# Development of Assays for Newborn Screening and for Post-Screening Evaluation of Disease Severity

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2022 · $576,499

## Abstract

PROJECT SUMMARY
The overall goal of this project is to develop tandem mass spectrometry for newborn screening
of a subset of congenital diseases in neonates in which initiation of early treatment leads to a
better patient quality of life. A punch from a dried blood spot on a newborn screening card is
used as the sample to measure the activities of relevant enzymes and to quantify disease
biomarkers. These assays are multiplexed together in order to minimize the workload in the
newborn screening laboratory so that an increasing number of diseases can be included (as
new treatments become available). Tandem mass spectrometry is also being developed for use
in the second stage of newborn screening in order to reduce the number of false positives
resulting from only a single stage of analysis. This is important to reduce patient follow-up and
associated costs and to reduce family anxiety. A third component is to test the hypothesis that
the lower the level of residual function of a particular protein, the more severe is the disease.
Predicting disease severity including age of onset of symptoms is important in planning follow-
up and treatment options in newborns who screen positive for a disease.

## Key facts

- **NIH application ID:** 10508062
- **Project number:** 2R01DK067859-22
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Michael H Gelb
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $576,499
- **Award type:** 2
- **Project period:** 1999-08-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10508062

## Citation

> US National Institutes of Health, RePORTER application 10508062, Development of Assays for Newborn Screening and for Post-Screening Evaluation of Disease Severity (2R01DK067859-22). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10508062. Licensed CC0.

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