Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Characterization of Global DNA Methylome in Mild Cognitive Impairment and Alzheimer's Disease

ABSTRACT: Characterization of Global DNA Methylome in Mild Cognitive Impairment and Alzheimer's Disease

PROVIDER: PRJNA860378 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

microRNA microarray profile in the plasma of mild cognitive impairment due to Alzheimer's disease

Project description:To explore the microRNAs associated with pathology of early Alzheimer's disease, we detected the microRNA profiles in the plasma of subjects with mild cognitive impairment due to Alzheimer's disease and gender-, age-, education-matched normal control elderly.

2020-03-20 | GSE147232 | GEO

Long noncoding RNA microarray profile in the plasma of mild cognitive impairment due to Alzheimer's disease

Project description:To explore the lncRNAs associated with pathology of early Alzheimer's disease, we detected the lncRNA profiles in the plasma of subjects with mild cognitive impairment due to Alzheimer's disease and gender-, age-, education-matched normal control elderly.

2023-03-01 | GSE225304 | GEO

Alzheimer, Mild Cognitive impairment and control samples from AddneuroMed Cohort (batch 2)

Project description:Alzheimer case-control samples originate from the EU funded AddNeuroMed Cohort, which is a large cross-European AD biomarker study relying on human blood as the source of RNA. The design is case-control. Cases are either Alzheimer's disease patients, subjects with mild cognitive impairment or age and gender matched controls.

2015-08-05 | E-GEOD-63061 | biostudies-arrayexpress

Characterization of Global DNA Methylome in Mild Cognitive Impairment and Alzheimer's Disease

Project description:Genome-wide profiling of DNA methylation in blood leukocytes from Chinese patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The Illumina Infinium MethylationEPIC BeadChip array (850K chip) was used to detect DNA methylation profiles throughout approximately 850,000 CpG sites in peripheral blood white cells of MCI- and AD-affected Chinese patients, as well as cognitively healthy controls. All samples included 20 Chinese patients with MCI, 20 Chinese patients with AD, and 20 cognitively healthy controls.

2023-09-11 | GSE208623 | GEO

Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's Disease

Project description:Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's Disease

| PRJNA1190346 | ENA

Detection of Alzheimer’s Disease at Mild Cognitive Impairment and Disease Progression Using Autoantibodies as Blood-based Biomarkers

Project description:Human serum samples from Alzheimer's disease driven mild cognitive impairment and non-diseased controls were probed onto human protein microarrays in order to identify differentially expressed autoantibody biomarkers that could be used as diagnostic indicators. Other neurodegenerative and non-neurodegenerative diseases were also used to help measure the specificity of the selected biomarkers.

2016-05-03 | GSE74763 | GEO

microRNA microarray profile in the plasma of mild cognitive impairment due to Alzheimer's disease

Project description:microRNA microarray profile in the plasma of mild cognitive impairment due to Alzheimer's disease

| PRJNA613461 | ENA

Long noncoding RNA microarray profile in the plasma of mild cognitive impairment due to Alzheimer's disease

Project description:Long noncoding RNA microarray profile in the plasma of mild cognitive impairment due to Alzheimer's disease

| PRJNA934925 | ENA

Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's Disease

Project description:Background: Effective treatment for Alzheimer’s disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention. Methods: Blood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n=28), stable MCI (S-MCI, n=39), and AD patients (n=49). Statistical DESeq2 analysis and machine learning methods were employed to identify differentially expressed genes (DEGs) and develop prediction models. Results: We discovered a remarkable gender-specific difference in DEGs that distinguish P-MCI from S-MCI. Machine learning models achieved high accuracy in distinguishing P-MCI from S-MCI (AUC 0.93), AD from S-MCI (AUC 0.94), and AD from P-MCI (AUC 0.92). An 8-gene signature was identified for distinguishing P-MCI from S-MCI.

2024-11-30 | GSE282742 | GEO

Alzheimer, Mild Cognitive impairment and control samples from AddneuroMed Cohort

Project description:Alzheimer, Mild Cognitive impairment and control samples from AddneuroMed Cohort

| PRJNA266528 | ENA