Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's Disease
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE282742 | GEO | 2024/11/30
REPOSITORIES: GEO
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Project description:Mild cognitive impairment (MCI) is an early stage of memory loss that affects cognitive abilities, such as language or virtual/spatial comprehension. This cognitive decline is mostly ob-served with the aging of individuals. Recently, MCI has been considered as a prodromal phase of Alzheimer’s disease (AD), with a 10-15% conversion rate. However, the existing diagnostic methods fail to provide precise and well-timed diagnoses, and the pathophysiology of MCI is not fully understood. Alterations of serum N-glycan expression could represent essential con-tributors to the overall pathophysiology of neurodegenerative diseases and be used as a poten-tial marker to assess MCI diagnosis using non-invasive procedures. Herein, we undertook an LC-MS/MS glycomics approach to determine and characterize potential N-glycan markers in de-pleted blood serum samples from MCI patients. For the first time, we profiled the isomeric gly-come of the low abundant serum glycoproteins extracted from serum samples of control and MCI patients using an LC-MS/MS analytical strategy. Additionally, the MRM validation of the identified data showed five isomeric N-glycans with the ability to discriminate between healthy and MCI patients: the sialylated N-glycans HexNAc5,Hex6,Neu5Ac3 and HexNAc6,Hex7,Neu5Ac4 with single AUCs of 0.92 and 0.87 respectively, and combined AUC of 0.96; and the sialylat-ed-fucosylated N-glycans HexNAc4,Hex5,Fuc,Neu5Ac, HexNAc5,Hex6,Fuc,Neu5Ac2 and HexNAc6,Hex7,Fuc,Neu5Ac3 with single AUCs of 0.94, 0.67, and 0.88 respectively, and combined AUC of 0.98. According to ingenuity pathway analysis (IPA) and in line with recent publications the identified N-glycans may play an important role in neuroinflammation. Process that plays a fundamental role in the progression of neurodegenerative diseases.
2023-03-11 | PXD036704 | Pride
Project description:<h4><strong>INTRODUCTION </strong>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><p><strong>METHODS </strong>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.</p><p><strong>RESULTS </strong>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.</p><p><strong>DISCUSSION </strong>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.</p>
2024-01-11 | MTBLS8662 | MetaboLights
Project description:Type 2 diabetes mellitus (T2DM) is an independent risk factor of Alzheimer's disease (AD), thus, identifying who among the increasing T2DM populations may develop into AD is important for early intervention. By using TMT-labeling coupled high-throughput mass spectrometry, we conducted a comprehensive plasma proteomic analysis in none-T2DM people (Ctrl, n=30), and the age-/sex-matched T2DM patients with mild cognitive impairment (T2DM-MCI, n=30) or T2DM without MCI (T2DM-nMCI, n=25). The candidate biomarkers identified by proteomics and bioinformatics analyses were verified by ELISA, and their diagnostic capabilities were evaluated with machine learning. A total of 53 differentially expressed proteins (DEPs) were identified in T2DM-MCI compared with T2DM-nMCI patients. These DEPs were significantly enriched in multiple biological processes, such as amyloid neuropathies, CNS disorders, and metabolic acidosis. Among the DEPs, alpha-1-antitrypsin (SERPINA1), major viral protein (PRNP), valosin-containing protein (VCP) showed strong collection with AD high-risk genes APP, MAPT, APOE, PSEN1, and PSEN2. And the levels of PP2A cancer inhibitor (CIP2A), PRNP, corticotropin releasing factor binding protein (CRHBP) were significantly increased, while the level of VCP was decreased in T2DM-MCI patients compared with the T2DM-nMCI, and these changes were correlated with the mini-mental state examination (MMSE) score. Further machine learning data showed that increases of CIP2A, ratio of β-amyloid (rAβ42/40), and rGSK-3β(T/S9) (ratio of total to serine-9-phosphorylated glycogen synthase kinase-3β) had the greatest power to identify mild cognitive decline in T2DM patients.
2023-03-11 | PXD027870 | Pride
Project description:Alzheimer’s dementia (AD) is the most common form of dementia and without readily available clinical biomarkers. Blood-derived proteins are routinely used for diagnostics, however, comprehensive plasma profiling is challenging due to the large dynamic range in protein concentrations. Extracellular vesicles (EVs) can cross the blood-brain barrier and may provide a source for AD related biomarkers. We investigated plasma-derived EV proteins for biomarkers towards AD diagnostics from 10 AD patients, 10 Mild Cognitive Impairment (MCI) patients, and 9 healthy controls (Con) using liquid chromatography - tandem mass spectrometry (LC-MS/MS). EV enrichment was performed using a double centrifugation of 100,000 × g, 1h, 4°C with a wash of the pellet. Some AD patients presented with highly elevated FXIIIA1 (log2 FC: 4.6, p-value: 0.005) and FXIIIB (log2 FC: 4.9, p-value: 0.018). A group of proteins was identified discriminating Con and AD (AUC: 0.91, CI: 0.67-1.00) with ORM2 (AUC: 1.00, CI: 1.00-1.00), RBP4 (AUC: 0.99, CI: 0.95-1.00), and HYDIN (AUC: 0.89, CI: 0.72-1.00) found especially relevant for AD. This indicates that EVs provide an easily accessible matrix for possible biomarkers for AD. Some of the MCI patients, with similar protein profiles as the AD group progressed to AD within a 2-year timespan.
2022-02-16 | PXD024216 | Pride
Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
2024-01-08 | GSE252118 | GEO
Project description: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.
2020-11-11 | GSE150693 | GEO
Project description:The objective of this study was to develop and validate predictive models to assess the risk of death in patients with acute diquat (DQ) poisoning using innovative machine learning techniques. The study analyzed data from 201 consecutive patients admitted for deliberate oral intake of DQ between February 2018 and August 2023 at the First Hospital and Shengjing Hospital of China Medical University. Initial clinical data were collected, and four machine learning methods—logistic regression, random forest, support vector machine (SVM), and gradient boosting—were applied to build the prediction models. The dataset was split into a training set and a test set in an 8:2 ratio. The performance of these models was evaluated in terms of discrimination, calibration, and clinical decision curve analysis (DCA). Additionally, the SHapley Additive ExPlanations (SHAP) interpretation tool was used to provide an intuitive explanation of the risk of death in patients with DQ poisoning by calculating the contribution and impact of each feature on the final prediction. The areas under the receiver operating characteristic curves (AUCs) for the models were 0.91 for logistic regression, 0.98 for random forest, 0.96 for SVM, and 0.94 for gradient boosting. The random forest model demonstrated the best predictive performance with the highest AUC of 0.98, highest F1-score (0.90), highest Matthews correlation coefficient (0.79), highest accuracy (0.90), and the lowest Brier score (0.07). The study concludes that these machine learning models, combined with SHAP, provide reliable and interpretable tools for predicting the death risk in patients with acute DQ poisoning, with the random forest model being identified as the best performing model.
2024-07-18 | MODEL2407180001 | BioModels
Project description:To identify the potential biomarkers of Alzheimer's disease (AD) based on circulating microRNAs (miRNAs), we developed a new approach using feature selection and linear mixed model. The miRNA sequencing data of 105 plasma and 112 serum samples from 112 subjects including 28 AD cases, 63 mild cognitive impairment (MCI), and 21 controls were used to identify cerebrospinal fluid biomarkers associated miRNAs. The potential of these miRNAs as biomarkers of AD or MCI was researched and validated via both internal and external dataset. Patient classification was effectuated in compliance with the NIA-AA criteria for “MCI due to AD” and “Dementia due to AD”.
2024-02-26 | GSE215789 | GEO
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
Project description:Extensive literature has explored the beneficial effects of music in age-related cognitive disorders (ACD), but limited knowledge exists regarding its impact on gene expression. We analyzed transcriptomes of ACD patients and healthy controls, pre-post a music session (n=60), and main genes/pathways were compared to those dysregulated in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) as revealed by a multi-cohort study (n=1269 MCI/AD and controls). Music was associated with 2.3 times more whole-genome gene expression, particularly on neurodegeneration-related genes, in ACD than controls. Co-expressed gene-modules and pathways analysis demonstrated that music impacted autophagy, vesicle and endosome organization, biological processes commonly dysregulated in MCI/AD. Notably, the data indicated a strong negative correlation between musically-modified genes/pathways in ACD and those dysregulated in MCI/AD. These findings highlight the compensatory effect of music on genes/biological processes affected in MCI/AD, providing insights into the molecular mechanisms underlying the benefits of music on these disorders.
2023-10-26 | GSE239282 | GEO