Alzheimer Disease Pathogenesis: The Role of Autoimmunity

Genetically determined dietary habits and risk of Alzheimer's disease: a Mendelian randomization study

Background

Emerging evidence have suggested that dietary habits have potential implication on the development of Alzheimer's disease (AD). However, elucidating the causal relationship between specific dietary factors and AD risk remains a challenge. Therefore, our study endeavors to investigate the causal association between dietary habits and the risk of AD.

Materials and methods

We analyzed data on 231 dietary habits sourced from the UK Biobank and MRC-IEU, and AD data obtained from the FinnGen database. Employing a framework based on the classic two-sample Mendelian randomization (MR) study, we utilized the inverse-variance weighted (IVW) method as the primary analysis. Additionally, we conducted Steiger filtering and other methods to mitigate horizontal pleiotropy. The robustness of our overall findings was confirmed through multiple sensitivity analysis methods, and forward MR and reverse MR to address potential reverse causality bias.

Results

Our study evaluated the causal effect between 231 dietary habits involving over 500,000 participants of European ancestry, and 10,520 AD cases. Only oily fish intake demonstrated a significant protective causal relationship with AD following FDR correction (raw p-value = 1.28e-4, FDR p-value = 0.011, OR = 0.60, 95%CI: 0.47-0.78). Additionally, six dietary habits potentially influenced AD risk, with protective causal effects observed for average monthly intake of other alcoholic drinks (raw p-value = 0.024, FDR p-value = 0.574, OR = 0.57, 95%CI: 0.35-0.93) and tea intake (raw p-value = 0.047, FDR p-value = 0.581, OR = 0.78, 95%CI: 0.603-1.00). Conversely, detrimental causal effects were observed for the average weekly champagne plus white wine intake (raw p-value = 0.006, FDR p-value = 0.243, OR = 2.96, 95%CI: 1.37-6.38), Danish pastry intake (raw p-value = 0.036, FDR p-value = 0.574, OR = 13.33, 95%CI: 1.19-149.69), and doughnut intake (raw p-value = 0.039, FDR p-value = 0.574, OR = 7.41, 95%CI: 1.11-49.57). Moreover, the protective effect of goat's cheese intake phenotype exhibited statistical significance only in the IVW method (raw p-value<0.05).

Conclusion

Our results provide genetic support for a protective causal effect of oily fish intake on AD risk. Additionally, average monthly intake of other alcoholic drinks and tea consumption were also related with a lower risk of AD. Conversely, average weekly champagne plus white wine intake, Danish pastry intake, and doughnut intake were causally associated with increased risk of AD.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

Alzheimer's disease prediction algorithm based on de-correlation constraint and multi-modal feature interaction

Alzheimer's disease (AD) is a neurodegenerative disease characterized by various pathological changes. Utilizing multimodal data from Fluorodeoxyglucose positron emission tomography(FDG-PET) and Magnetic Resonance Imaging(MRI) of the brain can offer comprehensive information about the lesions from different perspectives and improve the accuracy of prediction. However, there are significant differences in the feature space of multimodal data. Commonly, the simple concatenation of multimodal features can cause the model to struggle in distinguishing and utilizing the complementary information between different modalities, thus affecting the accuracy of predictions. Therefore, we propose an AD prediction model based on de-correlation constraint and multi-modal feature interaction. This model consists of the following three parts: (1) The feature extractor employs residual connections and attention mechanisms to capture distinctive lesion features from FDG-PET and MRI data within their respective modalities. (2) The de-correlation constraint function enhances the model's capacity to extract complementary information from different modalities by reducing the feature similarity between them. (3) The mutual attention feature fusion module interacts with the features within and between modalities to enhance the modal-specific features and adaptively adjust the weights of these features based on information from other modalities. The experimental results on ADNI database demonstrate that the proposed model achieves a prediction accuracy of 86.79% for AD, MCI and NC, which is higher than the existing multi-modal AD prediction models.

A glance through the effects of CD4+ T cells, CD8+ T cells, and cytokines on Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia. Unfortunately, despite numerous studies, an effective treatment for AD has not yet been established. There is remarkable evidence indicating that the innate immune mechanism and adaptive immune response play significant roles in the pathogenesis of AD. Several studies have reported changes in CD8+ and CD4+ T cells in AD patients. This mini-review article discusses the potential contribution of CD4+ and CD8+ T cells reactivity to amyloid β (Aβ) protein in individuals with AD. Moreover, this mini-review examines the potential associations between T cells, heme oxygenase (HO), and impaired mitochondria in the context of AD. While current mathematical models of AD have not extensively addressed the inclusion of CD4+ and CD8+ T cells, there exist models that can be extended to consider AD as an autoimmune disease involving these T cell types. Additionally, the mini-review covers recent research that has investigated the utilization of machine learning models, considering the impact of CD4+ and CD8+ T cells.

Metabolomic Signatures of Alzheimer's Disease Indicate Brain Region-Specific Neurodegenerative Progression

Pathological mechanisms contributing to Alzheimer's disease (AD) are still elusive. Here, we identified the metabolic signatures of AD in human post-mortem brains. Using 1H NMR spectroscopy and an untargeted metabolomics approach, we identified (1) metabolomic profiles of AD and age-matched healthy subjects in post-mortem brain tissue, and (2) region-common and region-unique metabolome alterations and biochemical pathways across eight brain regions revealed that BA9 was the most affected. Phenylalanine and phosphorylcholine were mainly downregulated, suggesting altered neurotransmitter synthesis. N-acetylaspartate and GABA were upregulated in most regions, suggesting higher inhibitory activity in neural circuits. Other region-common metabolic pathways indicated impaired mitochondrial function and energy metabolism, while region-unique pathways indicated oxidative stress and altered immune responses. Importantly, AD caused metabolic changes in brain regions with less well-documented pathological alterations that suggest degenerative progression. The findings provide a new understanding of the biochemical mechanisms of AD and guide biomarker discovery for personalized risk prediction and diagnosis.

Beyond the amyloid hypothesis: how current research implicates autoimmunity in Alzheimer's disease pathogenesis

The amyloid hypothesis has so far been at the forefront of explaining the pathogenesis of Alzheimer's Disease (AD), a progressive neurodegenerative disorder that leads to cognitive decline and eventual death. Recent evidence, however, points to additional factors that contribute to the pathogenesis of this disease. These include the neurovascular hypothesis, the mitochondrial cascade hypothesis, the inflammatory hypothesis, the prion hypothesis, the mutational accumulation hypothesis, and the autoimmunity hypothesis. The purpose of this review was to briefly discuss the factors that are associated with autoimmunity in humans, including sex, the gut and lung microbiomes, age, genetics, and environmental factors. Subsequently, it was to examine the rise of autoimmune phenomena in AD, which can be instigated by a blood-brain barrier breakdown, pathogen infections, and dysfunction of the glymphatic system. Lastly, it was to discuss the various ways by which immune system dysregulation leads to AD, immunomodulating therapies, and future directions in the field of autoimmunity and neurodegeneration. A comprehensive account of the recent research done in the field was extracted from PubMed on 31 January 2022, with the keywords "Alzheimer's disease" and "autoantibodies" for the first search input, and "Alzheimer's disease" with "IgG" for the second. From the first search, 19 papers were selected, because they contained recent research on the autoantibodies found in the biofluids of patients with AD. From the second search, four papers were selected. The analysis of the literature has led to support the autoimmune hypothesis in AD. Autoantibodies were found in biofluids (serum/plasma, cerebrospinal fluid) of patients with AD with multiple methods, including ELISA, Mass Spectrometry, and microarray analysis. Through continuous research, the understanding of the synergistic effects of the various components that lead to AD will pave the way for better therapeutic methods and a deeper understanding of the disease.

Immune Regulatory Functions of Macrophages and Microglia in Central Nervous System Diseases

Macrophages can be characterized as a very multifunctional cell type with a spectrum of phenotypes and functions being observed spatially and temporally in various disease states. Ample studies have now demonstrated a possible causal link between macrophage activation and the development of autoimmune disorders. How these cells may be contributing to the adaptive immune response and potentially perpetuating the progression of neurodegenerative diseases and neural injuries is not fully understood. Within this review, we hope to illustrate the role that macrophages and microglia play as initiators of adaptive immune response in various CNS diseases by offering evidence of: (1) the types of immune responses and the processes of antigen presentation in each disease, (2) receptors involved in macrophage/microglial phagocytosis of disease-related cell debris or molecules, and, finally, (3) the implications of macrophages/microglia on the pathogenesis of the diseases.

Exercise suppresses neuroinflammation for alleviating Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease, with the characteristics of neurofibrillary tangle (NFT) and senile plaque (SP) formation. Although great progresses have been made in clinical trials based on relevant hypotheses, these studies are also accompanied by the emergence of toxic and side effects, and it is an urgent task to explore the underlying mechanisms for the benefits to prevent and treat AD. Herein, based on animal experiments and a few clinical trials, neuroinflammation in AD is characterized by long-term activation of pro-inflammatory microglia and the NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) inflammasomes. Damaged signals from the periphery and within the brain continuously activate microglia, thus resulting in a constant source of inflammatory responses. The long-term chronic inflammatory response also exacerbates endoplasmic reticulum oxidative stress in microglia, which triggers microglia-dependent immune responses, ultimately leading to the occurrence and deterioration of AD. In this review, we systematically summarized and sorted out that exercise ameliorates AD by directly and indirectly regulating immune response of the central nervous system and promoting hippocampal neurogenesis to provide a new direction for exploring the neuroinflammation activity in AD.

Bioinformatics investigation on blood-based gene expressions of Alzheimer's disease revealed ORAI2 gene biomarker susceptibility: An explainable artificial intelligence-based approach

The progressive, chronic nature of Alzheimer's disease (AD), a form of dementia, defaces the adulthood of elderly individuals. The pathogenesis of the condition is primarily unascertained, turning the treatment efficacy more arduous. Therefore, understanding the genetic etiology of AD is essential to identifying targeted therapeutics. This study aimed to use machine-learning techniques of expressed genes in patients with AD to identify potential biomarkers that can be used for future therapy. The dataset is accessed from the Gene Expression Omnibus (GEO) database (Accession Number: GSE36980). The subgroups (AD blood samples from frontal, hippocampal, and temporal regions) are individually investigated against non-AD models. Prioritized gene cluster analyses are conducted with the STRING database. The candidate gene biomarkers were trained with various supervised machine-learning (ML) classification algorithms. The interpretation of the model prediction is perpetrated with explainable artificial intelligence (AI) techniques. This experiment revealed 34, 60, and 28 genes as target biomarkers of AD mapped from the frontal, hippocampal, and temporal regions. It is identified ORAI2 as a shared biomarker in all three areas strongly associated with AD's progression. The pathway analysis showed that STIM1 and TRPC3 are strongly associated with ORAI2. We found three hub genes, TPI1, STIM1, and TRPC3, in the network of the ORAI2 gene that might be involved in the molecular pathogenesis of AD. Naive Bayes classified the samples of different groups by fivefold cross-validation with 100% accuracy. AI and ML are promising tools in identifying disease-associated genes that will advance the field of targeted therapeutics against genetic diseases.

Investigating neurological symptoms of infectious diseases like COVID-19 leading to a deeper understanding of neurodegenerative disorders such as Parkinson's disease

Apart from common respiratory symptoms, neurological symptoms are prevalent among patients with COVID-19. Research has shown that infection with SARS-CoV-2 accelerated alpha-synuclein aggregation, induced Lewy-body-like pathology, caused dopaminergic neuron senescence, and worsened symptoms in patients with Parkinson's disease (PD). In addition, SARS-CoV-2 infection can induce neuroinflammation and facilitate subsequent neurodegeneration in long COVID, and increase individual vulnerability to PD or parkinsonism. These findings suggest that a post-COVID-19 parkinsonism might follow the COVID-19 pandemic. In order to prevent a possible post-COVID-19 parkinsonism, this paper reviewed neurological symptoms and related findings of COVID-19 and related infectious diseases (influenza and prion disease) and neurodegenerative disorders (Alzheimer's disease, PD and amyotrophic lateral sclerosis), and discussed potential mechanisms underlying the neurological symptoms and the relationship between the infectious diseases and the neurodegenerative disorders, as well as the therapeutic and preventive implications in the neurodegenerative disorders. Infections with a relay of microbes (SARS-CoV-2, influenza A viruses, gut bacteria, etc.) and prion-like alpha-synuclein proteins over time may synergize to induce PD. Therefore, a systematic approach that targets these pathogens and the pathogen-induced neuroinflammation and neurodegeneration may provide cures for neurodegenerative disorders. Further, antiviral/antimicrobial drugs, vaccines, immunotherapies and new therapies (e.g., stem cell therapy) need to work together to treat, manage or prevent these disorders. As medical science and technology advances, it is anticipated that better vaccines for SARS-CoV-2 variants, new antiviral/antimicrobial drugs, effective immunotherapies (alpha-synuclein antibodies, vaccines for PD or parkinsonism, etc.), as well as new therapies will be developed and made available in the near future, which will help prevent a possible post-COVID-19 parkinsonism in the 21st century.

Alzheimer’s Disease and Inflammaging

Alzheimer’s disease is one of the most common age-related neurodegenerative disorders. The main theory of Alzheimer’s disease progress is the amyloid-β cascade hypothesis. However, the initial mechanisms of insoluble forms of amyloid-β formation and hyperphosphorylated tau protein in neurons remain unclear. One of the factors, which might play a key role in senile plaques and tau fibrils generation due to Alzheimer’s disease, is inflammaging, i.e., systemic chronic low-grade age-related inflammation. The activation of the proinflammatory cell phenotype is observed during aging, which might be one of the pivotal mechanisms for the development of chronic inflammatory diseases, e.g., atherosclerosis, metabolic syndrome, type 2 diabetes mellitus, and Alzheimer’s disease. This review discusses the role of the inflammatory processes in developing neurodegeneration, activated during physiological aging and due to various diseases such as atherosclerosis, obesity, type 2 diabetes mellitus, and depressive disorders.

Integrated analysis and identification of hub genes as novel biomarkers for Alzheimer’s disease

Alzheimer’s disease (AD) is an intractable and progressive neurodegenerative disorder that can lead to severe cognitive decline, impaired speech, short-term memory loss, and finally an inability to function in daily life. For patients, their families, and even all of society, AD can impart great emotional pressure and economic costs. Therefore, this study aimed to investigate potential diagnostic biomarkers of AD. Using the Gene Expression Omnibus (GEO) database, the expression profiles of genes were extracted from the GSE5281, GSE28146, and GSE48350 microarray datasets. Then, immune-related genes were identified by the intersections of differentially expressed genes (DEGs). Functional enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA), were performed. Subsequently, random forest models and least absolute shrinkage and selection operator regression were used to further screen hub genes, which were then validated using receiver operating characteristic (ROC) curve analysis. Finally, 153 total immune-related DEGs were identified in relation to AD. DO analysis of these immune-related DEGs showed that they were enriched in “lung disease,” “reproductive system disease,” and “atherosclerosis.” Single GSEA of hub genes showed that they were particularly enriched in “oxidative phosphorylation.” ROC analysis of AGAP3 yielded an area under the ROC curve of 0.878 for GSE5281, 0.727 for GSE28146, and 0.635 for GSE48350. Moreover, immune infiltration analysis demonstrated that AGAP3 was related to follicular helper T cells, naïve CD4 T cells, naïve B cells, memory B cells, macrophages M0, macrophages M1, macrophages M2, resting natural killer (NK) cells, activated NK cells, monocytes, neutrophils, eosinophils, and activated mast cells. These results indicate that identifying immune-related DEGs might enhance the current understanding of the development and prognosis of AD. Furthermore, AGAP3 not only plays a vital role in AD progression and diagnosis but could also serve as a valuable target for further research on AD.

Current Advancement of Immunomodulatory Drugs as Potential Pharmacotherapies for Autoimmunity Based Neurological Diseases

Dramatic advancement has been made in recent decades to understand the basis of autoimmunity-mediated neurological diseases. These diseases create a strong influence on the central nervous system (CNS) and the peripheral nervous system (PNS), leading to various clinical manifestations and numerous symptoms. Multiple sclerosis (MS) is the most prevalent autoimmune neurological disease while NMO spectrum disorder (NMOSD) is less common. Furthermore, evidence supports the presence of autoimmune mechanisms contributing to the pathogenesis of amyotrophic lateral sclerosis (ALS), which is a neurodegenerative disorder characterized by the progressive death of motor neurons. Additionally, autoimmunity is believed to be involved in the basis of Alzheimer’s and Parkinson’s diseases. In recent years, the prevalence of autoimmune-based neurological disorders has been elevated and current findings strongly suggest the role of pharmacotherapies in controlling the progression of autoimmune diseases. Therefore, this review focused on the current advancement of immunomodulatory drugs as novel approaches in the management of autoimmune neurological diseases and their future outlook.

Autoimmune Disease Classification Based on PubMed Text Mining

Autoimmune diseases (AIDs) are often co-associated, and about 25% of patients with one AID tend to develop other comorbid AIDs. Here, we employ the power of datamining to predict the comorbidity of AIDs based on their normalized co-citation in PubMed. First, we validate our technique in a test dataset using earlier-reported comorbidities of seven knowns AIDs. Notably, the prediction correlates well with comorbidity (R = 0.91) and validates our methodology. Then, we predict the association of 100 AIDs and classify them using principal component analysis. Our results are helpful in classifying AIDs into one of the following systems: (1) gastrointestinal, (2) neuronal, (3) eye, (4) cutaneous, (5) musculoskeletal, (6) kidneys and lungs, (7) cardiovascular, (8) hematopoietic, (9) endocrine, and (10) multiple. Our classification agrees with experimentally based taxonomy and ranks AID according to affected systems and gender. Some AIDs are unclassified and do not associate well with other AIDs. Interestingly, Alzheimer’s disease correlates well with other AIDs such as multiple sclerosis. Finally, our results generate a network classification of autoimmune diseases based on PubMed text mining and help map this medical universe. Our results are expected to assist healthcare workers in diagnosing comorbidity in patients with an autoimmune disease, and to help researchers in identifying common genetic, environmental, and autoimmune mechanisms.

Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.

A combination of multiple autoantibodies is associated with the risk of Alzheimer’s disease and cognitive impairment

Autoantibodies are self-antigen reactive antibodies that play diverse roles in the normal immune system, tissue homeostasis, and autoimmune and neurodegenerative diseases. Anti-neuronal autoantibodies have been detected in neurodegenerative disease serum, with unclear significance. To identify diagnostic biomarkers of Alzheimer’s disease (AD), we analyzed serum autoantibody profiles of the HuProt proteome microarray using the discovery set of cognitively normal control (NC, n = 5) and AD (n = 5) subjects. Approximately 1.5-fold higher numbers of autoantibodies were detected in the AD group (98.0 ± 39.9/person) than the NC group (66.0 ± 39.6/person). Of the autoantigen candidates detected in the HuProt microarray, five autoantigens were finally selected for the ELISA-based validation experiment using the validation set including age- and gender-matched normal (NC, n = 44), mild cognitive impairment (MCI, n = 44) and AD (n = 44) subjects. The serum levels of four autoantibodies including anti-ATCAY, HIST1H3F, NME7 and PAIP2 IgG were significantly different among NC, MCI and/or AD groups. Specifically, the anti-ATCAY autoantibody level was significantly higher in the AD (p = 0.003) and MCI (p = 0.015) groups compared to the NC group. The anti-ATCAY autoantibody level was also significantly correlated with neuropsychological scores of MMSE (rs = − 0.229, p = 0.012), K-MoCA (rs = − 0.270, p = 0.003), and CDR scores (rs = 0.218, p = 0.016). In addition, a single or combined occurrence frequency of anti-ATCAY and anti-PAIP2 autoantibodies was significantly associated with the risk of MCI and AD. This study indicates that anti-ATCAY and anti-PAIP2 autoantibodies could be a potential diagnostic biomarker of AD.

Multiomics Profiling of Alzheimer's Disease Serum for the Identification of Autoantibody Biomarkers

New biomarkers of Alzheimer's disease (AD) with a diagnostic value in preclinical and prodromal stages are urgently needed. AD-related serum autoantibodies are potential candidate biomarkers. Here, we aimed at identifying AD-related serum autoantibodies using protein microarrays and mass spectrometry-based methods. To this end, an untargeted complementary screening using high-density (42,100 antigens) and low-density (384 antigens) planar protein-epitope signature tag (PrEST) arrays and an immunoprecipitation protocol coupled to mass spectrometry analysis were used for serum autoantibody profiling. From the untargeted screening phase, 377 antigens corresponding to 338 proteins were selected for validation. Out of them, IVD, CYFIP1, and ADD2 seroreactivity was validated using 128 sera from AD patients and controls by PrEST-suspension bead arrays, and ELISA or luminescence Halotag-based bead immunoassay using full-length recombinant proteins. Importantly, IVD, CYFIP1, and ADD2 showed in combination a noticeable AD diagnostic ability. Moreover, IVD protein abundance in the prefrontal cortex was significantly two-fold higher in AD patients than in controls by western blot and immunohistochemistry, whereas CYFIP1 and ADD2 were significantly down-regulated in AD patients. The panel of AD-related autoantigens identified by a comprehensive multiomics approach may provide new insights of the disease and should help in the blood-based diagnosis of Alzheimer's disease. Mass spectrometry raw data are available in the ProteomeXchange database with the access number PXD028392.

The role of the immune system in Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder where the accumulation of amyloid plaques and the formation of tau tangles are the prominent pathological hallmarks. Increasing preclinical and clinical studies have revealed that different components of the immune system may act as important contributors to AD etiology and pathogenesis. The recognition of misfolded Aβ and tau by immune cells can trigger a series of complex immune responses in AD, and then lead to neuroinflammation and neurodegeneration. In parallel, genome-wide association studies have also identified several immune related loci associated with increased - risk of AD by interfering with the function of immune cells. Other immune related factors, such as impaired immunometabolism, defective meningeal lymphatic vessels and autoimmunity might also be involved in the pathogenesis of AD. Here, we review the data showing the alterations of immune cells in the AD trajectory and seek to demonstrate the crosstalk between the immune cell dysfunction and AD pathology. We then discuss the most relevant research findings in regards to the influences of gene susceptibility of immune cells for AD. We also consider impaired meningeal lymphatics, immunometabolism and autoimmune mechanisms in AD. In addition, immune related biomarkers and immunotherapies for AD are also mentioned in order to offer novel insights for future research.