Poster_Metagene_Athen_140_73 V109.pdf

Selective Screening in urine for inborn errors of metabolism using NMR analysis linked to METAGENE knowledgebase

  • Dr. Manfred Spraul / Bruker BioSpin GmbH, Rheinstetten
  • Dr.med. Frauendienst-Egger / Kreiskliniken Reutlingen, Reutlingen
  • Hermann Götz / TDB Software, Schwabach
  • Claire Cannet / Bruker BioSpin GmbH, Rheinstetten
  • Dr. Bernd Beedgen / Dep Ped,Div Neuroped Metab Med, Heidelberg
  • Prof.Dr.med. Friedrich Trefz / Metabolic Consulting, Reutlingen
  • Markus Godejohann / Bruker BioSpin GmbH, Rheinstetten
  • Dr. Hartmut Schäfer / Bruker BioSpin GmbH, Rheinstetten
  • PD Dr. phil. nat. Jürgen G. Okun / Dietmar-Hopp-Stoffwechselzentrum Zentrum für Kinder- und Jugendmedizin, Universitätsklinikum Heidelberg
  • Dr. rer. nat. Claus-Dieter Langhans / Dietmar-Hopp-Stoffwechselzentrum Zentrum für Kinder- und Jugendmedizin, Universitätsklinikum Heidelberg
  • Dr.med. Michaela Klinke / Dietmar-Hopp-Stoffwechselzentrum Zentrum für Kinder- und Jugendmedizin, Universitätsklinikum Heidelberg
  • PD Dr. med. Dorothea Haas / Dietmar-Hopp-Stoffwechselzentrum Zentrum für Kinder- und Jugendmedizin, Universitätsklinikum Heidelberg
  • Prof.Dr.med. Georg F. Hoffmann / Dietmar-Hopp-Stoffwechselzentrum Zentrum für Kinder- und Jugendmedizin, Universitätsklinikum Heidelberg

Introduction:

NMR analysis in urine is a new approach for highly quantitative and reproducible measurement of a high number of analytes with different substance classes running on one platform. Because of the huge number of information provided by the NMR report an automatic evaluation showing out of normal range results and their interpretation for possible diagnoses is desirable.

Methods:

Quantitative NMR analysis ( Bruker Biospin Avance IVDr, B.I.Quant-URTM) was performed automatically for 152 metabolites of 12 substance classes. Metagene, a knowledgebase for Analysis support of inborn errors of metabolism (www.metagene.de) was adopted for direct interpretation of the NMR reports. Ranking of potential diagnoses explainable by the metabolic findings in the report is done by comparison to the disease database in Metagene containing 209 diseases and differential diagnoses.

Results:

In 60 known metabolic diseases the diagnosis was made by conventional analysis and data based automatic ranking. Comparison to the NMR based method showed high concordance. Using click boxes to add additional information as age and clinical symptoms to the quantitative results, rational ranking was highly improved. Diseases which may be well-defined by one characteristic metabolite show the best rankings:L- Alloisoleucine in MSUD, Galactitol in GALACTOSEMIA, Argininosuccinic acid in ARGININOSUCCINIC ACIDURIA (ASL .

1. NMR-Analysis and NMR-Report

2. MAPLE SIRUP URENE DISEASE

3. GALACTOSEMIA

4. ARGININOSUCCINIC ACIDURIA

Conclusion:

NMR analysis provides an excellent tool for using automatic analysis to further enable high throughput screening of urine samples and to improve yield of genetic metabolic diseases in the metabolic laboratory.