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Purpose: Metachromatic leukodystrophy (MLD) is a lysosomal storage disease caused by arylsulfatase A deficiency, which leads to progressive demyelination in both the central and peripheral nervous systems, resulting in significant gross motor deterioration. This study aimed to analyze data concerning neuroimaging and clinical phenotypes of MLD patients, categorized by disease subtype. Methods: Patients diagnosed with MLD based on arylsulfatase A enzymatic activity, demyelination observed in brain magnetic resonance images, and/or pathogenic mutations were included in this study. The medical charts of 10 patients with confirmed MLD were retrospectively reviewed. We used a simplified magnetic resonance imaging (MRI) scoring system and clinical status, including survival. We analyzed the correlations between the scores of specific neuroimaging lesions and clinical status in two groups, categorized as late-infantile and juvenile types based on the age at symptom onset. Results: We detected a positive relationship between clinical function deterioration and MRI score (rho=0.59, P=0.002) in patients with MLD. This correlation was stronger in the late-infantile type (rho=0.700, P=0.003) than in the juvenile type (rho=0.513, P=0.029). A strong relationship was also noted in patients with high signal intensities in the pons and basal ganglia, and cerebellar atrophy, but not in those with lesions in the midbrain. MLD with a high MRI score was associated with poor clinical function. Conclusion: The identified correlations between modified MRI scores and clinical function scales may help predict the prognosis of patients with MLD, thereby aiding in the identification of treatment options and enhancing the quality of life for these patients. [ABSTRACT FROM AUTHOR]

Background: The increasing prevalence of neurocognitive disorders (NCDs) in the aging population worldwide has become a significant concern due to subjectivity of evaluations and the lack of precise diagnostic methods and specific indicators. Developing personalized diagnostic strategies for NCDs has therefore become a priority. Results: Multimodal electroencephalography (EEG) data of a matched cohort of normal aging (NA) and NCDs seniors were recorded, and their faecal samples and urine exosomes were collected to identify multi-omics signatures and metabolic pathways in NCDs by integrating metagenomics, proteomics, and metabolomics analysis. Additionally, experimental verification of multi-omics signatures was carried out in aged mice using faecal microbiota transplantation (FMT). We found that NCDs seniors had low EEG power spectral density and identified specific microbiota, including Ruminococcus gnavus, Enterocloster bolteae, Lachnoclostridium sp. YL 32, and metabolites, including L-tryptophan, L-glutamic acid, gamma-aminobutyric acid (GABA), and fatty acid esters of hydroxy fatty acids (FAHFAs), as well as disturbed biosynthesis of aromatic amino acids and TCA cycle dysfunction, validated in aged mice. Finally, we employed a support vector machine (SVM) algorithm to construct a machine learning model to classify NA and NCDs groups based on the fusion of EEG data and multi-omics profiles and the model demonstrated 92.69% accuracy in classifying NA and NCDs groups. Conclusions: Our study highlights the potential of multi-omics profiling and EEG data fusion in personalized diagnosis of NCDs, with the potential to improve diagnostic precision and provide insights into the underlying mechanisms of NCDs. 69k9eiJKfEC56J752KW8KD Video Abstract [ABSTRACT FROM AUTHOR]