropometric, biochemical parameters and HDL lipid peroxidation (HDLox) were assessed. Genotyping was performed using TaqMan-real-time-PCR based method. The association of P376L-rs74830766 with cardiovascular-risk-factors and CVD events were evaluated. RESULTS: Carriers of the P376L variant were significantly more likely than non-carriers to develop CVD using multivariate analyses adjusted for traditional CVD risk factors defined as: age, sex, BMI, presence of diabetes, or hypertension, positive smoking habit, and total cholesterol (OR: 3.75, 95%CI: 1.76-7.98, p = 0.001). In an adjusted model, there was a two fold increase in cardiovascular endpoints among individuals who were heterozygous for the P376L variant (hazard ratio, 2.08; 95% CI, 1.12-to 3.84, p = 0.02). Although there was no association between the presence of the P376L variant and HDL-C level, serum HDLox, measured as dysfunctional HDL, was 13% higher among carriers of the P376L variant than non-carriers. CONCLUSION: We have found that carriers of the P376L variant possessed higher HDLox and were at increased risk of CVD in a representative population-based cohort, as compared to non-carriers.

Incidence and risk factors of common viral infections among renal transplant recipients during the first year post-transplant in North-eastern Iran.

Despite major therapeutic advances, management of viral infections in renal transplant recipients is still a major challenge. Hence, it is urgently needed to establish protocols for appropriate control and the prevention of viral infection. We evaluated demographic/clinical characteristics, frequency, and risk factors of symptomatic viral infections in renal transplant recipients during the 1st year posttransplant, in northeastern Iran. We retrospectively reviewed medical files of 247 patients including 146 males and 101 females who had undergone renal transplantation at Montaserie organ transplantation hospital of Mashhad during 2012-2014. These patients were followed up for one year after transplantation for the detection of any symptomatic viral infection. Demographic and clinical characteristics of recipients were collected and analyzed using the Statistical Package for Social Sciences version 18 software; P < 0.05 was considered as statistically significant. Data were presented using descriptive statistics. Furthermore, logistic regression analysis was used to determine risk factors for infection. The mean age of the patients was 34.94 ± 13.89 years. During the 1st year posttransplant, 68 episodes of viral infections were detected in 64 patients (25.9%). Cytomegalovirus (CMV, 21.9%), Varicella Zoster virus (2.8%), herpes simplex virus (2.0%), and human polyomavirus BK virus (0.8%) were the most common symptomatic viral infections found. Age of the patients was the only significant risk factor for viral infections (odds ratio = 1.066; 95% confidence interval: 1.002-1.134; P = 0.042). The incidence of symptomatic viral infections, particularly CMV disease, is high in our center. Hence, it is recommended to use appropriate prophylaxis and monitor the patients during the first six months post-transplant.

Association Between Trace Element Status and Depression in HTLV-1-Infected Patients: a Retrospective Cohort Study.

Depression and Anxiety are two important public health problems that are known to be associated with viral infections. The association between the intake of nutrients such as zinc and copper with symptoms of depression has been studied previously. The aim of the current study was to investigate the association between depression with human T cell lymphotropic virus type 1 (HTLV-1) infection and serum content of zinc and copper in a large Iranian population cohort. The study population consisted of 279 HTLV-1-positive patients who were identified after recruitment as part of a large cohort study: the Mashhad Stroke and Heart Association Disorder (MASHAD) study. They were divided into two groups of diagnosed with or without depression based on their symptoms. Serum zinc and copper levels of all subjects were measured using the flame atomic absorption spectrometry. The population sample comprised of 279 individuals infected with HTLV-1 of whom 192 (68.8%) were women. The mean serum zinc in the group with and without depression was 78.69 ± 13.79 µg/dl and 86.87 ± 19.44 µg/dl, respectively (p < 0.001). Also, the serum copper level was higher in the depressive group (116.75 ± 39.56) than in the non-depressive group (104.76 ± 30.77) (p 0.004). The association between serum zinc and copper with depression in HTLV-1-infected patients which was shown in this study could be considered in the treatment strategies in these patients.

Oxidative stress and inflammation, two features associated with a high percentage body fat, and that may lead to diabetes mellitus and metabolic syndrome.

Obesity is an important feature of the metabolic syndrome and is associated with an increased risk of type 2 diabetes mellitus, cardiovascular disease, and some cancers. The aim of this study was to determine the relationship between body fat percentage and an imbalance of the prooxidant/antioxidant balance (PAB), serum superoxide dismutase (SOD1) and inflammation (serum hs-CRP) and increase risk of metabolic syndrome and diabetes mellitus. In this study, 9154 individuals were recruited as part of the Mashhad Stroke and Heart Association Disorder (MASHAD) study. Subjects were categorized into two groups according to body fat percentage as defined >25% in male and > 30% in female, according to gender. Biochemical factors, including serum PAB, SOD1, and hs-CRP were measured in all subjects. SPSS version 18 was used for statistical analyses for all. GraphPad Prism 6 for figures was used. Of total number of subjects (9154), 6748 (73.7%) were found to have a high body fat (BF) percentage. Serum hs-CRP and PAB were significantly higher in individuals with a high BF percentage (P < 0.05) but SOD1 was not significantly different between the two groups (P > 0.05). BF percentage, serum PAB and serum hs-CRP were significantly higher in individuals with metabolic syndrome and diabetes versus those without metabolic syndrome and diabetes mellitus (P < 0.05), however serum SOD1 was significantly lower in individuals with metabolic syndrome (P < 0.005). Oxidative stress and inflammation are two factors that may link the presence of high BF percentage with the development of metabolic syndrome, diabetes, and cardiovascular disease. © 2018 BioFactors, 45(1):35-42, 2019.

A Comparison between Decision Tree and Random Forest in Determining the Risk Factors Associated with Type 2 Diabetes.

BACKGROUND: We aimed to identify the associated risk factors of type 2 diabetes mellitus (T2DM) using data mining approach, decision tree and random forest techniques using the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) Study program. STUDY DESIGN: A cross-sectional study. METHODS: The MASHAD study started in 2010 and will continue until 2020. Two data mining tools, namely decision trees, and random forests, are used for predicting T2DM when some other characteristics are observed on 9528 subjects recruited from MASHAD database. This paper makes a comparison between these two models in terms of accuracy, sensitivity, specificity and the area under ROC curve. RESULTS: The prevalence rate of T2DM was 14% among these subjects. The decision tree model has 64.9% accuracy, 64.5% sensitivity, 66.8% specificity, and area under the ROC curve measuring 68.6%, while the random forest model has 71.1% accuracy, 71.3% sensitivity, 69.9% specificity, and area under the ROC curve measuring 77.3% respectively. CONCLUSIONS: The random forest model, when used with demographic, clinical, and anthropometric and biochemical measurements, can provide a simple tool to identify associated risk factors for type 2 diabetes. Such identification can substantially use for managing the health policy to reduce the number of subjects with T2DM .

Hookah smoking is strongly associated with diabetes mellitus, metabolic syndrome and obesity: a population-based study.

OBJECTIVES: The adverse effects of cigarette smoking have been widely studied before, whilst the effects of hookah smoking has received less attention, although it is a common habit in the Middle East. Here we have investigated the effects of cigarette and hookah smoking on biochemical characteristics in a representative population sample derived from the Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study, from Northeastern Iran. STUDY DESIGN: A total of 9840 subjects from the MASHAD population study were allocated to five groups; non-smokers (6742), ex-smokers (976), cigarette smokers (864), hookah smokers (1067), concomitant cigarette and hookah smokers (41). METHODS: Baseline characteristics were recorded in a questionnaire. Biochemical characteristics were measured by routine methods. Data were analyzed using SPSS software and p < 0.05 was considered significant. RESULTS: After adjustment for age and sex; the presence of CVD, obesity, metabolic syndrome, DM and dyslipidemia were significantly (p < 0.001) related to smoking status. After multivariate analysis, HDL (p < 0.001), WBC (p < 0.001), MCV (p < 0.05), PLT (p < 0.01) and RDW (p < 0.001), and the presence of CVD (p < 0.01), obesity (p < 0.001), metabolic syndrome (p < 0.05) and DM (p < 0.01) remained significant between cigarette smokers and non-smokers. Between hookah smokers and non-smokers; uric acid (p < 0.001), PLT (p < 0.05) and RDW (p < 0.05), and the presence of obesity (p < 0.01), metabolic syndrome (p < 0.001), diabetes (p < 0.01) and dyslipidemia (p < 0.01) remained significant after logistic regression. CONCLUSION: There was a positive association between hookah smoking and metabolic syndrome, diabetes, obesity and dyslipidemia which was not established in cigarette smoking.

Association of rs6921438 A

BACKGROUND: Genome-wide association studies (GWAS) have identified common variants at the Vascular-Endothelial-Growth-Factor (VEGF) gene locus, which appear to be associated with plasma VEGF concentrations. These factors are among the major risk factors for cardiovascular disease and metabolic syndrome (MetS). We have investigated the association between serum VEGF concentrations and a VEGF genetic variant (rs6921438 A

Interaction between a variant of CDKN2A/B-gene with lifestyle factors in determining dyslipidemia and estimated cardiovascular risk: A step toward personalized nutrition.

BACKGROUND & AIMS: Several genome-wide-association-studies have identified genetic variants in a region on chromosome 9p21 that are associated with an increased risk of Cardiovascular disease (CVD) and diabetes. Here we have explored the interaction of a genetic variant of the CDKN2A/B-rs10811661 gene locus with cardiovascular risk factors and environmental-exposures (e.g., diet and physical activity) in 1165 individuals recruited from the Mashhad-Stroke and Heart-Atherosclerotic-Disorders cohort. METHODS: Genotyping was carried out using TaqMan-real-time-PCR based method. The association of CDKN2A/B-rs10811661 locus and its interaction with dietary intake in association with the main determinants of dyslipidemia, and cardiovascular-risk-factors were assessed in 2 cohorts. RESULTS: Our data showed that obese subjects with a TT genotype had a higher level of TG, TG/HDL ratio and Hs-CRP, compared to the subjects with the wild type genotype, or individuals with a normal BMI. Moreover, the presence of a TT genotype was associated with increased risk of hypercholesterolemia, insulin resistance and CVD. These effects were more pronounced in the sub-group with low physical activity and a high dietary energy intake (e.g., the interaction between TT genotype and total energy intake on serum cholesterol was positive (RERI: 0.2, 95%CI (-0.96-1.3), AP: 0.1, 95%CI (-0.5-0.7) and SI: 1.2, 95%CI (0.3-5.1))). CONCLUSIONS: We have found a significant association between the CDKN2A-rs10811661 polymorphism with cardiovascular risk factors and dyslipidemia in a non-diabetic population. It is possible that a low energy diet and high physical activity could ameliorate the unfavorable effects of T allele of CDKN2A/B locus. Functional analysis is warranted to investigate the value of this genetic biomarker of CVD risk in obese people.

Depression and anxiety both associate with serum level of hs-CRP: A gender-stratified analysis in a population-based study.

BACKGROUND: Depression and anxiety are two important mood disorders that are frequently associated with chronic diseases such as cardiovascular diseases (CVDs). Hyper-inflammation is related to both CVDs and psychological conditions such as depression and anxiety. Therefore, inflammation may partially explain the relationship between depression and cardiovascular disease. OBJECTIVE: The objective of this study was to perform a gender-stratified examination of the association between symptoms of depression/anxiety disorders and serum hs-CRP and inflammation linked conditions in a large Iranian population. METHODS: Symptoms of depression and anxiety disorders and serum hs-CRP levels were measured in 9759 participants (40% males and 60% females) aged 35-65 years, enrolled in a population-based cohort (MASHAD) study in north-eastern Iran. Symptoms of depression and anxiety were evaluated with Beck Depression and Anxiety Inventories. According to the scores of depression and anxiety, individuals were categorized into four groups of no or minimal, low, moderate and severe categories. RESULTS: The median serum hs-CRP concentration increased with increasing severity of depression and anxiety disorders. Male participants with severe depression had significantly higher levels of hs-CRP (p<0.001); however, this relationship was less marked among women (p=0.04). Subjects with severe anxiety also had significantly higher levels of hs-CRP (p<0.001). Moreover, women with severe depression and anxiety had higher BMI. There was also a positive association between current smoking habit and depression/anxiety disorders. CONCLUSION: Depression and anxiety disorders are associated with elevated levels of hs-CRP, particularly among men. Also, there is a significant positive association between depression/anxiety disorders and inflammation linked conditions such as smoking and obesity; however, in the case of obesity this association is only present in women.

The application of a decision tree to establish the parameters associated with hypertension.

INTRODUCTION: Hypertension is an important risk factor for cardiovascular disease (CVD). The goal of this study was to establish the factors associated with hypertension by using a decision-tree algorithm as a supervised classification method of data mining. METHODS: Data from a cross-sectional study were used in this study. A total of 9078 subjects who met the inclusion criteria were recruited. 70% of these subjects (6358 cases) were randomly allocated to the training dataset for the constructing of the decision-tree. The remaining 30% (2720 cases) were used as the testing dataset to evaluate the performance of decision-tree. Two models were evaluated in this study. In model I, age, gender, body mass index, marital status, level of education, occupation status, depression and anxiety status, physical activity level, smoking status, LDL, TG, TC, FBG, uric acid and hs-CRP were considered as input variables and in model II, age, gender, WBC, RBC, HGB, HCT MCV, MCH, PLT, RDW and PDW were considered as input variables. The validation of the model was assessed by constructing a receiver operating characteristic (ROC) curve. RESULTS: The prevalence rates of hypertension were 32% in our population. For the decision-tree model I, the accuracy, sensitivity, specificity and area under the ROC curve (AUC) value for identifying the related risk factors of hypertension were 73%, 63%, 77% and 0.72, respectively. The corresponding values for model II were 70%, 61%, 74% and 0.68, respectively. CONCLUSION: We have developed a decision tree model to identify the risk factors associated with hypertension that maybe used to develop programs for hypertension management.

Impact of Cigarette Smoking on Serum Pro- and Anti-Inflammatory Cytokines and Growth Factors.

Inflammation plays a key role in the initiation, progression, and clinical manifestation of atherosclerosis. Cigarette smoking is a risk factor for atherosclerosis and cardiovascular disease. The aim of the current study was to investigate the serum concentrations of 12 cytokines and growth factors (EGF, INF-γ, IL-1α/-1ß/-2/-4/-6/-8/-10, MCP-1, TNF-α, and VEGF) in an Iranian population, including 192 smokers, comparing these values with concentrations in nonsmokers. One hundred and ninety-two cases were enrolled from the Mashhad University of Medical Sciences. Of these cases, 82 were cigarette smokers and 110 were nonsmokers. Sex and age were matched for the two groups. The serum concentration of 12 cytokines and growth factors were determined using EV-3513-cytokine-biochip arrays, by competitive chemiluminescence immunoassays. The level of serum MCP-1 was significantly ( p < .001) lower in the female group of cigarette smokers (mean = 88.1 dL/ng), compared with nonsmokers (mean = 155.6 dL/ng). There were no significant differences for the other cytokines and growth factors between the groups. Our finding demonstrate the association of MCP-1 with cigarette smoking, supporting further studies in larger population on evaluating the role of cigarette smoking on pro-/anti-inflammatory cytokines.

Association of a Vascular Endothelial Growth Factor genetic variant with Serum VEGF level in subjects with Metabolic Syndrome.

BACKGROUND: The metabolic syndrome (MetS) is a clustering of metabolic disorders that is associated with an increased risk of developing cardiovascular-disease, diabetes, and related diseases. Against this background, Vascular Endothelial Growth Factor (VEGF) plays an essential role in angiogenesis, vascular permeability, and hematopoiesis and its increased level is reported to be associated with increasing the risk of developing cardiovascular-disease, stroke and diabetes. Therefore the aim of present study was to explore the association of serum VEGF level and its associated genetic-polymorphism, rs10738760 (A>G) at 9p24.2, in 850 subjects with/without MetS. METHODS: MetS was defined according to the International-Diabetes-Federation criteria. Genotyping was carried out using Polymerase chain reaction-amplification refractory mutation system. Anthropometric/biochemical parameters, including FBG, Triglyceride, HDL, TC, etc., were determined followed by univariate and multivariate analyses. RESULTS: MetS patients had significantly higher levels of BMI, waist-circumference, cholesterol, triglyceride, Hs-CRP and SBP/DBP, while the HDL-C levels was lower in patients group, compared to control group (P<0.05). Moreover, our analysis showed that MetS patients with GA or AA genotypes had a significantly (P=0.03) higher serum level of VEGF. CONCLUSIONS: we demonstrate an association between a VEGF genetic variant with MetS, suggesting its role as a risk stratification factor for MetS.

The prevalence of metabolic syndrome increases with serum high sensitivity C-reactive protein concentration in individuals without a history of cardiovascular disease: a report from a large Persian cohort.

Background Metabolic syndrome is defined by a clustering of cardiovascular risk factors and is associated with a heightened inflammatory state. A raised serum high-sensitivity C-reactive protein, a marker of inflammation, is also known to associate with cardiovascular risk. We have investigated the relationship between the presence of metabolic syndrome and serum high-sensitivity C-reactive protein concentration in a large representative Persian population cohort without a history of cardiovascular disease. Methods The MASHAD study population cohort comprised 9778 subjects, who were recruited from the city of Mashhad, Iran, between 2007 and 2008. Several cardiovascular risk factors were measured in this population without cardiovascular disease. Individuals were categorized into quartiles of serum high-sensitivity C-reactive protein concentration: first quartile - 0.72 (0.59-0.85) (median [range]) mg/L, second quartile - 1.30 (1.14-1.4) mg/L, third quartile - 2.29 (1.92-2.81) mg/L and fourth quartile - 6.63 (4.61-11.95) mg/L, respectively. The prevalence of metabolic syndrome in each quartile was determined using either International Diabetes Federation or Adult Treatment Panel III criteria. Results The prevalence of metabolic syndrome was highest in the fourth quartile for serum high-sensitivity C-reactive protein (1220 subjects [50.0%]), and significantly higher than that in the first quartile (reference group) (634 subjects [25.9%]) ( P < 0.001). A positive smoking habit (OR, 1.47 [1.26-1.70], P < 0.001) and the presence of either metabolic syndrome-International Diabetes Federation (OR, 1.35 [1.18-1.55], P < 0.001) or metabolic syndrome-ATPIII (OR, 1.40 [1.18-1.50], P < 0.001) were strong predictors of a fourth quartile for serum high-sensitivity C-reactive protein concentration. Conclusions There was a significant association between high concentrations of serum high-sensitivity C-reactive protein and the presence of metabolic syndrome among individuals without a history of cardiovascular disease in our Persian cohort.