Krabbe's disease (KD) is a fatal neurodegenerative disorder, with the early-infantile form (EIKD) defined by onset of symptoms before age 6 months. Early and highly accurate identification of EIKD is required to maximize benefits of hematopoietic stem cell transplantation treatment. This study investigates the potential for accurate prediction of EIKD based on a novel newborn screening (NBS) tool developed from two biomarkers, galactocerebrosidase (GALC) enzyme activity and galactosylsphingosine concentration (psychosine [PSY]). Normative information about PSY and GALC, derived from distinct samples of normal newborns, was used to develop the novel diagnostic tool. Bivariate normal limits (BVNL) were constructed, assuming a multivariate normal distribution of natural logarithms of GALC and PSY of normal newborns. The (lnGALC, lnPSY) points for newborns in various "abnormal groups," including one group of infants who subsequently suffered EIKD, were plotted on a graph of BVNL. The points for all EIKD patients fell outside of BVNL (100% sensitivity). In a simulation study to compare the false-positive rate of existing univariate methods of diagnosis with our new BVNL-based method, we generated 100 million normal newborn data points. All fell within BVNL (i.e., zero false positives), whereas 5,682 false positives were observed when applying a two-tiered univariate method of the type suggested in the literature. These results suggest that (lnGALC, lnPSY) BVNLs will allow highly accurate prediction of EIKD, whereas two-tiered univariate approaches will not. Redevelopment of the BVNL based on GALCs and PSYs measured on a common large sample of normal newborns is required for NBS use. © 2016 Wiley Periodicals, Inc.
Keywords: bivariate normal limits; early identification; early-onset disease; followup; late-onset disease; newborn screening.
© 2016 Wiley Periodicals, Inc.