Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer's Disease

The Tryptophan Metabolite Indole-3-Propionic Acid Raises Kynurenic Acid Levels in the Rat Brain In Vivo

Alterations in the composition of the gut microbiota may be causally associated with several brain diseases. Indole-3-propionic acid (IPrA) is a tryptophan-derived metabolite, which is produced by intestinal commensal microbes, rapidly enters the circulation, and crosses the blood-brain barrier. IPrA has neuroprotective properties, which have been attributed to its antioxidant and bioenergetic effects. Here, we evaluate an alternative and/or complementary mechanism, linking IPrA to kynurenic acid (KYNA), another neuroprotective tryptophan metabolite. Adult Sprague-Dawley rats received an oral dose of IPrA (200 mg/kg), and both IPrA and KYNA were measured in plasma and frontal cortex 90 minutes, 6 or 24 hours later. IPrA and KYNA levels increased after 90 minutes and 6 hours (brain IPrA: ~56- and ~7-fold; brain KYNA: ~4- and ~3-fold, respectively). In vivo microdialysis, performed in the medial prefrontal cortex and in the striatum, revealed increased KYNA levels (~2.5-fold) following the administration of IPrA (200 mg/kg, p.o), but IPrA failed to affect extracellular KYNA when applied locally. Finally, treatment with 100 or 350 mg IPrA, provided daily to the animals in the chow for a week, resulted in several-fold increases of IPrA and KYNA levels in both plasma and brain. These results suggest that exogenously supplied IPrA may provide a novel strategy to affect the function of KYNA in the mammalian brain.

Progress on early diagnosing Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects both cognition and non-cognition functions. The disease follows a continuum, starting with preclinical stages, progressing to mild cognitive and behavioral impairment, ultimately leading to dementia. Early detection of AD is crucial for better diagnosis and more effective treatment. However, the current AD diagnostic tests of biomarkers using cerebrospinal fluid and/or brain imaging are invasive or expensive, and mostly are still not able to detect early disease state. Consequently, there is an urgent need to develop new diagnostic techniques with higher sensitivity and specificity during the preclinical stages of AD. Various non-cognitive manifestations, including behavioral abnormalities, sleep disturbances, sensory dysfunctions, and physical changes, have been observed in the preclinical AD stage before occurrence of notable cognitive decline. Recent research advances have identified several biofluid biomarkers as early indicators of AD. This review focuses on these non-cognitive changes and newly discovered biomarkers in AD, specifically addressing the preclinical stages of the disease. Furthermore, it is of importance to explore the potential for developing a predictive system or network to forecast disease onset and progression at the early stage of AD.

Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed.

METHOD

Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis.

RESULTS

Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others.

CONCLUSIONS

This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.

Digestive System Diseases, Genetic Risk, and Incident Dementia: A Prospective Cohort Study

INTRODUCTION

Although digestive system disease affects gut microbiota and their metabolites associated with dementia risk, the association between digestive system diseases and incident dementia has not yet been established.

METHODS

This cohort analysis included 458,181 participants free of baseline dementia in the UK Biobank (2006-2021). The associations of 14 digestive system diseases with dementia incidence were examined in 2022 using Cox proportional hazards regression models. Analyses were performed to differentiate the associations for early-onset (age <65 years) and late-onset (age ≥65 years) dementia. Interaction and stratification analyses were performed for polygenic risk score and APOE.

RESULTS

During a median follow-up of 12.4 years, 6,415 incident dementia cases were diagnosed. Eleven digestive system diseases showed significant associations with an increased risk of dementia after controlling for covariates and multiple testing. Compared with hazard ratios for individuals without digestive system diseases, the hazard ratios of dementia increased from 1.15 (95% confidence interval=1.09, 1.23) for patients with intestinal diverticular disease to 2.31 (95% confidence interval=1.98, 2.70) for patients with cirrhosis. The associations were different between certain digestive system diseases and dementia by onset age. The associations appeared to be stronger for cirrhosis (Q=0.001), irritable bowel syndrome (Q<0.001), gastritis and duodenitis (Q=0.002), gastroesophageal reflux disease (Q<0.001), ulcerative colitis (Q=0.047), gallbladder disease (Q=0.012), and peptic ulcer (Q=0.030) with early-onset dementia. There were no interactions for polygenic risk score or APOE (p>0.05).

CONCLUSIONS

These findings suggest an increased need for dementia prevention among patients with digestive system diseases.

Influence of gut microbiota on the development of most prevalent neurodegenerative dementias and the potential effect of probiotics in elderly: A scoping review

Dementia is one of today's greatest public health challenges. Its high socio-economic impact and difficulties in diagnosis and treatment are of increasing concern to an aging world population. In recent years, the study of the relationship between gut microbiota and different neurocognitive disorders has gained a considerable interest. Several studies have reported associations between gut microbiota dysbiosis and some types of dementia. Probiotics have been suggested to restore dysbiosis and to improve neurocognitive symptomatology in these dementias. Based on these previous findings, the available scientific evidence on the gut microbiota in humans affected by the most prevalent dementias, as well as the probiotic trials conducted in these patients in recent years, have been here reviewed. Decreased concentrations of short-chain fatty acids (SCFA) and other bacterial metabolites appear to play a major role in the onset of neurocognitive symptoms in Alzheimer disease (AD) and Parkinson disease dementia (PDD). Increased abundance of proinflammatory taxa could be closely related to the more severe clinical symptoms in both, as well as in Lewy Bodies dementia. Important lack of information was noted in Frontotemporal dementia behavioral variant. Moreover, geographical differences in the composition of the gut microbiota have been reported in AD. Some potential beneficial effects of probiotics in AD and PDD have been reported. However, due to the controversial results further investigations are clearly necessary.

The fusion of multi-omics profile and multimodal EEG data contributes to the personalized diagnostic strategy for neurocognitive disorders

BACKGROUND

The increasing prevalence of neurocognitive disorders (NCDs) in the aging population worldwide has become a significant concern due to subjectivity of evaluations and the lack of precise diagnostic methods and specific indicators. Developing personalized diagnostic strategies for NCDs has therefore become a priority.

RESULTS

Multimodal electroencephalography (EEG) data of a matched cohort of normal aging (NA) and NCDs seniors were recorded, and their faecal samples and urine exosomes were collected to identify multi-omics signatures and metabolic pathways in NCDs by integrating metagenomics, proteomics, and metabolomics analysis. Additionally, experimental verification of multi-omics signatures was carried out in aged mice using faecal microbiota transplantation (FMT). We found that NCDs seniors had low EEG power spectral density and identified specific microbiota, including Ruminococcus gnavus, Enterocloster bolteae, Lachnoclostridium sp. YL 32, and metabolites, including L-tryptophan, L-glutamic acid, gamma-aminobutyric acid (GABA), and fatty acid esters of hydroxy fatty acids (FAHFAs), as well as disturbed biosynthesis of aromatic amino acids and TCA cycle dysfunction, validated in aged mice. Finally, we employed a support vector machine (SVM) algorithm to construct a machine learning model to classify NA and NCDs groups based on the fusion of EEG data and multi-omics profiles and the model demonstrated 92.69% accuracy in classifying NA and NCDs groups.

CONCLUSIONS

Our study highlights the potential of multi-omics profiling and EEG data fusion in personalized diagnosis of NCDs, with the potential to improve diagnostic precision and provide insights into the underlying mechanisms of NCDs. Video Abstract.

Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer's Disease

Late-onset Alzheimer's disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer's disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer's disease at various timepoints within the disease course, including at the preclinical stage. In this review, we discuss recent advances in such blood biomarkers for determining disease risk. We highlight how leveraging polygenic risk scores, based on genome-wide association studies, can help stratify individuals along their risk profile. We summarize studies analyzing protein biomarkers, as well as report on recent proteomic- and metabolomic-based prediction models. Finally, we discuss how a combination of multi-omic blood biomarkers can potentially be used in memory clinics for diagnosis and to assess the dynamic risk an individual has for developing Alzheimer's disease dementia.

Mild cognitive impairment estimation based on functional near-infrared spectroscopy

Patients with mild cognitive impairment (MCI) are at a high risk of developing future dementia. However, early identification and active intervention could potentially reduce its morbidity and the incidence of dementia. Functional near-infrared spectroscopy (fNIRS) has been proposed as a noninvasive modality for detecting oxygenation changes in the time-varying hemodynamics of the prefrontal cortex. This study sought to provide an effective method for detecting patients with MCI using fNIRS and the Wisconsin card sorting test (WCST) to evaluate changes in blood oxygenation. The results revealed that all groups with a lower mini-mental state examination grade had a higher increase in HHb concentration during a modified WCST (MCST). The increase in the change in oxygenated hemoglobin concentration in the stroke group was smaller than that in the normal group due to weak cerebrovascular reactivity.

The role of the indoles in microbiota-gut-brain axis and potential therapeutic targets: A focus on human neurological and neuropsychiatric diseases

At present, a large number of relevant studies have suggested that the changes in gut microbiota are related to the course of nervous system diseases, and the microbiota-gut-brain axis is necessary for the proper functioning of the nervous system. Indole and its derivatives, as the products of the gut microbiota metabolism of tryptophan, can be used as ligands to regulate inflammation and autoimmune response in vivo. In recent years, some studies have found that the levels of indole and its derivatives differ significantly between patients with central nervous system diseases and healthy individuals, suggesting that they may be important mediators for the involvement of the microbiota-gut-brain axis in the disease course. Tryptophan metabolites produced by gut microbiota are involved in multiple physiological reactions, take indole for example, it participates in the process of inflammation and anti-inflammatory effects through various cellular physiological activities mediated by aromatic hydrocarbon receptors (AHR), which can influence a variety of neurological and neuropsychiatric diseases. This review mainly explores and summarizes the relationship between indoles and human neurological and neuropsychiatric disorders, including ischemic stroke, Alzheimer's disease, Parkinson's disease, multiple sclerosis, cognitive impairment, depression and anxiety, and puts forward that the level of indoles can be regulated through various direct or indirect ways to improve the prognosis of central nervous system diseases and reverse the dysfunction of the microbiota-gut-brain axis. This article is part of the Special Issue on "Microbiome & the Brain: Mechanisms & Maladies".

Deep learning analysis of UPLC-MS/MS-based metabolomics data to predict Alzheimer's disease

OBJECTIVE

Metabolic biomarkers can potentially inform disease progression in Alzheimer's disease (AD). The purpose of this study is to identify and describe a new set of diagnostic biomarkers for developing deep learning (DL) tools to predict AD using Ultra Performance Liquid Chromatography Mass Spectrometry (UPLC-MS/MS)-based metabolomics data.

METHODS

A total of 177 individuals, including 78 with AD and 99 with cognitive normal (CN), were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort along with 150 metabolomic biomarkers. We performed feature selection using the Least Absolute Shrinkage and Selection Operator (LASSO). The H2O DL function was used to build multilayer feedforward neural networks to predict AD.

RESULTS

The LASSO selected 21 metabolic biomarkers. To develop DL models, the 21 biomarkers identified by LASSO were imported into the H2O package. The data was split into 70% for training and 30% for validation. The best DL model with two layers and 18 neurons achieved an accuracy of 0.881, F1-score of 0.892, and AUC of 0.873. Several metabolomic biomarkers involved in glucose and lipid metabolism, in particular bile acid metabolites, were associated with APOE-ε4 allele and clinical biomarkers (Aβ42, tTau, pTau), cognitive assessments [the Alzheimer's Disease Assessment Scale-cognitive subscale 13 (ADAS13), the Mini-Mental State Examination (MMSE)], and hippocampus volume.

CONCLUSIONS

This study identified a new set of diagnostic metabolomic biomarkers for developing DL tools to predict AD. These biomarkers may help with early diagnosis, prognostic risk stratification, and/or early treatment interventions for patients at risk for AD.

Sebacic Acid as a Potential Age-Related Biomarker of Liver Aging: Evidence Linking Mice and Human

The aging process is complicated and involves diverse organ dysfunction; furthermore, the biomarkers that are able to reflect biological aging are eagerly sought after to monitor the system-wide decline associated with the aging process. To address this, we performed a metabolomics analysis using a longitudinal cohort study from Taiwan (N = 710) and established plasma metabolomic age using a machine learning algorithm. The resulting estimation of age acceleration among the older adults was found to be correlated with HOMA-insulin resistance. In addition, a sliding window analysis was used to investigate the undulating decrease in hexanoic and heptanoic acids that occurs among the older adults at different ages. A comparison of the metabolomic alterations associated with aging between humans and mice implied that ω-oxidation of medium-chain fatty acids was commonly dysregulated in older subjects. Among these fatty acids, sebacic acid, an ω-oxidation product produced by the liver, was significantly decreased in the plasma of both older humans and aged mice. Notably, an increase in the production and consumption of sebacic acid within the liver tissue of aged mice was observed, along with an elevation of pyruvate-to-lactate conversion. Taken together, our study reveals that sebacic acid and metabolites of ω-oxidation are the common aging biomarkers in both humans and mice. The further analysis suggests that sebacic acid may play an energetic role in supporting the production of acetyl-CoA during liver aging, and thus its alteration in plasma concentration potentially reflects the aging process.

Does the Gut Microbial Metabolome Really Matter? The Connection between GUT Metabolome and Neurological Disorders

Herein we gathered updated knowledge regarding the alterations of gut microbiota (dysbiosis) and its correlation with human neurodegenerative and brain-related diseases, e.g., Alzheimer’s and Parkinson’s. This review underlines the importance of gut-derived metabolites and gut metabolic status as the main players in gut-brain crosstalk and their implications on the severity of neural conditions. Scientific evidence indicates that the administration of probiotic bacteria exerts beneficial and protective effects as reduced systemic inflammation, neuroinflammation, and inhibited neurodegeneration. The experimental results performed on animals, but also human clinical trials, show the importance of designing a novel microbiota-based probiotic dietary supplementation with the aim to prevent or ease the symptoms of Alzheimer’s and Parkinson’s diseases or other forms of dementia or neurodegeneration.

Gut Microbiota Dysbiosis after Traumatic Brain Injury Contributes to Persistent Microglial Activation Associated with Upregulated Lyz2 and Shifted Tryptophan Metabolic Phenotype

Traumatic brain injury (TBI) is a common cause of disability and mortality, affecting millions of people every year. The neuroinflammation and immune response post-TBI initially have neuroprotective and reparative effects, but prolonged neuroinflammation leads to secondary injury and increases the risk of chronic neurodegenerative diseases. Persistent microglial activation plays a critical role in chronic neuroinflammation post-TBI. Given the bidirectional communication along the brain–gut axis, it is plausible to suppose that gut microbiota dysbiosis post-TBI influences microglial activation. In the present study, hippocampal microglial activation was observed at 7 days and 28 days post-TBI. However, in TBI mice with a depletion of gut microbiota, microglia were activated at 7 days post-TBI, but not at 28 days post-TBI, indicating that gut microbiota contributes to the long-term activation of microglia post-TBI. In addition, in conventional mice colonized by the gut microbiota of TBI mice using fecal microbiota transplant (FMT), microglial activation was observed at 28 days post-TBI, but not at 7 days post-TBI, supporting the role of gut microbiota dysbiosis in persistent microglial activation post-TBI. The RNA sequencing of the hippocampus identified a microglial activation gene, Lyz2, which kept upregulation post-TBI. This persistent upregulation was inhibited by oral antibiotics and partly induced by FMT. 16s rRNA gene sequencing showed that the composition and function of gut microbiota shifted over time post-TBI with progressive dysbiosis, and untargeted metabolomics profiling revealed that the tryptophan metabolic phenotype was differently reshaped at 7 days and 28 days post-TBI, which may play a role in the persistent upregulation of Lyz2 and the activation of microglia. This study implicates that gut microbiota and Lyz2 are potential targets for the development of novel strategies to address persistent microglial activation and chronic neuroinflammation post-TBI, and further investigations are warranted to elucidate the specific mechanism.

Biological Effects of Indole-3-Propionic Acid, a Gut Microbiota-Derived Metabolite, and Its Precursor Tryptophan in Mammals' Health and Disease

Actions of symbiotic gut microbiota are in dynamic balance with the host’s organism to maintain homeostasis. Many different factors have an impact on this relationship, including bacterial metabolites. Several substrates for their synthesis have been established, including tryptophan, an exogenous amino acid. Many biological processes are influenced by the action of tryptophan and its endogenous metabolites, serotonin, and melatonin. Recent research findings also provide evidence that gut bacteria-derived metabolites of tryptophan share the biological effects of their precursor. Thus, this review aims to investigate the biological actions of indole-3-propionic acid (IPA), a gut microbiota-derived metabolite of tryptophan. We searched PUBMED and Google Scholar databases to identify pre-clinical and clinical studies evaluating the impact of IPA on the health and pathophysiology of the immune, nervous, gastrointestinal and cardiovascular system in mammals. IPA exhibits a similar impact on the energetic balance and cardiovascular system to its precursor, tryptophan. Additionally, IPA has a positive impact on a cellular level, by preventing oxidative stress injury, lipoperoxidation and inhibiting synthesis of proinflammatory cytokines. Its synthesis can be diminished in the presence of different risk factors of atherosclerosis. On the other hand, protective factors, such as the introduction of a Mediterranean diet, tend to increase its plasma concentration. IPA seems to be a promising new target, linking gut health with the cardiovascular system.