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Type 2 Diabetes Dataset

Mendeley Data - The Sex Independent Angle Of Type 2 Diabetes

Mendeley Data - The Sex Independent Angle Of Type 2 Diabetes

You are viewing a previous version of this dataset. Version 4 is the latest version of this dataset. The Sex Independent Angle of Type 2 Diabetes Published: 5 May 2018 | Version 2 | DOI: 10.17632/zx4tjnph4x.2 Biochemistry, Genetics and Molecular Biology Patients were randomly recruited from two hospitals and divided into two groups. One group had no form of diabetes and served as controls (n = 50). The other group had the condition of type 2 diabetes (n = 71). Measurements of minimal waist, umbilical waist and hip circumference were taken. Fasting blood samples from subjects were analyzed for glycated hemoglobin, blood glucose and lipid profile. All sex independent anthropometric variables and blood data were compared between groups with and without type 2 diabetes. Patients with type 2 diabetes had a significantly higher Angle Index (AI) value as compared to controls (p < 0.001). AI was the superior sex independent anthropometric index in relation to type 2 diabetes (AUC = 0.717; p < 0.001) as compared to other sex independent variables. AI correlated with glycated hemoglobin [Spearman's Correlation (r) = 0.28, p = 0.003) and fasting blood glucose (r = 0.31, p = 0.001) levels. Patients with type 2 diabetes were four times more likely to have an AI > 184 degrees [OR 4.2 (1.8 9.9)] as compared to controls. Angle Index (AI) was a superior sex independent index for discriminating between patients with type 2 diabetes and patients without diabetes, as compared to waist circumference, abdominal volume index, conicity index, blood pressure readings, triglyceride levels and very low density lipoprotein (VLDL) levels. Supplementary Information Spreadsheet .ods This is the raw data for paper entitled, "The application of trigonometry to waist and hip measurements in relation to Continue reading >>

Diabetes - Datasets - Wprdc

Diabetes - Datasets - Wprdc

The Western Pennsylvania Regional Data Center (WPRDC) is a project led by the University Center of Social and Urban Research (UCSUR) at the University of Pittsburgh ("University") in collaboration with City of Pittsburgh and The County of Allegheny in Pennsylvania. The WPRDC and the WPRDC Project is supported by a grant from the Richard King Mellon Foundation. This Data Use Agreement covers the terms and conditions that you must agree to before you access or use the Data on deposit with the WPRDC. By using the data available on the WPRDC website portal, you agree to the terms and conditions of your access to the WPRDC and your use of the Data on deposit with the WPRDC. Intending to be legally bound, you agree to the following: Effective Date: The effective date of the Agreement is the date of your signature or click through acceptance of these terms, and the terms shall continue to bind you with every subsequent attempt to access or use Data from the WPRDC. "Data" for purposes of this Agreement, shall mean all information of varying formats which has been deposited with the WPRDC by The City, The County, and other third parties, to make such information available for public access. Data may cover, but is not limited to topics including property ownership, budgets, transportation, education, public safety, public services, and geographic information. Data may consist of, but is not limited to administrative records created by government or other organizations, statistical information designed to improve the function of government and organizations, and information created about government and organizations. The Data on deposit with the WPRDC is not intended to and should not contain Non-Public Information, as defined below. "Non-Public Information" for the purposes of t Continue reading >>

Type 2 Diabetes Knowledge Portal

Type 2 Diabetes Knowledge Portal

The is an open-access resource for human genetic information on type 2 diabetes (T2D). It is a central repository for data from large genomic studies that identify DNA variants whose presence is linked to altered risk of having T2D or related traits, such as elevated blood glucose levels. Pinpointing these DNA variants, and the genes they affect, will spur novel insights into how T2D develops and suggest new potential targets for drugs or other therapies to treat T2D. The T2D Knowledge Portal aggregates many data sets, formerly disparate, in a framework that allows data sharing and analysis while properly crediting researchers and protecting patient privacy. It provides a user-friendly interface that enables all scientists — not only specialized geneticists, but also those from other disciplines including molecular biology and drug development — to mine the data by searching for information on particular genes or variants, and to find variants that are associated with particular traits. It provides a summary of each T2D-associated genetic variant, and also allows users to dig deeper into the data and run on-the-fly genetic analyses. New data sets are added to the knowledgebase as they are generated, continually increasing the power of analyses performed via the portal. This project is a collaborative effort of scientists and software engineers at the Broad Institute, University of Michigan, University of Oxford, and many other collaborators, and is part of a worldwide scientific consortium with contributors from academia, industry, and non-profit organizations. Financial support is provided by the Accelerating Medicines Partnership in Type 2 Diabetes — a collaboration of the National Institutes of Health, five major pharmaceutical companies, and three large non-pr Continue reading >>

Mpinet

Mpinet

Four datasets were used in the main study, including one metastatic prostate cancer dataset, two type 2 diabetes datasets and one drug sensitivity dataset. Detailed information regarding the metabolites of interest for each dataset is as follows: Metastatic prostate cancer data set: The metabolite set contains 93 differential metabolites associated with metastatic prostate cancer identified using Wilcoxon rank-sum tests (P <0.1) from multiple metabolomic profiles initially analyzed by Sreekumar et al.(1). Type 2 diabetes data set 1: The interesting metabolites that associated with type 2 diabetes were obtained by text mining, including studies of Wang et al. (2), Zeng et al. (3), Yi et al. (4), Wang-Sattler et al. (5), Rhee et al. (6), Floegel et al. (7) and Daimon et al. (8). These metabolites were mainly obtained by MS-based experiment. Then, we extracted type 2 diabetes associated metabolites from HMDB database (9) and added these metabolites to the interesting metabolite data set obtained from literatures. In total, 65 metabolites are involved in type 2 diabetes data set 1. Type 2 diabetes data set 2: The metabolite set contains 66 metabolites related with type 2 diabetes from multiplatform metabolomic profiles study of Suhre et al. (10) (significant p-value < 0.05). Drug sensitivity data set: In total, sensitivity related metabolites of 121 drugs are identified based on the drug GI50 data (11) and metabolite measurement data (by calculating the Pearson correlation of the drug log(GI50) value and the metabolites measurement across 58 NCI60 cell lines. The drug sensitivity significantly related metabolites cutoff is set FDR at 60%. (FDR <0.6). The interesting metabolites for each dataset can be downloaded here: 1. Sreekumar, A., Poisson, L.M., Rajendiran, T.M., Khan Continue reading >>

T2d@zju: A Knowledgebase Integrating Heterogeneous Connections Associated With Type 2 Diabetes Mellitus

[email protected]: A Knowledgebase Integrating Heterogeneous Connections Associated With Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2D), affecting >90% of the diabetic patients, is one of the major threats to human health. A comprehensive understanding of the mechanisms of T2D at molecular level is essential to facilitate the related translational research. Here, we introduce a comprehensive and up-to-date knowledgebase for T2D, i.e. [email protected] [email protected] contains three levels of heterogeneous connections associated with T2D, which is retrieved from pathway databases, proteinprotein interaction databases and literature, respectively. In current release, [email protected] contains 1078 T2D related entities such as proteins, protein complexes, drugs and others together with their corresponding relationships, which include 3069 manually curated connections, 14 893 proteinprotein interactions and 26 716 relationships identified by text-mining technology. Moreover, [email protected] provides a user-friendly web interface for users to browse and search data. A Cytoscape Web-based interactive network browser is available to visualize the corresponding network relationships between T2D-related entities. The functionality of [email protected] is shown by means of several case studies. Diabetes mellitus is one of the major threats to human health, which is expected to affect 552 million people by 2030 ( 1 ). More than 90% of the diabetic patients are affected with type 2 diabetes mellitus (T2D) ( 2 ). Although several mechanisms of T2D have been proposed, including metabolic overload, mitochondrial dysfunction, inflammatory mediators, deposition of toxic amyloid fibrils and etc, the pathogenesis of T2D is still under investigation ( 3 , 4 ). Hence, a comprehensive understanding of the mechanisms of T2D at molecular level is urgently needed. Nowadays, the existing knowledge of T2D is located in different formats at s Continue reading >>

Type 2 Diabetes Mellitus Prediction Model Based On Data Mining

Type 2 Diabetes Mellitus Prediction Model Based On Data Mining

Type 2 diabetes mellitus prediction model based on data mining Author links open overlay panel HanWu ShengqiYang Due to its continuously increasing occurrence, more and more families are influenced by diabetes mellitus. Most diabetics know little about their health quality or the risk factors they face prior to diagnosis. In this study, we have proposed a novel model based on data mining techniques for predicting type 2 diabetes mellitus (T2DM). The main problems that we are trying to solve are to improve the accuracy of the prediction model, and to make the model adaptive to more than one dataset. Based on a series of preprocessing procedures, the model is comprised of two parts, the improved K-means algorithm and the logistic regression algorithm. The Pima Indians Diabetes Dataset and the Waikato Environment for Knowledge Analysis toolkit were utilized to compare our results with the results from other researchers. The conclusion shows that the model attained a 3.04% higher accuracy of prediction than those of other researchers. Moreover, our model ensures that the dataset quality is sufficient. To further evaluate the performance of our model, we applied it to two other diabetes datasets. Both experiments' results show good performance. As a result, the model is shown to be useful for the realistic health management of diabetes. Continue reading >>

Data Available For Download

Data Available For Download

We are releasing the summary data from our genome-wide meta analyses of glycaemic traits, in order to enable other researchers to examine particular variants or loci of their interest for association with these traits. The files include p-values and direction of effect at over 2 million directly genotyped or imputed single nucleotide polymorphisms (SNPs), as well as frequency information from the HapMap project (release 27). Acknowleding the data When using data from the downloadable meta-analyses results please acknowledge the source of the data as follows: "Data on glycaemic traits have been contributed by MAGIC investigators and have been downloaded from www.magicinvestigators.org". In addition to the above acknowledgement, please cite the relevant paper. Downloading the data The data can be downloaded from the magic directory on the Sanger FTP site: ftp://ftp.sanger.ac.uk/pub/magic/ Datasets HbA1c summary statistics (ancestry-specific and transethnic) Ancestry-specific and transethnic genome-wide meta-analysis summary statistics for association with HbA1c in up to 159,940 individuals from 82 cohorts of European (N=123,665), African (N=7,564), East Asian (N=20,838) and South Asian (N=8,874) ancestry. HbA1c trait values are untransformed and adjusted for age, sex and study-specific covariates. File format details are provided in the accompanying README. Please cite: The following data can be downloaded: Modified Stumvoll Insulin Sensitivity Index (ISI) datasets Meta-analysis results files for the modified Stumvoll Insulin Sensitivity Index (ISI). Discovery was performed in 16,753 individuals, and replication was attempted for the 23 most significant novel loci in 13,354 independent individuals. Data are provided for the discovery effort. Details of the models are with Continue reading >>

Github - Andreagrandi/ml-pima-notebook: Experiments With Pima Indians Diabetes Dataset And Machine Learning

Github - Andreagrandi/ml-pima-notebook: Experiments With Pima Indians Diabetes Dataset And Machine Learning

Experiments with Pima Indians Diabetes dataset and Machine Learning If nothing happens, download GitHub Desktop and try again. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The Pima are a group of Native Americans living in Arizona. A genetic predisposition allowed this group to survive normally to a diet poor of carbohydrates for years. In the recent years, because of a sudden shift from traditional agricultural crops to processed foods, together with a decline in physical activity, made them develop the highest prevalence of type 2 diabetes and for this reason they have been subject of many studies. The dataset includes data from 768 women with 8 characteristics, in particular: Plasma glucose concentration a 2 hours in an oral glucose tolerance test Body mass index (weight in kg/(height in m)^2) The last column of the dataset indicates if the person is affected (1) by diabetes or not (0). The original dataset is available at UCI Machine Learning Repository and can be downloaded from this address: The type of dataset and problem is a classic supervised binary classification. Given a number of elements all with certain characteristics (features), we want to build a machine learning model to identify people affected by type 2 diabetes. To solve the problem we will have to analyse the data, do any required transformation and normalisation, apply a machine learning algorithm, train a model, check the performance of the trained model and iterate with other algorithms until we find the most performant for our type of dataset. For the presentation part I've used RISE, which is being installed by requirements.txt but it has to be co Continue reading >>

Complexity Of Type 2 Diabetes Mellitus Data Sets Emerging From Nutrigenomic Research: A Case For Dimensionality Reduction?

Complexity Of Type 2 Diabetes Mellitus Data Sets Emerging From Nutrigenomic Research: A Case For Dimensionality Reduction?

Complexity of Type 2 Diabetes Mellitus Data Sets Emerging from Nutrigenomic Research: A Case for Dimensionality Reduction? 1 Center of Excellence in Nutritional Genomics, University of California at Davis. Davis, CA 95616 2 Laboratory of Nutrigenomic Medicine, Department of Surgery, University of Illinois Chicago, Chicago, IL 60612 3 NuGO (European Nutrigenomics Organisation) www.nugo.org 1 Center of Excellence in Nutritional Genomics, University of California at Davis. Davis, CA 95616 2 Laboratory of Nutrigenomic Medicine, Department of Surgery, University of Illinois Chicago, Chicago, IL 60612 3 NuGO (European Nutrigenomics Organisation) www.nugo.org The publisher's final edited version of this article is available at Mutat Res See other articles in PMC that cite the published article. Nutrigenomics promises personalized nutrition and an improvement in preventing, delaying, and reducing the symptoms of chronic diseases such as diabetes. Nutritional genomics is the study of how foods affect the expression of genetic information in an individual and how an individual's genetic makeup affects the metabolism and response to nutrients and other bioactive components in food. The path to those promises has significant challenges, from experimental designs that include analysis of genetic heterogeneity to the complexities of food and environmental factors. One of the more significant complications in developing the knowledge base and potential applications is how to analyze high-dimensional datasets of genetic, nutrient, metabolomic (clinical), and other variables influencing health and disease processes. Type 2 diabetes mellitus (T2DM) is used as an illustration of the challenges in studying complex phenotypes with nutrigenomics concepts and approaches. Type 2 diabetes (T2D Continue reading >>

Uci Machine Learning Repository: Diabetes 130-us Hospitals For Years 1999-2008 Data Set

Uci Machine Learning Repository: Diabetes 130-us Hospitals For Years 1999-2008 Data Set

Diabetes 130-US hospitals for years 1999-2008 Data Set Abstract: This data has been prepared to analyze factors related to readmission as well as other outcomes pertaining to patients with diabetes. The data are submitted on behalf of the Center for Clinical and Translational Research, Virginia Commonwealth University, a recipient of NIH CTSA grant UL1 TR00058 and a recipient of the CERNER data. John Clore (jclore '@' vcu.edu), Krzysztof J. Cios (kcios '@' vcu.edu), Jon DeShazo (jpdeshazo '@' vcu.edu), and Beata Strack (strackb '@' vcu.edu). This data is a de-identified abstract of the Health Facts database (Cerner Corporation, Kansas City, MO). The dataset represents 10 years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes. Information was extracted from the database for encounters that satisfied the following criteria. (1) It is an inpatient encounter (a hospital admission). (2) It is a diabetic encounter, that is, one during which any kind of diabetes was entered to the system as a diagnosis. (3) The length of stay was at least 1 day and at most 14 days. (4) Laboratory tests were performed during the encounter. (5) Medications were administered during the encounter. The data contains such attributes as patient number, race, gender, age, admission type, time in hospital, medical specialty of admitting physician, number of lab test performed, HbA1c test result, diagnosis, number of medication, diabetic medications, number of outpatient, inpatient, and emergency visits in the year before the hospitalization, etc. Detailed description of all the atrributes is provided in Table 1 Beata Strack, Jonathan P. DeShazo, Chris Gennings, Juan L. Olmo, Sebastian Ventura, Krz Continue reading >>

Defining Characteristics Of Diabetic Patients By Using Data Mining Tools

Defining Characteristics Of Diabetic Patients By Using Data Mining Tools

Defining Characteristics of Diabetic Patients by Using Data Mining Tools Faculty of Business Administration,Department of QuantitativeMethods,Istanbul University, Turkey Received date: November 25, 2016; Accepted date: November 29, 2016; Published date: November 30, 2016 Citation: Gursoy UTS. DefiningCharacteristics of Diabetic Patients by UsingData Mining Tools. J Hosp Med Manage.2016, 2:2. Visit for more related articles at Journal of Hospital & Medical Management Most organizations have large databases that contain wealth of potentially accessible information. Data mining techniques can be used to discover hidden patterns that are unknown a priori. Data mining is the process of selection, exploration and modelling of large quantities of data. Data mining has worthy applications in finance, communication, education, marketing and health management. In this study health management is chosen as an application area. It is very important to encountered similarities of past period cases and definition of patient profile in the health services quickly and to decide correctly. It is aimed to define specific characteristics of diabetic patients in Turkey by using Cluster Analysis and Association Rules Diabetic patients; Association rules; Cluster analysis; Data mining Among chronic diseases , diabetes is increasingly becoming athreat to all age groups on a global scale. Diabetes mellitusprevention and control studies are being conducted commonly.As well as making lifestyle changes, people with diabetes oftenneed additional treatments such as medication like insulin tocontrol their diabetes, blood pressure and blood fats. Diabetes,often referred as diabetes mellitus, describes a group ofmetabolic diseases in which the person has high blood glucose(blood sugar), either because Continue reading >>

Three New Genetic Risk Factors For Kidney Disease In Type 2 Diabetes

Three New Genetic Risk Factors For Kidney Disease In Type 2 Diabetes

Kidney News Findings Three new genetic risk factors for kidney disease in type 2 diabetes Three new genetic risk factors for kidney disease in type 2 diabetes Three genetic variables are identified as predictors of chronic kidney disease (CKD) in Chinese patients with type 2 diabetes, according to a study in Kidney International. The study used a new three-stage procedure to test the hypothesis that genetic variants associated with type 2 diabetes, obesity, and fasting plasma glucose might be associated with type 2 diabetes-related CKD. This process was carried out using a large clinicogenomic dataset from a prospective cohort of 2755 patients with type 2 diabetes from the Hong Kong Diabetes Registry. The model included 25 clinical variables and 36 genetic variants associated with type 2 diabetes, obesity, or fasting plasma glucose. Clinical, genetic, and clinicogenomic models were compared, and the effect of the top selected genetic variants on the clinicogenomic model was assessed. The selected genetic variants were subsequently validated in two independent cohorts. Of the top six single-nucleotide polymorphisms selected from the clinico- genomic data, three were associated with significant improvement in prediction performance. These were the rs478333 variant of the gene G6PC2 and the rs7754840 and rs7756992 variants of CDKAL1. Patients with the rs478333 variant had a faster decline in eGFRgreater than 4 percent per year. On meta-analysis in replication cohorts, the associations for rs478333 and rs7754840 remained significant after adjustment for conventional risk factors. The three implicated gene variants seem to be novel predictors of CKD associated with type 2 diabetes in a Chinese population. Jian et al. believe that their three-step process may be useful for s Continue reading >>

Machine Learning: Pima Indians Diabetes

Machine Learning: Pima Indians Diabetes

Sat 14 April 2018| in Development | tags: Machine Learning Python scikit-learn tutorial The Pima are a group of Native Americans living in Arizona. A genetic predisposition allowed this group to survive normally to a diet poor of carbohydrates for years. In the recent years, because of a sudden shift from traditional agricultural crops to processed foods, together with a decline in physical activity, made them develop the highest prevalence of type 2 diabetes and for this reason they have been subject of many studies. The dataset includes data from 768 women with 8 characteristics, in particular: Plasma glucose concentration a 2 hours in an oral glucose tolerance test Body mass index (weight in kg/(height in m)^2) The last column of the dataset indicates if the person has been diagnosed with diabetes (1) or not (0) The original dataset is available at UCI Machine Learning Repository and can be downloaded from this address: The type of dataset and problem is a classic supervised binary classification. Given a number of elements all with certain characteristics (features), we want to build a machine learning model to identify people affected by type 2 diabetes. To solve the problem we will have to analyse the data, do any required transformation and normalisation, apply a machine learning algorithm, train a model, check the performance of the trained model and iterate with other algorithms until we find the most performant for our type of dataset. # We import the libraries needed to read the datasetimport osimport pandas as pdimport numpy as np # We placed the dataset under datasets/ sub folderDATASET_PATH = 'datasets/' # We read the data from the CSV filedata_path = os.path.join(DATASET_PATH, 'pima-indians-diabetes.csv')dataset = pd.read_csv(data_path, header=None)# Bec Continue reading >>

Diabetes Research In Children Network (direcnet)

Diabetes Research In Children Network (direcnet)

Diabetes Research in Children Network (DirecNet) The Accuracy of Continuous Glucose Monitors in Children with Type 1 Diabetes A Pilot Study to Assess the Accuracy of Continuous Glucose Monitors in Normal Children A Pilot Study to Evaluate the GlucoWatch 2 Biographer in the Management of Type 1 Diabetes in Children A Randomized Trial to Assess the Effectiveness of the GlucoWatch Biographer in the Management of Type 1 Diabetes in Children The Effect of Exercise on the Development of Hypoglycemia in Children with Type 1 Diabetes The Effect of Basal Insulin During Exercise on the Development of Hypoglycemia in Children with Type 1 Diabetes A Pilot Study to Evaluate the Navigator Continuous Glucose Sensor in the Management of Type 1 Diabetes in Children Evaluation of Counter-regulatory Hormone Responses during Hypoglycemia and the Accuracy of Continuous Glucose Monitors in Children with T1DM A Pilot Study to Evaluate the Safety of Terbutaline in Children with Type 1 Diabetes A Randomized Clinical Trial to Assess the Efficacy and Safety of Real-Time Continuous Glucose Monitoring in the Management of Type 1 Diabetes in Young Children (4 to <10 Year Olds) A Pilot Study to Assess the Feasibility of Real-Time Continuous Glucose Monitoring in the Management of Infants and Toddlers with Type 1 Diabetes Relationship Between Loss of Beta Cell Function and Loss of the Alpha Cell Response to Hypoglycemia Early in Type 1 Diabetes Effect of Metabolic Control at Onset of Diabetes on Progression of Type 1 Diabetes Cognitive and Neuroanatomical Consequences of Type 1 Diabetes in Young Children 2007 -2018 Jaeb Center for Health Research All forms and reports on this site require Adobe Acrobat Reader. Click HERE to download a free copy of Adobe. Although JCHR supports various web browsers, o Continue reading >>

Our Data T1d Exchange

Our Data T1d Exchange

Storm prep tip for people with T1D: Ask for an extra supply of all medications, including insulin and glucagon, if prescribed. Call your pharmacy, and if they say no, contact your healthcare provider for an altered prescription. From @DiabetesDisast1 As we enter into the turbulent weather season, please remember that you can call 1-800-DIABETES for individuals with diabetes care needs and 1-314-INSULIN for physicians and health care providers to request diabetes-related help and diabetes supplies in storm-affected areas. A while back, we talked with a researcher whose work has focused on the relationship between autoantibodies and the progression of type 1 diabetes: myglu.org/articles/5 T1D risk and autoantibodies - a look at some important research from a few years ago on the subject: myglu.org/articles/a FDA issues new warning on SGLT2 drugs. Read to know the symptoms to watch for: myglu.org/articles/f Don't assume all ER doctors know more about type 1 diabetes than you do. Make sure someone can advocate for you. Do you celebrate a diaversary? One mom's thoughts on the tradition: myglu.org/articles/d #InnovationTuesday - Here's a T1D Exchange Diabetes Innovation Challenge finalist who wants to create an oral medication to shrink fat deposits. While the treatment is targeted for people with #T2D , we know that people with #T1D could benefit, as well. youtu.be/NNCNZ92CzVg Sharing a 2014 T1D Exchange study about women with #T1D and bone disease rates: myglu.org/articles/h Our partner Nemours is recruiting young adults with #T1D between ages 18 and 25. If you are no longer followed in pediatric care, you can participate in a research study about the transition from pediatric to adult diabetes care bit.ly/transitionstu Copyright 2017 Unitio, Inc , All rights reserved. Continue reading >>

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