A dysmorphometric analysis to investigate facial phenotypic signatures as a foundation for non-invasive monitoring of lysosomal storage disorders

Stefanie Kung, M. Walters, P. Claes, Jack Goldblatt, Peter Le Souef, Gareth Baynam

    Research output: Contribution to journalArticle

    5 Citations (Scopus)

    Abstract

    Background: Some lysosomal storage disorders (LSDs), including Muccopolysaccharidosis type 1 (MPSI), are associated with characteristic facies. Methods such as three-dimensional (3D) facial scanning and geometric morphometric techniques can potentially generate detailed objective descriptions of these facial phenotypes. This approach can facilitate discriminating the inherent overlap in facial phenotypes within these disease spectra, and the non-invasive monitoring of disease progression and treatment.

    Methods: 3D facial images of three MPS I-affected individuals and 400 reference subjects (aged 5–25 years) were obtained using a 3dMD camera (Atlanta, Georgia). Images were fitted with an anthropometric mask, comprising a set of spatially dense quasi-landmarks. A statistical face-space was constructed from the reference image set and the MPS I-affected individuals were compared to this face-space utilising an emerging methodology known as dysmorphometrics. This facilitated simultaneous identification of harmonic and discordant facial regions. A relative significant discordance (RSD) score quantified proportional facial discordance for a given individual, whilst a root-mean-squared-error (RMSE) score measured the degree of facial discordance providing a severity measure.

    Results: A consistent facial pattern, with differential severities, primarily affecting the frontal, nasal, infraorbital and cheek regions, was detected in all three individuals. As expected, there was greater discordance (RMSE, RSD) with clinically severe MPS I when compared to attenuated disease.

    Conclusions: Objective detection and localisation of MPS I facial characteristics was achieved, and severity scores were attributed. This spatially dense dysmorphometric facial phenotyping technique has the potential to be used for non-invasive treatment monitoring and as a discriminatory tool.
    Original languageEnglish
    Pages (from-to)31-39
    Number of pages9
    JournalJournal of Inherited Metabolic Disease
    Volume2012
    Issue number5
    DOIs
    Publication statusPublished - 2012

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