Application of machine learning algorithms for the differential diagnosis of peroxisomal disorders.
Journal:
Journal of biochemistry
Published Date:
Jan 1, 2019
Abstract
We have established diagnostic thresholds of very long-chain fatty acids (VLCFA) for the differential diagnosis of peroxisomal disorders using the machine learning tools. The plasma samples of 131 controls and 90 cases were tested for VLCFA using gas chromatography-mass spectrometry following stable isotope dilution. These data were used to construct association rules and for recursive partitioning. The C26/22 in healthy controls ranged between 0.008 and 0.01. The C26 levels between 1.61 and 3.34 µmol/l and C26/C22 between 0.05 and 0.10 are diagnostic of X-linked adrenoleukodystrophy (X-ALD). Very high levels of C26 (>3.34 µmol/l) and C26/C22 ratio (>0.10) are diagnostic of Zellweger syndrome (ZS). Significant elevation of phytanic acid was observed in Refsum (t = 6.14, P < 0.0001) and Rhizomelic chondrodysplasia punctata (RCDP) (t = 16.72, P < 0.0001). The C26/C22 ratio is slightly elevated in RCDP (t = 2.58, P = 0.01) while no such elevation was observed in Refsum disease (t = 0.86, P = 0.39). The developed algorithm exhibited greater clinical utility (AUC: 0.99-1.00) in differentiating X-ALD, ZS and healthy controls. The algorithm has greater clinical utility in the differential diagnosis of peroxisomal disorders based on VLCFA pattern. Plasmalogens will add additional value in differentiating RCDP and Refsum disease.