Proteomics as a reliable approach for discovery of blood-based Alzheimer's disease biomarkers: A systematic review and meta-analysis

The IGF System and Aging

There is strong evidence that IGF signaling is involved in fundamental aspects of the aging process. However, the extracellular part of the IGF system is complex with various receptors, ligand effectors, high-affinity IGF-binding proteins, proteinases, and endogenous inhibitors that all, along with their biological context, must be considered. The IGF system components are evolutionarily conserved, underscoring the importance of understanding this system in physiology and pathophysiology. This review will briefly describe the different components of the IGF system and then discuss past and current literature regarding IGF and aging, with a focus on cellular senescence, model organisms of aging, centenarian genetics, and 3 age-related diseases-pulmonary fibrosis, Alzheimer disease, and macular degeneration-in appropriate murine models and in humans. Commonalities in mechanism suggest conditions where IGF system components may be disease drivers and potential targets in promoting healthy aging in humans.

Role of miRNA regulation in IGFBP-2 overexpression and neuronal ferroptosis: Insights into the Nrf2/SLC7A11/GPX4 pathway in Alzheimer's disease

Alzheimer's disease (AD) is a common neurodegenerative disease with pathological features including amyloid plaque deposits and neurofibrillary tangles. In this study, the expressions of miRNA, IGFBP-2 and neuronal ferritin were detected by qPCR, Western blot and immunohistochemistry. The regulatory effects of miRNA on IGFBP-2 and neuronal ferritin were further verified by intervention experiments with miRNA mimics and inhibitors. Double luciferase reporter gene assay and RNA immunoprecipitation were used to investigate the interaction between miRNA and target genes. Finally, the effect of miRNA on Nrf2/SLC7A11/GPX4 pathway was evaluated by antioxidant enzyme activity and oxidative stress marker detection. The overexpression of IGFBP-2 was found to be significantly increased with the deposition of neuronal ferritin. Expression levels of specific mirnas were significantly down-regulated in AD models and negatively correlated with IGFBP-2 and neuronal ferritin expression. Intervention experiments with miRNA mimics and inhibitors have confirmed that these mirnas can regulate the expression of IGFBP-2 and neuronal ferritin. Further studies revealed that these mirnas affect antioxidant enzyme activity and oxidative stress levels by targeting key genes in the Nrf2/SLC7A11/GPX4 pathway, thereby regulating the deposition of neuronal ferritin.

Blood plasma proteomic markers of Alzheimer's disease, current status and application prospects

INTRODUCTION

Identifying early risks of developing Alzheimer's disease (AD) is a major challenge as the number of patients with AD steadily increases and requires innovative solutions. Current molecular diagnostic modalities, such as cerebrospinal fluid (CSF) testing and positron emission tomography (PET) imaging, exhibit limitations in their applicability for large-scale screening. In recent years, there has been a marked shift toward the development of blood plasma-based diagnostic tests, which offer a more accessible and clinically viable alternative for widespread use. Furthermore, advances in large-scale proteomics technologies have boosted an interest in identifying novel biomarkers and developing panels of AD-associated proteins.

AREAS COVERED

This review mainly examines the results of recent searches for proteomic markers of AD in blood plasma (from 2022-2024 PubMed), focuses on some aspects for special attention in further studies, and discusses the prospects for their further application.

EXPERT OPINION

Recent advances in AD plasma/serum proteomic studies are largely driven using novel Olink/PEA and SomaScan/aptamer technologies, which complement the 'gold standard' of MS-based quantitative proteomics (MRM/SRM), and particularly expand the capabilities for studying low-abundant proteins.

Faecal intestinal permeability and intestinal inflammatory markers in older adults with age-related disorders: A systematic review and meta-analysis

This systematic review and meta-analysis appraised previous findings to uncover potential faecal intestinal permeability and intestinal inflammatory markers in older adults. A comprehensive literature search led to the identification of ten eligible studies with findings of potential faecal intestinal permeability (zonulin and alpha-1-antitrypsin) and intestinal inflammatory markers [calprotectin, lactoferrin and neutrophil gelatinase-associated lipocalin (NGAL)]. Most of the cases (n > 2) [Parkinson's disease (PD) and Alzheimer's disease (AD)] exhibited higher faecal alpha-1-antitrypsin, zonulin and calprotectin levels. The present meta-analysis confirmed significantly higher faecal alpha-1-antitrypsin in older persons with PD compared to non-PD [MD = 22.92 mg/dL; 95 % CI = 14.02-31.81, p < 0.00001; I2 = 0 % (p = 0.73)]. There was, however, no significant difference in faecal zonulin between PD and non-PD individuals [MD = 26.88 ng/mL; 95 % CI = -29.26-83.01, p = 0.35; I2 = 94 % (p < 0.0001)]. Meanwhile, faecal calprotectin was higher in older adults with GI symptoms, multiple system atrophy (MSA) or PD than the healthy controls [MD = 9.51 μg/g; 95 % CI = 0.07-18.95, p = 0.05; I2 = 84 % (p < 0.00001)]. Altogether, faecal calprotectin appears to be a potential intestinal inflammatory marker whereas previous findings on faecal alpha-1-antitrypsin as an intestinal permeability marker remain limited and require further validation.

Proteomics profiling of extracellular vesicle for identification of potential biomarkers in Alzheimer's disease: A comprehensive review

The intricate origins and diverse symptoms of Alzheimer's disease (AD) pose significant challenges for both diagnosis and treatment. Exosomes and microvesicles, which carry disease-specific cargo from a variety of central nervous system cell types, have emerged as promising reservoirs of biomarkers for AD. Research on the screening of possible biomarkers in Alzheimer's disease using proteomic profiling of EVs is systematically reviewed in this comprehensive review. We highlight key methodologies employed in EV isolation, characterization, and proteomic analysis, elucidating their advantages and limitations. Furthermore, we summarize the evolving landscape of EV-associated biomarkers implicated in AD pathogenesis, including proteins involved in amyloid-beta metabolism, tau phosphorylation, neuroinflammation, synaptic dysfunction, and neuronal injury. The literature review highlights the necessity for robust validation strategies and standardized protocols to effectively transition EV-based biomarkers into clinical use. In the concluding section, this review delves into potential future avenues and technological advancements pivotal in crafting EV-derived biomarkers applicable to AD diagnostics and prognostics. This review contributes to our comprehension of AD pathology and the advancement of precision medicine in neurodegenerative diseases, hinting at a promising era in AD precision medicine.

A multi-ethnic proteomic profiling analysis in Alzheimer's disease identifies the disparities in dysregulation of proteins and pathogenesis

Background

Alzheimer's disease (AD) is the most common type of dementia that affects the elderly population. Lately, blood-based proteomics have been intensively sought in the discovery of AD biomarkers studies due to the capability to link external environmental factors with the development of AD. Demographic differences have been shown to affect the expression of the proteins in different populations which play a vital role in the degeneration of cognitive function.

Method

In this study, a proteomic study focused on Malaysian Chinese and Malay prospects was conducted. Differentially expressed proteins (DEPs) in AD patients and normal controls for Chinese and Malays were identified. Functional enrichment analysis was conducted to further interpret the biological functions and pathways of the DEPs. In addition, a survey investigating behavioural practices among Chinese and Malay participants was conducted to support the results from the proteomic analysis.

Result

The variation of dysregulated proteins identified in Chinese and Malay samples suggested the disparities of pathways involved in this pathological condition for each respective ethnicity. Functional enrichment analysis supported this assumption in understanding the protein-protein interactions of the identified protein signatures and indicate that differentially expressed proteins identified from the Chinese group were significantly enriched with the functional terms related to Aβ/tau protein-related processes, oxidative stress and inflammation whereas neuroinflammation was associated with the Malay group. Besides that, a significant difference in sweet drinks/food intake habits between these two groups implies a relationship between sugar levels and the dysregulation of protein APOA4 in the Malay group. Additional meta-analysis further supported the dysregulation of proteins TF, AHSG, A1BG, APOA4 and C4A among AD groups.

Conclusion

These findings serve as a preliminary understanding in the molecular and demographic studies of AD in a multi-ethnic population.

Which neuroimaging and fluid biomarkers method is better in theranostic of Alzheimer's disease? An umbrella review

Biomarkers are measured to evaluate physiological and pathological processes as well as responses to a therapeutic intervention. Biomarkers can be classified as diagnostic, prognostic, predictor, clinical, and therapeutic. In Alzheimer's disease (AD), multiple biomarkers have been reported so far. Nevertheless, finding a specific biomarker in AD remains a major challenge. Three databases, including PubMed, Web of Science, and Scopus were selected with the keywords of Alzheimer's disease, neuroimaging, biomarker, and blood. The results were finalized with 49 potential CSF/blood and 35 neuroimaging biomarkers. To distinguish normal from AD patients, amyloid-beta42 (Aβ42), plasma glial fibrillary acidic protein (GFAP), and neurofilament light (NFL) as potential biomarkers in cerebrospinal fluid (CSF) as well as the serum could be detected. Nevertheless, most of the biomarkers fairly change in the CSF during AD, listed as kallikrein 6, virus-like particles (VLP-1), galectin-3 (Gal-3), and synaptotagmin-1 (Syt-1). From the neuroimaging aspect, atrophy is an accepted biomarker for the neuropathologic progression of AD. In addition, Magnetic resonance spectroscopy (MRS), diffusion weighted imaging (DWI), diffusion tensor imaging (DTI), tractography (DTT), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI), can be used to detect AD. Using neuroimaging and CSF/blood biomarkers, in combination with artificial intelligence, it is possible to obtain information on prognosis and follow-up on the different stages of AD. Hence physicians could select the suitable therapy to attenuate disease symptoms and follow up on the efficiency of the prescribed drug.

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.

Comparison of Commonly Measured Plasma and Cerebrospinal Fluid Proteins and Their Significance for the Characterization of Cognitive Impairment Status

BACKGROUND

Although cerebrospinal fluid (CSF) amyloid-β42 peptide (Aβ42) and phosphorylated tau (p-tau) and blood p-tau are valuable for differential diagnosis of Alzheimer's disease (AD) from cognitively normal (CN) there is a lack of validated biomarkers for mild cognitive impairment (MCI).

OBJECTIVE

This study sought to determine how plasma and CSF protein markers compared in the characterization of MCI and AD status.

METHODS

This cohort study included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who had baseline levels of 75 proteins measured commonly in plasma and CSF (257 total, 46 CN, 143 MCI, and 68 AD). Logistic regression, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF) methods were used to identify the protein candidates for the disease classification.

RESULTS

We observed that six plasma proteins panel (APOE, AMBP, C3, IL16, IGFBP2, APOD) outperformed the seven CSF proteins panel (VEGFA, HGF, PRL, FABP3, FGF4, CD40, RETN) as well as AD markers (CSF p-tau and Aβ42) to distinguish the MCI from AD [area under the curve (AUC) = 0.75 (plasma proteins), AUC = 0.60 (CSF proteins) and AUC = 0.56 (CSF p-tau and Aβ42)]. Also, these six plasma proteins performed better than the CSF proteins and were in line with CSF p-tau and Aβ42 in differentiating CN versus MCI subjects [AUC = 0.89 (plasma proteins), AUC = 0.85 (CSF proteins) and AUC = 0.89 (CSF p-tau and Aβ42)]. These results were adjusted for age, sex, education, and APOEϵ4 genotype.

CONCLUSIONS

This study suggests that the combination of 6 plasma proteins can serve as an effective marker for differentiating MCI from AD and CN.

Mass Spectrometry-Based Proteomics for Discovering Salivary Biomarkers in Periodontitis: A Systematic Review

Periodontitis is one of the primary causes of tooth loss, and is also related to various systemic diseases. Early detection of this condition is crucial when it comes to preventing further oral damage and the associated health complications. This study offers a systematic review of the literature published up to April 2023, and aims to clearly explain the role of proteomics in identifying salivary biomarkers for periodontitis. Comprehensive searches were conducted on PubMed and Web of Science to shortlist pertinent studies. The inclusion criterion was those that reported on mass spectrometry-driven proteomic analyses of saliva samples from periodontitis cohorts, while those on gingivitis or other oral diseases were excluded. An assessment for risk of bias was carried out using the Newcastle-Ottawa Scale and Quality Assessment of Diagnostic Accuracy Studies or the NIH quality assessment tool, and a meta-analysis was performed for replicable candidate biomarkers, i.e., consistently reported candidate biomarkers (in specific saliva samples, and periodontitis subgroups, reported in ≥2 independent cohorts/reports) were identified. A Gene Ontology enrichment analysis was conducted using the Database for Annotation, Visualization, and Integrated Discovery bioinformatics resources, which consistently expressed candidate biomarkers, to explore the predominant pathway wherein salivary biomarkers consistently manifested. Of the 15 studies included, 13 were case-control studies targeting diagnostic biomarkers for periodontitis participants (periodontally healthy/diseased, n = 342/432), while two focused on biomarkers responsive to periodontal treatment (n = 26 participants). The case-control studies were considered to have a low risk of bias, while the periodontitis treatment studies were deemed fair. Summary estimate and confidence/credible interval, etc. determination for the identified putative salivary biomarkers could not be ascertained due to the low number of studies in each case. The results from the included case-control studies identified nine consistently expressed candidate biomarkers (from nine studies with 230/297 periodontally healthy/diseased participants): (i) those that were upregulated: alpha-amylase, serum albumin, complement C3, neutrophil defensin, profilin-1, and S100-P; and (ii) those that were downregulated: carbonic anhydrase 6, immunoglobulin J chain, and lactoferrin. All putative biomarkers exhibited consistent regulation patterns. The implications of the current putative marker proteins identified were reviewed, with a focus on their potential roles in periodontitis diagnosis and pathogenesis, and as putative therapeutic targets. Although in its early stages, mass spectrometry-based salivary periodontal disease biomarker proteomics detection appeared promising. More mass spectrometry-based proteomics studies, with or without the aid of already available clinical biochemical approaches, are warranted to aid the discovery, identification, and validation of periodontal health/disease indicator molecule(s). Protocol registration number: CRD42023447722; supported by RD-02-202410 and GRF17119917.

Proteomics as a tool to decipher plant responses in arbuscular mycorrhizal interactions: a meta-analysis

The beneficial symbiosis between plants and arbuscular mycorrhizal (AM) fungi leads to a deep reprogramming of plant metabolism, involving the regulation of several molecular mechanisms, many of which are poorly characterized. In this regard, proteomics is a powerful tool to explore changes related to plant-microbe interactions. This study provides a comprehensive proteomic meta-analysis conducted on AM-modulated proteins at local (roots) and systemic (shoots/leaves) level. The analysis was implemented by an in-depth study of root membrane-associated proteins and by a comparison with a transcriptome meta-analysis. A total of 4262 differentially abundant proteins were retrieved and, to identify the most relevant AM-regulated processes, a range of bioinformatic studies were conducted, including functional enrichment and protein-protein interaction network analysis. In addition to several protein transporters which are present in higher amounts in AM plants, and which are expected due to the well-known enhancement of AM-induced mineral uptake, our analysis revealed some novel traits. We detected a massive systemic reprogramming of translation with a central role played by the ribosomal translational apparatus. On one hand, these new protein-synthesis efforts well support the root cellular re-organization required by the fungal penetration, and on the other they have a systemic impact on primary metabolism.

Proteomics for the Discovery of Clinical Delirium Biomarkers: A Systematic Review of Major Studies

Delirium represents a significant health care burden, diagnosed in more than 2 million elderly Americans each year. In the surgical population, delirium remains the most common complication among elderly patients, and is associated with longer hospital stays, higher costs of care, increased mortality, and functional impairment. The pathomechanism of disease is poorly understood, with current diagnostic approaches somewhat subjective and arbitrary, and definitive diagnostic biomarkers are currently lacking. Despite the recent interest in delirium research, biomarker discovery for it remains new. Most attempts to discover biomarkers are targeted studies that seek to assess the involvement of one or more members of a focused panel of candidates in delirium. For a more unbiased, system-biology view, we searched literature from Medical Literature Analysis and Retrieval System Online (MEDLINE), Cochrane Central, Web of Science, SCOPUS, and Dimensions between 2016 and 2021 for untargeted proteomic discovery studies for biomarkers of delirium conducted on human geriatric subjects. Two reviewers conducted an independent review of all search results and resolved discordance by consensus. From an overall search of 1172 publications, 8 peer-reviewed studies met our defined inclusion criteria. The 370 unique perioperative biomarkers identified in these reports are enriched in pathways involving activation of the immune system, inflammatory response, and the coagulation cascade. The most frequently identified biomarker was interleukin-6 (IL-6). By reviewing the distribution of protein biomarker candidates from these studies, we conclude that a panel of proteins, rather than a single biomarker, would allow for discriminating delirium cases from noncases. The paucity of hypothesis-generating studies in the peer-reviewed literature also suggests that a system-biology view of delirium pathomechanisms has yet to fully emerge.

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.

Association of plasma brain-derived neurotrophic factor with Alzheimer's disease and its influencing factors in Chinese elderly population

OBJECTIVE

To explore the association of plasma brain-derived neurotrophic factor (BDNF) levels with Alzheimer's disease and its influencing factors.

MATERIALS AND METHODS

A total of 1,615 participants were included in the present study. Among all subjects, 660 were cognitive normal controls (CNCs), 571 were mild cognitive impairment (MCI) patients, and 384 were dementia with Alzheimer's type (DAT) patients. BDNF in blood samples collected from these subjects was analyzed via the Luminex assay. Additionally, DNA extraction and APOE4 genotyping were performed on leukocytes using a blood genotyping DNA extraction kit. All data were processed with SPSS 20.0 software. Analysis of variance (ANOVA) or analysis of covariance (ANCOVA) was used to compare differences among groups on plasma BDNF. Pearson and Spearman correlation analysis examined the correlation between BDNF and cognitive impairment, and linear regression analysis examined the comprehensive effects of diagnosis, gender, age, education, and sample source on BDNF.

RESULTS

BDNF levels in DAT patients were higher than those in CNC and MCI patients (P < 0.01). BDNF levels were significantly correlated with CDR, MMSE, and clinical diagnosis (P < 0.001). Age, education, occupation, and sample source had significant effects on BDNF differences among the CNC, MCI, and DAT groups (P < 0.001). BDNF first decreased and then increased with cognitive impairment in the ApoE4-negative group (P < 0.05).

CONCLUSION

Plasma BDNF levels decreased in the MCI stage and increased in the dementia stage and were affected by age, education, occupation, and sample source. Unless the effects of sample heterogeneity and methodological differences can be excluded, plasma BDNF is difficult to become a biomarker for the early screening and diagnosis of AD.

Omics-based biomarkers discovery for Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorders presenting with the pathological hallmarks of amyloid plaques and tau tangles. Over the past few years, great efforts have been made to explore reliable biomarkers of AD. High-throughput omics are a technology driven by multiple levels of unbiased data to detect the complex etiology of AD, and it provides us with new opportunities to better understand the pathophysiology of AD and thereby identify potential biomarkers. Through revealing the interaction networks between different molecular levels, the ultimate goal of multi-omics is to improve the diagnosis and treatment of AD. In this review, based on the current AD pathology and the current status of AD diagnostic biomarkers, we summarize how genomics, transcriptomics, proteomics and metabolomics are all conducing to the discovery of reliable AD biomarkers that could be developed and used in clinical AD management.

Proteomic Markers and Early Prediction of Alzheimer's Disease

Alzheimer's disease (AD) is the most common socially significant neurodegenerative pathology, which currently affects more than 30 million elderly people worldwide. Since the number of patients grows every year and may exceed 115 million by 2050, and due to the lack of effective therapies, early prediction of AD remains a global challenge, solution of which can contribute to the timely appointment of a preventive therapy in order to avoid irreversible changes in the brain. To date, clinical assays for the markers of amyloidosis in cerebrospinal fluid (CSF) have been developed, which, in conjunction with the brain MRI and PET studies, are used either to confirm the diagnosis based on obligate clinical criteria or to predict the risk of AD developing at the stage of mild cognitive impairment (MCI). However, the problem of predicting AD at the asymptomatic stage remains unresolved. In this regard, the search for new protein markers and studies of proteomic changes in CSF and blood plasma are of particular interest and may consequentially identify particular pathways involved in the pathogenesis of AD. Studies of specific proteomic changes in blood plasma deserve special attention and are of increasing interest due to the much less invasive method of sample collection as compared to CSF, which is important when choosing the object for large-scale screening. This review briefly summarizes the current knowledge on proteomic markers of AD and considers the prospects of developing reliable methods for early identification of AD risk factors based on the proteomic profile.

Prognosis of Alzheimer's Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning

Early recognition of the risk of Alzheimer's disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA-plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels.

Crossing borders in Alzheimer's disease: A T cell's perspective

Alzheimer's disease (AD) is the most common form of dementia affecting millions of people worldwide. While different immunotherapies are imminent, currently only disease-modifying medications are available and a cure is lacking. Over the past decade, immunological interfaces of the central nervous system (CNS) and their role in neurodegenerative diseases received increasing attention. Specifically, emerging evidence shows that subsets of circulating CD8⁺ T cells cross the brain barriers and associate with AD pathology. To gain more insight into how the adaptive immune system is involved in disease pathogenesis, we here provide a comprehensive overview of the contribution of T cells to AD pathology, incorporating changes at the brain barriers. In addition, we review studies that provide translation of these findings by targeting T cells to combat AD pathology and cognitive decline. Importantly, these data show that immunological changes in AD are not confined to the CNS and that AD-associated systemic immune changes appear to affect brain homeostasis.

A Novel Panel of Plasma Proteins Predicts Progression in Prodromal Alzheimer's Disease

BACKGROUND

A cheap and minimum-invasive method for early identification of Alzheimer's disease (AD) pathogenesis is key to disease management and the success of emerging treatments targeting the prodromal phases of the disease.

OBJECTIVE

To develop a machine learning-based blood panel to predict the progression from mild cognitive impairment (MCI) to dementia due to AD within a four-year time-to-conversion horizon.

METHODS

We created over one billion models to predict the probability of conversion from MCI to dementia due to AD and chose the best-performing one. We used Alzheimer's Disease Neuroimaging Initiative (ADNI) data of 379 MCI individuals in the baseline visit, from which 176 converted to AD dementia.

RESULTS

We developed a machine learning-based panel composed of 12 plasma proteins (ApoB, Calcitonin, C-peptide, CRP, IGFBP-2, Interleukin-3, Interleukin-8, PARC, Serotransferrin, THP, TLSP 1-309, and TN-C), and which yielded an AUC of 0.91, accuracy of 0.91, sensitivity of 0.84, and specificity of 0.98 for predicting the risk of MCI patients converting to dementia due to AD in a horizon of up to four years.

CONCLUSION

The proposed machine learning model was able to accurately predict the risk of MCI patients converting to dementia due to AD in a horizon of up to four years, suggesting that this model could be used as a minimum-invasive tool for clinical decision support. Further studies are needed to better clarify the possible pathophysiological links with the reported proteins.

Plasma protein biomarker model for screening Alzheimer disease using multiple reaction monitoring-mass spectrometry

Alzheimer disease (AD) is a leading cause of dementia that has gained prominence in our aging society. Yet, the complexity of diagnosing AD and measuring its invasiveness poses an obstacle. To this end, blood-based biomarkers could mitigate the inconveniences that impede an accurate diagnosis. We developed models to diagnose AD and measure the severity of neurocognitive impairment using blood protein biomarkers. Multiple reaction monitoring-mass spectrometry, a highly selective and sensitive approach for quantifying targeted proteins in samples, was used to analyze blood samples from 4 AD groups: cognitive normal control, asymptomatic AD, prodromal AD), and AD dementia. Multimarker models were developed using 10 protein biomarkers and apolipoprotein E genotypes for amyloid beta and 10 biomarkers with Korean Mini-Mental Status Examination (K-MMSE) score for predicting Alzheimer disease progression. The accuracies for the AD classification model and AD progression monitoring model were 84.9% (95% CI 82.8 to 87.0) and 79.1% (95% CI 77.8 to 80.5), respectively. The models were more accurate in diagnosing AD, compared with single APOE genotypes and the K-MMSE score. Our study demonstrates the possibility of predicting AD with high accuracy by blood biomarker analysis as an alternative method of screening for AD.

Neurobiological Highlights of Cognitive Impairment in Psychiatric Disorders

This review is focused on several psychiatric disorders in which cognitive impairment is a major component of the disease, influencing life quality. There are plenty of data proving that cognitive impairment accompanies and even underlies some psychiatric disorders. In addition, sources provide information on the biological background of cognitive problems associated with mental illness. This scientific review aims to summarize the current knowledge about neurobiological mechanisms of cognitive impairment in people with schizophrenia, depression, mild cognitive impairment and dementia (including Alzheimer's disease).The review provides data about the prevalence of cognitive impairment in people with mental illness and associated biological markers.

Proteomic analysis of human hippocampal subfields provides new insights into the pathogenesis of Alzheimer's disease and the role of glial cells

The hippocampus and entorhinal cortex (EC), the earliest affected areas, are considered relative to early memory loss in Alzheimer's disease (AD). The hippocampus is composed of heterogeneous subfields that are affected in a different order and varying degrees during AD pathogenesis. In this study, we conducted a comprehensive proteomic analysis of the hippocampal subfields and EC region in human postmortem specimens obtained from the Chinese human brain bank. Bioinformatics analysis identified region‐consistent differentially expressed proteins (DEPs) which associated with astrocytes, and region‐specific DEPs which associated with oligodendrocytes and the myelin sheath. Further analysis illuminated that the region‐consistent DEPs functioned as connection of region‐specific DEPs. Moreover, in region‐consistent DEPs, the expression level of S100A10, a marker of protective astrocytes, was increased in both aging and AD patients. Immunohistochemical analysis confirmed an increase in the number of S100A10‐positive astrocytes in all hippocampal subfields and the EC region of AD patients. Dual immunofluorescence results further showed that S100A10‐positive astrocytes contained apoptotic neuron debris in AD patients, suggesting that S100A10‐positive astrocytes may protect brain through phagocytosis of apoptotic neurons. In region‐specific DEPs, the proteome showed a specific reduction of oligodendrocytes and myelin markers in CA1, CA3, and EC regions of AD patients. Immunohistochemical analysis confirmed the loss of myelin in EC region. Above all, these results highlight the role of the glial cells in AD and provide new insights into the pathogenesis of AD and potential therapeutic strategies.

Blood Lipid Biomarkers in Early Parkinson's Disease and Parkinson's Disease with Mild Cognitive Impairment

BACKGROUND

Lipid biomarkers have potential neuroprotective effects in Parkinson's disease (PD) and there is limited evidence in the field.

OBJECTIVE

This study aims to investigate the association between comprehensive blood lipid biomarkers and PD.

METHODS

A total of 205 PD patients and 102 non-PD subjects were included from Early Parkinson's disease Longitudinal Singapore (PALS) cohort. We investigated 6 serum lipid biomarkers including total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (Apo A1), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein B (Apo B). PD patients were further classified into mild cognitive impairment (MCI) and normal cognition (NC) subgroups. We conducted a cross-sectionals study to examine the association between lipids and PD and further explored the relationship between lipids and PD-MCI.

RESULTS

PD patients had significantly lower level of lipid panel including TC, TG, HDL-C, Apo A1, LDL-C, and Apo B (all p < 0.05). TC, TG, Apo A1, and Apo B levels were independent protective factors (p < 0.05) for PD in the logistic regression model. PD-MCI group had significantly higher mean TC, TG, and Apo A1 levels compared to PD-NC group. Higher TC, TG, and Apo A1 levels were independent risk factors (p < 0.05) for PD-MCI.

CONCLUSION

We demonstrated that PD patients had significantly lower levels of lipid biomarkers while PD-MCI patients had higher levels of TC, TG, and Apo A1. TC, TG, and Apo A1 may be useful biomarkers for PD-MCI.

Levels of Angiotensin-Converting Enzyme and Apolipoproteins Are Associated with Alzheimer's Disease and Cardiovascular Diseases

Angiotensin-converting enzyme-1 (ACE1) and apolipoproteins (APOs) may play important roles in the development of Alzheimer's disease (AD) and cardiovascular diseases (CVDs). This study aimed to examine the associations of AD, CVD, and endocrine-metabolic diseases (EMDs) with the levels of ACE1 and 9 APO proteins (ApoAI, ApoAII, ApoAIV, ApoB, ApoCI, ApoCIII, ApoD, ApoE, and ApoH). Non-Hispanic white individuals including 109 patients with AD, 356 mild cognitive impairment (MCI), 373 CVD, 198 EMD and controls were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Multivariable general linear model (GLM) was used to examine the associations. ApoE ε4 allele was associated with AD, as well as ApoAIV, ApoB and ApoE proteins, but not associated with CVD and EMD. Both AD and CVD were associated with levels of ACE1, ApoB, and ApoH proteins. AD, MCI and EMD were associated with levels of ACE1, ApoAII, and ApoE proteins. This is the first study to report associations of ACE1 and several APO proteins with AD, MCI, CVD and EMD, respectively, including upregulated and downregulated protein levels. In conclusion, as specific or shared biomarkers, the levels of ACE1 and APO proteins are implicated for AD, CVD, EMD and ApoE ε4 allele. Further studies are required for validation to establish reliable biomarkers for these health conditions.

Integrated Genomic, Transcriptomic and Proteomic Analysis for Identifying Markers of Alzheimer's Disease

There is an urgent need to identify biomarkers for Alzheimer’s disease (AD), but the identification of reliable blood-based biomarkers has proven to be much more difficult than initially expected. The current availability of high-throughput multi-omics data opens new possibilities in this titanic task. Candidate Single Nucleotide Polymorphisms (SNPs) from large, genome-wide association studies (GWAS), meta-analyses exploring AD (case-control design), and quantitative measures for cortical structure and general cognitive performance were selected. The Genotype-Tissue Expression (GTEx) database was used for identifying expression quantitative trait loci (eQTls) among candidate SNPs. Genes significantly regulated by candidate SNPs were investigated for differential expression in AD cases versus controls in the brain and plasma, both at the mRNA and protein level. This approach allowed us to identify candidate susceptibility factors and biomarkers of AD, facing experimental validation with more evidence than with genetics alone.

Apolipoprotein A1, the neglected relative of Apolipoprotein E and its potential role in Alzheimer's disease

Lipoproteins are multi-molecule assemblies with the primary function of transportation and processing of lipophilic substances within aqueous bodily fluids (blood, cerebrospinal fluid). Nevertheless, they also exert other physiological functions such as immune regulation. In particular, neurons are both sensitive to uncontrolled responses of the immune system and highly dependent on a controlled and sufficient supply of lipids. For this reason, the role of certain lipoproteins and their protein-component (apolipoproteins, Apo's) in neurological diseases is perceivable. ApoE, for example, is well-accepted as one of the major risk factors for sporadic Alzheimer's disease with a protective allele variant (ε2) and a risk-causing allele variant (ε4). ApoA1, the major protein component of high-density lipoproteins, is responsible for transportation of excess cholesterol from peripheral tissues to the liver. The protein is synthesized in the liver and intestine but also can enter the brain via the choroid plexus and thereby might have an impact on brain lipid homeostasis. This review focuses on the role of ApoA1 in Alzheimer's disease and discusses whether its role within this neurodegenerative disorder is specific or represents a general neuroprotective mechanism.

Interbatch Reliability of Blood-Based Cytokine and Chemokine Measurements in Community-Dwelling Older Adults: A Cross-Sectional Study

Blood-based inflammatory markers hold considerable promise for diagnosis and prognostication of age-related neurodegenerative disease, though a paucity of research has empirically tested how reliably they can be measured across different experimental runs ("batches"). We quantified the interbatch reliability of 13 cytokines and chemokines in a cross-sectional study of 92 community-dwelling older adults (mean age = 74; 48% female). Plasma aliquots from the same blood draw were parallelly processed in 2 separate batches using the same analytic platform and procedures (high-performance electrochemiluminescence by Meso Scale Discovery). Interbatch correlations (Pearson's r) ranged from small and nonsignificant (r = .13 for macrophage inflammatory protein-1 alpha [MIP-1α]) to very large (r > .90 for interferon gamma [IFNγ], interleukin-10 [IL-10], interferon gamma-induced protein 10 [IP-10], MIP-1β, thymus and activation-regulated chemokine [TARC]) with most markers falling somewhere in between (.67 ≤ r ≤ .90 for IL-6, tumor necrosis factor alpha [TNF-α], Eotaxin, Eotaxin-3, monocyte chemoattractant protein-1 [MCP-1], MCP-4, macrophage-derived chemokine [MDC]). All markers, except for IL-6 and MCP-4, showed significant differences in absolute values between batches, with discrepancies ranging in effect size (Cohen's d) from small to moderate (0.2 ≤ |d| ≤ 0.5 for IL-10, IP-10, MDC) to large or very large (0.68 ≤ |d| ≤ 1.5 for IFNγ, TNF-α, Eotaxin, Eotaxin-3, MCP-1, MIP-1α, MIP-1β, TARC). Relatively consistent associations with external variables of interest (age, sex, systolic blood pressure, body mass index, cognition) were observed across batches. Taken together, our results suggest heterogeneity in measurement reliability of blood-based cytokines and chemokines, with some analytes outperforming others. Future work is needed to evaluate the generalizability of these findings while identifying potential sources of batch effect measurement error.

Blood biomarkers for the diagnosis of amnestic mild cognitive impairment and Alzheimer's disease: A systematic review and meta-analysis

The development of blood-based biomarkers of Alzheimer's disease (AD) pathology as tools for screening the general population is essential, but persists controversies. We aimed to evaluate the effects of AD core pathological biomarkers on blood, and systematical searched Embase, PubMed and Cochrane for eligible studies. Biomarker performance was rated by random-effects meta-analysis based on the ratio of means method and multivariable-adjusted effect estimates. Finally, 150 articles were included, which demonstrated T-tau (average ratio: 1.25-1.62), P-tau 181 (1.36-2.16) and NfL (1.24-1.86) were increased, and AβPPr (0.65-0.88) were decreased from controls to amnestic mild cognitive impairment (aMCI) to AD. Furthermore, Aβ42, Aβ42/Aβ40 ratio and P-tau 217 using ultrasensitive platforms also had great diagnostic accuracy from controls to aMCI to AD. Consequently, significantly changes of blood AD core biomarkers were verified in comparison between AD, aMCI and control, supporting biomarkers were strongly valid in identifying AD and aMCI, which provides a new prospect of AD early diagnosis and progressive monitoring. This study is registered with PROSPERO, number CRD42020191927.

Alzheimer disease

Alzheimer disease (AD) is biologically defined by the presence of β-amyloid-containing plaques and tau-containing neurofibrillary tangles. AD is a genetic and sporadic neurodegenerative disease that causes an amnestic cognitive impairment in its prototypical presentation and non-amnestic cognitive impairment in its less common variants. AD is a common cause of cognitive impairment acquired in midlife and late-life but its clinical impact is modified by other neurodegenerative and cerebrovascular conditions. This Primer conceives of AD biology as the brain disorder that results from a complex interplay of loss of synaptic homeostasis and dysfunction in the highly interrelated endosomal/lysosomal clearance pathways in which the precursors, aggregated species and post-translationally modified products of Aβ and tau play important roles. Therapeutic endeavours are still struggling to find targets within this framework that substantially change the clinical course in persons with AD.

Why Inclusion Matters for Alzheimer's Disease Biomarker Discovery in Plasma

BACKGROUND

African American/Black adults have a disproportionate incidence of Alzheimer's disease (AD) and are underrepresented in biomarker discovery efforts.

OBJECTIVE

This study aimed to identify potential diagnostic biomarkers for AD using a combination of proteomics and machine learning approaches in a cohort that included African American/Black adults.

METHODS

We conducted a discovery-based plasma proteomics study on plasma samples (N = 113) obtained from clinically diagnosed AD and cognitively normal adults that were self-reported African American/Black or non-Hispanic White. Sets of differentially-expressed proteins were then classified using a support vector machine (SVM) to identify biomarker candidates.

RESULTS

In total, 740 proteins were identified of which, 25 differentially-expressed proteins in AD came from comparisons within a single racial and ethnic background group. Six proteins were differentially-expressed in AD regardless of racial and ethnic background. Supervised classification by SVM yielded an area under the curve (AUC) of 0.91 and accuracy of 86%for differentiating AD in samples from non-Hispanic White adults when trained with differentially-expressed proteins unique to that group. However, the same model yielded an AUC of 0.49 and accuracy of 47%for differentiating AD in samples from African American/Black adults. Other covariates such as age, APOE4 status, sex, and years of education were found to improve the model mostly in the samples from non-Hispanic White adults for classifying AD.

CONCLUSION

These results demonstrate the importance of study designs in AD biomarker discovery, which must include diverse racial and ethnic groups such as African American/Black adults to develop effective biomarkers.