One of the main challenges for healthcare systems is the increasing prevalence of neurodegenerative pathologies together with the rapidly aging populations. The enormous progresses made in the field of biomedical research and informatics have been crucial for improving the knowledge of how genes, epigenetic modifications, aging, nutrition, drugs and microbiome impact health and disease. In fact, the availability of high technology and computational facilities for large-scale analysis enabled a deeper investigation of neurodegenerative disorders, providing a more comprehensive overview of disease and encouraging the development of a precision medicine approach for these pathologies. On this subject, the creation of collaborative networks among medical centers, research institutes and highly qualified specialists can be decisive for moving the precision medicine from the bench to the bedside. To this purpose, the present review has been thought to discuss the main components which may be part of precise and personalized treatment programs applied to neurodegenerative disorders. Parkinson Disease will be taken as an example to understand how precision medicine approach can be clinically useful and provide substantial benefit to patients. In this perspective, the realization of web-based networks can be decisive for the implementation of precision medicine strategies across different specialized centers as well as for supporting clinical/therapeutical decisions and promoting a more preventive and participative medicine for neurodegenerative disorders. These collaborative networks are essentially addressed to find innovative, sustainable and effective strategies able to provide optimal and safer therapies, discriminate at risk individuals, identify patients at preclinical or early stage of disease, set-up individualized and preventative strategies for improving prognosis and patient's quality of life.

Recent advancements in Next-Generation Sequencing (NGS) technologies have revolutionized genomic research, presenting unprecedented opportunities for personalized medicine and population genetics. However, issues such as data silos, privacy concerns, and regulatory challenges hinder large-scale data integration and collaboration. Federated Learning (FL) has emerged as a transformative solution, enabling decentralized data analysis while preserving privacy and complying with regulations such as the General Data Protection Regulation (GDPR). This review explores the potential use of FL in genomics, detailing its methodology, including local model training, secure aggregation, and iterative improvement. Key challenges, such as heterogeneous data integration and cybersecurity risks, are examined alongside regulations like GDPR. In conclusion, successful implementations of FL in global and national initiatives demonstrate its scalability and role in supporting collaborative research. Finally, we discuss future directions, including AI integration and the necessity of education and training, to fully harness the potential of FL in advancing precision medicine and global health initiatives.

Background. Parkinson’s disease (PD) is mostly characterized by alpha-synuclein (SNCA) aggregation and loss of nigrostriatal dopamine-containing neurons. In this study a novel SNCA multiplication is described in two siblings affected by severe parkinsonism featuring early onset dyskinesia, psychiatric symptoms, and cognitive deterioration. Methods. SNCA dosage was performed using High-Density Comparative Genomic Hybridization Array (CGH-Array), Multiple Ligation Dependent Probe Amplification (MLPA), and Quantitative PCR (qPCR). Genetic analysis was associated with clinical evaluation. Results. Genetic analysis of siblings showed for the first time a 351 Kb triplication containing SNCA gene along with 6 exons of MMRN1 gene in 4q22.1 and a duplication of 1,29 Mb of a genomic region flanking the triplication. Conclusions. The identification of this family indicates a novel mechanism of SNCA gene multiplication, which confirms the genomic instability in this region and provides data on the genotype-phenotype correlation in PD patients.