{"title":"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","authors":"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","doi":"10.1007/s40200-024-01384-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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. <b>Conclusions:</b> The comprehensive output of this work, along with sex-specific attributes, will improve our understanding of the underlying intermediates involved in dyslipidemia.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s40200-024-01384-9.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":"23 1","pages":"1057-1069"},"PeriodicalIF":1.8000,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11196250/pdf/","citationCount":"0","resultStr":"{\"title\":\"Plasma acylcarnitines and amino acids in dyslipidemia: An integrated metabolomics and machine learning approach.\",\"authors\":\"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\",\"doi\":\"10.1007/s40200-024-01384-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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. <b>Conclusions:</b> The comprehensive output of this work, along with sex-specific attributes, will improve our understanding of the underlying intermediates involved in dyslipidemia.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s40200-024-01384-9.</p>\",\"PeriodicalId\":15635,\"journal\":{\"name\":\"Journal of Diabetes and Metabolic Disorders\",\"volume\":\"23 1\",\"pages\":\"1057-1069\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11196250/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Diabetes and Metabolic Disorders\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s40200-024-01384-9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q4\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Diabetes and Metabolic Disorders","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40200-024-01384-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
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.
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.