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Nicotinamide as potential biomarker for Alzheimer's disease: A translational study based on metabolomics.

ABSTRACT: Introduction: The metabolic routes altered in Alzheimer's disease (AD) brain are poorly understood. As the metabolic pathways are evolutionarily conserved, the metabolic profiles carried out in animal models of AD could be directly translated into human studies. Methods: We performed untargeted Nuclear Magnetic Resonance metabolomics in hippocampus of McGill-R-Thy1-APP transgenic (Tg) rats, a model of AD-like cerebral amyloidosis and the translational potential of these findings was assessed by targeted Gas Chromatography-Electron Impact-Mass Spectrometry in plasma of participants in the German longitudinal cohort AgeCoDe. Results: In rat hippocampus 26 metabolites were identified. Of these 26 metabolites, nine showed differences between rat genotypes that were nominally significant. Two of them presented partial least square-discriminant analysis (PLS-DA) loadings with the larger absolute weights and the highest Variable Importance in Projection (VIP) scores and were specifically assigned to nicotinamide adenine dinucleotide (NAD) and nicotinamide (Nam). NAD levels were significantly decreased in Tg rat brains as compared to controls. In agreement with these results, plasma of AD patients showed significantly reduced levels of Nam in respect to cognitively normal participants. In addition, high plasma levels of Nam showed a 27% risk reduction of progressing to AD dementia within the following 2.5 years, this hazard ratio is lost afterwards. Discussion: To our knowledge, this is the first report showing that a decrease of Nam plasma levels is observed couple of years before conversion to AD, thereby suggesting its potential use as biomarker for AD progression.

SUBMITTER: Dalmasso MC

PROVIDER: S-EPMC9853457 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Nicotinamide as potential biomarker for Alzheimer's disease: A translational study based on metabolomics.

Dalmasso María C MC Arán Martín M Galeano Pablo P Perin Silvina S Giavalisco Patrick P Martino Adami Pamela V PV Novack Gisela V GV Castaño Eduardo M EM Cuello A Claudio AC Scherer Martin M Maier Wolfgang W Wagner Michael M Riedel-Heller Steffi S Ramirez Alfredo A Morelli Laura L

Frontiers in molecular biosciences 20230106

<b>Introduction:</b> The metabolic routes altered in Alzheimer's disease (AD) brain are poorly understood. As the metabolic pathways are evolutionarily conserved, the metabolic profiles carried out in animal models of AD could be directly translated into human studies. <b>Methods:</b> We performed untargeted Nuclear Magnetic Resonance metabolomics in hippocampus of McGill-R-Thy1-APP transgenic (Tg) rats, a model of AD-like cerebral amyloidosis and the translational potential of these findings wa ...[more]

PMID: 36685284

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Project description:Metabolomics is increasingly being applied towards the identification of biomarkers for disease diagnosis, prognosis and risk prediction. Unfortunately among the many published metabolomic studies focusing on biomarker discovery, there is very little consistency and relatively little rigor in how researchers select, assess or report their candidate biomarkers. In particular, few studies report any measure of sensitivity, specificity, or provide receiver operator characteristic (ROC) curves with associated confidence intervals. Even fewer studies explicitly describe or release the biomarker model used to generate their ROC curves. This is surprising given that for biomarker studies in most other biomedical fields, ROC curve analysis is generally considered the standard method for performance assessment. Because the ultimate goal of biomarker discovery is the translation of those biomarkers to clinical practice, it is clear that the metabolomics community needs to start "speaking the same language" in terms of biomarker analysis and reporting-especially if it wants to see metabolite markers being routinely used in the clinic. In this tutorial, we will first introduce the concept of ROC curves and describe their use in single biomarker analysis for clinical chemistry. This includes the construction of ROC curves, understanding the meaning of area under ROC curves (AUC) and partial AUC, as well as the calculation of confidence intervals. The second part of the tutorial focuses on biomarker analyses within the context of metabolomics. This section describes different statistical and machine learning strategies that can be used to create multi-metabolite biomarker models and explains how these models can be assessed using ROC curves. In the third part of the tutorial we discuss common issues and potential pitfalls associated with different analysis methods and provide readers with a list of nine recommendations for biomarker analysis and reporting. To help readers test, visualize and explore the concepts presented in this tutorial, we also introduce a web-based tool called ROCCET (ROC Curve Explorer & Tester, http://www.roccet.ca). ROCCET was originally developed as a teaching aid but it can also serve as a training and testing resource to assist metabolomics researchers build biomarker models and conduct a range of common ROC curve analyses for biomarker studies.

Project description:IntroductionAlzheimer'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.MethodsWe 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.ResultsFifty-seven 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.DiscussionIn 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.

Project description:The purpose of our study was to identify microRNAs (miRNAs) as early detectable peripheral biomarkers in Alzheimer's disease (AD). To achieve our objective, we assessed miRNAs in serum samples from AD patients and Mild cognitive impairment (MCI) subjects relative to healthy controls. We used Affymetrix microarray analysis and validated differentially expressed miRNAs using qRT-PCR. We further validated miRNA data using AD postmortem brains, amyloid precursor protein transgenic mice and AD cell lines. We identified a gradual upregulation of four miRNAs: miR-455-3p, miR-4668-5p, miR-3613-3p and miR-4674. A fifth miRNA, mir-6722, was down-regulated in persons with AD and mild cognitive impairment compared with controls. Validation analysis by qRT-PCR showed significant upregulation of only miR-455-3p (P = 0.007) and miR-4668-5p (P = 0.016) in AD patients compared with healthy controls. Furthermore, qRT-PCR analysis of the AD postmortem brains with different Braak stages also showed upregulation of miR-455-3p (P = 0.016). However, receiver operating characteristic curves (ROC) curve analysis revealed a significant area under curve (AUC) value only for miR-455-3p in the serum (AUROC = 0.79; P = 0.015) and brains (AUROC = 0.86; P = 0.016) of AD patients. Expression analysis of amyloid precursor protein transgenic mice also revealed high level of mmu-miR-455-3p (P = 0.004) in the cerebral cortex (AD-affected) region of brain and low in the non-affected area, i.e. cerebellum. Furthermore, human and mouse neuroblastoma cells treated with the amyloid-β(1-42) peptide also showed a similarly higher expression of miR-455-3p. Functional analysis of differentially expressed miRNAs via the miR-path indicated that miR-455-3p was associated in the regulation of several biological pathways. Genes associated with these pathways were found to have a crucial role in AD pathogenesis. An increase in miR-455-3p expression found in AD patients and Aβ pathologies unveiled its biomarker characteristics and a precise role in AD pathogenesis.

Dataset Information

Nicotinamide as potential biomarker for Alzheimer's disease: A translational study based on metabolomics.

Publications

Nicotinamide as potential biomarker for Alzheimer's disease: A translational study based on metabolomics.

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