Background: Alkaptonuria (AKU; OMIM: 203500) is a classic Mendelian genetic disorder described by Garrod already in 1902. It causes urine to turn black upon exposure to air and also leads to ochronosis as well as early osteoarthritis.Main body of the abstract: Our objective is the implementation of a Precision Medicine (PM) approach to AKU. We present here a novel ApreciseKUre database facilitating the collection, integration and analysis of patient data in order to create an AKU-dedicated "PM Ecosystem" in which genetic, biochemical and clinical resources can be shared among registered researchers. In order to exploit the ApreciseKUre database, we developed an analytic method based on Pearson's correlation coefficient and P value that generates as refreshable correlation matrix. A complete statistical analysis is obtained by associating every pair of parameters to examine the dependence between multiple variables at the same time.Short conclusions: Employing this analytic approach, we showed that some clinically used biomarkers are not suitable as prognostic biomarkers in AKU for a more reliable patients' clinical monitoring. We believe this database could be a good starting point for the creation of a new clinical management tool in AKU, which will lead to the development of a deeper knowledge network on the disease and will advance its treatment. Moreover, our approach can serve as a personalization model paradigm for other inborn errors of metabolism or rare diseases in general.

Spiga, O., Cicaloni, V., Bernini, A., Zatkova, A., Santucci, A. (2017). ApreciseKUre: an approach of Precision Medicine in a Rare Disease. BMC MEDICAL INFORMATICS AND DECISION MAKING, 17(42), 1-5 [10.1186/s12911-017-0438-0].

ApreciseKUre: an approach of Precision Medicine in a Rare Disease

Spiga, O
;
Cicaloni, V;Bernini, A
Formal Analysis
;
Santucci, A
Supervision
2017-01-01

Abstract

Background: Alkaptonuria (AKU; OMIM: 203500) is a classic Mendelian genetic disorder described by Garrod already in 1902. It causes urine to turn black upon exposure to air and also leads to ochronosis as well as early osteoarthritis.Main body of the abstract: Our objective is the implementation of a Precision Medicine (PM) approach to AKU. We present here a novel ApreciseKUre database facilitating the collection, integration and analysis of patient data in order to create an AKU-dedicated "PM Ecosystem" in which genetic, biochemical and clinical resources can be shared among registered researchers. In order to exploit the ApreciseKUre database, we developed an analytic method based on Pearson's correlation coefficient and P value that generates as refreshable correlation matrix. A complete statistical analysis is obtained by associating every pair of parameters to examine the dependence between multiple variables at the same time.Short conclusions: Employing this analytic approach, we showed that some clinically used biomarkers are not suitable as prognostic biomarkers in AKU for a more reliable patients' clinical monitoring. We believe this database could be a good starting point for the creation of a new clinical management tool in AKU, which will lead to the development of a deeper knowledge network on the disease and will advance its treatment. Moreover, our approach can serve as a personalization model paradigm for other inborn errors of metabolism or rare diseases in general.
Spiga, O., Cicaloni, V., Bernini, A., Zatkova, A., Santucci, A. (2017). ApreciseKUre: an approach of Precision Medicine in a Rare Disease. BMC MEDICAL INFORMATICS AND DECISION MAKING, 17(42), 1-5 [10.1186/s12911-017-0438-0].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1126745
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