Characterization of facial phenotypes of children with congenital hypopituitarism and their parents: a matched case-control study

Shared facial phenotype of patients with mucolipidosis type IV: A clinical observation reaffirmed by next generation phenotyping

BACKGROUND

Mucolipidosis type IV (ML-IV) is a rare autosomal-recessive lysosomal storage disease, caused by mutations in MCOLN1. ML-IV manifests with developmental delay, esotropia and corneal clouding. While the clinical phenotype is well-described, the diagnosis of ML-IV is often challenging and elusive.

OBJECTIVE

Our experience with ML-IV patients brought to the clinical observation that they share common and identifiable facial features, not yet described in the literature to date. Here, we utilized a computerized facial analysis tool to establish this association.

METHODS

Using the DeepGestalt algorithm, 50 two-dimensional facial images of ten ML-IV patients were analyzed, and compared to unaffected controls (n = 98) and to individuals affected with other genetic disorders (n = 99). Results were expressed in terms of the area-under-the-curve (AUC) of the receiver-operating-characteristic curve (ROC).

RESULTS

When compared to unaffected cases and to cases diagnosed with syndromes other than ML-IV, the ML-IV cohort showed an AUC of 0.822 (p value < 0.01) and an AUC of 0.885 (p value < 0.001), respectively.

CONCLUSIONS

We describe recognizable facial features typical in patients with ML-IV. Reaffirmed by the DeepGestalt technology, the described common facial phenotype adds to the tools currently available for clinicians and may thus assist in reaching an earlier diagnosis of this rare and underdiagnosed disorder.

Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.

PITUITARY STALK INTERRUPTION SYNDROME: REPORT OF TWO CASES AND LITERATURE REVIEW

Pituitary stalk interruption syndrome (PSIS) consisting of the triad: ectopic posterior pituitary (EPP), thin or absent pituitary stalk and anterior pituitary hypoplasia is a rare pituitary malformation with variable degrees of pituitary insufficiency, from isolated growth hormone deficiency to TSH, gonadotropin and ACTH deficiency which may occur in time, with normo, hyper or hypoprolactinemia and central diabetes insipidus in up to 10% of cases. Also, extrapituitary malformations have been described in some cases. Genetic defects were identified only in 5% of cases. MRI findings are considered predictive for the endocrine phenotype. We aim to describe two cases with PSIS without central diabetes insipidus, anosmia and extrapituitary malformations, except for minor head dysmorphic features. The first case was referred at the age of 4 years for short stature (-4SDS for height, bone age 2 years), diagnosed with severe GH deficiency and developed central hypothyroidism and hypoprolactinemia during five-years follow-up. The second case, a 26 year old male with birth asphyxia, cryptorchidism, poor growth in childhood and adolescence (-3 to -4 height SDS), absent puberty and normal adult height (-1.18 SDS; bone age 15.5 years and active growth plates) had GH, TSH, ACTH deficiency and low normal PRL levels. Increasing medical awareness on PSIS clinical and endocrine heterogeneity may help a more early and accurate diagnosis. Corroboration of neuroimaging and endocrine data will improve our knowledge and understanding and will create premises for molecular diagnosis, genetic counseling and a better patients' management.