Dataset Information

Prognosis prediction model for Alzheimer’s disease conversion from mild cognitive impairment by integrative analysis of multi-omics data

ABSTRACT: Mild cognitive impairment (MCI) is a clinical precursor of Alzheimer’s disease (AD). Many MCI subjects convert to AD, although others remain stable as MCI or sometimes return to cognitive normal. Currently, there are no curative treatments for patients who are already in AD, and therefore biomarkers for early detection of high risk MCI-to-AD conversion subjects are rapidly required. Here, we investigated potential biomarkers from blood-based miRNA (miR) expression profiles and genomic data of 197 Japanese MCI patients. Using the candidate biomarkers, we constructed a prognosis prediction model based on a Cox proportional hazard model. The final prediction model, composed of 24 miR-eQTLs (i.e. SNP-miRNA pairs) and three clinical factors (age, sex and ApoE ε4 carriers), successfully classified MCI patients into two groups, low and high, in terms of risk of MCI-to-AD conversion (log-rank trend test P=3.44×10^(-4)), and achieved a concordance index of 0.702 on an independent test set. Network-based meta-analysis using target genes of the miR-eQTLs further revealed four important hub genes (SHC1, FOXO1, GSK3B, and PTEN) associated with the pathogenesis of AD. Statistically significant differences were observed in PTEN expression between MCI and AD and SHC1 expression between cognitively normal elder subjects (CN) and AD when examining RNA-seq data from 610 blood samples (PTEN, P=0.023; SHC1, P=0.049), although FOXO1 and GSK3B showed low levels of expression in blood. Accurate prediction of MCI-to-AD conversion enables earlier appropriate intervention for those MCI patients and can lead to a reduction of MCI patients that convert to AD with high risk. We believe that further investigation with larger sample sizes will contribute to practical clinical use of our approach in MCI-to-AD conversion in the near future.

ORGANISM(S): Homo sapiens

PROVIDER: GSE150693 | GEO | 2020/11/11

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:<h4>INTRODUCTION Alzheimer's disease is a prevalent disease with a heavy global burden and is suggested to be a metabolic disease in the brain in recent years. The metabolome is considered to be the most promising phenotype which reflects changes in genetic, transcript, and protein profiles as well as environmental effects. Aiming to obtain a comprehensive understanding and convenient diagnosis of MCI and AD from another perspective, researchers are working on AD metabolomics. Urine is more convenient which could reflect the change of disease at an earlier stage. Thus, we conducted a cross-sectional study to investigate novel diagnostic panels.</h4>METHODS We first enrolled participants from China-Japan Friendship Hospital from April 2022 to November 2022, collected urine samples and conducted an LC-MS/MS analysis. In parallel, clinical data were collected and clinical examinations were performed. After statistical and bioinformatics analyzes, significant risk factors and differential urinary metabolites were determined. We attempt to investigate diagnostic panels based on machine learning including LASSO and SVM.RESULTS 57 AD patients, 43 MCI patients and 62 CN subjects were enrolled. A total of 2,140 metabolites were identified among which 125 significantly differed between the AD and CN groups, including 46 upregulated ones and 79 downregulated ones. In parallel, there were 93 significant differential metabolites between the MCI and CN groups, including 23 upregulated ones and 70 downregulated ones. AD diagnostic panel (30 metabolites+ age + APOE) achieved an AUC of 0.9575 in the test set while MCI diagnostic panel (45 metabolites+ age + APOE) achieved an AUC of 0.7333 in the test set. Atropine, S-Methyl-L-cysteine-S-oxide, D-Mannose 6-phosphate (M6P), Spiculisporic Acid, N-Acetyl-L-methionine, 13,14-dihydro-15-keto-tetranor Prostaglandin D2, Pyridoxal 5'-Phosphate (PLP) and 17(S)-HpDHA were considered valuable for both AD and MCI diagnosis and defined as hub metabolites. Besides, diagnostic metabolites were weakly correlated with cognitive functions.DISCUSSION In conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN. Atropine, M6P and PLP were evidence-based hub metabolites in AD.

Dataset Information

Prognosis prediction model for Alzheimer’s disease conversion from mild cognitive impairment by integrative analysis of multi-omics data

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets