Introduction: Phosphomannomutase-2 deficiency (PMM2-CDG) is associated with a recognisable facial pattern. There are no early severity predictors for this disorder and no phenotype-genotype correlation. We performed a detailed dysmorphology evaluation to describe facial gestalt and its changes over time, to train digital recognition facial analysis tools and to identify early severity predictors.
Methods: Paediatric PMM2-CDG patients were evaluated and compared with controls. A computer-assisted recognition tool was trained. Through the evaluation of dysmorphic features (DFs), a simple categorisation was created and correlated with clinical and neurological scores, and neuroimaging.
Results: Dysmorphology analysis of 31 patients (4-19 years of age) identified eight major DFs (strabismus, upslanted eyes, long fingers, lipodystrophy, wide mouth, inverted nipples, long philtrum and joint laxity) with predictive value using receiver operating characteristic (ROC) curveanalysis (p<0.001). Dysmorphology categorisation using lipodystrophy and inverted nipples was employed to divide patients into three groups that are correlated with global clinical and neurological scores, and neuroimaging (p=0.005, 0.003 and 0.002, respectively). After Face2Gene training, PMM2-CDG patients were correctly identified at different ages.
Conclusions: PMM2-CDG patients' DFs are consistent and inform about clinical severity when no clear phenotype-genotype correlation is known. We propose a classification of DFs into major and minor with diagnostic risk implications. At present, Face2Gene is useful to suggest PMM2-CDG. Regarding the prognostic value of DFs, we elaborated a simple severity dysmorphology categorisation with predictive value, and we identified five major DFs associated with clinical severity. Both dysmorphology and digital analysis may help physicians to diagnose PMM2-CDG sooner.
Keywords: automated facial analysis software; cerebellar disorders; congenital disorders of glycosylation; dysmorphology; phosphomannomutase.
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