A new integrated and interactive tool applicable to inborn errors of metabolism: Application to alkaptonuria. (1st December 2018)

• Record Type: Journal Article
• Authors: Spiga, Ottavia; Cicaloni, Vittoria; Zatkova, Andrea; Millucci, Lia; Bernardini, Giulia; Bernini, Andrea; Marzocchi, Barbara; Bianchini, Monica; Zugarini, Andrea; Rossi, Alberto; Zazzeri, Matteo; Trezza, Alfonso; Frediani, Bruno; Ranganath, Lakshminarayan; Braconi, Daniela; Santucci, Annalisa
• Is Part Of: Computers in biology and medicine. Volume 103(2018)
• Page Start: 1
• Page End: 7
• Publication Date: 2018-12-01
• Subjects: Precision medicine -- Alkaptonuria -- Rare disease -- Database -- Biomarkers -- Data analysis; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.10.002
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: This paper describes our experience with the development and implementation of a database for the rare disease Alkaptonuria (AKU, OMIM: 203500). AKU is an autosomal recessive disorder caused by a gene mutation leading to the accumulation of homogentisic acid (HGA). Analogously to other rare conditions, currently there are no approved biomarkers to monitor AKU progression or severity. Although some biomarkers are under evaluation, an extensive biomarker analysis has not been undertaken in AKU yet. In order to fill this gap, we gained access to AKU-related data that we carefully processed, documented and stored in a database, which we named ApreciseKUre. We undertook a suitable statistical analysis by associating every couple of potential biomarkers to highlight significant correlations. Our database is continuously updated allowing us to find novel unpredicted correlations between AKU biomarkers and to confirm system reliability. ApreciseKUre includes data on potential biomarkers, patients' quality of life and clinical outcomes facilitating their integration and possibly allowing a Precision Medicine approach in AKU. This framework may represent an online tool that can be turned into a best practice model for other rare diseases. Highlights: AKU database integrates data on potential biomarkers and it is proposed as a supporting online tool for clinicians and researchers. AKU database could be the main driver to implement a PM approach to AKU working also as model for other rare disease. Correlation matrix might predict possible links between different biomarkers/parameters.