血脂异常中的血浆酰基肉碱和氨基酸:代谢组学与机器学习的综合方法

IF 1.8 Q4 ENDOCRINOLOGY & METABOLISM
Journal of Diabetes and Metabolic Disorders Pub Date : 2024-02-24 eCollection Date: 2024-06-01 DOI:10.1007/s40200-024-01384-9
Ali Etemadi, Farima Hassanzadehkiabi, Maryam Mirabolghasemi, Mehdi Ahmadi, Hojat Dehghanbanadaki, Shaghayegh Hosseinkhani, Fatemeh Bandarian, Niloufar Najjar, Arezou Dilmaghani-Marand, Nekoo Panahi, Babak Negahdari, Mohammadali Mazloomi, Mohammad Hossein Karimi-Jafari, Farideh Razi, Bagher Larijani
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引用次数: 0

摘要

目的:发现与血脂异常发生相关的潜在中间产物有助于更好地了解血脂异常的病理生理学,改变这些中间产物将是治疗血脂异常的一种有前景的预防和治疗策略:整个数据集选自伊朗 30 个省的非传染性疾病(NCDs)风险因素监测(伊朗 STEPs 2016 国家报告),其中包括 1200 名受试者,并根据其甘油三酯(TG)、总胆固醇(TC)、高密度脂蛋白胆固醇(HDL-C)和非 HDL-C 的水平将其分为正常和异常四个二元类别。使用串联质谱法评估了每类血脂异常中 20 种氨基酸和 30 种酰基肉碱的血浆浓度。然后,利用这些属性和基线特征数据来检验机器学习(ML)算法能否对病例和对照进行分类:我们的 ML 框架能准确预测 TG 的二元类别。在测试的模型中,SVM 模型表现突出,其 AUC 为 0.81,测试准确率的标准偏差为 0.04,表现略胜一筹。因此,该模型被选为 TG 分类的最佳模型。此外,研究结果表明,丙氨酸、苯丙氨酸、蛋氨酸、C3、C14:2 和 C16 在区分高 TG 患者和正常 TG 对照组方面具有很强的能力。结论:这项工作的全面成果以及性别特异性属性将提高我们对血脂异常所涉及的基本中间产物的认识:在线版本包含补充材料,可在 10.1007/s40200-024-01384-9 上查阅。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plasma acylcarnitines and amino acids in dyslipidemia: An integrated metabolomics and machine learning approach.

Purpose: The Discovery of underlying intermediates associated with the development of dyslipidemia results in a better understanding of pathophysiology of dyslipidemia and their modification will be a promising preventive and therapeutic strategy for the management of dyslipidemia.

Methods: The entire dataset was selected from the Surveillance of Risk Factors of Noncommunicable Diseases (NCDs) in 30 provinces of Iran (STEPs 2016 Country report in Iran) that included 1200 subjects and was stratified into four binary classes with normal and abnormal cases based on their levels of triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and non-HDL-C.Plasma concentrations of 20 amino acids and 30 acylcarnitines in each class of dyslipidemia were evaluated using Tandem mass spectrometry. Then, these attributes, along with baseline characteristics data, were used to check whether machine learning (ML) algorithms could classify cases and controls.

Results: Our ML framework accurately predicts TG binary classes. Among the models tested, the SVM model stood out, performing slightly better with an AUC of 0.81 and a standard deviation of test accuracy at 0.04. Consequently, it was chosen as the optimal model for TG classification. Moreover, the findings showed that alanine, phenylalanine, methionine, C3, C14:2, and C16 had great power in differentiating patients with high TG from normal TG controls. Conclusions: The comprehensive output of this work, along with sex-specific attributes, will improve our understanding of the underlying intermediates involved in dyslipidemia.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-024-01384-9.

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来源期刊
Journal of Diabetes and Metabolic Disorders
Journal of Diabetes and Metabolic Disorders Medicine-Internal Medicine
CiteScore
4.80
自引率
3.60%
发文量
210
期刊介绍: Journal of Diabetes & Metabolic Disorders is a peer reviewed journal which publishes original clinical and translational articles and reviews in the field of endocrinology and provides a forum of debate of the highest quality on these issues. Topics of interest include, but are not limited to, diabetes, lipid disorders, metabolic disorders, osteoporosis, interdisciplinary practices in endocrinology, cardiovascular and metabolic risk, aging research, obesity, traditional medicine, pychosomatic research, behavioral medicine, ethics and evidence-based practices.As of Jan 2018 the journal is published by Springer as a hybrid journal with no article processing charges. All articles published before 2018 are available free of charge on springerlink.Unofficial 2017 2-year Impact Factor: 1.816.
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