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.