A cross-sectional quantitative analysis of the natural history of Farber disease: an ultra-orphan condition with rheumatologic and neurological cardinal disease features
Author(s) -
Matthias Zielonka,
Sven F. Garbade,
Stefan Kölker,
Georg F. Hoffmann,
Markus Ries
Publication year - 2017
Publication title -
genetics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.509
H-Index - 128
eISSN - 1530-0366
pISSN - 1098-3600
DOI - 10.1038/gim.2017.133
Subject(s) - medicine , disease , natural history , clinical trial , biomarker , generalizability theory , pediatrics , enzyme replacement therapy , population , age of onset , natural history study , biochemistry , chemistry , environmental health , statistics , mathematics
PurposeFarber disease (OMIM 22800) is an ultrarare progressive multisystemic neurodevelopmental storage disorder caused by a deficiency of the lysosomal enzyme acid ceramidase (AC). Hard clinical end points for future clinical trials remain to be defined.MethodsWe quantitatively analyzed published cases with Farber disease (N = 96). The main outcome variables were survival and diagnostic delay. As a potential predictor of survival, the influence of residual AC enzyme activity was investigated. The analysis was performed in compliance with STROBE criteria.ResultsThe median survival period of the study population was 3 years. The median age at disease onset was 3 months, and the median age at diagnosis was 17 months. The median diagnostic delay was 13.75 months. Patients with residual AC activity in fibroblasts at more than 5.1% of the normal level survived significantly longer than patients with residual AC activity below this threshold. In addition, higher residual AC activity was associated with a later onset of symptoms.ConclusionFarber disease onset is in infancy. Diagnostic delay is typically substantial. Our data suggest a phenotype-biomarker association with implications for future clinical and therapeutic trials. In the absence of a prospective multicenter natural-history study protocol, we believe that our modeling approach, based on published case descriptions, is the best and most timely approximation for generalizability.
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