gene predictor for diabetes - Prediction of type 2 diabetes using diabetes mellitus and platelet count genomewide polygenic risk score Genetic Prediction of Future Type 2 Diabetes PLOS Medicine Predicting Diabetes Mellitus With Machine Learning Techniques Genetics of Type 2 Diabetes Diabetes in America NCBI Bookshelf Genetic polymorphisms as predictors of incident diabetes None of the four polymorphisms was significantly related to incident diabetes in either men or women using either the three genotypes or recessive dominant or additive models of inheritance online appendix Table Introduction Type 2 diabetes T2D is a multifactorial disease in which environmental triggers interact with genetic variants in the predisposition to the disease T2D is characterized by impaired insulin secretion and insulin action in target tissues such as muscle and liver Many patients with a genetic predisposition to T2D also have a predisposition to weight gain and obesity is a DNA methylation ie the attachment of a methyl group to the DNA is an epigenetic mark indicative of gene activity Applying the Omics integration procedure for type 2 diabetes prediction A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults Diabetes Care 2016393337344 PMC free article Google Scholar 58 Redondo MJ Oram RA Steck AK Genetic risk scores for type 1 diabetes prediction and diagnosis Curr Diab Rep 20171712129 In summary the field of type 2 diabetes genetics has experienced a steep discovery curve and efforts now focus on translation of findings to improve understanding of pathophysiology and augment disease risk prediction Progress has been uneven with can diabetes insipidus be cured most efforts focused on common variants and populations of European ancestry Previous research has indicated that having knowledge of firstdegree relatives with diabetes is a more robust predictor than established genetic variants for type 2 diabetes 27 This suggests The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes with a slight increase in the area under the receiveroperatingcharacteristic The Framingham Offspring and Finnish studies reported that incorporating PRS calculated from dozens to hundreds of genetic variants with genomewide significance level to a clinical type 2 diabetes risk prediction model modestly improved its performance 9 10 however this combination of PRS with clinical data did not enhance the performance In order to deal with the high dimensional datasets Razavian et al 2015 built prediction models based on logistic regression for different onsets of type 2 diabetes prediction Georga et al 2013 focused on the glucose and used support vector regression SVR to predict diabetes which is as a multivariate regression problem Identifying top ten predictors of type 2 diabetes through machine Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine Predicting Diabetes Clinical Biological and Genetic Approaches Predicting type 2 diabetes via machine learning integration of multiple Walford G A et al Metabolite traits and genetic risk provide complementary information for the prediction of future type 2 diabetes Diabetes Care 37 25082514 2014 Clinical Risk Factors DNA Variants and the Development of Type 2 Diabetes AIenhanced integration of genetic and medical adi husada diabetes center imaging data for risk
diabetes mellitus and stroke
diabetes mellitus menurun ke anak
