Proposal of an Algorithm to Early Detect Attenuated Type I Mucopolysaccharidosis (MPS Ia) among Children with Growth

Federico Baronio1, Stefano Zucchini1, Francesco Zulian2

  • 1Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy.

Insights

Short stature in children may indicate attenuated Mucopolysaccharidosis Type I (MPS Ia). A new algorithm helps pediatric endocrinologists identify milder clinical signs, aiming to reduce diagnostic delays for this rare disease.

Area of Science:

  • Pediatric Endocrinology
  • Rare Diseases
  • Metabolic Disorders

Background:

  • Diagnostic delay is common in attenuated Mucopolysaccharidosis Type I (MPS Ia) due to rarity and variable presentation.
  • Short stature and impaired growth are frequent but often overlooked signs of MPS Ia in children.
  • Paediatric endocrinologists often lack training in recognizing early, milder clinical manifestations of MPS Ia.

Purpose of the Study:

  • To develop a clinical approach for diagnosing MPS Ia in children presenting with short stature or stunted growth.
  • To improve the diagnostic pathway for attenuated MPS Ia.
  • To enhance awareness among paediatric endocrinologists regarding MPS Ia.

Main Methods:

  • A consensus-based methodology was employed.
  • A multidisciplinary panel (pediatric endocrinologists, metabolic disorder specialists, radiologists, rheumatologists) shared expertise.
  • An algorithm was developed for diagnosing MPS Ia in children over 5 years old with short stature and suggestive signs.

Main Results:

  • An algorithm was created to aid in suspecting MPS Ia in children over 5 years old with short stature and specific clinical indicators.
  • The algorithm provides a structured approach for identifying potential MPS Ia cases.
  • The study highlights key clinical signs suggestive of MPS Ia in the context of growth issues.

Conclusions:

  • The proposed algorithm can serve as a valuable tool for improving paediatric endocrinologists' awareness of MPS Ia.
  • Implementation of the algorithm has the potential to significantly reduce diagnostic delays for MPS Ia patients.
  • Early and accurate diagnosis of MPS Ia is crucial for timely management and improved patient outcomes.

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