Public Awareness of Early and Late Complications of Type 2 Diabetes - Application of Latent Profile Analysis in Determining Questionnaire Cut-Off Points

OBJECTIVES: A questionnaire was designed to determine public understanding of early and late complications of Type 2 diabetes mellitus (T2DM). METHODS: A cross-sectional study was performed in participants who were selected using a multi-stage sampling method and a standard questionnaire of 67 questions was proposed. An expert panel selected 53 closed-ended questions for content validity to be included in the questionnaire. The reliability of the questionnaire was tested using Cronbach’s alpha coefficient giving a score of 0.84. RESULTS: Of the 825 participants, 443 (57.6%) were male, and 322 (41.87%) were 40 years or more. The proportion of low-, moderate- and high- awareness about T2DM and its complications was 29.26%, 62.68%, and 8.06%, respectively. Friends (56.31%) and internet and social networks (20.55%) were the 2 major sources of awareness, respectively. The medical staff (e.g., physicians) had the lowest share in the level of public awareness (3.64%) compared to other sources. CONCLUSION: These results present data that shows the general population awareness of T2DM is low. Healthcare policymakers need to be effective at raising awarenes of diabetes and it should be through improved education.

Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors / 한국역학회지

OBJECTIVES: To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model. METHODS: This study was conducted on a sample of 234 individuals, in whom T2DM was diagnosed using hemoglobin A1c levels. A multilayer perceptron artificial neural network was used to identify demographic risk factors for T2DM and their importance. The DeLong method was used to compare the models by fitting in sequential steps. RESULTS: Variables found to be significant at a level of p < 0.2 in a univariate logistic regression analysis (age, hypertension, waist circumference, body mass index [BMI], sedentary lifestyle, smoking, vegetable consumption, family history of T2DM, stress, walking, fruit consumption, and sex) were entered into the model. After 7 stages of neural network modeling, only waist circumference (100.0%), age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoking (49.3%), and a family history of T2DM (37.2%) were identified as predictors of the diagnosis of T2DM. CONCLUSIONS: In this study, waist circumference and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests.

Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors / 한국역학회지

OBJECTIVES: To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model.METHODS: This study was conducted on a sample of 234 individuals, in whom T2DM was diagnosed using hemoglobin A1c levels. A multilayer perceptron artificial neural network was used to identify demographic risk factors for T2DM and their importance. The DeLong method was used to compare the models by fitting in sequential steps.RESULTS: Variables found to be significant at a level of p < 0.2 in a univariate logistic regression analysis (age, hypertension, waist circumference, body mass index [BMI], sedentary lifestyle, smoking, vegetable consumption, family history of T2DM, stress, walking, fruit consumption, and sex) were entered into the model. After 7 stages of neural network modeling, only waist circumference (100.0%), age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoking (49.3%), and a family history of T2DM (37.2%) were identified as predictors of the diagnosis of T2DM.CONCLUSIONS: In this study, waist circumference and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests.

Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors / 한국역학회지

OBJECTIVES@#To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model.@*METHODS@#This study was conducted on a sample of 234 individuals, in whom T2DM was diagnosed using hemoglobin A1c levels. A multilayer perceptron artificial neural network was used to identify demographic risk factors for T2DM and their importance. The DeLong method was used to compare the models by fitting in sequential steps.@*RESULTS@#Variables found to be significant at a level of p < 0.2 in a univariate logistic regression analysis (age, hypertension, waist circumference, body mass index [BMI], sedentary lifestyle, smoking, vegetable consumption, family history of T2DM, stress, walking, fruit consumption, and sex) were entered into the model. After 7 stages of neural network modeling, only waist circumference (100.0%), age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoking (49.3%), and a family history of T2DM (37.2%) were identified as predictors of the diagnosis of T2DM.@*CONCLUSIONS@#In this study, waist circumference and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests.

relationship between clinicobiochemical markers and depression in women with polycystic ovary syndrome

Previous studies have demonstrated that clinical features of 16TPolycystic ovary syndrome16T [PCOS] are associated with a lower degree of health, self, and sex satisfaction. Our study aimed to investigate possible associations between depression and different clinicobiochemical markers of PCOS. In a cross-sectional analytic study, 120 PCOS women aged 18-45 yr, were enrolled. Beck Depression Inventory was used to assess depression. Also, all participants underwent biochemical studies. Individuals with 15 points and more in Beck test were referred to a psychiatrist to participate in a complementary interview for the diagnosis of depression based on Diagnostic and Statistical Manual of Mental Disorders IV [DSMIV-TR] criteria. Among the study participants, 82 women [68.3%] were non-depressed, and 38 patients [31.7%] had some degrees of depression. According to the psychiatric interview, 10 patients [8.3%] had major depression, 22 patients [18.3%] had minor depression and 6 patients [5%] had dysthymia. We failed to show any significant difference in body mass index, hirsutism, infertility, serum total testosterone, lipid profile, and the homeostasis model assessment of insulin resistance [HOMA-IR] between depressed and non-depressed subjects [p>0.05]. Using Spearman correlation, we did not find a positive correlation between BDI scores and clinicobiochemical markers for all PCOS subjects [-0.139 0.05]. In spite of high rate of depression in women with PCOS, there was no significant association between Clinicobiochemical Markers and depression

Insulin therapy: Side effects and their management

Insulin has an important and vital role in control of blood glucose for all diabetic patients. With increasing the prevalence of diabetes mellitus, insulin consumption will be increased too. Different types of insulin are available, i.e. short, rapid, intermediate and long acting insulin and also mixed insulins. The side effects of insulin may lead to the lack of a desirable control of blood sugar or discontinuing insulin injection by the patients. On the other hand sometimes the side effects of insulin can be serious and fatal. Therefore, information about these complications and how to deal with them are very important. Hypoglycemia, weight gain, antibody-mediated insulin resistance, lipodystrophy [atrophy or hypertrophy], edema, postural hypotension and allergic reactions to insulin are major side effects of insulin therapy