Annals of Clinical and Translational Neurology最新文献

Objective: We performed a pilot screen to assess the utility of the NULISA™ (Nucleic-acid-Linked Immuno-Sandwich Assay) platform in the identification of amyotrophic lateral sclerosis (ALS) biomarkers.

Methods: Plasma from 86 individuals (48 ALS, 18 asymptomatic C9orf72 repeat expansion carriers (AsymC9), and 20 healthy controls) was analyzed via a multiplexed NULISA™ assay that includes 120 neurodegeneration-associated proteins. Statistical analysis of NULISA™ results was performed to identify proteins differentially expressed in plasma and their correlation with disease-associated parameters.

Results: ALS plasma showed elevation of the established biomarkers, neurofilament light chain (NEFL) and neurofilament heavy chain (NEFH). Compared to controls and AsymC9, microtubule-associated protein tau (MAPT), phosphorylated tau 181 (pTau181), phosphorylated tau 217 (pTau217), phosphorylated tau 231 (pTau231), and phosphorylated TDP-43 (pTDP-43) were elevated in ALS. NEFL levels positively correlated with pTau181, pTau217, pTau231, and pTDP-43. MAPT and pTDP-43 were also correlated with pTau181, pTau217 and pTau231. Elevated pTau was negatively correlated with survival and ALSFRS-R. Spinal onset ALS was associated with higher pTau181, pTau217, and pTau231.

Interpretation: We confirm previous reports showing elevated pTau181 in ALS plasma and show elevation of other phosphorylated tau forms, pTau217 and pTau231, typically observed in Alzheimer's disease. We provide preliminary data showing the detection and elevation of pTDP-43-409/410 in a subset of ALS samples compared to healthy controls. Neurofilament and tau levels are highly correlated suggesting their elevation may reflect a common pathology and disease state. Total and phosphorylated tau are correlated with multiple disease measures, such as ALS duration, ALSFRS-R, and site of onset.

Objectives: An increasing body of evidence indicates altered DNA methylation in Parkinson's disease, yet the reproducibility and utility of such methylation changes are largely unexplored. We aimed to further elucidate the role of dysregulated DNA methylation in Parkinson's disease and to evaluate the biomarker potential of methylation-based profiling.

Methods: We conducted an epigenome-wide association study (EWAS) in whole blood, including 280 Parkinson's disease and 279 control participants from Oslo, Norway. Next, we took advantage of data from the Parkinson's Progression Markers Initiative (PPMI) and a previously published EWAS to conduct a whole blood EWAS meta-analysis in Parkinson's disease, incorporating results from a total of 3068 participants. Finally, we generated multiple methylation-based scores for each Oslo and PPMI participant and tested their association with disease status, individually and in a joint multiscore model.

Results: In EWAS meta-analysis, we confirm SLC7A11 hypermethylation and nominate a novel differentially methylated CpG near LPIN1. A joint multiscore model incorporating polygenic risk and methylation-based estimates of epigenetic Parkinson's disease risk, smoking, and leukocyte proportions differentiated patients from control participants with an area under the receiver-operator curve of 0.82 in the Oslo cohort and 0.65 in PPMI.

Interpretation: Our results highlight the power of DNA methylation profiling to capture multiple aspects of disease risk, indicating a biomarker potential for precision medicine in neurodegenerative disorders. The reproducibility of specific differentially methylated CpGs across data sets was limited but may improve if future studies are designed to account for disease stage and incorporate environmental exposure data.

Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning algorithms enhances various radiomics components, including image normalization, region of interest segmentation, feature extraction, feature selection, and model construction and can potentially improve model accuracy and performance. Moreover, investigating specific tumor habitats of glioblastomas aids in a better understanding of glioblastoma aggressiveness and the development of effective treatment strategies. Additionally, advanced imaging techniques, such as diffusion-weighted imaging, perfusion-weighted imaging, magnetic resonance spectroscopy, magnetic resonance fingerprinting, functional MRI, and positron emission tomography, can provide supplementary information for tumor characterization and classification. Furthermore, radiomics analysis helps understand the glioma immune microenvironment by predicting immune-related biomarkers and characterizing immune responses within tumors. Integrating multi-omics data, such as genomics, transcriptomics, proteomics, and pathomics, with radiomics, aids the understanding of the biological significance of the underlying radiomics features and improves the prediction of genetic mutations, prognosis, and treatment response in patients with glioma. Addressing challenges, such as model reproducibility, model generalizability, model interpretability, and multi-omics data integration, is crucial for the clinical translation of radiomics in glioma.