Prevalence and risk factors of hepatitis C virus infection in Amol city, north of Iran: A population-Based study (2008-2011)

Background: Hepatitis C Virus (HCV) infection is one of the most important causes of chronic liver disease and related problems in the world.There are few population-based studies on the prevalence and risk factors of hepatitis C infection in Iran, which could not provide enough information. Moreover, the prevalence and risk factors of hepatitis C infection are not similar in all parts of Iran. Objectives: The aim of this survey was to determine the prevalence and risk factors of HCV infection in the general population of the city of Amol, north of Iran. Patients and Methods: This was a population-based study. Using a cluster sampling approach, 6145 individuals of both genders and different ages were involved from general population of urban and rural areas of Amol, The inclusion criteria were Iranian nationality, willing to participate in the study, and lifelong residence in Amol city and surrounding areas. Anti-hepatitis C antibody was measured by a third generation of ELISA. The positive results were confirmed by Recombinant Immuno Blot Assay (RIBA) and quantitative HCV-RNA polymerase chain reaction (PCR) tests. Potential risk factors of HCV transmission were recorded. Results: The mean age of participants was 42.70 ± 17.10 years. Of these participants, 57.2 (n = 3483) were male. Anti-HCV antibody was positive in 12 individuals from which five were RIBA positive. Three of these subjects were PCR positive. The prevalence of HCV was more predominant among males than females. The common risk factors among the study subjects included history of minor or major surgery (34.7), unsterile punctures (21.2), history of traditional phlebotomy (5.8), and history of hepatitis among close relatives (5.7). In univariate regression analysis, unsterile punctures and history of infection in family members were associated with HCV infection. Conclusions: We confirm that in Amol city and surrounding areas, the prevalence of true HCV infection is 0.05, which is lower than that previously reported from Iran. © 2013, Kowsar Corp.; Published by Kowsar Corp

The best obesity indices to use in a single factor model indicating metabolic syndrome: A population based study

Objective: Although metabolic syndrome (MetS) is a major health problem worldwide, there is no universal agreement on its definition. One of the major disagreements is dealing with the issue of obesity in this definition. This study was conducted to determine a preferably better index of obesity which can be interrelated with other components of MetS in a single factor model of MetS. Design: Out of 6140 participants of a cohort study of subjects aged 10-90 years in northern Iran, the baseline data of 5616 participants aged 18-75 was considered. Confirmatory factor analysis was conducted using AMOS software to evaluate a single factor model of MetS in which blood pressure, triglyceride (TG), high density lipoprotein (HDL), fasting blood sugar (FBS) and obesity measures including waist circumference (WC), body mass index (BMI), waist to hip ratio (WHR) and waist to height ratio (WHtR) were used as indicators of metabolic syndrome. Four single factor models differing from each other by obesity indices were evaluated. The models were evaluated in all 5616 subjects and 4931 subjects without diabetes mellitus according to sex separately. Results: All single factor models had appropriate fit indices with CFI > 0.95, GFI > 0.95 and RMSEA < 0.08 in non-diabetic population, wherein all models obtained the best values of fit indices in men and good fit indices in women. In the general population of men, the single factor models built based on WHR (Chi-square=6.9, df=2, P-value=0.031, RMSEA = 0.028, CI = 0.007-0.052, CFI = 0.994, GFI = 0.999 and AIC = 22.9) and WHtR (Chi-square = 9.97, df = 2, P-value = 0.007, RMSEA = 0.036, CI = 0.016-0.059, CFI = 0.992, GFI = 0.998 and AIC = 25.97) were fitted properly with data while in th general population of women, the model based on WHR obtained better fit indices (Chi-square = 7.5, df = 2, P-value = 0.023, RMSEA = 0.033, CI = 0.011-0.060, CFI = 0.994, GFI = 0.998 and AIC = 23.5). Models based on WHtR obtained better regression weights than WHR. Conclusion: While single factor validity of MetS was confirmed in almost all models, the best models were different according to sex and population of study. © 2016, Academy of Medical Sciences of I.R. Iran, INIA. All rights reserved

The Best Obesity Indices to Discriminate Type 2 Diabetes Mellitus

Background: It is expected that the number of people with diabetes will reach 435 million by 2030. Obesity is considered the most important predictor of type 2 diabetes mellitus (T2DM). We conducted the present study to determine the best usual discriminator indices of obesity to diagnose diabetes mellitus (DM). Methods: Of 6143 subjects aged 10-90 years from a baseline cohort study, the data of 5772 participants aged >18 years and without history of type 1 diabetes were utilized to analyze in this study. The cohort study was carried out in northern Iran and sampling frame was provided from related local health centers. The capability of obesity indices, including body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), and body adiposity index (BAI), in the discrimination of DM was evaluated. Discriminatory capabilities were evaluated using the receiver operating characteristic (ROC) curve. Logistic regression analysis was performed to determine the strength of association between obesity indices and DM. Results: The areas under ROC curve of BAI, BMI, WC, and WHR were 0.6244 (0.5918-0.6570), 0.6214 (0.5908-0.6520), 0.6636 (0.6341-0.6930), and 0.7303 (0.7032-0.7575) in men and 0.5961 (0.5674-0.6249), 0.5963 (0.5690-0.6235), 0.6850 (0.6593-0.7108), and 0.7529 (0.7297-0.7761) in women, respectively. In the multivariate model, one unit increase in Z-score of BMI, WC, and WHR increased the chance of DM by 49, 65, and 51 in men and by 17, 51, and 67 in women, respectively. No association was found between DM and BAI in this model. Conclusions: While WHR had an appropriate discriminatory capability for T2DM in the population of northern Iran, BAI and BMI did not. © 2016, Mary Ann Liebert, Inc

Nonalcoholic fatty liver: The association with metabolic abnormalities, body mass index and central obesity - A population-based study

Background: To assess the prevalence of nonalcoholic fatty liver (NAFL) in Iran and to evaluate correlates of NAFL in categories of body mass index (BMI). Methods: Using a cluster random sampling approach, 7723 subjects over 18 years of age underwent abdominal ultrasonography, laboratory evaluations, blood pressure, and anthropometric measurements and were interviewed to obtain baseline characteristics. Prevalence of NAFL according to BMI and waist to hip ratio and its association with metabolic abnormalities in categories of BMI were assessed in multivariate analysis. Results: The overall prevalence of NAFL was 35.2 95% confidence interval (CI) 34.1-36.3. A significant number of subjects with BMI <30 had NAFL 22.1% (CI 21.0-23.2). Waist to hip ratio for 38.2% (CI 35.6-40.8) of the subjects with NAFL, and BMI <30 was higher than normal values. The odds ratio for association of NAFL and dyslipidemias were higher in subjects with BMI <30 versus those with BMI �30: (1) hypertriglyceridemia: 2.21 vs. 1.57, P=0.006; (2) lower high-density lipoprotein: 1.29 versus 0.98, P=0.046. Higher low-density lipoprotein also revealed greater association with NAFL in subjects with BMI <25 than those with BMI �25 (odds ratio 1.84 vs. 1.1, P=0.015). Conclusions: NAFL shows stronger association with central obesity compared to high BMI. NAFL has stronger association with dyslipidemias in subjects with low compared with high BMI. © Mary Ann Liebert, Inc. 2015