Show Summary Details

Page of

PRINTED FROM OXFORD HANDBOOKS ONLINE (www.oxfordhandbooks.com). © Oxford University Press, 2022. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a title in Oxford Handbooks Online for personal use (for details see Privacy Policy and Legal Notice).

date: 06 July 2022

Alzheimer’s Disease: Causes, Mechanisms, and Steps Toward Prevention

Abstract and Keywords

Alzheimer’s disease (AD) is the most common form of dementia in the elderly; it is clinically characterized by progressive memory loss and catastrophic cognitive dysfunction. Neuropathologically, the brains of AD patients are characterized by abundant beta-amyloid plaques, neurofibrillary tangles, and neuroinflammation. To date, this fatal disease ranks as the sixth leading cause of death; 5.8 million people in the United States are estimated to have the disease, and the total incidence of AD-related dementia is projected to grow to 16 million by 2050. Currently, there is no cure or any reliable means for pre-symptomatic diagnosis of AD. AD is a genetically heterogenous and multifactorial disease, and a variety of molecular mechanisms have been suggested to underlie its etiology and pathogenesis. A better understanding of pathogenic mechanisms underlying the development of AD pathology and symptoms would accelerate the development of effective therapeutic strategies for preventing and treating AD. Here, we present a comprehensive overview of the pathogenetic and molecular mechanisms underlying AD along with current therapeutic and lifestyles interventions being investigated for the prevention and treatment of this devastating neurological disorder.

Keywords: Alzheimer’s disease, amyloid hypothesis, genetic risk factors, neuroinflammation, infection, therapeutic targets, lifestyle (SHIELD)

Etiology

The two key pathological hallmarks of AD, first described by Alois Alzheimer in 1907, are senile plaques and neurofibrillary tangles (NFTs) in brain regions responsible for higher brain function (i.e., cerebral cortex and limbic system) (Alzheimer, 1987). Senile plaques (amyloid plaques) are extracellular deposits of a small protein called amyloid-β (Aβ), while NFTs are filamentous accumulations of aggregated hyperphosphorylated tau protein (pTau) inside neurons (Tanzi & Bertram, 2005).

Aβ is a 4 kDa protein that exhibits heterogeneity in amino acid sequence and in various biophysical states. This peptide results from proteolytic cleavage of a larger transmembrane amyloid precursor protein (APP), which is synthesized in ribosomes on the endoplasmic reticulum for transport to the Golgi apparatus. APP is a type-I transmembrane glycoprotein consisting of an N-terminal signal peptide, a large ectodomain with N-glycosylation sites, an alternatively spliced Kunitz-type serine protease inhibitor (KPI) domain, an Aβ region (comprised of twenty-eight amino acids of the ectodomain and eleven to fourteen amino acids of the adjacent transmembrane domain), a single membrane-spanning helix, and a short cytoplasmic domain (Hardy, 1997; Price & Sisodia, 1998). APP is metabolized by two competing pathways: α-secretase pathway resulting in non-toxic products (non-amyloidogenic pathway) and serial cleavage by β- and γ-secretase generating Aβ40/42 (amyloidogenic pathway) (Tanzi & Bertram, 2005).

The non-amyloidogenic pathway involves α-secretase which cleaves within the Aβ encoding region, releasing sAPPα, a soluble fragment, into the extracellular space. The metalloprotease type enzyme, α-secretase, is a member of the ADAM (A Disintegrin and Metalloproteases) family and is a nootropic and neuroprotective enzyme critical for neuronal development. Several members of the ADAM family proteases, including ADAM9, ADAM10, and ADAM17, possess α-secretase activity at least under in vitro conditions. Among them, ADAM10 has been shown to be the major α-secretase responsible for ectodomain shedding of APP in mouse brain (Jorissen et al., 2010; Kuhn et al., 2010; Postina et al., 2004). sAPPα appears to have neuroprotective and neurotrophic properties in vitro and in vivo (Mattson et al., 1993; Ring et al., 2007). The cleavage of APP within the Aβ domain precludes the formation of Aβ. The remaining C-terminal fragment of APP, including eighty-three amino acid residues (C83), is subsequently cleaved by γ-secretase, generating p3 (Aβ17-40) and APP intracellular domain (AICD), both of which are released into the extracellular space and cytosol, respectively. AICD can potentially translocate to the nucleus and function as a transcription factor (Cao & Sudhof, 2004).

In the amyloidogenic pathway, β-secretase cleaves N-terminal of Aβ, releasing a soluble fragment, sAPPβ, into the extracellular space, while the remaining C-terminal fragment of ninety-nine amino acid residues (C99) is still membrane bound. β-secretase, also called β-APP-Converting Enzyme (BACE), is a pepsin structured aspartyl protease, found at the Golgi apparatus, endosome, and in the cell membrane. γ-Secretase is a multi-subunit protease complex which consists of presenilin (PSEN) 1 and 2, nicastrin, presenilin enhancer 2 (PEN2), and anterior pharynx-defective phenotype-1 (APH-1). PSEN1 is the catalytic component of γ-secretase responsible for the intramembranous proteolysis of several type I membrane proteins, including APP and Notch-1 (De Strooper, 2003; Sisodia & St. George-Hyslop, 2002). sAPPβ can render proapoptotic and neurodegenerative effects on neuronal cells upon further processing (Nikolaev, McLaughlin, O’Leary, & Tessier-Lavigne, 2009). C99 is further cleaved by γ-secretase at the C-terminal region of the Aβ sequence, generating AICD and Aβ peptides. Approximately 90 percent of secreted Aβ peptides are Aβ40, whereas ~10 percent of secreted Aβ peptides are Aβ42 and Aβ43 (Iwatsubo et al., 1994; Mann et al., 1996; Mann et al., 1997). Aβ peptides are released into the extracellular space where they accumulate and contribute to amyloid plaque formation. Aβ40, which ends at residue 40, is a soluble form of the peptide, and Aβ42 is more prone to aggregation due to its high hydrophobicity, aggregation, and fibrillization potential, as it also initiates formation of pathological oligomers, fibrils, and plaques (Selkoe, 2001).

Tau is an intracellular protein belonging to a family of microtubule-associated proteins, which contributes to microtubule assembly and stabilization. Tau, which is located on chromosome 17, is produced by alternative splicing from a single gene designated as MAPT (microtubule-associated protein tau) in humans. Tau is neurotoxic when it is hyperphosphorylated. Because of the effects of hyperphosphorylated tau species binding poorly to microtubules and altering its stability, other cytoskeletal constituents, intracellular transport, cellular geometry, and/or neuronal viability could be affected. Further, pTau is prone to self-aggregate resulting in the formation of insoluble fibrils. Tau hyperphosphorylation promotes paired helical filaments formations, eventually evolving into NFTs and dystrophic neurites (Iqbal, Liu, Gong, Alonso Adel, & Grundke-Iqbal, 2009). Depending on the site, tau phosphorylation has the ability to cause different effects in its biological function and pathogenic role. Tau phosphorylation, catalyzed by protein kinases, at Ser262, Thr231, and Ser235 inhibits its binding to microtubules by ~35 percent, ~25 percent, and ~10 percent, respectively (Sengupta et al., 1998). An inappropriate activation of tau kinases, such as glycogen synthase kinase-3β (GSK-3β) and cyclin-dependent kinase 5 (cdk-5), has been demonstrated to cause pTau and subsequent NFT formation (Dolan & Johnson, 2010).

Genetic Risk Factors

Age is the most well-established risk factor for AD, followed by family history and gender: two-thirds of AD patients are women. Prevalence of AD is increased with age and doubles with every five-year increase in age. Nearly one-third of those over age 85 have AD. With every ten-year increase in age, result there are decreases in cognitive performance (Barnes et al., 2015). AD is classified as familial and sporadic. Familial, early-onset (< 60 years), autosomal-dominant forms of AD (FAD) represent only a small fraction of all AD cases (< 5 percent of cases) but can progress more rapidly than the sporadic form. FAD can be caused by fully penetrant mutations in the amyloid beta protein precursor (APP on chromosome 21), presenilin 1 (PSEN1 on chromosome 14), and presenilin 2 (PSEN2 on chromosome 1) genes (Tanzi, 2013).

APP was first isolated and mapped to chromosome 21 in 1987 (Goldgaber, Lerman, McBride, Saffiotti, & Gajdusek, 1987; Tanzi et al., 1987). Genetic linkage of FAD to genetic markers on chromosome 21 in the vicinity of APP was concurrently reported (St. George-Hyslop et al., 1987). However, the first pathogenic mutation in APP was not reported until 1990: the sequencing of exons 16 and 17 of APP, which encodes the Aβ portion, revealed a pathogenic mutation in APP resulting in Dutch hereditary cerebral hemorrhage with amyloidosis (Levy et al., 1990). Resequencing the same two APP exons in FAD families revealed the FAD London mutation (V717I) in APP (Goate et al., 1991). In 1995, several FAD mutations in PSEN1 were uncovered (Sherrington et al., 1995). Also, PSEN2 on chromosome 1, which harbors the N141I mutation in Volga-German FAD families, was found (Levy-Lahad et al., 1995). To date, 60 mutations have been reported for APP, 309 for PSEN1, and 53 for PSEN2. Most FAD mutations increase the ratio of Aβ42:Aβ40 (Tanzi & Bertram, 2005). The relative increase in Aβ42 promotes peptides to aggregate into oligomers and, eventually amyloid fibrils (Jarrett, Berger, & Lansbury, 1993). One mutation (Swedish) in APP increases all species of Aβ, which results in amino acid substitution within the Aβ domain and leads to increased Aβ aggregation.

Sporadic, late-onset AD (LOAD), the more common form of the disease, involves onset over the age of 60 owing to multifactorial genetic, environmental, and lifestyle factors (Bertram, Lill, & Tanzi, 2010). Symptoms become apparent in people by their mid-60s. In a large twin study, the contribution of heritability to sporadic AD was estimated to be as high as 80 percent (Gatz et al., 2006). Although over three dozen putative genetic risk factors for AD have been reported in literature, only the apolipoprotein (APOE) gene on chromosome 19 has been consistently found to be associated with sporadic AD in independent studies (Tanzi & Bertram, 2005). APOE is a 34 kDa glycoprotein and is the major serum protein functioning in cholesterol storage, transport, and metabolism (Saunders et al., 1993). The APOE gene has three isoforms: ε2, ε3, and ε4. We carry one from each parent constituting an allele. The ε3 form is the most common form; having the ε2 form could decrease risk of AD. Carrying the ε4 form is associated with increased risk for developing the disease as well as earlier age of disease onset (Fleisher et al., 2013). One ε4 allele may increase the risk of AD two- to threefold, whereas two copies may increase the risk about twelve-fold (Corder et al., 1993; Roses, 1996). The presence of ε4 can influence the brain pathology, particularly by enhancing Aβ burden (Farlow, 1997; Gomez-Isla et al., 1996; Rebeck, Reiter, Strickland, & Hyman, 1993; Schmechel et al., 1993). However, APOE ε4 carriers are not fated to develop AD as some carriers show intact cognitive function (Verghese, Castellano, & Holtzman, 2011).

Genomic-wide association studies (GWAS) have demonstrated other susceptibility genes. The first GWAS in AD was family-based and identified the CD33 and ataxin-1 (ATXN1) genes as AD loci (Bertram et al., 2008). CD33 is discussed later in this chapter (see Pathogenic Mechanism: Neuroinflammation). ATXN1 has been shown to regulate the transcription of the beta-secretase gene (BACE1), the rate-limiting enzyme in the production of Aβ (Suh et al., 2019). Here, we summarize three case-control AD GWAS studies. Figure 1 shows AD GWAS loci that were identified from the three case-control GWAS studies. In 2013, a study conducted by a group of researchers aligned under the auspices of the International Genomics of Alzheimer’s Disease Project (IGAP), entailed the analysis of ~75,000 individuals and identified twenty genome-wide significant AD risk loci including APOE locus (Lambert et al., 2013). Among the twenty loci, eleven were identified as newly associated with LOAD at the time, which are SORL1, SLC24A4, INPP5D, CASS4, ZCWPW1, PTK2B, FERMT2, HLA-DRB5, MEF2C, NME8, and CELF1. These newly associated loci give support to some previously suspected pathways, including APP (SORL1 and CASS4) and tau (CASS4 and FERMT2), in pathology. A number of candidate genes at these loci are involved in pathways that are enriched for association signal in AD GWAS, such as immune response and inflammation (HLA-DRB5DRB1, INPP5D and MEF2C), which also are reflected in the association of AD with CR1 and TREM2, cell migration (PTK2B), and lipid transport and endocytosis (SORL1). These results further suggest that new pathways underlying AD exist, including hippocampal synaptic function (MEF2C and PTK2B), cytoskeletal function and axonal transport (CELF1, NME8 and CASS4), regulation of gene expression and post-translational modification of proteins, and microglial and myeloid cell function (INPP5D).

Jansen et al. (2019) included an overall sample size of ~455,000 individuals and identified nine novel loci, all of which were additionally tested in an independent replication sample of 180,000 individuals (Jansen et al., 2019). This study identified twenty-nine loci, and nine of them were newly identified loci. They are ADAMTS4, HESX1, CLNK, CNTNP2, ADAM10, APH1B, KAT8, ALPK2, and AC074212.3. ADAM10, which encodes the key enzyme that cleaves APP to preclude Aβ generation, has previously been shown to contain rare variants segregating with AD status in families. We showed that two rare highly penetrant ADAM10 mutations, Q170H and R181G, dramatically impair the ability of ADAM10 to cleave APP at the α-secretase site of APP in vitro and in vivo (Kim et al., 2009; Suh et al., 2013). The analyses of Jansen and colleagues confirmed that CD33, which was originally identified in a GWAS (Bertram et al., 2010), is associated with AD risk at genome-wide significance (for CD33, see Pathogenic Mechanism: Neuroinflammation). Using the gene-based P values, Jansen et al. (2019) performed gene-set analysis for curated biological pathway and tissue/single-cell expression. Four gene ontology gene sets that were significantly associated with AD risk are lipid complex (BIN1, GLU, SORL1, and APOE), regulation of APP catabolic process (CLU, PICALM, SORL1, ABCA7, and APOE), high-density lipoprotein particle (CLU and APOE), and protein lipid complex assembly (BIN1, ABCA7, and APOE). Linking gene-based P values to tissue- and cell type-specific gene sets, Jansen et al. (2019) found the brain cell types with the highest expression of AD risk genes were microglia, a cell type that is involved in the brain’s immune system response. Astrocytes ependymal was the next cell type with high expression of AD risk genes.

Since the IGAP-2013 publication, the consortium continued collecting additional independent AD cases and controls culminating in an updated GWAS on approximate 94,000 individuals and identified twenty-five genome-wide significant loci, five of which were reported as novel: ADAM10, IQCK, ACE, ADAMTS1, and WWOX (Kunkle et al., 2019). Since ADAM10 was first published in the GWAS by Jansen et al., it should be counted as “novel” findings in their study (Jansen et al., 2019). Pathway analysis implicates immunity (CR1, CLU, INPP5D, and HLA-DRB1), lipid metabolism (APOE, BIN1, CLU, ABCA7, and SORL1), tau binding proteins (APOE and BIN1), and regulation of APP catabolic process and Aβ formation (APOE, CLU, PICALM, ABCA7, and SORL1), showing that genetic variants affecting APP and Aβ processing are associated not only with FAD but also with LOAD.

Alzheimer’s Disease: Causes, Mechanisms, and Steps Toward Prevention

Figure 1. Significantly AD-associated loci identified in the GWAS analysis of studies by Lambert et al. (2013), Jansen et al. (2019), and Kunkle et al. (2019).

Several non-genetic risk factors have been reported. Smoking increases risk for AD, and nicotine did not protect from AD (Anstey, von Sanden, Salim, & O’Kearney, 2007; Durazzo, Mattsson, Weiner, & Alzheimer’s Disease Neuroimaging, 2014). While light alcohol consumption reduces the incidence of AD, alcohol abuse can cause alcoholic dementia and heavy drinkers have increased risk of AD in later life (Anttila et al., 2004; Panza et al., 2012; Piazza-Gardner, Gaffud, & Barry, 2013). Uncontrolled hypertension has been shown to increase AD risk (Kivipelto et al., 2005; Lattanzi, Luzzi, Provinciali, & Silvestrini, 2014). People with lower education have higher risk of AD than those more educated (Meng & D’Arcy, 2012). The importance of any one of the genetic and non-genetic factors in affecting the risk for AD may differ among individuals.

Pathogenic Mechanism

AD, as a multifactorial disease, involves various mechanisms that play key roles in its pathogenesis. Increased understanding of the pathogenic mechanisms will be foundational in the development of new, effective therapeutic strategies to prevent onset and/or progression of AD. We provide an overview about the pathogenic mechanism linked to AD: amyloid hypothesis, neuroinflammation, and the role of infection.

The Amyloid Hypothesis

The identification of Aβ as the main component of senile plaques by George Glenner and Caine Wong (1984) resulted in the original formation of the “amyloid hypothesis.” According to the hypothesis, later reinterpreted and renamed as the “amyloid cascade hypothesis” (Hardy & Higgins, 1992), Aβ and its aggregates are the initial pathological trigger of the disease, subsequently leading to the formation of NFTs, neuronal degeneration, synaptic loss, and ultimately dementia (Tanzi, 2013). One tenet of this hypothesis is that plaques precede NFTs (and other subsequent AD pathologies). Central to the amyloid cascade hypothesis is the observation that, first, Aβ is toxic to neurons cultured in petri dishes and animal models in various ways (Selkoe, 2001; Tanzi & Bertram, 2005). As a proof of concept, Aβ immunotherapy decreases Aβ levels in mice, which rescues cognitive deficits (Roskam, Neff, Schwarting, Bacher, & Dodel, 2010). Second, most mutations in APP, PSEN1, and PSEN2 genes causing FAD increase Aβ42 accumulation (Selkoe, 2001; Tanzi & Bertram, 2005). Third, tau alteration and formation of NFTs are insufficient to induce amyloid plaques. For example, tau mutations led to severe tau deposition in frontotemporal dementia, but amyloid plaque is absent in the disease. Thus, NFTs or wildtype tau seen in AD brains might have been deposited after changes in initial Aβ plaque formation (Hardy & Selkoe, 2002). Supporting this, some AD patients have many neocortical plaques but no or few NFTs. Fourth, in mouse models, the levels of NFTs are higher in mice expressing both APP and tau mutations than those expressing only tau mutation (Lewis et al., 2001), suggesting that altered APP processing occurs before tau alterations in AD pathogenic cascade. In 3xTg transgenic mice expressing APP, PSEN1, and tau mutations, Aβ influences formation of tau pathology (Bloom, Ren, & Glabe, 2005).

As the most influential model of AD pathology for nearly three decades, the amyloid cascade hypothesis has been the basis of numerous studies and proposed potential treatments. However, concerns and limitations about the hypothesis have also been identified (Herrup, 2015). This hypothesis has not been easy to test, partly due to the absence of appropriate experimental models. For example, in transgenic mouse models, the presence of an APP mutation alone or in combination with PSEN1 can result in Aβ deposition, but it does not appear to induce tau pathology (Armstrong, 2014). However, experimental validation of the amyloid hypothesis of AD has been provided through genetically engineered human neural stem cells that overexpress FAD-related genes combined with a three-dimensional (3D) culture condition inducing robust AD pathogenesis, including extracellular aggregation of Aβ and accumulation of pTau as NFTs (called “Alzheimer’s in a dish”) (Choi et al., 2014; Kim et al., 2015). Inhibition of Aβ generation with β- or γ-secretase inhibitors not only decreased Aβ pathology but also attenuated tauopathy in the 3D culture system, suggesting that amyloid deposition is the essential first step leading to the formation of NFTs containing pathogenic form of the tau protein.

The production and clearance of Aβ are balanced in a normal state. However, in AD case, increased total Aβ levels, increased Aβ42:Aβ40 ratio, and/or decreased Aβ degradation/clearance cause aggregation and accumulation of Aβ in the brain (Selkoe, 2001; Tanzi & Bertram, 2005). Major pathways that remove Aβ peptides from the brain are: proteolytic degradation by neprilysin (NEP) and insulin degrading enzyme (IDE); uptake by microglia and astrocytes; and passive flow into the cerebrospinal fluid (CSF) and sequestration into the vascular compartment by soluble low-density lipoprotein receptor related protein 1 (LRP1) (Braak & Del Tredici, 2011; Yasojima, McGeer, & McGeer, 2001).

Neuroinflammation

Clinicopathological correlation studies showed individuals with intact cognition at time of death even in the presence of abundant Aβ plaques and NFTs (“resilient” brain) (Riley, Snowdon, Desrosiers, & Markesbery, 2005). Interestingly, there were less inflammatory cells, which are glial fibrillary acidic protein (GFAP)-positive astrocytes and CD68-positive microglia, in those who were clinically normal versus those who had clinical dementia (Perez-Nievas et al., 2013). Number of neurons, cortical thickness, and markers of synaptic integrity were preserved in clinically normal individuals but not in demented AD cases. These results suggest that inflammatory responses, rather than Aβ and/or tau pathologies, correlate with the extent of brain atrophy and cognitive decline, leading to development of dementia. Once amyloid begins to form, the NFTs themselves cause neuronal cell death, but not enough to lead to dementia. However, when amyloid and NFT-driven neurodegeneration hit a threshold such that the innate immune system of the brain reacts with significant neuroinflammation, rampant cell death occurs, leading to symptoms that characterize dementia and AD.

Neuroinflammation in AD is due to the accumulation of activated microglia and reactive astrocytes. Microglial activation has two major impacts on AD progression: (1) activated microglia produce cytokines and chemokines that lead to phagocytic activity contributing to Aβ clearance and degradation (Janelsins et al., 2005), and (2) prolonged microglial activation starts off a pro-inflammatory cascade of synaptic dysfunction that includes mitochondrial damage, additional microglia activation, and then neuronal death (Akiyama et al., 2000). Microglial activation produces and releases pro-inflammatory cytokines, such as interleukin-1β (IL-1β), IL-6, IL-8, tumor necrosis factor α (TNFα), chemokines, and reactive oxygen species (ROS), resulting in neuronal and vascular degeneration in AD brains (Takahashi, Capetillo-Zarate, Lin, Milner, & Gouras, 2010). These cytokines stimulate adjacent neurons-astrocytes that increase additional Aβ42 (Zaheer et al., 2008). Furthermore, the cognate receptors of these pro-inflammatory cytokines are upregulated in AD patients (Dal Pra et al., 2015).

The aforementioned “Alzheimer’s in a dish” model successfully recapitulates the plaques and NFTs but did not induce strong neuroinflammation due to lack of microglia (Choi et al., 2014; Kim et al., 2015). To investigate inflammatory mechanism(s) of neurodegeneration in AD, microglia was added into the 3D culture model on a multichambered microfluidic chip consisting of two circular chambers, one inside the other, to measure the migration of microglia from the outer chamber into the amyloid-loaded inner chamber through connecting channels, called a “triculture system” (Park et al., 2018). Human microglia that were loaded to the outer chamber soon showed structural changes signifying their activation, such as upregulation of activation marker CD11b, and migrated through the connecting channels into the inner chamber where amyloid and tau pathologies are developed. A neutralizing antibody to Chemokine (C-C motif) ligand 2 (CCL2)/Monocyte Chemoattractant Protein-1 (MCP-1) partially blocked the migration of microglia, indicating they are molecules responsible for attracting microglia.

Once reaching the inner chamber, the microglia directly attacked neurons, causing visible damage to key structures, such as retraction of neurites, while levels of inflammatory factors, like TNFα, IL-6, and IL-8, rose significantly. Remarkably, microglia in the triculture system directly cleaved neurite by physical contact and axotomy, and their infiltration are associated with the death of around 50 percent of the neurons and astrocytes by soluble factors such as inducible nitric oxide synthase (iNOS). This death, occurring mostly near microglial cells, suggests that microglia may be critical mediators of neuron death. The neuronal loss by recruited microglia was through an interferon-γ (IFNγ) and toll-like receptor 4 (TLR4)-dependent mechanism. Blocking either of these two receptors in microglia prevented neuroinflammation. These results suggest that inflammation causally drives massive cell death in AD.

Genetic factors lead to neuroinflammation, and many AD risk factors are associated with genes highly expressed in microglia, such as APOE, TREM2, and Clusterin. CD33, the first gene found to be associated with neuroinflammation, carries the genetic code for receptors found on microglia (Bertram et al., 2008). CD33 is expressed in microglial cells in the human brain, and CD33 protein levels along with the number of CD33-positive microglia are increased in AD brains (Griciuc et al., 2013). The minor allele of the CD33 SNP rs3865444, which protects against AD, shows association with reduced CD33 expression and insoluble Aβ42 levels in AD brain. Conversely, the amount of CD33-immunoreactive microglia is positively correlated with insoluble Aβ42 levels and amyloid plaque burden in AD brain. CD33 is both required and sufficient for inhibiting the microglial uptake of Aβ42, which leads to impaired Aβ42 clearance. Knocking out the CD33 gene in AD transgenic APPswePS1ΔE9 mice reduced insoluble Aβ42 levels and Aβ plaque burden, which indicates that CD33 promotes Aβ42 pathology in vivo.

A rare variant of the TREM2 gene provides increased risk for late-onset AD (Guerreiro et al., 2013; Jonsson et al., 2013). TREM2, as an innate immune receptor, similar to CD33, is expressed in a subset of myeloid cells, such as microglia (Klesney-Tait, Turnbull, & Colonna, 2006). TREM2, which is upregulated in amyloid plaque-associated microglia in aging AD transgenic APP23 mice and CRND8 transgenic mice, induces the phagocytic clearance of amyloid proteins (Frank et al., 2008; Guerreiro et al., 2013; Melchior et al., 2010). TREM2 interacts with its ligand TREM2-L, which is expressed by apoptotic neurons, and is involved in removal of dying neuronal cells by microglia (Hsieh et al., 2009).

CD33 and TREM2 interact with each other, and TREM2 acts downstream of CD33 in modulating cognition, amyloid pathology, neurodegeneration, microgliosis, and microglial gene expression (Griciuc et al., 2019). Knocking out CD33 attenuated Aβ pathology and ameliorated cognition in AD transgenic 5xFAD mice; concomitantly, knocking out additional TREM2 abrogated those effects. The knockout of TREM2 in 5xFAD mice showed exacerbated Aβ pathology and neurodegeneration as well as reduced number of microglial cells; these effects were not rescued by additional CD33 knockout. RNA-seq profiling of microglia indicated that genes involved in phagocytosis and signaling (IL-6, IL-8, acute phase response) are upregulated in 5xFAD;CD33-/- mice and downregulated in 5xFAD;TREM2-/- mice. Knocking out CD33 in 5xFAD mice caused an uptick in gene expression related to microglial activation, phagocytosis, and cytokine production, while knocking out TREM2 downregulated expression of genes involved in these same processes. Differential microglial gene expression in 5xFAD;CD33-/- mice depended on the presence of TREM2. Gene expression changes occurred in 5xFAD;TREM2-/- mice independently of CD33. These results suggest that TREM2 acts downstream of CD33. This CD33 and TREM2 crosstalk includes regulation of the IL-1β/IL-RN (IL-1 receptor antagonist) axis and a gene set in the receptor activity chemokine cluster.

Role of Infection in AD

Innate immunity is a non-specific defense mechanism occurring immediately or within hours of an antigen’s appearance in the body. It is naturally present and is not due to prior sensitization to an antigen from an infection or vaccination. The primary effector molecules of innate immunity are antimicrobial peptides (AMPs). Aβ is shown to be a potent AMP that protects against infection, and Aβ deposition may be directly promoted by elevated viral presence.

When a lethal dose of herpes simplex virus 1 (HSV-1) is injected into the brain of AD transgenic 5xFAD mice, Aβ42 oligomers bind HSV-1 through glycoproteins, surface components of the viral envelope that are essential for viral entry into host cells, and rapidly fibrillizes and forms sticky nets (Eimer et al., 2018). It is hypothesized that Aβ completely sequestered the virus and began to break down viral envelopes, destroying the microbes. This response protected neurons from herpes virus infection after a viral challenge. Consequently, viral infections caused amyloidosis in 5xFAD mice: amyloid plaques are formed rapidly (within forty-eight hours of viral injection) surrounding viral particles. Plaques in the brain function as extracellular traps for viruses, as part of the brain’s immune system. Injection of non-lethal doses of HSV-1 in 5xFAD mice lead to advanced amyloidosis three weeks later, suggesting that a low-level viral infection in the brain can still accelerate amyloid formation.

These results suggest that (1) Aβ functions as an antimicrobial peptide against invading pathogens—microbial seeding of plaques can be an innate immune response to defend host cells from infection; (2) the formation of amyloid fibrils in response to pathogens is relevant to the development of AD; and (3) viral seeding of plaque can be a prequel to the amyloid hypothesis. HSV-1 infections conferred a 2.5-fold increased risk of developing dementia in a Taiwanese population study of over 33,000 adults (Tzeng et al., 2018). Interestingly, the risk returned to baseline for those treated with antiviral medication, supporting the idea that infections stimulate AD. Viruses or bacteria are not necessary to generate amyloid plaques in the brain, given other mechanisms, like genetics. Though, viruses of many types (herpes, human endogenous retroviruses, etc.) could be reactivated in old age, instantly seeding the amyloid, forming a big mass around the virus, and trapping it to protect the nerves cells in the brain, which lead to the plaques, tangles, and neuroinflammation that trigger AD.

Prevention and Therapy

The development of preventative targets as well as disease modifying treatments and habits will be critical factors in the successful management or prevention of AD. We thus present efforts on the discovery of (1) the current AD medications which compensate for the loss of cholinergic system or block the action of excessive glutamate; (2) anti-amyloid agents, including α-secretase activators, BACE1 inhibitors, γ-secretase inhibitors and modulators, and immunization with Aβ, and strategies targeting neuroinflammation; and (3) lifestyle habits that help to prevent AD, such as sleep, handling stress, interacting with others, exercise, learning new things, and diet.

Current Medications

There are currently no medications for reversing the pathogenic processes of AD. Current available treatments do not halt the progression of AD but delay symptomatic decline (Farlow & Cummings, 2007). Four prescription drugs are currently approved by the U.S. Food and Drug Administration (FDA) to treat people diagnosed with AD: three acetylcholinesterase inhibitors (AChEIs), including Razadyne® (galantamine), Exelon® (rivastigmine), and Aricept® (donepezil), and one N-methyl D-aspartate (NMDA) antagonist, Namenda® (memantine). AChEIs are for mild to moderate disease. In AD, the earliest and main pathology is impairment of the cholinergic system in the brain, particularly areas associated with learning and memory (Selkoe, 1991). In spite of their different pharmacological properties, those three AChEIs work by blocking the enzyme acetylcholinesterase (AChE) in order to inhibit the breakdown of acetylcholine (ACh), an important neurotransmitter associated with memory (Birks, 2006). Therefore, AChEIs increase cholinergic transmission at the synaptic cleft. However, AChEIs may eventually lose their effectiveness since the brain produces less ACh as AD progresses. Namenda® (memantine) is prescribed to treat moderate to severe AD by preventing excess levels of glutamate from damaging the brain (Greig, 2015). Glutamate is the main excitatory neurotransmitter in the brain and its toxicity is also considered in AD pathogenesis. Animal experiments have suggested that people who developed AD might also have excess glutamate in their brain. Glutamate functions by activating the voltage-gated calcium channels, causing influx of calcium into the cell, and facilitating apoptosis. Additionally, glutamate promotes formation of senile plaques and NFTs (Engelhart et al., 2002). Memantine prevents nerve cell deaths due to excess glutamate, without affecting normal nerve signal transmission. Because NMDA antagonists work differently from AChEIs, combination therapy of the two types of drugs is rational from a pharmacological perspective. Moderate to severe AD patients who received donepezil in addition to memantine showed significant improvements for cognition, activities of daily living (ADLs), global outcome, and behavior (Tariot et al., 2004).

Therapeutic Targets Focusing on Beta-amyloid

Many therapeutic approaches have been aimed at reducing Aβ levels to improve patients’ cognitive function. α-Secretase activators are useful for treatment of AD since Aβ formation is inhibited when APP is cleaved by α-secretase. α-Secretase, also called TNFα Converting Enzyme (TACE) because of its similarities with tumor necrosis factors (TNF), can be induced by protein kinase C (PKC) activators such as phorbol ester. Drugs that stimulate receptors through PKC, muscarinic agonists, statins, steroid hormones (estrogens and testosterone), and nonsteroidal anti-inflammatory drugs may increase α-secretase activity. At mild to moderate AD, Etazolate (EHT-0202), a γ-aminobutyric acid (GABA) A receptor modulator and a phosphodiesterase 4 (PDE4) inhibitor, has completed phase 2 studies (Marcade et al., 2008). Bryostatin-1, a potent activator of PKCε, showed improvements of cognitive functions in the phase 2 studies and compassionate use trials (Etcheberrigaray et al., 2004; Nelson et al., 2017).

Since β-secretase (BACE1) is required to produce Aβ peptides, pharmacological inhibition of BACE1 has been intensively pursued as a therapeutic approach to treat AD patients. However, multiple trials of BACE1 inhibitors were suspended due to their inability to slow cognitive decline or because of adverse side effects, despite reducing Aβ plaque formation. Verubecestat (MK-8931, Merck) is the first small-molecular BACE1 inhibitor with oral availability and blood-brain barrier (BBB) permeability (Scott et al., 2016). Although up to 80 percent reduction of Aβ40, Aβ42, and sAPPβ in cerebrospinal fluid (CSF) was detected and a small reduction in plaque load was confirmed by amyloid positron emission tomography (PET) in participants taking verubecestat, the clinical trial was terminated, as verubecestat exhibited no cognitive function improvement in AD patients (Egan et al., 2018). AD patients on verubecestat showed decline on the cognitive scale at the same rate as those in the placebo group. Patients with prodromal AD on verubecestat actually scored worse on cognitive tests than prodromal AD patients on placebo. Lanabecestat (AstraZeneca/Eli Lilly) and Atabecestat (Janssen) have also failed in clinical trials due to lack of efficacy and toxicity, respectively.

γ-Secretase, an aspartyl protease, is a multi-subunit complex involved in the cleavage of APP along with other type I transmembrane proteins. Therefore, γ-secretase inhibitors (GSIs) have been the subject of study for the treatment of AD. N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT) is the first dipeptide compound showing GSI activity in vivo tests. Administration of DAPT decreased the levels of cortical Aβ in a dose-dependent manner in AD transgenic PDAPP and Tg2576 mice (Dovey et al., 2001; Lanz et al., 2003). Toxicities associated with GSIs, in spite of their bioavailability and brain penetrant characteristics, provide challenges in the development of therapeutic approaches targeting γ-secretase. Common side effects of GSIs are hematological and gastrointestinal toxicity, skin reactions, and hair color changes (Fleisher et al., 2008; Maillard, Adler, & Pear, 2003; Nicolas et al., 2003; Stanger, Datar, Murtaugh, & Melton, 2005; Xia et al., 2001). LY-450139/semagacestat were shown to decrease plasma Aβ levels significantly in clinical studies, but they have been discontinued due to impaired cognitive function, increased skin cancer, and infection risk. (Bateman et al., 2009; Doody et al., 2013). The side effects of GSIs are, at least in part, due to downregulation of Notch processing.

To overcome the above-mentioned toxicity problem, a group of small molecules with a promising therapeutic mechanism, known as γ-secretase modulators (GSMs), modulates the cleavage activity of γ-secretase and specifically reduces the levels of the fibrillogenic Aβ42 peptide without inhibiting γ-secretase-mediated proteolysis of Notch or causing accumulation of carboxyl-terminal fragments of APP (Brendel et al., 2015; Imbimbo et al., 2007; Kounnas et al., 2010; Rogers et al., 2012). GSMs spare the ε-site processing of Notch; thus, these compounds are presumably safer and better tolerated than GSIs. A series of GSMs with promising biological activities have been developed and characterized (Kounnas et al., 2010). The initial aminothiazole class of compounds displayed high potency for inhibiting the secretion of the Aβ42 peptide; they suffered from poor aqueous solubility. Soluble GSMs (SGSMs), another class of aminothiazole GSMs, were developed and improved with certain physicochemical properties, such as increased aqueous solubility (Wagner et al., 2014). SGSMs effectively reduced the levels of Aβ using cell-based models (D’Avanzo et al., 2015; Wagner et al., 2014). Additionally pharmacokinetic assessments of this aminothiazole class of SGSMs in mice identified a lead compound, SGSM-36, that showed good brain penetration, good clearance, half-life, and volume of distribution (Rynearson et al., 2016). These results bolster the support for continued development of SGSMs as a potential therapy for AD.

Activating the immune system has been explored as a solution for the main Aβ problem in AD pathophysiology. Immunization with Aβ therapeutic effects was reported in AD transgenic mice (Bard et al., 2000; Schenk et al., 1999). However, Aducanumab (Biogen/Eisai, passive immunotherapy, administration of antibodies generated against Aβ), Solanezumab (Eli Lilly, passive immunotherapy), Bapineuzumab (Elan/Wyeth/Pfizer, passive immunotherapy), and Immunoglobulin (Baxter) have all failed in phase 3 trials due to the lack of efficacy. BAN2401, an antibody that selectively binds to Aβ protofibrils, is currently on a phase 3 trial with patients with early AD. Two identical phase 3 clinical trials of Aducanumab treatment (ENGAGE and EMERGE) were discontinued in March 2019, when a futility analysis indicated negligible chance of treatment efficacy. On October 22, 2019, Biogen announced that subsequent analysis of a larger data set showed EMERGE had met its primary endpoint. The group of participants in EMERGE who received high dose of Aducanumab (10 mg/kg) showed a 23 percent reduction in the rate of decline on the Clinical Dementia Rating Scale–Sum of Boxes (CDR-SB) score. This group also showed less decline on secondary endpoints Mini-Mental State Examination (MMSE), Alzheimer’s Disease Assessment Scale Cognitive Subscale 13 Items (ADAS-Cog-13), and Alzheimer’s Disease Cooperative Study–Activities of Daily Living Inventory–Mild Cognitive Impairment version (ADCS-ADL-MCI). The ENGAGE did not meet the primary endpoint. However, an exploratory analysis suggested that a subgroup of people who had received at least ten 10 mg/kg doses declined more slowly (trending positive). Results from a tau PET imaging substudy suggest a dose-dependent decline in MK6240 tracer uptake in medial temporal brain regions after Aducanumab treatment, indicating that it reduced tangle pathology. The FDA recently approved a re-dosing amendment for patients enrolled in phase 3 clinical trials of Biogen’s Aducanumab. As a result, following a failed futility analysis, new trial results were released suggesting Aducanumab not only clears amyloid plaque but may improve cognition in very mild AD patients. If confirmed, these data would provide the strongest support for the amyloid hypothesis, to date.

AD begins with Aβ deposition and tangles that trigger the disease presymptomatically. Most clinical trials were suspended due to their inability to improve cognitive function, despite reducing Aβ plaque formation. Since amyloid develops very early in AD progression, even twenty years before the first symptoms appear, targeting Aβ in AD patients who already have dementia is unlikely to stop the disease. Although Aβ is still a critical factor in AD, managing its deposition will require early intervention following early detection of β-amyloid in the brain (e.g. by imaging, or perhaps, someday by a blood-based test). Likewise, targeting tangles will need to be achieved presymptomatically in a secondary prevention approach. The best bet for treating AD patients suffering with dementia will likely entail the reduction of neuroinflammation as an innate immune reaction to Aβ, tangles, and initiating events of neurodegeneration. Targeting neuroinflammation can be achieved by both protecting neurons from oxidative damage and preventing activation of glia to a pro-inflammatory state. For example, enhancing microglial phagocytic ability by inhibiting CD33 and/or activating TREM2 to prevent Aβ pathology and memory dysfunction might be considered one of the best drug targets or potential therapies for AD as it could both convert microglia from the pro-inflammatory state and reduce the initiating pathology of plaques and tangles. Finally, antiviral drugs might also be effective in reducing the risk of AD in a primary prevention approach. AD is a complex disease, and it is unlikely that any one drug or intervention will successfully treat it. A combination approach might be needed to treat the disease, particularly focusing on administering the right therapy at the right stage of the disease (e.g., presymptomatically or following the presentation of symptoms).

Lifestyle

Although drug treatment is desperately needed, people can take steps to reduce their risk of developing AD. Preventing AD from ever starting might also require avoiding brain inflammation as much as possible, particularly in later life. A recent study shows that unhealthy lifestyles can increase chances of developing dementia; conversely, favorable lifestyles can lower dementia risk, regardless of genetic risk (Lourida et al., 2019). Preventive measures include sleep, handling stress, interacting with others, exercise, learning new things, and diet (SHIELD). A combination approach may also include lifestyle changes.

Sleep

Emerging evidence shows that the sleep-wake cycle directly influences Aβ levels in the brain. Sleep helps promote the brain’s neural cleaning glymphatic system. During sleep (rapid eye movement, or REM, sleep), amyloid production decreases, and the brain can produce more fluid to flush excess plaque out of the brain. Animal studies suggest a possible causal relationship between disrupted sleep and AD pathology. Evidence showed increased wakefulness and decreased sleep beginning around when amyloid plaques start to accumulate in AD transgenic APPswePS1ΔE9 mice (6 months of age) as well as significantly disrupted sleep patterns when plaques become widespread (9 months of age) (Roh et al., 2012). This study demonstrates diurnal variation in the level of soluble Aβ in the interstitial fluid (ISF), which increases during wakefulness and decreases during sleep. Active immunization with Aβ prior to amyloid deposition prevented the formation of amyloid plaques, maintained diurnal variation in the level of soluble Aβ, and normalized sleep-wake patterns in APPswePS1ΔE9 mice (Roh et al., 2012). In both mice and humans, levels of soluble Aβ fluctuate with the sleep-wake cycle in a diurnal pattern (Huang et al., 2012; Kang et al., 2009). Sleep deprivation leads to the acute effect of increased Aβ concentrations; chronic sleep deprivation results in accelerated Aβ deposition into insoluble amyloid plaques in AD transgenic APPswe and APPswePS1ΔE9 mice (Kang et al., 2009). Enhanced sleep through treatment with an orexin receptor antagonist has been shown to decrease Aβ plaque deposition in these models.

These results suggest that a form of Aβ that accumulates leads to sleep-wake disruptions and that sleep deprivation increases the concentration of soluble Aβ causing chronic accumulation of Aβ, whereas sleep extension has the opposite effect. Individuals with early Aβ deposition and normal cognitive function report sleep abnormalities, as do individuals with very mild AD-related dementia. Sleep and AD may influence each other in many ways that impact the diagnosis and treatment of AD (Ju, Lucey, & Holtzman, 2014). Therefore, developing good sleep habit supports brain resilience over time. However, it is not yet clear whether poor sleep is a cause of AD or one of its earliest symptoms.

Handling Stress

Stress exposure has been associated with AD risk (Hoeijmakers, Lesuis, Krugers, Lucassen, & Korosi, 2018; Machado et al., 2014). Major stress exposures accelerate the onset of FAD and exacerbate the progress of AD-related symptoms and neuropathology in sporadic AD (Hoogendijk, Meynen, Endert, Hofman, & Swaab, 2006; Mejia, Giraldo, Pineda, Ardila, & Lopera, 2003; Wilson, Begeny, Boyle, Schneider, & Bennett, 2011). Animal studies have also shown that stress exposures caused cognitive decline, increased APP misprocessing, reduced Aβ clearance, and enhanced tau hyperphosphorylation (Green, Billings, Roozendaal, McGaugh, & LaFerla, 2006; Hsiao, Kuo, Chen, & Gean, 2012; Lee, 2009; Lesuis et al., 2016; Lesuis, Weggen, Baches, Lucassen, & Krugers, 2018; Tran, Srivareerat, & Alkadhi, 2010). Therefore, minimizing and managing stress can reduce AD risk. Regular meditation may help relieve stress, as meditation changes gene expression that works against inflammation. Also, a full one-week intensive meditation course has shown changes in genes that affect the amount of amyloid in the brain (Epel et al., 2016; Mills et al., 2016; Peterson et al., 2016).

Interacting with Others

It has been shown that loneliness increases the risk of AD and those individuals tend to perform worse on cognitive performance tasks (Wilson et al., 2007). Measurements of Pittsburg compound B-PET indicate the association of loneliness and higher brain amyloid burden (Donovan et al., 2016). Amyloid-positive participants were seven and a half times more likely to report feeling loneliness. APOE ε4 carriers showed stronger associations with loneliness (Donovan et al., 2016). Thus, staying socially engaged, such as with family and friends, to prevent loneliness may support brain health.

Exercise

Regular physical activity has shown to be an important protective factor against cognitive decline and dementia. Physical activity, as opposed to engaging in none, has been linked to lower risks of cognitive impairment, AD, and dementia of any type. High levels of physical activity were reported to show reduced risks of cognitive impairment and AD (Laurin, Verreault, Lindsay, MacPherson, & Rockwood, 2001). Acute aerobic exercise of moderate intensity increased the levels of brain-derived neurotrophic factor (BDNF), a neurotrophic factor which plays a critical role in learning and memory and promotes synaptogenesis, in AD patients (Coelho et al., 2014). In AD transgenic APPswePS1ΔE9 mice, voluntary exercise (running) lowered even amyloid plaques in the brain by increasing the activity of an Aβ degrading enzyme NEP (Lazarov et al., 2005). Treadmill running demonstrated to reverse cognitive impairment and showed improved Aβ and tau pathology as well as suppression of decreased synaptic proteins and BDNF in the brains of AD transgenic 3xTg mice (Cho et al., 2015). Voluntary wheel running reduced inflammatory response, glial activation, in APPswePS1ΔE9 mice (Ke, Huang, Liang, & Hsieh-Li, 2011; Tapia-Rojas, Aranguiz, Varela-Nallar, & Inestrosa, 2016). It also increased neurogenesis, a process of generating new neurons that play critical roles in cognitive functions, in AD transgenic 5xFAD mice (Choi et al., 2018). Those who were inactive (in intellectual, passive, or physical activities) in midlife (early and middle adulthood) have a 250 percent increased risk of developing AD (Friedland et al., 2001). AD patients have been shown to have lower premorbid activity levels in measures of intellectual, passive, and physical activities.

Regular cardiovascular exercise can provide benefits by elevating heart rate and increasing blood flow to the brain. The American Heart Association recommends at least two and a half hours of physical activity per week, which can also help reduce risks for high blood pressure, heart disease, stroke, obesity, and diabetes, all of which can negatively affect cognitive health.

Learning New Things

Mental exercise is just as important as physical exercise in preventing and delaying the onset of cognitive decline. Brain stimulating activities show mitigation of longitudinally measured cognitive decline in a study of 214 persons ages 44 to 86 (Hultsch, Hertzog, Small, & Dixon, 1999). Evaluation of 6,162 persons showed that cognitive function was related to composite measures of the frequency and intensity of cognitive activity (Wilson et al., 1999). Learning new skills can build new nerve connections and strengthen old synapses to maintain optimal brain health. Mentally stimulating activities have been shown to lower the risk of mild cognitive impairment (MCI), which precedes dementia (Krell-Roesch et al., 2019). Mentally stimulating activities may include reading, learning a new language, playing a new musical instrument, adopting a new hobby, using a computer, participating in social activities, playing games, or enjoying craft activities.

Diet

Dietary pattern can be a contributor to the incidence of AD. Unhealthy dietary patterns can promote oxidative stress and inflammation, accumulation of Aβ, and subsequent development of AD (Samadi, Moradi, Moradinazar, Mostafai, & Pasdar, 2019). Unhealthy diets include high-fat diet (Laitinen et al., 2006; Morris et al., 2003a), high-glycemic diet (Luchsinger, Tang, & Mayeux, 2007), sugar-sweetened beverages (Pase et al., 2017), high-cholesterol diet (Ylilauri et al., 2017), and Western diet consisting of high intake of total fat, saturated fat, cholesterol, sodium, processed foods, refined grains, simple carbohydrates, and sugar (Hariharan, Vellanki, & Kramer, 2015; Rai et al., 2017). Adherence to a healthy dietary pattern has neuroprotective effects on AD prevention. There is a beneficial association between AD and consumption of a Mediterranean diet, which is characterized by an increased intake of fiber, fruits, beans, poultry, nuts, fish, olive oil, and vegetables; a moderate consumption of alcohol; and a lower consumption of red meat and dairy products (Gardener et al., 2012; Hill et al., 2018; Scarmeas, Stern, Tang, Mayeux, & Luchsinger, 2006). Adherence to the Mediterranean diet may affect not only the risk of AD but also mortality in AD (Scarmeas, Luchsinger, Mayeux, & Stern, 2007). Other healthy dietary patterns that can lower the risk of AD include dietary approaches to stop hypertension (DASH) (Morris et al., 2015); a low-fat diet (Hill et al., 2018); consumption of fruits, vegetables, fish, legumes, high protein, and cereals (Eskelinen, Ngandu, Tuomilehto, Soininen, & Kivipelto, 2011; Fernando et al., 2018); a seafood-rich diet (Devore et al., 2009; Morris et al., 2003b); and a soy-based food diet (Ozawa et al., 2013).

References

Akiyama, H., Barger, S., Barnum, S., Bradt, B., Bauer, J., Cole, G. M., … Wyss-Coray, T. (2000). Inflammation and Alzheimer’s disease. Neurobiology of Aging, 21(3), 383–421.Find this resource:

Alzheimer, A. (Jarvik, L., & Greenson, H., trans.). About a peculiar disease of the cerebral cortex. By Alois Alzheimer, 1907. (1987). Alzheimer Disease and Associated Disorders, 1, 3–8.Find this resource:

Anstey, K. J., von Sanden, C., Salim, A., & O’Kearney, R. (2007). Smoking as a risk factor for dementia and cognitive decline: A meta-analysis of prospective studies. American Journal of Epidemiology, 166(4), 367–378. doi:10.1093/aje/kwm116Find this resource:

Anttila, T., Helkala, E. L., Viitanen, M., Kareholt, I., Fratiglioni, L., Winblad, B., Soininen, H., Tuomilehto, J., Nissinen, A., & Kivipelto, M. (2004). Alcohol drinking in middle age and subsequent risk of mild cognitive impairment and dementia in old age: A prospective population-based study. British Medical Journal, 329(7465), 539. doi:10.1136/bmj.38181.418958.BEFind this resource:

Armstrong, R. A. (2014). A critical analysis of the “amyloid cascade hypothesis.” Folia Neuropathologica, 52(3), 211–225.Find this resource:

Bard, F., Cannon, C., Barbour, R., Burke, R. L., Games, D., Grajeda, H., Guido, T., Hu, K., Huang, J., Johnson-Wood, K., Khan, K., Kholodenko, D., Lee, M., Lieberburg, I., Motter, R., Nguyen, M., Soriano, F., Vasquez, N., Weiss, K., Welch, B., Seubert, P., Schenk, D., & Yednock, T. (2000). Peripherally administered antibodies against amyloid beta-peptide enter the central nervous system and reduce pathology in a mouse model of Alzheimer disease. Nature Medicine, 6(8), 916–919. doi:10.1038/78682Find this resource:

Barnes, J., Dickerson, B. C., Frost, C., Jiskoot, L. C., Wolk, D., & van der Flier, W. M. (2015). Alzheimer’s disease first symptoms are age dependent: Evidence from the NACC dataset. Alzheimer’s Dementia, 11(11), 1349–1357. doi:10.1016/j.jalz.2014.12.007Find this resource:

Bateman, R. J., Siemers, E. R., Mawuenyega, K. G., Wen, G., Browning, K. R., Sigurdson, W. C., Yarasheski, K. E., Friedrich, S. W., Demattos, R. B., May, P. C., Paul, S. M., & Holtzman, D. M. (2009). A gamma-secretase inhibitor decreases amyloid-beta production in the central nervous system. Annals of Neurology, 66(1), 48–54. doi:10.1002/ana.21623Find this resource:

Bertram, L., Lange, C., Mullin, K., Parkinson, M., Hsiao, M., Hogan, M. F., Schjeide, B. M., Hooli, B., Divito, J., Ionita, I., Jiang, H., Laird, N., Moscarillo, T., Ohlsen, K. L., Elliott, K., Wang, X., Hu-Lince, D., Ryder, M., Murphy, A., Wagner, S. L., Blacker, D., Becker, K. D., & Tanzi, R. E. (2008). Genome-wide association analysis reveals putative Alzheimer’s disease susceptibility loci in addition to APOE. American Journal of Human Genetics, 83(5), 623–632. doi:10.1016/j.ajhg.2008.10.008Find this resource:

Bertram, L., Lill, C. M., & Tanzi, R. E. (2010). The genetics of Alzheimer disease: Back to the future. Neuron, 68(2), 270–281. doi:10.1016/j.neuron.2010.10.013Find this resource:

Birks, J. (2006). Cholinesterase inhibitors for Alzheimer’s disease. Cochrane Database System Review, 1, CD005593s. doi:10.1002/14651858.CD005593Find this resource:

Bloom, G. S., Ren, K., & Glabe, C. G. (2005). Cultured cell and transgenic mouse models for tau pathology linked to beta-amyloid. Biochimica et Biophysica Acta, 1739(2–3), 116–124. doi:10.1016/j.bbadis.2004.08.008Find this resource:

Braak, H., & Del Tredici, K. (2011). Alzheimer’s pathogenesis: Is there neuron-to-neuron propagation? Acta Neuropathologica, 121(5), 589–595. doi:10.1007/s00401-011-0825-zFind this resource:

Brendel, M., Jaworska, A., Herms, J., Trambauer, J., Rotzer, C., Gildehaus, F. J., Carlsen, J., Cumming, P., Bylund, J., Luebbers, T., Bartenstein, P., Steiner, H., Haass, C., Baumann, K., & Rominger, A. (2015). Amyloid-PET predicts inhibition of de novo plaque formation upon chronic gamma-secretase modulator treatment. Molecular Psychiatry, 20(10), 1179–1187. doi:10.1038/mp.2015.74Find this resource:

Cao, X., & Sudhof, T. C. (2004). Dissection of amyloid-beta precursor protein-dependent transcriptional transactivation. Journal of Biological Chemistry, 279(23), 24601–24611. doi:10.1074/jbc.M402248200Find this resource:

Cho, J., Shin, M. K., Kim, D., Lee, I., Kim, S., & Kang, H. (2015). Treadmill running reverses cognitive declines due to Alzheimer’s disease. Medicine and Science in Sports Exercise, 47(9), 1814–1824. doi:10.1249/MSS.0000000000000612Find this resource:

Choi, S. H., Bylykbashi, E., Chatila, Z. K., Lee, S. W., Pulli, B., Clemenson, G. D., Kim, E., Rompala, A., Oram, M. K., Asselin, C., Aronson, J., Zhang, C., Miller, S. J., Lesinski, A., Chen, J. W., Kim, D. Y., van Praag, H., Spiegelman, B. M., Gage, F. H., & Tanzi, R. E. (2018). Combined adult neurogenesis and BDNF mimic exercise effects on cognition in an Alzheimer’s mouse model. Science, 361(6406). doi:10.1126/science.aan8821Find this resource:

Choi, S. H., Kim, Y. H., Hebisch, M., Sliwinski, C., Lee, S., D’Avanzo, C., Chen, H., Hooli, B., Asselin, C., Muffat, J., Klee, J. B., Zhang, C., Wainger, B. J., Peitz, M., Kovacs, D. M., Woolf, C. J., Wagner, S. L., Tanzi, R. E., & Kim, D. Y. (2014). A three-dimensional human neural cell culture model of Alzheimer’s disease. Nature, 515(7526), 274–278. doi:10.1038/nature13800Find this resource:

Coelho, F. G., Vital, T. M., Stein, A. M., Arantes, F. J., Rueda, A. V., Camarini, R., Teodorov, E., & Santos-Galduroz, R. F. (2014). Acute aerobic exercise increases brain-derived neurotrophic factor levels in elderly with Alzheimer’s disease. Journal of Alzheimer’s Disease, 39(2), 401–408. doi:10.3233/JAD-131073Find this resource:

Corder, E. H., Saunders, A. M., Strittmatter, W. J., Schmechel, D. E., Gaskell, P. C., Small, G. W., Roses, A. D., Haines, J. L., & Pericak-Vance, M. A. (1993). Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science, 261(5123), 921–923. doi:10.1126/science.8346443Find this resource:

D’Avanzo, C., Sliwinski, C., Wagner, S. L., Tanzi, R. E., Kim, D. Y., & Kovacs, D. M. (2015). gamma-Secretase modulators reduce endogenous amyloid beta42 levels in human neural progenitor cells without altering neuronal differentiation. FASEB Journal, 29(8), 3335–3341. doi:10.1096/fj.15–271015Find this resource:

Dal Pra, I., Chiarini, A., Gui, L., Chakravarthy, B., Pacchiana, R., Gardenal, E., Whitfield, J. F., & Armato, U. (2015). Do astrocytes collaborate with neurons in spreading the “infectious” abeta and Tau drivers of Alzheimer’s disease? Neuroscientist, 21(1), 9–29. doi:10.1177/1073858414529828Find this resource:

De Strooper, B. (2003). Aph-1, Pen-2, and nicastrin with presenilin generate an active gamma-secretase complex. Neuron, 38(1), 9–12.Find this resource:

Devore, E. E., Grodstein, F., van Rooij, F. J., Hofman, A., Rosner, B., Stampfer, M. J., Witteman, J. C., & Breteler, M. M. (2009). Dietary intake of fish and omega-3 fatty acids in relation to long-term dementia risk. American Journal of Clinical Nutrition, 90(1), 170–176. doi:10.3945/ajcn.2008.27037Find this resource:

Dolan, P. J., & Johnson, G. V. (2010). The role of tau kinases in Alzheimer’s disease. Current Opinion in Drug Discovery & Development, 13(5), 595–603.Find this resource:

Donovan, N. J., Okereke, O. I., Vannini, P., Amariglio, R. E., Rentz, D. M., Marshall, G. A., Johnson, K. A., & Sperling, R. A. (2016). Association of higher cortical amyloid burden with loneliness in cognitively normal older adults. JAMA Psychiatry, 73(12), 1230–1237. doi:10.1001/jamapsychiatry.2016.2657Find this resource:

Doody, R. S., Raman, R., Farlow, M., Iwatsubo, T., Vellas, B., Joffe, S., … Semagacestat Study Group. (2013). A phase 3 trial of semagacestat for treatment of Alzheimer’s disease. New England Journal of Medicine, 369(4), 341–350. doi:10.1056/NEJMoa1210951Find this resource:

Dovey, H. F., John, V., Anderson, J. P., Chen, L. Z., de Saint Andrieu, P., Fang, L. Y., … Audia, J. E. (2001). Functional gamma-secretase inhibitors reduce beta-amyloid peptide levels in brain. Journal of Neurochemistry, 76(1), 173–181. doi:10.1046/j.1471–4159.2001.00012.xFind this resource:

Durazzo, T. C., Mattsson, N., Weiner, M. W., & Alzheimer’s Disease Neuroimaging, I. (2014). Smoking and increased Alzheimer’s disease risk: A review of potential mechanisms. Alzheimer’s Dementia, 10(3 Suppl.), S122–S145. doi:10.1016/j.jalz.2014.04.009Find this resource:

Egan, M. F., Kost, J., Tariot, P. N., Aisen, P. S., Cummings, J. L., Vellas, B., … Michelson, D. (2018). Randomized trial of verubecestat for mild-to-moderate Alzheimer’s disease. New England Journal of Medicine, 378(18), 1691–1703. doi:10.1056/NEJMoa1706441Find this resource:

Eimer, W. A., Vijaya Kumar, D. K., Navalpur Shanmugam, N. K., Rodriguez, A. S., Mitchell, T., Washicosky, K. J., … Moir, R. D. (2018). Alzheimer’s disease-associated beta-amyloid is rapidly seeded by herpesviridae to protect against brain infection. Neuron, 99(1), 56–63. doi:10.1016/j.neuron.2018.06.030Find this resource:

Engelhart, M. J., Geerlings, M. I., Ruitenberg, A., van Swieten, J. C., Hofman, A., Witteman, J. C., & Breteler, M. M. (2002). Dietary intake of antioxidants and risk of Alzheimer disease. JAMA, 287(24), 3223–3229. doi:10.1001/jama.287.24.3223Find this resource:

Epel, E. S., Puterman, E., Lin, J., Blackburn, E. H., Lum, P. Y., Beckmann, N. D., … Schadt, E. E. (2016). Meditation and vacation effects have an impact on disease-associated molecular phenotypes. Translational Psychiatry, 6(8), e880. doi:10.1038/tp.2016.164Find this resource:

Eskelinen, M. H., Ngandu, T., Tuomilehto, J., Soininen, H., & Kivipelto, M. (2011). Midlife healthy-diet index and late-life dementia and Alzheimer’s disease. Dementia and Geriatric Cognitive Disorders Extra, 1(1), 103–112. doi:10.1159/000327518Find this resource:

Etcheberrigaray, R., Tan, M., Dewachter, I., Kuiperi, C., Van der Auwera, I., Wera, S., … Alkon, D. L. (2004). Therapeutic effects of PKC activators in Alzheimer’s disease transgenic mice. Proceedings of the National Academy of Sciences USA, 101(30), 11141–11146. doi:10.1073/pnas.0403921101Find this resource:

Farlow, M. R. (1997). Alzheimer’s disease: Clinical implications of the apolipoprotein E genotype. Neurology, 48(5 Suppl. 6), S30–S34. doi:10.1212/wnl.48.5_suppl_6.30sFind this resource:

Farlow, M. R., & Cummings, J. L. (2007). Effective pharmacologic management of Alzheimer’s disease. American Journal of Medicine, 120(5), 388–397. doi:10.1016/j.amjmed.2006.08.036Find this resource:

Fernando, W., Rainey-Smith, S. R., Gardener, S. L., Villemagne, V. L., Burnham, S. C., Macaulay, S. L., … AIBL Research Group. (2018). Associations of dietary protein and fiber intake with brain and blood amyloid-beta. Journal of Alzheimer’s Disease, 61(4), 1589–1598. doi:10.3233/JAD-170742Find this resource:

Fleisher, A. S., Chen, K., Liu, X., Ayutyanont, N., Roontiva, A., Thiyyagura, P., … Reiman, E. M. (2013). Apolipoprotein E epsilon4 and age effects on florbetapir positron emission tomography in healthy aging and Alzheimer disease. Neurobiology of Aging, 34(1), 1–12. doi:10.1016/j.neurobiolaging.2012.04.017Find this resource:

Fleisher, A. S., Raman, R., Siemers, E. R., Becerra, L., Clark, C. M., Dean, R. A., … Thal, L. J. (2008). Phase 2 safety trial targeting amyloid beta production with a gamma-secretase inhibitor in Alzheimer disease. Archives of Neurology, 65(8), 1031–1038. doi:10.1001/archneur.65.8.1031Find this resource:

Frank, S., Burbach, G. J., Bonin, M., Walter, M., Streit, W., Bechmann, I., & Deller, T. (2008). TREM2 is upregulated in amyloid plaque-associated microglia in aged APP23 transgenic mice. Glia, 56(13), 1438–1447. doi:10.1002/glia.20710Find this resource:

Friedland, R. P., Fritsch, T., Smyth, K. A., Koss, E., Lerner, A. J., Chen, C. H., … Debanne, S. M. (2001). Patients with Alzheimer’s disease have reduced activities in midlife compared with healthy control-group members. Proceedings of the National Academy of Sciences USA, 98(6), 3440–3445.Find this resource:

Gardener, S., Gu, Y., Rainey-Smith, S. R., Keogh, J. B., Clifton, P. M., Mathieson, S. L., … Group, A. R. (2012). Adherence to a Mediterranean diet and Alzheimer’s disease risk in an Australian population. Translational Psychiatry, 2, e164. doi:10.1038/tp.2012.91Find this resource:

Gatz, M., Reynolds, C. A., Fratiglioni, L., Johansson, B., Mortimer, J. A., Berg, S., … Pedersen, N. L. (2006). Role of genes and environments for explaining Alzheimer disease. Archives of General Psychiatry, 63(2), 168–174. doi:10.1001/archpsyc.63.2.168Find this resource:

Glenner, G. G., & Wong, C. W. (1984). Alzheimer’s disease: Initial report of the purification and characterization of a novel cerebrovascular amyloid protein. Biochemical and Biophysical Research Communications, 120(3), 885–890.Find this resource:

Goate, A., Chartier-Harlin, M. C., Mullan, M., Brown, J., Crawford, F., Fidani, L., … James, L. (1991). Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease. Nature, 349(6311), 704–706. doi:10.1038/349704a0Find this resource:

Goldgaber, D., Lerman, M. I., McBride, O. W., Saffiotti, U., & Gajdusek, D. C. (1987). Characterization and chromosomal localization of a cDNA encoding brain amyloid of Alzheimer’s disease. Science, 235(4791), 877–880. doi:10.1126/science.3810169Find this resource:

Gomez-Isla, T., West, H. L., Rebeck, G. W., Harr, S. D., Growdon, J. H., Locascio, J. J., … Hyman, B. T. (1996). Clinical and pathological correlates of apolipoprotein E epsilon 4 in Alzheimer’s disease. Annals of Neurology, 39(1), 62–70. doi:10.1002/ana.410390110Find this resource:

Green, K. N., Billings, L. M., Roozendaal, B., McGaugh, J. L., & LaFerla, F. M. (2006). Glucocorticoids increase amyloid-beta and tau pathology in a mouse model of Alzheimer’s disease. Journal of Neuroscience, 26(35), 9047–9056. doi:10.1523/JNEUROSCI.2797–06.2006Find this resource:

Greig, S. L. (2015). Memantine ER/donepezil: A review in Alzheimer’s disease. CNS Drugs, 29(11), 963–970. doi:10.1007/s40263-015-0287-2Find this resource:

Griciuc, A., Patel, S., Federico, A. N., Choi, S. H., Innes, B. J., Oram, M. K., … Tanzi, R. E. (2019). TREM2 acts downstream of CD33 in modulating microglial pathology in Alzheimer’s disease. Neuron, 103(5), 820–835. doi:10.1016/j.neuron.2019.06.010Find this resource:

Griciuc, A., Serrano-Pozo, A., Parrado, A. R., Lesinski, A. N., Asselin, C. N., Mullin, K., Hooli, B., Choi, S. H., Hyman, B. T., &… Tanzi, R. E. (2013). Alzheimer’s disease risk gene CD33 inhibits microglial uptake of amyloid beta. Neuron, 78(4), 631–643. doi:10.1016/j.neuron.2013.04.014Find this resource:

Guerreiro, R., Wojtas, A., Bras, J., Carrasquillo, M., Rogaeva, E., Majounie, E., … Alzheimer Genetic Analysis, G. (2013). TREM2 variants in Alzheimer’s disease. New England Journal of Medicine, 368(2), 117–127. doi:10.1056/NEJMoa1211851Find this resource:

Hardy, J. (1997). Amyloid, the presenilins and Alzheimer’s disease. Trends in Neurosciences, 20(4), 154–159.Find this resource:

Hardy, J. A., & Higgins, G. A. (1992). Alzheimer’s disease: The amyloid cascade hypothesis. Science, 256(5054), 184–185.Find this resource:

Hardy, J., & Selkoe, D. J. (2002). The amyloid hypothesis of Alzheimer’s disease: Progress and problems on the road to therapeutics. Science, 297(5580), 353–356.Find this resource:

Hariharan, D., Vellanki, K., & Kramer, H. (2015). The Western diet and chronic kidney disease. Current Hypertension Report, 17(3), 16. doi:10.1007/s11906-014-0529-6Find this resource:

Herrup, K. (2015). The case for rejecting the amyloid cascade hypothesis. Nature Neuroscience, 18(6), 794–799. doi:10.1038/nn.4017Find this resource:

Hill, E., Clifton, P., Goodwill, A. M., Dennerstein, L., Campbell, S., & Szoeke, C. (2018). Dietary patterns and beta-amyloid deposition in aging Australian women. Alzheimer’s Dementia, 4, 535–541. doi:10.1016/j.trci.2018.09.007Find this resource:

Hoeijmakers, L., Lesuis, S. L., Krugers, H., Lucassen, P. J., & Korosi, A. (2018). A preclinical perspective on the enhanced vulnerability to Alzheimer’s disease after early-life stress. Neurobiology of Stress, 8, 172–185. doi:10.1016/j.ynstr.2018.02.003Find this resource:

Hoogendijk, W. J., Meynen, G., Endert, E., Hofman, M. A., & Swaab, D. F. (2006). Increased cerebrospinal fluid cortisol level in Alzheimer’s disease is not related to depression. Neurobiology of Aging, 27(5), 780 e781–780 e782. doi:10.1016/j.neurobiolaging.2005.07.017Find this resource:

Hsiao, Y. H., Kuo, J. R., Chen, S. H., & Gean, P. W. (2012). Amelioration of social isolation-triggered onset of early Alzheimer’s disease-related cognitive deficit by N-acetylcysteine in a transgenic mouse model. Neurobiology of Disease, 45(3), 1111–1120. doi:10.1016/j.nbd.2011.12.031Find this resource:

Hsieh, C. L., Koike, M., Spusta, S. C., Niemi, E. C., Yenari, M., Nakamura, M. C., & Seaman, W. E. (2009). A role for TREM2 ligands in the phagocytosis of apoptotic neuronal cells by microglia. Journal of Neurochemistry, 109(4), 1144–1156. doi:10.1111/j.1471–4159.2009.06042.xFind this resource:

Huang, Y., Potter, R., Sigurdson, W., Santacruz, A., Shih, S., Ju, Y. E., … Bateman, R. J. (2012). Effects of age and amyloid deposition on Abeta dynamics in the human central nervous system. Archives of Neurology, 69(1), 51–58. doi:10.1001/archneurol.2011.235Find this resource:

Hultsch, D. F., Hertzog, C., Small, B. J., & Dixon, R. A. (1999). Use it or lose it: Engaged lifestyle as a buffer of cognitive decline in aging? Psychology and Aging, 14(2), 245–263.Find this resource:

Imbimbo, B. P., Del Giudice, E., Colavito, D., D’Arrigo, A., Dalle Carbonare, M., Villetti, G., … Leon, A. (2007). 1-(3’,4’-Dichloro-2-fluoro[1,1’-biphenyl]-4-yl)-cyclopropanecarboxylic acid (CHF5074), a novel gamma-secretase modulator, reduces brain beta-amyloid pathology in a transgenic mouse model of Alzheimer’s disease without causing peripheral toxicity. Journal of Pharmacology and Experimental Therapeutics, 323(3), 822–830. doi:10.1124/jpet.107.129007Find this resource:

Iqbal, K., Liu, F., Gong, C. X., Alonso Adel, C., & Grundke-Iqbal, I. (2009). Mechanisms of tau-induced neurodegeneration. Acta Neuropathologica, 118(1), 53–69. doi:10.1007/s00401-009-0486-3Find this resource:

Iwatsubo, T., Odaka, A., Suzuki, N., Mizusawa, H., Nukina, N., & Ihara, Y. (1994). Visualization of A beta 42(43) and A beta 40 in senile plaques with end-specific A beta monoclonals: evidence that an initially deposited species is A beta 42(43). Neuron, 13(1), 45–53.Find this resource:

Janelsins, M. C., Mastrangelo, M. A., Oddo, S., LaFerla, F. M., Federoff, H. J., & Bowers, W. J. (2005). Early correlation of microglial activation with enhanced tumor necrosis factor-alpha and monocyte chemoattractant protein-1 expression specifically within the entorhinal cortex of triple transgenic Alzheimer’s disease mice. Journal of Neuroinflammation, 2, 23. doi:10.1186/1742-2094-2-23Find this resource:

Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., …Posthuma, D. (2019). Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nature Genetics, 51(3), 404–413. doi:10.1038/s41588-018-0311-9Find this resource:

Jarrett, J. T., Berger, E. P., & Lansbury, P. T., Jr. (1993). The carboxy terminus of the beta amyloid protein is critical for the seeding of amyloid formation: Implications for the pathogenesis of Alzheimer’s disease. Biochemistry, 32(18), 4693–4697.Find this resource:

Jonsson, T., Stefansson, H., Steinberg, S., Jonsdottir, I., Jonsson, P. V., Snaedal, J., … Stefansson, K. (2013). Variant of TREM2 associated with the risk of Alzheimer’s disease. New England Journal of Medicine, 368(2), 107–116. doi:10.1056/NEJMoa1211103Find this resource:

Jorissen, E., Prox, J., Bernreuther, C., Weber, S., Schwanbeck, R., Serneels, L., … Saftig, P. (2010). The disintegrin/metalloproteinase ADAM10 is essential for the establishment of the brain cortex. Journal of Neuroscience, 30(14), 4833–4844. doi:10.1523/JNEUROSCI.5221–09.2010Find this resource:

Ju, Y. E., Lucey, B. P., & Holtzman, D. M. (2014). Sleep and Alzheimer disease pathology—A bidirectional relationship. Nature Reviews. Neurology, 10(2), 115–119. doi:10.1038/nrneurol.2013.269Find this resource:

Kang, J. E., Lim, M. M., Bateman, R. J., Lee, J. J., Smyth, L. P., Cirrito, J. R., … Holtzman, D. M. (2009). Amyloid-beta dynamics are regulated by orexin and the sleep-wake cycle. Science, 326(5955), 1005–1007. doi:10.1126/science.1180962Find this resource:

Ke, H. C., Huang, H. J., Liang, K. C., & Hsieh-Li, H. M. (2011). Selective improvement of cognitive function in adult and aged APP/PS1 transgenic mice by continuous non-shock treadmill exercise. Brain Research, 1403, 1–11. doi:10.1016/j.brainres.2011.05.056Find this resource:

Kim, M., Suh, J., Romano, D., Truong, M. H., Mullin, K., Hooli, B., … Tanzi, R. E. (2009). Potential late-onset Alzheimer’s disease-associated mutations in the ADAM10 gene attenuate {alpha}-secretase activity. Human Molecular Genetics, 18(20), 3987–3996.Find this resource:

Kim, Y. H., Choi, S. H., D’Avanzo, C., Hebisch, M., Sliwinski, C., Bylykbashi, E., … Kim, D. Y. (2015). A 3D human neural cell culture system for modeling Alzheimer’s disease. Nature Protocols, 10(7), 985–1006. doi:10.1038/nprot.2015.065Find this resource:

Kivipelto, M., Ngandu, T., Fratiglioni, L., Viitanen, M., Kareholt, I., Winblad, B., … Nissinen, A. (2005). Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease. Archives of Neurology, 62(10), 1556–1560. doi:10.1001/archneur.62.10.1556Find this resource:

Klesney-Tait, J., Turnbull, I. R., & Colonna, M. (2006). The TREM receptor family and signal integration. Natural Immunology, 7(12), 1266–1273. doi:10.1038/ni1411Find this resource:

Kounnas, M. Z., Danks, A. M., Cheng, S., Tyree, C., Ackerman, E., Zhang, X., … Wagner, S. L. (2010). Modulation of gamma-secretase reduces beta-amyloid deposition in a transgenic mouse model of Alzheimer’s disease. Neuron, 67(5), 769–780. doi:10.1016/j.neuron.2010.08.018Find this resource:

Krell-Roesch, J., Syrjanen, J. A., Vassilaki, M., Machulda, M. M., Mielke, M. M., Knopman, D. S., … Geda, Y. E. (2019). Quantity and quality of mental activities and the risk of incident mild cognitive impairment. Neurology, 93(6), e548-e558. doi:10.1212/WNL.0000000000007897Find this resource:

Kuhn, P. H., Wang, H., Dislich, B., Colombo, A., Zeitschel, U., Ellwart, J. W., … Lichtenthaler, S. F. (2010). ADAM10 is the physiologically relevant, constitutive alpha-secretase of the amyloid precursor protein in primary neurons. EMBO Journal, 29(17), 3020–3032. doi:10.1038/emboj.2010.167Find this resource:

Kunkle, B. W., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., … Environmental Risk for Alzheimer’s Disease, C. (2019). Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing. Nature Genetics, 51(3), 414–430. doi:10.1038/s41588-019-0358-2Find this resource:

Laitinen, M. H., Ngandu, T., Rovio, S., Helkala, E. L., Uusitalo, U., Viitanen, M., … Kivipelto, M. (2006). Fat intake at midlife and risk of dementia and Alzheimer’s disease: A population-based study. Dementia and Geriatric Cognitive Disorders, 22(1), 99–107. doi:10.1159/000093478Find this resource:

Lambert, J. C., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., … Amouyel, P. (2013). Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nature Genetics, 45(12), 1452–1458. doi:10.1038/ng.2802Find this resource:

Lanz, T. A., Himes, C. S., Pallante, G., Adams, L., Yamazaki, S., Amore, B., & Merchant, K. M. (2003). The gamma-secretase inhibitor N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester reduces A beta levels in vivo in plasma and cerebrospinal fluid in young (plaque-free) and aged (plaque-bearing) Tg2576 mice. Journal of Pharmacology and Experimental Therapeutics, 305(3), 864–871. doi:10.1124/jpet.102.048280Find this resource:

Lattanzi, S., Luzzi, S., Provinciali, L., & Silvestrini, M. (2014). Blood pressure variability predicts cognitive decline in Alzheimer’s disease patients. Neurobiology of Aging, 35(10), 2282–2287. doi:10.1016/j.neurobiolaging.2014.04.023Find this resource:

Laurin, D., Verreault, R., Lindsay, J., MacPherson, K., & Rockwood, K. (2001). Physical activity and risk of cognitive impairment and dementia in elderly persons. Archives of Neurology, 58(3), 498–504.Find this resource:

Lazarov, O., Robinson, J., Tang, Y. P., Hairston, I. S., Korade-Mirnics, Z., Lee, V. M., …Sisodia, S. S. (2005). Environmental enrichment reduces Abeta levels and amyloid deposition in transgenic mice. Cell, 120(5), 701–713.Find this resource:

Lee, L. J. (2009). Neonatal fluoxetine exposure affects the neuronal structure in the somatosensory cortex and somatosensory-related behaviors in adolescent rats. Neurotoxicity Research, 15(3), 212–223. doi:10.1007/s12640-009-9022-4Find this resource:

Lesuis, S. L., Maurin, H., Borghgraef, P., Lucassen, P. J., Van Leuven, F., & Krugers, H. J. (2016). Positive and negative early life experiences differentially modulate long term survival and amyloid protein levels in a mouse model of Alzheimer’s disease. Oncotarget, 7(26), 39118–39135. doi:10.18632/oncotarget.9776Find this resource:

Lesuis, S. L., Weggen, S., Baches, S., Lucassen, P. J., & Krugers, H. J. (2018). Targeting glucocorticoid receptors prevents the effects of early life stress on amyloid pathology and cognitive performance in APP/PS1 mice. Translational Psychiatry, 8(1), 53. doi:10.1038/s41398-018-0101-2Find this resource:

Levy, E., Carman, M. D., Fernandez-Madrid, I. J., Power, M. D., Lieberburg, I., van Duinen, S. G., … Frangione, B. (1990). Mutation of the Alzheimer’s disease amyloid gene in hereditary cerebral hemorrhage, Dutch type. Science, 248(4959), 1124–1126. doi:10.1126/science.2111584Find this resource:

Levy-Lahad, E., Wasco, W., Poorkaj, P., Romano, D. M., Oshima, J., Pettingell, W. H., … Tanzi, R. E. (1995). Candidate gene for the chromosome 1 familial Alzheimer’s disease locus. Science, 269(5226), 973–977. doi:10.1126/science.7638622Find this resource:

Lewis, J., Dickson, D. W., Lin, W. L., Chisholm, L., Corral, A., Jones, G., … McGowan, E. (2001). Enhanced neurofibrillary degeneration in transgenic mice expressing mutant tau and APP. Science, 293(5534), 1487–1491. doi:10.1126/science.1058189Find this resource:

Lourida, I., Hannon, E., Littlejohns, T. J., Langa, K. M., Hypponen, E., Kuzma, E., & Llewellyn, D. J. (2019). Association of lifestyle and genetic risk with incidence of dementia. JAMA, 322(5), 430–437. doi:10.1001/jama.2019.9879Find this resource:

Luchsinger, J. A., Tang, M. X., & Mayeux, R. (2007). Glycemic load and risk of Alzheimer’s disease. Journal of Nutrition, Health & Aging, 11(3), 238–241.Find this resource:

Machado, A., Herrera, A. J., de Pablos, R. M., Espinosa-Oliva, A. M., Sarmiento, M., Ayala, A., … Cano, J. (2014). Chronic stress as a risk factor for Alzheimer’s disease. Reviews in Neurosciences, 25(6), 785–804. doi:10.1515/revneuro-2014-0035Find this resource:

Maillard, I., Adler, S. H., & Pear, W. S. (2003). Notch and the immune system. Immunity, 19(6), 781–791.Find this resource:

Mann, D. M., Iwatsubo, T., Cairns, N. J., Lantos, P. L., Nochlin, D., Sumi, S. M., … Haltia, M. (1996). Amyloid beta protein (Abeta) deposition in chromosome 14-linked Alzheimer’s disease: Predominance of Abeta42(43). Annals of Neurology, 40(2), 149–156. doi:10.1002/ana.410400205Find this resource:

Mann, D. M., Iwatsubo, T., Nochlin, D., Sumi, S. M., Levy-Lahad, E., & Bird, T. D. (1997). Amyloid (Abeta) deposition in chromosome 1-linked Alzheimer’s disease: The Volga German families. Annals of Neurology, 41(1), 52–57. doi:10.1002/ana.410410110Find this resource:

Marcade, M., Bourdin, J., Loiseau, N., Peillon, H., Rayer, A., Drouin, D., … Desire, L. (2008). Etazolate, a neuroprotective drug linking GABA(A) receptor pharmacology to amyloid precursor protein processing. Journal of Neurochemistry, 106(1), 392–404. doi:10.1111/j.1471–4159.2008.05396.xFind this resource:

Mattson, M. P., Cheng, B., Culwell, A. R., Esch, F. S., Lieberburg, I., & Rydel, R. E. (1993). Evidence for excitoprotective and intraneuronal calcium-regulating roles for secreted forms of the beta-amyloid precursor protein. Neuron, 10(2), 243–254.Find this resource:

Mejia, S., Giraldo, M., Pineda, D., Ardila, A., & Lopera, F. (2003). Nongenetic factors as modifiers of the age of onset of familial Alzheimer’s disease. International Psychogeriatrics, 15(4), 337–349.Find this resource:

Melchior, B., Garcia, A. E., Hsiung, B. K., Lo, K. M., Doose, J. M., Thrash, J. C., … Carson, M. J. (2010). Dual induction of TREM2 and tolerance-related transcript, Tmem176b, in amyloid transgenic mice: Implications for vaccine-based therapies for Alzheimer’s disease. ASN Neuro, 2(3), e00037. doi:10.1042/AN20100010Find this resource:

Meng, X., & D’Arcy, C. (2012). Education and dementia in the context of the cognitive reserve hypothesis: A systematic review with meta-analyses and qualitative analyses. PLoS One, 7(6), e38268. doi:10.1371/journal.pone.0038268Find this resource:

Mills, P. J., Wilson, K. L., Pung, M. A., Weiss, L., Patel, S., Doraiswamy, P. M., … Tanzi, R. E. (2016). The self-directed biological transformation initiative and well-being. Journal of Alternative and Complementary Medicine, 22(8), 627–634. doi:10.1089/acm.2016.0002Find this resource:

Morris, M. C., Evans, D. A., Bienias, J. L., Tangney, C. C., Bennett, D. A., Aggarwal, N., … Wilson, R. S. (2003a). Dietary fats and the risk of incident Alzheimer disease. Archives of Neurology, 60(2), 194–200. doi:10.1001/archneur.60.2.194Find this resource:

Morris, M. C., Evans, D. A., Bienias, J. L., Tangney, C. C., Bennett, D. A., Wilson, R. S., … Schneider, J. (2003b). Consumption of fish and n-3 fatty acids and risk of incident Alzheimer disease. Archives of Neurology, 60(7), 940–946. doi:10.1001/archneur.60.7.940Find this resource:

Morris, M. C., Tangney, C. C., Wang, Y., Sacks, F. M., Bennett, D. A., & Aggarwal, N. T. (2015). MIND diet associated with reduced incidence of Alzheimer’s disease. Alzheimer’s Dementia, 11(9), 1007–1014. doi:10.1016/j.jalz.2014.11.009Find this resource:

Nelson, T. J., Sun, M. K., Lim, C., Sen, A., Khan, T., Chirila, F. V., & Alkon, D. L. (2017). Bryostatin effects on cognitive function and PKCvarepsilon in Alzheimer’s disease phase IIa and expanded access trials. Journal of Alzheimer’s Disease, 58(2), 521–535. doi:10.3233/JAD-170161Find this resource:

Nicolas, M., Wolfer, A., Raj, K., Kummer, J. A., Mill, P., van Noort, M., … Radtke, F. (2003). Notch1 functions as a tumor suppressor in mouse skin. Nature Genetics, 33(3), 416–421. doi:10.1038/ng1099Find this resource:

Nikolaev, A., McLaughlin, T., O’Leary, D. D., & Tessier-Lavigne, M. (2009). APP binds DR6 to trigger axon pruning and neuron death via distinct caspases. Nature, 457(7232), 981–989. doi:10.1038/nature07767Find this resource:

Ozawa, M., Ninomiya, T., Ohara, T., Doi, Y., Uchida, K., Shirota, T., … Kiyohara, Y. (2013). Dietary patterns and risk of dementia in an elderly Japanese population: The Hisayama study. American Journal of Clinical Nutrition, 97(5), 1076–1082. doi:10.3945/ajcn.112.045575Find this resource:

Panza, F., Frisardi, V., Seripa, D., Logroscino, G., Santamato, A., Imbimbo, B. P., … Solfrizzi, V. (2012). Alcohol consumption in mild cognitive impairment and dementia: Harmful or neuroprotective? International Journal of Geriatric Psychiatry, 27(12), 1218–1238. doi:10.1002/gps.3772Find this resource:

Park, J., Wetzel, I., Marriott, I., Dreau, D., D’Avanzo, C., Kim, D. Y., … Cho, H. (2018). A 3D human triculture system modeling neurodegeneration and neuroinflammation in Alzheimer’s disease. Nature Reviews. Neuroscience, 21(7), 941–951. doi:10.1038/s41593-018-0175-4Find this resource:

Pase, M. P., Himali, J. J., Jacques, P. F., DeCarli, C., Satizabal, C. L., Aparicio, H., … Seshadri, S. (2017). Sugary beverage intake and preclinical Alzheimer’s disease in the community. Alzheimer’s Dementia, 13(9), 955–964. doi:10.1016/j.jalz.2017.01.024Find this resource:

Perez-Nievas, B. G., Stein, T. D., Tai, H. C., Dols-Icardo, O., Scotton, T. C., Barroeta-Espar, I., … Gomez-Isla, T. (2013). Dissecting phenotypic traits linked to human resilience to Alzheimer’s pathology. Brain, 136(Pt. 8), 2510–2526. doi:10.1093/brain/awt171Find this resource:

Peterson, C. T., Lucas, J., John-Williams, L. S., Thompson, J. W., Moseley, M. A., Patel, S., … Chopra, D. (2016). Identification of altered metabolomic profiles following a Panchakarma-based ayurvedic intervention in healthy subjects: The self-directed biological transformation initiative (SBTI). Scientific Reports, 6, 32609. doi:10.1038/srep32609Find this resource:

Piazza-Gardner, A. K., Gaffud, T. J., & Barry, A. E. (2013). The impact of alcohol on Alzheimer’s disease: A systematic review. Aging & Mental Health, 17(2), 133–146. doi:10.1080/13607863.2012.742488Find this resource:

Postina, R., Schroeder, A., Dewachter, I., Bohl, J., Schmitt, U., Kojro, E., … Fahrenholz, F. (2004). A disintegrin-metalloproteinase prevents amyloid plaque formation and hippocampal defects in an Alzheimer disease mouse model. Journal of Clinical Investigation, 113(10), 1456–1464.Find this resource:

Price, D. L., & Sisodia, S. S. (1998). Mutant genes in familial Alzheimer’s disease and transgenic models. Annual Review of Neuroscience, 21, 479–505.Find this resource:

Rai, S. K., Fung, T. T., Lu, N., Keller, S. F., Curhan, G. C., & Choi, H. K. (2017). The Dietary Approaches to Stop Hypertension (DASH) diet, Western diet, and risk of gout in men: Prospective cohort study. British Medical Journal, 357, j1794. doi:10.1136/bmj.j1794Find this resource:

Rebeck, G. W., Reiter, J. S., Strickland, D. K., & Hyman, B. T. (1993). Apolipoprotein E in sporadic Alzheimer’s disease: Allelic variation and receptor interactions. Neuron, 11(4), 575–580. doi:10.1016/0896-6273(93)90070-8Find this resource:

Riley, K. P., Snowdon, D. A., Desrosiers, M. F., & Markesbery, W. R. (2005). Early life linguistic ability, late life cognitive function, and neuropathology: Findings from the Nun study. Neurobiology of Aging, 26(3), 341–347. doi:10.1016/j.neurobiolaging.2004.06.019Find this resource:

Ring, S., Weyer, S. W., Kilian, S. B., Waldron, E., Pietrzik, C. U., Filippov, M. A., … Muller, U. C. (2007). The secreted beta-amyloid precursor protein ectodomain APPs alpha is sufficient to rescue the anatomical, behavioral, and electrophysiological abnormalities of APP-deficient mice. Journal of Neuroscience, 27(29), 7817–7826. doi:10.1523/JNEUROSCI.1026–07.2007Find this resource:

Rogers, K., Felsenstein, K. M., Hrdlicka, L., Tu, Z., Albayya, F., Lee, W., … Koenig, G. (2012). Modulation of gamma-secretase by EVP-0015962 reduces amyloid deposition and behavioral deficits in Tg2576 mice. Molecular Neurodegeneration, 7, 61. doi:10.1186/1750-1326-7-61Find this resource:

Roh, J. H., Huang, Y., Bero, A. W., Kasten, T., Stewart, F. R., Bateman, R. J., & Holtzman, D. M. (2012). Disruption of the sleep-wake cycle and diurnal fluctuation of beta-amyloid in mice with Alzheimer’s disease pathology. Science Translational Medicine, 4(150), 150ra122. doi:10.1126/scitranslmed.3004291Find this resource:

Roses, A. D. (1996). Apolipoprotein E alleles as risk factors in Alzheimer’s disease. Annual Review of Medicine, 47, 387–400. doi:10.1146/annurev.med.47.1.387Find this resource:

Roskam, S., Neff, F., Schwarting, R., Bacher, M., & Dodel, R. (2010). APP transgenic mice: the effect of active and passive immunotherapy in cognitive tasks. Neuroscience Biobehavioral Reviews, 34(4), 487–499. doi:10.1016/j.neubiorev.2009.10.006Find this resource:

Rynearson, K. D., Buckle, R. N., Barnes, K. D., Herr, R. J., Mayhew, N. J., Paquette, W. D., … Wagner, S. L. (2016). Design and synthesis of aminothiazole modulators of the gamma-secretase enzyme. Bioorganic & Medicinal Chemistry Letters, 26(16), 3928–3937. doi:10.1016/j.bmcl.2016.07.011Find this resource:

Samadi, M., Moradi, S., Moradinazar, M., Mostafai, R., & Pasdar, Y. (2019). Dietary pattern in relation to the risk of Alzheimer’s disease: A systematic review. Neurological Sciences, 40(10), 2031–2043. doi:10.1007/s10072-019-03976-3Find this resource:

Saunders, A. M., Strittmatter, W. J., Schmechel, D., St. George-Hyslop, P. H., Pericak-Vance, M. A., Joo, S. H., … Roses, A. D. (1993). Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer’s disease. Neurology, 43(8), 1467–1472. doi:10.1212/wnl.43.8.1467Find this resource:

Scarmeas, N., Luchsinger, J. A., Mayeux, R., & Stern, Y. (2007). Mediterranean diet and Alzheimer disease mortality. Neurology, 69(11), 1084–1093. doi:10.1212/01.wnl.0000277320.50685.7cFind this resource:

Scarmeas, N., Stern, Y., Tang, M. X., Mayeux, R., & Luchsinger, J. A. (2006). Mediterranean diet and risk for Alzheimer’s disease. Annals of Neurology, 59(6), 912–921. doi:10.1002/ana.20854Find this resource:

Schenk, D., Barbour, R., Dunn, W., Gordon, G., Grajeda, H., Guido, T., … Seubert, P. (1999). Immunization with amyloid-beta attenuates Alzheimer-disease-like pathology in the PDAPP mouse. Nature, 400(6740), 173–177. doi:10.1038/22124Find this resource:

Schmechel, D. E., Saunders, A. M., Strittmatter, W. J., Crain, B. J., Hulette, C. M., Joo, S. H., … Roses, A. D. (1993). Increased amyloid beta-peptide deposition in cerebral cortex as a consequence of apolipoprotein E genotype in late-onset Alzheimer disease. Proceedings of the National Academy of Sciences USA, 90(20), 9649–9653. doi:10.1073/pnas.90.20.9649Find this resource:

Scott, J. D., Li, S. W., Brunskill, A. P., Chen, X., Cox, K., Cumming, J. N., … Stamford, A. W. (2016). Discovery of the 3-Imino-1,2,4-thiadiazinane 1,1-dioxide derivative verubecestat (MK-8931)-A beta-site amyloid precursor protein cleaving enzyme 1 inhibitor for the treatment of Alzheimer’s disease. Journal of Medicinal Chemistry, 59(23), 10435–10450. doi:10.1021/acs.jmedchem.6b00307Find this resource:

Selkoe, D. J. (1991). The molecular pathology of Alzheimer’s disease. Neuron, 6(4), 487–498.Find this resource:

Selkoe, D. J. (2001). Alzheimer’s disease: Genes, proteins, and therapy. Physiological Reviews, 81(2), 741–766.Find this resource:

Sengupta, A., Kabat, J., Novak, M., Wu, Q., Grundke-Iqbal, I., & Iqbal, K. (1998). Phosphorylation of tau at both Thr 231 and Ser 262 is required for maximal inhibition of its binding to microtubules. Archives of Biochemistry Biophysics, 357(2), 299–309. doi:10.1006/abbi.1998.0813Find this resource:

Sherrington, R., Rogaev, E. I., Liang, Y., Rogaeva, E. A., Levesque, G., Ikeda, M., … St. George-Hyslop, P. H. (1995). Cloning of a gene bearing missense mutations in early-onset familial Alzheimer’s disease. Nature, 375(6534), 754–760. doi:10.1038/375754a0Find this resource:

Sisodia, S. S., & St. George-Hyslop, P. H. (2002). gamma-Secretase, Notch, Abeta and Alzheimer’s disease: Where do the presenilins fit in? Nature Reviews. Neuroscience, 3(4), 281–290.Find this resource:

St. George-Hyslop, P. H., Tanzi, R. E., Polinsky, R. J., Haines, J. L., Nee, L., Watkins, P. C., … Gusella, J. F. (1987). The genetic defect causing familial Alzheimer’s disease maps on chromosome 21. Science, 235(4791), 885–890. doi:10.1126/science.2880399Find this resource:

Stanger, B. Z., Datar, R., Murtaugh, L. C., & Melton, D. A. (2005). Direct regulation of intestinal fate by Notch. Proceedings of the National Academy of Sciences USA, 102(35), 12443–12448. doi:10.1073/pnas.0505690102Find this resource:

Suh, J., Choi, S. H., Romano, D. M., Gannon, M. A., Lesinski, A. N., Kim, D. Y., & Tanzi, R. E. (2013). ADAM10 missense mutations potentiate beta-amyloid accumulation by impairing prodomain chaperone function. Neuron, 80(2), 385–401. doi:10.1016/j.neuron.2013.08.035Find this resource:

Suh, J., Romano, D. M., Nitschke, L., Herrick, S. P., DiMarzio, B. A., Dzhala, V., … Tanzi, R. E. (2019). Loss of ataxin-1 potentiates Alzheimer’s pathogenesis by elevating cerebral BACE1 transcription. Cell, 178(5), 1159–1175 e1117. doi:10.1016/j.cell.2019.07.043Find this resource:

Takahashi, R. H., Capetillo-Zarate, E., Lin, M. T., Milner, T. A., & Gouras, G. K. (2010). Co-occurrence of Alzheimer’s disease ss-amyloid and tau pathologies at synapses. Neurobiology of Aging, 31(7), 1145–1152. doi:10.1016/j.neurobiolaging.2008.07.021Find this resource:

Tanzi, R. E. (2013). A brief history of Alzheimer’s disease gene discovery. Journal of Alzheimer’s Disease, 33(Suppl. 1), S5–S13. doi:10.3233/JAD-2012-129044Find this resource:

Tanzi, R. E., & Bertram, L. (2005). Twenty years of the Alzheimer’s disease amyloid hypothesis: A genetic perspective. Cell, 120(4), 545–555. doi:10.1016/j.cell.2005.02.008Find this resource:

Tanzi, R. E., Gusella, J. F., Watkins, P. C., Bruns, G. A., St. George-Hyslop, P., Van Keuren, M. L., … Neve, R. L. (1987). Amyloid beta protein gene: cDNA, mRNA distribution, and genetic linkage near the Alzheimer locus. Science, 235(4791), 880–884. doi:10.1126/science.2949367Find this resource:

Tapia-Rojas, C., Aranguiz, F., Varela-Nallar, L., & Inestrosa, N. C. (2016). Voluntary running attenuates memory loss, decreases neuropathological changes and induces neurogenesis in a mouse model of Alzheimer’s disease. Brain Pathology, 26(1), 62–74. doi:10.1111/bpa.12255Find this resource:

Tariot, P. N., Farlow, M. R., Grossberg, G. T., Graham, S. M., McDonald, S., Gergel, I., & Memantine Study Group. (2004). Memantine treatment in patients with moderate to severe Alzheimer disease already receiving donepezil: A randomized controlled trial. JAMA, 291(3), 317–324. doi:10.1001/jama.291.3.317Find this resource:

Tran, T. T., Srivareerat, M., & Alkadhi, K. A. (2010). Chronic psychosocial stress triggers cognitive impairment in a novel at-risk model of Alzheimer’s disease. Neurobiology of Disease, 37(3), 756–763. doi:10.1016/j.nbd.2009.12.016Find this resource:

Tzeng, N. S., Chung, C. H., Lin, F. H., Chiang, C. P., Yeh, C. B., Huang, S. Y., … Chien, W. C. (2018). Anti-herpetic medications and reduced risk of dementia in patients with herpes simplex virus infections—A nationwide, population-based cohort study in Taiwan. Neurotherapeutics, 15(2), 417–429. doi:10.1007/s13311-018-0611-xFind this resource:

Verghese, P. B., Castellano, J. M., & Holtzman, D. M. (2011). Apolipoprotein E in Alzheimer’s disease and other neurological disorders. The Lancet. Neurology, 10(3), 241–252. doi:10.1016/S1474-4422(10)70325-2Find this resource:

Wagner, S. L., Zhang, C., Cheng, S., Nguyen, P., Zhang, X., Rynearson, K. D., … Tanzi, R. E. (2014). Soluble gamma-secretase modulators selectively inhibit the production of the 42-amino acid amyloid beta peptide variant and augment the production of multiple carboxy-truncated amyloid beta species. Biochemistry, 53(4), 702–713. doi:10.1021/bi401537vFind this resource:

Wilson, R. S., Begeny, C. T., Boyle, P. A., Schneider, J. A., & Bennett, D. A. (2011). Vulnerability to stress, anxiety, and development of dementia in old age. American Journal of Geriatric Psychiatry, 19(4), 327–334. doi:10.1097/JGP.0b013e31820119daFind this resource:

Wilson, R. S., Bennett, D. A., Beckett, L. A., Morris, M. C., Gilley, D. W., Bienias, J. L., … Evans, D. A. (1999). Cognitive activity in older persons from a geographically defined population. Journals of Gerontology. Series B. Psychological Sciences and Social Sciences, 54(3), P155–160. doi:10.1093/geronb/54b.3.p155Find this resource:

Wilson, R. S., Krueger, K. R., Arnold, S. E., Schneider, J. A., Kelly, J. F., Barnes, L. L., … Bennett, D. A. (2007). Loneliness and risk of Alzheimer disease. Archives of General Psychiatry, 64(2), 234–240. doi:10.1001/archpsyc.64.2.234Find this resource:

Xia, X., Qian, S., Soriano, S., Wu, Y., Fletcher, A. M., Wang, X. J., … Zheng, H. (2001). Loss of presenilin 1 is associated with enhanced beta-catenin signaling and skin tumorigenesis. Proceedings of the National Academy of Sciences USA, 98(19), 10863–10868. doi:10.1073/pnas.191284198Find this resource:

Yasojima, K., McGeer, E. G., & McGeer, P. L. (2001). Relationship between beta amyloid peptide generating molecules and neprilysin in Alzheimer disease and normal brain. Brain Research, 919(1), 115–121. doi:10.1016/s0006-8993(01)03008-6Find this resource:

Ylilauri, M. P., Voutilainen, S., Lonnroos, E., Mursu, J., Virtanen, H. E., Koskinen, T. T., … Virtanen, J. K. (2017). Association of dietary cholesterol and egg intakes with the risk of incident dementia or Alzheimer disease: The Kuopio ischaemic heart disease risk factor study. American Journal of Clinical Nutrition, 105(2), 476–484. doi:10.3945/ajcn.116.146753Find this resource:

Zaheer, A., Zaheer, S., Thangavel, R., Wu, Y., Sahu, S. K., & Yang, B. (2008). Glia maturation factor modulates beta-amyloid-induced glial activation, inflammatory cytokine/chemokine production and neuronal damage. Brain Research, 1208, 192–203. doi:10.1016/j.brainres.2008.02.093Find this resource: