Defining the Phenotype and Assessing Severity in Phosphoglucomutase-1 Deficiency

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• 作者：Sunnie Yan-Wai Wong ; BA¹ ; ^{swong1@tulane.edu" class="auth_mail" title="E-mail the corresponding author} ; Lesa J. Beamer ; PhD² ; Therese Gadomski ; BS¹ ; Tomas Honzik ; MD ; PhD³ ; Miski Mohamed ; MD ; PhD⁴ ; Saskia B. Wortmann ; MD ; PhD⁵ ; Katja S. Brocke Holmefjord ; MD⁶ ; Marit Mork ; MD⁶ ; Francis Bowling ; MD ; PhD⁷ ; Jolanta Sykut-Cegielska ; MD ; PhD⁸ ; Dieter Koch ; MD⁹ ; Amanda Ackermann ; MD ; PhD¹⁰ ; Charles A. Stanley ; MD¹⁰ ; Daisy Rymen ; MD ; PhD¹¹ ; Avraham Zeharia ; MD¹² ; Moeen Al-Sayed ; MD¹³ ; Thomas Marquardt ; MD¹⁴ ; Jaak Jaeken ; MD ; PhD¹⁵ ; Dirk Lefeber ; PhD¹⁶ ; Donald F. Conrad ; PhD¹⁷ ; Tamas Kozicz ; MD ; PhD¹ ; Eva Morava ; MD ; PhD¹ ; ⁴
• 关键词：congenital disorder of glycosylation ; congenital malformation ; cleft palate ; bifid uvula ; dilated cardiomyopathy ; hypoglycemia ; myopathy ; small stature ; hormonal deficiency ; coagulopathy ; hepatopathy
• 刊名：The Journal of Pediatrics
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：175
• 期：Complete
• 页码：130-136.e8
• 全文大小：1082 K

文摘

To define phenotypic groups and identify predictors of disease severity in patients with phosphoglucomutase-1 deficiency (PGM1-CDG).

Study design

We evaluated 27 patients with PGM1-CDG who were divided into 3 phenotypic groups, and group assignment was validated by a scoring system, the Tulane PGM1-CDG Rating Scale (TPCRS). This scale evaluates measurable clinical features of PGM1-CDG. We examined the relationship between genotype, enzyme activity, and TPCRS score by using regression analysis. Associations between the most common clinical features and disease severity were evaluated by principal component analysis.

Results

We found a statistically significant stratification of the TPCRS scores among the phenotypic groups (P < .001). Regression analysis showed that there is no significant correlation between genotype, enzyme activity, and TPCRS score. Principal component analysis identified 5 variables that contributed to 54% variance in the cohort and are predictive of disease severity: congenital malformation, cardiac involvement, endocrine deficiency, myopathy, and growth.

Conclusions

We established a scoring algorithm to reliably evaluate disease severity in patients with PGM1-CDG on the basis of their clinical history and presentation. We also identified 5 clinical features that are predictors of disease severity; 2 of these features can be evaluated by physical examination, without the need for specific diagnostic testing and thus allow for rapid assessment and initiation of therapy.