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Published on: October 21, 2014
Can psychosine and galactocerebrosidase activity predict early-infantile Krabbe's disease presymptomatically?
Randy L Carter1,2, Lawrence Wrabetz2,3, Kabir Jalal1,2
1Department of Biostatistics, Population Health Observatory, School of Public Health and Health Professions, University at Buffalo, Buffalo, New York.
Insights
This study introduces a novel newborn screening tool for early infantile Krabbe's disease (EIKD) using galactocerebrosidase (GALC) enzyme activity and psychosine (PSY) concentration. The bivariate normal limits (BVNL) method demonstrated 100% sensitivity and zero false positives, outperforming existing univariate approaches.
Area of Science:
- Biochemistry
- Genetics
- Pediatrics
Background:
- Krabbe's disease (KD) is a fatal neurodegenerative disorder, with early infantile Krabbe's disease (EIKD) presenting before 6 months.
- Early and accurate EIKD diagnosis is crucial for effective hematopoietic stem cell transplantation.
- Current diagnostic methods may lack the sensitivity and specificity required for newborn screening.
Purpose of the Study:
- To develop and evaluate a novel newborn screening (NBS) tool for predicting EIKD.
- To assess the accuracy of bivariate normal limits (BVNL) using galactocerebrosidase (GALC) enzyme activity and psychosine (PSY) concentration.
- To compare the performance of the BVNL method against existing univariate diagnostic approaches.
Main Methods:
- Constructed bivariate normal limits (BVNL) based on natural logarithms of GALC and PSY from normal newborns.
- Assumed a multivariate normal distribution for GALC and PSY in the newborn population.
- Compared the diagnostic accuracy and false-positive rates of the BVNL method versus univariate methods through simulation studies.
Main Results:
- All EIKD patient data points fell outside the constructed BVNL, indicating 100% sensitivity.
- The BVNL method yielded zero false positives in a simulation of 100 million normal newborns.
- Existing two-tiered univariate methods produced 5,682 false positives in the same simulation.
Conclusions:
- The (lnGALC, lnPSY) BVNL approach offers highly accurate prediction of EIKD.
- BVNL-based NBS is superior to univariate methods, significantly reducing false positives.
- Further refinement of BVNL using a common large sample of normal newborns is necessary for NBS implementation.
Abstract:
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.
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