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The IHHP design was previously reported [ 20 ]. Isfahan is a city with a population of , the second most populous metropolitan area in Iran after Tehran. In , the population of Arak and Najafabad, was and , respectively. These areas were selected because of the socioeconomic, demographic picture and health profile similarities to the other large cities in Iran. The overall prevalence of CVD was estimated as Participants were recruited from and followed-up for at least ten years.

All subjects signed the informed consent form for the experimental procedure. For controlling selection bias, the samples were randomly selected from a healthy population. The participants were selected by multistage random cluster sampling.

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The study population was first stratified by their living area urban vs rural. We then randomly selected census blocks from each county and divided them into clusters. Within each cluster, households were randomly selected for enumeration. From each household, we randomly selected one eligible individual. The exclusion criteria were: having a stroke, ischemic heart attack, coronary heart disease, and heart failure. Meanwhile, the random sampling was used without any restriction for the elderly, as to overcome underestimation or volunteer bias [ 16 ].

These subjects were followed up until CVDs occurred. All participants had no clinical history of chronic diseases and were interviewed by trained personnel registered Nurses, Dietitians and General Practitioners , using standard questionnaires assessing lifestyle habits, various sociodemographic predictors, and clinical or biological characteristics.

They were followed up by repeating these measurements every five years, and biennially by phone calls looking for the occurrence of any of primary or secondary events. However, as it happens in cohort studies, there were lost to follow-up participants in each of these follow-up phases Fig 1. An important reason for the loss to follow-up was a change in phone numbers, based on the government new policy in the whole country.

It was part of a network capacity expansion policy, without any particular distribution. It was thus completely random, not biasing the follow-up [ 14 ]. Participants were interviewed by trained personnel to complete standardized questionnaires, including questions on cardiovascular risk factors, and also clinical examinations, electrocardiography and laboratory evaluation [ 19 ]. After sitting for five minutes, trained physicians measured the blood pressure by standard mercury Sphygmomanometers, using the right arm of resting participants.

Blood pressure measurements were repeated after 15 minutes, and the average of two measures was reported. While shoes were removed, height was measured to the closest centimeter by a trained technician. A calibrated scale was used to measure weight in light clothing. Weight kg was divided by height in squared meter m 2 , and represented as body mass index BMI. The smallest circumference at or below the costal margin was taken as waist circumference WC. The hip circumference was taken at the level of the greater trochanter. Fasting 12 h blood samples FBS were taken from the participants.


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Moreover, it was under the external quality control of St. Rafael University, Leuven and Belgium [ 18 ]. A 2-hour post-load plasma glucose 2 hpp test was performed, using the samples from entire participants, without including diabetic subjects. A cell counter AL was used to measure hematological parameters. Low-density and very low-density lipoproteins were precipitated, using dextran sulfate-magnesium, and then serum high-density lipoprotein-cholesterol HDL-C was measured [ 21 ]. Since , each participant has been followed up for every two years.

In , full structured interview, clinical and blood-sample testing were repeated as the baseline examination. Telephone interviews were implemented in , , , and and if they were deceased, attempts were made to contact all living participants or their first-degree relatives. When the phone interviews were unsuccessful, the participants were visited at their home address for the follow-up. Structured questionnaire was asked based on being alive, hospitalized and experiencing specific neurological symptoms. For deaths out of hospital, death registries from the provincial mortality database were used to obtain death certificates.

Verbal autopsies were performed before death by a trained Nurse based on predefined questionnaire, including medical history, signs and symptoms. Additional secondary interviews for hospitalized cases were performed where information was incomplete or inconsistent. When there were not any inconsistent dates or diagnosis, or the records were not obtainable, we used the original medical records of MI and stroke registry database of the Surveillance Department of ICRC, to verify the reported events.

Cardiovascular diseases (CVDs)

If we were not able to find hospitalization data in the database, trained Nurses investigated the medical records [ 14 ]. A professional panel, consisting of Cardiologists and Neurologists have reviewed all the documents and made a decision about the diagnosis of each CVD event. Such events were defined as either acute coronary syndromes, consisting of fatal or non-fatal myocardial infarction and unstable angina, sudden cardiac death, and fatal or non-fatal stroke.

The detailed description of the above-mentioned end points, risk factor measurements, and data collection have been provided in the previous reports [ 14 , 15 , 19 ]. The development of the risk function in ICS was assessed, according to the following three steps: I Refitted Framingham function: multiple Cox proportional-hazards regression models were derived, using the same variables of Framingham equation and ICS database. The most important risk factors were then hierarchically included in the model based on the higher hazard ratio HR. At each step, significant risk factors remained in the model.

The final model, referred to PARS risk function, had the best discrimination and calibration The protocol: dx. After testing the assumption of proportionality, cox proportional hazards model was used. The Schoenfeld residuals were used for testing the proportional hazards PH assumption for risk factors.

Further evaluation was applied to check the PH assumption, regarding the risk factors plotted graphically with the log-cumulative hazard plots as a function of survival time, comparing Kaplan-Meier survival estimates and Cox adjusted estimates plotted on the same graph. If the graphical approaches suggested that there is some violation of PH assumption, an extended cox model was run based on an appropriate function of survival time.

It was performed by defining the product term involving time-independent variable with some function of time g t or Heaviside function and testing the coefficient of the product term. In our analysis, there was no significant coefficient of the time-dependent variable based on product terms. Two internal validation resampling methods namely as fold cross-validation and bootstrapping were used to obtain unbiased estimates of predictive accuracy. The above two techniques are used to assess, if the developed risk scores could be generalized to an independent data set.

In this method, the data set is randomly divided into ten equal size groups. Among which, the model is tested on a single validation group, while the model is estimated, using the other nine groups training data. Such validation is then repeated ten times, in which each of the groups is used one time, for validation. Then the average of ten validations was calculated.

Moreover, internal bootstrapping was used to obtain unbiased estimates of predictive accuracy. Fifty thousand random samples were bootstrapped. Finally, overall bias in predicting CVD incidence was estimated as [ 13 , 25 ]: 2.

Introduction

We constructed year risk assessment charts of CVD incidence, using important risk factors. The smoking variable comprised of current smokers. Statistical modeling and analysis were performed, using SAS software, version 9. Matlab version 8. Our missing data were missing at random MAR. The baseline characteristics and the prevalence of CVD risk factors were compared, among lost to follow-up and loyal subjects, using the sensitivity analysis.

Participants without any event and loss to follow-up events were considered as censored. The main reasons for not participating were related to address and or phone number change, and to a less extent, not willing to take part in multiple follow-ups. There were 2. A number of The loss to follow-up rate was 6.

The prevalence of the CVD risk factors and also baseline characteristics were not significantly different between lost to follow-ups and loyal subjects [ 14 , 16 ]. The Baseline examination of participant risk factors is shown in Table 1. The Average age for men and women was The majority of women have a high WHR In comparison with women, the smoking rate was far greater in men The SBP level has almost similar frequency distribution in men and women.

Heart Disease Fact Sheet

Higher levels of TC were more prevalent in women, compared to men. The prevalence of diabetes mellitus was less in men, compared with women 9. IHD comprised of 39 20 women and 19 men fatal and 36 women and 77 men non-fatal MI, women and men UA, and 81 26 women and 55 men sudden cardiac deaths. Ischemic stroke composed of 30 16 women and 14 men fatal and cases 57 women and 54 men of non-fatal stroke.

A vast overlap in gene expression alterations has also been identified in obesity-driven insulin resistance IR and atherosclerosis, which are critical pathogenic mechanisms underlying DM and CVD, respectively [ 6 ]. Moreover, many of these alterations are related to the inflammatory response pathway [ 6 ].

The specific inflammatory processes involved in DM versus CVD may differ as well [ 12 , 13 , 14 , 15 ]. In DM, enlarged adipocytes and altered secretion of adipokines play a significant role in promoting inflammation in adipose tissue [ 16 ]. Other inflammation-related mechanisms, such as endothelial dysfunction, atherosclerosis and increased plaque vulnerability, play important roles in CVD [ 6 , 8 ]. A wide range of inflammatory markers have been proposed as risk factors for both DM and CVD [ 3 , 7 , 8 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ].

Among them, several markers have already been widely studied, such as total and differential leukocyte counts [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ] and C-reactive protein CRP [ 3 , 7 , 8 ].

Heart Disease Fact Sheet|Data & Statistics|DHDSP|CDC

However, correlations among markers have usually been weak or moderate [ 24 , 25 ], which indicates that they potentially reflect different aspects of inflammation. For clinical purposes, it is necessary to identify reliable and specific markers that can help to discriminate between elevated risks of DM and CVD to enable targeted therapies. Therefore, we conducted the present study to investigate the inflammatory profiles of DM and CVD and to distinguish their shared and specific markers.

A baseline examination was conducted on 11, males and 17, females between March and September and included peripheral venous blood samples, physical examination, and a self-administered questionnaire. Of the initial 28, participants, complete information on leukocyte counts and covariates was available for 27, participants Additional file 1. Based on this cohort, total and differential leukocyte counts, as well as neutrophil to lymphocyte ratio NLR , were studied in relation to DM and CVD cohort analysis 1.

Four hundred and 62 subjects with DM [i. Finally, we excluded those with missing information for each analysis of the specific inflammatory marker. Therefore, different cohort analyses cohort analyses 2—8 were conducted on slightly different populations when investigating different markers Additional file 1.