Purpose
To develop a safe and noninvasive in vivo assay of hepatic propionate oxidative capacity.
Methods
A modified 1-13C-propionate breath test was administered to 57 methylmalonic acidemia (MMA) subjects, including 19 transplant recipients, and 16 healthy volunteers. Isotopomer enrichment (13CO2/12CO2) was measured in exhaled breath after an enteral bolus of sodium-1-13C-propionate, and normalized for CO2 production. 1-13C-propionate oxidation was then correlated with clinical, laboratory, and imaging parameters collected via a dedicated natural history protocol.
Results
Lower propionate oxidation was observed in patients with the severe mut0 and cblB subtypes of MMA, but was near normal in those with the cblA and mut− forms of the disorder. Liver transplant recipients demonstrated complete restoration of 1-13C-propionate oxidation to control levels. 1-13C-propionate oxidation correlated with cognitive test result, growth indices, bone mineral density, renal function, and serum biomarkers. Test repeatability was robust in controls and in MMA subjects (mean coefficient of variation 6.9% and 12.8%, respectively), despite widely variable serum methylmalonic acid concentrations in the patients.
Conclusion
Propionate oxidative capacity, as measured with 1-13C-propionate breath testing, predicts disease severity and clinical outcomes, and could be used to assess the therapeutic effects of liver-targeted genomic therapies for MMA and related disorders of propionate metabolism.
TRIAL REGISTRATION
This clinical study is registered in www.clinicaltrials.gov with the ID: NCT00078078. Study URL: http://clinicaltrials.gov/ct2/show/NCT00078078
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.
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