Front. Aging Neurosci. Frontiers in Aging Neuroscience Front. Aging Neurosci. 1663-4365 Frontiers Media S.A. 10.3389/fnagi.2025.1652754 Neuroscience Mini Review Exploring efficient and effective mammalian models for Alzheimer’s disease Kayano Mitsunori * Research Center for Global Agromedicine, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Japan

Edited by: Vijay Karkal Hegde, Texas Tech University, United States

Reviewed by: Bikash Medhi, Post Graduate Institute of Medical Education and Research (PGIMER), India

 *Correspondence: Mitsunori Kayano, kayano@obihiro.ac.jp 14 08 2025 2025 17 1652754 24 06 2025 31 07 2025 Copyright © 2025 Kayano. 2025 Kayano

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

The aim of this study was to explore and discuss efficient and effective mammalian models for Alzheimer’s disease (AD). In this study, efficient AD models are characterized by a small body size, a short lifespan, and rapid development of the main pathology including amyloid plaque formation. Effective AD models are expected to exhibit not only the main pathology, but also co-pathology associated with other neurodegenerative diseases (e.g., Lewy body dementia), systemic disturbances such as disrupted central–peripheral homeostasis, and sleep-circadian failures. This reflects recent findings indicating that AD is far more multifactorial than previously assumed. Although further investigation is required, non-human primates, particularly common marmosets (Callithrix jacchus), and dogs (Canis lupus familiaris) are candidates of promising and effective AD models. Tree shrews (Tupaia belangeri), guinea pigs (Cavia porcellus), and evolutionary related species including degus (Octodon degus) constitute an alternative group of AD models that remain underexplored but potentially efficient and effective. These mammalian models, together with hypothesis-driven mouse models and advances in data science technologies including omics and imaging analyses, may lead to breakthroughs in AD research, resulting in the development of effective prevention and treatment for AD.

 amyloid-β α-synuclein blood brain barrier cerebral amyloid angiopathy marmoset dog tree shrew rodent section-at-acceptance Alzheimer’s Disease and Related Dementias 1 Introduction

Alzheimer’s disease (AD), the most common form of dementia, is characterized by the presence of β-amyloid (Aβ)-containing extracellular plaques and tau-containing intracellular neurofibrillary tangles in the brain (Hardy and Selkoe, 2002; Bloom, 2014; Scheltens et al., 2021). In amyloid hypotheses, form(s) of Aβ, such as plaques and soluble oligomers, in the brain initiates a pathophysiological cascade leading the tau pathology, neuro-inflammation related to activation of microglia and astrocyte, neuronal death and cognitive decline (Selkoe and Hardy, 2016; Cline et al., 2018). However, prevention and treatment targeting the brain Aβ have not been as successful as the amyloid hypotheses had expected.

AD may be far more multifactorial than previously assumed, regarding co-pathology and systemic abnormalities. For example, emerging evidences have supported that AD brains frequently share the pathology (co-pathology) associated with other dementias such as Lewy body dementia (LBD) and frontotemporal lobe dementia (FTLD) through the interplay among Aβ, tau, α-synuclein and TAR DNA-binding protein of 43 kDa (TDP-43) (Robinson et al., 2018; Sengupta and Kayed, 2022). Also, AD may extend beyond the brain, involving systemic alterations (Wang et al., 2017; Cheng et al., 2020; Xu et al., 2025). At least 10 to 20 multilevel factors at molecular, cellular, tissue-organ and individual levels are then associated with AD: (1) molecular level: Aβ, tau, α-synuclein, TDP-43 and apolipoprotein E (APOE; a major risk factor for AD) (Yamazaki et al., 2019; Serrano-Pozo et al., 2021; Jackson et al., 2024), (2) cellular level: astrocyte, microglia, oligodendrocyte, T-cell and neutrophil (Huang et al., 2022; Gericke et al., 2023), (3) tissue-organ level: cortex, hippocampus, hypothalamus, liver, pancreas, kidney and gut (Wang et al., 2017; Xu et al., 2025), and (4) individual level: infection (Moir et al., 2018; Vojtechova et al., 2022), sleep-circadian failure (Ju et al., 2014; van Erum et al., 2018), cardiovascular diseases (Stampfer, 2006; Tini et al., 2020), diabetes (Sims-Robinson et al., 2010; Takeda et al., 2010; Arnold et al., 2018) and epilepsy (Palop and Mucke, 2009; Horváth et al., 2016).

Interestingly, many mammalian species spontaneously exhibit the amyloid plaque as they age (Mckean et al., 2021; Sharma et al., 2023; Ferrer, 2024; Supplementary material). Several mammalian species also naturally present the tau pathology (Mckean et al., 2021) and some show symptoms as well (Osella et al., 2007; Prpar Mihevc and Majdič, 2019). The most surprising fact is that dogs, a mammalian species which is evolutionally divergent from human in mammals (Figure 1), can naturally develop AD-like disorder without any interventions: it is canine cognitive dysfunction (CCD) (Osella et al., 2007; Landsberg et al., 2017; Prpar Mihevc and Majdič, 2019). CCD dogs can exhibit the amyloid plaque and tau pathology, and, very surprisingly, they present symptoms exactly like human such as disorientation (Osella et al., 2007). This fact encourages us and evokes idea of naturally onset mammals for AD, implying that key progression pathways of the multifactorial AD may be fundamentally conserved in mammals.

Divergence of sporadic mammalian models for the multifactorial AD. Non-human primates, particularly common marmosets, and dogs can be promising and effective animal models for the multifactorial AD. Tree shrews, guinea pigs and degus constitute an alternative group of AD models that remain underexplored but potentially efficient and effective. Surprising fact is that dog, a mammalian species which is evolutionally divergent from human, can naturally develop human-like AD without any interventions. This supports the use of naturally occurring animal models for AD. The possibility of large and small mammals including pig, cattle, horse, rabbit, ferret, Mongolian gerbil as the AD model is discussed in the Supplementary material. **, promising and effective models (marmosets and dogs); *, underexplored but potentially efficient and effective models (tree shrews, guinea pigs and degus).

Diagram showing sporadic mammalian models in translational research connecting humans and mice. It includes rodents (guinea pigs and degus), rabbits,tree shrews, primates (marmosets), pig, horse and carnivores (dogs), with some marked by asterisks indicating significance.

This review provides the summary of the possibility of effective mammalian models for the multifactorial AD. Also, the efficiency including a small body size, a short life span and rapid disease progression of the animals is discussed.

 2 Key criteria for efficient and effective animal models for Alzheimer’s disease

Animal models are expected to be efficient and effective. For the efficiency, the AD models need to have a small body size (low maintenance costs), a short lifespan (short generation time) and rapid disease progression: models are particularly required to exhibit the Aβ accumulation and plaques as early as possible with preserved effectiveness.

For the effectiveness, animal models for AD are expected to exhibit at least the amyloid and tau pathology, and also co-pathology such as those involving α-synuclein and systemic alterations including direct dysfunction in peripheral tissues, breakdown of blood-brain-barrier (BBB) and disruption of central and peripheral homeostasis. This reflects recent findings indicating that AD is far more multifactorial than previously assumed, involving co-pathology and systemic alterations. Although α-synuclein can be detected in the brain, particularly as a component of the amyloid plaque (Ueda et al., 1993), it can be detected in peripheral, particularly in gut (Xu et al., 2025). This may construct the gut-to-brain axis of α-synuclein spreading. AD patients also show systemic alterations. Typical systemic alterations associated with AD include the Aβ and tau accumulations in peripheral tissues and organs (Xu et al., 2025), peripheral inflammation and dysfunction (Wang et al., 2017) and breakdown of BBB (Bowman et al., 2007; Sweeney et al., 2018; Cai et al., 2018). Especially, BBB impairment is thought to be critical in the multifactorial AD: since BBB maintains physiological and immunological homeostasis in central nervus system (CNS) and periphery, and BBB leakage may precede the senile plaque (Ujiie et al., 2003), implying that BBB alterations may be linked to the true initiator(s) of AD. BBB dysfunction is believed to be associated with cerebral amyloid angiopathy (CAA) (Kalaria, 1999; Carrano et al., 2012; Magaki et al., 2018) and epilepsy (van Vliet et al., 2007; Löscher and Friedman, 2020; Greene et al., 2022). APOE may also associate with BBB dysfunction (Montagne et al., 2020; Liu et al., 2022). Finally, sleep and circadian disorders are probably associated with huge numbers of factors in the multifactorial AD including co-pathology and systemic alterations (Ju et al., 2014; Musiek and Holtzman, 2016; Cuddapah et al., 2019; Patke et al., 2020; Nassan and Videnovic, 2022). We need to sleep for our health (Buysse, 2014; Mander et al., 2017): not only for AD, but also for other neurodegenerative diseases (Malhotra, 2018; Husain, 2021).

In summary, for the efficiency, the models are required to exhibit the amyloid and related pathology as early as possible. In addition, a smaller body size is preferred to minimize maintenance costs. The effective models for the multifactorial AD should exhibit at least the amyloid and tau pathology. Also, co-pathology of α-synuclein and related accumulation in the brain and body are expected to be observed. For the systemic abnormalities in AD, the models need to present direct alterations in peripheral tissues, BBB dysfunction and/or related disorders such as CAA and epilepsy that are associated with disrupted peripheral-central homeostasis. Moreover, sleep-circadian failures, which are related to co-pathology and systemic abnormalities, are desirable features to be observed. However, it seems impossible to obtain detailed studies for all aspects of co-pathology, systemic alterations and sleep-circadian failures in underexplored animals. This study then evaluates the effective model as the possibility of either co-pathology or at least one of the systemic alterations, and roughly discusses (1) whether each animal species is diurnal and (2) selective sleep-circadian reports in each species.

 3 Efficient and effective mammalian models for Alzheimer’s disease 3.1 Non-human primate: common marmosets

Non-human primates (NHPs) including common marmosets (marmoset; Callithrix jacchus) are probably promising and effective models for AD (Mckean et al., 2021; Stonebarger et al., 2021; Rizzo et al., 2023). In particular, marmosets, a small primate species (∼300 g body weight in wild), are an emerging model for AD and other neurodegenerative diseases (Rizzo et al., 2023; Pérez-Cruz and Rodríguez-Callejas, 2023; Huhe et al., 2025). Marmosets are considered to be aged in 8–10 years and have average lifespan of 12 years (Table 1) (Pérez-Cruz and Rodríguez-Callejas, 2023). Sporadic amyloid plaques are observable as early as 7 years (Rizzo et al., 2023), and aged marmosets exhibit both 3-repeat (3R) and 4-repeat (4R) tau isoforms in their brain, implying highly toxic patterns of tau expressions similar to human (Huhe et al., 2025). Aggregation of α-synuclein in the marmoset brain and body is thought to be possible (Shimozawa et al., 2017): the natural aggregation of α-synuclein in the olfactory bulb of 6 years old marmoset (without injections of toxic seeds) has been reported (Kobayashi et al., 2016). Also, colitis may be associated with the alteration in α-synuclein expression and phosphorylation in the myenteric plexus of marmosets (Resnikoff et al., 2019). CCA pathology (Rizzo et al., 2023) and epilepsy (Yang et al., 2022) which are associated with BBB disruption are naturally observed in marmosets: direct BBB characteristics of marmosets have also been well-studied (Hoshi et al., 2013; Parks et al., 2023). The marmoset is diurnal, and the sleep and circadian rhythm of marmosets has been well-studied (Erkert, 1989; Crofts et al., 2001; Koshiba et al., 2021; Bukhtiyarova et al., 2022).

Comparison of human and selective mammalian models for efficient and effective Alzheimer’s disease (AD) research.

Species Body size*1 Lifespan (y: years) Plaque (y: years) Tau Alpha-synuclein Systemic abnormalities*2 Diurnal
Human (Homo sapience) Large 80 y > 50–60 y Yes Yes Yes Yes
Marmoset (Callithrix jacchus) Small 12 y > 7–10 y Yes Probable Probable Yes
Dog (Canis lupus familiaris) Middle 15 y > 8–10 y Yes Probable Probable Yes
Tree shrew (Tupaia belangeri) Small 6–8 y > 5–6 y*3 Yes Probable Possible Yes
Guinea pig (Cavia porcellus) Small 5–7 y > 4 y Yes Possible Probable Yes
Degu (Octodon degus) Small 5–7 y > 3 y*4 Yes ND Probable Possible
Rat (Rattus norvegicus) Small 2 y ND ND ND Possible*5 No
Mouse (Mus musculus) Tiny 2 y ND ND ND Possible*5 No

Body size, lifespan and the timing of plaque deposition are associated with the efficiency. Others are related to the effectiveness for the multifactorial Alzheimer’s disease (AD) regarding the main pathology (plaque and tau), co-pathology, systemic alterations and diurnality. Yes/No/Probable/Possible is determined by their natural and wild situation, at most with diet/metal/environmental supplementation (without any drug-induced and/or genetic manipulation). ND means “not detected.”

*1 Body size: tiny (< 100 g), small (< 1 kg), middle (< 30 kg), and large (> 30 kg).

*2 “Probable/Possible” refers possibility of direct alterations in peripheral tissues, blood-brain-barrier (BBB) disruption and related diseases such as cerebral amyloid angiopathy (CAA) and epilepsy that are associated with disrupted peripheral-central homeostasis.

*3 In tree shrews, the presence of the amyloid plaque is thought to be relatively rare.

*4 Degus with APOE4 allele and wild-captured or early generations after the wild-capture may frequently develop the amyloid pathology.

*5 BBB characteristic and dysfunction with aging have been studied in mice (Poduslo et al., 2001; Daneman et al., 2010; Braniste et al., 2014; Goodall et al., 2018) and rats (Yang et al., 1994; Enerson and Drewes, 2006; Hawkins et al., 2007).

 3.2 Companion animal (divergent from primates): dogs

Dogs, a Carnivora species, are another candidate of promising and effective models for AD (Cotman and Head, 2008; Prpar Mihevc and Majdič, 2019; Ambrosini et al., 2019). Dog is evolutionally divergent from human in mammals (more distant than rodents; Figure 1), but can naturally develop AD-like disorder without any interventions. Sever cognitive decline can be observed in aged dog: it is referred as CCD. Aged dogs can naturally exhibit both amyloid plaque (over 8 to 10 years-old) and tau pathology (Schmidt et al., 2015; Ozawa et al., 2016; Smolek et al., 2016; Table 1), and, very surprisingly, present symptoms exactly like human such as disorientation (Osella et al., 2007). Accumulation of α-synuclein has been presented in spinal cord and hippocampus of 10–12 years old beagle dogs (Ahn et al., 2012; Ahn et al., 2013), although it may be not very frequent and/or breed-genetical specific (Uchida et al., 2003). CAA including micro-bleeding in the brain and epilepsy (probable BBB disruption) seems relatively frequent in aged dogs (Wisniewski et al., 1996; Ozawa et al., 2016; Nešić et al., 2017). Direct association studies between epilepsy and BBB disfunction in dogs have been reported (Hanael et al., 2019; Hanael et al., 2024). Sleep-circadian cycle has been widely studied in dogs (Adams and Johnson, 1993; Bódizs et al., 2020; Reicher et al., 2021).

 3.3 Close to primates: tree shrews

Tree shrews (northern tree shrew; Tupaia belangeri), a species in the order Scandentia and widely distributed in South and Southeast Asia, are a possible efficient and effective model for AD (Fan et al., 2018; Li et al., 2023). The advantages of tree shrew as model animals are a small body weight (100–150 g), a short lifespan (6–8 years) and low maintenance costs (Table 1). Tree shrew has a much closer genetic and physiological affinities to primates than those of rodents (Figure 1) and has been used as models for basic science and many types of diseases including brain development, infection (particularly hepatitis viruses), depression, social stress and aging (Fan et al., 2018; Li et al., 2023; Yao et al., 2024). Aβ aggregates, plaque-like structures and increased phosphorylated tau protein have been detected in the brain of 5–6 years-old tree shrews (Yamashita et al., 2012; Fan et al., 2018; Li et al., 2023), although the plaque deposition may be rare (Pawlik et al., 1999; Yamashita et al., 2010; Li et al., 2023). The α-synuclein protein sequence of tree threw is 97.1% identical to that of human, implying the tree shrew’s α-synuclein might have similar functions compared to human (Wu et al., 2015). A higher expression and aggregates of α-synuclein has been observed in the brain of tree shrews (Wu et al., 2019). Although CAA, epilepsy and related BBB leakages have not been reported, gut-to-brain axis in cognition (Guo et al., 2021; Wang et al., 2023) and circadian rhythm (Meijer et al., 1990; Legros et al., 2007; Coolen et al., 2012; Luo et al., 2020; Dimanico et al., 2021) of tree shrews has been studied well.

 3.4 Rodents: guinea pigs and degus

Rodents other than mice and rats can be other candidates of the efficient and effective models for AD. The order Rodentia (rodents) is divided into three suborders: Sciuromorpha (squirrel-like) and Myomorpha (mouse and rat-like), and Hystricomorph (porcupine-like). In particular, certain hystricomorph rodents, including guinea pigs (Cavia porcellus: Sharman et al., 2013; Wahl et al., 2022) and degus (Octodon degus: Inestrosa et al., 2005; Hurley et al., 2018), are emerging AD model rodents. A relatively smaller body size and a shorter lifespan of such rodents than other mammalian models suggest their potential as one of the most efficient models for AD. Moreover, such rodents can be effective and are expected to bridge the translational gap between mouse to human, since they are rodents like mice and rats, but have potential to naturally onset AD.

The guinea pig, a hystricomorph rodent with an average lifespan of 5–7 years and a body weight of 700 to 1,000 g, is an emerging sporadic model for AD (Sharman et al., 2013; Wahl et al., 2022; Table 1). Guinea pigs have been used in research for over 200 years (Harkness et al., 2002), including more recent studies of cerebral cortices (Hatakeyama et al., 2017), infectious diseases (Connolly et al., 1999; Lowen et al., 2006; Padilla-Carlin et al., 2008) and pharmacological, environmental, and dietary interventions (Rakic et al., 1989; Kim et al., 2017; Li et al., 2021). Guinea pigs have the identical Aβ42 sequence to human (Salazar et al., 2016) and express both 3R and 4R tau isoforms (Sharman et al., 2013). The Aβ aggregates (Wahl et al., 2022) and plaques (Bates et al., 2014) can be observed at over 1 and 4 years old, respectively. Although this study has not found any report of the aggregates and/or deposition of α-synuclein in the brain of guinea pigs, the aggregation has been observed in the gut (Sharrad et al., 2013; Sharrad et al., 2017): notice that two possible pathways (brain-first and body (gut)-first) for the synuclein spreading have been reported (Borghammer and Van Den Berge, 2019; Nuzum et al., 2022). The BBB of guinea pigs has been well-studied (Rakic et al., 1989; Uva et al., 2008), including the transport of Aβ at BBB (Martel et al., 1996). No CAA researches were found in this study. Pharmacologically-induced epilepsy in guinea pig have been widely investigated, and the association between epilepsy and BBB permeability using induced-epilepsy model of the guinea pig has been studied (Uva et al., 2008). Also a gut-to-brain (microbiome-hypothalamus) axis in guinea pigs has been investigated (Li et al., 2021; Nuzum et al., 2022). Guinea pigs are diurnal, and extensive studies have been reported related to sleep and circadian system in guinea pigs (Kurumiya and Kawamura, 1988; Akita et al., 2001; Liu et al., 2020).

The degu, another hystricomorph rodent from central Chile with an average lifespan of 5 to 7 years and a body weight of less than 300 g, has the potential as one of the most efficient and effective mammalian models for AD (Inestrosa et al., 2005; Cisternas et al., 2018; Hurley et al., 2018; Tan et al., 2022; Table 1). Degus are highly social (Rivera et al., 2016) and thought to have advanced cognitive abilities (Kumazawa-Manita et al., 2013), although they are a small rodent. Degus are the emerging candidate of multimorbidity-systemic models, since recent studies have reported that degus naturally develop visual impairments (Datiles and Fukui, 1989; Szabadfi et al., 2015; Hurley et al., 2018), endocrinological and metabolic dysfunctions including diabetes (Datiles and Fukui, 1989; Rivera et al., 2018; Hurley et al., 2018) and neoplasi (Anderson et al., 1990; Lester et al., 2005; Švara et al., 2020; Ikeda et al., 2024). Interestingly, related to AD pathology, degus spontaneously represent the accumulation of Aβ and phosphorylated tau in the brain: it may start between 1 and 3 years old (Ardiles et al., 2012). The amyloid plaque can be observed at 3–5 years old (Cisternas et al., 2018). Although some studies have reported contradictory results regarding the potential of degus as a model for AD research (Steffen et al., 2016; Bourdenx et al., 2017), degus with the higher risk APOE4 allele and wild-captured or early generations after the wild-capture may frequently develop the AD pathology (Hurley et al., 2022). No α-synuclein researches in degus were found in this study. However, degus can be one of the best (efficient and effective) models for investigating systemic alterations in AD, because they can naturally exhibit CAA (van Groen et al., 2011) and epilepsy (Ikai et al., 2021) (like marmosets and dogs), implying a higher risk for BBB dysfunction. Degus can be diurnal (Kas and Edgar, 1999a; Lee, 2004; Bonmati-Carrion et al., 2017), and sleep and circadian functions including the association between sleep deprivation and cognitive decline have been studied well (Kas and Edgar, 1999b; Ocampo-Garcés et al., 2013; Tarragon et al., 2014; Estrada et al., 2015).

 4 Conclusion and perspective

Interestingly, many mammalian species spontaneously exhibit the amyloid plaques as they age (Mckean et al., 2021; Sharma et al., 2023; Ferrer, 2024; Supplementary material). This study updated the information of mammalian models for the multifactorial AD, including co-pathology and systemic alterations. However, it was impossible to investigate prevalence and frequency of such new issues for relatively underexplored animals. This study was based on case reports of mammals. This is a limitation of this review.

Although further investigation is required, NHPs, particularly marmosets, and dogs are candidates of promising and effective AD models. Previous studies have showed that marmosets and dogs can present the amyloid plaque at 7–10 years old. Tree shrews, guinea pigs and degus constitute an alternative group of AD models that remain underexplored but potentially efficient and effective. Particularly, in guinea pigs and degus, the amyloid plaque can be detected in 3–4 years: this seems the earliest (the most efficient) so far in naturally onset mammals. However the possibility of co-pathology and systemic alterations in the three species warrants further investigation for more robust and effective AD models.

Emerging evidences suggest that the key progression pathways of the multifactorial AD may be fundamentally conserved in several mammalian species. The efficient and effective mammalian model provides opportunities to investigate the long-term and spatio (systemic)-temporal observations that are potentially crucial in current AD research but are highly resource-intensive and time-consuming to implement in epidemiological studies of NHPs (except marmosets) and human. The mammalian models in this study, together with hypothesis-driven mouse models and also advances in data science technologies including omics and imaging analyses, may bridge the translational gap between mouse and human and lead to breakthroughs in AD research.

 Author contributions

MK: Funding acquisition, Conceptualization, Investigation, Writing – original draft, Writing – review & editing, Project administration, Visualization.

 Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by JSPS KAKENHI (Grant Number: JP22K10503).

The author is grateful to all his colleagues and collaborators who participated in valuable discussions. He also appreciates the support of the technical staff.

 Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Generative AI statement

The authors declare that no Generative AI was used in the creation of this manuscript.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi.2025.1652754/full#supplementary-material

 References Adams G. J. Johnson K. G. (1993). Sleep-wake cycles and other night-time behaviours of the domestic dog (Canis familiaris). Appl. Anim. Behav. Sci. 36 233–248. 10.1016/0168-1591(93)90013-F Ahn J. H. Choi J. H. Park J. H. Yan B. C. Kim I. H. Lee J. C. (2012). Comparison of alpha-synuclein immunoreactivity in the spinal cord between the adult and aged beagle dog. Lab. Anim. Res. 28 165–170. 10.5625/lar.2012.28.3.165 23091516 Ahn J. H. Park J. H. Yan B. C. Lee J. C. Choi J. H. Lee C. H. (2013). Comparison of alpha-synuclein immunoreactivity in the hippocampus between the adult and aged beagle dogs. Cell. Mol. Neurobiol. 33 75–84. 10.1007/s10571-012-9873-8 22972205 Akita M. Ishii K. Kuwahara M. Tsubone H. (2001). The daily pattern of heart rate, body temperature, and locomotor activity in guinea pigs. Exp. Anim. 50 409–415. 10.1538/expanim.50.409 11769543 Ambrosini Y. M. Borcherding D. C. Kanthasamy A. G. Kim H. Willette A. A. Jergens A. E. (2019). The gut–brain axis in neurodegenerative diseases and relevance of the canine model: A review. Front. Aging Neurosci. 11:130. 10.3389/fnagi.2019.00130 31275138 Anderson W. I. Steinberg H. King J. M. (1990). Bronchioloalveolar carcinoma with renal and hepatic metastases in a degu (Octodon degus). J. Wildl. Dis. 26 129–131. 10.7589/0090-3558-26.1.129 2154626 Ardiles Á. O. Tapia-Rojas C. C. Mandal M. Alexandre F. Kirkwood A. Inestrosa N. C. (2012). Postsynaptic dysfunction is associated with spatial and object recognition memory loss in a natural model of Alzheimer’s disease. Proc. Natl. Acad. Sci. U.S.A. 109 13835–13840. 10.1073/pnas.1201209109 22869717 Arnold S. E. Arvanitakis Z. Macauley-Rambach S. L. Koenig A. M. Wang H. Y. Ahima R. S. (2018). Brain insulin resistance in type 2 diabetes and Alzheimer disease: Concepts and conundrums. Nat. Rev. Neurol. 14 168–181. 10.1038/nrneurol.2017.185 29377010 Bates K. Vink R. Martins R. Harvey A. Morganti-Kossmann M. C. (2014). Aging, cortical injury and Alzheimer’s disease-like pathology in the guinea pig brain. Neurobiol. Aging 35 1345–1351. 10.1016/j.neurobiolaging.2013.11.020 24360504 Bloom G. S. (2014). Amyloid-β and tau: The trigger and bullet in Alzheimer disease pathogenesis. JAMA Neurol. 71 505–508. 10.1001/jamaneurol.2013.5847 24493463 Bódizs R. Kis A. Gácsi M. Topál J. (2020). Sleep in the dog: comparative, behavioral and translational relevance. Curr. Opin. Behav. Sci. 33 25–33. 10.1016/j.cobeha.2019.12.006 Bonmati-Carrion M. A. Baño-Otalora B. Madrid J. A. Rol M. A. (2017). Light color importance for circadian entrainment in a diurnal (Octodon degus). and a nocturnal (Rattus norvegicus). rodent. Sci. Rep. 7:8846. 10.1038/s41598-017-08691-7 28821732 Borghammer P. Van Den Berge N. (2019). Brain-first versus gut-first Parkinson’s disease: a hypothesis. J. Parkinsons Dis. 9 S281–S295. 10.3233/JPD-191721 31498132 Bourdenx M. Dovero S. Thiolat M. L. Bezard E. Dehay B. (2017). Lack of spontaneous age-related brain pathology in Octodon degus: A reappraisal of the model. Sci. Rep. 7:45831. 10.1038/srep45831 28374864 Bowman G. L. Kaye J. A. Moore M. Waichunas D. Carlson N. E. Quinn J. F. (2007). Blood–brain barrier impairment in Alzheimer disease: Stability and functional significance. Neurology 68 1809–1814. 10.1212/01.wnl.0000262031.18018.1a 17515542 Braniste V. Al-Asmakh M. Kowal C. Anuar F. Abbaspour A. Tóth M. (2014). The gut microbiota influences blood–brain barrier permeability in mice. Sci. Transl. Med. 6:263ra158. 10.1126/scitranslmed.3009759 25411471 Bukhtiyarova O. Chauvette S. Seigneur J. Timofeev I. (2022). Brain states in freely behaving marmosets. Sleep 45:zsac106. 10.1093/sleep/zsac106 35576961 Buysse D. J. (2014). Sleep health: Can we define it? Does it matter? Sleep 37 9–17. 10.5665/sleep.3298 24470692 Cai Z. Qiao P. F. Wan C. Q. Cai M. Zhou N. K. Li Q. (2018). Role of blood–brain barrier in Alzheimer’s disease. J. Alzheimer’s Dis. 63 1223–1234. 10.3233/JAD-180098 29782323 Carrano A. Hoozemans J. J. M. van der Vies S. M. van Horssen J. de Vries H. E. Rozemuller A. J. M. (2012). Neuroinflammation and blood–brain barrier changes in capillary amyloid angiopathy. Neurodegener. Dis. 10 329–331. 10.1159/000335183 22327547 Cheng Y. Tian D.-Y. Wang Y.-J. (2020). Peripheral clearance of brain-derived Aβ in Alzheimer’s disease: Pathophysiology and therapeutic perspectives. Transl. Neurodegener. 9:16. 10.1186/s40035-020-00195-1 32381118 Cisternas P. Zolezzi J. M. Lindsay C. Rivera D. S. Martinez A. Bozinovic F. (2018). New insights into the spontaneous human Alzheimer’s disease-like model Octodon degus: Unraveling amyloid-β peptide aggregation and age-related amyloid pathology. J. Alzheimer’s Dis. 66 1145–1163. 10.3233/JAD-180729 30412496 Cline E. N. Bicca M. A. Viola K. L. Klein W. L. Watterson D. M. Rogers J. (2018). The Amyloid-β oligomer hypothesis: Beginning of the third decade. J. Alzheimer’s Dis. 64 S567–S610. 10.3233/JAD-179941 29843241 Connolly B. M. Steele K. E. Davis K. J. Geisbert T. W. Kell W. M. Jaax N. K. (1999). Pathogenesis of experimental Ebola virus infection in guinea pigs. J. Infect. Dis. 179 S203–S217. 10.1086/514305 9988186 Coolen A. Hoffmann K. Barf R. P. Fuchs E. Meerlo P. (2012). Telemetric study of sleep architecture and sleep homeostasis in the day-active tree shrew Tupaia belangeri. Sleep 35 879–888. 10.5665/sleep.1894 22654207 Cotman C. W. Head E. (2008). The canine (dog). model of human aging and disease: Dietary, environmental and immunotherapy approaches. J. Alzheimer’s Dis. 15 685–707. 10.3233/JAD-2008-15413 19096165 Crofts H. S. Wilson S. Muggleton N. G. Nutt D. J. Scott E. A. Pearce P. C. (2001). Investigation of the sleep electrocorticogram of the common marmoset (Callithrix jacchus). using radiotelemetry. Clin. Neurophysiol. 112 2265–2273. 10.1016/S1388-2457(01)00699-X 11738198 Cuddapah V. A. Zhang S. L. Sehgal A. (2019). Regulation of the blood–brain barrier by circadian rhythms and sleep. Trends Neurosci. 42 500–510. 10.1016/j.tins.2019.05.001 31253251 Daneman R. Zhou L. Agalliu D. Cahoy J. D. Kaushal A. Barres B. A. (2010). The mouse blood–brain barrier transcriptome: A new resource for understanding the development and function of brain endothelial cells. PLoS One 5:e13741. 10.1371/journal.pone.0013741 21060791 Datiles M. B. Fukui H. (1989). Cataract prevention in diabetic Octodon degus with Pfizer’s sorbinil. Curr. Eye Res. 8 233–237. 10.3109/02713688908997564 2523284 Dimanico M. M. Klaassen A. L. Wang J. Kaeser M. Harvey M. Rasch B. (2021). Aspects of tree shrew consolidated sleep structure resemble human sleep. Commun. Biol. 4:722. 10.1038/s42003-021-02234-7 34117351 Enerson B. E. Drewes L. R. (2006). The rat blood–brain barrier transcriptome. J. Cereb. Blood Flow Metab. 26 959–973. 10.1038/sj.jcbfm.9600249 16306934 Erkert H. G. (1989). Characteristics of the circadian activity rhythm in common marmosets (Callithrix jacchus). Am. J. Primatol. 17 271–286. 10.1002/ajp.1350170403 31964051 Estrada C. López D. Conesa A. Fernández-Gómez F. J. Gonzalez-Cuello A. Toledo F. (2015). Cognitive impairment after sleep deprivation rescued by transcranial magnetic stimulation application in Octodon degus. Neurotox. Res. 28 361–371. 10.1007/s12640-015-9544-x 26194615 Fan Y. Luo R. Su L. Y. Xiang Q. Yu D. Xu L. (2018). Does the genetic feature of the Chinese tree shrew (Tupaia belangeri chinensis). support its potential as a viable model for Alzheimer’s disease research? J. Alzheimer’s Dis. 61 1015–1028. 10.3233/JAD-170594 29332044 Ferrer I. (2024). Alzheimer’s disease neuropathological change in aged non-primate mammals. Int. J. Mol. Sci. 25:8118. 10.3390/ijms25158118 39125687 Gericke C. Kirabali T. Flury R. Mallone A. Rickenbach C. Kulic L. (2023). Early β-amyloid accumulation in the brain is associated with peripheral T cell alterations. Alzheimers Dement. 19 5642–5662. 10.1002/alz.13136 37314431 Goodall E. F. Wang C. Simpson J. E. Baker D. J. Drew D. R. Heath P. R. (2018). Age-associated changes in the blood-brain barrier: Comparative studies in human and mouse. Neuropathol. Appl. Neurobiol. 44 328–340. 10.1111/nan.12408 28453876 Greene C. Hanley N. Reschke C. R. Reddy A. Mäe M. A. Connolly R. (2022). Microvascular stabilization via blood–brain barrier regulation prevents seizure activity. Nat. Commun. 13:2003. 10.1038/s41467-022-29657-y 35422069 Guo Y. Wang L. Lu J. Jiao J. Yang Y. Zhao H. (2021). GinsenosideRg1 improves cognitive capability and affects the microbiota of large intestine of tree shrew model for Alzheimer’s disease. Mol. Med. Rep. 23:291. 10.3892/mmr.2021.11931 33649817 Hanael E. Baruch S. Altman R. K. Chai O. Rapoport K. Peery D. (2024). Blood–brain barrier dysfunction and decreased transcription of tight junction proteins in epileptic dogs. J. Vet. Intern. Med. 38 2237–2248. 10.1111/jvim.17099 38842297 Hanael E. Veksler R. Friedman A. Bar-Klein G. Senatorov V. V. Kaufer D. (2019). Blood–brain barrier dysfunction in canine epileptic seizures detected by dynamic contrast-enhanced magnetic resonance imaging. Epilepsia 60 1005–1016. 10.1111/epi.14739 31032909 Hardy J. Selkoe D. J. (2002). The amyloid hypothesis of Alzheimer’s disease: Progress and problems on the road to therapeutics. Science 297 353–356. 10.1126/science.1072994 12130773 Harkness J. E. Murray K. A. Wagner J. E. (2002). Biology and diseases of guinea pigs. Lab. Anim. Sci. 203–246. 10.1016/B978-012263951-7/50009-0 Hatakeyama J. Sato H. Shimamura K. (2017). Developing guinea pig brain as a model for cortical folding. Dev. Growth Differ. 59 286–301. 10.1111/dgd.12371 28585227 Hawkins B. T. Lundeen T. F. Norwood K. M. Brooks H. L. Egleton R. D. (2007). Increased blood–brain barrier permeability and altered tight junctions in experimental diabetes in the rat: Contribution of hyperglycaemia and matrix metalloproteinases. Diabetologia 50 202–211. 10.1007/s00125-006-0485-z 17143608 Horváth A. Szűcs A. Barcs G. Noebels J. L. Kamondi A. (2016). Epileptic seizures in Alzheimer disease: A review. Alzheimer Dis. Assoc. Disord. 30:186–192. 10.1097/WAD.0000000000000134 26756385 Hoshi Y. Uchida Y. Tachikawa M. Inoue T. Ohtsuki S. Terasaki T. (2013). Quantitative atlas of blood–brain barrier transporters, receptors, and tight junction proteins in rats and common marmoset. J. Pharm. Sci. 102 3343–3355. 10.1002/jps.23575 23650139 Huang L. T. Zhang C. P. Wang Y. B. Wang J. H. (2022). Association of peripheral blood cell profile with Alzheimer’s disease: A meta-analysis. Front. Aging Neurosci. 14:888946. 10.3389/fnagi.2022.888946 35601620 Huhe H. Shapley S. M. Duong D. M. Wu F. Ha S. K. Choi S. H. (2025). Marmosets as model systems for the study of Alzheimer’s disease and related dementias: Substantiation of physiological tau 3R and 4R isoform expression and phosphorylation. Alzheimers Dement. 21:1. 10.1002/alz.14366 39559898 Hurley M. J. Deacon R. M. Beyer K. Ioannou E. Ibáñez A. Teeling J. L. (2018). The long-lived Octodon degus as a rodent drug discovery model for Alzheimer’s and other age-related diseases. Ageing Res. Rev. 47 19–35. 10.1016/j.arr.2018.05.004 29803716 Hurley M. J. Urra C. Garduno B. M. Bruno A. Kimbell A. Wilkinson B. (2022). Genome sequencing variations in the Octodon degus, an unconventional natural model of aging and Alzheimer’s disease. Front. Aging Neurosci. 14:894994. 10.3389/fnagi.2022.894994 35860672 Husain M. (2021). Sleep and neurodegenerative diseases. Brain 144 695–696. 10.1093/brain/awab031 33755108 Ikai Y. Shinohara A. Nagura-Kato G. Shichijo H. Koshimoto C. (2021). Evaluation index of epilepsy-like seizures observed in common degu (Octodon degus). Honyurui Kagaku. 61 3–11. 10.11238/mammalianscience.61.3 Ikeda M. Kondo H. Hamada F. Yamashita T. Shibuya H. (2024). Disseminated histiocytic sarcoma in a degu (Octodon degus). J. Vet. Med. Sci. 86 529–532. 10.1292/jvms.24-0081 38556322 Inestrosa N. C. Reyes A. E. Chacón M. A. Cerpa W. Villalón A. Montiel J. (2005). Human-like rodent amyloid-β-peptide determines Alzheimer pathology in aged wild-type Octodon degu. Neurobiol. Aging 26 1023–1028. 10.1016/j.neurobiolaging.2004.09.016 15748782 Jackson R. J. Hyman B. T. Serrano-Pozo A. (2024). Multifaceted roles of APOE in Alzheimer disease. Nat. Rev. Neurol. 20 457–474. 10.1038/s41582-024-00988-2 38906999 Ju Y. E. S. Lucey B. P. Holtzman D. M. (2014). Sleep and Alzheimer disease pathology—a bidirectional relationship. Nat. Rev. Neurol. 10 115–119. 10.1038/nrneurol.2013.269 24366271 Kalaria R. N. (1999). The blood-brain barrier and cerebrovascular pathology in Alzheimer disease. Ann. N. Y. Acad. Sci. 893 113–125. 10.1111/j.1749-6632.1999.tb07821.x 10672233 Kas M. J. H. Edgar D. M. (1999a). A nonphotic stimulus inverts the diurnal–nocturnal phase preference in Octodon degus. J. Neurosci. 19 328–333. 10.1523/JNEUROSCI.19-01-00328.1999 9870962 Kas M. J. H. Edgar D. M. (1999b). Circadian timed wakefulness at dawn opposes compensatory sleep responses after sleep deprivation in Octodon degus. Sleep 22 1045–1053. 10.1093/sleep/22.8.1045 10617165 Kim N. H. Park J. H. Park J. S. Joung Y. H. (2017). The effect of deoxycholic acid on secretion and motility in the rat and guinea pig large intestine. J. Neurogastroenterol. Motil. 23 606–615. 10.5056/jnm16201 28554984 Kobayashi R. Takahashi-Fujigasaki J. Shiozawa S. Hara-Miyauchi C. Inoue T. Okano H. J. (2016). α-Synuclein aggregation in the olfactory bulb of middle-aged common marmoset. Neurosci. Res. 106 55–61. 10.1016/j.neures.2015.11.006 26643383 Koshiba M. Watarai-Senoo A. Karino G. Ozawa S. Kamei Y. Honda Y. (2021). A susceptible period of photic day-night rhythm loss in common marmoset social behavior development. Front. Behav. Neurosci. 14:539411. 10.3389/fnbeh.2020.539411 33603653 Kumazawa-Manita N. Hama H. Miyawaki A. Iriki A. (2013). Tool use specific adult neurogenesis and synaptogenesis in rodent (Octodon degus). hippocampus. PLoS One 8:e58649. 10.1371/journal.pone.0058649 23516527 Kurumiya S. Kawamura H. (1988). Circadian oscillation of the multiple unit activity in the guinea pig suprachiasmatic nucleus. J. Comp. Physiol. A 162 301–308. 10.1007/BF00606118 3351787 Landsberg G. Maďari A. Žilka N. (2017). Canine and feline dementia: molecular basis, diagnostics and therapy. Cham: Springer International Publishing. 10.1007/978-3-319-53219-6 Lee T. M. (2004). Octodon degus: A diurnal, social, and long-lived rodent. ILAR J. 45 14–24. 10.1093/ilar.45.1.14 14752204 Legros C. Chalivoix S. Gabriel C. Mocaer E. Delagrange P. Fuchs E. (2007). First evidence of melatonin receptors distribution in the suprachiasmatic nucleus of tree shrew brain. Neuro Endocrinol. Lett. 28 267–273. 10.1159/000112678 7128484 Lester P. A. Rush H. G. Sigler R. E. (2005). Renal transitional cell carcinoma and choristoma in a degu (Octodon degus). Contemp. Top. Lab Anim Sci. 44 41–44. 10.1093/ilarjournal/ilj040 Li H. Xiang B. L. Li X. Cong L. Li Y. Miao Y. (2023). Cognitive deficits and Alzheimer’s disease-like pathologies in the aged Chinese tree shrew (Tupaia belangeri chinensis). Mol. Neurobiol. 61 1892–1908. 10.1007/s12035-023-03663-7 37814108 Li J. Zhu S. Lv Z. Dai H. Wang Z. Wei Q. (2021). Drinking water with saccharin sodium alters the microbiota-gut-hypothalamus axis in guinea pig. Anim. 11:1875. 10.3390/ani11071875 34201842 Liu C. C. Zhao J. Fu Y. Inoue Y. Ren Y. Chen Y. (2022). Peripheral apoE4 enhances Alzheimer’s pathology and impairs cognition by compromising cerebrovascular function. Nat. Neurosci. 25 1020–1033. 10.1038/s41593-022-01127-0 35915180 Liu W. Zhang Y. Chen Q. Liu S. Xu W. Shang W. (2020). Melatonin alleviates glucose and lipid metabolism disorders in Guinea pigs caused by different artificial light rhythms. J. Diabetes Res. 2020:4927403. 10.1155/2020/4927403 33150187 Löscher W. Friedman A. (2020). Structural, molecular, and functional alterations of the blood–brain barrier during epileptogenesis and epilepsy: A cause, consequence, or both? Int. J. Mol. Sci. 21:591. 10.3390/ijms21020591 31963328 Lowen A. Mubareka S. Tumpey T. García-Sastre A. Palese P. (2006). The guinea pig as a transmission model for human influenza viruses. Proc. Natl. Acad. Sci. U.S.A. 103 9988–9992. 10.1073/pnas.0604157103 16785447 Luo P. H. Shu Y. M. Ni R. J. Liu Y. J. Zhou J. N. Yan J. (2020). A characteristic expression pattern of core circadian genes in the diurnal tree shrew (Tupaia belangeri). Neuroscience 437 145–160. 10.1016/j.neuroscience.2020.04.027 32339628 Magaki S. Tang Z. Tung S. Williams C. K. Lo D. Yong W. H. (2018). The effects of cerebral amyloid angiopathy on integrity of the blood–brain barrier. Neurobiol. Aging 70 70–77. 10.1016/j.neurobiolaging.2018.06.004 30007166 Malhotra R. K. (2018). Neurodegenerative disorders and sleep. Sleep Med. Clin. 13 63–70. 10.1016/j.jsmc.2017.09.006 29412984 Mander B. A. Winer J. R. Jagust W. J. Walker M. P. (2017). Sleep and human aging. Neuron 94 19–36. 10.1016/j.neuron.2017.02.004 28384471 Martel C. L. Mackic J. B. McComb J. G. Ghiso J. Zlokovic B. V. (1996). Blood–brain barrier uptake of the 40 and 42 amino acid sequences of circulating Alzheimer’s amyloid beta in guinea pigs. Neurosci. Lett. 206 157–160. 10.1016/S0304-3940(96)12462-9 8710175 Mckean N. E. Handley R. R. Snell R. G. (2021). A review of the current mammalian models of Alzheimer’s disease and challenges that need to be overcome. Int. J. Mol. Sci. 22:13168. 10.3390/ijms222313168 34884970 Meijer J. H. Daan S. Overkamp G. J. F. Hermann P. M. (1990). The two-oscillator circadian system of tree shrews (Tupaia belangeri) and its response to light and dark pulses. J. Biol. Rhythms 5 1–16. 10.1177/074873049000500101 2133115 Moir R. D. Lathe R. Rudolph E. Tanzi R. E. (2018). The antimicrobial protection hypothesis of Alzheimer’s disease. Alzheimers Dement. 14 1602–1614. 10.1016/j.jalz.2018.06.3040 30314800 Montagne A. Nation D. A. Sagare A. P. Barisano G. Sweeney M. D. Chakhoyan A. (2020). APOE4 leads to blood–brain barrier dysfunction predicting cognitive decline. Nature 581 71–76. 10.1038/s41586-020-2247-3 32376954 Musiek E. S. Holtzman D. M. (2016). Mechanisms linking circadian clocks, sleep, and neurodegeneration. Science 354 1004–1008. 10.1126/science.aah4968 27885006 Nassan M. Videnovic A. (2022). Circadian rhythms in neurodegenerative disorders. Nat. Rev. Neurol. 18:7–24. 10.1038/s41582-021-00577-7 34759373 Nešić S. Kukolj V. Marinković D. Vučićević I. Jovanović M. (2017). Histological and immunohistochemical characteristics of cerebral amyloid angiopathy in elderly dogs. Vet Q. 37:1–7. 10.1080/01652176.2016.1235301 27669976 Nuzum N. D. Loughman A. Szymlek-Gay E. A. Teo W. P. Hendy A. M. Macpherson H. (2022). To the gut microbiome and beyond: The brain-first or body-first hypothesis in Parkinson’s disease. Front. Microbiol. 13:791213. 10.3389/fmicb.2022.791213 35432226 Ocampo-Garcés A. Hernández F. Palacios A. G. (2013). REM sleep phase preference in the crepuscular Octodon degus assessed by selective REM sleep deprivation. Sleep 36:1247–1256. 10.5665/sleep.2896 23904685 Osella M. C. Re G. Odore R. Girardi C. Badino P. Barbero R. (2007). Canine cognitive dysfunction syndrome: Prevalence, clinical signs and treatment with a neuroprotective nutraceutical. Appl. Anim. Behav. Sci. 105 297–310. 10.1016/j.applanim.2006.11.007 Ozawa M. Chambers J. K. Uchida K. Nakayama H. (2016). The relation between canine cognitive dysfunction and age-related brain lesions. J. Vet. Med. Sci. 78 997–1006. 10.1292/jvms.15-0624 26922972 Padilla-Carlin D. J. McMurray D. N. Hickey A. J. (2008). The guinea pig as a model of infectious diseases. Comp. Med. 58 324–340. 10.4149/CM_2008_058_4_324 Palop J. J. Mucke L. (2009). Epilepsy and cognitive impairments in Alzheimer disease: A network dysfunction perspective. Arch. Neurol. 66 435–440. 10.1001/archneurol.2009.15 19204149 Parks T. V. Szuzupak D. Choi S. H. Alikaya A. Mou Y. Silva A. C. (2023). Noninvasive disruption of the blood-brain barrier in the marmoset monkey. Commun. Biol. 6:806. 10.1038/s42003-023-05185-3 37532791 Patke A. Young M. W. Axelrod S. (2020). Molecular mechanisms and physiological importance of circadian clocks. Nat. Rev. Mol. Cell Biol. 20 521–537. 10.1038/s41580-019-0179-2 31768006 Pawlik M. Fuchs E. Walker L. C. Levy E. Silhol S. Calenda A. (1999). Primate-like amyloid-β sequence but no cerebral amyloidosis in aged tree shrews. Neurobiol. Aging 20 47–51. 10.1016/S0197-4580(99)00017-2 10466892 Pérez-Cruz C. Rodríguez-Callejas J. D. (2023). The common marmoset as a model of neurodegeneration. Trends Neurosci. 46 394–409. 10.1016/j.tins.2023.02.002 36907677 Poduslo J. F. Curran G. L. Wengenack T. M. Malester B. Duff K. (2001). Permeability of proteins at the blood–brain barrier in the normal adult mouse and double transgenic mouse model of Alzheimer’s disease. Neurobiol. Dis. 8 555–567. 10.1006/nbdi.2001.9402 Prpar Mihevc S. Majdič G. (2019). Canine cognitive dysfunction and Alzheimer’s disease – two facets of the same disease? Front. Neurosci. 13:604. 10.3389/fnins.2019.00604 31249505 Rakic L. M. Zlokovic B. V. Davson H. Segal M. B. Begley D. J. Lipovac M. N. (1989). Chronic amphetamine intoxication and blood–brain barrier permeability to inert polar molecules studied in the vascularly perfused guinea pig brain. J. Neurol. Sci. 94 41–50. 10.1016/0022-510x(89)90216-5 2515257 Reicher V. Kis A. Simor P. Bódizs R. Gácsi M. (2021). Interhemispheric asymmetry during NREM sleep in the dog. Sci. Rep. 11:18817. 10.1038/s41598-021-98178-3 34552141 Resnikoff H. Metzger J. M. Lopez M. Bondarenko V. Mejia A. Simmons H. A. (2019). Colonic inflammation affects myenteric alpha-synuclein in nonhuman primates. J. Inflamm. Res. 12 113–126. 10.2147/JIR.S196552 31123415 Rivera D. S. Inestrosa N. C. Bozinovic F. (2016). On cognitive ecology and the environmental factors that promote Alzheimer disease: Lessons from Octodon degus (Rodentia: Octodontidae). Biol. Res. 49:10. 10.1186/s40659-016-0074-7 26897365 Rivera D. S. Lindsay C. B. Codocedo J. F. Carreño L. E. Cabrera D. Arrese M. A. (2018). Long-term, fructose-induced metabolic syndrome-like condition is associated with higher metabolism, reduced synaptic plasticity and cognitive impairment in Octodon degus. Mol. Neurobiol. 55 9169–9187. 10.1007/s12035-018-0969-0 29654490 Rizzo S. J. Homanics G. Schaeffer D. J. Schaeffer L. Park J. E. Oluoch J. (2023). Bridging the rodent to human translational gap: Marmosets as model systems for the study of Alzheimer’s disease. Alzheimers Dement. Transl. Res. Clin. Interv. 9:e12417. 10.1002/trc2.12417 37614242 Robinson J. L. Lee E. B. Xie S. X. Rennert L. Suh E. Bredenberg C. (2018). Neurodegenerative disease concomitant proteinopathies are prevalent, age-related and APOE4-associated. Brain 141 2181–2193. 10.1093/brain/awy146 29878075 Salazar C. Valdivia G. Ardiles A. O. Ewer J. Palacios A. G. (2016). Genetic variants associated with neurodegenerative Alzheimer disease in natural models. Biol. Res. 49:14. 10.1186/s40659-016-0072-9 26919851 Scheltens P. de Strooper B. Kivipelto M. Frisoni G. B. Salloway S. Van der Flier W. M. (2021). Seminar: Alzheimer’s disease – pathophysiology, diagnosis, and treatments. Lancet 397 1577–1590. 10.1016/S0140-6736(20)32205-4 33667416 Schmidt F. Boltze J. Jäger C. Hofmann S. Willems N. Seeger J. (2015). Detection and quantification of β-amyloid, pyroglutamyl Aβ, and tau in aged canines. J. Neuropathol. Exp. Neurol. 74 912–923. 10.1097/NEN.0000000000000230 26247394 Selkoe D. J. Hardy J. (2016). The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med. 8 595–608. 10.15252/emmm.201606210 27025652 Sengupta U. Kayed R. (2022). Amyloid β, Tau, and α-Synuclein aggregates in the pathogenesis, prognosis, and therapeutics for neurodegenerative diseases. Prog. Neurobiol. 214:102270. 10.1016/j.pneurobio.2022.102270 35447272 Serrano-Pozo A. Das S. Hyman B. T. (2021). APOE and Alzheimer’s disease: Advances in genetics, pathophysiology, and therapeutic approaches. Lancet Neurol. 20 68–80. 10.1016/S1474-4422(20)30412-9 33340485 Sharma H. Chang K. A. Hulme J. An S. S. A. (2023). Mammalian models in Alzheimer’s research: An update. Cells 12:2459. 10.3390/cells12202459 37887303 Sharman M. J. Moussavi Nik S. H. Chen M. M. Ong D. Wijaya L. Laws S. M. (2013). The guinea pig as a model for sporadic Alzheimer’s disease (AD): The impact of cholesterol intake on expression of AD-related genes. PLoS One 8:e66235. 10.1371/journal.pone.0066235 23805206 Sharrad D. F. Chen B. N. Gai W. P. Vaikath N. El-Agnaf O. M. Brookes S. J. H. (2017). Rotenone and elevated extracellular potassium concentration induce cell-specific fibrillation of α-synuclein in axons of cholinergic enteric neurons in the guinea-pig ileum. Neurogastroenterol. Motil. 29:e12985. 10.1111/nmo.12985 27997067 Sharrad D. F. de Vries E. Brookes S. J. H. (2013). Selective expression of α-synuclein-immunoreactivity in vesicular acetylcholine transporter-immunoreactive axons in the guinea pig rectum and human colon. J. Comp. Neurol. 521 657–676. 10.1002/cne.23198 22821666 Shimozawa A. Ono M. Takahara D. Tarutani A. Imura S. Masuda-Suzukake M. (2017). Propagation of pathological α-synuclein in marmoset brain. Acta Neuropathol. Commun. 5:12. 10.1186/s40478-017-0413-0 28148299 Sims-Robinson C. Kim B. Rosko A. J. Feldman E. L. (2010). How does diabetes accelerate Alzheimer disease pathology? Nat. Rev. Neurol. 6 551–559. 10.1038/nrneurol.2010.130 20842183 Smolek T. Madari A. Farbakova J. Kandrac O. Jadhav S. Cente M. (2016). Tau hyperphosphorylation in synaptosomes and neuroinflammation are associated with canine cognitive impairment. J. Comp. Neurol. 524 874–895. 10.1002/cne.23877 26239295 Stampfer M. J. (2006). Cardiovascular disease and Alzheimer’s disease: Common links. J. Intern. Med. 260 211–223. 10.1111/j.1365-2796.2006.01687.x 16918818 Steffen J. Krohn M. Paarmann K. Schwitlick C. Brüning T. Marreiros R. (2016). Revisiting rodent models: Octodon degus as Alzheimer’s disease model? Acta Neuropathol. Commun. 4:91. 10.1186/s40478-016-0363-y 27566602 Stonebarger G. A. Bimonte-Nelson H. A. Urbanski H. F. (2021). The rhesus macaque as a translational model for neurodegeneration and Alzheimer’s disease. Front. Aging Neurosci. 13:734173. 10.3389/fnagi.2021.734173 34539388 Švara T. Gombač M. Poli A. Račnik J. Zadravec M. (2020). Spontaneous tumors and non-neoplastic proliferative lesions in pet degus (Octodon degus). Vet. Sci. 7:32. 10.3390/vetsci7010032 32183187 Sweeney M. D. Sagare A. P. Zlokovic B. V. (2018). Blood–brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat. Rev. Neurol. 14 133–150. 10.1038/nrneurol.2017.188 29377008 Szabadfi K. Estrada C. Fernandez-Villalba E. Tarragon E. Setalo G. Izura V. (2015). Retinal aging in the diurnal Chilean rodent (Octodon degus): Histological, ultrastructural and neurochemical alterations of the vertical information processing pathway. Front. Cell Neurosci. 9:126. 10.3389/fncel.2015.00126 25954153 Takeda S. Sato N. Uchio-Yamada K. Sawada K. Kunieda T. Takeuchi D. (2010). Diabetes-accelerated memory dysfunction via cerebrovascular inflammation and Aβ deposition in an Alzheimer mouse model with diabetes. Proc. Natl. Acad. Sci. U. S. A. 107 7036–7041. 10.1073/pnas.1000645107 20231468 Tan Z. Garduño B. M. Aburto P. F. Chen L. Ha N. Cogram P. (2022). Cognitively impaired aged Octodon degus recapitulate major neuropathological features of sporadic Alzheimer’s disease. Acta Neuropathol. Commun. 10:182. 10.1186/s40478-022-01481-x 36529803 Tarragon E. Lopez D. Estrada C. Gonzalez-Cuello A. Ros C. M. Lamberty Y. (2014). Memantine prevents reference and working memory impairment caused by sleep deprivation in both young and aged Octodon degus. Neuropharmacology 85 206–214. 10.1016/j.neuropharm.2014.05.023 24878242 Tini G. Scagliola R. Monacelli F. La Malfa G. Porto I. Brunelli C. (2020). Alzheimer’s disease and cardiovascular disease: A particular association. Cardiol. Res. Pract. 2020:2617970. 10.1155/2020/2617970 32454996 Uchida K. Kihara N. Hashimoto K. Nakayama H. Yamaguchi R. Tateyama S. (2003). Age-related histological changes in the canine substantia nigra. J. Vet. Med. Sci. 65 179–185. 10.1292/jvms.65.179 12655111 Ueda K. Fukushima H. Masliah E. Xia Y. Iwai A. Yoshimoto M. (1993). Molecular cloning of cDNA encoding an unrecognized component of amyloid in Alzheimer disease. Proc. Natl. Acad. Sci. U. S. A. 90 11282–11286. 10.1073/pnas.90.23.11282 8248242 Ujiie M. Dickstein D. L. Carlow D. A. Jefferies W. A. (2003). Blood–brain barrier permeability precedes senile plaque formation in an Alzheimer disease model. Microcirculation 10 463–470. 10.1038/sj.mn.7800212 14745459 Uva L. Librizzi L. Marchi N. Noe F. Bongiovanni R. Vezzani A. (2008). Acute induction of epileptiform discharges by pilocarpine in the in vitro isolated guinea-pig brain requires enhancement of blood–brain barrier permeability. Neuroscience 151 303–312. 10.1016/j.neuroscience.2007.10.037 18082973 van Erum J. Van Dam D. De Deyn P. P. (2018). Sleep and Alzheimer’s disease: A pivotal role for the suprachiasmatic nucleus. Sleep Med. Rev. 40 17–27. 10.1016/j.smrv.2017.07.005 29102282 van Groen T. Kadish I. Popović N. Popović M. Caballero-Bleda M. Baño-Otálora B. (2011). Age-related brain pathology in Octodon degus: Blood vessel, white matter and Alzheimer-like pathology. Neurobiol. Aging 32 1651–1661. 10.1016/j.neurobiolaging.2009.10.008 19910078 van Vliet E. A. da Costa Araújo S. Redeker S. van Schaik R. Aronica E. (2007). Blood–brain barrier leakage may lead to progression of temporal lobe epilepsy. Brain 130 521–534. 10.1093/brain/awl318 17124188 Vojtechova I. Machacek T. Kristofikova Z. Stuchlik A. Petrasek T. (2022). Infectious origin of Alzheimer’s disease: Amyloid beta as a component of brain antimicrobial immunity. PLoS Pathog. 18:e1010929. 10.1371/journal.ppat.1010929 36395147 Wahl D. Moreno J. Santangelo K. S. Zhang Q. Afzali M. F. Walsh M. A. (2022). Nontransgenic guinea pig strains exhibit hallmarks of human brain aging and Alzheimer’s disease. J. Gerontol. A Biol. Sci. Med. Sci. 77 1766–1774. 10.1093/gerona/glac073 35323931 Wang J. Gu B. J. Masters C. L. Wang Y.-J. (2017). A systemic view of Alzheimer disease – insights from amyloid-β metabolism beyond the brain. Nat. Rev. Neurol. 13 612–623. 10.1038/nrneurol.2017.111 28960209 Wang L. Lu J. Yang Y. Zhao Y. Wang P. Jiao J. (2023). Mechanism of cognitive impairment induced by D-galactose and L-glutamate through gut–brain interaction in tree shrews. Synapse 77:e22274. 10.1002/syn.22274 37211869 Wisniewski H. M. Becker J. T. Kowall N. W. (1996). The origin of amyloid in cerebral vessels of aged dogs. Brain Res. 715 151–157. 10.1016/0006-8993(95)01156-0 8821753 Wu Z. C. Gao J. H. Du T. F. Tang D. H. Chen N. H. Yuan Y. H. (2019). Alpha-synuclein is highly prone to distribution in the hippocampus and midbrain in tree shrews, and its fibrils seed Lewy body-like pathology in primary neurons. Exp. Gerontol. 116 37–45. 10.1016/j.exger.2018.12.008 30553024 Wu Z. C. Huang Z. Q. Jiang Q. F. Dai J. J. Zhang Y. Gao J. H. (2015). Human and tree shrew alpha-synuclein: Comparative cDNA sequence and protein structure analysis. Appl. Biochem. Biotechnol. 177 957–966. 10.1007/s12010-015-1789-6 26265394 Xu B. Lei X. Yang Y. Yu J. Chen J. Xu Z. (2025). Peripheral proteinopathy in neurodegenerative diseases. Transl. Neurodegener. 14:2. 10.1186/s40035-024-00461-6 39819742 Yamashita A. Fuchs E. Taira M. Hayashi M. (2010). Amyloid beta (Aβ). protein- and amyloid precursor protein (APP)-immunoreactive structures in the brains of aged tree shrews. Curr. Aging Sci. 3 230–238. 10.2174/1874609811003030230 20735344 Yamashita A. Fuchs E. Taira M. Yamamoto T. Hayashi M. (2012). Somatostatin-immunoreactive senile plaque-like structures in the frontal cortex and nucleus accumbens of aged tree shrews and Japanese macaques. J. Med. Primatol. 41 147–157. 10.1111/j.1600-0684.2012.00540.x 22512242 Yamazaki Y. Zhao N. Caulfield T. R. Liu C.-C. Bu G. (2019). Apolipoprotein E and Alzheimer disease: Pathobiology and targeting strategies. Nat. Rev. Neurol. 15 501–518. 10.1038/s41582-019-0228-7 31367008 Yang G. Y. Betz A. L. Chenevert T. L. Brunberg J. A. Hoff J. T. (1994). Experimental intracerebral hemorrhage: Relationship between brain edema, blood flow, and blood–brain barrier permeability in rats. J. Neurosurg. 81 93–102. 10.3171/jns.1994.81.1.0093 8207532 Yang X. Chen Z. Wang Z. He G. Li Z. Shi Y. (2022). A natural marmoset model of genetic generalized epilepsy. Mol. Brain 15:16. 10.1186/s13041-022-00901-2 35144651 Yao Y.-G. Lu L. Ni R.-J. Bi R. Chen C. Chen J.-Q. (2024). Study of tree shrew biology and models: A booming and prosperous field for biomedical research. Zool. Res. 45 877–909. 10.24272/j.issn.2095-8137.2024.199 39004865