Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, MD, USA.
NIH Library, Office of Research Services, National Institutes of Health, Bethesda, MD, USA.
Genet Med. 2021 Aug;23(8):1534-1542. doi: 10.1038/s41436-021-01173-2. Epub 2021 May 18.
To conduct a proof-of-principle study to identify subtypes of propionic acidemia (PA) and associated biomarkers.
Data from a clinically diverse PA patient population ( https://clinicaltrials.gov/ct2/show/NCT02890342 ) were used to train and test machine learning models, identify PA-relevant biomarkers, and perform validation analysis using data from liver-transplanted participants. k-Means clustering was used to test for the existence of PA subtypes. Expert knowledge was used to define PA subtypes (mild and severe). Given expert classification, supervised machine learning (support vector machine with a polynomial kernel, svmPoly) performed dimensional reduction to define relevant features of each PA subtype.
Forty participants enrolled in the study; five underwent liver transplant. Analysis with k-means clustering indicated that several PA subtypes may exist on the biochemical continuum. The conventional PA biomarkers, plasma total 2-methylctirate and propionylcarnitine, were not statistically significantly different between nontransplanted and transplanted participants motivating us to search for other biomarkers. Unbiased dimensional reduction using svmPoly revealed that plasma transthyretin, alanine:serine ratio, GDF15, FGF21, and in vivo 1-C-propionate oxidation, play roles in defining PA subtypes.
Support vector machine prioritized biomarkers that helped classify propionic acidemia patients according to severity subtypes, with important ramifications for future clinical trials and management of PA.
进行原理验证研究，以确定丙酸血症（PA）的亚型和相关生物标志物。
使用来自临床多样的 PA 患者群体的数据（https://clinicaltrials.gov/ct2/show/NCT02890342）来训练和测试机器学习模型，确定与 PA 相关的生物标志物，并使用来自肝移植参与者的数据进行验证分析。k-均值聚类用于测试 PA 亚型的存在。专家知识用于定义 PA 亚型（轻度和重度）。根据专家分类，监督机器学习（具有多项式核的支持向量机，svmPoly）进行降维以定义每个 PA 亚型的相关特征。
本研究共纳入 40 名参与者，其中 5 名接受了肝移植。k-均值聚类分析表明，在生化连续体上可能存在几种 PA 亚型。血浆总 2-甲基柠檬酸和丙酰肉碱等传统 PA 生物标志物在未接受移植和接受移植的参与者之间无统计学差异，这促使我们寻找其他生物标志物。使用 svmPoly 进行无偏降维揭示了血浆转甲状腺素、丙氨酸：丝氨酸比、GDF15、FGF21 和体内 1-C-丙酸氧化在定义 PA 亚型方面发挥作用。
支持向量机优先考虑了有助于根据严重程度亚型对丙酸血症患者进行分类的生物标志物，这对未来的临床试验和 PA 管理具有重要意义。
