Showing 7 results for Hadaegh
D Khalili, F Hadaegh, M Tohidi, A Ghasemi, F Sheikholeslami, F Azizi,
Volume 4, Issue 3 (20 2009)
Abstract
Background & Objectives: Triglyceride/HDL-cholesterol ratio (TG/HDL-C) has been shown as an indicator for metabolic syndrome (MetS). This study aimed to detect the role of this ratio to predict coronary heart disease (CHD) outcome in an Iranian men population with high prevalence of MetS.
Methods: 1824 men ≥ 40 years old, free of clinical cardiovascular disease at baseline, were included in the study from February 1999 to August 2001. Serum level of total cholesterol (TC), HDL-C, TG, and risk factors of CHD including age, systolic blood pressure, diastolic blood pressure, body mass index, diabetes, smoking and family history of cardiovascular diseases were measured at initial phase of study.
Results: During a median follow up of 6.5 years until March 2007, a total of 163 new CHD events occurred. According to a Cox proportional hazard modeling, after adjustment for TC and other risk factors, men in the top quartile of TG/HDL-C relative to first quartile had a significant hazard ratio (HR) of 1.85 (95% CI, 1.07-3.17). Combined HR for TC and TG/HDL-C (men in the top quartiles of both TC and TG/HDL-C relative to first quartiles) after adjustment for other risk factors was 6.13 (95% CI, 2.37-15.86).
Conclusions: The evaluation of both TG/HDL-C ratio and TC should be considered for CHD risk prediction in Iranian male population.
M Sedehi, Y Mehrabi, A Kazemnejad, V Joharimajd, F Hadaegh,
Volume 6, Issue 4 (16 2011)
Abstract
Background & Objective: Mixed outcomes arise when, in a multivariate model, response variables measured on different scales such as binary and continuous. Artificial neural networks (ANN) can be used for modeling in situations where classic models have restricted application when some of their assumptions are not met. In this paper, we propose a method based on ANNs for modeling mixed binary and continuous outcomes.
Methods: Univariate and bivariate models were evaluated based on two different sets of simulated data. The scaled conjugate gradient (SCG) algorithm was used for optimization. To end the algorithm and finding optimum number of iteration and learning coefficient, mean squared error (MSE) was computed. Predictive accuracy rate criterion was employed for selection of appropriate model. We also used our model in medical data for joint prediction of metabolic syndrome (binary) and HOMA-IR (continues) in Tehran Lipid and Glucose Study (TLGS). The codes were written in R 2.9.0 and MATLAB 7.6.
Results: The predictive accuracy for univariate and bivariate models based on simulated dataset Ι, where two outcomes associated with a common covariate, were shown to be approximately similar. However, in simulated dataset ΙΙ in which two outcomes associated with different covariates, predictive accuracy in bivariate models were seen to be larger than that of univariate models.
Conclusions: It is indicated that the predictive accuracy gain is higher in bivariate model, when the outcomes share a different set of covariates with higher level of correlation between the outcomes.
Ss Azimi, D Khalili, F Hadaegh, Y Mehrabi, P Yavari, F Azizi,
Volume 7, Issue 4 (16 2012)
Abstract
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Background
& Objectives: Cardiovascular
disease is the first cause of death and third place in disease burden in Iran.
This study estimates population attributable fraction (PAF) of cardiovascular
risk factors in Tehran population. PAF is one of the important parameters of
measuring population affect of risk factors and evaluating potential impact of
preventive strategies in community level.
Methods: In this study 5868 participants above
30 years old of Tehran lipid and glucose study (TLGS) were employed and 501 CVD
events detected during 10 years follow-up. Direct estimate of adjusted PAFs
using logistic regression which is one of less biased exist methods of PAF
calculation were applied.
Results: Highest modifiable cardiovascular
risk factor PAFs, in sequence, was smoking (14.16%), hypertension (11.73%),
diabetes (7.32%), hypercholesterolemia (6.85%) and central obesity (5.91%) for
men, and hypertension (19.25%), diabetes (18.82%), central obesity(9.88%) and
hypercholesterolemia (7.95%), for women. Also PAF of hazardous age and
premature family history of CVD, as most important nonmodifiable CVD risk
factors, were 36.09%, 16.61% and 3.95%, 7.56% for men and women respectively.
Conclusion:
According to the difference of risk factors PAFs ranking in men and women, it
is suitable that CVD preventive interventions to be prioritized by sex
separately. In this regard, besides special attention to control tobacco use in
men, hypertension and diabetes in both sexes and high cholesterol in men and
central obesity in women respectively, should be given in priority of preventive
strategies.
Mh Panahi , P Yavari, D Khalili, Y Mehrabi, F Hadaegh, F Azizi,
Volume 9, Issue 4 (3-2014)
Abstract
Background & Objectives: We studied the risk of Chronic Kidney Disease (CKD), Metabolic Syndrome (MetS), and their interaction on the incidence of Coronary Heart Disease (CHD).
Methods: A population of 6568 participants (43.4% male) with a mean age of 48.4 years for males and 46.7 years for females and a median follow-up of 10.1 years was investigated. They were divided into 4 groups at baseline: CKD-/MetS-, CKD+/MetS-, CKD-/MetS+, CKD+/MetS+. Hazard Ratios (HRs) were calculated for each group and were compared to the first group using multivariate Cox regression analysis adjusted for age, education, smoking, total cholesterol, and the family history of cardiovascular diseases.
Results: Men with CKD (without MetS) showed an HR of 1.74 (CI 95%: 1.16-2.60) for CHD events. The measured value was 2.34 (1.77-3.08) for men with MetS (without CKD). The respective results were in women 1.18
(0.64-2.19) and 2.59 (1.73-3.88). CKD and MetS had a significant negative interaction with CHD events (HR=0.40, 0.24-0.66). The interaction was not significant in women (P value=0.48).
Conclusion: The results of this study indicated that CKD without MetS was a risk factor for coronary heart disease in men but not in women.
M Nazarzadeh, D Khalili, B Eshrati, F Hadaegh, F Azizi,
Volume 9, Issue 4 (3-2014)
Abstract
Background & Objectives: The case-cohort study is one of the youngest designs in epidemiology and some methodological aspects of it are still in debate. This study aimed at comparing the estimated hazard ratio, standard error, and interaction hazard ratio between the case-cohort and cohort studies for assessing the relationship between diabetes and cardiovascular diseases.
Methods: A total of 1701 men and 2253 women aged between 40 and 75 years were considered as the main cohort. Subcohort sampling was performed using simple random sampling with a sampling fraction of 0.3%. The hazard ratio of the cohort study was calculated using Cox regression model and the 3 methods of Prentice, Self-Prentice, and Barlow were used for calculating the hazard ratio of the case-cohort study. The mentioned regression models were used to assess the interactions.
Results: The results of the two studies were similar in populations with higher incidence (cohort of men) and lower incidence (the cohort of women) when frequency percent of exposure variable was greater than 10%. When the sample size of the initial cohort was less than 1250 subjects, discrepancies were observed between the results of the two studies. In addition, the standard error of the case-cohort study was higher than the cohort study. The results of both studies were similar in assessing the considered interactions.
Conclusion: The results are similar when the initial cohort sample sizes are sufficient. Meanwhile, unlike the percentage of exposure frequency, the outcome incidence has a negligible impact on the discrepancy between the results while the effect of the relative frequency of the exposure levels on the results discrepancy is noticeable.
Mh Panahi, P Yavari, D Khalili, Y Mehrabi, F Hadaegh, F Azizi,
Volume 11, Issue 1 (Vol 11, No 1 2015)
Abstract
Background & Objectives: Some studies on chronic diseases have indicated that obesity may result in a paradoxically longer survival. The present study was aimed to investigate the effect of Chronic Kidney Diseases (CKD) and Metabolic Syndrome (MetS) on the incidence of Coronary Heart Disease (CHD).
Methods: In order to record time to CHD events, a sample of 6,507 individuals (mean age 47.4 years, 43.4% males) was followed for nearly 10 years. Participants were also categorized into four groups according to presence/absence of CKD and MetS. Then, using a Multivariate Cox Regression, the Hazard Ratio (HR) of each group was estimated relative to individuals free of both CKD and MetS separately for obese (BMI&ge27.1 Kg/m2) and non-obese persons (BMI<27.1 Kg/m2).
Results: HR for non-obese patients with CKD but not MetS was obtained 2.06 (95%CI: 1.28-3.31) in men and 2.56 (1.04-6.31) in women. However, these associations were not significant for obese patients. Furthermore, among non-obese men and women with MetS alone, HR was estimated 2.52 (1.71-3.73) and 4.68 (2.20-9.95), respectively. For obese patients, these values were 1.70 (1.05-2.78) and 1.90 (1.16-3.13), respectively.
Conclusion: The results reflect that among those who had MetS alone, the risk of CHD incidence was twice higher in non-obese individuals compared to obese patients.
S Akbarpour, Y Jahangiri-Noudeh, M Lotfaliany , N Zafari, D Khalili, M Tohidi, Ma Mansournia, F Azizi, F Hadaegh,
Volume 11, Issue 2 (Vol 11, No 2 2015)
Abstract
Background & Objectives: Considering the importance of CVD risk factors in diabetic and non-diabetic populations and the high prevalence of diabetes and cardiovascular risk factors, we studied the trend of anthropometric indexes, blood pressure, smoking and lipids in diabetic and non-diabetic populations.
Methods: The data of Tehran Lipid and Glucose Study was used in this investigation. The study population comprised 1045 diabetic and 5136 non-diabetic subjects. To investigate the secular longitudinal trends, the Generalized Estimation Equation method was employed. All statistical models were adjusted for age to eliminate the potential confounding effect of age. The interaction between the diabetes status and each phase of the study was checked in a separate model in GEE.
Results: Over a decade, the serum levels of TC, TGs, LDL-C and non HDL-C decreased although about 60% of the diabetic population did not reach the therapeutic goals of non-HDL-C and LDL-C levels until 2011. Control of hypertension was more successful in females, but about 60% of both males and females with diabetes were still hypertensive at the end of follow-up period. Smoking increased during the follow-up.
Conclusion: This study showed that among CVD risk factors, healthcare professionals paid more attention to hypercholesterolemia neglecting other risk factors including hypertension and central obesity.
