Objectives Since the prevalence of hypophosphatasia (HPP), a rare genetic disease, seems to be underestimated in clinical practice, in this study, a new diagnostic algorithm to identify missed cases of HPP was developed and implemented. Methods Analytical determinations recorded in the Clinical Analysis Unit of the Hospital Universitario Clínico San Cecilio in the period June 2018 – December 2020 were reviewed. A new clinical algorithm to detect HPP-misdiagnosed cases was used including the following steps: confirmation of persistent hypophosphatasemia, exclusion of secondary causes of hypophosphatasemia, determination of serum pyridoxal-5′-phosphate (PLP) and genetic study of ALPL gene. Results Twenty-four subjects were selected to participate in the study and genetic testing was carried out in 20 of them following clinical algorithm criteria. Eighty percent of patients was misdiagnosed with HPP following the current standard clinical practice. Extrapolating these results to the current Spanish population means that there could be up to 27,177 cases of undiagnosed HPP in Spain. In addition, we found a substantial proportion of HPP patients affected by other comorbidities, such as autoimmune diseases (∼40 %). Conclusions This new algorithm was effective in detecting previously undiagnosed cases of HPP, which appears to be twice as prevalent as previously estimated for the European population. In the near future, our algorithm could be globally applied routinely in clinical practice to minimize the underdiagnosis of HPP. Additionally, some relevant findings, such as the high prevalence of autoimmune diseases in HPP-affected patients, should be investigated to better characterize this disorder.
Non-alcoholic fatty liver disease (NAFLD) seems to have some molecular links with atherosclerosis (ATH); however, the molecular pathways which connect both pathologies remain unexplored to date. The identification of common factors is of great interest to explore some therapeutic strategies to improve the outcomes for those affected patients. Differentially expressed genes (DEGs) for NAFLD and ATH were extracted from the GSE89632 and GSE100927 datasets, and common up- and downregulated DEGs were identified. Subsequently, a protein–protein interaction (PPI) network based on the common DEGs was performed. Functional modules were identified, and the hub genes were extracted. Then, a Gene Ontology (GO) and pathway analysis of common DEGs was performed. DEGs analysis in NAFLD and ATH showed 21 genes that were regulated similarly in both pathologies. The common DEGs with high centrality scores were ADAMTS1 and CEBPA which appeared to be down- and up-regulated in both disorders, respectively. For the analysis of functional modules, two modules were identified. The first one was oriented to post-translational protein modification, where ADAMTS1 and ADAMTS4 were identified, and the second one mainly related to the immune response, where CSF3 was identified. These factors could be key proteins with an important role in the NAFLD/ATH axis.
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