Tehran University Medical Journal

Background: Jaundice is the common cause of hospitalization of infants in the first month after birth. Therefore, detection of risk factors associated with jaundice can effect on its process and complications. This study aimed to determine the prevalence and characteristics of diabetic mother's infants and comparing with infants with unknown jaundice.
Methods: In this cross-sectional study, among 2,800 infants with jaundice in Ghaem hospital in Mashhad during the 2007 to 2014, features of 59 infants of diabetic mother's (case group) and 78 infants with unknown jaundice (control group) were analyzed. After confirming of jaundice (Bilirubin ≥ 17 mg/dl) in newborns based on examination of pediatrician and laboratory results, a researcher made questionnaire containing maternal demographic data, (maternal age, parity, maternal problems during pregnancy, route of delivery). Also neonatal characteristics including age, sex, birth weight, current weight, duration of hospitalization, current age, age of recovery and laboratory data (Bilirubin, direct bilirubin, hematocrit, platelet, sodium, potassium, blood urea nitrogen, Cr, TSH, T4) were assessed. After data collection and recording information in SPSS software, version 19.5 (IBM SPSS, Armonk, NY, USA), by using tables, charts and statistical indices, the study was evaluated. Data were analyzed using statistical tests such as Mann-Whitney, Chi-square tests after normality control. Comparison of the two groups in normal distribution with t-test and for non-standard data with Mann-Whitney test. Also for definitive variables Chi-square test was used. P-value less than 0.50 was the significant level minimum.

Background: The prevalence of gestational diabetes is increasing among pregnant women. It is associated with an increased risk of congenital heart disease, including hypertrophic cardiomyopathy. The aim of this study was to evaluate the effect of maternal diabetes control (based on HbA1c) on their hypertrophic cardiomyopathy in newborns.
Methods: This case-control study was performed on 60 neonates born in Afzalipour Hospital (Kerman University of Medical Sciences) from May to November 2014 in two groups of eligible infants using the convenience sampling method. Information about the age, sex, weight, gestational age, maternal age, obstetric history, gestational diabetes through the checklist were collected. Then Doppler echocardiography, M- Mode, Doppler tissue was conducted on two groups. Echocardiographic criteria including ventricular septal thickness and blood HbA1c mothers in both groups were compared. To compare quantitative and qualitative variables between the two groups’ Independent samples t‐test and Chi-square test was used. A significant level of 0.05 was considered in all of the statistical samples and SPSS software, ver. 20 (IBM, Armonk, NY, USA) was used to analyze the data.

Cancer is the major cause of death in the world and the rate of mortality is higher in developed countries. Therefore, lifestyle could be effective in promoting the cancer. The pancreatic tumors, are 8th cause of mortality due to cancer, which have several types, among them ductal adenocarcinoma is the most common and includes 85% of cases. Since, it is almost impossible to diagnosis the tumor in early stages of the disease, it contributes to high rates of mortality, although if it diagnosis in early stage and the surgery performed for them only 10-20% of patients will be survived. Metastasis occurs when the tumor is smaller than 2 cm in size and because the pancreas is located in the depth of abdomen, typically, it happens after tumor is spread to other organs. A combination of medical imaging, blood tests, and examination of tissue samples are usually made for diagnosis and based on the cancer stage, surgery, radiotherapy and chemotherapy are chosen as treatment options. Some rare genetic variations can cause pancreatic cancer and about 5-10% of cases are linked to inherited genes. However, major risk factors are including age, obesity, tobacco smoking and diabetes. Smoking counts for about 25% of cases, and the diabetes is the main symptoms of pancreatic cancer, which observed in about 80% of cases. But, it is still unclear whether diabetes is a predisposing factor in pancreatic cancer, or the outcome of tumor progression. Recent studies have shown that, diabetes is unique in pancreatic cancer which is not related to common types. Currently, CA 19-9 is the only reliable tumor marker for pancreatic cancer that its frequency also increases in non-bad conditions, such as pancreatitis and obstructive jaundice, so is not sensitive and specific enough for diagnosis of this cancer. Due to researches continue to find more specific markers. In this review the etiology of pancreatic cancer, diabetes associated with this type of cancer and significant biomarkers for diagnosis will be considered.

Background: The prevalence of diabetes mellitus due to existence of various factors is growing rapidly. The insulin resistance phenomenon is one of the main underlying causes of this disease, due to various reasons such as a decrease in serum levels of vitamin D3. In this study, we evaluated the relationship between plasma level of vitamin D3 and some other main plasma factors.
Methods: This clinical trial study carried out on type 2 diabetes mellitus subjects who referred to diabetes clinic of Baqiyatallah Hospital in Tehran city during June to September 2016. First demographic data of all patients were collected. Then the blood samples for evaluation of blood glucose, HbA1c, triglyceride, cholesterol, HDL, LDL and vitamin D3 were obtained. Based on plasma level of vitamin D3, diabetic subjects were divided into two separate treatment groups (with and without vitamin D3 deficiency) and then above-mentioned factors were compared between these two groups. Afterward, in group with vitamin D3 deficiency, treatment by vitamin D3 triggered (50000 unit per week for eight weeks orally) until plasma level of vitamin D3 reached above 30 ng/mL. Then, treatment continued by vitamin D3 (50000 Unit per week orally) and calcium (1.5 g/day PO) until 60 days. Finally, the patients were evaluated about fasting blood glucose, HbA1c, HDL, LDL and blood pressure.
Results: In total, 187 subjects (99 men and 88 women) were evaluated in this study. Total hypovitaminose incidence was 0.65% which was higher in subjects with higher body mass index (BMI). Serum content of vitamin D3 and calcium, significantly changed the values of fasting blood sugar (FBS), HbA1c, HDL and TG, but has no significant effects on other factors.
Conclusion: Our results clearly revealed that hypovitaminose vitamin D3 is one of the main factors that can leading to rise in plasma glucose and dyslipidemia occurrence. Also, our data are demonstrated that treatment by vitamin D3 and calcium can modify fasting blood glucose and dyslipidemia in type 2 diabetes mellitus subjects.

Background: Regarding the increased risk of developing type 2 diabetes in pre-diabetic people, identifying pre-diabetes and determining of its risk factors seems so necessary. In this study, it is aimed to compare ordinary logistic regression and robust logistic regression models in modeling pre-diabetes risk factors.
Methods: This is a cross-sectional study and conducted on 6460 people, over 30 years old, who have participated in the screening of diabetes plan in Mashhad city that it was done by Mashhad University of Medical Sciences from October to December 2010. According to the fasting blood sugar criteria, 5414 individuals were identified as healthy and 1046 individuals were identified as pre-diabetic. Age, gender, body mass index, systolic blood pressure, diastolic blood pressure and waist-to-hip ratio were measured for every participant. The data was entered into the Microsoft Excel 2013 (Microsoft Corp., Redmond, WA, USA) and then analysis of the data was done in R Project for Statistical Computing, Version R 3.1.2 (www.r-project.org). Ordinary logistic regression model was fitted on the data. The outliers were identified. Then Mallow, WBY and BY robust logistic regression models were fitted on the data. And then, the robust models were compared with each other and with ordinary logistic regression model according to goodness of fit and prediction ability using Pearson's chi-square and area under the receiver operating characteristic (ROC) curve respectively.
Results: Among the variables that were included in the ordinary logistic regression model and three robust logistic models, age, body mass index and systolic blood pressure were statistically significant (P< 0.01) but waist-to-hip ratio was not statistically significant (P> 0.1). There were 552 outliers with misclassification error in the ordinary logistic regression model. Pearson's chi-square value and area under the ROC curve value in the Mallow model were almost the same as for ordinary logistic regression model. But it was relatively higher in BY and WBY models.
Conclusion: Based on results of this study age, overweight and hypertension are risk factors of prediabetes. Also, WBY and BY models were better than ordinary logistic regression model, according to goodness of fit criteria and prediction ability.

Background: Gestational diabetes is one of the common causes of maternal and fetal complications. Due to fetal and maternal complications of diabetes, it is very important to reduce the prevalence of diabetes and its consequences. The relationship between vitamin D deficiency and type 2 diabetes has been reported. There is little information about the relationship between serum vitamin D levels and the risk of gestational diabetes mellitus (GDM). The aim of this study was to determine the relationship between the levels of vitamin D and gestational diabetes.
Methods: This case-control study was conducted in health centers of Urmia University of Medical Sciences in May 2015 until March 2016. A total of 100 pregnant women with gestational diabetes and 100 healthy pregnant women were entered into the study by nonrandom and available sampling. The level of vitamin D was measured and levels were divided into three levels. Vitamin D levels were considered less than 20 ng/ml, 20-30 ng/ml and more than 30 ng/ml as deficiency, insufficiency and sufficient, respectively. Exclusion criteria include pre-pregnancy glucose tolerance, history of medical disease, and supplementation with vitamin D.
Results: The mean age of women in the study group was 30.31±5 years and in the control group was 28.83±4.95 years (P=0.06). The vitamin D levels in GDM and control groups were 7.25±4.76 ng/ml and 11.93±16.12 ng/ml, which is lower in the gestational diabetes than the control group (P=0.01). The severe deficiency of vitamin D in the gestational diabetes group and in control group were 34% and 27% respectively (P<0.0001). There was a significant difference in mean fasting plasma glucose level between gestational diabetes group and healthy pregnant group (P<0.001). There was no relationship between vitamin D levels and body mass index of pregnant women (P=0.1).
Conclusion: In this study, the majority of patients had vitamin D deficiency and in the gestational diabetes group, vitamin D deficiency was significantly higher than the control group. The severe deficiency of vitamin D in the gestational diabetes group was higher than patients without gestational diabetes.

Background: Diabetes mellitus has several complications. The Late diagnosis of diabetes in people leads to the spread of complications. Therefore, this study has been done to determine the possibility of predicting diabetes type 2 by using data mining techniques.
Methods: This is a descriptive-analytic study that was conducted as a cross-sectional study. The study population included people referring to health centers in Mohammadieh City in Qazvin Province, Iran, from April to June 2015 for screening for diabetes. The 5-step CRISP method was used to implement this study. Data were collected from March 2015 to June 2015. In this study, 1055 persons with complete information were included in the study. Of these, 159 were healthy and 896 were diabetic. A total of 11 characteristics and risk factors were examined, including the age, sex, systolic and diastolic blood pressure, family history of diabetes, BMI, height, weight, waistline, hip circumference and diagnosis. The results obtained by support vector machine (SVM), decision tree (DT) and the k-nearest neighbors algorithm (k-NN) were compared with each other. Data was analyzed using MATLAB® software, version 3.2 (Mathworks Inc., Natick, MA, USA).
Results: Data analysis showed that in all criteria, the best results were obtained by decision tree with accuracy (0.96) and precision (0.89). The k-NN methods were followed by accuracy (0.96) and precision (0.83) and support vector machine with accuracy (0.94) and precision (0.85). Also, in this study, decision tree model obtained the highest degree of class accuracy for both diabetes classes and healthy in the analysis of confusion matrix.
Conclusion: Based on the results, the decision tree represents the best results in the class of test samples which can be recommended as a model for predicting diabetes type 2 using risk factor data.

Background: Gestational diabetes mellitus (GDM) is one of the most common medical complications in pregnancy, which is associated with many serious consequences for mother and her fetus. Body mass index (BMI) in pregnant women is considered as one of most effective factor for the incidence of GDM. The aim of this study was to determine the relationship between BMI at pregnant women in the early months of pregnancy and the incidence of GDM.
Methods: In this retrospective cohort study, the case of six hundred fifty-nine pregnant women who referred to health centers in Kermanshah City from September 2010 to September 2012 by convenience sampling method were selected and investigated. This study was sponsored by Kermanshah University of Medical Sciences. Height and weight were measured for each woman at the beginning of pregnancy and maternal body mass index (BMI) was calculated based on height and weight measurements. Then the pregnant women were divided into four groups based on BMI: thin (BMI less than 18.9 kg/m2), normal (BMI between 19 kg/m2 and 24.9 kg/m2), overweight (BMI between 25 kg/m2 and 29.9 kg/m2) and obese (BMI more than 30 kg/m2). Those women who had diabetes at the beginning of pregnancy were excluded from the study. GDM was considered as fasting blood glucose ≥92 between 26-30 weeks of gestation.
Results: The mean±SD age of pregnant women was 27.7±5.85 year and the mean of BMI was 24.4±4.0 kg/m2. The GDM was shown in 30.7% of women. Association between BMI and GDM were statistically significant (P<0.001). The risk of GDM onset was 1.24 times, for each unit increased in BMI, (P<0.001). The risk of GDM was significantly higher in overweight [OR=2.97, CI (2.01-4.39)] and obese [OR=16.89, CI (8.46-33.70)] women. Being underweight increased the risk of GDM onset up to 1.19 times, but not significant.
Conclusion: There is a significant relationship between maternal BMI in pregnant women at the beginning of pregnancy with GDM onset. Increased BMI is correlated with an increase in the incidence of GDM.

Background: Diabetes mellitus as a chronic disease is the most common disease caused by metabolic disorders and it is one of the most important health issues all around the world. Nowadays, data mining methods are applied in different fields of sciences due to data mining methods capability. Therefore, in this study, we compared the efficiency of data mining methods in predicting type 2 diabetes.
Methods: In this cross-sectional study, the data of 7,000 participants in the Diabetes Screening Project in Samen, Mashhad City, Iran, were considered in 2016. There were 540 untreated diabetic patients. The Samen Project was included in the routine examinations of diabetes patients like blood glucose, eyes health, nephropathy, and legs health. So, in order to maintain balance, 600 healthy individuals were selected in a proportional volume sampling in this study. Therefore, the total sample size was 1140 people. In this study, people with diabetes aged over 30 years old were enrolled and participants with the previous history of type 2 diabetes, with normal blood glucose due to drug use or other issues at the time of the study, were excluded.
Results: All three models (Logistic regression, simple Bayesian and support vector machine models) had the same test accuracy (86%), however, in terms of area under the receiver operating characteristic (ROC) curve (AUC), logistic regression and simple Bayesian models had better performance (AUC=90% against AUC=88%). In the simple Bayesian model and logistic regression, body mass index (BMI) and age variables were the most important variables, while BMI and blood pressure variables were the most important factors in the support vector machine model.
Conclusion: According to the results, all three models had the same accuracy. In terms of area under the curve (AUC), logistic and simple Bayes models had better performance than the support vector machine model. Totally all three models had almost the same performance. Based on all three models, BMI was the most important variable.

Background: Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnancy, which is associated with serious complications. In the event of early diagnosis of this disease, some of the maternal and fetal complications can be prevented. The aim of this study was to early predict gestational diabetes mellitus by two statistical models including artificial neural network (ANN) and decision tree and also comparing these models in the diagnosis of GDM.
Methods: In this modeling study, among the cases of pregnant women who were monitored by health care centers of Kermanshah City, Iran, from 2010 to 2012, four hundred cases were selected, therefore the information in these cases was analyzed in this study. Demographic information, mother's maternal pregnancy rating, having diabetes at the beginning of pregnancy, fertility parameters and biochemical test results of mothers was collected from their records. Perceptron ANN and decision tree with CART algorithm models were fitted to the data and those performances were compared. According to the accuracy, sensitivity, specificity criteria and surface under the receiver operating characteristic (ROC) curve (AUC), the superior model was introduced.
Results: Following the fitting of an artificial neural network and decision tree models to data set, the following results were obtained. The accuracy, sensitivity, specificity and area under the ROC curve were calculated for both models. All of these values were more in the neural network model than the decision tree model. The accuracy criterion for these models was 0.83, 0.77, the sensitivity 0.62, 0.56 and specificity 0.95, 0.87, respectively. The surface under the ROC curve in ANN model was significantly higher than decision tree (0.79, 0.74, P=0.03).
Conclusion: In predicting and categorizing the presence and absence of gestational diabetes mellitus, the artificial neural network model had a higher accuracy, sensitivity, specificity, and surface under the receiver operating characteristic curve than the decision tree model. It can be concluded that the perceptron artificial neural network model has better predictions and closer to reality than the decision tree model.

Background: Celiac disease is a chronic inflammation of small intestine which is caused by an increased permanent sensitivity to a protein named gluten. This protein is present in some cereals such as wheat, barley, and rye. The immunologic response to this protein can cause clinical symptoms in people with specific human leukocyte antigens (HLAs) (including HLADQ2 or HLADQ8). Most studies have reported an increased incidence of celiac disease in patients with diabetes mellitus type I. This study aimed to determine the prevalence of the celiac disease in patients with diabetes mellitus type I under the age of 18 years old.
Methods: This cross-sectional, analytic descriptive study was performed on forty children with diabetes mellitus type I in Sanandaj Diabetes Association (Kurdistan University of Medical Sciences), Iran, from September 2012 to September 2013. After obtaining consent from their parents, demographic data, including gender, age, family history of diabetes, duration of illness, symptoms of celiac disease, were recorded in the questionnaire. The measurement of the tissue transglutaminase (tTG) antibody and total immunoglobulin type A in the serum was necessary for the screening of celiac disease. Therefore in the laboratory, 5 ml of the venous blood sample was taken and then the serum levels of tTG antibody (from immunoglobulin type A) and total serum levels of this immunoglobulin were measured by the enzyme-linked immunosorbent assay (ELISA) method. Upper endoscopy with multiple biopsies from small intestine was performed in patients with positive serological screening. Finally, the disease was evaluated by histological finding.
Results: Forty children with diabetes mellitus type I included 19 boys (47.5%) and 21 girls (52.5%) were enrolled in the study. The mean age of these patients was 10.53±4.05. The prevalence of celiac disease was 7.5% in these individuals. In the subjects, there was no significant relationship between gastrointestinal symptoms and celiac disease.
Conclusion: In the present study, the prevalence of the celiac disease in type 1 diabetic patients was 7.5% which is higher than the normal population.

Background: Nitric oxide (NO) produced by endothelial NO synthase (eNOS) mediates a large range of processes, and abnormality in the production of NO has been implicated in diabetic complications including diabetic nephropathy (DN). G894T polymorphism in the eNOS gene has been shown to decreased activity the NO levels of plasma. The association between eNOS Glu298Asp gene polymorphism and DN risk is still controversial. The present study investigated the effect of eNOS gene G894T polymorphism on susceptibility to type 2 diabetes (T2D) and DN and measures of kidney function in a population with and without diabetes.
Methods: This case-control study was carried out at the diabetes specialist clinic of Golestan Hospital of Ahvaz Jundishapur University of Medical Sciences, Iran, from September 2016 to December 2017. The study comprised 132 patients with T2D (with and without nephropathy). They were compared to 66 normal subjects. The subjects were genotyped for the eNOS G894T polymorphism by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Blood glucose, HbA1c, BUN, creatinine and urinary albumin were evaluated by a biochemistry analyzer.
Results: Higher prevalence of the mutant T allele and homozygous TT genotypes and biochemical parameters) like FBS, TG, and BUN) were seen in T2D patients compared to healthy subjects. For T2DM, the odds ratios (ORs) for the TT genotype and the T allele carrier were 3.1 (P=0.0001) and 2.6 (P=0.0001), respectively. In contrast to the significant association between the eNOS G894T polymorphism and T2D, we could not find a significant correlation to the DN. For DN, the ORs for the TT genotype and the T allele carrier were 1.1 (P=0.76) and 0.8 (P=0.6). For decreased epidermal growth factor receptor (EGFR) below 60 ml/min/ 1.73 m2 in diabetic patients, the OR for TT was 0.8 (P=0.7).
Conclusion: Our results confirm that the risk of T allele and TT genotype of the eNOS G894T polymorphism were significantly associated with T2D, The TT genotype of this polymorphism also conferred the risk of developing T2D, but they were not correlated with DN and decreased eGFR.

Background: Coronary artery ectasia (CAE) is dilatation of an arterial segment to a diameter at least 1.5 times that of the adjacent normal coronary artery. The incidence of coronary artery ectasia is distinct in different countries that can be found in 1.2% to 5% of angiographic examinations.
Methods: This is a retrospective study that was conducted from September 2019 to February 2020 in Kermanshah University of Medical Sciences and the results were reported briefly. To obtain the desired articles, electronic searches were conducted in databases including the Scopus, PubMed, and Science Direct databases without time limited until October 2019. The keywords used were Coronary Artery Ectasia AND (Diabetes OR "Diabetes Mellitus"). This was done by two individuals separately and the final results were confirmed by a third person. Mixed method appraisal tool (MMAT) was used to evaluate the quality of studies. The structure of writing and the process of performing and reporting the study are based on the PRISMA checklist.

Background: Cholecystectomy is one of the most common abdominal surgeries and its preferred method is laparoscopy. The difficulty of laparoscopic cholecystectomy in diabetic patients is not clear and the preferred method of cholecystectomy in these patients is still under controversy. Therefore, this study was performed to evaluate the difficulty of laparoscopic cholecystectomy in diabetic and non-diabetic patients.
Methods: This retrospective analytical study was performed in Shahid Beheshti Hospital and Forghani Educational and Medical Center from April 2019 to April 2020. Samples were easily selected and 86 people in two groups of diabetic and non-diabetic patients were included in the study. All patient records were reviewed based on inclusion and exclusion criteria for factors such as age, sex, diet, duration of surgery, bleeding, adhesions, and open surgery, and finally, diabetes as a risk factor. It was compared between the two groups. Data were analyzed in SPSS software version 22, an independent t-test was used to analyze quantitative data and the chi-square test was used to analyze qualitative data. In this study, a significance level of less than 0.05 was considered.
Results: Abdominal scar, palpable gallbladder and gallstone were not significantly different between the two groups (P=0.33). But the history of cholecystectomy attacks was significantly different between the two groups (P<0.05). Laboratory values were not significant (P>0.05) . Hard operations in diabetic patients were more than nondiabetic
patients and even two cases of open surgery were seen in the group of diabetic
patients, but there was no significant relationship (P=0.09). Intraoperative bleeding was
statistically significant between the two groups (P=0.02), But adhesion during the
operation was not related (P=0.38).
Conclusion: Finally, our study showed that diabetes can be a predictive risk factor for the difficulty of cholecystectomy.

Background: Examining the course of changes in predictive indicators of future diabetes, such as blood sugar in high-risk individuals including pre-diabetic patients, can provide valuable information about the incidence of diabetes in these individuals. This study aimed to classify people at risk (pre-diabetes) based on the course of changes in their blood sugar and blood lipid and to investigate the incidence of diabetes in these classes on a sample of patients who were referred to the Endocrine and Metabolism Research Center of Isfahan.
Methods: This cohort study was performed based on the information of the Isfahan Diabetes Prevention Plan (IDPs). This project was implemented from April 2004 to March 2018 in the clinics of the Endocrine and Metabolism Research Center of Isfahan. The subjects in this study include 1228 pre-diabetic patients who participated in this project. Demographic and clinical variables of patients including blood sugar and lipid-blood variables were obtained using a questionnaire and laboratory measurements. Also in this study, the number of clinical variables was recorded 3 times. Data analysis was performed using the latent class growth trees model in R software version v4. (R v4.1.0)

Results: The mean (standard deviation) age of participants was 44 (6.86) years. Subjects were classified into two classes of low-risk impaired blood sugar (n=1165) and high-risk impaired blood sugar (n=63) based on the trend of changes in blood sugar levels. Blood sugar levels were reported in the first class (104.28) and the second class (132.41). Conclusion: In the present study, it was concluded that there is a significant relationship between the incidence of diabetes and the different classes formed based on the course of changes in blood sugar of at-risk individuals. Therefore, by classifying people at risk, the incidence of this disease can be predicted and thus prevented. Also,measures such as managing the blood sugar and lifestyle variables of pre-diabetic patients through nutrition counseling classes and regular periodic tests can be used to reduce the incidence of diabetes in the future is used in people with pre-diabetes who are at high risk for the disease.

Conclusion: the present study showed the effect of moderate-intensity continuous training on the expression of RBP4 and GLUT4 genes in soleus muscle which can be effective in glucose uptake. It was also shown that moderate-intensity continuous training can minimize the complications of diabetes by reducing RBP4 gene expression.

 

Background: Psoriasis is a chronic and recurrent inflammatory disease that involves skin, joints and different organ systems. It is associated with Multiple morbidities such as cardiovascular disorders, diabetes, hypertension, hyperlipidemia and chronic kidney disease (CKD). Due to the high importance of the association between psoriasis and CKD which results in major side effects the aim of this study was to evaluation of CKD and associated factors in Psoriasis patients at Razi Hospital, Tehran, Iran.
Methods: This retrospective study was conducted as a cross-sectional descriptive and analytical study to evaluate the frequency of CKD and associated factors in psoriatic patients admitted to the Razi Hospital whose last time of admission was from June 2018 to January 2019. According to the K/DOQI guideline, CKD is defined as the GFR<60 mL/min/1.73 m² during at least a period of three months. GFR was calculated based on the MDRD formula. The sample size was equal to 265. The hospital documents of inpatients who have been admitted to Razi Hospital wards or follow-up clinics during 2017-2019 were used for collecting information and data. This information has been extracted based on the initial checklist for data collection. Collected data has been analyzed and performed by using SPSS 25 software.
Results: The study found that 18 (6.8%) of psoriasis patients had CKD. Patients were in the age range of 3.5-92 years, the majority of them were in the age range of 18.65–79.7 years. 171 (64.5%) patients were male and 94 (35.5%) were female. 41 (15.5%) patients had diabetes, 94 (35.5%) had hyperlipidemia and 41 (15.5%) had hypertension. History of NSAID, Methotrexate, Cyclosporine, Acitretin, Infliximab, and Adalimumab medication use among 9 (3.4%), 205 (77.4%), 56 (21.1%), 147 (55.5%), 30 (11.3%), and 28 (10.6%) patients were observed, respectively. Also, 54 (20.4%) had a history of phototherapy. 217 (81.9%) of the psoriatic patients had CPP (Chronic Plaque Psoriasis) and 48 (18.1%) had PP (pustular Psoriasis) and finally, 21 (7.9%) of the patients had psoriatic arthritis.

Conclusion: The prevalence of CKD was shown to increase by age. The other correlated factors are diabetes, hypertension, and hyperlipidemia. On the other hand, there was not found any significant correlation between drugs (NSAIDs, Methotrexate, Cyclosporine, Acitretin, Infliximab, Adalimumab) and CKD prevalence. There was also no significant correlation between phototherapy, psoriasis type and psoriatic arthritis, duration of psoriasis and CKD prevalence.

Results: After treatment in placebo group the level of LDL-C (median 0.90 mg/dl to 0.82, P=0.008) and HDL-C (median 0.40 mg/dl to 0.35, P=0.090) were decreased, while in the empagliflozin group the levels of LDL-C (median 0.87 mg/dl to 0.96, P=0.875) and HDL-C (median 0.38 mg/dl to 0.48), P=0.007) increased. Treatment with Empagliflozin and placebo had no significant effect on changing the levels of total cholesterol, TG and eGFR (P>0.05). The weight loss and FBS reduction in the empagliflozin group were significantly higher than placebo (P=0.001 and P=0.048, respectively).