首页|Ulster University Reports Findings in Type 2 Diabetes (Exploring metformin monotherapy response in Type-2 diabetes: Computational insights through clinical, genomic, and proteomic markers using machine learning algorithms)
Ulster University Reports Findings in Type 2 Diabetes (Exploring metformin monotherapy response in Type-2 diabetes: Computational insights through clinical, genomic, and proteomic markers using machine learning algorithms)
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New research on Nutritional and Metabolic Diseases and Conditions - Type 2 Diabetes is the subject of a report. According to news reporting from Londonderry, United Kingdom, by NewsRx journalists, research stated, "In 2016, the UK had 4.5 million people with diabetes, predominantly Type-2 Diabetes Mellitus (T2DM). The NHS allocates £10 billion (9% of its budget) to manage diabetes." The news correspondents obtained a quote from the research from Ulster University, "Metformin is the primary treatment for T2DM, but 35% of patients don't benefit from it, leading to complications. This study aims to delve into metformin's efficacy using clinical, genomic, and proteomic data to uncover new biomarkers and build a Machine Learning predictor for early metformin response detection. Here we report analysis from a T2DM dataset of individuals prescribed metformin monotherapy from the Diastrat cohort recruited at the Altnagelvin Area Hospital, Northern Ireland. In the clinical data analysis, comparing responders (those achieving HbA1c 48 mmol/mol) to non-responders (with HbA1c ＞48 mmol/mol), we identified that creatinine levels and bodyweight were more negatively correlated with response than nonresponse. In genomic analysis, we identified statistically significant (p-value ＜0.05) variants rs6551649 (LPHN3), rs6551654 (LPHN3), rs4495065 (LPHN3) and rs7940817 (TRPC6) which appear to differentiate the responders and non-responders. In proteomic analysis, we identified 15 statistically significant (pvalue ＜0.05, q-value ＜0.05) proteomic markers that differentiate controls, responders, non-responders and treatment groups, out of which the most significant were HAOX1, CCL17 and PAI that had fold change 2. A machine learning model was build; the best model predicted non-responders with 83% classification accuracy."
LondonderryUnited KingdomEuropeAlgorithmsAntidiabetic AgentsBiguanidesBiomarkersCyborgsDiagnostics and ScreeningDrugs and TherapiesEmerging TechnologiesGeneticsHealth and MedicineHypoglycemic AgentsMachine LearningMetforminMetformin TherapyNon-Insulin Dependent Diabetes MellitusNon-SulfonylureasNutritional and Metabolic Diseases and ConditionsPharmaceuticalsProteomicsType 2 Diabetes
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