The Anti-Amyloid Monoclonal Antibody Lecanemab: 16 Cautionary Notes

Abstract

After the CLARITY-AD clinical trial results of lecanemab were interpreted as positive, and supporting the amyloid hypothesis, the drug received accelerated Food and Drug Administration approval. However, we argue that benefits of lecanemab treatment are uncertain and may yield net harm for some patients, and that the data do not support the amyloid hypothesis. We note potential biases from inclusion, unblinding, dropouts, and other issues. Given substantial adverse effects and subgroup heterogeneity, we conclude that lecanemab’s efficacy is not clinically meaningful, consistent with numerous analyses suggesting that amyloid-β and its derivatives are not the main causative agents of Alzheimer’s disease dementia.

Keywords

Alzheimer’s disease amyloid-β antibody lecanemab subgroup analysis

INTRODUCTION

With nearly 40 million Alzheimer’s disease (AD) patients in the world and up to a million deaths per year, new treatments are urgently needed [1, 2]. Monoclonal amyloid-β peptide (Aβ) antibodies have received enormous attention as treatments inspired by the amyloid cascade hypothesis [3 –6]. However, many previous antibodies failed in clinical trials and produced adverse effects [7 –10]. Donanemab, aducanumab, and lecanemab were possible exceptions by showing some claimed benefits [11 –13], although these are modest and highly debated [14 –17]. The Food and Drug Administration (FDA) recently gave accelerated approval of aducanumab, endorsing drug-induced changes in brain amyloid-levels measured by positron emission tomography (amyloid-PET) as a surrogate measurement of clinical efficacy [15, 18]. This decision caused widespread controversy, with 10 out of 11 independent FDA advisors voting against approval, and three resigning in protest when approval was nevertheless granted against their recommendation [16 , 20]. More recently, another antibody lecanemab was approved by FDA also via the accelerated amyloid-PET surrogate claim in January 2023 [21 –23].

THE AMYLOID HYPOTHESIS

The current definition of AD is predicated on the presence of amyloid deposition of the amyloid-β (Aβ) peptide in the brain [2 , 24–26]. This disease nosology is based on the amyloid hypothesis stating that an increase in Aβ aggregation, in one or more in vivo molecular forms acting on some processes in the brain, is the primary cause of disease [3 , 6] and thus that therapies that reduce Aβ should have a strong beneficial impact [10]. However, dementia associated with AD is a complex disorder [27 –32], with genome-wide association studies implicating risk genes unrelated to Aβ processing [33 –37], as well as diverse metabolic [38, 39], vascular [40 –42], and other important risk factors [1 , 43]. Furthermore, familial AD (fAD) which results in genetically determined increase in Aβ deposition in the brain, represents only a very small percentage of total cases with the large majority occurring sporadically [44]. It is not yet clear that Aβ is the cause of dementia in fAD, or that fAD and sporadic AD (sAD) are the same disease, or that Aβ is a major risk factor for sAD, as amyloid load correlates only modestly with clinical presentation [45 –48] (see also below). Many people have brain Aβ amyloid fulfilling diagnostic criteria for AD yet without symptoms [45 , 49–51], and technical challenges limit the use of amyloid-PET as a surrogate [52 –57], with the amyloid-PET-clinical relationship being uncertain and sometimes possibly based on misinterpreted data [52, 58].

LECANEMAB

The monoclonal antibody lecanemab was designed to target the N-terminal region of Aβ protofibrils deriving from the Arctic mutant, which favors protofibril formation [22]. The new data for lecanemab from the phase III CLARITY-AD trial [23] showed a modest but statistically significant clinical effect of slowing cognitive decline during the 18-month study period [23]. The primary endpoint was the change in the Clinical Dementia Rating–Sum of Boxes (CDR-SB) [59, 60], a broadly accepted 18-point assessment scale based on interviews covering six categories of cognitive and functional performance (memory, orientation, problem solving, community, home and hobbies, and personal care). Although partly subjective, it has shown good consistency [61 –63]. The trial also reported significant effects on secondary endpoints, notably the 14-item version of the 90-point Alzheimer’s Disease Assessment Scale [64] (ADAS-Cog14) [65], covering word recall, commands, object cognition (constructional praxis), naming, planned object-interaction and motoric skill (ideational praxis), orientation, word recognition, word instruction memory, spoken language comprehension, spoken language ability, word finding difficulty, delayed recall, digit cancellation, and maze tasks. A limitation of this scale is that significance depends on the number of items included (e.g., 14 items may yield significance in cases where 11 items do not) and the differentially weighed items, with the largest weight for items that often change the most [66]. For the primary endpoint, the mean change versus the baseline (3.2) was approximately – 0.45 difference versus placebo (1.21 with lecanemab and 1.66 with placebo), whereas for ADAS-Cog14 the effect was – 1.44. This was described as a 27% reduced cognitive decline versus placebo [23]. It was also claimed that lecanemab slows disease progression relative to placebo by about half a year [23]. These effects were associated with a reduction of amyloid-PET signal, but also with increased levels of CSF Aβ₄₂. However, upon analyzing these data, we propose that there are many uncertainties that should be considered in relation to lecanemab.

CAUTIONARY NOTES REGARDING LECANEMAB’S EFFICACY

Several points regarding both the efficiency and risk-benefit assessment are noteworthy:

Lecanemab use was associated with a 27% slowing of decline based on the CDR-SB measure of cognition, relative to the placebo group. However, this absolute reduction relative to placebo was 0.45 on an 18-point scale and highly heterogeneous among subgroups (see below). The authors stated that “A definition of clinically meaningful effects in the primary end point of the CDR-SB score has not been established” but recent literature estimates this to be equal to 1 [67, 68]. That is, an effect below 1 cannot be expected to be perceived by patients. Furthermore, the 0.45 points reduction in decline seen in the lecanemab group is a maximal effect achieved after careful patient selection (59.6% of initially screened individuals at many sites did not meet inclusion criteria or fulfilled exclusion criteria) and may be subject to uncertainties and potential biases (see below). The problem in real life is that doctors are unlikely to exclude such a high proportion of patients from treatment, and the risks for patients that would have been excluded from the trial, such as those with comorbidity, are unknown. It is also unknown whether the “maximal benefits” seen in the trial will persist over longer drug exposure, or alternatively after drug use is ceased [23].

Bias from unblinding of patients due to protocols related to ARIA and infusion reactions could reduce the effect further [69]. Although the authors performed sensitivity analysis to address this [23], a quarter of the treatment arm had such effects. The risk of functional unblinding from ARIA cannot be overcome by blinded raters when patients learned they are on treatment by side effects. Such unblinding biases responses on subjective scales such as CDR-SB, ADCOMS, and ADCS-ADL-MCI.

The endpoint measurements are based on cohorts with more dropouts due to severe events in the treatment group. There were almost double dropout rates in the treatment group due to severe adverse events (see also Fig. S6 in [23] – “time to worsening of global CDR score”, rate of progression to “next stage”). Distinct dropout characteristics are a well-known source of bias [70, 71] and could explain some of the difference between the primary and secondary endpoints and the highly heterogenous subgroups. Specifically, higher severe-cause dropouts in the treatment arm made the cohorts unbalanced and plausibly related to covariates of health or behavior that could translate into an artificial relative effect on the endpoint curve. This concern could have been addressed by providing dropout statistics related to each subgroup. Thus, the trial data need to be analyzed with standard methods to at least show how large an impact this bias has on the endpoint curves [71, 72].

The effect was extremely heterogenous in the subgroup analysis. For example, all endpoints showed 100–300% more effect in men. For the primary endpoint the effect in women was only 12%, versus 43% for men, an enormous 3.5-fold difference in impact (Fig. S1B in [23]). This needs to be understood either biologically, as an artefact relating to the biases discussed above, or at least, as a point of note for the label regarding the lesser effect and therefore lower benefit-risk ratio in women (who are more at risk of AD) if eventually approved. The evidence for non-random dropout effects (attrition bias) in the trial raises the concern that a larger dropout bias in Americans, old participants, and males could contribute to the much larger, unexplained apparent efficacy for these groups relative to Europeans, women, and young participants, but as mentioned above, the trial authors unfortunately did not report dropout statistics on subgroups.

The APOE ɛ4 genotype was associated with a consistent reduction of the clinical benefit of lecanemab (non-carriers most benefit, heterozygotes less benefit, and homozygotes even less benefit). APOE ɛ4 is a known risk factor for AD and has been thought to enhance Aβ pathology [29 , 74]. Quite apart from clinical implications, the fact that people at higher AD risk due to APOE ɛ4 showed less benefit with lecanemab is hard to reconcile with the amyloid-based disease hypothesis (i.e., if patients are at increased risk, they should have responded better to a treatment targeting the primary cause). Together with population covariates influencing outcome in the subgroups, this suggests the involvement of important non-amyloid etiologies.

Europeans had only 41% of the benefit of the drug measured on the primary endpoint compared to Americans (CDR-SB; 14 versus 34%, Fig. S1A in [23]). Given the very large confidence intervals, some of this could be real population covariates or mostly low sampling certainty, invoking the need for further data. This is particularly true since in the current trial data, population health covariates influence the claimed efficiency by the same magnitude as the effects observed, which suggests that the trial may have identified previously unidentified covariates of AD risk and progression.

The subgroup analysis for the ADAS-Cog14 score also shows a concerning tendency that the use of symptomatic medicine at baseline dominates as a covariate of the endpoint efficacy, with almost double the efficacy of lecanemab if the patient is already on a symptomatic medication (Fig. S2 in [23]). This is not expected from a causal disease-modifying treatment.

Further supporting our concern in point 7 are previous trials of non-causal (symptomatic) drugs such as donepezil showing remarkably similar effect curve shapes [75, 76]. If lecanemab had been disease modifying one would expect a far more significant effect. The similarity of the effects could suggest that lecanemab does not work by disease modification but by some other non-specific effect or a common bias in the trial, such as drop-out, unblinding or cohort selection bias discussed above.

For both the primary endpoint CDR-SB and for ADAS-Cog14, age was a major covariate of efficacy, with the drug having essentially no effect on the primary endpoint for patients <65 years (6% versus 23–40% in the higher ages, large confidence interval, Fig. S1B in [23]) and half effect by ADAS-Cog14 (14% versus 29–30% for older ages, Fig. S2B in [23]). Notably this 6% CDR-SB effect for combined sex <65 years includes women with less than 1/3 benefit as judged from the sex-stratified estimate for all ages (12% women, 43% men, Fig. S1B in [23]).

The combined subgroup results suggest that effects could in fact be negative (i.e., harmful compared to placebo) in some patient groups, such as APOE ɛ4 allele carriers, Europeans below 65 years, women, and especially combinations of these. These effects probably reflect demographic risk factors and show the importance of other covariates determining disease outcome than Aβ amyloid alone. However, subgroup stratification of adverse effects and clinical effects of composite groups are missing for those in whom administration could plausibly be net harmful (e.g., female APOE ɛ4 carriers under 65 years).

URGENT POINTS OF CAUTION REGARDING ADVERSE EFFECTS OF LECANEMAB

One of the advantages of lecanemab is its development based on the Arctic mutation (E22G) that is assumed to form protofibrils quickly, making the antibody attack N-terminal epitopes of protofibrils [22] and supposedly producing fewer amyloid-related imaging abnormalities (ARIA), the most prevailing adverse effect seen for these types of antibodies [21]. Yet, the adverse effects in Table 3 of the trial paper [23] were very substantial and included 12.6% ARIA-E versus 1.7% placebo, and 17.3% ARIA-H versus 9% placebo [23].

The twice as high drop-out rate in the treatment group due to serious adverse effects [23] not only risks biasing the efficacy estimates in the endpoint curves, but is also by itself a red flag regarding the risk-benefit balance of lecanemab: An overall good drug in balance of benefits and adverse effects would not be expected to give so many more adverse effects in the treatment group. Long-term follow up of all patients, including those who left the trial, is essential before drawing conclusions about the drug’s safety.

Another major concern is the evidence for brain atrophy in both the Phases II and III of lecanemab [21]. Brain volume changes are also seen with other antibodies [77]. Just as amyloid accumulation does not cause brain swelling, amyloid clearance is not very likely to explain the brain atrophy, as postmortem studies in preclinical models and patients indicate that the overall volume of amyloid deposition accounts for less that 1% of the neocortex [78, 79], i.e., this could indicate neuronal damage. Segmentation techniques can be used to quantify brain and cerebrospinal volumes [80 –82]. We believe such methodologies as well as FDG-PET imaging data should be combined with magnetic resonance imaging for comprehensive determination of brain function and structure following antibody treatments. The lack of rigorous data ruling out brain volume changes due to treatment related tissue damage is of utmost concern for clinicians and patients. This issue is emphasized by a very recent meta-analysis, indicating that antibody treatments accelerate AD-like changes in brain volume [83].

Another important red flag is the reports of three deaths during the trial, several associated with ARIA [84 –86], although anticoagulants may have contributed to the observed brain swelling and hemorrhage [84, 85]. The trial authors stated that no deaths were associated with ARIA [23]. However, independent assessments suggest lecanemab contributed to death in at least two subsequent cases [85, 86], which urgently requires analysis, as fatal outcomes due to ARIA (with a prevalence of 12.6% ARIA-E and and 17.3% ARIA-H) could change the risk-benefit balance considerably, especially given lack of long-term follow-up information. Cerebral amyloid angiopathy is a major risk factor of and shares pathology with ARIA [87], which could explain the anticoagulant relationship and raises concerns about the real-world cost-benefit tradeoff associated with monitoring risk factors during lecanemab administration.

That ARIA increases with APOE ɛ4 genotype, as also seen for gantenerumab [88] and bapineuzumab [9], strongly argues for caution of use in these patients [89] and illustrates the importance of having both adverse effects and clinical benefits stratified better on population subgroups, in order to first do no harm.

Importantly, especially in context of the above-described heterogeneity and uncertainty, the trial was short, and we do not know the long-term effects of treatment; there may be an unexpected long-term impact (e.g., of ARIA) that changes the risk-benefit substantially. In addition to the many concerns, we must prepare for the eventuality that the real-world effects of lecanemab may be much smaller than reported in the trial, due to the biases and heterogeneity discussed above. We note that Alzheimer patients often have comorbidities that will make them more vulnerable to side effect risks and less likely to respond to treatment.

In sum, the data point to limited if any benefit on cognition and potential net detrimental effects at the very least for particular patient subgroups (e.g., women, Europeans, people under 65, and APOE ɛ4 carriers). Taken as a whole, the data do not provide compelling evidence for benefits of lecanemab on cognition, while the risks remain potentially significant and poorly understood.

DISCUSSION: CLARITY-AD IN CONTEXT

There was evidence before the CLARITY-AD trial of a limited impact of reducing Aβ in AD patients [10]. The amyloid hypothesis, the theoretical basis for the Aβ reduction strategy, has been criticized for its simplicity and inconsistencies [25 , 90–98]. Many fAD mutations associate with reduced Aβ production [95 , 99–105] but increase the Aβ₄₂/Aβ₄₀ ratio [106 –108]. Aβ toxicity conclusions were drawn from Aβ applied to cells at 1000-fold physiological Aβ concentrations [27, 99]. The absence of fAD mutations in the α- and β-secretases that prevent or initiate the production of Aβ [109] also suggests against Aβ processing as a major single cause of AD. Along similar lines, no known mutations yield fAD risk in the key metalloproteases degrading Aβ, such as insulin degrading enzyme [110 –112] and neprilysin [113 –115], expected if Aβ overload really caused disease [98]. Such anomalies among many others [25 , 116] illustrate that lowering of Aβ by itself cannot have a major impact.

The expected small benefit of amyloid-reducing drugs has been confirmed in clinical trials, including now lecanemab: We disagree that disease modification was demonstrated, since biomarker changes were not correlated directly to same subgroup clinical outcomes or studied for causal relationships. There was no evidence of dose-response effect as this was a single-dose trial. ARIA is known to be dose-dependent from previous antibody trials [117], and we expect at least the same level of dose-response benefit as adverse effects if a drug is disease-modifying.

The claimed beneficial effects with lecanemab in some subgroups (but not clearly in all, see above) [23] are not understood and could also be due to e.g., reconstitution of beneficial Aβ monomers [118]. While the association between brain amyloid and cognitive impairment is poor, reaching 5:1 ratio by age 85 [119, 120], the correlation between low soluble Aβ₄₂ and dementia is high: Most individuals with AD have low CSF Aβ₄₂ [121]. Soluble Aβ₄₂ increased substantially due to lecanemab treatment (Fig. S5 in [23]), which has been associated with a net positive clinical effect by itself [121, 122].

We also note that the CLARITY-AD trial confirms previous data in suggesting that Aβ by itself is not a major cause of AD [99]. Lecanemab offers further evidence that anti-amyloid therapies are unlikely to produce clinically meaningful benefits in broader patient groups.

REAL-WORLD CLINICAL USE OF LECANEMAB

It could be claimed that a modest decline of cognition in a relatively healthy patient cohort over the 18 months of the CLARITY-AD trial would make positive effects harder to identify, and therefore, that any benefit should be considered meaningful. However, this statement is wishful as uncertainty of effect is not an argument for treatment with so many red flags. The 59.6% patient exclusion from the trial suggests that the CLARITY-AD cohort is far from representative of real-world settings, where desperate patients and busy clinicians meet. Under the pressures of intense clinical practice with broader patient groups, the drug is unlikely to work as well as in the ideal settings monitored by CLARITY-AD, especially in context of the biases and subgroup heterogeneity discussed above. In other words, the uncertainty of benefit is very much to the direction of smaller, if any, effect.

In terms of risk, while it is thus unclear whether the short 18-month trial documents meaningful benefits, the long-term risks need to be better studied and clarified. Stringent monitoring of side effects will be much more difficult in real-world settings where many comorbidities are the rule, not the exception. Accordingly, the long-term risk-benefit balance of the drug is very unclear, with some patient groups (e.g., women under 65 years or APOE ɛ4 carriers) potentially at risk of net harm from administration. The data are insufficient to allow us to estimate this issue, which suggests against therapeutic use in real-world settings without a much better understanding of the subgroup risk-benefit balances.

Finally, the issue of financial sustainability should be addressed, as these costly and marginally effective therapies may deplete funding on public and private health budgets.

CONCLUSIONS

Analysis of the available data from CLARITY-AD suggests that lecanemab’s efficacy is below that accepted as clinically meaningful. There are potential biases in the cohort from inclusion bias, unblinding, and dropouts, among other issues, which may affect the veracity of the data. Substantial adverse effects and subgroup heterogeneity is clearly present. Thus, the translation of the clinical trial data into real world effects is very uncertain. All these issues are consistent with a large body of previous data suggesting that Aβ plays a minor role in etiology despite its clear role in pathology.

The present discussion is urgent for everyone—patients, clinicians, and researchers. There is reason for concern based on an objective assessment of the available data. To end the scourge of AD on our families and our society, we must ensure scientific and medical rigor focused on developing other etiology-based treatments for this devastating disease.

Footnotes

ACKNOWLEDGMENTS

The authors have no acknowledgments to report.

FUNDING

The authors have no funding to report related to this paper.

CONFLICT OF INTEREST

Dr. Sensi has received grant support from the Alzheimer’s Association, The Italian Ministry of Health and the Italian Ministry of Research. He is a scientific advisory board member without compensation for SINDEM the Italian Neurological Society for the Study of Dementia. He serves on the editorial boards of the Journal of Alzheimer’s Disease, PLOS One, Frontiers in Aging, Frontiers in Neuroscience, and Frontiers in Psychiatry.

Dr. Perry is a consultant for Synaptogenix and Nervgen and Editor-in-Chief of the Journal of Alzheimer’s Disease.

Dr. Espay has received consultant fees from Neuroderm, Amneal, Acadia, Acorda, Bexion, Kyowa Kirin, Sunovion, Supernus (formerly, USWorldMeds), Avion Pharmaceuticals, and Herantis Pharma; honoraria for speakership for Avion; and publishing royalties from Lippincott Williams & Wilkins, Cambridge University Press, and Springer. He cofounded REGAIN Therapeutics and is co-owner of a patent on synthetic soluble nonaggregating peptide analogues. He serves on the editorial boards of the Journal of Parkinson’s Disease, Journal of Alzheimer’s Disease, European Journal of Neurology, Movement Disorders Clinical Practice, and JAMA Neurology.

Stefano L. Sensi, George Perry, and Alberto J. Espay are Editorial Board Members of this journal but were not involved in the peer-review process nor had access to any information regarding its peer-review.

Other authors did not report any conflicts of interest related to this work.

DATA AVAILABILITY

The data discussed are available in the paper and supporting information of Van Dyck et al. [].

References

GBD 2016 Dementia Collaborators (2019) Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 18, 88–106.

Blennow

, de Leon

, Zetterberg

(2015) Alzheimer’s disease. Lancet 368, 387–403.

Wong

, Quaranta

, Glenner

(1985) Neuritic plaques and cerebrovascular amyloid in Alzheimer disease are antigenically related. Proc Natl Acad Sci U S A 82, 8729–8732.

Glenner

, Wong

(1987) Amyloidosis in Alzheimer’s disease and Down’s syndrome. In Molecular Neuropathology of Aging, Davies

, Finch

, eds. Cold Spring Harbor Laboratory Press, pp. 253–265.

Hardy

(2006) Alzheimer’s disease: The amyloid cascade hypothesis - an update and reappraisal. J Alzheimers Dis 9, 151–153.

Masters

, Simms

, Weinman

, Multhaup

, McDonald

, Beyreuther

(1985) Amyloid plaque core protein in Alzheimer disease and Down syndrome. Proc Natl Acad Sci U S A 82, 4245–4249.

Honig

, Vellas

, Woodward

, Boada

, Bullock

, Borrie

, Hager

, Andreasen

, Scarpini

, Liu-Seifert

, Case

, Dean

, Hake

, Sundell

, Poole Hoffmann

, Carlson

, Khanna

, Mintun

, DeMattos

, Selzler

, Siemers

(2018) Trial of solanezumab for mild dementia due to Alzheimer’s disease. N Engl J Med 378, 321–330.

Doody

, Thomas

, Farlow

, Iwatsubo

, Vellas

, Joffe

, Kieburtz

, Raman

, Sun

, Aisen

, Siemers

, Liu-Seifert

, Mohs

; Alzheimer’s Disease Cooperative Study Steering Committee; Solanezumab Study Group (2014) Phase 3 trials of solanezumab for mild-to-moderate Alzheimer’s disease. N Engl J Med 370, 311–321.

Salloway

, Sperling

, Fox

, Blennow

, Klunk

, Raskind

, Sabbagh

, Honig

, Porsteinsson

, Ferris

, Reichert

, Ketter

, Nejadnik

, Guenzler

, Miloslavsky

, Wang

, Lu

, Lull

, Tudor

, Liu

, Grundman

, Yuen

, Black

, Brashear

; Bapineuzumab 301 and 302 Clinical Trial Investigators (2014) Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. N Engl J Med 370, 322–333.

10.

Richard

, den Brok

MGHE

, van Gool

(2021) Bayes analysis supports null hypothesis of anti-amyloid beta therapy in Alzheimer’s disease. Alzheimers Dement 17, 1051–1055.

11.

Mintun

, Lo

, Duggan Evans

, Wessels

, Ardayfio

, Andersen

, Shcherbinin

, Sparks

, Sims

, Brys

, Apostolova

, Salloway

, Skovronsky

(2021) Donanemab in early Alzheimer’s disease. N Engl J Med 384, 1691–1704.

12.

Howard

, Liu

(2020) Questions EMERGE as Biogen claims aducanumab turnaround. Nat Rev Neurol 16, 63–64.

13.

Mahase

(2022) Lecanemab trial finds slight slowing of cognitive decline, but clinical benefits are uncertain. BMJ 379, o2912.

14.

Espay

(2021) Donanemab in early Alzheimer’s disease. N Engl J Med 385, 666–667.

15.

Knopman

, Jones

, Greicius

(2021) Failure to demonstrate efficacy of aducanumab: An analysis of the EMERGE and ENGAGE trials as reported by Biogen, December 2019. Alzheimers Dement 17, 696–701.

16.

Alexander

, Emerson

, Kesselheim

(2021) Evaluation of Aducanumab for Alzheimer disease: Scientific evidence and regulatory review involving efficacy, safety, and futility. JAMA 325, 1717–1718.

17.

The Lancet (2022) Lecanemab for Alzheimer’s disease: Tempering hype and hope. Lancet 400, 1899.

18.

Alexander

, Knopman

, Emerson

, Ovbiagele

, Kryscio

, Perlmutter

, Kesselheim

(2021) Revisiting FDA approval of aducanumab. N Engl J Med 385, 769–771.

19.

Schneider

(2022) Aducanumab trials EMERGE but don’t ENGAGE. J Prev Alzheimers Dis 2022, 193–196.

20.

Vogel

, Kanevsky

, Che

, Swanson

, Muik

, Vormehr

, Kranz

, Walzer

, Hein

, Güler

, Loschko

, Maddur

, Ota-Setlik

, Tompkins

, Cole

, Lui

, Ziegenhals

, Plaschke

, Eisel

, Dany

, Fesser

, Erbar

, Bates

, Schneider

, Jesionek

, Sänger

, Wallisch

A-K

, Feuchter

, Junginger

, Krumm

, Heinen

, Adams-Quack

, Schlereth

, Schille

, Kröner

, de la Caridad Güimil Garcia

, Hiller

, Fischer

, Sellers

, Choudhary

, Gonzalez

, Vascotto

, Gutman

, Fontenot

, Hall-Ursone

, Brasky

, Griffor

, Han

, Su

AAH

, Lees

, Nedoma

, Mashalidis

, Sahasrabudhe

, Tan

, Pavliakova

, Singh

, Fontes-Garfias

, Pride

, Scully

, Ciolino

, Obregon

, Gazi

, Carrion

, Alfson

, Kalina W

, Kaushal

, Shi

P-Y

, Klamp

, Rosenbaum

, Kuhn

, Türeci

, Dormitzer

, Jansen

, Sahin

(2021) BNT162b vaccines protect rhesus macaques from SARS-CoV-2. Nature 592, 283–289.

21.

Swanson

, Zhang

, Dhadda

, Wang

, Kaplow

, Lai

RYK

, Lannfelt

, Bradley

, Rabe

, Koyama

, Reyderman

, Berry

, Gordon

, Kramer

, Cummings

(2021) A randomized, double-blind, phase 2b proof-of-concept clinical trial in early Alzheimer’s disease with lecanemab, an anti-Aβ protofibril antibody. Alzheimers Res Ther 13, 80.

22.

Söderberg

, Johannesson

, Nygren

, Laudon

, Eriksson

, Osswald

, Möller

, Lannfelt

(2022) Lecanemab, Aducanumab, and Gantenerumab — binding profiles to different forms of amyloid-beta might explain efficacy and side effects in clinical trials for Alzheimer’s disease. Neurotherapeutics 20, 195–206.

23.

van Dyck

, Swanson

, Aisen

, Bateman

, Chen

, Gee

, Kanekiyo

, Li

, Reyderman

, Cohen

, Froelich

, Katayama

, Sabbagh

, Vellas

, Watson

, Dhadda

, Irizarry

, Kramer

, Iwatsubo

(2023) Lecanemab in early Alzheimer’s disease. N Engl J Med 388, 9–21.

24.

Johnson

, Minoshima

, Bohnen

, Donohoe

, Foster

, Herscovitch

, Karlawish

, Rowe

, Carrillo

, Hartley

, Hedrick

, Pappas

, Thies

(2013) Appropriate use criteria for amyloid PET: A report of the Amyloid Imaging Task Force, the Society of Nuclear Medicine and Molecular Imaging, and the Alzheimer’s Association. J Nucl Med 54, 476–490.

25.

Morris

, Clark

, Vissel

(2014) Inconsistencies and controversies surrounding the amyloid hypothesis of Alzheimer’s disease. Acta Neuropathol Commun 2, 135.

26.

Morris

, Clark

, Vissel

(2018) Questions concerning the role of amyloid-β in the definition, aetiology and diagnosis of Alzheimer’s disease. Acta Neuropathol 136, 663–689.

27.

Neve

, Robakis

(1998) Alzheimer’s disease: A re-examination of the amyloid hypothesis. Trends Neurosci 21, 15–19.

28.

Karantzoulis

, Galvin

(2011) Distinguishing Alzheimer’s disease from other major forms of dementia. Expert Rev Neurother 11, 1579–1591.

29.

Tanzi

(2012) The genetics of Alzheimer disease. Cold Spring Harb Perspect Med 2, a006296.

30.

Carreiras

, Mendes

, Perry

, Francisco

, Marco-Contelles

(2013) The multifactorial nature of Alzheimer’s disease for developing potential therapeutics. Curr Top Med Chem 13, 1745–1770.

31.

Heneka

, Carson

, El Khoury

, Landreth

, Brosseron

, Feinstein

, Jacobs

, Wyss-Coray

, Vitorica

, Ransohoff

, Herrup

, Frautschy

, Finsen

, Brown

, Verkhratsky

, Yamanaka

, Koistinaho

, Latz

, Halle

, Petzold

, Town

, Morgan

, Shinohara

, Perry

, Holmes

, Bazan

, Brooks

, Hunot

, Joseph

, Deigendesch

, Garaschuk

, Boddeke

, Dinarello

, Breitner

, Cole

, Golenbock

, Kummer

(2015) Neuroinflammation in Alzheimer’s disease. Lancet Neurol 14, 388–405.

32.

, Qiao

, Guo

, Wang

(2020) Cryo-EM structures of HKU2 and SADS-CoV spike glycoproteins provide insights into coronavirus evolution. Nat Commun 11, 3070.

33.

Medway

, Morgan

(2014) Review: The genetics of Alzheimer’s disease; putting flesh on the bones. Neuropathol Appl Neurobiol 40, 97–105.

34.

Bertram

, Tanzi

(2009) Genome-wide association studies in Alzheimer’s disease. Hum Mol Genet 18, R137–R145.

35.

Hollingworth

, Harold

, Jones

, Owen

, Williams

(2011) Alzheimer’s disease genetics: Current knowledge and future challenges. Int J Geriatr Psychiatry 26, 793–802.

36.

Harold

, Abraham

, Hollingworth

, Sims

, Gerrish

, Hamshere

, Pahwa

, Moskvina

, Dowzell

, Williams

, Jones

, Thomas

, Stretton

, Morgan

, Lovestone

, Powell

, Proitsi

, Lupton

, Brayne

, Rubinsztein

, Gill

, Lawlor

, Lynch

, Morgan

, Brown

, Passmore

, Craig

, McGuinness

, Todd

, Holmes

, Mann

, Smith

, Love

, Kehoe

, Hardy

, Mead

, Fox

, Rossor

, Collinge

, Maier

, Jessen

, Schürmann

, Heun

, van den Bussche

, Heuser

, Kornhuber

, Wiltfang

, Dichgans

, Frölich

, Hampel

, Hüll

, Rujescu

, Goate

, Kauwe

, Cruchaga

, Nowotny

, Morris

, Mayo

, Sleegers

, Bettens

, Engelborghs

, De Deyn

, Van Broeckhoven

, Livingston

, Bass

, Gurling

, McQuillin

, Gwilliam

, Deloukas

, Al-Chalabi

, Shaw

, Tsolaki

, Singleton

, Guerreiro

, Mühleisen

, Nöthen

, Moebus

, Jöckel

, Klopp

, Wichmann

, Carrasquillo

, Pankratz

, Younkin

, Holmans

, O’Donovan

, Owen

, Williams

(2009) Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet 41, 1088–1093.

37.

Karch

, Goate

(2015) Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol Psychiatry 77, 43–51.

38.

Virta

, Heikkila

, Perola

, Koskenvuo

, Raiha

, Rinne

, Kaprio

(2013) Midlife cardiovascular risk factors and late cognitive impairment. Eur J Epidemiol 28, 405–416.

39.

Diaz

(2009) Obesity: Overweight as a risk factor for dementia. Nat Rev Endocrinol 5, 587.

40.

Love

, Miners

(2016) Cerebrovascular disease in ageing and Alzheimer’s disease. Acta Neuropathol 131, 645–658.

41.

Ronnemaa

, Zethelius

, Lannfelt

, Kilander

(2011) Vascular risk factors and dementia: 40-year follow-up of a population-based cohort. Dement Geriatr Cogn Disord 31, 460–466.

42.

Sweeney

, Montagne

, Sagare

, Nation

, Schneider

, Chui

, Harrington

, Pa

, Law

, Wang

DJJ

, Jacobs

, Doubal

, Ramirez

, Black

, Nedergaard

, Benveniste

, Dichgans

, Iadecola

, Love

, Bath

, Markus

, Al-Shahi Salman

, Allan

, Quinn

, Kalaria

, Werring

, Carare

, Touyz

, Williams

SCR

, Moskowitz

, Katusic

, Lutz

, Lazarov

, Minshall

, Rehman

, Davis

, Wellington

, González

, Yuan

, Lockhart

, Hughes

, Chen

CLH

, Sachdev

, O’Brien

, Skoog

, Pantoni

, Gustafson

, Biessels

, Wallin

, Smith

, Mok

, Wong

, Passmore

, Barkof

, Muller

, Breteler

MMB

, Román

, Hamel

, Seshadri

, Gottesman

, van Buchem

, Arvanitakis

, Schneider

, Drewes

, Hachinski

, Finch

, Toga

, Wardlaw

, Zlokovic

(2019) Vascular dysfunction-the disregarded partner of Alzheimer’s disease. Alzheimers Dement 15, 158–167.

43.

Kunkle

, Grenier-Boley

, Sims

, Bis

, Damotte

, Naj

, Boland

, Vronskaya

, van der Lee

, Amlie-Wolf

, et al. (2019) Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing. Nat Genet 51, 414–430.

44.

Van Cauwenberghe

, Van Broeckhoven

, Sleegers

(2016) The genetic landscape of Alzheimer disease: Clinical implications and perspectives. Genet Med 18, 421–430.

45.

Giannakopoulos

, Herrmann

, Bussière

, Bouras

, Kövari

, Perl

, Morrison

, Gold

, Hof

(2003) Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer’s disease. Neurology 60, 1495–1500.

46.

Villemagne

, Pike

, Chételat

, Ellis

, Mulligan

, Bourgeat

, Ackermann

, Jones

, Szoeke

, Salvado

, Martins

, O’Keefe

, Mathis

, Klunk

, Ames

, Masters

, Rowe

(2011) Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease. Ann Neurol 69, 181–192.

47.

Bush

, Tanzi

(2008) Therapeutics for Alzheimer’s disease based on the metal hypothesis. Neurotherapeutics 5, 421–432.

48.

Jung

, Whitwell

, Duffy

, Strand

, Machulda

, Senjem

, Jack

, Lowe

, Josephs

(2016) Regional β-amyloid burden does not correlate with cognitive or language deficits in Alzheimer’s disease presenting as aphasia. Eur J Neurol 23, 313–319.

49.

Aizenstein

, Nebes

, Saxton

, Price

, Mathis

, Tsopelas

, Ziolko

, James

, Snitz

, Houck

, Bi

, Cohen

, Lopresti

, DeKosky

, Halligan

, Klunk

(2008) Frequent amyloid deposition without significant cognitive impairment among the elderly. Arch Neurol 65, 1509–1517.

50.

Bennett

, Schneider

, Arvanitakis

, Kelly

, Aggarwal

, Shah

, Wilson

(2006) Neuropathology of older persons without cognitive impairment from two community-based studies. Neurology 66, 1837–1844.

51.

Bouwman

, Schoonenboom

, Verwey

, van Elk

, Kok

, Blankenstein

, Scheltens

, van der Flier

(2009) CSF biomarker levels in early and late onset Alzheimer’s disease. Neurobiol Aging 30, 1895–1901.

52.

Moghbel

, Saboury

, Basu

, Metzler

, Torigian

, Långström

, Alavi

(2012) Amyloid-β imaging with PET in Alzheimer’s disease: Is it feasible with current radiotracers and technologies? Eur J Nucl Med Mol Imaging 39, 202–208.

53.

Høilund-Carlsen

, Alavi

(2021) Aducanumab (marketed as aduhelm) approval is likely based on misinterpretation of PET imaging data. J Alzheimers Dis 84, 1457–1460.

54.

Kepe

, Moghbel

, Långström

, Zaidi

, Vinters H

, Huang

, Satyamurthy

, Doudet

, Mishani

, Cohen

(2013) Amyloid-β positron emission tomography imaging probes: A critical review. J Alzheimers Dis 36, 613–631.

55.

McKhann

, Knopman

, Chertkow

, Hyman

, Jack

Jr , Kawas

, Klunk

, Koroshetz

, Manly

, Mayeux

, Mohs

, Morris

, Rossor

, Scheltens

, Carrillo

, Thies

, Weintraub

, Phelps

(2011) The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 263–269.

56.

Alavi

, Barrio

, Werner

, Khosravi

, Newberg

, Høilund-Carlsen

(2020) Suboptimal validity of amyloid imaging-based diagnosis and management of Alzheimer’s disease: Why it is time to abandon the approach. Eur J Nucl Med Mol Imaging 47, 225–230.

57.

Høilund-Carlsen

, Barrio

, Gjedde

, Werner

, Alavi

(2018) Circular inference in dementia diagnostics. J Alzheimers Dis 63, 69–73.

58.

Høilund-Carlsen

, Revheim

, Alavi

, Satyamurthy

, Barrio

(2022) Amyloid PET: A questionable single primary surrogate efficacy measure on Alzheimer immunotherapy trials. J Alzheimers Dis 90, 1395–1399.

59.

Hughes

, Berg

, Danziger

, Coben

, Martin

(1982) A new clinical scale for the staging of dementia. Br J Psychiatry 140, 566–572.

60.

Morris

(1993) The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology 43, 2412–2414.

61.

Coley

, Andrieu

, Jaros

, Weiner

, Cedarbaum

, Vellas

(2011) Suitability of the Clinical Dementia Rating-Sum of Boxes as a single primary endpoint for Alzheimer’s disease trials. Alzheimers Dement 7, 602–610.

62.

O’Bryant

, Waring

, Cullum

, Hall

, Lacritz

, Massman

, Lupo

, Reisch

, Doody

, Consortium

TAR

(2008) Staging dementia using Clinical Dementia Rating Scale Sum of Boxes scores: A Texas Alzheimer’s research consortium study. Arch Neurol 65, 1091–1095.

63.

Huang

, Tseng

, Chen

, Chiu

(2021) Diagnostic accuracy of the Clinical Dementia Rating Scale for detecting mild cognitive impairment and dementia: A bivariate meta-analysis. Int J Geriatr Psychiatry 36, 239–251.

64.

Rosen

, Mohs

, Davis

(1984) A new rating scale for Alzheimer’s disease. Am J Psychiatry 141, 1356–1364.

65.

Mohs

, Knopman

, Petersen

, Ferris

, Ernesto

, Grundman

, Sano

, Bieliauskas

, Geldmacher

, Clark

, Thal

(1997) Development of cognitive instruments for use in clinical trials of antidementia drugs: Additions to the Alzheimer’s Disease Assessment Scale that broaden its scope. Alzheimer Dis Assoc Disord 11, 13–21.

66.

Kuller

, Lopez

(2021) ENGAGE and EMERGE: Truth and consequences? Alzheimers Dement 17, 692–695.

67.

Lansdall

, McDougall

, Butler

, Delmar

, Pross

, Qin

, McLeod

, Zhou

, Kerchner

, Doody

(2022) Establishing clinically meaningful change on outcome assessments frequently used in trials of mild cognitive impairment due to Alzheimer’s disease. J Prev Alzheimers Dis 10, 9–18.

68.

Andrews

, Desai

, Kirson

, Zichlin

, Ball

, Matthews

(2019) Disease severity and minimal clinically important differences in clinical outcome assessments for Alzheimer’s disease clinical trials. Alzheimers Dement (N Y) 5, 354–363.

69.

Gleason

, Ayton

, Bush

(2021) Unblinded by the light: ARIA in Alzheimer’s clinical trials. Eur J Neurol 28, e1.

70.

Bell

, Kenward

, Fairclough

, Horton

(2013) Differential dropout and bias in randomised controlled trials: When it matters and when it may not. BMJ 346, e8668.

71.

Nunan

, Aronson

, Bankhead

(2018) Catalogue of bias: Attrition bias. BMJ Evid Based Med 23, 21–22.

72.

Lane

(2008) Handling drop-out in longitudinal clinical trials: A comparison of the LOCF and MMRM approaches. Pharm Stat J Appl Stat Pharm Ind 7, 93–106.

73.

Pastor

, Roe

, Villegas

, Bedoya

, Chakraverty

, García

, Tirado

, Norton

, Ríos

, Martínez

, Kosik

, Lopera

, Goate

(2003) Apolipoprotein Eɛ4 modifies Alzheimer’s disease onset in an E280A PS1 kindred. Ann Neurol 54, 163–169.

74.

Liu

, Zhao

, Fu

, Wang

, Linares

, Tsai

, Bu

(2017) ApoE4 accelerates early seeding of amyloid pathology. Neuron 96, 1024–1032.

75.

Rogers

, Doody

, Mohs

, Friedhoff

, Group

(1998) Donepezil improves cognition and global function in Alzheimer disease: A 15-week, double-blind, placebo-controlled study. Arch Intern Med 158, 1021–1031.

76.

Seltzer

, Zolnouni

, Nunez

, Goldman

, Kumar

, Ieni

, Richardson

(2004) Efficacy of donepezil in early-stage Alzheimer disease: A randomized placebo-controlled trial. Arch Neurol 61, 1852–1856.

77.

Novak

, Fox

, Clegg

, Nielsen

, Einstein

, Lu

, Tudor

, Gregg

, Di

, Collins

, Wyman

, Yuen

, Grundman

, Brashear

, Liu

(2016) Changes in brain volume with Bapineuzumab in mild to moderate Alzheimer’s disease. J Alzheimers Dis 49, 1123–1134.

78.

Madsen

, Folke

, Pakkenberg

(2018) Stereological quantification of plaques and tangles in neocortex from Alzheimer’s disease patients. J Alzheimers Dis 64, 723–734.

79.

Reilly

, Games

, Rydel

, Freedman

, Schenk

, Young

, Morrison

, Bloom

(2003) Amyloid deposition in the hippocampus and entorhinal cortex: Quantitative analysis of a transgenic mouse model. Proc Natl Acad Sci U S A 100, 4837–4842.

80.

Kohn

, Tanna

, Herman

, Resnick

, Mozley

, Gur

, Alavi

, Zimmerman

, Gur

(1991) Analysis of brain and cerebrospinal fluid volumes with MR imaging. Part I. Methods, reliability, and validation. Radiology 178, 115–122.

81.

Tanna

, Kohn

, Horwich

, Jolles

, Zimmerman

, Alves

, Alavi

(1991) Analysis of brain and cerebrospinal fluid volumes with MR imaging: Impact on PET data correction for atrophy. Part II. Aging and Alzheimer dementia. Radiology 178, 123–130.

82.

Tumeh

, Alavi

, Houseni

, Greenfield

, Chryssikos

, Newberg

, Torigian

, Moonis

(2007) Structural and functional imaging correlates for age-related changes in the brain. Semin Nucl Med 37, 69–87.

83.

Alves

, Kallinowski

, Ayton

(2023) Accelerated brain volume loss caused by anti-β-amyloid drugs: A systematic review and meta-analysis. Neurology 100, e2144–e2124.

84.

Christensen

(2022) Experimental Alzheimer’s drug may have contributed to death of study participant, according to reports. CNN. https://edition.cnn.com/2022/10/28/health/alzheimers-drug-lecanemab-trial/index.html

85.

Piller

(2022) Second death linked to potential antibody treatment for Alzheimer’s disease. Science. https://www.science.org/content/article/second-death-linked-potential-antibody-treatment-alzheimer-s-disease

86.

Piller

(2022) Scientists tie third clinical trial death to experimental Alzheimer’s drug. Science. https://www.science.org/content/article/scientists-tie-third-clinical-trial-death-experimental-alzheimer-s-drug

87.

Yamada

(2015) Cerebral amyloid angiopathy: Emerging concepts. J Stroke 17, 17–30.

88.

Ostrowitzki

, Lasser

, Dorflinger

, Scheltens

, Barkhof

, Nikolcheva

, Ashford

, Retout

, Hofmann

, Delmar

, Klein

, Andjelkovic

, Dubois

, Boada

, Blennow

, Santarelli

, Fontoura

; SCarlet RoAD Investigators (2017) A phase III randomized trial of gantenerumab in prodromal Alzheimer’s disease. Alzheimers Res Ther 9, 95.

89.

Sperling

, Salloway

, Brooks

, Tampieri

, Barakos

, Fox

, Raskind

, Sabbagh

, Honig

, Porsteinsson

(2012) Amyloid-related imaging abnormalities in patients with Alzheimer’s disease treated with bapineuzumab: A retrospective analysis. Lancet Neurol 11, 241–249.

90.

Ricciarelli

, Fedele

(2017) The amyloid cascade hypothesis in Alzheimer’s disease: It’s time to change our mind. Curr Neuropharmacol 15, 926–935.

91.

Smith

, Joseph

, Perry

(2000) Arson. Tracking the culprit in Alzheimer’s disease. Ann N Y Acad Sci 924, 35–38.

92.

Smith

, Casadesus

, Joseph

, Perry

(2002) Amyloid-β and τ serve antioxidant functions in the aging and Alzheimer brain. Free Radic Biol Med 33, 1194–1199.

93.

Lee

, Casadesus

, Zhu

, Takeda

, Perry

, Smith

(2004) Challenging the amyloid cascade hypothesis: Senile plaques and amyloid-β as protective adaptations to Alzheimer disease. Ann N Y Acad Sci 1019, 1–4.

94.

Rogaeva

, Meng

, Lee

, Gu

, Kawarai

, Zou

, Katayama

, Baldwin

, Cheng

, Hasegawa

, Chen

, Shibata

, Lunetta

, Pardossi-Piquard

, Bohm

, Wakutani

, Cupples

, Cuenco

, Green

, Pinessi

, Rainero

, Sorbi

, Bruni

, Duara

, Friedland

, Inzelberg

, Hampe

, Bujo

, Song

Y-Q

, Andersen

, Willnow

, Graff-Radford

, Petersen

, Dickson

, Der

, Fraser

, Schmitt-Ulms

, Younkin

, Mayeux

, Farrer

, St George-Hyslop

(2007) The neuronal sortilin-related receptor SORL1 is genetically associated with Alzheimer disease. Nat Genet 39, 168–177.

95.

Barnard

, Bush

, Ceccarelli

, Cooper

, de Jager

, Erickson

, Fraser

, Kesler

, Levin

, Lucey

, Morris

, Squitti

(2014) Dietary and lifestyle guidelines for the prevention of Alzheimer’s disease. Neurobiol Aging 35, S74–S78.

96.

Sorrentino

, Iuliano

, Polverino

, Jacini

, Sorrentino

(2014) The dark sides of amyloid in Alzheimer’s disease pathogenesis. FEBS Lett 588, 641–652.

97.

Harrison

, Owen

(2016) Alzheimer’s disease: The amyloid hypothesis on trial. Br J Psychiatry 208, 1–3.

98.

Kepp

(2017) Ten challenges of the amyloid hypothesis of Alzheimer’s disease. J Alzheimers Dis 55, 447–457.

99.

Robakis

(2011) Mechanisms of AD neurodegeneration may be independent of Abeta and its derivatives. Neurobiol Aging 32, 372–379.

100.

Sun

, Zhou

, Yang

, Shi

(2016) Analysis of 138 pathogenic mutations in presenilin-1 on the in vitro production of Aβ42 and Aβ40 peptides by γ-secretase. Proc Natl Acad Sci U S A 114, E476–E485.

101.

Tiwari

, Kepp

(2016) β-amyloid pathogenesis: Chemical properties versus cellular levels. Alzheimers Dement 12, 184–194.

102.

Georgakopoulos

, Litterst

, Ghersi

, Baki

, Xu

, Serban

, Robakis

(2006) Metalloproteinase/Presenilin1 processing of ephrinB regulates EphB-induced Src phosphorylation and signaling. EMBO J 25, 1242–1252.

103.

Woodruff

, Young

, Martinez

, Buen

, Gore

, Kinaga

, Li

, Yuan

, Zhang

, Goldstein

(2013) The Presenilin-1 δE9 mutation results in reduced γ-secretase activity, but not total loss of PS1 function, in isogenic human stem cells. Cell Rep 5, 974–985.

104.

Shioi

, Georgakopoulos

, Mehta

, Kouchi

, Litterst

, Baki

, Robakis

(2007) FAD mutants unable to increase neurotoxic Aβ42 suggest that mutation effects on neurodegeneration may be independent of effects on Abeta. J Neurochem 101, 674–681.

105.

Shen

, Kelleher

(2007) The presenilin hypothesis of Alzheimer’s disease: Evidence for a loss-of-function pathogenic mechanism. Proc Natl Acad Sci U S A 104, 403–409.

106.

Mehra

, Kepp

(2022) Understanding familial Alzheimer’s disease: The fit-stay-trim mechanism of γ-secretase. Wiley Interdiscip Rev Comput Mol Sci 12, e1556.

107.

Somavarapu

, Kepp

(2017) Membrane dynamics of γ-Secretase provides a molecular basis for β-amyloid binding and processing. ACS Chem Neurosci 8, 2424–2436.

108.

Somavarapu

, Kepp

(2016) Loss of stability and hydrophobicity of presenilin 1 mutations causing Alzheimer’s disease. J Neurochem 137, 101–111.

109.

Herrup

(2015) The case for rejecting the amyloid cascade hypothesis. Nat Neurosci 18, 794–799.

110.

Qiu

, Walsh

, Ye

, Vekrellis

, Zhang

, Podlisny

, Rosner

, Safavi

, Hersh

, Selkoe

(1998) Insulin-degrading enzyme regulates extracellular levels of amyloid beta-protein by degradation. J Biol Chem 273, 32730–32738.

111.

Bulloj

, Leal

, Xu

, Castaño

, Morelli

(2010) Insulin-degrading enzyme sorting in exosomes: A secretory pathway for a key brain amyloid-β degrading protease. J Alzheimers Dis 19, 79–95.

112.

Farris

, Mansourian

, Chang

, Lindsley

, Eckman

, Frosch

, Eckman

, Tanzi

, Selkoe

, Guénette

(2003) Insulin-degrading enzyme regulates the levels of insulin, amyloid β-protein, and the β-amyloid precursor protein intracellular domain in vivo. Proc Natl Acad Sci U S A 100, 4162–4167.

113.

Malgieri

, Grasso

(2014) The clearance of misfolded proteins in neurodegenerative diseases by zinc metalloproteases: An inorganic perspective. Coord Chem Rev 260, 139–155.

114.

Miners

, Barua

, Kehoe

, Gill

, Love

(2011) Abeta-degrading enzymes: Potential for treatment of Alzheimer disease. J Neuropathol Exp Neurol 70, 944–959.

115.

Carson

, Turner

(2002) β-Amyloid catabolism: Roles for neprilysin (NEP) and other metallopeptidases? J Neurochem 81, 1–8.

116.

Mullane

, Williams

(2018) Alzheimer’s disease (AD) therapeutics – 1: Repeated clinical failures continue to question the amyloid hypothesis of AD and the current understanding of AD causality. Biochem Pharmacol 158, 359–375.

117.

Salloway

, Chalkias

, Barkhof

, Burkett

, Barakos

, Purcell

, Suhy

, Forrestal

, Tian

, Umans

, Wang

, Singhal

, Budd Haeberlein

, Smirnakis

(2022) Amyloid-related imaging abnormalities in 2 Phase 3 studies evaluating Aducanumab in patients with early Alzheimer disease. JAMA Neurol 79, 13–21.

118.

Giuffrida

, Caraci

, Pignataro

, Cataldo

, De Bona

, Bruno

, Molinaro

, Pappalardo

, Messina

, Palmigiano

, Garozzo

, Nicoletti

, Rizzarelli

, Copani

(2009) β-amyloid monomers are neuroprotective. J Neurosci 29, 10582–10587.

119.

Brookmeyer

, Abdalla

(2018) Estimation of lifetime risks of Alzheimer’s disease dementia using biomarkers for preclinical disease. Alzheimers Dement 14, 981–988.

120.

Jack

Jr , Therneau

, Weigand

, Wiste

, Knopman

, Vemuri

, Lowe

, Mielke

, Roberts

, Machulda

, Graff-Radford

, Jones

, Schwarz

, Gunter

, Senjem

, Rocca

, Petersen

(2019) Prevalence of biologically vs clinically defined Alzheimer spectrum entities using the National Institute on Aging-Alzheimer’s Association Research Framework. JAMA Neurol 76, 1174–1183.

121.

Sturchio

, Dwivedi

, Young

, Malm

, Marsili

, Sharma

, Mahajan

, Hill

, Andaloussi

SEL

, Poston

, Manfredsson

, Schneider

, Ezzat

, Espay

(2021) High cerebrospinal amyloid-β 42 is associated with normal cognition in individuals with brain amyloidosis. eClinicalMedicine 38, 100988.

122.

Sturchio

, Dwivedi

, Malm

, Wood

MJA

, Cilia

, Sharma

, Hill

, Schneider

, Graff-Radford

, Mori

, Nübling

, El Andaloussi

, Svenningsson

, Ezzat

, Espay

; Dominantly Inherited Alzheimer Consortia (DIAN) (2022) High soluble amyloid-β 42 predicts normal cognition in amyloid-positive individuals with Alzheimer’s disease-causing mutations. J Alzheimers Dis 90, 333–348.