PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s40200-024-01384-9.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s40200-024-01384-9.
摘要：
与血脂异常的发展相关的潜在中间体的发现导致对血脂异常的病理生理学的更好理解,并且它们的修饰将是管理血脂异常的有希望的预防和治疗策略。
■整个数据集选自伊朗30个省的非传染性疾病（NCDs）危险因素监测（2016年伊朗国家报告），其中包括1200名受试者，并根据他们的甘油三酯（TG）水平分为正常和异常病例四个二元类别。总胆固醇(TC),高密度脂蛋白胆固醇（HDL-C），和非HDL-C使用串联质谱法评估每类血脂异常中20种氨基酸和30种酰基肉碱的血浆浓度。然后，这些属性，连同基线特征数据，用于检查机器学习(ML)算法是否可以对病例和对照进行分类。
■我们的ML框架可以准确预测TG二进制类。在测试的模型中，SVM模型脱颖而出，表现略好，AUC为0.81，测试精度的标准偏差为0.04。因此,它被选为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患者与正常TG对照组方面具有很大的作用。结论：这项工作的综合成果，以及特定性别的属性，将提高我们对血脂异常的潜在中间体的理解。
■在线版本包含补充材料，可在10.1007/s40200-024-01384-9获得。
■整个数据集选自伊朗30个省的非传染性疾病（NCDs）危险因素监测（2016年伊朗国家报告），其中包括1200名受试者，并根据他们的甘油三酯（TG）水平分为正常和异常病例四个二元类别。总胆固醇(TC),高密度脂蛋白胆固醇（HDL-C），和非HDL-C使用串联质谱法评估每类血脂异常中20种氨基酸和30种酰基肉碱的血浆浓度。然后，这些属性，连同基线特征数据，用于检查机器学习(ML)算法是否可以对病例和对照进行分类。
■我们的ML框架可以准确预测TG二进制类。在测试的模型中，SVM模型脱颖而出，表现略好，AUC为0.81，测试精度的标准偏差为0.04。因此,它被选为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患者与正常TG对照组方面具有很大的作用。结论：这项工作的综合成果，以及特定性别的属性，将提高我们对血脂异常的潜在中间体的理解。
■在线版本包含补充材料，可在10.1007/s40200-024-01384-9获得。
