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

IF 4.3 Q1 ENDOCRINOLOGY & METABOLISM
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引用次数: 0

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.

2 型糖尿病患者对胰高血糖素样肽-1 治疗反应的临床、基因组和蛋白质组标记的计算方法:利用机器学习算法进行探索性分析。
导言:2021 年,国际糖尿病联合会报告称,全球有 5.37 亿人患有糖尿病。虽然胰高血糖素样肽-1激动剂在糖尿病治疗中具有显著疗效,但约有40%的患者对这种疗法反应不佳。本研究旨在利用机器学习预测个体对胰高血糖素样肽-1疗法的反应状态,从而提高治疗效果:我们分析了北爱尔兰分层医学中心招募的 Diastrat 队列中的 2 型糖尿病数据集。该数据集包括接受胰高血糖素样肽-1疗法的患者,其反应状态由糖化血红蛋白水平≤53 mmol/mol决定。我们确定了基因组和蛋白质组标记,并开发了机器学习模型来预测治疗反应:结果:研究发现 5 个基因组变异和 45 个蛋白质组标记有助于区分胰高血糖素样肽-1 治疗应答者和非应答者,机器学习模型的预测准确率达到 95%:这项研究证明了机器学习在预测2型糖尿病患者对胰高血糖素样肽-1疗法反应方面的潜力。这些研究结果表明,整合基因组学和蛋白质组学数据可以显著提高个性化治疗方法的效果,从而改善那些可能对胰高血糖素样肽-1疗法反应不佳的患者的预后。要证实这些结果并将其转化为临床实践,还需要在更大的队列中进行进一步的研究和验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
22.90
自引率
2.00%
发文量
248
审稿时长
51 days
期刊介绍: Diabetes and Metabolic Syndrome: Clinical Research and Reviews is the official journal of DiabetesIndia. It aims to provide a global platform for healthcare professionals, diabetes educators, and other stakeholders to submit their research on diabetes care. Types of Publications: Diabetes and Metabolic Syndrome: Clinical Research and Reviews publishes peer-reviewed original articles, reviews, short communications, case reports, letters to the Editor, and expert comments. Reviews and mini-reviews are particularly welcomed for areas within endocrinology undergoing rapid changes.
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