Romanian Medical Journal (Dec 2020)

Dyke-Davidoff-Masson syndrome: An endocrine perspective

  • Cristina Dumitrescu,
  • Anda Dumitrascu,
  • Corina Chirita,
  • Eugenia Petrova,
  • Nicoleta Dumitru,
  • Adina Ghemigian,
  • Mara Carsote,
  • Dana Terzea,
  • Ana Valea

DOI
https://doi.org/10.37897/RMJ.2020.4.3
Journal volume & issue
Vol. 67, no. 4
pp. 349 – 352

Abstract

Read online

Dyke-Davidoff-Masson syndrome (DDMS) represents a severe disease that is caused by brain anomalies of different mechanisms early in life during fetal period or within first years after birth. This is a brief point of view of DDMS through an endocrine perspective. In DDMS, low TSH (thyroid stimulating hormone) hypothyroidism is part of a variable cocktail of endocrine anomalies like pituitary insufficiency that actually seem less relevant opposite to massive neurological damage which marks the clinical picture. Overall, the most spectacular anomaly and the most specific in DMMS is the imaging aspect of the brain hemiatrophy. The syndrome itself, even with a very low prevalence in general population (the level of statistical evidence is case report or series), has a heterogeneous presentation and a large area of clinical combo involving not only neurological field. On the other hand, cerebral hemiatrophy (also a rare neuroimaging finding) may have other causes, either acquired or congenital. Seizures induced by brain damage may be presented long before the imaging recognition of the syndrome is actually done. The endocrine disturbances vary and they may be subtle like central hypothyroidism or life threatening like adrenal crisis due to secondary adrenal insufficiency. Overall, DDMS represents a complex challenge from severe neurological deterioration to neuro-imagery features centered on the brain hemiatrophy to skin and bone anomalies as well as endocrine disorders which are either deficiency of pituitary hormones or diabetes mellitus and autoimmune thyroiditis. Early recognition is useful for long term prognosis, a multidisciplinary approach is essential. Underlying causes and specific clusters of classification are still running under a large shadow of unknown data.

Keywords