ArticleAnnals of clinical and translational neurology2019
A metabolomic approach to identifying biomarkers in blood of Alzheimer's disease.
Article in Annals of clinical and translational neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Kynurenine pathway dysregulation in cognitive impairment and dementia: a systematic review and meta-analysis.GeroScience · 2026Pooled it
- Lipids as Early and Minimally Invasive Biomarkers for Alzheimer's Disease.Current neuropharmacology · 2022Pooled it
- Personalized whole-body modeling links gut microbiota to metabolic perturbations in Alzheimer's disease.Gut microbes · 2026Article
- Integrated multi-omics profiling reveals novel molecular biomarkers and pathways associated with Fragile X-associated tremor/ataxia syndrome.Frontiers in molecular neuroscience · 2026Article
- Artificial intelligence in drug development for delirium and Alzheimer's disease.Acta pharmaceutica Sinica. B · 2025Review
- Alterations of amino acids in older adults with Alzheimer's Disease and Vascular Dementia.Amino acids · 2025Article
- Discovery of novel metabolic biomarkers in blood serum for diagnosis of Alzheimer's disease.Journal of Alzheimer's disease : JAD · 2024Article
- SDMA as a marker and mediator in cerebrovascular disease.Clinical science (London, England : 1979) · 2024Review
- Palmitoyl-L-carnitine induces tau phosphorylation and mitochondrial dysfunction in neuronal cells.PloS one · 2024Article
- Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Alteration of Metabolic Profiles during the Progression of Alzheimer's Disease.Brain sciences · 2023Article
- Metabolomic Signatures of Alzheimer's Disease Indicate Brain Region-Specific Neurodegenerative Progression.International journal of molecular sciences · 2023Article
- Construction of the XGBoost model for early lung cancer prediction based on metabolic indices.BMC medical informatics and decision making · 2023Article
- Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?International journal of molecular sciences · 2023Review
- Discovering Hair Biomarkers of Alzheimer's Disease Using High Resolution Mass Spectrometry-Based Untargeted Metabolomics.Molecules (Basel, Switzerland) · 2023Article
- Comparing Symmetric Dimethylarginine and Amyloid-β42 as Predictors of Alzheimer's Disease Development.Journal of Alzheimer's disease reports · 2023Article
- Metabolic changes in the plasma of mild Alzheimer's disease patients treated with Hachimijiogan.Frontiers in pharmacology · 2023Article
- Multi-Omics, an Integrated Approach to Identify Novel Blood Biomarkers of Alzheimer's Disease.Metabolites · 2022Article
- Age andFrontiers in aging neuroscience · 2022Article
- Identification of Serum Biomarkers Associated With Emergence Agitation After General Anesthesia in Adult Patients: A Metabolomics Analysis.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aims to identify metabolites with altered levels of expression in patients with early and progressive stages of Alzheimer's disease (AD). Methods: All participants of the study underwent genetic screening and were diagnosed using both neuropsychological assessment and amyloid imaging before metabolome analysis. According to these assessments, the patients were classified as normal ( Results: Using a targeted metabolomic approach, we found that plasma levels of C3, C5, and C5-DC acylcarnitines, arginine, phenylalanine, creatinine, symmetric dimethylarginine (SDMA) and phosphatidylcholine ae C38:2 were significantly altered in patients with early and progressive stages of AD. We created a predictive model based on the decision tree that included three main parameters: age, arginine and C5 plasma concentrations. The model distinguished AD patients from other participants with 60% sensitivity and 86.7% specificity. For healthy controls, the sensitivity was 85.7% and specificity was 61.5%. Multivariate ROC analysis to develop a decision tree showed that our model reached moderate diagnostic power in differentiating between older adults who are cognitively normal (AUC = 0.77) and those with AD (AUC = 0.72). Interpretation: The plasma levels of arginine and valeryl carnitine, together with subject age, are promising as biomarkers for the diagnosis of AD in older adults.
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