An Observation Study of Urinary Biomarkers Exploratory in Alzheimer's Disease using High Resolution Mass Spectrometry

Metabolic Signature of Insulin Resistance and Risk of Alzheimer's Disease

BACKGROUND

Substantial evidence supports the relationship between peripheral insulin resistance (IR) and the development of Alzheimer's disease (AD)-dementia. However, the mechanisms explaining these associations are only partly understood. We aimed to identify a metabolic signature of IR associated with the progression from mild cognitive impairment (MCI) to AD-dementia.

METHODS

This is a case-control study on 400 MCI subjects, free of type 2 diabetes, within the ACE cohort, including individuals ATN + and ATN-. After a median of 2.1 years of follow-up, 142 subjects converted to AD-dementia. IR was assessed using the homeostasis model assessment for insulin resistance (HOMA-IR). A targeted multiplatform approach profiled over 600 plasma metabolites. Elastic net penalized linear regression with 10-fold cross-validation was employed to select those metabolites associated with HOMA-IR. The prediction ability of the signature was assessed using support vector machine and performance metrics. The metabolic signature was associated with AD-dementia risk using a multivariable Cox regression model. Using counterfactual-based mediation analysis, we investigated the mediation role of the metabolic signature between HOMA-IR and AD-dementia. The metabolic pathways in which the metabolites were involved were identified using MetaboAnalyst.

RESULTS

The metabolic signature comprised 18 metabolites correlated with HOMA-IR. After adjustments by confounders, the signature was associated with increased AD-dementia risk (HR = 1.234; 95% CI = 1.019-1.494; p < .05). The metabolic signature mediated 35% of the total effect of HOMA-IR on AD-dementia risk. Significant metabolic pathways were related to glycerophospholipid and tyrosine metabolism.

CONCLUSIONS

We have identified a blood-based metabolic signature that reflects IR and may enhance our understanding of the biological mechanisms through which IR affects AD-dementia.

Advances and future trends in the detection of beta-amyloid: A comprehensive review

The neurodegenerative condition known as Alzheimer's disease is typified by the build-up of beta-amyloid plaques within the brain. The timely and precise identification of beta-amyloid is essential for understanding disease progression and developing effective therapeutic interventions. This comprehensive review explores the diverse landscape of beta-amyloid detection methods, ranging from traditional immunoassays to cutting-edge technologies. The review critically examines the strengths and limitations of established techniques such as ELISA, PET, and MRI, providing insights into their roles in research and clinical settings. Emerging technologies, including electrochemical methods, nanotechnology, fluorescence techniques, point-of-care devices, and machine learning integration, are thoroughly discussed, emphasizing recent breakthroughs and their potential for revolutionizing beta-amyloid detection. Furthermore, the review delves into the challenges associated with current detection methods, such as sensitivity, specificity, and accessibility. By amalgamating knowledge from multidisciplinary approaches, this review aims to guide researchers, clinicians, and policymakers in navigating the complex landscape of beta-amyloid detection, ultimately contributing to advancements in Alzheimer's disease diagnostics and therapeutics.

Integration of genomic and transcriptomic layers in RNA-Seq data leads to protein interaction modules with improved Alzheimer's disease associations

Alzheimer's disease (AD) is the most common neurodegenerative disease, and it is currently untreatable. RNA sequencing (RNA-Seq) is commonly used in the literature to identify AD-associated molecular mechanisms by analysing changes in gene expression. RNA-Seq data can also be used to detect genomic variants, enabling the identification of the genes with a higher load of deleterious variants in patients compared with controls. Here, we analysed AD RNA-Seq datasets to obtain differentially expressed genes and genes with a higher load of pathogenic variants in AD, and we combined them in a single list. We mapped these genes on a human protein-protein interaction network to discover subnetworks perturbed by AD. Our results show that utilizing gene pathogenicity information from RNA-Seq data positively contributes to the disclosure of AD-related mechanisms. Moreover, dividing the discovered subnetworks into highly connected modules reveals a clearer picture of altered molecular pathways that, otherwise, would not be captured. Repeating the whole pipeline with human metabolic network genes led to results confirming the positive contribution of gene pathogenicity information and enabled a more detailed identification of altered metabolic pathways in AD.

Protective signature of xanthohumol on cognitive function of APP/PS1 mice: a urine metabolomics approach by age

Alzheimer's disease (AD) has an increasing prevalence, complicated pathogenesis and no effective cure. Emerging evidences show that flavonoid compounds such as xanthohumol (Xn) could play an important role as a dietary supplement or traditional Chinese herbal medicine in the management of diseases such as AD. This study aims to analyze the target molecules of Xn in the prevention and treatment of AD, and its potential mechanism from the perspective of metabolites. APP/PS1 mice 2- and 6-months old were treated with Xn for 3 months, respectively, the younger animals to test for AD-like brain disease prevention and the older animals to address therapeutic effects on the disease. Memantine (Mem) was selected as positive control. Behavioral tests were performed to assess the course of cognitive function. Urine samples were collected and analyzed by high-performance liquid chromatography (HPLC) with tandem mass spectrometry (MS/MS) coupled with online Compound Discoverer software. Morris Water Maze (MWM) tests showed that Xn, like Mem, had a therapeutic but not a preventive effect on cognitive impairment. The expression levels of urinary metabolites appeared to show an opposite trend at different stages of Xn treatment, downregulated in the prevention phase while upregulated in the therapy phase. In addition, the metabolic mechanisms of Xn during preventive treatment were also different from that during therapeutic treatment. The signaling pathways metabolites nordiazepam and genistein were specifically regulated by Xn but not by Mem in the disease prevention stage. The signaling pathway metabolite ascorbic acid was specifically regulated by Xn in the therapeutic stage. In conclusion, dietary treatment with Xn altered the urinary metabolite profile at different stages of administration in APP/PS1 mice. The identified potential endogenous metabolic biomarkers and signal pathways open new avenues to investigate the pathogenesis and treatment of AD.

Role of the Gut Bacteria-Derived Metabolite Phenylacetylglutamine in Health and Diseases

Over the past few decades, it has been well established that gut microbiota-derived metabolites can disrupt gut function, thus resulting in an array of diseases. Notably, phenylacetylglutamine (PAGln), a bacterial derived metabolite, has recently gained attention due to its role in the initiation and progression of cardiovascular and cerebrovascular diseases. This meta-organismal metabolite PAGln is a byproduct of amino acid acetylation of its precursor phenylacetic acid (PAA) from a range of dietary sources like egg, meat, dairy products, etc. The microbiota-dependent metabolism of phenylalanine produces PAA, which is a crucial intermediate that is catalyzed by diverse microbial catalytic pathways. PAA conjugates with glutamine and glycine in the liver and kidney to predominantly form phenylacetylglutamine in humans and phenylacetylglycine in rodents. PAGln is associated with thrombosis as it enhances platelet activation mediated through the GPCRs receptors α2A, α2B, and β2 ADRs, thereby aggravating the pathological conditions. Clinical evidence suggests that elevated levels of PAGln are associated with pathology of cardiovascular, cerebrovascular, and neurological diseases. This Review further consolidates the microbial/biochemical synthesis of PAGln and discusses its role in the above pathophysiologies.

Urine metabolomics phenotyping and urinary biomarker exploratory in mild cognitive impairment and Alzheimer's disease

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.

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

Fifty-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.

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.

Targeting amyloid proteins for clinical diagnosis of neurodegenerative diseases

Abnormal aggregation and accumulation of pathological amyloid proteins such as amyloid-β, Tau, and α-synuclein play key pathological roles and serve as histological hallmarks in different neurodegenerative diseases (NDs) such as Alzheimer's disease (AD) and Parkinson's disease (PD). In addition, various post-translational modifications (PTMs) have been identified on pathological amyloid proteins and are subjected to change during disease progression. Given the central role of amyloid proteins in NDs, tremendous efforts have been made to develop amyloid-targeting strategies for clinical diagnosis and molecular classification of NDs. In this review, we summarize two major strategies for targeting amyloid aggregates, with a focus on the trials in AD diagnosis. The first strategy is a positron emission tomography (PET) scan of protein aggregation in the brain. We mainly focus on introducing the development of small-molecule PET tracers for specifically recognizing pathological amyloid fibrils. The second strategy is the detection of PTM biomarkers on amyloid proteins in cerebrospinal fluid and plasma. We discuss the pathological roles of different PTMs in diseases and how we can use the PTM profile of amyloid proteins for clinical diagnosis. Finally, we point out the potential technical challenges of these two strategies, and outline other potential strategies, as well as a combination of multiple strategies, for molecular diagnosis of NDs.

Amyloid beta-correlated plasma metabolite dysregulation in Alzheimer's disease: an untargeted metabolism exploration using high-resolution mass spectrometry toward future clinical diagnosis

Introduction

Alzheimer's disease (AD) is a leading cause of dementia, and it has rapidly become an increasingly burdensome and fatal disease in society. Despite medical research advances, accurate recognition of AD remains challenging. Epidemiological evidence suggests that metabolic abnormalities are tied to higher AD risk.

Methods

This study utilized case-control analyses with plasma samples and identified a panel of 27 metabolites using high-resolution mass spectrometry in both the Alzheimer's disease (AD) and cognitively normal (CN) groups. All identified variables were confirmed using MS/MS with detected fragmented ions and public metabolite databases. To understand the expression of amyloid beta proteins in plasma, ELISA assays were performed for both amyloid beta 42 (Aβ42) and amyloid beta 40 (Aβ40).

Results

The levels of plasma metabolites PAGln and L-arginine were found to significantly fluctuate in the peripheral blood of AD patients. In addition, ELISA results showed a significant increase in amyloid beta 42 (Aβ42) in AD patients compared to those who were cognitively normal (CN), while amyloid beta 40 (Aβ40) did not show any significant changes between the groups. Furthermore, positive correlations were observed between Aβ42/Aβ40 and PAGln or L-arginine, suggesting that both metabolites could play a role in the pathology of amyloid beta proteins. Binary regression analysis with these two metabolites resulted in an optimal model of the ROC (AUC = 0.95, p < 0.001) to effectively discriminate between AD and CN.

Discussion

This study highlights the potential of advanced high-resolution mass spectrometry (HRMS) technology for novel plasma metabolite discovery with high stability and sensitivity, thus paving the way for future clinical studies. The results of this study suggest that the combination of PAGln and L-arginine holds significant potential for improving the diagnosis of Alzheimer's disease (AD) in clinical settings. Overall, these findings have important implications for advancing our understanding of AD and developing effective approaches for its future clinical diagnosis.