Acta Scientific Paediatrics (ASPE)

Research Article Volume 6 Issue 10

Type 1 Diabetes in Adolescents: About 207 Cases Monitored at the Abass Ndao Hospital Center in Dakar (Senegal)

Srilatha Kadali1,2 Shaik Mohammad Naushad2 and Vijaya Lakshmi Bodiga1*

1Department of Clinical Biochemistry and Molecular Biology, Institute of Genetics and Hospital for Genetics Diseases, Osmania University, Begumpet, Hyderabad, India
2Department of Biochemical Genetics, YODA Lifeline Diagnostics Pvt. Ltd, Ameerpet, Hyderabad, India

*Corresponding Author: Vijaya Lakshmi Bodiga, Department of Clinical Biochemistry and Molecular Biology, Institute of Genetics and Hospital for Genetics Diseases, Osmania University, Begumpet, Hyderabad, India

Received: November 13, 2023; Published: November 27, 2023


In view of significant overlapping clinical features in mucopolysaccharidoses (MPS) subtypes, clinicians face difficulty in differential diagnosis, thus requiring the need for a machine learning-based clinical tool for the provisional diagnosis of MPS subtypes. Out of 520 patients with suspicion of MPS, 296 patients were identified with MPS types. We considered 53 clinical symptoms of MPS patients (n = 255) for the differential diagnosis to derive the model. The diagnosis was based on specific enzyme assays. Among mucopolysaccharidoses, MPS I was the most common disease in our study. Different machine learning tools were tested, out of which classification and regression tree (CART) was more promising. The overall prediction showed 79.92% accuracy in determining the subtype of MPS with a precision of 89.31%. Phenotype-based provisional diagnosis of MPS can be emerging as a useful effective tool for clinicians, thus eliminating the need to perform a whole panel of enzymes. Improved therapeutic efficacy can be attained through early diagnosis and specific intervention. Additionally, this study estimated the prevalence of MPS disorders and established the disease-specific cut-offs of enzyme activity that can distinguish affected from carriers.

Keywords: Mucopolysaccharidoses; Clinical Phenotype; Prenatal Diagnosis; Provisional Diagnosis; Machine Learning


  1. Byers S., et al. “Glycosaminoglycan accumulation and excretion in the mucopolysaccharidoses: characterization and basis of a diagnostic test for MPS”. Molecular Genetics and Metabolism4 (1998): 282-290.
  2. Coutinho Maria Francisca., et al. “Glycosaminoglycan storage disorders: a review”. Biochemistry Research International 2012 (2012): 471325.
  3. Wiśniewska Karolina., et al. “Misdiagnosis in mucopolysaccharidoses”. Journal of applied genetics3 (2022): 475-495.
  4. Chuang, C K., et al. “Diagnostic screening for mucopolysaccharidoses by the dimethylmethylene blue method and two-dimensional electrophoresis”. Zhonghua yi xue za zhi = Chinese Medical Journal; Free China ed1 (2001): 15-22.
  5. Wood T., et al. “Expert recommendations for the laboratory diagnosis of MPS VI”. Molecular Genetics and Metabolism1 (2012): 73-82.
  6. Oguma Toshihiro., et al. “Analytical method for the determination of disaccharides derived from keratan, heparan, and dermatan sulfates in human serum and plasma by high-performance liquid chromatography/turbo ionspray ionization tandem mass spectrometry”. Analytical biochemistry1 (2007): 79-86.
  7. Lehman Thomas JA., et al. “Diagnosis of the mucopolysaccharidoses”. Rheumatology (Oxford, England)5 (2011): v41-48.
  8. Shapria E., et al. “Fluorometric assays in biochemical genetics: a laboratory Manual”. New York: Oxford university press, New York (1989): 19-46.
  9. Miron Patricia Minehart. “Preparation, culture, and analysis of amniotic fluid samples”. Current Protocols in Human Genetics Chapter 8 (2012): 8.4.
  10. Marwaha Ashish., et al. “The point-of-care use of a facial phenotyping tool in the genetics clinic: Enhancing diagnosis and education with machine learning”. American journal of medical genetics. Part A4 (2021): 1151-1158.
  11. Kadali Srilatha., et al. “Biochemical, machine learning and molecular approaches for the differential diagnosis of Mucopolysaccharidoses”. Molecular and Cellular Biochemistry1-2 (2019): 27-37.
  12. Kubaski, Francyne., et al. “Diagnosis of Mucopolysaccharidoses”. Diagnostics (Basel, Switzerland)3 (2020): 172.
  13. Verma Prashant K., et al. “Spectrum of Lysosomal storage disorders at a medical genetics center in northern India”. Indian Pediatrics10 (2012): 799-804.
  14. Sheth Jayesh., et al. “Burden of lysosomal storage disorders in India: experience of 387 affected children from a single diagnostic facility”. JIMD Reports 12 (2014): 51-63.
  15. Gupta Nalini., et al. “Lysosomal Storage Disorders in India: A Mini Review”. Journal of Mucopolysaccharide Rare Disisease1 (2018): 1-6.
  16. Kadali Srilatha., et al. “The utility of two-dimensional electrophoresis in diagnosis of mucopolysaccharidosis disorders”. Clinica Chimica Acta; International Journal of Clinical Chemistry 457 (2016): 36-40.
  17. Fateen Ekram., et al. “Mucopolysaccharidoses diagnosis in the era of enzyme replacement therapy in Egypt”. Heliyon8 (2021): e07830.
  18. Cheema, Huma Arshad., et al. “Mucopolysaccharidoses - Clinical Spectrum and Frequency of Different Types”. Journal of the College of Physicians and Surgeons--Pakista: JCPSP2 (2017): 80-83.
  19. Ben Turkia Hadhami., et al. “Incidence of mucopolysaccharidoses in Tunisia 1999-2021”. La Tunisie Medicale11 (2009): 782-785.
  20. Malm Gunilla., et al. “Mucopolysaccharidoses in the Scandinavian countries: incidence and prevalence”. Acta Paediatrica (Oslo, Norway: 1992)11 (2008): 1577-1581.
  21. Teke Kısa., et al. “Evaluation of Demographic and Clinical Characteristics of Patients with Mucopolysaccharidosis”. Journal of Pediatric Research 4 (2017): 59-62.
  22. Al-Jasmi Fatma A., et al. “Prevalence and Novel Mutations of Lysosomal Storage Disorders in United Arab Emirates: LSD in UAE”. JIMD Reports 10 (2013): 1-9.
  23. Josahkian, Juliana Alves., et al. “Updated birth prevalence and relative frequency of mucopolysaccharidoses across Brazilian regions”. Genetics and Molecular Biology1 (2017): e20200138.
  24. Lin Hsiang-Yu., et al. “Incidence of the mucopolysaccharidoses in Taiwan, 1984-2004”. American Journal of Medical Genetics. Part A5 (2009): 960-964.
  25. Ngu Lock-Hock., et al. “Case report of treatment experience with idursulfase beta (Hunterase) in an adolescent patient with MPS II”. Molecular Genetics and Metabolism Reports 12 (2017): 28-32.
  26. Chen, Xueru., et al. “Demographic characteristics and distribution of lysosomal storage disorder subtypes in Eastern China”. Journal of Human Genetics4 (2016): 345-349.
  27. Khan Shaukat A., et al. “Epidemiology of mucopolysaccharidoses”. Molecular Genetics and Metabolism3 (2017): 227-240.
  28. Lowry RB., et al. “An update on the frequency of mucopolysaccharide syndromes in British Columbia”. Human Genetics3 (1990): 389-390.
  29. Moreno-Giraldo LJ., et al. “Genomic variability of the mucopolysaccharidosis complex in southwestern Colombia”. Genetic Molecular Research2 (2020): GMR18502.
  30. Mendoza-Ruvalcaba Sandra Del Carmen., et al. “Biochemical diagnosis of mucopolysaccharidosis in a Mexican reference center”. Genetics and Molecular Biology1 (2020): e20180347.
  31. Poupetová Helena., et al. “The birth prevalence of lysosomal storage disorders in the Czech Republic: comparison with data in different populations”. Journal of Inherited Metabolic Disease4 (2010): 387-96.
  32. Puckett Yana., et al. “Epidemiology of mucopolysaccharidoses (MPS) in United States: challenges and opportunities”. Orphanet Journal of Rare Diseases1 (2021): 241.
  33. Poorthuis BJ., et al. “The frequency of lysosomal storage diseases in The Netherlands”. Human Genetics1-2 (1999): 151-156.
  34. Meikle PJ., et al. “Prevalence of lysosomal storage disorders”. JAMA3 (1999): 249-254.
  35. Lawrence Roger., et al. “Glycan-based biomarkers for mucopolysaccharidoses”. Molecular Genetics and Metabolism2 (2014): 73-83.
  36. Akella Radha Rama Devi and Srilatha Kadali. “Amniotic fluid glycosaminoglycans in the prenatal diagnosis of mucopolysaccharidoses - A useful biomarker”. Clinica Chimica Acta; International Journal of Clinical Chemistry 460 (2016): 63-66.
  37. Pinto Rui., et al. “Prevalence of lysosomal storage diseases in Portugal”. European Journal of Human Genetics: EJHG2 (2004): 87-92.
  38. Aboulnasr Aly A., et al. “Prenatal diagnosis of mucopolysaccharidoses type II by two-dimensional electrophoresis and mass spectrometry in amniotic fluid”. The journal Of Obstetrics and Gynaecology Research3 (2022): 682-687.


Citation: Vijaya Lakshmi Bodiga., et al. “Phenotyping and Provisional Diagnosis of Mucopolysaccharidoses Based on Machine Learning".Acta Scientific Paediatrics 6.10 (2023): 33-43.


Copyright: © 2023 Vijaya Lakshmi Bodiga., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate32%
Acceptance to publication20-30 days

Indexed In

News and Events

Contact US