Iranian Journal of

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Background: Diabetes type 1 is an autoimmune disease that is associated with other autoimmune disorders like Hashimoto thyroiditis. The purpose of this study was to determine the prevalence of autoimmune thyroid disease (ATD) in type 1 diabetic patients in the south of Iran (Bandar Abbas).
Methods: A cross-sectional study, was conducted 48 type 1 diabetics and 41 age and sex matched healthy controls were evaluated for the presence of ATD. Blood sample were collected from all of the subjects for the measurement of thyroid autoantobodies [anti thyroid peroxidase (anti-TPO) and anti thyroglobulin (anti-TG)], T3, T4, TSH, RT3U and HbA1c.
Results: Prevalence of positive anti-TPO and anti-TG was 29 % and 29% respectively in diabetic patients and 2% and 7% respectively in control group (P<0.05). The prevalence of ATD (positive anti TPO or anti TG) in diabetic patients and control subjects was 35% and 7% respectively (P<0.05). The prevalence of positive anti TPO and anti TG was higher in girls. There was no association between the prevalence of positive autoantibody and duration or age of onset of diabetes. 17.6% of diabetic patients with positive autoantibody had thyroid dysfunction (subclinical hypothyroidism and hyperthyroidism).
Conclusion: Regarding high prevalence of ATD in type 1 diabetic patients in Bandar Abbas (approximately 1 out of 3 patients), screening for ATD and thyroid dysfunction is recommended for all type 1 diabetic patiens and in those with positive autoantibody consecutively.

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Background: The aim of this study was to determine the level of agreement between the impaired fasting glucose (IFG) and abnormal glucose tolerance before and after application of the new IFG definition and to evaluate the impact of adding common clinical data on this agreement.
Methods: A cross sectional population based study was carried out in an Iranian urban population which enrolled 8766 men and women over 20 years. Fasting and 2-hour plasma glucose were measured in all subjects excluding those with previously diagnosed diabetes and fasting plasma glucose ≥126 mg/dl. The diagnostic parameters and kappa coefficient between the previous and revised definitions of IFG for detecting impaired glucose tolerance (IGT) and dysglycemia (IGT and diabetes) were calculated. Logistic regression and ROC curve analysis were used to determine the independent clinical risk factors and their optimal cut-points associated with IGT and dysglycemia. Results: After using the new criteria, sensitivity of IFG for detecting IGT or dysglycemia increased but specificity and positive likelihood ratio (LR+) decreased and the κ slightly improved (0.16 to 0.29 for IGT and 0.24 to 0.35 for dysglycemia). Adding the clinical data to the revised criteria considerably improved the agreement between IFG with IGT and dysglycemia (κ increased from 0.286 to 0.470 for IGT and from 0.354 to 0.574 for dysglycemia). This also increased the LR+ from 3.86 to 14.5 and from 4.46 to 17.4 respectively for detecting IGT or dysglycemia.
Conclusion: The new IFG definition in combination with common clinical risk factors most likely predicts IGT and dysglycemia.

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Background: The aim of this study was to examine the prevalence of the metabolic syndrome (MetS) and its association with coronary heart disease (CHD) in Iranian older individuals.

Methods: In this cross-sectional study, the prevalence of the MetS was determined according to the Third Adult Treatment Panel (ATPIII), the World Health Organization (WHO) and the International Diabetes Federation (IDF) definitions in 720 men and women aged≥65 years. Logistic regression analysis was used to estimate the Odds Ratio (OR) of developing CHD in model 1 an age adjusted model, in model 2 adjusted for age, smoking status, premature history of CHD and LDL cholesterol and in model 3 adjusted for mentioned variables in model 2 plus the components of the MetS according to each definition.

Results: The prevalence of MetS was 50.8%, 41.9% and 41.8% by ATPIII, IDF and the WHO definitions, respectively. IDF had high agreement with the ATPIII definition. In model 2, the ATPIII and the WHO definitions of MetS were associated with CHD by the odds ratio of 1.6 (1.1-2.2) and 1.7 (1.9-2.4), respectively. In model 3, obesity (WHO definition) and high blood pressure (ATPIII and WHO definitions) were associated with CHD.

Conclusion: As defined by the ATPIII and WHO definitions, the MetS was associated with CHD even after adjustment for the conventional CHD risks, but after further adjustment for their components none of these definitions showed association with CHD.

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Background: Non- alcoholic fatty liver disease (NAFLD) is a pathogenic factor of insulin resistance and type 2 diabetes. On the other hand, the circulating liver enzymes including Aspartate aminotransferase (AST), Alanin aminotranferase (ALT) and Gamma glutamyl transferase (GGT) are commonly elevated in asymptomatic patients with NAFLD.

Methods: As a nested case-control study, AST, ALT, GGT as well as classic diabetes risk factors, homeostatic model assessment of insulin resistance(HOMA- IR) and C-reactive protein (CRP) were measured in 133 non-diabetic subjects at baseline (68 cases and 65  controls). Conditional logistic regression was used to calculate the odds ratio (OR) of diabetes associated with different hepatic markers. We used factor analysis for clustering of classic diabetes risk factors.

Results: In Univariate analysis, both ALT and GGT were associated with diabetes with ORs of 3.07(1.21-7.79) and 2.91(1.29-6.53), respectively. After adjustment for CRP and insulin, ALT and GGT were still predictive of incident diabetes. When the model was further adjusted for anthropometric, blood pressure and metabolic factors resulted from factor analysis (full model), only ALT was independently associated with diabetes [OR=3.06 (1.01-9.26)]. No difference was found between the area under the receiver operating characteristic curves of the models with and without ALT (0.820 and 0.802 respectively, P=0.4)

Conclusion: ALT is associated with incident type 2 diabetes independent of classic risk factors. However, its addition to the classic risk factors does not improve the prediction of diabetes.