Amyloid-Beta Aggregation and Plaque Formation

Amyloid-Beta Aggregation and Plaque Formation in Alzheimer's Disease

This article is part of the NeuroWiki Alzheimer's Disease Knowledge Base

Overview

Introduction to Amyloid-Beta

Amyloid-beta (Aβ) is a 36-43 amino acid peptide that plays a central role in the pathogenesis of Alzheimer's disease (AD). This peptide is produced normally throughout life through the proteolytic cleavage of the amyloid precursor protein (APP), a type I transmembrane protein expressed highly in neurons and other cell types. While Aβ is generated throughout life under physiological conditions, the aggregation and accumulation of this peptide into toxic species is considered a hallmark of Alzheimer's disease neuropathology.

The amyloid hypothesis posits that the accumulation of Aβ in the brain is the primary driver of Alzheimer's disease pathogenesis, leading to downstream events including tau hyperphosphorylation, neurofibrillary tangle formation, synaptic loss, and ultimately neuronal death. Understanding the production, aggregation, and toxicity of Aβ is therefore fundamental to understanding AD and developing effective therapeutic interventions.

Amyloid Precursor Protein (APP) Processing

APP is a single-pass transmembrane glycoprotein that exists in three major isoforms in humans: APP695, APP751, and APP770. Neurons primarily express the shorter APP695 isoform, which lacks the Kunitz-type protease inhibitor domain present in the larger isoforms.

APP undergoes proteolytic processing through two mutually exclusive pathways:

Amyloid-Beta Aggregation and Plaque Formation in Alzheimer's Disease

This article is part of the NeuroWiki Alzheimer's Disease Knowledge Base

Overview

Introduction to Amyloid-Beta

Amyloid-beta (Aβ) is a 36-43 amino acid peptide that plays a central role in the pathogenesis of Alzheimer's disease (AD). This peptide is produced normally throughout life through the proteolytic cleavage of the amyloid precursor protein (APP), a type I transmembrane protein expressed highly in neurons and other cell types. While Aβ is generated throughout life under physiological conditions, the aggregation and accumulation of this peptide into toxic species is considered a hallmark of Alzheimer's disease neuropathology.

The amyloid hypothesis posits that the accumulation of Aβ in the brain is the primary driver of Alzheimer's disease pathogenesis, leading to downstream events including tau hyperphosphorylation, neurofibrillary tangle formation, synaptic loss, and ultimately neuronal death. Understanding the production, aggregation, and toxicity of Aβ is therefore fundamental to understanding AD and developing effective therapeutic interventions.

Amyloid Precursor Protein (APP) Processing

APP is a single-pass transmembrane glycoprotein that exists in three major isoforms in humans: APP695, APP751, and APP770. Neurons primarily express the shorter APP695 isoform, which lacks the Kunitz-type protease inhibitor domain present in the larger isoforms.

APP undergoes proteolytic processing through two mutually exclusive pathways:

Non-amyloidogenic pathway: In this predominant pathway in healthy cells, APP is first cleaved by α-secretase within the Aβ domain, preventing Aβ formation. The principal α-secretases are members of the ADAM (a disintegrin and metalloprotease) family, particularly ADAM10 and ADAM17. This cleavage releases a large soluble ectodomain (sAPPα) and leaves a membrane-bound C-terminal fragment (C83). The C83 fragment can then be cleaved by γ-secretase to release a non-amyloidogenic p3 peptide.

Amyloidogenic pathway: In this pathway, APP is first cleaved by β-site APP cleaving enzyme 1 (BACE1), also known as memapsin 2, at the N-terminus of the Aβ domain. BACE1 is an aspartyl protease with optimal activity at acidic pH, and its expression is enriched in neurons. Cleavage by BACE1 releases a soluble ectodomain (sAPPβ) and generates a C-terminal fragment (C99). The C99 fragment then undergoes cleavage by the γ-secretase complex, which consists of four core components: presenilin (PSEN1 or PSEN2), nicastrin, APH-1, and PEN-2. This γ-secretase cleavage occurs at multiple sites within the transmembrane domain, producing Aβ peptides of varying lengths, primarily Aβ40 and Aβ42.

γ-Secretase and the Production of Different Aβ Species

The γ-secretase complex cleaves C99 at multiple sites, resulting in Aβ peptides of varying lengths. The major species produced are:

• Aβ1-40 (Aβ40): The most abundant species (~80-90% of total Aβ)
• Aβ1-42 (Aβ42): Less abundant (~5-10%) but more hydrophobic and prone to aggregation
• Aβ1-43 (Aβ43): Minor species with even greater aggregation propensity

The Amyloid Cascade Hypothesis

Historical Development

The amyloid cascade hypothesis was first articulated by John Hardy and David Allsop in 1991 and subsequently refined in a seminal paper by Hardy and Selkoe in 2002. The hypothesis proposes that the accumulation and aggregation of Aβ in the brain is the primary cause of Alzheimer's disease, initiating a pathogenic cascade that leads to neurofibrillary tangle formation, synaptic loss, neuronal death, and dementia.

The foundation for this hypothesis was laid by several critical discoveries:

Current Status and Modifications

While the amyloid cascade hypothesis has dominated AD research for over three decades, it has undergone significant refinement:

• Soluble oligomers may be more toxic than insoluble plaques
• Aβ accumulation may be necessary but not sufficient for neurodegeneration
• The relationship between Aβ and tau pathology may be bidirectional

Aggregation Kinetics

Nucleation-Dependent Polymerization

The aggregation of Aβ follows nucleation-dependent polymerization kinetics, characterized by a lag phase followed by rapid growth:

1. Primary Nucleation: Spontaneous formation of a stable oligomeric nucleus from monomers.

2. Elongation: Monomers add to growing chain ends, leading to fibril growth.

3. Secondary Nucleation: Formation of new nuclei on existing fibril surfaces.

4. Fragmentation: Breakage of fibrils increases growth sites.

Structural Intermediates

• Monomers: Soluble, non-aggregated Aβ
• Oligomers: Small soluble aggregates (dimers, trimers, Aβ*56)
• Protofibrils: Curved, worm-like intermediates
• Mature Fibrils: Linear structures with cross-β sheet architecture
• Amyloid Plaques: Large insoluble deposits

Soluble Oligomers vs. Insoluble Plaques

Evidence for Oligomer Toxicity

Clinical correlation studies demonstrate that soluble Aβ levels correlate better with cognitive impairment than plaque burden[@mclean1999]. Animal model studies show that injection of Aβ oligomers impairs synaptic plasticity and memory[@lambert1998].

Evidence for Plaque Toxicity

Plaques are not merely inert end-products:

• Surrounded by dystrophic neurites and activated microglia
• Vascular amyloid (CAA) impairs cerebral blood flow
• Serve as reservoirs for toxic oligomers via secondary nucleation

Current Consensus

Both oligomers and plaques contribute to toxicity through different mechanisms. Soluble oligomers are likely proximal toxins, while insoluble plaques create local inflammatory microenvironments.

Structural Biology of Aβ Isoforms

Major Aβ Isoforms

| Isoform | Length | Abundance | Aggregation Propensity |
|---------|--------|-----------|----------------------|
| Aβ40 | 40 aa | 80-90% | Lower |
| Aβ42 | 42 aa | 5-10% | Higher |
| Aβ43 | 43 aa | Minor | Highest |

Aggregation-Prone Regions

• Hydrophobic cluster (17-21, LVFFA): Critical for β-sheet formation
• C-terminal hydrophobic domain: Determines aggregation propensity

Cellular Mechanisms of Aβ Toxicity

Synaptic Toxicity

Aβ oligomers cause:

• Direct binding to synaptic receptors (PrPᶜ, NMDA receptors)
• Impaired spine density and morphology
• Reduced long-term potentiation (LTP)[@walsh2002]

Oxidative Stress

Aβ induces oxidative stress through:

• ROS production via metal interactions
• Mitochondrial dysfunction
• Lipid and protein peroxidation[@butterfield2002]

Neuroinflammation

Chronic neuroinflammation involves:

• Microglial activation surrounding plaques
• Cytokine release (IL-1β, TNF-α, IL-6)
• NLRP3 inflammasome activation[@heneka2015]

Mitochondrial Dysfunction

Aβ causes:

• Reduced ATP production
• Impaired mitophagy
• Cytochrome c release and apoptosis[@reddy2008]

Neuroimaging Biomarkers

PET Amyloid Imaging

| Radioligand | Developer | Status |
|-------------|-----------|--------|
| Pittsburgh compound B (PiB) | University of Pittsburgh | Research use |
| Florbetapir (Amyvid) | Eli Lilly | FDA approved |
| Florbetaben (Neuraceq) | Piramal Imaging | FDA approved |
| Flutemetamol (Vizamyl) | GE Healthcare | FDA approved |

Therapeutic Approaches

Immunotherapy

Passive Immunization:

| Antibody | Developer | Mechanism | Status |
|----------|-----------|-----------|--------|
| Aducanumab | Biogen/Eisai | Binds aggregated Aβ | FDA approved (accelerated) |
| Lecanemab | Eisai/Biogen | Binds protofibrils | FDA approved |
| Donanemab | Eli Lilly | Binds plaque Aβ | FDA approved |

Lecanemab CLARITY-AD Results[@van2023]:

• 27% slowing of cognitive decline on CDR-SB
• 55.43 centiloid reduction in brain amyloid at 18 months
• ARIA-E in 21.5% of treated patients

• 35% slowing of clinical decline on iADRS
• 47% achieved amyloid negativity by 12 months

Secretase Inhibitors

BACE Inhibitors (all failed in Phase III):

• Verubecestat: No cognitive benefit, liver toxicity
• Atabecestat: Terminated for liver safety
• Lanabecestat: Insufficient efficacy

Aggregation Inhibitors

• scyllo-Inositol: Phase II showed acceptable safety but failed primary endpoint
• Clioquinol: Metal chelator showed mixed results

1. Animal Models of Amyloid Pathology

Animal models have been instrumental in advancing our understanding of amyloid-beta (Aβ) aggregation and its role in Alzheimer's disease (AD) pathogenesis. These models recapitulate key features of amyloid pathology and have provided valuable insights into disease mechanisms and therapeutic interventions.

APP/PS1 Transgenic Mice

The APP/PS1 mouse model represents one of the most widely used preclinical models for studying Aβ pathology. This double-transgenic model expresses a chimeric mouse/human amyloid precursor protein (APP) carrying the Swedish familial AD mutation (K670N/M671L) combined with a mutant human presenilin-1 (PS1) gene with the exon 9 deletion (ΔE9) mutation. The Swedish APP mutation increases Aβ production by enhancing β-secretase cleavage, while the PS1 mutation increases the Aβ42/Aβ40 ratio by altering γ-secretase activity.

APP/PS1 mice develop age-dependent Aβ deposition beginning at approximately 6 months of age, with progressive accumulation of amyloid plaques in the hippocampus and cortex. These mice exhibit cognitive deficits that correlate with amyloid burden, making them suitable for testing anti-amyloid interventions. Studies using this model have demonstrated that Aβ vaccination can reduce plaque load and improve cognitive performance, providing foundational evidence for immunotherapeutic approaches[@jankowsky2004].

3xTg-AD Mice

The 3xTg-AD model was developed by introducing three mutant genes: APP Swedish, PS1 M146V, and tau P301L. This triple-transgenic model is unique in that it develops both Aβ plaques and neurofibrillary tangles (NFTs), mimicking the two hallmark pathological features of AD. The mice exhibit an age-dependent progression of pathology, with Aβ deposition preceding tau pathology, reflecting the hypothesized temporal sequence in human disease.

Importantly, 3xTg-AD mice display synaptic dysfunction and behavioral deficits before overt neuronal loss, making this model particularly valuable for studying early disease mechanisms. The presence of both pathological hallmarks allows researchers to investigate interactions between Aβ and tau, as well as test interventions targeting both pathways simultaneously[@oddo2003].

5xFAD Mice

The 5xFAD model carries five familial AD mutations: APP Swedish (K670N/M671L), Florida (I716V), and London (V717I) mutations, along with two PS1 mutations (M146L and L286V). This model exhibits rapid and robust Aβ42 production and deposition, with plaque formation beginning as early as 1.5-2 months of age. The aggressive phenotype makes 5xFAD mice particularly useful for studying early Aβ aggregation processes and testing interventions that require rapid disease manifestation.

5xFAD mice develop pronounced amyloid pathology in the hippocampus, cortex, and subiculum, accompanied by gliosis and neuronal loss. The model has been extensively used to study microglial responses to amyloid deposition and the role of inflammation in AD progression. Notably, 5xFAD mice have contributed to understanding how Aβ affects neurogenesis and synaptic function[@oakley2006].

Limitations of Current Models

Despite their utility, animal models of AD have significant limitations that must be considered when interpreting findings. First, most models are based on familial AD mutations, which account for less than 5% of human AD cases. The majority of AD patients have sporadic late-onset disease with different underlying mechanisms.

Second, rodent models do not naturally develop Alzheimer's disease, and the artificial expression of human transgenes does not fully replicate the complex biology of human aging. The brains of mice and humans differ substantially in size, structure, and complexity of neural networks.

Third, the amyloid cascade hypothesis, while influential, may not capture the full complexity of AD pathogenesis. Recent clinical trial failures have prompted reconsideration of whether Aβ reduction alone is sufficient to halt disease progression in humans.

Fourth, species differences in Aβ sequence and metabolism may affect aggregation kinetics and response to interventions. Mouse Aβ differs from human Aβ at three amino acid positions, potentially influencing its aggregation properties[@drummond2017].

2. Biomarkers for Aβ Metabolism

The development of biomarkers for Aβ metabolism has revolutionized AD diagnosis and monitoring, enabling detection of pathology before clinical symptoms emerge.

Cerebrospinal Fluid Aβ42

CSF Aβ42 levels are consistently decreased in AD patients compared to healthy controls. This reduction reflects the preferential deposition of Aβ42 into amyloid plaques, effectively sequestering the peptide from the soluble CSF compartment. Meta-analyses have demonstrated that CSF Aβ42 has a sensitivity of approximately 80-90% and specificity of 80-90% for distinguishing AD from healthy controls.

The CSF Aβ42/Aβ40 ratio has emerged as an even more reliable marker, as it controls for individual differences in overall Aβ production. The ratio shows less variability and better diagnostic accuracy than Aβ42 alone, reducing the influence of factors that globally affect Aβ levels[@blennow2010].

Cerebrospinal Fluid Aβ40

Unlike Aβ42, CSF Aβ40 levels remain relatively unchanged in AD patients. This observation supports the concept that AD pathology specifically involves Aβ42 aggregation, while Aβ40 serves as a marker of general APP metabolism. The Aβ42/Aβ40 ratio therefore provides a more specific indicator of amyloidogenic processing than Aβ42 measurements alone.

CSF Tau/Aβ42 Ratio

The combination of Aβ and tau biomarkers has been incorporated into the AT(N) classification system for AD research and diagnosis. Elevated CSF tau and phosphorylated tau (p-tau) combined with decreased Aβ42 defines "A+T+N+" biological AD, enabling detection of AD pathology independent of clinical syndrome. The tau/Aβ42 ratio improves diagnostic specificity and has been incorporated into clinical trial enrollment criteria[@jack2018].

Blood-Based Biomarkers

Recent advances in ultrasensitive assay technologies have enabled detection of Aβ and tau species in blood, representing a major breakthrough in AD biomarker development.

Plasma p-tau181: Phosphorylated tau at threonine 181 (p-tau181) reliably detects Aβ pathology, with elevated levels observed in Aβ-positive individuals across the disease spectrum. Plasma p-tau181 shows excellent correlation with PET-measured amyloid burden and can identify preclinical AD with high accuracy[@karikari2020].

Plasma p-tau217: Phosphorylated tau at threonine 217 (p-tau217) has emerged as an exceptionally accurate blood biomarker for AD. Studies have demonstrated that plasma p-tau217 can distinguish AD from other neurodegenerative diseases with high specificity and shows strong correlation with cerebral amyloid burden. The biomarker appears to be elevated even in preclinical stages and may track disease progression[@palmqvist2019].

Blood Aβ measures: Plasma Aβ42/Aβ40 ratio measured by mass spectrometry or immunoassays can detect cerebral amyloid with reasonable accuracy. While not as accurate as PET or CSF biomarkers, plasma Aβ measures offer a minimally invasive screening approach for clinical trials and population screening.

3. Aβ Metabolism and Clearance

Understanding the mechanisms governing Aβ production, aggregation, and clearance is essential for developing therapeutic strategies.

Proteolytic Degradation

Several proteases contribute to Aβ catabolism, including neprilysin, insulin-degrading enzyme (IDE), and angiotensin-converting enzyme (ACE).

Neprilysin: This zinc-dependent metalloprotease is the major Aβ-degrading enzyme in the brain. Neprilysin expression decreases with aging, and this decline correlates with increased amyloid accumulation. Gene therapy approaches to increase neprilysin have shown promise in animal models, reducing Aβ burden and improving cognitive function. However, delivery challenges and the broad substrate specificity of neprilysin have limited translation to human studies[@iwata2001].

Insulin-Degrading Enzyme (IDE): IDE degrades both Aβ and insulin, suggesting potential interactions between metabolic and amyloid pathways. IDE polymorphisms have been linked to AD risk, and mice lacking IDE develop Aβ accumulation. The enzyme's role in cellular Aβ clearance makes it an attractive therapeutic target, though its large size and complex structure have complicated inhibitor development.

Perivascular Drainage

The glymphatic system and perivascular drainage pathways provide major routes for Aβ clearance from the brain. Perivascular drainage along basement membranes of cerebral blood vessels is proposed as a key mechanism for Aβ removal, particularly during sleep when convective flow increases.

Arterial stiffening and age-related changes in vascular integrity impair perivascular drainage, contributing to Aβ accumulation. This relationship between vascular health and Aβ clearance may explain the link between cardiovascular risk factors and AD risk.

Microglial Phagocytosis

Microglia, the resident immune cells of the brain, actively phagocytose and degrade Aβ. The microglia-mediated clearance involves receptors including CD14, TLRs, and complement receptors. However, chronic microglial activation in AD can lead to a dysfunctional state characterized by impaired phagocytosis and excessive inflammatory cytokine production.

Recent single-cell studies have identified disease-associated microglia (DAM) that appear to have enhanced Aβ clearance capabilities. Understanding how to promote and sustain this protective microglial phenotype represents an active area of investigation[@kerenshaul2017].

Transport Across the Blood-Brain Barrier

The blood-brain barrier (BBB) mediates bidirectional transport of Aβ between brain and blood. LRP1 (low-density lipoprotein receptor-related protein 1) on the brain side mediates efflux of free Aβ, while RAGE (receptor for advanced glycation end products) on the vascular side facilitates Aβ influx into the brain.

Soluble Aβ can be cleared via BBB transport, and enhancing this pathway represents a potential therapeutic strategy. Age-related decline in BBB function and altered expression of Aβ transporters may contribute to Aβ accumulation in sporadic AD.

4. Aβ in Down Syndrome

Down syndrome (DS), caused by trisomy 21, provides unique insights into Aβ biology due to the presence of the APP gene on chromosome 21.

APP Triplication

Individuals with DS have three copies of chromosome 21 and consequently three copies of the APP gene. This gene dosage effect leads to increased APP expression and elevated Aβ production beginning early in life. The resulting Aβ accumulation can be detected in individuals with DS as young as 8-12 years old.

Early Amyloid Pathology

Neuropathological studies have demonstrated that virtually all individuals with DS develop significant amyloid pathology by age 40, with many showing pathology earlier. The prevalence of AD dementia in DS increases substantially after age 50, with approximately 50-70% developing dementia by age 60.

The DS population therefore represents a naturally occurring model of Aβ accumulation driven by gene dosage, providing opportunities to study Aβ deposition in the absence of familial AD mutations[@lott2001].

Therapeutic Implications

The understanding that APP overproduction drives Aβ accumulation in DS has implications for therapeutic development. Approaches to reduce APP expression or Aβ production may be particularly relevant for DS, and clinical trials in this population are underway.

Additionally, DS provides a unique opportunity to test preventive interventions in individuals with biomarker evidence of amyloid accumulation before symptom onset. The uniform genetic etiology of DS (chromosome 21 trisomy) offers advantages for clinical trial design compared to the heterogeneous sporadic AD population.

5. Genetic Risk Factors

APOE ε4 Effects on Aβ

The apolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor for late-onset AD, increasing risk approximately 3-4 fold in heterozygotes and 12-15 fold in homozygotes compared to ε3/ε3 individuals.

APOE influences Aβ metabolism through multiple mechanisms. It affects Aβ aggregation kinetics, with ε4 promoting faster fibril formation. APOE4 is less efficient at promoting Aβ clearance than other isoforms, leading to reduced proteolytic degradation and impaired perivascular drainage. Additionally, APOE4 is associated with reduced synaptic protection and increased neuroinflammation[@bu2009].

Human PET studies have demonstrated that APOE ε4 carriers have greater amyloid burden than non-carriers at comparable disease stages, suggesting that APOE genotype influences Aβ accumulation rates.

Other Risk Genes

Genome-wide association studies (GWAS) have identified numerous AD risk loci, many of which affect Aβ metabolism.

Clusterin (CLU): Also known as apolipoprotein J, clusterin is a chaperone protein that can bind Aβ and influence its aggregation and clearance. CLU polymorphisms are associated with altered AD risk and may influence Aβ transport across the BBB.

PICALM: The phosphatidylinositol binding clathrin assembly protein (PICALM) is involved in clathrin-mediated endocytosis, a process relevant to APP trafficking and Aβ production. PICALM variants affect AD risk possibly through influences on synaptic function and Aβ clearance[@harold2009].

CR1: Complement receptor 1 (CR1) is expressed on microglia and is involved in immune regulation. CR1 polymorphisms influence AD risk, potentially through effects on complement-mediated Aβ clearance and microglial activation.

Other GWAS-implicated genes affecting Aβ metabolism include ABCA7 (involved in lipid metabolism and Aβ clearance), SORL1 (affecting APP trafficking), and PLCG2 (influencing microglial signaling). The collective influence of these variants, combined with APOE, accounts for a substantial portion of the heritability of late-onset AD[@jansen2019].

References

[@oddo2003]: Oddo S, Caccamo A, Shepherd JD, et al. Triple-transgenic model of Alzheimer's disease with plaques and tangles: intracellular Aβ and synaptic dysfunction. Neuron. 2003;39(3):409-421.

[@oakley2006]: Oakley H, Cole SL, Logan S, et al. Intraneuronal β-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial Alzheimer's disease mutations: potential factors in amyloid plaque formation. J Neurosci. 2006;26(40):10129-10140.

[@drummond2017]: Drummond E, Wisniewski T. Alzheimer's disease: experimental models and reality. Acta Neuropathol. 2017;133(2):155-175.

[@blennow2010]: Blennow K, Hampel H, Weiner M, Zetterberg H. Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol. 2010;6(3):131-144.

[@jack2018]: Jack CR Jr, Bennett DA, Blennow K, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535-562.

[@karikari2020]: Karikari TK, Pascoal TA, Ashton NJ, et al. Blood phosphorylated tau 181 as a biomarker for Alzheimer's disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. Lancet Neurol. 2020;19(5):422-433.

[@palmqvist2019]: Palmqvist S, Janelidze S, Stomrud E, et al. Performance of Fully Automated Plasma Assays as Screening Tests for Alzheimer Disease-Related β-Amyloid Status. JAMA Neurol. 2019;76(9):1060-1069.

[@iwata2001]: Iwata N, Tsubuki S, Takaki Y, et al. Metabolic regulation of brain Aβ by neprilysin. Science. 2001;292(5521):1550-1552.

[@kerenshaul2017]: Keren-Shaul H, Spinrad A, Weiner A, et al. A Unique Microglia Type Associated with Restricting Development of Alzheimer's Disease. Cell. 2017;169(7):1276-1290.e17.

[@lott2001]: Lott IT, Head E. Down syndrome and Alzheimer's disease: A link between development and aging. Ment Retard Dev Disabil Res Rev. 2001;7(3):172-178.

[@bu2009]: Bu G. Apolipoprotein E and its receptors in Alzheimer's disease: pathways, pathogenesis and therapy. Nat Rev Neurosci. 2009;10(5):333-344.

[@harold2009]: Harold D, Abraham R, Hollingworth P, et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease. Nat Genet. 2009;41(10):1088-1093.

[@jansen2019]: Jansen IE, Savage JE, Watanabe K, et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk. Nat Genet. 2019;51(3):404-413.

See Also

• [Alzheimer's Disease](/diseases/alzheimers-disease)
• [Tau Pathology Pathway](/mechanisms/tau-pathology)
• APP Gene
• PSEN1 Gene
• BACE1 Gene
• [Amyloid Precursor Protein](/genes/app)
• Neuroinflammation in AD
• Mitochondrial Dysfunction in AD

Pathway Diagram

The following diagram shows the key molecular relationships involving Amyloid-Beta Aggregation and Plaque Formation discovered through SciDEX knowledge graph analysis: