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Hematologic Complications with Age in Shwachman-Diamond Syndrome.

Elissa FurutaniShanshan LiuAshley E GalvinSarah SteltzMaggie M MalschSara Katharine LovelessLeann MountJordan Henry LarsonKelan QueenanAlison A BertuchMark Daniel FlemingJohn M GansnerAmy E GeddisRabi HannaSioban B KeelBonnie W LauJeffrey M LiptonRobert LorsbachTaizo A NakanoAdrianna VlachosWinfred C WangStella M DaviesEdie WellerKasiani C MyersAkiko Shimamura
Published in: Blood advances (2021)
Shwachman-Diamond Syndrome (SDS) is an inherited bone marrow failure syndrome with leukemia predisposition. An understanding of the hematologic complications of SDS with age could guide clinical management, but data are limited for this rare disease. We conducted a cohort study of 153 subjects from 143 families with confirmed biallelic SBDS mutations enrolled on the North American Shwachman Diamond Registry or Bone Marrow Failure Registry. The SBDS c.258+2T>C variant was present in all but one patient. To evaluate association of blood counts with age, a total of 2146 blood counts were analyzed for 119 subjects. Absolute neutrophil counts were positively associated with age (P<0.0001). Hemoglobin was also positively associated with age up to 18 years (P<0.0001) but thereafter the association was negative (P=0.0079). Platelet counts and marrow cellularity were negatively associated with age (P<0.0001). Marrow cellularity did not correlate with blood counts. Severe marrow failure necessitating transplant developed in 8 subjects at a median age of 1.7 years (range 0.4-39.5), with 7 of 8 requiring transplant prior to age 8 years. Twenty-six subjects (17%) developed a myeloid malignancy (16 MDS and 10 AML) at a median age of 12.3 years (range 0.5-45.0) for MDS, and 28.4 years (range 14.4-47.3) for AML. A lymphoid malignancy developed in one patient at the age of 16.9 years. Hematologic complications were the major cause of mortality (17/20 deaths, 85%). These data inform surveillance of hematologic complications in SDS.
Keyphrases
  • bone marrow
  • case report
  • risk factors
  • type diabetes
  • mesenchymal stem cells
  • machine learning
  • cardiovascular disease
  • peripheral blood
  • big data