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Computational approaches for clinical, genomic and proteomic markers of response to glucagon-like peptide-1 therapy in type-2 diabetes mellitus: An exploratory analysis with machine learning algorithms

Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2024

Villikudathil A., Mc Guigan D., English A.

Disease areaApplication areaSample typeProducts
Metabolic Diseases
Patient Stratification
Data Science
Plasma
Olink Target 96

Olink Target 96

Abstract

Introduction
In 2021, the International Diabetes Federation reported that 537 million people worldwide are living with diabetes. While glucagon-like peptide-1 agonists provide significant benefits in diabetes management, approximately 40 % of patients do not respond well to this therapy. This study aims to enhance treatment outcomes by using machine learning to predict individual response status to glucagon-like peptide-1 therapy.

Methods
We analysed a type-2 diabetes mellitus dataset from the Diastrat cohort, recruited at the Northern Ireland Centre for Stratified Medicine. The dataset included individuals prescribed glucagon-like peptide-1 therapy, with response status determined by glycated haemoglobin levels of ≤53 mmol/mol. We identified genomic and proteomic markers and developed machine learning models to predict therapy response.

Results
The study found 5 genomic variants and 45 proteomic markers that help differentiate glucagon-like peptide-1 therapy responders from non-responders, achieving 95 % prediction accuracy with a machine learning model.

Conclusion
This study demonstrates the potential of machine learning in predicting the response to glucagon-like peptide-1 therapy in individuals with type-2 diabetes mellitus. These findings suggest that integrating genomic and proteomic data can significantly enhance personalized treatment approaches, potentially improving outcomes for patients who might otherwise not respond well to glucagon-like peptide-1 therapy. Further research and validation in larger cohorts are necessary to confirm these results and translate them into clinical practice.

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