Iranian Journal of

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Background and Objectives: analogues insulin is similar to human insulin but have differences in cell structure that leading to differences in characteristics of pharmacodynamics and pharmacokinetics.
Methods: A systematic review of published studies between 2009 until June 2010 in Cochrane Library (CENTRAL and Cochrane Systematic Reviews), DARE, NHS EEDs, and CRD databases were searched.
Results: Only nine articles were identified to be included in this review. Most of these studies demonstrated that there were no more or less risks caused by rapid acting insulin compared with human insulin in terms of safety. The risk of sever hypoglycemia significantly, reduced by using long acting insulin analogue. Although it has been shown that insulin analogues are not cost effective, some of these studies pointed out that that insulin Aspart 30 compared with other analogues has more cost effectiveness.
Conclusion: Although this type of insulin seems very effective for patients with diabetes, due to the less cost effectiveness in comparison to human insulin, it is recommended that government's resources for this technology will be allocated just to the children and elderly people.

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Background & Objectives: Knowledge of the magnitude of attributable burden of cardiovascular diseases (CVDs) due to diabetes is necessary for health policy, priority setting and preventing CVD deaths. Our study aimed at estimating the attribute of proportion of diabetes to the burden of cardiovascular diseases in Kermanshah, West of Iran.

Methods: World Health Organization Comparative Risk Assessment methodology was used to calculating Potential Impact Fraction (PIF). Data on the Prevalence of newly diagnosed diabetes mellitus (People who have fasting plasma glucose (FPG) equal or greater than 126 mg/dl) were obtained from 3rd Iranian surveillance of risk factors of non- communicable diseases and data on corresponding measures of effect were derived from a national- specific study with age and multivariate adjusted hazard ratios.

Results: Based on multivariate- adjusted hazard ratios, by reducing the percent of women with diabetes from 8.1 percent to the zero level and the feasible minimum risk level i.e. 4 percent, 11.2% and 5.7% of attributable Disability Adjusted Life Years (DALYs) to CVD are avoidable, respectively. The corresponding value for men at the theoretical (zero level) and feasible minimum risk level (3 percent) were 5.6% and 2.9%, respectively.

Conclusion: To better planning, decision making and priority setting, PIF should be applied to updated and revised burden of CVDs in Iranian Health system.

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Background & Objectives: Macrosomia is a term applied to newborns with a birth weight more than of 4000 gr which cause different maternal and neonatal complications. Several risk factors has been known for macrosomia. The purpose of this cross-sectional study was to estimate the frequency and evaluate of risk factors of macrosomia in Asalian hospital of Khorramabad in 2010.
Methods: This study was undertaken in Khorramabad, Iran. The data collection instrument was a questionnaire containing 10 variables as the risk factors of macrosomia. The data were analyzed using the SPSS software.
 Results: 59 cases of macrosomia were found in 500 living births, and the frequency of macrosomia was 11.8%. Also, 69.5% of the neonates were male and 30.5% were female. Maternal risk factors were mother's age at pregnancy, mother's obesity (BMI>=30), weight gain more than 18 kg during pregnancy, history of diabetes mellitus, history of macrosomia, prolonged gestational age, and multiparity (parity>=5). There was no significant relationship between mother's job and macrosomia.
Conclusion: The prevalence of macrosomia in Khorramabad was high (11.8%). Preventing pregnancy in mothers over 35 years of age by contraception ways, preventing maternal obesity before pregnancy, and control of blood glucose during pregnancy by suitable diet and insulin therapy are recommended to prevent macrosomia.

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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.

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Background & Objectives: In recent years, different decision support systems (DSS) have been used to predict and diagnose diseases. The purpose of this paper was to compare some DSSs and to evaluate their accuracy in predicting diabetes. 

Methods: In this research, determination and optimization of the weights of the neural network were undertaken using genetic algorithm and Levenberg-Marquardt (GALM). Traditional and K-Fold Cross Validation were used to verify the models. Finally, the proposed model (i.e. GALM) was compared using logistic regression and genetic algorithm based on area under curve (AUC), and Confusion Matrix.

Results: After evaluating the results, the model based on the GALM algorithm showed better sensitivity and specificity in comparison with models based on the logistic regression (LR) and genetic algorithm (GA). Furthermore, among other models, the proposed model had a high sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and a small negative likelihood.

Conclusion: The results showed that the GALM model with a sensitivity, specificity, PPV, NPV, and AUC of 98.7, 90.01, 91.8, 98.3 and 0.979 respectively was an appropriate model for predicting diabetes in comparison with models of GA and LR.

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Background and Objectives: Missing data is a big challenge in the research. According to the type of the study and of the variables, different ways have been proposed to work with these data. This study compared five popular imputation approaches in addressing missing data in the questionnaires.

Methods: In this study, 500 questionnaires were used for self-medication in diabetic patients. Missing in the observations was artificially generated by random selection of questions and then deleting them. Five imputation ways included: 1) the mean of the questions, 2) the mean of the person, 3) the mode of the person, 4) linear regression, and 5) EM algorithm. For each method, the mean and standard deviation were compared with imputation. The Spearman correlation coefficient, the percentage of incorrectly classified and kappa statistic were also calculated.

Results: A kappa higher than 0.81 represented almost perfect agreement at 10% missingness. The EM algorithm showed the highest level of agreement with the results of actual data with a Kappa of 0.886. With increasing missingness to 30%, the EM algorithm and the mean of  the person showed a rather similar agreement with a Kappa of 0.697 and 0.687, respectively.

Conclusion: In this study, the EM algorithm was the most accurate method for handling missing data in all models. The mean of the person method is easy for handling missing data, especially for most non statisticians.

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Background and Objectives: Diabetic patients are always at risk of hypertension. In this paper, the main goal was to design a native cost sensitive model for the diagnosis of hypertension among diabetics considering the prior probabilities.

Methods: In this paper, we tried to design a cost sensitive model for the diagnosis of hypertension in diabetic patients, considering the distribution of the disease in the general population. Among the data mining algorithms, Decision Tree, Artificial Neural Network, K-Nearest Neighbors, Support Vector Machine, and Logistic Regression were used. The data set belonged to Azarbayjan-e-Sharqi, Iran.

Results: For people with diabetes, a systolic blood pressure more than 130 mm Hg increased the risk of hypertension. In the non-cost-sensitive scenario, Youden's index was around 68%. On the other hand, in the cost-sensitive scenario, the highest Youden's index (47.11%) was for Neural Network. However, in the cost-sensitive scenario, the value of the imposed cost was important, and Decision Tree and Logistic Regression show better performances.

Conclusion: When diagnosing a disease, the cost of miss-classifications and also prior probabilities are the most important factors rather than only minimizing the error of classification on the data set.

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Background and Objectives: Neuropathy is a common complication of diabetes that can cause disability in diabetic patients. The aim of this study was to determine of effective factors in the Event Time of neuropathy in type 2 diabetic patients using the Cox proportional hazards model.

Methods: This study included 371 patients with type II diabetes without neuropathy who were registered at Fereydunshahr Diabetes Clinic. Subjects were followed up for the development of neuropathy between 2006 until March 2016. The data were analyzed using the R software (ver. 3.2.3). The test was conducted at an error level of 5%.

Results: At the end of 10 years of study, the cumulative incidence and prevalence of neuropathy was 30.7% and 41.6%, respectively. The Kaplan-Meier method showed the mean time to detection of neuropathy was 76.6 ± 5 months after the first diagnosis of diabetes (83.8 ± 8 in men and 72.7 ± 6 in women). The semi-parametric Cox regression model revealed the one-year, two-year, five-year, and eight-year disease-free survival was 0.867, 0.819, 0.647, and 0.527, respectively. Also, four variables of duration of diabetes, sex, family history of diabetes, and HbA1c can be considered as strong determinants of the time of development of neuropathy in the semi-parametric model (COX) (P<0.05).

Conclusion: Optimal glycemic control and regular evaluation of legs in elderly patients, especially women with a positive family history, decrease the occurrence and progression of neuropathy and improve the quality of life in diabetic patients.

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Background and Objectives: Ischemic heart disases are the main cause of mortality and morbidity in Iran. Diabetes mellitus (DM) is one of the most prevalent diseases in Yazd. However, little is known about the share of CHD associated with DM in Yazd.This study aimed to determine the potential impact fraction of ischemic heart disease associated with diabetes mellitus in Yazd-Iran.
Methods: The potential impact fraction (PIF) equation was used to calculate the estimates of CHD associated with DM in Yazd. Prevalence diabetes in Yazd were calculated from Yazd Health Study (YAHS) data, conducted in 2013- 2014 in Yazd Greater Area. The relative risk eof IHD associated with DM was extracted from Tehran Lipid and Glucose study.
Results: According to this study, by hypothetical reduction of DM prevalence in women from the 20.6 percent to null or optimistically to a minimum risk level of 20.3 percent, 23.6% and 0.3% of IHD will be reduced consequently. The corresponding value for men considering the DM prevalence of 15.9 percent at the theoretical zero level and feasible minimum prevalence of 17.7 percent, were 10.6% and -1.2%, respectively.
Conclusion: Given the high prevalence of DM and CHD in Yazd, more interventions to control DM is needed in Yazd by the health section.

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Background and Objectives: A significant portion of the total health expenditure is related to medicines. Evaluation of medication system indicators forms a basis for modifying the consumption pattern and cost containment. Therefore, the present study was conducted to investigate the trend of medication consumption indicators in Iran.
Methods: In this descriptive-cross-sectional study, medicine consumption per capita was calculated based on the medicine type, medicine form, and treatment category. The data were extracted from the Pharmacological Statistics of the Food and Drug Administration and the Statistics Center of Iran between 2012 and 2015. Catzung categorization was used for treatment categories. Data were analyzed using descriptive statistics and annual growth index by Excel 2013 software.
Results: During the study years, the highest medicine consumption per capita was related to metformin 500 (18.52), ranitidine 150 (15.45), and adult cold (15.29). The highest average consumption per capita in different treatment categories was related cardiac medications (97.65). The lowest and highest growth rates were related to glibenclamide 80 with -12% in 2014 and metformin 500 with 16.9% in 2013, respectively. Tablets were the most widely used form of medicine.
Conclusion: According to the results, medicine consumption per capita in Iran is higher than developed countries. Cardiac and diabetes medications have a high consumption per capita in Iran compared to other medications. Therefore, in order to rationalize drug prescription, strengthening the clinical evaluation system is recommended by developing and implementing clinical guidelines for common and high-cost diseases.

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Background and Objectives: Among non-communicable diseases, endocrine, nutritional, and metabolic diseases have increased in recent decades, especially in developing countries. This study was conducted to evaluate and predict deaths from this group of diseases in Iran
Methods: In this analytical cross-sectional study, the study population comprised all deaths of endocrine, nutritional, and metabolic diseases recorded in the Ministry of Health and Medical Education during the years 2006 to 2016. In order to forecast the trend of cause of death, the Lee Carter model was employed in the demographic package 18/1 of the R software version 3/3/1.
Results: The death rate from endocrine, nutritional, and metabolic diseases increased in all age and sex groups from 2006 to 2015. It was predicted that death rate of this group of diseases would reach 197/71 in 100,000 in the total population, 202/97 in 100,000 women, and 188/99 in 100,000 men in 2035.
Conclusion: Several factors play a role in endocrine and metabolic diseases, one of which is aging and aging population. Considering the change in the age structure of the population of Iran by 2035 and the increase in population’s age, an increasing trend is expected in the mortality rate due to these diseases. Because of this rapid increase, policymakers need to adopt intergovernmental population-based plans and policies in this regard.

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Background and Objectives: Researchers and health specialists are increasingly using self-reports to obtain information on chronic illnesses. This study was conducted to assess the validity of self-reports of diabetes based on a recent field survey in Mashhad.
 
Methods: In this cross-sectional study, we used the results of 2015 census in Mashhad, a population based survey of people over the age of 30 (n =307103), to determine the proportion of self-reported diabetes. The patient records of Sina Electronic Health Record system (SinaEHR®) coded as E11 and E12 approved by doctors were used as a reference. The sensitivity, specificity, positive predictive value, and negative predictive value were calculated with 95% confidence intervals.
 
Results: The sensitivity, specificity, PLR, NLR, PPV, and NPV of self-reported diabetes was 24.59% (95% CI: 23.97-25.21), 98.04% (95% CI: 97.99-98.09), 12.56% (95% CI: 12.11-13.02), 0.77% (95% CI: 0.76-0.78), 44.77% (95% CI: 43.89-45.67), and 95.27% (95% CI: 95.23-95.31), respectively. The sensitivity of self-reported diabetes was higher in men, Iranian individuals, single subjects, people aged 60 and over, individuals with a BMI of 18.5-25, and those with university education.
 
Conclusion: Although the sensitivity of self-reported diabetes was poor in this study, its specificity and positive and negative predictive values were good. Furthermore, the sensitivity of self-reported diabetes was higher in men, Iranian individuals, single subjects, people aged 60 and over, individuals with a BMI of 18.5-25, and those with university education. It seems that caution should be exercised in using self-reported data in epidemiological studies.

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Background and Objectives: Chronic renal failure (CRF) is an irreversible disorder of the renal function. The aim of this study was to  describe the features of patients with chronic renal failure and the factors associated with this disease among hemodialysis patients in a hospital in Tehrn in 2016.
 
Methods: In this cross-sectional descriptive study, patients presenting to the hemodialysis department of Shahid Ayatollah Ashrafi Esfahani Hospital who underwent continuous hemodialysis treatment for at least three months were studied. Data were gathered from the medical records of the patient s and, if necessary, additional interviews were conducted with the patients. Data were analyzed with descriptive and inferential statistics using the Stata software version 14.
 
Results: Of 359 patients, 230 (64.07%) were male with a mean age of 58.1 ± 1.09 years. The frequency (frequency percentage) of the most commonly known possible causes of renal failure was hypertension 91 (25.35%), hypertension and diabetes 84 (23.43%), and diabetes 78 (21.73%). The mortality rate was higher in diabetic patients compared to non-diabetic patients (50% vs 38.55%).
 
Conclusion: The main cause of chronic renal failure is hypertension and diabetes, and the mortality rate is higher in these patients than in other patients.

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Background and Objectives: Gestational diabetes mellitus (GDM) is a medical problem in pregnancy, and its late diagnosis can cause adverse effects in the mother and fetus. The purpose of this research was to estimate the accuracy parameters of a biomarker for early prediction of gestational diabetes in the absence of a perfect reference standard test.
 
Methods: This study was conducted in 523 pregnant women who presented to Mahdieh Hospital and Taleghani Hospital affiliated with Shahid Beheshti University of Medical Sciences, Tehran, Iran 2017-2018. As a predictor for detecting GDM, beta- human chorionic gonadotropin (β-hCG) measurements were recorded during 14-17th weeks’ gestation in a checklist. The Bayesian latent variable model was used to estimate the sensitivity, specificity, and area under receiver operating characteristic curve (AUC). Bayesian parameter estimation was calculated using the R2OpenBUGS package in R version 3.5.3.
 
Results: The median gestational age was 33 years. In the absence of a perfect reference test, the applied model had a sensitivity, specificity, and AUC of 78% (95% credible interval (CrI): 0.66-0.83), 83% (95% CrI: 0.74-0.89), and 0.72 (95% CrI: 0.64-0.88) for β-hCG, respectively. 
 
Conclusion: According to the results of this study, β-hCG may be an acceptable biomarker for early diagnosis of diabetes in pregnant women in the absence of a perfect reference test.

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Background and Objectives: Diabetes is considered as one of the most common endocrine disorders worldwide. The aim of this study was to investigate the factors associated with diabetic complications.
 
Methods: A case-control study was performed on the data of 70089 diabetic patients (4622 cases and 53613 controls) extracted from the SINA Electronic Health Record (SinaEHR®) in a population covered by Mashhad University of Medical Sciences in 2018. The effect of independent variables on the likelihood of diabetic complications was investigated using single-variable and multivariate logistic regression models with the control of the potential confounding effects.
 
Results: Using the multivariate logistic regression, the odds of developing diabetic complications were 0.35 (0.31-0.38) for living in the city, 0.73(0.67-0.79) for living in the suburbs and 0.31(0.28-0.33) for living in rural areas relative to the metropolises, 0.84 (0.78-0.91) for illiterate subjects, 0.70 (0.66-0.75) for physical activity, 1.51(1.34-1.71) for stage 1 hypertension and 1.87 (1.43-2.44) for stage 2 hypertension relative to normal blood pressure, 0.79(0.74-0.85) for uncontrolled low density lipoprotein and 1.42(1.33-1.51) for uncontrolled hemoglobin A1C.
 
Conclusion: Various risk factors were identified to increase the odds ratio of diabetic complications. The most important risk factors were uncontrolled glycosylated hemoglobin and stage 1 and 2 hypertension. Control of these factors can reduce the chance of diabetic complications in diabetic patients.
 

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Background and Objectives: The results of cohort studies could be used in evidence-based medicine in case they have a good quality and robust methodology. Therefore, this study was conducted to assess the quality of cohort studies investigating preconception risk factors of gestational diabetes.
 
Methods: In this cross-sectional study, the cohort studies investigating the preconception risk factors of gestational diabetes during 2008-2018 indexed in PubMed, Scopus and Science Direct were searched. Keywords used for search included cohort study, pre pregnancy, preconception, risk factor and gestational diabetes. Out of 312 retrieved articles, 26 were critically appraised using the STROBE checklist. The maximum and minimum score that each article could obtain was 34 and 0, respectively. Data were analyzed using SPSS version 16.
 
Results: Of 26 articles that were reviewed, the overall agreement rate with STROBE was 78.8%. Of these, 18 articles (69/23%) were rated as moderate and eight articles (30.76%) were graded as good quality. The method and result sections were the weakest parts of the published articles.
 
Conclusion: The quality of the reported cohort studies investigating preconception risk factors of gestational diabetes is not optimal. It is recommended that chief editors, reviewers, and authors as well as obstetricians and midwives use the STROBE criteria to appraise articles critically, before using their findings in clinical practice.

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Background and Objectives: The Covid-19 prevalence can threaten self-care behaviors and disease control in people with type 2 diabetes (T2D) through increasing anxiety and stress levels and limited access to medical services. This study was conducted to investigate the association of the Covid-19 pandemic with self-care behaviors in patients with T2D.
 
Methods: The present study was conducted on 318 adults with T2D who participated in the Tehran Lipid and Glucose Study (TLGS) between 1397 and 1399 and completed the summary of diabetes self-care activities (SDSCA) questionnaire. Data on self-care activities during the Covid-19 pandemic were collected 3-4 months after the announcement of the epidemic in the country via telephone calls. For data analysis, a multiple linear regression model and paired t-test were used.
 
Results: The mean total scores of self-care behaviors were 49.7±14.8 and 56.1±13.1 before and after the Covid-19 pandemic, respectively (P<0.001). After the Covid-19 pandemic, women's total self-care score increased with an increase in the diabetes duration (β= 0.31, P= 0.020) after adjusting for the aforementioned related conditions. This score showed a significant decrease in women with a high school education compared to those with higher education (β= -6.57, P= 0.024). Moreover, the diet and foot care subscales in both sexes and the blood glucose testing score in women were significantly higher after versus before the pandemic.
 
Conclusion: Following the Covid-19 pandemic, self-care behaviors, especially those related to diet, foot care, and blood glucose testing, improved in the short term