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Leptin is a 16-kD protein which is secreted from white adipocytes and, its discovery has generated enormous interest in the regulation of energy balance. Leptin has been implicated in the regulation of food intake, energy expenditure, and whole-body energy balance in animals and human. Plasma leptin levels correlate with fat storages and respond to changes in energy balance. It was initially proposed that leptin serves a primary role as an anti-obesity hormone, but this role is commonly thwarted by leptin resistance. The profound effects of leptin on regulating body energy balance, make it as a prime candidate for drug therapies of obesity in humans and animals. Despite the recent achievements in unearthing the role of leptin in the pathophysiology of obesity, many important questions still remained that must be responded. More studies with follow-up designs and genetic evaluations are warranted to understand the comprehensive role of leptin in human. In this letter we have a review of known effects of leptin on human obesity up to now.

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Background: Effect of mobile text message on blood glucose (HbA1c) control in providing type 2 diabetes care (diabetes mellitus non insulin dependent).

Methods: The present study is a systematic review with meta-analysis. A search of the most important electronic medical databases of medical resources from December 1992 to January 2017 in a systematic manner, including: CRD, Ovid Medline, PubMed, Cochrane Library, and moreover, by referring to the resources found in the articles and manual search on the site. Related to this technology and, if necessary, contacting experts. All randomized clinical trials and cohort studies were reviewed.
Results: Participants included nine studies (818), all randomized clinical trials, and quality assessment. The average decrease in Mobile SMS Services users compared to the control group (SMD-0.324, 95% CI, -0.526 to -0.121; I2 = 51.0). The analysis of subgroups showed that young patients are more likely to use diabetes programs, and the size of the effect increases with short intervals of interventions and the size of large samples.
Conclusion: Mobile SMS services may be considered as an effective component for helping control glycosylated hemoglobin and as a side intervention for the care of patients with type 2 diabetes.

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Backgrounds: Early detection of diabetes is critical to avoid complications and damage caused by this disease. The purpose of this paper is designing an intelligent system for Diabetes prediction (healthy or patient) by using regression method based on Multilayer Perceptron Neural Network.
Methods: In this descriptive-analytic study, an intelligent system is designed to classification diabetes patients. The system is simulated by MATLAB software 2015 (8.5.0.197613). In this study, used PID dataset in UCI Machine Learning Repository. The dataset is contained 768 records from Indian women and 8 diagnostic factors for Diabetes.
Results: The data were then divided randomly in 20 groups for training and testing, after preprocessing. 90% of the data is used for training phase and 10% for the test phase. The results obtained based on sensitivity, specificity, accuracy and precision were 0.4815, 0.9804, 0.8077 and 0.9286, respectively.
Conclusion: The obtained results, showed superiority of designed intelligent system to classify individuals (healthy and patient) in comparison with other methods implemented on this dataset. Using MLP- Regression has increased the accuracy of the proposed system.
 

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Background: Diabetes is one of the most dangerous and common diseases of the modern world. Since medical research usually has limited data available and medical data is very ambiguous, it seems appropriate to use the fuzzy model to find out the relationship between input and output in medical data. None of the previous articles of fuzzy regression have been used to predict complications of diabetes, including nephropathy. Therefore, in this study, a fuzzy regression model was used to predict nephropathy in a diabetic patient.
Methods: In the present study, GFR results of previous patient experiments were used to predict a deeper horizons of GFR and ultimately to predict renal disease. Chronic kidney disease has been stratified based on the amount of GFR, that fuzzy data has been constructed based on these levels. The GFR prediction was performed in the following steps. Step 1: Define fuzzy sets based on the GFR level, which is considered for each level of a fuzzy set. Step 2: Fuzzify patient data Based on fuzzy sets. Step 3: GFR prediction with fuzzy regression model. Step 4: Defuzzifying the predictions. Step 5: Evaluating the model efficiency. The RMSE error is used to compare the performance of the model.
Results: The results of GFR prediction showed that comparison RMSE was 10.09 with using simple linear regression model and 4.24 in fuzzy model.
Conclusions: fuzzy regression model can predict nephropathy in diabetic patients.

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Background: In Iran, with the advancement of technology and the development of registration statistics, the need to use data mining methods has attracted more attention from researchers. Regression and classification tree is one of the important methods in Big data modeling, which has attracted the attention of many researchers for community control and prediction. The purpose of this study is to determine the influencing variables on the occurrence of complications caused by diabetes.
Methods: This paper is a cross sectional-analytical study. In this research, all diabetic patients covered by Mashhad University of Medical Sciences in 2017 were extracted from the SINA system. The number of diabetics with complications was 5016 and diabetics without complications were 53613. The method of fitting the regression tree model and classification and measurement criteria of the model is the coefficient of determination and the area of the Rock curve and the Lift diagram.
Results: The rock curve for the fitted tree model is 73.8%, which shows the relatively high power of the model. Based on the Lift chart, the decision-making power of diabetes complications increases 3.5 times for the person who comes to visit.
Conclusion: The results of the regression model and tree classification showed that, in descending order, age, risk assessment factor, FBS, HbA1C, total activity time, cholesterol, FBS and HDL, cardiovascular disease, history of stroke, blood pressure, cholesterol Statin prescription, job with hard physical activity, living area, consumed oil, walking, consumption of vegetables and gender are more effective than other factors in the occurrence of diabetes complications.

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Background: Diabetes entails a great quantity of deaths each year and a great quantity of people living with the disease do not find out their health status early sufficient. In this paper, we advance a data mining-based model for prematurely diagnosis and prediction of diabetes.
Methods: Although K-means is simple and can be utilized for a vast diversity of data kinds, it is wholly sensitive to initial locations of cluster centers which specify the final cluster result, which either enables an efficiently and adequate clustered dataset for the logistic regression model, or presents a lesser amount of data as a result of wrong clustering of the main dataset, thereby restricting the proficiency of the logistic regression model. The main purpose of this study is was to specify procedures of ameliorating the k-means clustering and logistic regression accuracy consequence. Therefore, our algorithm comprises of principal component analysis technique, k-means technique and logistic regression model.
Results: The results obtained from this study show that the ability to obtain the result of K-means clustering accuracy is much higher than what other researchers have obtained in similar studies. Also, compared to the results obtained from other algorithms, the logistic regression model was implemented at an improved level in predicting the onset of diabetes. Another real advantage is that the proposed algorithm was able to successfully model a new dataset.
Conclusion: In general, the proposed approach can be effectively used in predicting and early diagnosis of diabetes.