Iranian Journal of

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Diabetes during pregnancy is either concurrent diabetes, diagnosed before pregnancy, or ‘gestational diabetes mellitus’ (GDM), first diagnosed in pregnancy. GDM is the commonest metabolic disorder of pregnancy, with a prevalence of one to 14 percent, depending on the reporting team. The prevalence of GDM in Tehran is 4.7%. Diabetes concurrent with pregnancy is diagnosed according to the recently revised criteria of the National Diabetes Data Group (NDDG). GDM is diagnosed with a 100-gram 3-hour glucose tolerance test (3hGTT100), with at least two abnormally high readings being required for a definite diagnosis. Screening for GDM is still a matter of dispute. Universal or selective screening? Each has its supporters. The American Diabetes Association (ADA) recommended universal screening until 1997, and this is still recommended for areas with a high prevalence of GDM. Diabetes during pregnancy has multiple potential consequences for mother and fetus fetal macrosomia being the commonest. The children of diabetic mothers are also more likely to become overweight and develop impaired glucose tolerance. ADA recommendations for glycemic control in diabetic mothers-to-be include maintaining their fasting blood glucose between 60 and 95mg/dl, and their postprandial blood glucose between 80 and 120mg/dl. Measurement of urinary ketones is recommended when the patient is on a calorie-restricted diet. Lifestyle changes are an integral part of management. Insulin requirements and calorie intake must be adjusted in line with weight gain as pregnancy progresses.

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Diabetes mellitus is characterized by several adverse consequences among which diabetic foot is a major complication. With a life long incidence of 15%, diabetic foot is accountable for more than 50% of non-traumatic lower limb amputations. Regular care, proper footwear and early treatment, but, have proved effective measures in preventing such outcome. The problem and features as infection, ulceration, or gangrene. Neuropathy, poor circulation, and decreased resistance to infection are the three major contributors to the development of diabetic foot which when present, foot deformities or minor trauma can readily lead to ulceration and infection. Not all diabetic foots are preventable, but appropriate preventive measures can dramatically reduce their occurrence.

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Background: Diabetic Neuropathy is the most common and troublesome complication of Diabetes Mellitus, leading to the greatest morbidity and mortality and resulting in a huge economic burden for diabetes care. Early diagnosis of distal symmetric sensorimotor polyneuropathy, a common complication of diabetes, may decrease morbidity by allowing potential therapeutic interventions.
Methods: In 68 diabetic patients after neuropathy screening by U.K and Michigan scores, Bilateral sural nerve conduction parameters as nerve conduction velocity, latency and amplitude were determined and analysed.
Results: 54.4% of patients had Rt sural abnormal response, 50% had abnormality in left side and 39.7% had bilateral abnormality. There was significant statistical correlation between Michigan physical score and electrophysiologic finding (P-value < 0.003) but no correlation with U.K score (P-value > 0.3). The most prevalent abnormal electrophysiologic finding was amplitude decrement of sural response. Conclusion: Sural nerve response is one of the simplest and most sensitive peripheral sensory nerves for electrophysiologic study of diabetic neuropathy and its evaluation is recommended in all diabetic patients in spite of normal physical examination and history for detection of subclinical neuropathic cases. For increment of sensitivity, amplitude measurement of sensory response is highly recommended.

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Background: To determine the prevalence and characteristics of subjects with isolated post challenge hyperglycemia (IPH) defined as fasting plasma glucose (FPG) <126 mg/dl and 2-hour plasma glucose (2h-PG) ≥200 mg/dl.

Methods: We evaluated data and results of Oral Glucose Tolerance Test (OGTT) from 9745 participants in Tehran Lipid and Glucose Study (5589 women and 4156 men) aged>20years, without previously diagnosed diabetes mellitus (DM). A logistic regression model for having IPH was developed among subjects with FPG<126mg/dl to predict.

Results: The prevalence of IPH was 3.1% (n=302). 26.5% (n=80) of subjects with IPH had FPG<100mg/dl. The odds ratios for IPH were statistically significant for FPG≥100mg/dl (OR=9.5 95% CI: 7.1-12.5), age≥40 years (OR=2.6 95% CI: 1.8 - 3.7), triglycerides≥200 mg/dl (OR=2.1 95% CI: 1.6-2.7), hypertension (OR=2.0, 95% CI: 1.5-2.6) and abnormal waist circumference (females≥80, males≥90cm) (OR=1.9, 95%CI: 1.3-2.8).

Conclusion: This analysis showed that in Tehran urban subjects with FPG<126 mg/dl, variables such as FPG≥100 mg/dl, older age, hypertriglyceridemia, hypertension and abnormal waist circumference were significantly associated with higher likelihood of IPH OGTT would hence be recommended in subjects with above mentioned characteristics.

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Background: LADA is a subgroup of type 1 diabetes mellitus characterized by its age at diagnosis being more than 30 years, presence of Glutamic Acid Decarboxylase₆₅ (GAD₆₅) autoantibody, and insulin independency in the first 6 months of diagnosis. Our aim was to determine the prevalence and predictors of LADA in a large population-based drug naïve newly diagnosed adult diabetics of Tehran urban population.

Methods: In this cross-sectional study, all newly diagnosed diabetics of Tehran Lipid and Glucose Study aged more than 30 years who were not initialy treated with insulin were tested for GAD antibody titer. Thirty two GAD antibody positive (LADA) and 556 GAD antidody negative patients (type 2 diabetics) were found and compared for anthropometric, clinical and laboratory features.

Results: Mean age of all 588 cases was 54.2±11.6. The prevalence of LADA was %5.44 (CI %95: %3.6-%7.3). Age, sex, BMI, family history of diabetes, diastolic blood pressure, glucose and lipid profile and the prevalence of metabolic syndrome were similar between LADA and type 2 diabetics. Systolic blood pressure were greater in type 2 diabetics than in LADA group. No model could predict the result of GAD antibody measurement.

Conclusion: autoimmune process is present in early stages of diabetes in %5.4 of newly diagnosed adult diabetics assumed to suffer type 2 diabetes mellitus. Using clinical and laboratory features, no model could reliably screen these cases. It seems reasonable to test GAD antibody in all such cases to early find persons more probable to have a more rapid process toward pancreas insufficiency.

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Background: Today many people are likely to have diabetes. Diabetes is a serious disease of modern societies and its on-time diagnosis have important role in the treatment of disease. Methods: In this paper, using Sugeno fuzzy inference systems and intelligent algorithms Firefly, a new method is presented for the detection of diabetes. The proposed method enables the use of a few simple fuzzy rules to detect diabetes with good accuracy. Results: Sugeno fuzzy inference system and firefly algorithm com

Background: Today many people are likely to have diabetes. Diabetes is a serious disease of modern societies and its on-time diagnosis have important role in the treatment of disease.

Methods: In this paper, using Sugeno fuzzy inference systems and intelligent algorithms Firefly, a new method is presented for the detection of diabetes. The proposed method enables the use of a few simple fuzzy rules to detect diabetes with good accuracy.

Results: Sugeno fuzzy inference system and firefly algorithm combines efficiency was 87.24 percent.

Conclusion: Experimental results show that this method deals on the data set is more accurate than the standard PID algorithm in this field.

bines efficiency was 87.24 percent. Conclusion: Experimental results show that this method deals on the data set is more accurate than the standard PID algorithm in this field.

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Background: On time diabetes diagnosis dramatically reduces the many injuries and damage in the community. Diabetes is a disease that requires a lot of care in addition to prevention, such as prediction the correct level of blood sugar fluctuations. The most important complications of such disease are anesthesia, coma and even death at final. Today, in these patients, determining the correct dose of insulin is based on the experience and knowledge of physicians along with the interaction of patients with them, although human error is inevitable.
Methods: This study includes 124 patients and 188 healthy suspects were examined based on 21 features which hold by 7 features for diagnosis and 14 features for predicting insulin dose. The proposed system was presented to identify or diagnose the disease at first, and finally the correct doses of insulin for patients have been determine. The proposed system has two stages (which include diagnosis and prediction) and several subsystems. In the diagnosis phase, some sub systems such as the Fuzzy system for the purpose of accurately estimating the disease progression in patients and the decision tree (DT) for the preparation of rules in the fuzzy system (the process of mapping the attribute space (individuals) to the output (the diagnostic result)) have used. Also, in the prediction phase of insulin dose, the BPSO algorithms are used to select the best features. Classification algorithms (SVMs) are used to categorize effective to non-effective and adaptive artificial neuropsychological (ANFIS) systems for ultimate patient prediction have used.
Results: The proposed system, based on the best features in the provided data base in the form of the combination and interaction, succeeded to achieve a 95.1% precision, of course due to comparing by other commonly used methods and its performance the proposed method have high precision.
Conclusion: The results were significantly improved compared to previous studies. Also, in comparison with the results of physicians, it is indicative of good performance in predicting the accuracy of the time series of glucose concentration because the proposed system succeeded in predicting blood sugar levels for up to 48 hours.

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