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Lysosomal storage diseases are a group of rare hereditary metabolic diseases. Due to a deficiency of lysosomal enzymes, complex substrates accumulate in the lysosomes of various organs. Depending on the affected enzyme, this results in clinically variable and chronic progressive multiorgan diseases. Diagnosis is often delayed. As clinical symptoms include the musculoskeletal system, an awareness of lysosomal storage diseases is of relevance to (pediatric) rheumatologists. This article is focused on Mucopolysaccharidosis type I‑S, Mucolipidosis type III, Gaucher disease and Fabry disease. When suspecting a lysosomal storage disease, enzyme activity should be determined in dried blood spots or leukocytes. For some diseases, specific biomarkers can additionally be analyzed. Diagnosis should be confirmed by genetic testing. As causal treatment options are available for three of the presented diseases, a timely diagnosis is very important., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Diagnosing a rare metabolic disease challenges physicians and affected individuals and their families. To support the diagnostic pathway, a diagnostic tool was developed using the experiences of the affected individuals gained in interviews., Methods: 17 interviews with parents or individuals with a selected rare metabolic disease (Mucopolysaccharidosis (MPS), M. Fabry and M. Gaucher) were performed and qualitatively analysed using the standardized methods of Colaizzi. The results are reflected in diagnostic questionnaires. The questionnaires were distributed and answered by parents or individuals with an established diagnosis of MPS, M. Fabry or M. Gaucher and a control group. Four combined data mining classifiers were trained to detect suspicious answer patterns in the questionnaires., Results: 56 questionnaires were used for training and cross-validation tests of the binary data mining system resulting in a sensitivity value of 91% for the diagnosis 'MPS'. Another 20 questionnaires which have not been used for the training process could be evaluated as a preliminary prospective test. Out of these 20 questionnaires the test delivered 18 correct diagnoses (90%)., Discussion and Conclusions: Questionnaires for diagnostic support based on interviews with parents and affected individuals were developed and answer patterns were analysed with an ensemble of classifiers. Although preliminary, the results illustrate the potential of answer pattern recognition using data mining techniques. This approach might prove useful for diagnostic support in selected metabolic diseases., Competing Interests: The authors declare that they have no competing interests., (© Georg Thieme Verlag KG Stuttgart · New York.)