Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease

Abstract

Alkaptonuria (AKU) is an ultrarare autosomal recessive disorder (MIM 203500) that is caused byby a complex set of mutations in homogentisate 1,2‐dioxygenasegene and consequent accumulation of homogentisic acid (HGA), causing a significant protein oxidation. A secondary form of amyloidosis was identified in AKU and related to high circulating serum amyloid A (SAA) levels, which are linked with inflammation and oxidative stress and might contribute to disease progression and patients' poor quality of life. Recently, we reported that inflammatory markers (SAA and chitotriosidase) and oxidative stress markers (protein thiolation index) might be disease activity markers in AKU. Thanks to an international network, we collected genotypic, phenotypic, and clinical data from more than 200 patients with AKU. These data are currently stored in our AKU database, named ApreciseKUre. In this work, we developed an algorithm able to make predictions about the oxidative status trend of each patient with AKU based on 55 predictors, namely circulating HGA, body mass index, total cholesterol, SAA, and chitotriosidase. Our general aim is to integrate the data of apparently heterogeneous patients with AKUAKU by using specific bioinformatics tools, in order to identify pivotal mechanisms involved in AKU for a preventive, predictive, and personalized medicine approach to AKU.—Cicaloni, V., Spiga, O., Dimitri, G. M., Maiocchi, R., Millucci, L., Giustarini, D., Bernardini, G., Bernini, A., Marzocchi, B., Braconi, D., Santucci, A. Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease. FASEB J. 33, 12696–12703 (2019). http://www.fasebj.org

Machine learning application for development of a data-driven predictive model able to investigate quality of life scores in a rare disease

“…The ApreciseKUre contains data from 203 patients, but only 129 have a complete and comprehensive set of information, which have been used in this study [26][27][28]. ApreciseKUre contains information about biomarkers and replies to questionnaires (for a full description of data deposited in Apreci-seKUre see [20].…”

HGDiscovery: An online tool providing functional and phenotypic information on novel variants of homogentisate 1,2- dioxigenase

“…An in-depth molecular characterization of the disease is needed in pharmacogenomics prediction for suitable medical treatment. To address the issue, ApreciseKUre platform was developed, which includes data on potential biomarkers, patients' quality of life, biochemical outcomes and clinical information facilitating their integration and analysis in order to shed light on pathological characterization of every AKU patient in a typical precision medicine perspective ( Cicaloni et al., 2019 ; Spiga et al., 2017 , 2018 , 2020 ).…”

Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria

“…To overcome the limitations due to the scarcity of specimens and data available for AKU and the wide range of AKU symptoms, we have recently established a comprehensive digital ecosystem, ApreciseKUre, that integrates patient-derived information (QoL scores, lifestyle), clinician-derived information (urine, blood, plasma analysis), mutational analysis (genotypes, protein stability) and therapeutic treatments offering an exhaustive visualization of different informative layers, to support clinicians and researchers in a PM approach to AKU [ 10 , 11 , 12 , 13 , 14 , 15 ]. The ApreciseKUre database can 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 [ 10 , 11 , 12 ].…”