Ontology highlight
ABSTRACT: Purpose
To conduct a proof-of-principle study to identify subtypes of propionic acidemia (PA) and associated biomarkers.Methods
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.Results
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 1-13C-propionate oxidation, play roles in defining PA subtypes.Conclusion
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.
SUBMITTER: Shchelochkov OA
PROVIDER: S-EPMC8354856 | biostudies-literature | 2021 Aug
REPOSITORIES: biostudies-literature
Shchelochkov Oleg A OA Manoli Irini I Juneau Paul P Sloan Jennifer L JL Ferry Susan S Myles Jennifer J Schoenfeld Megan M Pass Alexandra A McCoy Samantha S Van Ryzin Carol C Wenger Olivia O Levin Mark M Zein Wadih W Huryn Laryssa L Snow Joseph J Chlebowski Colby C Thurm Audrey A Kopp Jeffrey B JB Chen Kong Y KY Venditti Charles P CP
Genetics in medicine : official journal of the American College of Medical Genetics 20210518 8
<h4>Purpose</h4>To conduct a proof-of-principle study to identify subtypes of propionic acidemia (PA) and associated biomarkers.<h4>Methods</h4>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 knowled ...[more]