Summary
Objectives:
Marfan syndrome (MFS) is an autosomal dominant inherited connective tissue disorder caused by mutations in the fibrillin-1 (FBN1) gene with variable clinical manifestations in the cardiovascular, musculoskeletal and ocular systems.
Methods:
Data of molecular genetic analysis and a catalogue of clinical manifestations including aortic elastic parameters were mined in order to (i) assess aortic abnormality before and during medical treatment, and to (ii) identify novel correlations between the genotype and phenotype of the disease using hierarchical cluster analysis and logistic regression analysis. A score measure describing the similarity between a patient’s clinical symptoms and a characteristic phenotype class was introduced.
Results:
A probabilistic model for monitoring the loss of aortic elasticity was built on merely aortic parameters of 34 patients with classic MFS and 43 control subjects showing a sensitivity of 82% and a specificity of 96%. The clinical phenotypes of 100 individuals with classical or suspected MFS were clustered yielding four different phenotypic expressions. The highest correlation was found between FBN1 missense mutations, which manifested as ectopia lentis, skeletal major and skin minor criteria, and two out of four clustered phenotypes. The probability of the presence of a missense mutation in both phenotype classes is approximately 70%.
Conclusions:
Monitoring of aortic elastic properties during medical treatment may serve as additional criterion to indicate elective surgical interventions. Genotype-phenotype correlation may contribute to anticipate the clinical consequences of specific FBN1 mutations more comprehensively and may be helpful to identify MFS patients at risk at an early stage of disease.
Keywords
Marfan syndrome - fibrillin-1
(FBN1)
- aortic elasticity - phenotype-genotype correlation - data mining
