Metabolome in progression to Alzheimer's disease

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.

Zinc utilization by microglia in Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia defined by two key pathological characteristics in the brain, amyloid-β (Aβ) plaques and neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau. Microglia, the primary innate immune cells of the central nervous system (CNS), provide neuroprotection through Aβ and tau clearance but may also be neurotoxic by promoting neuroinflammation to exacerbate Aβ and tau pathogenesis in AD. Recent studies have demonstrated the importance of microglial utilization of nutrients and trace metals in controlling their activation and effector functions. Trace metals, such as zinc, have essential roles in brain health and immunity, and zinc dyshomeostasis has been implicated in AD pathogenesis. As a result of these advances, the mechanisms by which zinc homeostasis influences microglial-mediated neuroinflammation in AD is a topic of continuing interest since new strategies to treat AD are needed. Here, we review the roles of zinc in AD, including zinc activation of microglia, the associated neuroinflammatory response, and the application of these findings in new therapeutic strategies.

Assessing the contribution of the chemical exposome to neurodegenerative disease

Over the past few decades, numerous environmental chemicals from solvents to pesticides have been suggested to be involved in the development and progression of neurodegenerative diseases. Most of the evidence has accumulated from occupational or cohort studies in humans or laboratory research in animal models, with a range of chemicals being implicated. What has been missing is a systematic approach analogous to genome-wide association studies, which have identified dozens of genes involved in Alzheimer's disease, Parkinson's disease and other neurodegenerative diseases. Fortunately, it is now possible to study hundreds to thousands of chemical features under the exposome framework. This Perspective explores how advances in mass spectrometry make it possible to generate exposomic data to complement genomic data and thereby better understand neurodegenerative diseases.

Metabolomics and Risk of Dementia: A Systematic Review of Prospective Studies

BACKGROUND

The projected increase in the prevalence of dementia has sparked interest in understanding the pathophysiology and underlying causal factors in its development and progression. Identifying novel biomarkers in the preclinical or prodromal phase of dementia may be important for predicting early disease risk. Applying metabolomic techniques to prediagnostic samples in prospective studies provides the opportunity to identify potential disease biomarkers.

OBJECTIVE

The objective of this systematic review was to summarize the evidence on the associations between metabolite markers and risk of dementia and related dementia subtypes in human studies with a prospective design.

DESIGN

We searched PubMed, PsycINFO, and Web of Science databases from inception through December 8, 2023. Thirteen studies (mean/median follow-up years: 2.1-21.0 y) were included in the review.

RESULTS

Several metabolites detected in biological samples, including amino acids, fatty acids, acylcarnitines, lipid and lipoprotein variations, hormones, and other related metabolites, were associated with risk of developing dementia. Our systematic review summarized the adjusted associations between metabolites and dementia risk; however, our findings should be interpreted with caution because of the heterogeneity across the included studies and potential sources of bias. Further studies are warranted with well-designed prospective cohort studies that have defined study populations, longer follow-up durations, the inclusion of additional diverse biological samples, standardization of techniques in metabolomics and ascertainment methods for diagnosing dementia, and inclusion of other related dementia subtypes.

CONCLUSIONS

This study contributes to the limited systematic reviews on metabolomics and dementia by summarizing the prospective associations between metabolites in prediagnostic biological samples with dementia risk. Our review discovered additional metabolite markers associated with the onset of developing dementia and may help aid in the understanding of dementia etiology. The protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (https://www.crd.york.ac.uk/prospero/; registration ID: CRD42022357521).

Metabolites and MRI-Derived Markers of AD/ADRD Risk in a Puerto Rican Cohort

Objective

Several studies have examined metabolomic profiles in relation to Alzheimer's disease and related dementia (AD/ADRD) risk; however, few studies have focused on minorities, such as Latinos, or examined Magnetic-Resonance Imaging (MRI)-based outcomes.

Methods

We used multiple linear regression, adjusted for covariates, to examine the association between metabolite concentration and MRI-derived brain age deviation. Metabolites were measured at baseline with untargeted metabolomic profiling (Metabolon, Inc). Brain age deviation (BAD) was calculated at wave 4 (~ 9 years from Boston Puerto Rican Health Study (BPRHS) baseline) as chronologic age, minus MRI-estimated brain age, representing the rate of biological brain aging relative to chronologic age. We also examined if metabolites associated with BAD were similarly associated with hippocampal volume and global cognitive function at wave 4 in the BPRHS.

Results

Several metabolites, including isobutyrylcarnitine, propionylcarnitine, phenylacetylglutamine, phenylacetylcarnitine (acetylated peptides), p-cresol-glucuronide, phenylacetylglutamate, and trimethylamine N-oxide (TMAO) were inversely associated with brain age deviation. Taurocholate sulfate, a bile salt, was marginally associated with better brain aging. Most metabolites with negative associations with brain age deviation scores also were inversely associations with hippocampal volumes and wave 4 cognitive function.

Conclusion

The metabolites identifiedin this study are generally consistent with prior literature and highlight the role of BCAA, TMAO and microbially derived metabolites in cognitive decline.

Association of metabolic dysfunction with cognitive decline and Alzheimer's disease: A review of metabolomic evidence

The discovery of new biomarkers that can distinguish Alzheimer's disease (AD) from mild cognitive impairment (MCI) in the early stages will help to provide new diagnostic and therapeutic strategies and slow the transition from MCI to AD. Patients with AD may present with a concomitant metabolic disorder, such as diabetes, obesity, and dyslipidemia, as a risk factor for AD that may be involved in the onset of both AD pathology and cognitive impairment. Therefore, metabolite profiling, or metabolomics, can be very useful in diagnosing AD, developing new therapeutic targets, and evaluating both the course of treatment and the clinical course of the disease. In addition, studying the relationship between nutritional behavior and AD requires investigation of the role of conditions such as obesity, hypertension, dyslipidemia, and elevated glucose level. Based on this literature review, nutritional recommendations, including weight loss by reducing calorie and cholesterol intake and omega-3 fatty acid supplementation can prevent cognitive decline and dementia in the elderly. The underlying metabolic causes of the pathology and cognitive decline caused by AD and MCI are not well understood. In this review article, metabolomics biomarkers for diagnosis of AD and MCI and metabolic risk factors for cognitive decline in AD were evaluated.

Lipid metabolism and oxidative stress in patients with Alzheimer's disease and amnestic mild cognitive impairment

Lipid metabolism and oxidative stress are key mechanisms in Alzheimer's disease (AD). The link between plasma lipid metabolites and oxidative stress in AD patients is poorly understood. This study was to identify markers that distinguish AD and amnestic mild cognitive impairment (aMCI) from NC, and to reveal potential links between lipid metabolites and oxidative stress. We performed non-targeted lipid metabolism analysis of plasma from patients with AD, aMCI, and NC using LC-MS/MS. The plasma malondialdehyde (MDA), glutathione peroxidase (GSH-Px), and superoxide dismutase (SOD) levels were assessed. We found significant differences in lipid metabolism between patients with AD and aMCI compared to those in NC. AD severity is associated with lipid metabolites, especially TG (18:0_16:0_18:0) + NH4, TG (18:0_16:0_16:0) + NH4, LPC(16:1e)-CH3, and PE (20:0_20:4)-H. SPH (d16:0) + H, SPH (d18:1) + H, and SPH (d18:0) + H were high-performance markers to distinguish AD and aMCI from NC. The AUC of three SPHs combined to predict AD was 0.990, with specificity and sensitivity as 0.949 and 1, respectively; the AUC of three SPHs combined to predict aMCI was 0.934, with specificity and sensitivity as 0.900, 0.981, respectively. Plasma MDA concentrations were higher in the AD group than in the NC group (p = 0.003), whereas plasma SOD levels were lower in the AD (p < 0.001) and aMCI (p = 0.045) groups than in NC, and GSH-Px activity were higher in the AD group than in the aMCI group (p = 0.007). In addition, lipid metabolites and oxidative stress are widely associated. In conclusion, this study distinguished serum lipid metabolism in AD, aMCI, and NC subjects, highlighting that the three SPHs can distinguish AD and aMCI from NC. Additionally, AD patients showed elevated oxidative stress, and there are complex interactions between lipid metabolites and oxidative stress.

Metabolites and Cognitive Decline in a Puerto Rican Cohort

Background

Recent studies have identified plasma metabolites associated with cognitive decline and Alzheimer's disease; however, little research on this topic has been conducted in Latinos, especially Puerto Ricans.

Objective

This study aims to add to the growing body of metabolomics research in Latinos to better understand and improve the health of this population.

Methods

We assessed the association between plasma metabolites and global cognition over 12 years of follow-up in 736 participants of the Boston Puerto Rican Health Study (BPRHS). Metabolites were measured with untargeted metabolomic profiling (Metabolon, Inc) at baseline. We used covariable adjusted linear mixed models (LMM) with a metabolite * time interaction term to identify metabolites (of 621 measured) associated with ∼12 years cognitive trajectory.

Results

We observed strong inverse associations between medium-chain fatty acids, caproic acid, and the dicarboxylic acids, azelaic and sebacic acid, and global cognition. N-formylphenylalanine, a tyrosine pathway metabolite, was associated with improvement in cognitive trajectory.

Conclusions

The metabolites identified in this study are generally consistent with prior literature and highlight a role medium chain fatty acid and tyrosine metabolism in cognitive decline.

Modifications of Blood Molecular Components after Treatment with Low Ozone Concentrations

The ex vivo treatment of a limited volume of blood with gaseous oxygen-ozone (O2-O3) mixtures and its rapid reinfusion into the patient is a widespread medical procedure. O3 instantly reacts with the blood's antioxidant systems, disappearing before reinfusion, although the molecules formed act as messengers in the organism, inducing multiple antioxidant and anti-inflammatory responses. An appropriate dose of O3 is obviously essential to ensure both safety and therapeutic efficacy, and in recent years, the low-dose O3 concept has led to a significant reduction in the administered O3 concentrations. However, the molecular events triggered by such low concentrations in the blood still need to be fully elucidated. In this basic study, we analysed the molecular modifications induced ex vivo in sheep blood by 5 and 10 µg O3/mL O2 by means of a powerful metabolomics analysis in association with haemogas, light microscopy and bioanalytical assays. This combined approach revealed increased oxygenation and an increased antioxidant capacity in the O3-treated blood, which accorded with the literature. Moreover, original information was obtained on the impact of these low O3 concentrations on the metabolic pathways of amino acids, carbohydrates, lipids and nucleotides, with the modified metabolites being mostly involved in the preservation of the oxidant-antioxidant balance and in energy production.

Lipids and lipoproteins may play a role in the neuropathology of Alzheimer's disease

Alzheimer's disease (AD) and other classes of dementia are important public health problems with overwhelming social, physical, and financial effects for patients, society, and their families and caregivers. The pathophysiology of AD is poorly understood despite the extensive number of clinical and experimental studies. The brain's lipid-rich composition is linked to disturbances in lipid homeostasis, often associated with glucose and lipid abnormalities in various neurodegenerative diseases, including AD. Moreover, elevated low-density lipoprotein (LDL) cholesterol levels may be related to a higher probability of AD. Here, we hypothesize that lipids, and electronegative LDL (L5) in particular, may be involved in the pathophysiology of AD. Although changes in cholesterol, triglyceride, LDL, and glucose levels are seen in AD, the cause remains unknown. We believe that L5-the most electronegative subfraction of LDL-may be a crucial factor in understanding the involvement of lipids in AD pathology. LDL and L5 are internalized by cells through different receptors and mechanisms that trigger separate intracellular pathways. One of the receptors involved in L5 internalization, LOX-1, triggers apoptotic pathways. Aging is associated with dysregulation of lipid homeostasis, and it is believed that alterations in lipid metabolism contribute to the pathogenesis of AD. Proposed mechanisms of lipid dysregulation in AD include mitochondrial dysfunction, blood-brain barrier disease, neuronal signaling, inflammation, and oxidative stress, all of which lead ultimately to memory loss through deficiency of synaptic integration. Several lipid species and their receptors have essential functions in AD pathogenesis and may be potential biomarkers.

Cardiovascular Disease and Alzheimer's Disease: The Heart-Brain Axis

Cardiovascular disease (CVD) remains one of the leading causes of morbidity and mortality in aging adults across the United States. Prior studies indicate that the presence of atherosclerosis, the pathogenic basis of CVD, is linked with dementias. Alzheimer's disease (AD) and AD-related dementias are a major public health challenge in the United States. Recent studies indicate that ≈3.7 million Americans ≥65 years of age had clinical AD in 2017, with projected increases to 9.3 million by 2060. Treatment options for AD remain limited. Development of disease-modifying therapies are challenging due, in part, to the long preclinical window of AD. The preclinical incubation period of AD starts in midlife, providing a critical window for identification and optimization of AD risk factors. Studies link AD with CVD risk factors such as hypertension, inflammation, and dyslipidemia. Both AD and CVD are progressive diseases with decades-long development periods. CVD can clinically manifest several years earlier than AD, making CVD and its risk factors a potential predictor of future AD. The current review focuses on the state of literature on molecular and metabolic pathways modulating the heart-brain axis underlying the potential association of midlife CVD risk factors and their effect on AD and related dementias. Further, we explore potential CVD/dementia preventive strategies during the window of opportunity in midlife and the future of research in the field in the multiomics and novel biomarker use era.

Sensitivity of cognitive function tests to acute hypoxia in healthy subjects: a systematic literature review

Acute exposure to hypoxia can lead to cognitive impairment. Therefore, hypoxia may become a safety concern for occupational or recreational settings at altitude. Cognitive tests are used as a tool to assess the degree to which hypoxia affects cognitive performance. However, so many different cognitive tests are used that comparing studies is challenging. This structured literature evaluation provides an overview of the different cognitive tests used to assess the effects of acute hypoxia on cognitive performance in healthy volunteers. Less frequently used similar cognitive tests were clustered and classified into domains. Subsequently, the different cognitive test clusters were compared for sensitivity to different levels of oxygen saturation. A total of 38 articles complied with the selection criteria, covering 86 different cognitive tests. The tests and clusters showed that the most consistent effects of acute hypoxia were found with the Stroop test (where 42% of studies demonstrated significant abnormalities). The most sensitive clusters were auditory/verbal memory: delayed recognition (83%); evoked potentials (60%); visual/spatial delayed recognition (50%); and sustained attention (47%). Attention tasks were not particularly sensitive to acute hypoxia (impairments in 0%-47% of studies). A significant hypoxia level-response relationship was found for the Stroop test (p = 0.001), as well as three clusters in the executive domain: inhibition (p = 0.034), reasoning/association (p = 0.019), and working memory (p = 0.024). This relationship shows a higher test sensitivity at more severe levels of hypoxia, predominantly below 80% saturation. No significant influence of barometric pressure could be identified in the limited number of studies where this was varied. This review suggests that complex and executive functions are particularly sensitive to hypoxia. Moreover, this literature evaluation provides the first step towards standardization of cognitive testing, which is crucial for a better understanding of the effects of acute hypoxia on cognition.

CSF metabolites associated with biomarkers of Alzheimer's disease pathology

Introduction

Metabolomics technology facilitates studying associations between small molecules and disease processes. Correlating metabolites in cerebrospinal fluid (CSF) with Alzheimer's disease (AD) CSF biomarkers may elucidate additional changes that are associated with early AD pathology and enhance our knowledge of the disease.

Methods

The relative abundance of untargeted metabolites was assessed in 161 individuals from the Wisconsin Registry for Alzheimer's Prevention. A metabolome-wide association study (MWAS) was conducted between 269 CSF metabolites and protein biomarkers reflecting brain amyloidosis, tau pathology, neuronal and synaptic degeneration, and astrocyte or microglial activation and neuroinflammation. Linear mixed-effects regression analyses were performed with random intercepts for sample relatedness and repeated measurements and fixed effects for age, sex, and years of education. The metabolome-wide significance was determined by a false discovery rate threshold of 0.05. The significant metabolites were replicated in 154 independent individuals from then Wisconsin Alzheimer's Disease Research Center. Mendelian randomization was performed using genome-wide significant single nucleotide polymorphisms from a CSF metabolites genome-wide association study.

Results

Metabolome-wide association study results showed several significantly associated metabolites for all the biomarkers except Aβ42/40 and IL-6. Genetic variants associated with metabolites and Mendelian randomization analysis provided evidence for a causal association of metabolites for soluble triggering receptor expressed on myeloid cells 2 (sTREM2), amyloid β (Aβ40), α-synuclein, total tau, phosphorylated tau, and neurogranin, for example, palmitoyl sphingomyelin (d18:1/16:0) for sTREM2, and erythritol for Aβ40 and α-synuclein.

Discussion

This study provides evidence that CSF metabolites are associated with AD-related pathology, and many of these associations may be causal.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Association of gut microbiota, plasma and fecal metabolite profiles with intellectual development in school-age children

Background

Little is known about how the gut microbiota and metabolic profiles are related to cognitive outcomes in young children until now. It was hypothesized that the gut microbiota, the plasma and fecal metabolites significantly correlated with intelligence quotient (IQ) in school-age children in current study.

Methods

This cross-sectional study enrolled 452 children aged 6-9 years old. IQ was measured using the Wechsler Intelligence Scale for Children-Fourth Edition. Fecal microbiota, plasma and fecal metabolites were analyzed using 16S rRNA amplicon sequencing and targeted metabolomic technologies, respectively.

Results

Restricted maximum likelihood (REML) analyses showed that microbiota composition and fecal metabolites were associated with neither subscale nor full-scale IQ (P: 0.059-0.500). However, plasma metabolites were significantly correlated with the processing speed (P=0.008). In multiple regression analysis after adjusting for confounders and multiple test correction, benzoic acid, azelaic acid, adipic acid, suberic acid and malonic acid selected by the multivariate methods with unbiased variable selection were positively associated with processing speed index (PSI) [Pfalse discovery rate (FDR): 0.006-0.024], whereas pyruvic acid was negatively associated with the PSI and full-scale IQ (PFDR: 0.014-0.030).

Conclusions

In normal school-age children, certain plasma metabolites concentrations but not the gut microbiota composition nor fecal metabolites are correlated with intelligence.

Metabolic changes in the plasma of mild Alzheimer's disease patients treated with Hachimijiogan

Background: Alzheimer's disease (AD), the most prevalent form of dementia, is a debilitating, progressive neurodegeneration. Amino acids play a wide variety of physiological and pathophysiological roles in the nervous system, and their levels and disorders related to their synthesis have been related to cognitive impairment, the core feature of AD. Our previous multicenter trial showed that hachimijiogan (HJG), a traditional Japanese herbal medicine (Kampo), has an adjuvant effect for Acetylcholine estelase inhibitors (AChEIs) and that it delays the deterioration of the cognitive dysfunction of female patients with mild AD. However, there are aspects of the molecular mechanism(s) by which HJG improves cognitive dysfunction that remain unclear. Objectives: To elucidate through metabolomic analysis the mechanism(s) of HJG for mild AD based on changes in plasma metabolites. Methods: Sixty-seven patients with mild AD were randomly assigned to either an HJG group taking HJG extract 7.5 g/day in addition to AChEI or to a control group treated only with AChEI (HJG:33, Control:34). Blood samples were collected before, 3 months, and 6 months after the first drug administration. Comprehensive metabolomic analyses of plasma samples were done by optimized LC-MS/MS and GC-MS/MS methods. The web-based software MetaboAnalyst 5.0 was used for partial least square-discriminant analysis (PLS-DA) to visualize and compare the dynamics of changes in the concentrations of the identified metabolites. Results: The VIP (Variable Importance in Projection) score of the PLS-DA analysis of female participants revealed a significantly higher increase in plasma metabolite levels after HJG administration for 6 months than was seen in the control group. In univariate analysis, the aspartic acid level of female participants showed a significantly higher increase from baseline after HJG administration for 6 months when compared with the control group. Conclusion: Aspartic acid was a major contributor to the difference between the female HJG and control group participants of this study. Several metabolites were shown to be related to the mechanism of HJG effectiveness for mild AD.

APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge

The E4 allele of Apolipoprotein E (APOE) is associated with both metabolic dysfunction and a heightened pro-inflammatory response: two findings that may be intrinsically linked through the concept of immunometabolism. Here, we combined bulk, single-cell, and spatial transcriptomics with cell-specific and spatially resolved metabolic analyses in mice expressing human APOE to systematically address the role of APOE across age, neuroinflammation, and AD pathology. RNA sequencing (RNA-seq) highlighted immunometabolic changes across the APOE4 glial transcriptome, specifically in subsets of metabolically distinct microglia enriched in the E4 brain during aging or following an inflammatory challenge. E4 microglia display increased Hif1α expression and a disrupted tricarboxylic acid (TCA) cycle and are inherently pro-glycolytic, while spatial transcriptomics and mass spectrometry imaging highlight an E4-specific response to amyloid that is characterized by widespread alterations in lipid metabolism. Taken together, our findings emphasize a central role for APOE in regulating microglial immunometabolism and provide valuable, interactive resources for discovery and validation research.

Biomarkers of diagnosis, prognosis, pathogenesis, response to therapy: Convergence or divergence? Lessons from Alzheimer's disease and synucleinopathies

Alzheimer's disease (AD) is the most common disorder associated with cognitive impairment. Recent observations emphasize the pathogenic role of multiple factors inside and outside the central nervous system, supporting the notion that AD is a syndrome of many etiologies rather than a "heterogeneous" but ultimately unifying disease entity. Moreover, the defining pathology of amyloid and tau coexists with many others, such as α-synuclein, TDP-43, and others, as a rule, not an exception. Thus, an effort to shift our AD paradigm as an amyloidopathy must be reconsidered. Along with amyloid accumulation in its insoluble state, β-amyloid is becoming depleted in its soluble, normal states, as a result of biological, toxic, and infectious triggers, requiring a shift from convergence to divergence in our approach to neurodegeneration. These aspects are reflected-in vivo-by biomarkers, which have become increasingly strategic in dementia. Similarly, synucleinopathies are primarily characterized by abnormal deposition of misfolded α-synuclein in neurons and glial cells and, in the process, depleting the levels of the normal, soluble α-synuclein that the brain needs for many physiological functions. The soluble to insoluble conversion also affects other normal brain proteins, such as TDP-43 and tau, accumulating in their insoluble states in both AD and dementia with Lewy bodies (DLB). The two diseases have been distinguished by the differential burden and distribution of insoluble proteins, with neocortical phosphorylated tau deposition more typical of AD and neocortical α-synuclein deposition peculiar to DLB. We propose a reappraisal of the diagnostic approach to cognitive impairment from convergence (based on clinicopathologic criteria) to divergence (based on what differs across individuals affected) as a necessary step for the launch of precision medicine.

Ether Lipid-Mediated Antioxidant Defense in Alzheimer's Disease

One of the richest tissues in lipid content and diversity of the human body is the brain. The human brain is constitutively highly vulnerable to oxidative stress. This oxidative stress is a determinant in brain aging, as well as in the onset and progression of sporadic (late-onset) Alzheimer's disease (sAD). Glycerophospholipids are the main lipid category widely distributed in neural cell membranes, with a very significant presence for the ether lipid subclass. Ether lipids have played a key role in the evolution of the human brain compositional specificity and functionality. Ether lipids determine the neural membrane structural and functional properties, membrane trafficking, cell signaling and antioxidant defense mechanisms. Here, we explore the idea that ether lipids actively participate in the pathogenesis of sAD. Firstly, we evaluate the quantitative relevance of ether lipids in the human brain composition, as well as their role in the human brain evolution. Then, we analyze the implications of ether lipids in neural cell physiology, highlighting their inherent antioxidant properties. Finally, we discuss changes in ether lipid content associated with sAD and their physiopathological implications, and propose a mechanism that, as a vicious cycle, explains the potential significance of ether lipids in sAD.

An Alternative View of Familial Alzheimer's Disease Genetics

Probabilistic and parsimony-based arguments regarding available genetics data are used to propose that Hardy and Higgin's amyloid cascade hypothesis is valid but is commonly interpreted too narrowly to support, incorrectly, the primacy of the amyloid-β peptide (Aβ) in driving Alzheimer's disease pathogenesis. Instead, increased activity of the βCTF (C99) fragment of AβPP is the critical pathogenic determinant altered by mutations in the APP gene. This model is consistent with the regulation of APP mRNA translation via its 5' iron responsive element. Similar arguments support that the pathological effects of familial Alzheimer's disease mutations in the genes PSEN1 and PSEN2 are not exerted directly via changes in AβPP cleavage to produce different ratios of Aβ length. Rather, these mutations likely act through effects on presenilin holoprotein conformation and function, and possibly the formation and stability of multimers of presenilin holoprotein and/or of the γ-secretase complex. All fAD mutations in APP, PSEN1, and PSEN2 likely find unity of pathological mechanism in their actions on endolysosomal acidification and mitochondrial function, with detrimental effects on iron homeostasis and promotion of "pseudo-hypoxia" being of central importance. Aβ production is enhanced and distorted by oxidative stress and accumulates due to decreased lysosomal function. It may act as a disease-associated molecular pattern enhancing oxidative stress-driven neuroinflammation during the cognitive phase of the disease.

Alterations in Peripheral Metabolites as Key Actors in Alzheimer's Disease

Growing evidence supports that Alzheimer's disease (AD) could be regarded as a metabolic disease, accompanying central and peripheral metabolic disturbance. Nowadays, exploring novel and potentially alternative hallmarks for AD is needed. Peripheral metabolites based on blood and gut may provide new biochemical insights about disease mechanisms. These metabolites can influence brain energy homeostasis, maintain gut mucosal integrity, and regulate the host immune system, which may further play a key role in modulating the cognitive function and behavior of AD. Recently, metabolomics has been used to identify key AD-related metabolic changes and define metabolic changes during AD disease trajectory. This review aims to summarize the key blood- and microbial-derived metabolites that are altered in AD and identify the potential metabolic biomarkers of AD, which will provide future targets for precision therapeutic modulation.

Early Diagnosis of Alzheimer's Disease with Blood Test; Tempting but Challenging

The increasing prevalence of Alzheimer's disease (AD) has led to a health crisis. According to official statistics, more than 55 million people globally have AD or other types of dementia, making it the sixth leading cause of death. It is still difficult to diagnose AD and there is no definitive diagnosis yet; post-mortem autopsy is still the only definite method. Moreover, clinical manifestations occur very late in the course of disease progression; therefore, profound irreversible changes have already occurred when the disease manifests. Studies have shown that in the preclinical stage of AD, changes in some biomarkers are measurable prior to any neurological damage or other symptoms. Hence, creating a reliable, fast, and affordable method capable of detecting AD in early stage has attracted the most attention. Seeking clinically applicable, inexpensive, less invasive, and much more easily accessible biomarkers for early diagnosis of AD, blood-based biomarkers (BBBs) seem to be an ideal option. This review is an inclusive report of BBBs that have been shown to be altered in the course of AD progression. The aim of this report is to provide comprehensive insight into the research status of early detection of AD based on BBBs.

The role of dietary patterns and erythrocyte membrane fatty acid patterns on mild cognitive impairment

Background

Dietary fatty acids have been shown to be associated with the development of cognition. However, research on the role of fatty acid intake in dietary patterns and fatty acid patterns (FAPs) in the development of cognitive function is limited. The aim of this study was to explore the correlation between dietary patterns and FAPs and to provide available evidence for preventing mild cognitive impairment (MCI) through these patterns.

Materials and methods

The 973 participants aged between 65 and 85 were recruited from 2020 to 2021 for this multicenter research in Beijing. Neuropsychological tests were used for cognitive evaluation, and data of dietary intake in the past 12 months were collected with semi-quantitative food frequency questionnaire. The erythrocyte membrane fatty acid profile was tested by chromatography and mass spectrometry lipid profiling. Factor analysis was used to derive the main dietary patterns and FAPs. Pearson’s correlation or Spearman’s correlation was used to explore the association between dietary patterns and FAPs. Binary logistic regression was applied to examine the relationship between patterns and cognitive function.

Results

Six dietary patterns and six FAPs were identified, explaining 53.4 and 80.9% of the total variance separately. After adjusting all potential confounders, T3 of the pattern 1 and FAP2 were the independent protect factors for MCI, respectively (OR 0.601, 95% CI [0.395, 0.914]; OR 0.108, 95% CI [0.019, 0.623]). Rich of SM (26:0), SM (24:1), and SM (26:1) is the characteristic of FAP2. A positive correlation was found between component scores of dietary pattern1 and FAP2 (r = 0.441, p = 0.001). People who adhered to a reasonable intake of animal flesh consumed more various long-chain fatty acids as well.

Conclusion

The erythrocyte membrane metabolites, SM (26:0), SM (24:1), and SM (26:1), might function as early biomarkers for predicting or monitoring of cognitive aging in the elderly. The dietary pattern with recommended animal flesh consumption was significantly associated with FAP characterized by very long-chain SMs. This dietary pattern affected FAP, which might achieve the ultimate goal of neuroprotection through the very long-chain SMs. A rational intake of dietary fatty acids might be an effective way on preventing MCI in the elderly.

Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study

BACKGROUND

Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach and determined whether the selected metabolites improved prediction accuracy beyond the effect of age.

METHODS

Five thousand three hundred seventy-four participants from the Whitehall II study, mean age 55.8 (standard deviation (SD) 6.0) years in 1997-1999 when 233 metabolites were quantified using nuclear magnetic resonance metabolomics. Participants were followed for a median 21.0 (IQR 20.4, 21.7) years for clinically-diagnosed dementia (N=329). Elastic net penalized Cox regression with 100 repetitions of nested cross-validation was used to select models that improved prediction accuracy for incident dementia compared to an age-only model. Risk scores reflecting the frequency with which predictors appeared in the selected models were constructed, and their predictive accuracy was examined using Royston's R², Akaike's information criterion, sensitivity, specificity, C-statistic and calibration.

RESULTS

Sixteen of the 100 models had a better c-statistic compared to an age-only model and 15 metabolites were selected at least once in all 16 models with glucose present in all models. Five risk scores, reflecting the frequency of selection of metabolites, and a 1-SD increment in all five risk scores was associated with higher dementia risk (HR between 3.13 and 3.26). Three of these, constituted of 4, 5 and 15 metabolites, had better prediction accuracy (c-statistic from 0.788 to 0.796) compared to an age-only model (c-statistic 0.780), all p<0.05.

CONCLUSIONS

Although there was robust evidence for the role of glucose in dementia, metabolites measured in midlife made only a modest contribution to dementia prediction once age was taken into account.

Plasma metabolomic profiles of dementia: a prospective study of 110,655 participants in the UK Biobank

BACKGROUND

Plasma metabolomic profile is disturbed in dementia patients, but previous studies have discordant conclusions.

METHODS

Circulating metabolomic data of 110,655 people in the UK Biobank study were measured with nuclear magnetic resonance technique, and incident dementia records were obtained from national health registers. The associations between plasma metabolites and dementia were estimated using Cox proportional hazard models. The 10-fold cross-validation elastic net regression models selected metabolites that predicted incident dementia, and a 10-year prediction model for dementia was constructed by multivariable logistic regression. The predictive values of the conventional risk model, the metabolites model, and the combined model were discriminated by comparison of area under the receiver operating characteristic curves (AUCs). Net reclassification improvement (NRI) was used to estimate the change of reclassification ability when adding metabolites into the conventional prediction model.

RESULTS

Amongst 110,655 participants, the mean (standard deviation) age was 56.5 (8.1) years, and 51 186 (46.3%) were male. A total of 1439 (13.0%) developed dementia during a median follow-up of 12.2 years (interquartile range: 11.5-12.9 years). A total of 38 metabolites, including lipids and lipoproteins, ketone bodies, glycolysis-related metabolites, and amino acids, were found to be significantly associated with incident dementia. Adding selected metabolites (n=24) to the conventional dementia risk prediction model significantly improved the prediction for incident dementia (AUC: 0.824 versus 0.817, p =0.042) and reclassification ability (NRI = 4.97%, P = 0.009) for identifying high risk groups.

CONCLUSIONS

Our analysis identified various metabolomic biomarkers which were significantly associated with incident dementia. Metabolomic profiles also provided opportunities for dementia risk reclassification. These findings may help explain the biological mechanisms underlying dementia and improve dementia prediction.

Lipids as Early and Minimally Invasive Biomarkers for Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide. Specifically, typical late-onset AD is a sporadic form with a complex etiology that affects over 90% of patients. The current gold standard for AD diagnosis is based on the determination of amyloid status by analyzing cerebrospinal fluid samples or brain positron emission tomography. These procedures can be used widely as they have several disadvantages (expensive, invasive). As an alternative, blood metabolites have recently emerged as promising AD biomarkers. Small molecules that cross the compromised AD blood-brain barrier could be determined in plasma to improve clinical AD diagnosis at early stages through minimally invasive techniques. Specifically, lipids could play an important role in AD since the brain has a high lipid content, and they are present ubiquitously inside amyloid plaques. Therefore, a systematic review was performed with the aim of identifying blood lipid metabolites as potential early AD biomarkers. In conclusion, some lipid families (fatty acids, glycerolipids, glycerophospholipids, sphingolipids, lipid peroxidation compounds) have shown impaired levels at early AD stages. Ceramide levels were significantly higher in AD subjects, and polyunsaturated fatty acids levels were significantly lower in AD. Also, high arachidonic acid levels were found in AD patients in contrast to low sphingomyelin levels. Consequently, these lipid biomarkers could be used for minimally invasive and early AD clinical diagnosis.

Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease

Metabolites, the biochemical products of the cellular process, can be used to measure alterations in biochemical pathways related to the pathogenesis of Alzheimer's disease (AD). However, the relationships between systemic abnormalities in metabolism and the pathogenesis of AD are poorly understood. In this study, we aim to identify AD-specific metabolomic changes and their potential upstream genetic and transcriptional regulators through an integrative systems biology framework for analyzing genetic, transcriptomic, metabolomic, and proteomic data in AD. Metabolite co-expression network analysis of the blood metabolomic data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) shows short-chain acylcarnitines/amino acids and medium/long-chain acylcarnitines are most associated with AD clinical outcomes, including episodic memory scores and disease severity. Integration of the gene expression data in both the blood from the ADNI and the brain from the Accelerating Medicines Partnership Alzheimer's Disease (AMP-AD) program reveals ABCA1 and CPT1A are involved in the regulation of acylcarnitines and amino acids in AD. Gene co-expression network analysis of the AMP-AD brain RNA-seq data suggests the CPT1A- and ABCA1-centered subnetworks are associated with neuronal system and immune response, respectively. Increased ABCA1 gene expression and adiponectin protein, a regulator of ABCA1, correspond to decreased short-chain acylcarnitines and amines in AD in the ADNI. In summary, our integrated analysis of large-scale multiomics data in AD systematically identifies novel metabolites and their potential regulators in AD and the findings pave a way for not only developing sensitive and specific diagnostic biomarkers for AD but also identifying novel molecular mechanisms of AD pathogenesis.

Prospective study of dietary intake of branched-chain amino acids and the risk of primary open-angle glaucoma

PURPOSE

Metabolomic and preclinical studies suggest that branched-chain amino acids (BCAA) may be inversely associated with neurodegenerative diseases including glaucoma. We therefore assessed the long-term association between dietary intake of BCAA and incident primary open-angle glaucoma (POAG) and POAG subtypes.

METHODS

We followed biennially participants of the Nurses' Health Study (NHS; 65 531 women: 1984-2016), Health Professionals Follow-up Study (42 254 men: 1986-2016) and NHSII (66 904 women; 1991-2017). Eligible participants were 40+ years old and reported eye examinations. Repeated validated food frequency questionnaires were used to assess dietary intake of BCAA. Incident cases of POAG and POAG subtypes defined by visual field (VF) loss and untreated intraocular pressure (IOP) were confirmed by medical record review. Multivariable-adjusted relative risks (MVRRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models.

RESULTS

We identified 1946 incident POAG cases. The pooled MVRRs of POAG for the highest quintile (Q5 = 17.1 g/day) versus lowest quintile (Q1 = 11.2 g/day) of total BCAA intake was 0.93 (95% CI, 0.73-1.19; p_trend  = 0.45; p_{heterogeneity by sex}  = 0.24). For subtypes of POAG defined by IOP level or POAG with only peripheral VF loss, no associations were observed for men or women (p_trend  ≥ 0.20); however, for the POAG subtype with early paracentral VF loss, there was a suggestion of an inverse association in women (MVRR_Q5versusQ1  = 0.80 [95% CI, 0.57-1.12; p_trend  = 0.12]) but not in men (MVRR_Q5versusQ1  = 1.38 [95% CI, 0.81-2.34; p_trend  = 0.28; p_{heterogeneity by sex}  = 0.06]).

CONCLUSION

Higher dietary intake of BCAA was not associated with POAG risk.

Dihuang-Yinzi Alleviates Cognition Deficits via Targeting Energy-Related Metabolism in an Alzheimer Mouse Model as Demonstrated by Integration of Metabolomics and Network Pharmacology

Energy metabolism disturbance and the consequent reactive oxygen species (ROS) overproduction play a key and pathogenic role in the onset and progression of Alzheimer’s disease (AD). Dihuang-Yinzi (DHYZ) is a traditional Chinese herbal prescription clinically applied to treat AD and other neurodegenerative diseases for a long time. However, the systematical metabolic mechanism of DHYZ against AD remains largely unclear. Here we aimed to explore the mechanism of DHYZ in the treatment of AD comprehensively in an in vivo metabolic context by performing metabolomics analysis coupled with network pharmacology study and experimental validation. The network pharmacology was applied to dig out the potential target of DHYZ against AD. The metabolomics analysis based on UPLC-HRMS was carried out to profile the urine of 2× Tg-AD mice treated with DHYZ. By integrating network pharmacology and metabolomics, we found DHYZ could ameliorate 4 key energy-related metabolic pathways, including glycerophospholipid metabolism, nicotinate/nicotinamide metabolism, glycolysis, and tricarboxylic acid cycle. Besides, we identified 5 potential anti-AD targets of DHYZ, including DAO, HIF1A, PARP1, ALDH3B2, and ACHE, and 14 key differential metabolites involved in the 4 key energy-related metabolic pathways. Furthermore, DHYZ depressed the mitochondrial dysfunction and the resultant ROS overproduction through ameliorating glycerophospholipid metabolism disturbance. Thereby DHYZ increased nicotinamide adenine dinucleotide (NAD⁺) content and promoted glycolysis and tricarboxylic acid (TCA) cycle, and consequently improved oxidative phosphorylation and energy metabolism. In the present study, we provided a novel, comprehensive and systematic insight into investigating the therapeutic efficacy of DHYZ against AD via ameliorating energy-related metabolism.

Targeted Lipidomics To Measure Phospholipids and Sphingomyelins in Plasma: A Pilot Study To Understand the Impact of Race/Ethnicity in Alzheimer's Disease

The number of people suffering from Alzheimer's disease (AD) is increasing rapidly every year. One aspect of AD that is often overlooked is the disproportionate incidence of AD among African American/Black populations. With the recent development of novel assays for lipidomics analysis in recent times, there has been a drastic increase in the number of studies focusing on changes of lipids in AD. However, very few of these studies have focused on or even included samples from African American/Black individuals samples. In this study, we aimed to determine if the lipidome in AD is universal across non-Hispanic White and African American/Black individuals. To accomplish this, a targeted mass spectrometry lipidomics analysis was performed on plasma samples (N = 113) obtained from cognitively normal (CN, N = 54) and AD (N = 59) individuals from African American/Black (N = 56) and non-Hispanic White (N = 57) backgrounds. Five lipids (PS 18:0_18:0, PS 18:0_20:0, PC 16:0_22:6, PC 18:0_22:6, and PS 18:1_22:6) were altered between AD and CN sample groups (p value < 0.05). Upon racial stratification, there were notable differences in lipids that were unique to African American/Black or non-Hispanic White individuals. PS 20:0_20:1 was reduced in AD in samples from non-Hispanic White but not African American/Black adults. We also tested whether race/ethnicity significantly modified the association between lipids and AD status by including a race × diagnosis interaction term in a linear regression model. PS 20:0_20:1 showed a significant interaction (p = 0.004). The discovery of lipid changes in AD in this study suggests that identifying relevant lipid biomarkers for diagnosis will require diversity in sample cohorts.

Molecular Probes, Chemosensors, and Nanosensors for Optical Detection of Biorelevant Molecules and Ions in Aqueous Media and Biofluids

Synthetic molecular probes, chemosensors, and nanosensors used in combination with innovative assay protocols hold great potential for the development of robust, low-cost, and fast-responding sensors that are applicable in biofluids (urine, blood, and saliva). Particularly, the development of sensors for metabolites, neurotransmitters, drugs, and inorganic ions is highly desirable due to a lack of suitable biosensors. In addition, the monitoring and analysis of metabolic and signaling networks in cells and organisms by optical probes and chemosensors is becoming increasingly important in molecular biology and medicine. Thus, new perspectives for personalized diagnostics, theranostics, and biochemical/medical research will be unlocked when standing limitations of artificial binders and receptors are overcome. In this review, we survey synthetic sensing systems that have promising (future) application potential for the detection of small molecules, cations, and anions in aqueous media and biofluids. Special attention was given to sensing systems that provide a readily measurable optical signal through dynamic covalent chemistry, supramolecular host-guest interactions, or nanoparticles featuring plasmonic effects. This review shall also enable the reader to evaluate the current performance of molecular probes, chemosensors, and nanosensors in terms of sensitivity and selectivity with respect to practical requirement, and thereby inspiring new ideas for the development of further advanced systems.

Metabolic Features of Brain Function with Relevance to Clinical Features of Alzheimer and Parkinson Diseases

Brain metabolism is comprised in Alzheimer’s disease (AD) and Parkinson’s disease (PD). Since the brain primarily relies on metabolism of glucose, ketone bodies, and amino acids, aspects of these metabolic processes in these disorders—and particularly how these altered metabolic processes are related to oxidative and/or nitrosative stress and the resulting damaged targets—are reviewed in this paper. Greater understanding of the decreased functions in brain metabolism in AD and PD is posited to lead to potentially important therapeutic strategies to address both of these disorders, which cause relatively long-lasting decreased quality of life in patients.

Neurodegenerative Disorders of Alzheimer, Parkinsonism, Amyotrophic Lateral Sclerosis and Multiple Sclerosis: An Early Diagnostic Approach for Precision Treatment

Neurodegenerative diseases (NDs) are characterised by progressive dysfunction of synapses, neurons, glial cells and their networks. Neurodegenerative diseases can be classified according to primary clinical features (e.g., dementia, parkinsonism, or motor neuron disease), anatomic distribution of neurodegeneration (e.g., frontotemporal degenerations, extrapyramidal disorders, or spinocerebellar degenerations), or principal molecular abnormalities. The most common neurodegenerative disorders are amyloidosis, tauopathies, a-synucleinopathy, and TAR DNA-binding protein 43 (TDP-43) proteopathy. The protein abnormalities in these disorders have abnormal conformational properties along with altered cellular mechanisms, and they exhibit motor deficit, mitochondrial malfunction, dysfunctions in autophagic-lysosomal pathways, synaptic toxicity, and more emerging mechanisms such as the roles of stress granule pathways and liquid-phase transitions. Finally, for each ND, microglial cells have been reported to be implicated in neurodegeneration, in particular, because the microglial responses can shift from neuroprotective to a deleterious role. Growing experimental evidence suggests that abnormal protein conformers act as seed material for oligomerization, spreading from cell to cell through anatomically connected neuronal pathways, which may in part explain the specific anatomical patterns observed in brain autopsy sample. In this review, we mention the human pathology of select neurodegenerative disorders, focusing on how neurodegenerative disorders (i.e., Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis) represent a great healthcare problem worldwide and are becoming prevalent because of the increasing aged population. Despite many studies have focused on their etiopathology, the exact cause of these diseases is still largely unknown and until now with the only available option of symptomatic treatments. In this review, we aim to report the systematic and clinically correlated potential biomarker candidates. Although future studies are necessary for their use in early detection and progression in humans affected by NDs, the promising results obtained by several groups leads us to this idea that biomarkers could be used to design a potential therapeutic approach and preclinical clinical trials for the treatments of NDs.

A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.

OBJECTIVE

To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.

METHODS

We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.

RESULTS

The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.

CONCLUSION

Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.

Metabolic Signature of Leukocyte Telomere Length in Elite Male Soccer Players

Introduction: Biological aging is associated with changes in the metabolic pathways. Leukocyte telomere length (LTL) is a predictive marker of biological aging; however, the underlying metabolic pathways remain largely unknown. The aim of this study was to investigate the metabolic alterations and identify the metabolic predictors of LTL in elite male soccer players.

Methods: Levels of 837 blood metabolites and LTL were measured in 126 young elite male soccer players who tested negative for doping abuse at anti-doping laboratory in Italy. Multivariate analysis using orthogonal partial least squares (OPLS), univariate linear models and enrichment analyses were conducted to identify metabolites and metabolic pathways associated with LTL. Generalized linear model followed by receiver operating characteristic (ROC) analysis were conducted to identify top metabolites predictive of LTL.

Results: Sixty-seven metabolites and seven metabolic pathways showed significant associations with LTL. Among enriched pathways, lysophospholipids, benzoate metabolites, and glycine/serine/threonine metabolites were elevated with longer LTL. Conversely, monoacylglycerols, sphingolipid metabolites, long chain fatty acids and polyunsaturated fatty acids were enriched with shorter telomeres. ROC analysis revealed eight metabolites that best predict LTL, including glutamine, N-acetylglutamine, xanthine, beta-sitosterol, N2-acetyllysine, stearoyl-arachidonoyl-glycerol (18:0/20:4), N-acetylserine and 3-7-dimethylurate with AUC of 0.75 (0.64–0.87, p < 0.0001).

Conclusion: This study characterized the metabolic activity in relation to telomere length in elite soccer players. Investigating the functional relevance of these associations could provide a better understanding of exercise physiology and pathophysiology of elite athletes.

Dynamic metabolic profiles for HBeAg seroconversion in chronic hepatitis B (CHB) patients by gas chromatography-mass spectrometry (GC-MS)

Chronic hepatitis B (CHB) remains a major public health problem globally. HBeAg seroconversion is a vital hallmark for the improvement of CHB. The plasma metabolic profile has not been clear in CHB patients and searching metabolic candidates to represent HBeAg seroconversion is also difficult currently. In this study, CHB patients were recruited, followed and divided into the HBeAg-positive (HBeAg-pos.) group (n = 29) and the HBeAg-negative (HBeAg-neg.) group (n = 29) based on HBeAg seroconversion or not. The plasma metabolic profiles were measured by gas chromatography-mass spectrometry (GC-MS) at 0 week (0w), 24 weeks (24w) and 48 weeks (48w) after administration. The acquired data was analyzed using orthogonal partial least squares discriminate analysis (OPLS-DA) and the differential metabolites were further assessed by self and group comparison. No differences of age, gender and serological characteristics were observed between two groups at 0w and 48w separately. The OPLS-DA score plots depending on administration time displayed robust metabolic differences no matter HBeAg turned to be negative or not. According to VIP> 1.0, a total of 15 differential metabolites were same in the two groups, 7 differential metabolites (glycolic acid, D-talose, L-proline, L-(-)-arabitol, ethyl-alpha-D-glucopyranoside, L-leucine and dihydroxybutanoic acid) were derived from one group alone and considered as metabolic candidates. At 0w versus (vs.) 24w, only 3 of 7 candidates (L-proline, L-(-)-arabitol, dihydroxybutanoic acid) showed nonuniform in the two groups, while at 0w vs. 48w, all of them varied inconsistently. Conclusively the dynamic metabolic profiles assayed by GC-MS were different between CHB patients with and without HBeAg seroconversion. The 7 metabolic candidates probably had the ability to reflect the CHB progression for HBeAg seroconversion and 3 of them showed strong relationship with HbeAg seroconversion early.

Metabolic profile changes in serum of migraine patients detected using 1H-NMR spectroscopy

Background

Migraine is a common brain disorder but reliable diagnostic biomarkers in blood are still lacking. Our aim was to identify, using proton nuclear magnetic resonance (¹H-NMR) spectroscopy, metabolites in serum that are associated with lifetime and active migraine by comparing metabolic profiles of patients and controls.

Methods

Fasting serum samples from 313 migraine patients and 1512 controls from the Erasmus Rucphen Family (ERF) study were available for ¹H-NMR spectroscopy. Data was analysed using elastic net regression analysis.

Results

A total of 100 signals representing 49 different metabolites were detected in 289 cases (of which 150 active migraine patients) and 1360 controls. We were able to identify profiles consisting of 6 metabolites predictive for lifetime migraine status and 22 metabolites predictive for active migraine status. We estimated with subsequent regression models that after correction for age, sex, BMI and smoking, the association with the metabolite profile in active migraine remained. Several of the metabolites in this profile are involved in lipid, glucose and amino acid metabolism.

Conclusion

This study indicates that metabolic profiles, based on serum concentrations of several metabolites, including lipids, amino acids and metabolites of glucose metabolism, can distinguish active migraine patients from controls.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10194-021-01357-w.

Alterations in Glycerolipid and Fatty Acid Metabolic Pathways in Alzheimer's Disease Identified by Urinary Metabolic Profiling: A Pilot Study

An easily accessible and non-invasive biomarker for the early detection of Alzheimer's disease (AD) is needed. Evidence suggests that metabolic dysfunction underlies the pathophysiology of AD. While urine is a non-invasively collectable biofluid and a good source for metabolomics analysis, it is not yet widely used for this purpose. This small-scale pilot study aimed to examine whether the metabolic profile of urine from AD patients reflects the metabolic dysfunction reported to underlie AD pathology, and to identify metabolites that could distinguish AD patients from cognitively healthy controls. Spot urine of 18 AD patients (AD group) and 18 age- and sex-matched, cognitively normal controls (control group) were analyzed by mass spectrometry (MS). Capillary electrophoresis time-of-flight MS and liquid chromatography–Fourier transform MS were used to cover a larger range of molecules with ionic as well as lipid characteristics. A total of 304 ionic molecules and 81 lipid compounds of 12 lipid classes were identified. Of these, 26 molecules showed significantly different relative concentrations between the AD and control groups (Wilcoxon's rank-sum test). Moreover, orthogonal partial least-squares discriminant analysis revealed significant discrimination between the two groups. Pathway searches using the KEGG database, and pathway enrichment and topology analysis using Metaboanalyst software, suggested alterations in molecules relevant to pathways of glycerolipid and glycerophospholipid metabolism, thermogenesis, and caffeine metabolism in AD patients. Further studies of urinary metabolites will contribute to the early detection of AD and understanding of its pathogenesis.

Modulation of Neurolipid Signaling and Specific Lipid Species in the Triple Transgenic Mouse Model of Alzheimer's Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in aging populations. Recently, the regulation of neurolipid-mediated signaling and cerebral lipid species was shown in AD patients. The triple transgenic mouse model (3xTg-AD), harboring βAPP_Swe, PS1_M146V, and tau_P301L transgenes, mimics many critical aspects of AD neuropathology and progressively develops neuropathological markers. Thus, in the present study, 3xTg-AD mice have been used to test the involvement of the neurolipid-based signaling by endocannabinoids (eCB), lysophosphatidic acid (LPA), and sphingosine 1-phosphate (S1P) in relation to the lipid deregulation. [³⁵S]GTPγS autoradiography was used in the presence of specific agonists WIN55,212-2, LPA and CYM5442, to measure the activity mediated by CB₁, LPA₁, and S1P₁ G_i/0 coupled receptors, respectively. Consecutive slides were used to analyze the relative intensities of multiple lipid species by MALDI Mass spectrometry imaging (MSI) with microscopic anatomical resolution. The quantitative analysis of the astrocyte population was performed by immunohistochemistry. CB₁ receptor activity was decreased in the amygdala and motor cortex of 3xTg-AD mice, but LPA₁ activity was increased in the corpus callosum, motor cortex, hippocampal CA1 area, and striatum. Conversely, S1P₁ activity was reduced in hippocampal areas. Moreover, the observed modifications on PC, PA, SM, and PI intensities in different brain areas depend on their fatty acid composition, including decrease of polyunsaturated fatty acid (PUFA) phospholipids and increase of species containing saturated fatty acids (SFA). The regulation of some lipid species in specific brain regions together with the modulation of the eCB, LPA, and S1P signaling in 3xTg-AD mice indicate a neuroprotective adaptation to improve neurotransmission, relieve the myelination dysfunction, and to attenuate astrocyte-mediated neuroinflammation. These results could contribute to identify new therapeutic strategies based on the regulation of the lipid signaling in familial AD patients.

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

Assessing dementia conversion in patients with mild cognitive impairment (MCI) remains challenging owing to pathological heterogeneity. While many MCI patients ultimately proceed to Alzheimer’s disease (AD), a subset of patients remain stable for various times. Our aim was to characterize the plasma metabolites of nineteen MCI patients proceeding to AD (P-MCI) and twenty-nine stable MCI (S-MCI) patients by untargeted metabolomics profiling. Alterations in the plasma metabolites between the P-MCI and S-MCI groups, as well as between the P-MCI and AD groups, were compared over the observation period. With the help of machine learning-based stratification, a 20-metabolite signature panel was identified that was associated with the presence and progression of AD. Furthermore, when the metabolic signature panel was used for classification of the three patient groups, this gave an accuracy of 73.5% using the panel. Moreover, when specifically classifying the P-MCI and S-MCI subjects, a fivefold cross-validation accuracy of 80.3% was obtained using the random forest model. Importantly, indole-3-propionic acid, a bacteria-generated metabolite from tryptophan, was identified as a predictor of AD progression, suggesting a role for gut microbiota in AD pathophysiology. Our study establishes a metabolite panel to assist in the stratification of MCI patients and to predict conversion to AD.

Metabolomic Approaches to Investigate the Effect of Metformin: An Overview

Metformin is the first-line antidiabetic drug that is widely used in the treatment of type 2 diabetes mellitus (T2DM). Even though the various therapeutic potential of metformin treatment has been reported, as well as the improvement of insulin sensitivity and glucose homeostasis, the mechanisms underlying those benefits are still not fully understood. In order to explain the beneficial effects on metformin treatment, various metabolomics analyses have been applied to investigate the metabolic alterations in response to metformin treatment, and significant systemic metabolome changes were observed in biofluid, tissues, and cells. In this review, we compare the latest metabolomic research including clinical trials, animal models, and in vitro studies comprehensively to understand the overall changes of metabolome on metformin treatment.

Looking at time dependent differentiation of mesenchymal stem cells by culture media using MALDI-TOF-MS

Mesenchymal stem cells (MSCs) are multipotent cells which are popular in human regenerative medicine. These cells can renew themselves and differentiate into several specialized cell types including osteoblasts, adipocytes, and chondrocytes under physiological and experimental conditions. MSCs can secret a lot of components including proteins and metabolites. These components have significant effects on their surrounding cells and also can be used to characterize them. This characterization of multipotent MSCs plays a critical role in their therapeutic potential. The metabolic profile of culture media verified by applying matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF-MS) techniques. Also, the differentiation and development of MSCs have monitored through culture media metabolome or secretome (secreted metabolites). Totally, 24 potential metabolites were identified. Between them 12 metabolites are unique to BM-MSCs and 5 metabolites are unique to AD-MSCs. Trilineage differentiation including chondrocytes, osteoblasts, and adipocytes, as well as metabolites that are being differentiated, have been shown in different weeks. In the present study, the therapeutic effects of MSCs analyzed by decoding the metabolome for MSCs secretome via metabolic profiling using MALDI-TOF-MS techniques.

Metabolomic Alterations in the Blood and Brain in Association with Alzheimer's Disease: Evidence from in vivo to Clinical Studies

Alzheimer's disease (AD) has become a major health problem among the elderly population. Some evidence suggests that metabolic disturbance possibly plays a role in the pathophysiology of AD. Currently, the study of metabolomics has been used to explore changes in multiple metabolites in several diseases, including AD. Thus, the metabolomics research in AD might provide some information regarding metabolic dysregulations, and their possible associated pathophysiology. This review summarizes the information discovered regarding the metabolites in the brain and the blood from the metabolomics research of AD from both animal and clinical studies. Additionally, the correlation between the changes in metabolites and outcomes, such as pathological findings in the brain and cognitive impairment are discussed. We also deliberate on the findings of cohort studies, demonstrating the alterations in metabolites before changes of cognitive function. All of these findings can be used to inform the potential identity of specific metabolites as possible biomarkers for AD.

Challenges in LC-MS-based metabolomics for Alzheimer's disease early detection: targeted approaches versus untargeted approaches

BACKGROUND

Alzheimer's disease (AD) is one of the most common causes of dementia in old people. Neuronal deficits such as loss of memory, language and problem-solving are severely compromised in affected patients. The molecular features of AD are Aβ deposits in plaques or in oligomeric structures and neurofibrillary tau tangles in brain. However, the challenge is that Aβ is only one piece of the puzzle, and recent findings continue to support the hypothesis that their presence is not sufficient to predict decline along the AD outcome. In this regard, metabolomic-based techniques are acquiring a growing interest for either the early diagnosis of diseases or the therapy monitoring. Mass spectrometry is one the most common analytical platforms used for detection, quantification, and characterization of metabolic biomarkers. In the past years, both targeted and untargeted strategies have been applied to identify possible interesting compounds.

AIM OF REVIEW

The overall goal of this review is to guide the reader through the most recent studies in which LC-MS-based metabolomics has been proposed as a powerful tool for the identification of new diagnostic biomarkers in AD. To this aim, herein studies spanning the period 2009-2020 have been reported. Advantages and disadvantages of targeted vs untargeted metabolomic approaches have been outlined and critically discussed.

Omics sciences for systems biology in Alzheimer's disease: State-of-the-art of the evidence

Alzheimer's disease (AD) is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in biological alterations and disease spatial-temporal progression. Human in-vivo and post-mortem studies point out a failure of multi-level biological networks underlying AD pathophysiology, including proteostasis (amyloid-β and tau), synaptic homeostasis, inflammatory and immune responses, lipid and energy metabolism, oxidative stress. Therefore, a holistic, systems-level approach is needed to fully capture AD multi-faceted pathophysiology. Omics sciences - genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics - embedded in the systems biology (SB) theoretical and computational framework can generate explainable readouts describing the entire biological continuum of a disease. Such path in Neurology is encouraged by the promising results of omics sciences and SB approaches in Oncology, where stage-driven pathway-based therapies have been developed in line with the precision medicine paradigm. Multi-omics data integrated in SB network approaches will help detect and chart AD upstream pathomechanistic alterations and downstream molecular effects occurring in preclinical stages. Finally, integrating omics and neuroimaging data - i.e., neuroimaging-omics - will identify multi-dimensional biological signatures essential to track the clinical-biological trajectories, at the subpopulation or even individual level.

Biomarkers for the Clinical Diagnosis of Alzheimer's Disease: Metabolomics Analysis of Brain Tissue and Blood

With an increase in aging populations worldwide, age-related diseases such as Alzheimer's disease (AD) have become a global concern. At present, a cure for neurodegenerative disease is lacking. There is an urgent need for a biomarker that can facilitate the diagnosis, classification, prognosis, and treatment response of AD. The recent emergence of highly sensitive mass-spectrometry platforms and high-throughput technology can be employed to discover and catalog vast datasets of small metabolites, which respond to changed status in the body. Metabolomics analysis provides hope for a better understanding of AD as well as the subsequent identification and analysis of metabolites. Here, we review the state-of-the-art emerging candidate biomarkers for AD.

Circulating Plasma Metabolites and Cognitive Function in a Puerto Rican Cohort

BACKGROUND

Minorities, including mainland Puerto Ricans, are impacted disproportionally by Alzheimer's disease (AD), dementia, and cognitive decline. Studying blood metabolomics in this population has the potential to probe the biological underpinnings of this health disparity.

OBJECTIVE

We performed a comprehensive analysis of circulating plasma metabolites in relation to cognitive function in 736 participants from the Boston Puerto Rican Health Study (BPRHS) who underwent untargeted mass-spectrometry based metabolomics analysis and had undergone a battery of in-person cognitive testing at baseline.

METHODS

After relevant exclusions, 621 metabolites were examined. We used multivariable regression, adjusted for age, sex, education, apolipoprotein E genotype, smoking, and Mediterranean dietary pattern, to identify metabolites related to global cognitive function in our cohort. LASSO machine learning was used in a complementary analysis to identify metabolites that could discriminate good from poor extremes of cognition. We also conducted sensitivity analyses: restricted to participants without diabetes, and to participants with good adherence to Mediterranean diet.

RESULTS

Of 621 metabolites, FDR corrected (p < 0.05) multivariable linear regression identified 3 metabolites positively, and 10 negatively, associated with cognitive function in the BPRHS. In a combination of FDR-corrected linear regression, logistic regression regularized via LASSO, and sensitivity analyses restricted to participants without diabetes, and with good adherence to the Mediterranean diet, β-cryptoxanthin plasma concentration was consistently associated with better cognitive function and N-acetylisoleucine and tyramine O-sulfate concentrations were consistently associated with worse cognitive function.

CONCLUSION

This untargeted metabolomics study identified potential biomarkers for cognitive function in a cohort of Puerto Rican older adults.

Potential Neuroregenerative and Neuroprotective Effects of Uridine/Choline-Enriched Multinutrient Dietary Intervention for Mild Cognitive Impairment: A Narrative Review

In mild cognitive impairment (MCI) due to Alzheimer disease (AD), also known as prodromal AD, there is evidence for a pathologic shortage of uridine, choline, and docosahexaenoic acid [DHA]), which are key nutrients needed by the brain. Preclinical and clinical evidence shows the importance of nutrient bioavailability to support the development and maintenance of brain structure and function in MCI and AD. Availability of key nutrients is limited in MCI, creating a distinct nutritional need for uridine, choline, and DHA. Evidence suggests that metabolic derangements associated with ageing and disease-related pathology can affect the body's ability to generate and utilize nutrients. This is reflected in lower levels of nutrients measured in the plasma and brains of individuals with MCI and AD dementia, and progressive loss of cognitive performance. The uridine shortage cannot be corrected by normal diet, making uridine a conditionally essential nutrient in affected individuals. It is also challenging to correct the choline shortfall through diet alone, because brain uptake from the plasma significantly decreases with ageing. There is no strong evidence to support the use of single-agent supplements in the management of MCI due to AD. As uridine and choline work synergistically with DHA to increase phosphatidylcholine formation, there is a compelling rationale to combine these nutrients. A multinutrient enriched with uridine, choline, and DHA developed to support brain function has been evaluated in randomized controlled trials covering a spectrum of dementia from MCI to moderate AD. A randomized controlled trial in subjects with prodromal AD showed that multinutrient intervention slowed brain atrophy and improved some measures of cognition. Based on the available clinical evidence, nutritional intervention should be considered as a part of the approach to the management of individuals with MCI due to AD, including adherence to a healthy, balanced diet, and consideration of evidence-based multinutrient supplements.

CSF metabolites associate with CSF tau and improve prediction of Alzheimer's disease status

INTRODUCTION

Cerebrospinal fluid (CSF) total tau (t-tau) and phosphorylated tau (p-tau) are biomarkers of Alzheimer's disease (AD), yet much is unknown about AD-associated changes in tau metabolism and tau tangle etiology.

METHODS

We assessed the variation of t-tau and p-tau explained by 38 previously identified CSF metabolites using linear regression models in middle-age controls from the Wisconsin Alzheimer's Disease Research Center, and predicted AD/mild cognitive impairment (MCI) versus an independent set of older controls using metabolites selected by the least absolute shrinkage and selection operator (LASSO).

RESULTS

The 38 CSF metabolites explained 70.3% and 75.7% of the variance in t-tau and p-tau, respectively. Of these, seven LASSO-selected metabolites improved the prediction ability of AD/MCI versus older controls (area under the curve score increased from 0.92 to 0.97 and 0.78 to 0.93) compared to the base model.

DISCUSSION

These tau-correlated CSF metabolites increase AD/MCI prediction accuracy and may provide insight into tau tangle etiology.

Relevance of a Truncated PRESENILIN 2 Transcript to Alzheimer's Disease and Neurodegeneration

BACKGROUND

The PRESENILIN genes (PSEN1, PSEN2) encoding for their respective proteins have critical roles in many aspects of Alzheimer's disease (AD) pathogenesis. The PS2V transcript of PSEN2 encodes a truncated protein and is upregulated in AD brains; however, its relevance to AD and disease progression remains to be determined.

OBJECTIVE

Assess transcript levels in postmortem AD and non-AD brain tissue and in lymphocytes collected under the Australian Imaging Biomarker and Lifestyle (AIBL) study.

METHODS

Full length PSEN2 and PS2V transcript levels were assessed by quantitative digital PCR in postmortem brain tissue (frontal cortex and hippocampus) from control, AD, frontotemporal dementia (FTD), and Lewy body dementia (LBD). Transcript levels were also assessed in lymphocytes obtained from the Perth subset of the AIBL study (n = 160). Linear regression analysis was used to assess correlations between transcript copy number and brain volume and neocortical amyloid load.

RESULTS

PS2V levels increased in AD postmortem brain but PS2V was also present at significant levels in FTD and LBD brains. PS2V transcript was detected in lymphocytes and PS2V/PSEN2 ratios were increased in mild cognitive impairment (p = 0.024) and AD (p = 0.019) groups compared to control group. Increased ratios were significantly correlated with hippocampal volumes only (n = 62, β= -0.269, p = 0.03).

CONCLUSION

Taken together, these results suggest that PS2V may be a marker of overall neurodegeneration.