Next Article in Journal
Calculated Maximal Volume Ventilation (cMVV) as a Marker of Early Respiratory Failure in Amyotrophic Lateral Sclerosis (ALS)
Previous Article in Journal
Cellular and Molecular Mechanisms Underlying Synaptic Subcellular Specificity
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Animal Approaches to Studying Risk Factors for Parkinson’s Disease: A Narrative Review

by
R. H. Silva
1,*,
L. B. Lopes-Silva
1,
D. G. Cunha
1,
M. Becegato
1,
A. M. Ribeiro
2 and
J. R. Santos
3
1
Behavioral Neuroscience Laboratory, Department of Pharmacology, Universidade Federal de São Paulo, São Paulo 04021-001, SP, Brazil
2
Laboratory of Neuroscience and Bioprospecting of Natural Products, Department of Biosciences, Universidade Federal de São Paulo, Santos 11015-020, SP, Brazil
3
Behavioral and Evolutionary Neurobiology Laboratory, Department of Biosciences, Federal University of Sergipe, Itabaiana 49500-000, SE, Brazil
*
Author to whom correspondence should be addressed.
Brain Sci. 2024, 14(2), 156; https://doi.org/10.3390/brainsci14020156
Submission received: 14 December 2023 / Revised: 25 January 2024 / Accepted: 31 January 2024 / Published: 2 February 2024
(This article belongs to the Section Behavioral Neuroscience)

Abstract

:
Despite recent efforts to search for biomarkers for the pre-symptomatic diagnosis of Parkinson’s disease (PD), the presence of risk factors, prodromal signs, and family history still support the classification of individuals at risk for this disease. Human epidemiological studies are useful in this search but fail to provide causality. The study of well-known risk factors for PD in animal models can help elucidate mechanisms related to the disease’s etiology and contribute to future prevention or treatment approaches. This narrative review aims to discuss animal studies that investigated four of the main risk factors and/or prodromal signs related to PD: advanced age, male sex, sleep alterations, and depression. Different databases were used to search the studies, which were included based on their relevance to the topic. Although still in a reduced number, such studies are of great relevance in the search for evidence that leads to a possible early diagnosis and improvements in methods of prevention and treatment.

1. Introduction

Animal models are important resources for studying human diseases as they allow for the investigation of pathophysiological mechanisms and the screening of potential treatments. There are several protocols used in experimental studies of PD, both in non-human animals (mainly rodents) and cell culture. As illustrated in Table 1, the main animal models used for this purpose encompass pharmacological and genetic approaches [1,2,3,4,5,6,7]. In general, they achieve the validities that certify animal models [6,7], based on the core characteristics of the disease: hypofunction of the dopaminergic nigrostriatal pathway (construct validity) and the presence of motor alterations (face validity), which are ameliorated by classical antiparkinsonian drugs such as L-DOPA (predictive validity). Important additions to the construct aspects of many of these protocols are oxidative stress-induced damage, neuroinflammation, and increased levels of alpha-synuclein—although Lewy bodies (classical pathological hallmarks) are difficult to observe in PD animal models. Regarding phenomenological validity, several aspects of the deterioration of motor function are presented in similar or equivalent ways in relation to humans, such as akinesia, bradykinesia, rigidity, tremor, and balance alterations. Nevertheless, the gradual appearance of these alterations is not always achieved due to acute severe neuronal injuries caused by most protocols. In addition, non-motor symptoms, considered an important part of the disease, are rarely evaluated, partly for the same reason that makes it difficult to assess the progressivity of motor deficits.
Likewise, studying risk factors for PD in animal models can be challenging. As mentioned, understanding of the mechanisms underlying the influence of such factors on disease pathogenesis is crucial for the development of early diagnosis methods, allowing for preventive interventions. In addition, a similar response to known risk factors could add to the construct validity of animal models. Rodents do not present spontaneous PD. Thus, it is necessary to conduct studies that use a combination of variables that correspond to human risk factors with protocols that induce parkinsonian-like conditions in rodents.
Overall, literature reviews on animal models of Parkinson’s disease focus on the motor and neurodegenerative aspects of the condition. The present narrative review aims to draw attention to the importance of studying risk factors and prodromal signs of PD in animal models. To this purpose, animal studies that investigated four of the main risk factors and/or prodromal signs related to PD were discussed: advanced age, male sex, sleep alterations, and depression. An overview of the content discussed here is schematized in Figure 1.

2. Methods

This review discusses articles retrieved from Embase, Google Scholar, Medline, and Pubmed until December 2023, coupled with an examination of citations from relevant articles. Main terms (“Parkinson’s disease AND risk factors” OR “Parkinson’s disease AND animal model” OR “movement disorder AND risk factors AND sex, age, depression, sleep disturbances”) were used to search for relevant articles that investigated four risk factors related to PD (age, sex, sleep alterations, and depression). Furthermore, to perform a broad search, synonyms and truncated terms of the descriptors were added to the search strategy. The search was restricted to English-language articles. The title and abstract of the studies were analyzed separately by three authors, who excluded articles unrelated to the topic. Moreover, the selected studies were further revised through full-text screening. The final reference list was generated based on relevance to the topics covered in this review.

3. Parkinson’s Disease

Parkinson’s disease (PD) is the most common neurodegenerative motor dysfunction and the second most prevalent neurodegenerative disease associated with aging [8,9,10,11], and it has a higher incidence in men than in women [9]. PD is a multifactorial disease with no defined etiology. The underlying pathophysiology is characterized by the progressive death of dopaminergic neurons, leading to motor disorders such as bradykinesia, tremor at rest, muscle rigidity, and changes in posture and gait as the disease progresses [12,13,14]. However, studies show that in the early stages of the disease, PD patients present several non-motor dysfunctions, such as cognitive deficit, sleep disturbance, anxiety, depression, hyposmia, and constipation, which are related to other neuronal mechanisms such as noradrenergic and serotoninergic pathways [15,16,17,18].
There is a body of evidence suggesting that the factors involved in the pathogenesis of this disease interact to culminate in the neurodegenerative process. In other words, PD is defined as a condition of multifactorial etiology [19,20,21]. Currently, the diagnosis of PD relies on well-defined clinical criteria based on the cardinal motor symptoms that characterize the disease, and there are no standardized biomarkers for a possible early diagnosis [19,22]. Because of this, it is estimated that the diagnosis of PD is made several years after the beginning of the neurodegeneration process [23]. Thus, a possible PD diagnosis before the characteristic motor manifestation is of great relevance. Alternatively, the identification of individuals who are at high risk for this disease would pave the way for potentially effective preventive strategies in controlling or delaying neurodegeneration. For this purpose, it is important to characterize initial symptoms (usually non-motor, which may occur years before conventional diagnosis) and individual and environmental risk factors, as well as the interaction between risk factors and pathophysiological mechanisms of the disease [24,25,26,27].

4. Risk Factors for Parkinson’s Disease

Risk factors for PD are mainly studied through human epidemiological surveys [21]. Among all the conditions positively associated with the risk of PD, some are classic risk factors and others have pathogenic relevance, that is, they are considered initial symptoms of the disease. Risk factors are characteristics of the individual or the environment that increase the likelihood of the presence of the disease. Features with pathogenic relevance are the so-called prodromal markers. A priori, those markers are part of the pathological condition, such as non-motor signs that precede motor symptoms [27]. However, the identification of a risk factor or prodromal sign of PD is sometimes hampered by the absence of a method to identify the onset of the disease before the appearance of motor symptoms [19].
Regardless of their straight category, all these characteristics are highly relevant for identifying individuals at high risk [19,27]. As mentioned, the non-motor symptoms that precede motor impairment are considered prodromal signs that may result from the initial neurodegenerative process. Nevertheless, there is a possibility that these signs are pre-morbid, that is, occurring prior to the actual onset of the disease. In that case, they would qualify as risk factors [28,29]. Importantly, many of these signs are not routinely checked, which often leads to underdiagnosis [19]. In addition, these symptoms may be present in other pathological conditions or even in normal aging, unrelated to PD [30]. Finally, the pathophysiological mechanism underlying these symptoms is still unknown, and the time course of their emergence can vary between individuals and different populations at risk [25,27,31].
In epidemiological studies, evidence does not allow for a precise assessment of the chronology and causality of these events. In fact, except for age, no risk factor for PD has indisputable evidence of causality [21]. Furthermore, although many studies propose to survey such factors, little is known about the nature of their interaction with the mechanisms that initiate neurodegeneration [27]. In addition, one difficulty of epidemiological approaches is that several studies are based on self-reports of PD history instead of standardized clinical diagnoses [32,33,34,35,36,37,38,39,40]. Patients with other neurological diagnoses may present parkinsonism as a symptom, and even if the diagnosis is correct, there are subtypes of parkinsonism that can have different etiologies [41].
Other limitations regarding the precision of epidemiologic approaches to studying risk factors are: (1) geographical differences in the prevalence of the disease, which makes it difficult to compile studies carried out in different continents or countries [42]; (2) variability in the duration of follow-up for the study population [19]; and (3) heterogeneity in the clinical manifestation of PD [19]. In a very comprehensive review of meta-analyses, Bellou et al. [20] concluded that there is a large body of evidence in favor of associating PD risk with the most studied risk factors in the literature. However, in most cases, they cannot exclude possible methodological biases or alternative explanations. In particular, inverse causality has been pointed out as a possible reason for some risk factors. For example, the still poorly understood pre-motor phase of the disease could influence the patient’s habits or personality, resulting in exposure to certain environmental factors [43].
Finally, the relevance of studying risk factors lies not only in the possibility of preventive interventions but also in contributions to the investigation of pathophysiological mechanisms and the identification of therapeutic targets. Considering the above, it is essential to characterize risk factors for PD in animal models. Such studies could enable the investigation of the causality of risk factors, specify their temporal course, determine their prodromal role, and contribute to the unravel of the biological mechanisms responsible for the increased risk.
A large number of risk factors have already been reported for the development of PD [44]. Among the risk factors in epidemiological studies, the most significant is undoubtedly advanced age. In addition, the male sex, family history, the use of pesticides/herbicides and associated activities, sleep changes, and depression can be highlighted. Additionally, studies point to other factors such as ethnicity, high consumption of dairy products, and traumatic brain injury.
In this review, two important risk factors mentioned in the literature will not be addressed: family history and the use of pesticides. There is an influence of genetic inheritance on the development of PD [45], which results in a positive association between family history and the risk of developing the disease [19,46]. Cases of monogenic inheritance determining PD occur in only about 5% of patients. Therefore, the expressive association between family history and the risk of PD is probably due to the inheritance of multiple susceptibility loci that predispose to the development of the disease [19,21,41,45,47]. However, the natural aspect of this influence cannot be studied in rodent models because these species do not develop PD spontaneously. Nevertheless, studies on genetically modified animals have been carried out to model PD [48,49].
The use of pesticides and related activities (rural life, agricultural work, etc.) have been repeatedly identified as factors that increase the risk of developing PD [19,21,44,50,51,52], but not always [21,41,53]. Some compounds have been used in animal studies to induce PD, such as rotenone and paraquat [54,55,56]. These approaches have some advantages over other animal models, such as the presence of alpha–synuclein aggregates [57]. However, there are some constraints in studying them as risk factors in laboratory animals: (1) the extremely high toxicity of these compounds, which compromises the survival rates of the animals under study [58] and (2) the fact that pesticides have been reported as causal factors of parkinsonian symptoms in humans [59,60]. This causal relation would characterize this condition as drug-induced parkinsonism, not exposure to a risk factor for idiopathic PD.
Thus, this study will focus on: (1) age and male sex, well-known risk factors for PD, and (2) sleep disturbances and depression, conditions that have been proposed both as risk factors and prodromic characteristics of the disease [19,61,62,63]. First, main pharmacological models for PD will be briefly described. Then, the state-of-the-art of each of the risk factors in the context of animal studies will be discussed. The main results regarding such risk factors obtained in pharmacological rodent models—and discussed in the present review—are summarized in Table 2.

5. Neurotoxic and Pharmacological Rodent Models for PD

Currently, two types of animal models are most used to study PD: genetic models, based on the expression of genes related to the disease; and neurotoxic/pharmacological models, which use drugs that interfere with dopaminergic transmission. Among the toxins most used in animal models of PD, 6-hydroxydopamine (6-OHDA) stands out. This toxin enters dopaminergic and noradrenergic neurons through dopamine and noradrenaline transporters, respectively. However, it is worth mentioning that 6-OHDA does not cross the blood–brain barrier. Therefore, it is necessary to administer this substance directly to the animal’s central nervous system. Once inside dopaminergic neurons, the toxin acts mainly on the mitochondrial complex I of the respiratory chain, leading to oxidative stress and degeneration of neurons in the substantia nigra pars compacta (SNpc) and ventral tegmental area (VTA) [76,77,78].
Another toxin used to induce PD is 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), the precursor of 1-methyl-4-phenylpyridine (MPP+), a neurotoxin capable of causing damage in the nigrostriatal pathway. MPTP crosses the blood–brain barrier and is subsequently converted to MPP+ by monoaminoxidase-B (MAO-B), leading to the blockage of the electron transport chain in the mitochondrial complex I of the respiratory chain, which results in oxidative stress [76,78,79].
Rotenone is a neurotoxin used as a pesticide that can also be used as a PD inducer in animals. This toxin crosses the blood–brain barrier and, similar to the other already mentioned toxins, acts on the mitochondrial complex I of the respiratory chain [80], in addition to preventing cell proliferation and blocking the mitosis process [81]. Chronic administration of this substance in rats induces nigrostriatal neurodegeneration, as well as the formation of Lewy bodies [82].
Reserpine is an alkaloid extracted from Rauwolfia serpentina, used in the first animal model of PD [83]. Since then, this model has been widely used to test new treatments to PD. Reserpine blocks the vesicular membrane transporters responsible for the storage of monoamines (dopamine, norepinephrine, and serotonin) in the synaptic vesicles, thus causing the depletion of these neurotransmitters [84]. One of the limitations of this model is that reserpine not only depletes dopamine but also other monoamines. However, this animal model can mimic the biochemical and behavioral effects of the disease, since the reduction of striatal dopamine promotes symptoms such as akinesia, tremors, and cognitive deficits [85]. In addition, there is evidence that PD involves the impairment of multiple neurotransmission systems, including serotonin [17,86] and norepinephrine; [87,88]. Therefore, the involvement of neurotransmitters other than dopamine in the reserpine model of PD seems to be relevant for the construct validity of this model.
Originally, reserpine doses commonly used to induce parkinsonism in animals ranged from 1 to 10 mg/kg [89,90,91]. Such doses induce intense and immediate motor impairment. In the early 2010s, the repeated administration of a lower dose (0.1 mg/kg) was proposed. This protocol showed a gradual impairment in catalepsy in the bar test, in traveled distance in the open field, and orofacial movements occurring progressively over treatment [2,92,93]. Furthermore, Santos et al. [92] also demonstrated that the motor alterations presented by the animals were preceded by cognitive disabilities. In addition, a reduction in the tyrosine hydroxylase (TH) labeling and an increase in lipid peroxidation and neuroinflammatory parameters in the nigrostriatal dopaminergic pathway were also demonstrated [92,93]. A comparison between the progression of parkinsonian alterations induced by acute neurotoxins versus chronic reserpine is illustrated in Figure 2.

6. Age

Age has been linked to PD since the original description by James Parkinson. Currently, it is considered the most consistent risk factor for the disease [27,43,94,95]. A meta-analysis study reported that the prevalence ranges from 41 individuals per 100,000 in the 40 to 49 age group to 1900 individuals per 100,000 for the population over 80 [96]. Kim et al. [96] observed a prevalence of 1229 individuals per 100,000 for the Latin American population over 60. Indeed, advanced age is the most applicable factor for the inclusion of an individual in the population at risk for PD, along with family history [30].
Although the association between neurodegenerative diseases and aging seems obvious, this link is not found for all diseases that fall into this category. For example, Huntington’s disease and amyotrophic lateral sclerosis occur in younger individuals. Thus, for those neurodegenerative diseases that are associated with aging (such as PD), there must be some intrinsic factor of the aging process that leads to (or accelerates) its onset. However, the mechanisms involved in the influence of age on the development of PD are not completely understood. It has been proposed that normal aging and the pathophysiology of PD have common cellular mechanisms. In other words, the cellular aging that normally occurs in the nigrostriatal dopaminergic pathway would be exacerbated in PD due to a combination of genetic and environmental factors [97]. However, this hypothesis is not supported by some studies, which argue that the processes of normal aging and degeneration underlying PD—and other age-related neurodegenerative conditions—occur through distinct mechanisms [98,99,100,101].
Although some researchers have addressed this issue in non-human primates [97] few rodent studies have approached the specific association between aging and PD, weakening the translatability of preclinical findings [102]. In the study by Gupta et al. [64], 21-month-old male C57BL/6 mice received two MPTP injections intraperitoneally, the first at a dose of 30 mg/kg and the second at 15 mg/kg (due to the death of many animals). Three-month-old young adult mice C57BL/6 also received two doses of MPTP (30 mg/kg). After euthanasia, the brain was sliced for immunofluorescence analysis. Histological analysis showed a marked reduction in fluorescence in noradrenergic neurons of the locus coeruleus and in dopaminergic neurons of SNpc and VTA of elderly mice when compared to young controls [64].
In another study, Tremblay et al. [65] showed that pre-treatment with cystamine—an antioxidant and anti-apoptotic molecule—two days prior, and during 14 days after MPTP lesioning, increased the immunostaining for tyrosine hydroxylase in the striatum, as well as Nurr1 gene expression and increased the density of dopamine transporter in the substantia nigra in aged rodents. Similarly, Patki et al. [66] demonstrated that elderly mice (6 to 10 months) showed deficits in the activity of the respiratory chain in mitochondria, decreased antioxidant enzymes and cytochrome c, and a significant reduction in TH and DA uptake transporter. In addition, the older animals had impaired movement when compared to younger mice (6–10 weeks) subjected to the same protocol. It is worth mentioning that these changes were detected up to six weeks after the chronic protocol.
It has been demonstrated that aged animals are more susceptible to MPTP, and the neurotoxin induces a more pronounced reduction of TH in the aged brain [67]. The same study reported that astaxanthin-treated aged mice, when exposed to the MPTP neurotoxin, exhibited a significant loss of tyrosine hydroxylase throughout the nigrostriatal circuit compared to young mice. This suggests that aged animals respond differently to the MPTP toxin due to greater vulnerability of the aging brain.
Using a progressive animal model of parkinsonism, based on the administration of repeated injections of a low dose of reserpine (0.1 mg/kg), Melo et al. [68] observed that elderly rats (18–24 months) were more susceptible to the effects of the treatment when compared to adult animals (6–8 months). Indeed, aged animals developed motor alterations earlier than adult animals. In addition to the more severe motor changes, the authors observed that elderly rats showed a reduction in TH immunoreactivity in SNpc, dorsal striatum, and VTA. Furthermore, after treatment interruption, older animals did not show reversibility of the behavioral and dopaminergic changes caused by reserpine, supporting the hypothesis that the use of older animals better represents the behavioral and pathophysiological changes observed in the progressiveness of PD.

7. Sex

The greater prevalence of PD in males is well-recognized and reported by numerous studies. The relative risk reaches a ratio of 2:1 [21,27,94,95,103,104]. Of relevance, this proportion varies according to the age group. For example, Taylor et al. [105] reported that the male/female sex ratio is higher in older age groups [105]. In addition to age, it is interesting to highlight that other risk factors may act differently between the sexes, such as coffee consumption, physical activity, and the use of non-steroidal anti-inflammatory drugs [21]. In addition to being less likely to develop the disease, women may have a more benign motor phenotype, with a slower progression compared to men [106]. Additionally, there is evidence that the effectiveness of treatment with antiparkinsonian drugs depends on sex [107]. On the other hand, some non-motor symptoms such as nociceptive alterations and depression seem to be more prominent in women [108], although these findings are controversial [109]. Thus, clarifying the mechanisms related to the differential susceptibility to PD between sexes may be relevant to improving possible preventive and therapeutic strategies.
Epidemiological studies show that the incidence of PD in men remains higher than in women, even with increasing age. The incidence rate in men over 40 years of age is 61.21 cases per 100,000 inhabitants, while in women of the same age group, the incidence is 37.55 cases. The incidence rate among women is constantly increasing, from 3.26 cases per 100,000 inhabitants up to 49 years old to 103.48 cases up to 80 years old, with the peak between 70 and 79 years old. In men, in the same age groups, the incidence rate increases from 3.37 to 258.47 cases per 100,000 inhabitants, respectively, and this rate increases as patient survival increases. However, different rates are shown when they are restricted to specific geographic regions [10,96,110,111]. In addition, women with PD experience difficulties in receiving treatment and caregiving [112].
Because the risk of developing PD is evidently lower in females, the hypothesis that estrogen would be a protective factor against the development of the disease was raised. Indeed, the neuroprotective action of this hormone has been reported [113,114]. On the other hand, epidemiological studies show that the association between estrogen levels and protection against the development of PD is controversial [21,43,115,116]. There is some evidence of an increased risk of PD in women who have undergone ovariectomy or hysterectomy, used oral contraceptives, or have abstained from hormone replacement therapy [104,117,118,119]. However, a very comprehensive meta-analysis and well-controlled prospective studies did not show significant associations between the risk of PD and the use of oral contraceptives, surgical menopause, or hormone replacement therapy [19,116]. As a possible cause of this controversy, it has been demonstrated that the neuroprotective effect of estrogen occurs in the preclinical phase, postponing the degenerative effects of the disease, but it does not have any effect once the symptoms are already established [106]. Nevertheless, if estrogen promoted neuroprotection, the male/female prevalence ratio would be higher only at earlier ages, since estrogen levels are higher in younger women. Thus, it is likely that other factors are involved in the higher prevalence in males, such as cultural issues or some type of genetic susceptibility linked to sex chromosomes, but these factors have not yet been specifically investigated [21].
The neuroprotective effects of female hormones, especially estradiol (17β-estradiol), in the pathophysiology of neurodegenerative diseases have been demonstrated in various animal models [120,121]. In addition, it has been shown that estradiol has anti-inflammatory properties [122]; prevents neuronal death by increasing the endogenous synthesis of anti-apoptotic molecules [123]; acts on mitochondria, improving bioenergetic activity and basal mitochondrial respiration [124,125]; and increases levels of brain-derived neurotrophic factor (BDNF), a key molecule involved in neuronal survival, neurotransmission, dendritic growth, and cell communication in the central nervous system. In astrocytes, estradiol has neurotrophic activity, facilitating the secretion of growth factors, represses the expression of glial fibrillary acidic protein (GFAP), and reduces astrogliosis [126,127]. Neuroprotective effects of progesterone have also been demonstrated in a variety of experimental models [128,129]. Progesterone attenuates blood–brain barrier dysfunction [130]; promotes the survival of newborn neurons [131]; has anti-inflammatory [132] and antioxidant [133] properties; acts in the preservation of mitochondrial functions [134,135]; reduces GFAP levels [136]; and regulates BDNF production and release [129]—which can be interpreted as neuroprotection mechanisms.
Studies on sexual differences in animal models of PD provide a more accurate control of hormonal variation since most human studies depend on personal reporting, and hormonal measurements are not carried out. In addition, other physiological factors that could be involved in the differences in prevalence between sexes can be investigated. Nevertheless, few animal studies have addressed this issue. For example, in the study by Field et al. [69], animals of both sexes presenting 6-OHDA-induced unilateral lesions were submitted to a test that evaluates vertical exploration in a confined cylinder. The results showed that 6-OHDA-treated male animals reduced the use of the hind limbs compared to females, despite the deficit in forelimbs movements being similar between sexes. In addition, males were more likely to contact the cylinder wall with their dorsal surface to keep an erect posture. The study also showed that female animals had a less severe reduction in the number of dopaminergic cells compared to males.
Using the repeated reserpine-induced progressive PD model, ref. [70] showed that females were more resistant to the deleterious effect of the treatment. Indeed, this sex did not present reduced TH immunoreactivity in the dorsal striatum and VTA. Also applying the reserpine-induced progressive protocol, ref. [71] showed that female animals did not present cognitive alterations and TH immunoreactivity reduction. In addition, females presented attenuated motor impairment compared to males. These findings reinforce the notion that more studies comparing sexes should be conducted to better comprehend the mechanisms that lead to neuroprotection in females.

8. Sleep Alterations

Sleep and circadian rhythm disturbances are among the most common non-motor symptoms in PD, reaching 60 to 90% of patients. For many years, this group of symptoms was considered only as secondary signs, unrelated to the pathophysiology of the disease, despite the high prevalence [137,138,139]. Even now, such disturbances are underreported or underrecognized by patients with PD [140]. A study showed that 20% to 30% of patients failed to report sleep disorders to their healthcare providers. The high rate of non-declaration of these disturbances by patients to healthcare providers means that many symptoms remain untreated. Factors that lead to help-seeking can be the acceptance of symptoms, lack of awareness that the symptom is associated with PD, and belief that no effective treatments are available [141].
More recently, attention to non-motor symptoms of PD has increased, especially to sleep and circadian rhythm disorders. Currently, these symptoms are recognized as important causes of quality-of-life impairment, and PD is known to affect important brain regions and neurotransmission systems related to the control of the sleep–wake cycle [142,143]. Sleep disorders present in DP are mainly insomnia, excessive daytime sleepiness (EDS), restless legs syndrome, circadian rhythm disorders, and rapid eye movement (REM) sleep behavior disorder (RBD) [144,145,146].
Insomnia is thought to be the most common sleep disorder in PD, with prevalence varying from 30% to 80%. Patients often report sleep fragmentation and early awakenings rather than sleep initiation difficulty. PD patients with insomnia are usually at more advanced stages of the disease, showing important motor, psychiatric, and autonomic symptoms [140]. Regarding RBD, studies estimate the prevalence at 23–25% in PD patients and 2–4% in the general population [140]. Another study shows a prevalence of RBD close to 50% in patients with PD. If REM sleep without muscle atony (preclinical form of the disorder) is also considered, the estimated prevalence is 60% in PD patients. Importantly, the highest predictive value for the future development of PD is observed for polysomnography confirmed RBD [147]. In addition, RBD is associated with a worse prognosis for patients. In other words, there is a higher risk of more severe motor dysfunction, hallucinations, cognitive impairment, and autonomic dysfunction [147].
Autopsy brain studies revealed the presence of Lewy bodies in the pedunculopontine tegmental nucleus (PPN), locus coeruleus/subcoeruleus complex, and gigantocellular reticular nucleus in the medulla oblongata of PD patients who previously developed idiopathic RBD [140]. These regions are considered part of the neural circuit that regulates atonia during REM sleep and are linked to RBD pathology [148].
Restless Legs Syndrome (RLS) is proposed as another important symptom of PD and is closely associated with periodic limb movement of sleep (PLMS). Studies evaluating the frequency of RLS and PLMS in PD patients generated very discrepant results, ranging from 0% to 52.3% of patients with the condition. Some studies showed that RLS is present in 14–16% of patients [96,149]. For some authors, RLS indicates an increased risk for PD. A 0.37% incidence of PD has been found in the RLS population, while in control individuals, the incidence of PD was 0.13% [140]. On the other hand, there are studies that found a similar prevalence of RLS among PD patients and in the general population [150]. These discrepant results can be related to the diagnosis accuracy. RLS diagnosis may be confounded in patients with PD due to the potential overlap of motor symptoms. Therefore, because the prevalence and causes of RLS in PD are still unclear, this disturbance has not been considered an accurate predictor.
Excessive daytime sleepiness (EDS) is present in 15–50% of PD patients. This condition is characterized by an urge to fall asleep during different daily-life circumstances, with a severe negative impact on the overall quality of life. The degeneration of hypothalamic orexin cells (related to vigilance maintenance) is an essential factor in PD-related EDS [120,151].
The mechanisms underlying the circadian fluctuation of symptoms are not known, although it is probably related to a circadian variation in central dopaminergic transmission [143]. The sleep regulatory centers and circadian rhythm circuits—such as the hypothalamus and various brainstem nuclei involved in sleep–wake regulation—are affected by the neurodegenerative process. Neuropathological changes in these regions may begin before the degeneration of the substantia nigra and may be related to many of the non-motor characteristics seen in PD, such as sleep and circadian rhythm disturbances [138,152].
Of relevance, many of these changes can occur prior to the appearance of motor symptoms and the diagnosis. Thus, the presence of such sleep disorders can classify an individual as at high risk for PD. The risk of developing PD is very high among patients who suffer from RBD [61], which is considered a prodromal sign of the disease [61,153]. RBD precedes the onset of parkinsonism by 13 years on average, but this interval can reach so as far as over 20 years [140,154]. However, the prevalence of this disorder in recently diagnosed patients is limited, and therefore, it is questioned whether RBD would be a pre-motor symptom in all cases of idiopathic PD [46,155]. Thus, the investigation of these sleep disturbances as risk factors becomes relevant. In addition, activities that interfere with sleep and circadian rhythm, such as night shift work, have also been suggested as risk factors for PD, although this is still under debate [156,157,158]. In this sense, it is not clear whether there is a causal relationship between sleep loss and an increased risk of PD, or if the pre-diagnosis period of the disease already has changes that would lead to sleep changes. Thus, the chronology of events remains to be clarified, and the study of the relationship between sleep deficits and PD in animal models could provide causal or mechanistic evidence. However, few studies using animal models seek to study the interaction between PD and sleep disorders.
Toxin-based animal models have greatly contributed to the development of symptomatic treatments, mainly for motor symptoms. Notwithstanding, some toxin-based models also show prodromal symptoms. For example, 6-OHDA and MPTP protocols, widely known for reproducing motor deficits accompanied by dopaminergic neuronal death, can mimic sleep disorders in animals. Increased muscle tone during REM sleep, which is suggestive of an RBD-like phenotype, was reported in rats treated with 6-OHDA and in rhesus monkeys and marmosets treated with MPTP [74].
It has also been shown that these pharmacological models can mimic the insomnia state present in patients with PD. Rats with a unilateral 6-OHDA lesion of the medial forebrain bundle show decreased sleep time during their inactive phase (light) of the 24 h light–dark cycle [73]. Increased wake time during the 12 h dark period has also been observed in rats with a selective 6-OHDA lesion of the SNpc [74]. Animals submitted to bilateral 6-OHDA lesion in the ventral tegmental area (VTA) show reduced REM sleep during the light period and an increase in total sleep time during the dark phase [72]. These findings are in line with some of the disturbances observed in PD patients, who are affected by insomnia at night and daytime sleepiness.
Genetic models based on changes in the α-syn gene (SNCA) have also been created in recent years. Many features of sporadic PD are observed in transgenic mice overexpressing wild-type α-syn. The model induces progressive changes in dopamine release and striatal content, alpha-synuclein pathology, and deficits in motor and nonmotor functions, including sleep disturbances [159,160]. It was recently demonstrated that A53T α-syn BAC transgenic mice present an RBD-like phenotype, hyposmia, and decreased TH-positive neurons in the SNpc. All of these findings were seen in the absence of motor deficits, suggesting that this could be a prodromal PD mouse model [160].
Sleep and circadian rhythm disorders may be a key component of the non-motor symptoms of Parkinson’s disease [161]. In addition, sleep and circadian rhythm disorders are difficult to reproduce in animal models of PD, although some studies have succeeded. On the other hand, one particularly interesting aspect of sleep disorders in PD is their potential impact on other symptoms, whether motor or non-motor alterations, and in quality of life [162], reinforcing the relevance of addressing the interaction between PD and sleep in animal studies.

9. Depression

Depression is present in approximately 40 to 60% of patients with PD, and the presence of this symptom worsens the already poor quality of life of individuals with this disease [163,164,165,166,167]. It is important to note that depression, or other mood disorders, can occur prior to the onset of motor symptoms [168], and that early treatment of depression associated with PD can promote better acceptance of the signs of the disease [169,170]. Despite that, depression in PD patients is still underdiagnosed and undertreated [167].
In addition to manifesting as a non-motor symptom in a large number of patients, a history of depression has a positive association with the subsequent development of PD [27,171,172,173]. Indeed, twice as much risk is reported for an individual with a history of depression to be later diagnosed with PD [19,174]. The presence of clinically diagnosed depression can be used as a criterion for including individuals in populations at risk in prospective studies [62].
The pathophysiology of depression associated with PD is not completely understood [164]. Some hypotheses intend to provide a pathophysiological explanation for the higher prevalence of depression in PD patients [175]. Considering depression as a prodromal sign, a hypothesis to explain its occurrence would be the degeneration of brainstem nuclei, midbrain, and cortex [29]. However, depression can also have other causes [176] and is positively associated with advanced age itself [177]. When depression occurs prior to the diagnosis of PD, it is unclear if it is part of the disease, or whether the individual with depression is more susceptible, and whether, together with other risk factors, this susceptibility would lead to the development of PD [175]. In epidemiological studies, in general, it is not possible to identify the chronology and causality of events, in the same way as discussed for symptoms related to sleep.
Another factor to be considered is that one of the main risk factors for depression is chronic stress [178]. In this respect, it has been suggested that chronic stress, by increasing nervous system susceptibility, could be a causal factor for neurodegeneration in PD [179,180]. Accordingly, there is evidence that stress is a risk factor for the development of PD [181,182,183].
The study of mechanisms related to the interaction between depression, stress, and the development of PD could contribute to refining the identification of individuals at risk and improving treatment strategies for patients who have depression as a relevant aspect during PD. However, this relationship has not yet been studied systematically in animal models. Although such studies could help clarify mechanisms underlying PD-related depression neuropathology, most of the animal models of the disease do not reproduce human disease progression and do not comply with nondopaminergic deficits [184].
In other words, the difficulty in approaching depression in animal models of PD relies on the same context of investigations involving non-motor symptoms. Indeed, those evaluations can be hindered by the presence of motor impairment. Nevertheless, an effort has been made to circumvent these difficulties.
One of the most used tests that addresses depression-like behavior in rodents is the forced swimming test. The animal is subjected to a container filled with enough water to necessitate swimming, without being able to support the hind paws at the bottom. The time the animals spend in immobility (i.e., not trying to escape the recipient by swimming) is considered a measure of learned helplessness and interpreted as depressive-like behavior. Almost all studies with neurotoxin models of Parkinson’s disease showed a decreased swimming time and/or increased immobility time (see [185] for review). However, in those cases, it is not possible to separate the motor deficit from the effects on depression per se.
Anhedonia is a well-known depression sign [186] that can be addressed in rodents by the sucrose preference test [187]. A choice between regular water and sucrose solution is offered to rats or mice, which regularly prefer sucrose. If the preference is not observed, this is interpreted as anhedonic behavior, and hence considered depressive-like behavior. This behavior would be less affected by motor impairment (at least one that is not too severe). If only the sucrose preference, and not the total amount of drink, changes with neurotoxin treatment, the effect is probably specific for depressive-like behavior and not motor function. Studies with neurotoxic PD models have shown a decrease in rodent sucrose preference compared to controls, but this effect was not unequivocal, as some studies also showed no changes in sucrose preference [188]. This study will not provide details of previous work as a very comprehensive review on studies that address depression in animal models of PD has already been done [188]. Nevertheless, progressive PD models could be a more interesting approach to investigate depression because it could be assessed in different stages of the progression, including those with no or little motor deficit. For example, Soares et al. [75] investigated the relationship between the predisposition to depressive-like behavior and the development of motor alterations in the progressive model of PD in mice induced by reserpine. Animals were classified into groups of depressive-like profiles and received a low dose (0.1 mg/kg) of reserpine over 40 days. Anhedonic behavior was considered a depressive-like trait, and each mouse was submitted to the sucrose preference test. Based on their performance, mice were allocated into three groups: those with greater depressive-like behavior (predisposed), those with less depressive depressive-like behavior (non-predisposed), and those with intermediate levels. Only animals categorized at the extremes of the depressive-like spectrum were further divided into two subgroups, reserpine-treated or vehicle-treated. The catalepsy and oral movement tests were used to assess motor alterations, while the open field test was used to evaluate exploratory activity. Reserpine induced parkinsonian motor deficits. However, there were no differences between animals with different depressive-like behavior profiles. Thus, it was not possible to establish a relationship between parkinsonism and the propensity for depression based on the basal sucrose preference test under those experimental conditions. Thus, although depressive-like behavior is seen in animals that went through parkinsonism induction, more studies are needed to verify if a depressive profile could predispose the animals or increase susceptibility to alterations induced by PD models.

10. Conclusions

The increasing incidence of PD, combined with a lack of specific knowledge on risk factors, impacts a substantial number of individuals worldwide. There is a recent effort in finding biomarkers that could provide pre-symptomatic diagnosis of PD, including single-photon emission computed tomography imaging, positron emission tomography, olfactory alterations, skin and colonic biopsy, changed metabolites, gene sequencing, and α-synuclein quantification in body fluids [188,189,190,191,192,193]. Nevertheless, the identification of risk factors and evaluation of prodromal signs, together with family history, are still the main methods to classify individuals at risk for this disease. Human epidemiological studies are useful in this search, but this approach fails in providing causality.
The aim of the present review was to provide an overview of experimental animal studies related to the four main risk factors of PD—age, sex, sleep alterations, and depression. Overall, the review summarizes the available evidence, pointing to the need for a greater number of animal studies focusing on PD risk factors. Importantly, although several animal models have helped clarify PD pathophysiology, up to date, none of them has completely reproduced the entire natural history of the disease. An ideal model of prodromal PD would be one that reproduces various PD-specific premotor symptoms followed by the slowly progressive DA neurodegeneration. In conclusion, studies that aim to investigate well-known risk factors for PD in animal models can help elucidate mechanisms related to the disease’s etiology and contribute to future prevention or treatment approaches. Therefore, continuing to study risk factors and prodromal signs in animal models of PD is crucial.

11. Limitations of the Study

It is important to emphasize that this review does not intend to close the issue of studying risk factors for PD in animal models. Here, only four of the main risk factors were addressed, and others must be considered when approaching the subject. Furthermore, this work did not undertake a systematic approach. Therefore, more attention needs to be paid to factors such as variations in animal model used, length of protocols, species, and treatment in the studies carried out for each risk factor. Greater reliability in literature findings regarding how risk factors influence the onset and progression of PD will be beneficial for the development of new prevention and treatment approaches.

Author Contributions

Conceptualization: R.H.S., A.M.R. and J.R.S. Literature search: L.B.L.-S., D.G.C. and M.B. Full-text screening and writing the initial draft: R.H.S., J.R.S., L.B.L.-S., D.G.C. and M.B. Final revision and formatting: R.H.S., A.M.R. and J.R.S. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES, Finance Code 001), by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grants 2015/03354-3, 2017/26253-3, 2020/09015-4) and by Fundação de Apoio à Pesquisa e à Inovação Tecnológica de Sergipe (FAPITEC/SE. grant 794017/2013). R.H.S., A.M.R., and J.R.S. are recipients of research fellowships from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, grants 313631/2021-2, 408377/2021-6, 310403/2021-9, 408377/2021-6 and 312863/2022-5).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Pingale, T.; Gupta, G.L. Classic and evolving animal models in Parkinson’s disease. Pharmacol. Biochem. Behav. 2020, 199, 173060. [Google Scholar] [CrossRef] [PubMed]
  2. Leão, A.H.F.F.; Sarmento-Silva, A.J.; Santos, J.R.; Ribeiro, A.M.; Silva, R.H. Molecular, Neurochemical, and Behavioral Hallmarks of Reserpine as a Model for Parkinson’s Disease: New Perspectives to a Long-Standing Model. Brain Pathol. 2015, 25, 377–390. [Google Scholar] [CrossRef] [PubMed]
  3. Ko, W.K.D.; Bezard, E. Experimental animal models of Parkinson’s disease: A transition from assessing symptomatology to α-synuclein targeted disease modification. Exp. Neurol. 2017, 298, 172–179. [Google Scholar] [CrossRef]
  4. Kin, K.; Yasuhara, T.; Kameda, M.; Date, I. Animal models for Parkinson’s disease research: Trends in the 2000s. Int. J. Mol. Sci. 2019, 20, 5402. [Google Scholar] [CrossRef] [PubMed]
  5. Gubellini, P.; Kachidian, P. Animal models of Parkinson’s disease: An updated overview. Rev. Neurol. 2015, 171, 750–761. [Google Scholar] [CrossRef]
  6. Breger, L.S.; Fuzzati Armentero, M.T. Genetically engineered animal models of Parkinson’s disease: From worm to rodent. Eur. J. Neurosci. 2019, 49, 533–560. [Google Scholar] [CrossRef]
  7. Blandini, F.; Armentero, M.T. Animal models of Parkinson’s disease. FEBS J. 2012, 279, 1156–1166. [Google Scholar] [CrossRef]
  8. Mantri, S.; Fullard, M.E.; Beck, J.; Willis, A.W. State-level prevalence, health service use, and spending vary widely among Medicare beneficiaries with Parkinson disease. NPJ Park. Dis. 2019, 5, 1. [Google Scholar] [CrossRef]
  9. Michel, P.P.; Hirsch, E.C.; Hunot, S. Understanding Dopaminergic Cell Death Pathways in Parkinson Disease. Neuron 2016, 90, 675–691. [Google Scholar] [CrossRef]
  10. Elbaz, A.; Carcaillon, L.; Kab, S.; Moisan, F. Epidemiology of Parkinson’s disease. Rev. Neurol. 2016, 172, 14–26. [Google Scholar] [CrossRef]
  11. Dorsey, E.R.; Sherer, T.; Okun, M.S.; Bloemd, B.R. The emerging evidence of the Parkinson pandemic. J. Park. Dis. 2018, 8, S3–S8. [Google Scholar] [CrossRef] [PubMed]
  12. Boix, J.; von Hieber, D.; Connor, B. Gait analysis for early detection of motor symptoms in the 6-ohda rat model of parkinson’s disease. Front. Behav. Neurosci. 2018, 12, 39. [Google Scholar] [CrossRef] [PubMed]
  13. Andica, C.; Kamagata, K.; Hatano, T.; Okuzumi, A.; Saito, A.; Nakazawa, M.; Ueda, R.; Motoi, Y.; Kamiya, K.; Suzuki, M.; et al. Neurite orientation dispersion and density imaging of the nigrostriatal pathway in Parkinson’s disease: Retrograde degeneration observed by tract-profile analysis. Park. Relat. Disord. 2018, 51, 55–60. [Google Scholar] [CrossRef] [PubMed]
  14. Caminiti, S.P.; Presotto, L.; Baroncini, D.; Garibotto, V.; Moresco, R.M.; Gianolli, L.; Volonté, M.A.; Antonini, A.; Perani, D. Axonal damage and loss of connectivity in nigrostriatal and mesolimbic dopamine pathways in early Parkinson’s disease. NeuroImage Clin. 2017, 14, 734–740. [Google Scholar] [CrossRef] [PubMed]
  15. Mann, T.; Zilles, K.; Dikow, H.; Hellfritsch, A.; Cremer, M.; Piel, M.; Rösch, F.; Hawlitschka, A.; Schmitt, O.; Wree, A. Dopamine, Noradrenaline and Serotonin Receptor Densities in the Striatum of Hemiparkinsonian Rats following Botulinum Neurotoxin-A Injection. Neuroscience 2018, 374, 187–204. [Google Scholar] [CrossRef] [PubMed]
  16. Rana, A.Q.; Qureshi, A.R.M.; Shamli Oghli, Y.; Saqib, Y.; Mohammed, B.; Sarfraz, Z.; Rana, R. Decreased sleep quality in Parkinson’s patients is associated with higher anxiety and depression prevalence and severity, and correlates with pain intensity and quality. Neurol. Res. 2018, 40, 696–701. [Google Scholar] [CrossRef]
  17. Politis, M.; Wu, K.; Loane, C.; Kiferle, L.; Molloy, S.; Brooks, D.J.; Piccini, P. Staging of serotonergic dysfunction in Parkinson’s Disease: An in vivo 11C-DASB PET study. Neurobiol. Dis. 2010, 40, 216–221. [Google Scholar] [CrossRef]
  18. Li, Y.; Jiao, Q.; Du, X.; Bi, M.; Han, S.; Jiao, L.; Jiang, H. Investigation of behavioral dysfunctions induced by monoamine depletions in a mouse model of Parkinson’s disease. Front. Cell. Neurosci. 2018, 12, 241. [Google Scholar] [CrossRef]
  19. Noyce, A.J.; Bestwick, J.P.; Silveira-Moriyama, L.; Hawkes, C.H.; Giovannoni, G.; Lees, A.J.; Schrag, A. Meta-analysis of early nonmotor features and risk factors for Parkinson disease. Ann. Neurol. 2012, 72, 893–901. [Google Scholar] [CrossRef]
  20. Bellou, V.; Belbasis, L.; Tzoulaki, I.; Evangelou, E.; Ioannidis, J.P.A. Environmental risk factors and Parkinson’s disease: An umbrella review of meta-analyses. Park. Relat. Disord. 2015, 23, 1–9. [Google Scholar] [CrossRef]
  21. Kieburtz, K.; Wunderle, K.B. Parkinson’s disease: Evidence for environmental risk factors. Mov. Disord. 2013, 28, 8–13. [Google Scholar] [CrossRef]
  22. Daniela, B. Marker for a preclinical diagnosis of Parkinson’s disease as a basis for neuroprotection. J. Neural. Transm. 2006, 71, 123–132. [Google Scholar]
  23. O’Sullivan, S.S.; Williams, D.R.; Gallagher, D.A.; Massey, L.A.; Silveira-Moriyama, L.; Lees, A.J. Nonmotor symptoms as presenting complaints in Parkinson’s disease: A clinicopathological study. Mov. Disord. 2008, 23, 101–106. [Google Scholar] [CrossRef]
  24. Siderowf, A.; Stern, M.B. Premotor Parkinson’s disease: Clinical features, detection, and prospects for treatment. Ann. Neurol. 2008, 64, S139–S147. [Google Scholar] [CrossRef] [PubMed]
  25. Lang, A.E. A critical appraisal of the premotor symptoms of Parkinson’s disease: Potential usefulness in early diagnosis and design of neuroprotective trials. Mov. Disord. 2011, 26, 775–783. [Google Scholar] [CrossRef] [PubMed]
  26. Chahine, L.M.; Stern, M.B. Diagnostic markers for Parkinson’s disease. Curr. Opin. Neurol. 2011, 24, 309–317. [Google Scholar] [CrossRef] [PubMed]
  27. Lerche, S.; Seppi, K.; Behnke, S.; Liepelt-Scarfone, I.; Godau, J.; Mahlknecht, P.; Gaenslen, A.; Brockmann, K.; Srulijes, K.; Huber, H.; et al. Risk factors and prodromal markers and the development of Parkinson’s disease. J. Neurol. 2014, 261, 180–187. [Google Scholar] [CrossRef] [PubMed]
  28. Delledonne, A.; Klos, K.J.; Fujishiro, H.; Ahmed, Z.; Parisi, J.E.; Josephs, K.A.; Frigerio, R.; Burnett, M.; Wszolek, Z.K.; Uitti, R.J.; et al. Incidental Lewy Body Disease and Preclinical Parkinson Disease. Arch. Neurol. 2008, 65, 1074–1080. [Google Scholar] [CrossRef]
  29. Braak, H.; Del Tredici, K. Neuropathological Staging of Brain Pathology in Sporadic Parkinson’s disease: Separating the Wheat from the Chaff. J. Park. Dis. 2017, 7, S71–S85. [Google Scholar] [CrossRef]
  30. Berg, D.; Marek, K.; Ross, G.W.; Poewe, W. Defining at-risk populations for Parkinson’s disease: Lessons from ongoing studies. Mov. Disord. 2012, 27, 656–665. [Google Scholar] [CrossRef]
  31. Gaenslen, A.; Swid, I.; Liepelt-Scarfone, I.; Godau, J.; Berg, D. The patients’ perception of prodromal symptoms before the initial diagnosis of Parkinson’s disease. Mov. Disord. 2011, 26, 653–658. [Google Scholar] [CrossRef] [PubMed]
  32. Reedijk, M.; Huss, A.; Verheij, R.A.; Peeters, P.H.; Vermeulen, R.C.H. Parkinson’s disease case ascertainment in prospective cohort studies through combining multiple health information resources. PLoS ONE 2020, 15, e0234845. [Google Scholar] [CrossRef]
  33. Pouchieu, C.; Piel, C.; Carles, C.; Gruber, A.; Helmer, C.; Tual, S.; Marcotullio, E.; Lebailly, P.; Baldi, I. Pesticide use in agriculture and Parkinson’s disease in the AGRICAN cohort study. Int. J. Epidemiol. 2018, 47, 299–310. [Google Scholar] [CrossRef] [PubMed]
  34. Kyrozis, A.; Ghika, A.; Stathopoulos, P.; Vassilopoulos, D.; Trichopoulos, D.; Trichopoulou, A. Dietary and lifestyle variables in relation to incidence of Parkinson’s disease in Greece. Eur. J. Epidemiol. 2013, 28, 67–77. [Google Scholar] [CrossRef] [PubMed]
  35. Litvan, I.; Bhatia, K.P.; Burn, D.J.; Goetz, C.G.; Lang, A.E.; Mckeith, I.; Quinn, N.; Sethi, K.D.; Shults, C.; Wenning, G.K. Movement Disorders Society Scientific Issues Committee Report SIC Task Force Appraisal of Clinical Diagnostic Criteria for Parkinsonian Disorders. Mov. Disord. 2003, 18, 467–486. [Google Scholar] [CrossRef] [PubMed]
  36. Kamel, F.; Tanner, C.M.; Umbach, D.M.; Hoppin, J.A.; Alavanja, M.C.R.; Blair, A.; Comyns, K.; Goldman, S.; Korell, M.; Langston, J.; et al. Pesticide exposure and self-reported Parkinson’s disease in the agricultural health study. Am. J. Epidemiol. 2006, 165, 364–374. [Google Scholar] [CrossRef] [PubMed]
  37. Amboni, M.; Stocchi, F.; Abbruzzese, G.; Morgante, L.; Onofrj, M.; Ruggieri, S.; Tinazzi, M.; Zappia, M.; Attar, M.; Colombo, D.; et al. Prevalence and associated features of self-reported freezing of gait in Parkinson disease: The DEEP FOG study. Park. Relat. Disord. 2015, 21, 644–649. [Google Scholar] [CrossRef]
  38. Chaturvedi, S.; Ostbye, T.; Stoessl, A.J.; Merskey, H.; Hachinslci, V. Environmental Exposures in Elderly Canadians with Parkinson’s Disease. Can. J. Neurol. Sci. 1995, 22, 232–234. [Google Scholar] [CrossRef]
  39. Banks, S.J.; Bayram, E.; Shan, G.; LaBelle, D.R.; Bluett, B. Non-motor predictors of freezing of gait in Parkinson’s disease. Gait Posture 2019, 68, 311–316. [Google Scholar] [CrossRef]
  40. Jain, S.; Ton, T.G.; Perera, S.; Zheng, Y.; Stein, P.K.; Thacker, E.; Strotmeyer, E.S.; Newman, A.B.; Longstreth, W.T., Jr. Cardiovascular physiology in premotor Parkinson’s disease: A neuroepidemiologic study. Mov. Disord. 2012, 27, 988–995. [Google Scholar] [CrossRef]
  41. Breckenridge, C.B.; Berry, C.; Chang, E.T.; Sielken, R.L.; Mandel, J.S. Association between Parkinson’s disease and cigarette smoking, rural living, well-water consumption, farming and pesticide use: Systematic review and meta-analysis. PLoS ONE 2016, 11, e0151841. [Google Scholar] [CrossRef]
  42. Pringsheim, T.; Jette, N.; Frolkis, A.; Steeves, T.D.L. The prevalence of Parkinson’s disease: A systematic review and meta-analysis. Mov. Disord. 2014, 29, 1583–1590. [Google Scholar] [CrossRef] [PubMed]
  43. Delamarre, A.; Meissner, W.G. Épidémiologie, facteurs de risque environnementaux et génétiques de la maladie de Parkinson. Presse Medicale 2017, 46, 175–181. [Google Scholar] [CrossRef] [PubMed]
  44. Prajjwal, P.; Flores Sanga, H.S.; Acharya, K.; Tango, T.; John, J.; Rodriguez, R.S.C.; Marsool, M.D.M.; Sulaimanov, M.; Ahmed, A.; Hussin, O.A. Parkinson’s disease updates: Addressing the pathophysiology, risk factors, genetics, diagnosis, along with the medical and surgical treatment. Ann. Med. Surg. 2023, 85, 4887–4902. [Google Scholar] [CrossRef] [PubMed]
  45. Nalls, M.A.; Blauwendraat, C.; Vallerga, C.L.; Heilbron, K.; Bandres-Ciga, S.; Chang, D.; Tan, M.; Kia, D.A.; Noyce, A.J.; Xue, A.; et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: A meta-analysis of genome-wide association studies. Lancet Neurol. 2019, 18, 1091–1102. [Google Scholar] [CrossRef] [PubMed]
  46. Noyce, A.J.; Lees, A.J.; Schrag, A.E. The prediagnostic phase of Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 2016, 87, 871–878. [Google Scholar] [CrossRef] [PubMed]
  47. Campêlo, C.L.D.C.; Silva, R.H. Genetic Variants in SNCA and the Risk of Sporadic Parkinson’s Disease and Clinical Outcomes: A Review. Park. Dis. 2017, 2017, 4318416. [Google Scholar] [CrossRef] [PubMed]
  48. Wang, Z.; Chan, S.W.; Zhao, H.; Miu, K.K.; Chan, W.Y. Outlook of PINK1/Parkin signaling in molecular etiology of Parkinson’s disease, with insights into Pink1 knockout models. Zool. Res. 2023, 44, 559–576. [Google Scholar] [CrossRef]
  49. Bastioli, G.; Regoni, M.; Cazzaniga, F.; De Luca, C.M.G.; Bistaffa, E.; Zanetti, L.; Moda, F.; Valtorta, F.; Sassone, J. Animal models of autosomal recessive parkinsonism. Biomedicines 2021, 9, 812. [Google Scholar] [CrossRef]
  50. Brown, T.P.; Rumsby, P.C.; Capleton, A.C.; Rushton, L.; Levy, L.S. Pesticides and Parkinson’s disease—Is there a link? Environ. Health Perspect. 2006, 114, 156–164. [Google Scholar] [CrossRef]
  51. Priyadarshi, A.; Khuder, S.A.; Schaub, E.A.; Priyadarshi, S.S. Environmental risk factors and parkinson’s disease: A metaanalysis. Environ. Res. 2001, 86, 122–127. [Google Scholar] [CrossRef] [PubMed]
  52. Tanner, C.M.; Ross, G.W.; Jewell, S.A.; Hauser, R.A.; Jankovic, J.; Factor, S.A.; Bressman, S.; Deligtisch, A.; Marras, C.; Lyons, K.E.; et al. Occupation and Risk of Parkinsonism A Multicenter Case-Control Study. Arch. Neurol. 2009, 66, 1106–1113. [Google Scholar] [CrossRef] [PubMed]
  53. Li, A.A.; Mink, P.J.; McIntosh, L.J.; Teta, M.J.; Finley, B. Evaluation of epidemiologic and animal data associating pesticides with Parkinson’s disease. J. Occup. Environ. Med. 2005, 47, 1059–1087. [Google Scholar] [CrossRef] [PubMed]
  54. Vellingiri, B.; Chandrasekhar, M.; Sri Sabari, S.; Gopalakrishnan, A.V.; Narayanasamy, A.; Venkatesan, D.; Iyer, M.; Kesari, K.; Dey, A. Neurotoxicity of pes ticides—A link to neurodegeneration. Ecotoxicol. Environ. Saf. 2022, 243, 113972. [Google Scholar] [CrossRef] [PubMed]
  55. Ibarra-Gutiérrez, M.T.; Serrano-García, N.; Orozco-Ibarra, M. Rotenone-Induced Model of Parkinson’s Disease: Beyond Mitochondrial Complex I Inhibition. Mol. Neurobiol. 2023, 60, 1929–1948. [Google Scholar] [CrossRef] [PubMed]
  56. Bové, J.; Perier, C. Neurotoxin-based models of Parkinson’s disease. Neuroscience 2012, 211, 51–76. [Google Scholar] [CrossRef] [PubMed]
  57. Giráldez-Pérez, R.M.; Antolín-Vallespín, M.; Muñoz, M.D.; Sánchez-Capelo, A. Models of α-synuclein aggregation in Parkinson’s disease. Acta Neuropathol. Commun. 2014, 2, 176. [Google Scholar] [CrossRef]
  58. Zhang, Z.N.; Zhang, J.S.; Xiang, J.; Yu, Z.H.; Zhang, W.; Cai, M.; Li, X.T.; Wu, T.; Li, W.W.; Cai, D.F. Subcutaneous rotenone rat model of Parkinson’s disease: Dose exploration study. Brain Res. 2017, 1655, 104–113. [Google Scholar] [CrossRef]
  59. Grossman, J.T.; Filatov, A.; Hammond, T. Parkinson’s Disease: Unanticipated Sequela of an Attempted Suicide. Cureus 2020, 12, e9409. [Google Scholar] [CrossRef]
  60. Eriguchi, M.; Iida, K.; Ikeda, S.; Osoegawa, M.; Nishioka, K.; Hattori, N.; Nagayama, H.; Hara, H. Parkinsonism relating to intoxication with glyphosate. Intern. Med. 2019, 58, 1935–1938. [Google Scholar] [CrossRef]
  61. Postuma, R.B.; Gagnon, J.F.; Vendette, M.; Fantini, M.L.; Massicotte-Marquez, J.; Montplaisir, J. Quantifying the risk of neurodegenerative disease in idiopathic REM sleep behavior disorder. Neurology 2009, 72, 1296–1300. [Google Scholar] [CrossRef] [PubMed]
  62. Gaenslen, A.; Wurster, I.; Brockmann, K.; Huber, H.; Godau, J.; Faust, B.; Lerche, S.; Eschweiler, G.W.; Maetzler, W.; Berg, D. Prodromal features for Parkinson’s disease—Baseline data from the TREND study. Eur. J. Neurol. 2014, 21, 766–772. [Google Scholar] [CrossRef]
  63. Rojo, A.; Aguilar, M.; Garolera, M.T.; Cubo, E.; Navas, I.; Quintana, S. Depression in Parkinson’s disease: Clinical correlates and outcome. Park. Relat. Disord. 2003, 10, 23–28. [Google Scholar] [CrossRef] [PubMed]
  64. Gupta Gupta, M.B.; Thomas, R.; Bruemmer, V.; Sladek, J.; Felten, D. Aged mice are more sensitive to l-methyl-4-phenyl-1,2,3,6-tetrahydropyridine treatment than young adults. Neurosci. Lett. 1986, 70, 326–331. [Google Scholar] [CrossRef] [PubMed]
  65. Tremblay, M.È.; Saint-Pierre, M.; Bourhis, E.; Lévesque, D.; Rouillard, C.; Cicchetti, F. Neuroprotective effects of cystamine in aged parkinsonian mice. Neurobiol. Aging 2006, 27, 862–870. [Google Scholar] [CrossRef] [PubMed]
  66. Patki, G.; Che, Y.; Lau, Y.S. Mitochondrial dysfunction in the striatum of aged chronic mouse model of Parkinson’s disease. Front. Aging Neurosci. 2009, 1, 3. [Google Scholar] [CrossRef] [PubMed]
  67. Grimmig, B.; Daly, L.; Subbarayan, M.; Hudson, C.; Williamson, R.; Nash, K.; Bickford, P.C. Astaxanthin is neuroprotective in an aged mouse model of Parkinson’s disease. Oncotarget 2018, 9, 10388–10401. [Google Scholar] [CrossRef]
  68. Melo, J.E.C.; Santos, T.F.O.; Santos, R.S.; Franco, H.S.; Monteiro, M.C.N.; Bispo, J.M.M.; Mendonça, M.S.; Ribeiro, A.M.; Silva, R.H.; Gois, A.M.; et al. Aging accentuates decrease in tyrosine hydroxylase immunoreactivity associated with the increase in the motor impairment in a model of reserpine-induced parkinsonism. J. Chem. Neuroanat. 2022, 125, 102162. [Google Scholar] [CrossRef]
  69. Field, E.F.; Metz, G.A.; Pellis, S.M.; Whishaw, I.Q. Sexually dimorphic postural adjustments during vertical behaviour are altered in a unilateral 6-OHDA rat model of Parkinson’s disease. Behav. Brain Res. 2006, 174, 39–48. [Google Scholar] [CrossRef]
  70. Bispo, J.M.M.; Melo, J.E.C.; Gois, A.M.; Leal, P.C.; Lins, L.C.R.F.; Souza, M.F.; Medeiros, K.A.A.L.; Ribeiro, A.M.; Silva, R.H.; Marchioro, M.; et al. Sex differences in the progressive model of parkinsonism induced by reserpine in rats. Behav. Brain Res. 2019, 363, 23–29. [Google Scholar] [CrossRef]
  71. Lima, A.C.; Meurer, Y.S.R.; Bioni, V.S.; Cunha, D.M.G.; Gonçalves, N.; Lopes-Silva, L.B.; Becegato, M.; Soares, M.B.L.; Marinho, G.F.; Santos, J.R.; et al. Female Rats Are Resistant to Cognitive, Motor and Dopaminergic Deficits in the Reserpine-Induced Progressive Model of Parkinson’s Disease. Front. Aging Neurosci. 2021, 13, 757714. [Google Scholar] [CrossRef] [PubMed]
  72. Sakata, M.; Sei, H.; Toida, K.; Fujihara, H.; Urushihara, R.; Morita, Y. Mesolimbic dopaminergic system is involved in diurnal blood pressure q regulation. Brain Res. 2002, 928, 194–201. [Google Scholar] [CrossRef]
  73. Vo, Q.; Gilmour, T.P.; Venkiteswaran, K.; Fang, J.; Subramanian, T. Polysomnographic features of sleep disturbances and rem sleep behavior disorder in the unilateral 6-OHDA lesioned hemiparkinsonian rat. Park. Dis. 2014, 2014, 852965. [Google Scholar] [CrossRef]
  74. Qiu, M.H.; Yao, Q.L.; Vetrivelan, R.; Chen, M.C.; Lu, J. Nigrostriatal Dopamine Acting on Globus Pallidus Regulates Sleep. Cereb. Cortex 2016, 26, 1430–1439. [Google Scholar] [CrossRef] [PubMed]
  75. Soares, M.B.L.; Lopes-Silva, L.B.; Becegato, M.; Bioni, V.S.; Lima, A.C.; Ferreira, G.M.; Meurer, Y.; Silva, R.H. Reserpine-Induced Progressive Parkinsonism in Mice Predisposed and Non-Predisposed to Depressive-Like Behavior. J. Behav. Brain Sci. 2021, 11, 267–279. [Google Scholar] [CrossRef]
  76. Duty, S.; Jenner, P. Themed Issue: Translational Neuropharmacology-Using Appropriate Animal Models to Guide Clinical Drug Development Animal models of Parkinson’s disease: A source of novel treatments and clues to the cause of the disease. Br. J. Pharmacol. 2011, 164, 1357. [Google Scholar] [CrossRef]
  77. Schober, A. Classic toxin-induced animal models of Parkinson’s disease: 6-OHDA and MPTP. Cell Tissue Res. 2004, 318, 215–224. [Google Scholar] [CrossRef]
  78. Jagmag, S.A.; Tripathi, N.; Shukla, S.D.; Maiti, S.; Khurana, S. Evaluation of models of Parkinson’s disease. Front. Neurosci. 2016, 9, 503. [Google Scholar] [CrossRef] [PubMed]
  79. Narmashiri, A.; Abbaszadeh, M.; Ghazizadeh, A. The effects of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) on the cognitive and motor functions in rodents: A systematic review and meta-analysis. Neurosci. Biobehav. Rev. 2022, 140, 104792. [Google Scholar] [CrossRef]
  80. Talpade, D.J.; Greene, J.G.; Higgins, S.; Greenamyre, J.T. In Vivo Labeling of Mitochondrial Complex I (NADH:Ubiquinone Oxidoreductase) in Rat Brain Using [3H]Dihydrorotenone. J. Neurochem. 2000, 75, 2611–2621. [Google Scholar] [CrossRef]
  81. Srivastava, P.; Panda, D. Rotenone inhibits mammalian cell proliferation by inhibiting microtubule assembly through tubulin binding. FEBS J. 2007, 274, 4788–4801. [Google Scholar] [CrossRef] [PubMed]
  82. Sherer, T.B.; Betarbet, R.; Testa, C.M.; Seo, B.B.; Richardson, J.R.; Kim, J.H.; Miller, G.W.; Yagi, T.; Matsuno-Yagi, A.; Greenamyre, J.T. Mechanism of Toxicity in Rotenone Models of Parkinson’s Disease. J. Neurosci. 2003, 23, 10756–10764. [Google Scholar] [CrossRef] [PubMed]
  83. Carlsson, A.; Lindqvist, M.; Magnusson, T. 3,4-Dihydroxyphenylalanine and 5-hydroxytryptophan as reserpine antagonists. Nature 1957, 180, 1200. [Google Scholar] [CrossRef] [PubMed]
  84. Verheij, M.M.M.; Cools, A.R. Differential contribution of storage pools to the extracellular amount of accumbal dopamine in high and low responders to novelty: Effects of reserpine. Neurochemistry 2007, 100, 810–821. [Google Scholar] [CrossRef] [PubMed]
  85. Delfino, M.A.; Stefano, A.V.; Ferrario, J.E.; Taravini, I.R.E.; Murer, M.G.; Gershanik, O.S. Behavioral sensitization to different dopamine agonists in a parkinsonian rodent model of drug-induced dyskinesias. Behav. Brain Res. 2004, 152, 297–306. [Google Scholar] [CrossRef] [PubMed]
  86. Hornung, J.P. The human raphe nuclei and the serotonergic system. J. Chem. Neuroanat. 2003, 26, 331–343. [Google Scholar] [CrossRef] [PubMed]
  87. Goldstein, D.S.; Holmes, C.; Sharabi, Y. Cerebrospinal fluid biomarkers of central catecholamine deficiency in Parkinson’s disease and other synucleinopathies. Brain 2012, 135, 1900–1913. [Google Scholar] [CrossRef]
  88. Brunnström, H.; Friberg, N.; Lindberg, E.; Englund, E. Differential degeneration of the locus coeruleus in dementia subtypes. Clin. Neuropathol. 2011, 30, 104–110. [Google Scholar] [CrossRef]
  89. Baskin, P.; Salamone, J.; SALAt, J.; Vacuous, I. Vacuous Jaw Movements in Rats Induced by Acute Reserpine Administration: Interactions with Different Doses of Apomorphine. Pharmacol. Biochem. Behav. 1993, 46, 793–797. [Google Scholar] [CrossRef]
  90. Colpaert, F.C. Pharmacological characteristics of tremor, rigidity and hypokinesia induced by reserpine in rat. Neuropharmacology 1987, 26, 1431–1440. [Google Scholar] [CrossRef]
  91. Salamone, J.; Baskin, P.; Baskin, P. Vacuous Jaw Movements Induced by Acute Reserpine and Low-Dose Apomorphine: Possible Model of Parkinsonian Tremor Vacuous jaw movements in rats induced by acute reserpine and low-dose apomor-phine administration: Possible model ofparkinsonian tremor. Pharmacol. Biochem. Behav. 1994, 53, 179–183. [Google Scholar] [CrossRef]
  92. Santos, J.R.; Cunha, J.A.S.; Dierschnabel, A.L.; Campêlo, C.L.C.; Leão, A.H.F.F.; Silva, A.F.; Engelberth, R.C.; Izídio, G.S.; Cavalcante, J.S.; Abílio, V.C.; et al. Cognitive, motor and tyrosine hydroxylase temporal impairment in a model of parkinsonism induced by reserpine. Behav. Brain Res. 2013, 253, 68–77. [Google Scholar] [CrossRef]
  93. Fernandes, V.S.; Santos, J.R.; Leão, A.H.F.F.; Medeiros, A.M.; Melo, T.G.; Izídio, G.S.; Cabral, A.; Ribeiro, R.A.; Abílio, V.C.; Ribeiro, A.M.; et al. Repeated treatment with a low dose of reserpine as a progressive model of Parkinson’s disease. Behav. Brain Res. 2012, 231, 154–163. [Google Scholar] [CrossRef]
  94. Driver, J.A.; Logroscino, G.; Gaziano, J.M.; Kurth, T. Incidence and remaining lifetime risk of Parkinson disease in advanced age. Neurology 2009, 72, 432–438. [Google Scholar] [CrossRef]
  95. Van Den Eeden, S.K.; Tanner, C.M.; Bernstein, A.L.; Fross, R.D.; Leimpeter, A.; Bloch, D.A.; Nelson, L.M. Incidence of Parkinson’s disease: Variation by age, gender, and race/ethnicity. Am. J. Epidemiol. 2003, 157, 1015–1022. [Google Scholar] [CrossRef] [PubMed]
  96. Kim, D.J.; Isidro-Pérez, A.L.; Doering, M.; Llibre-Rodriguez, J.J.; Acosta, I.; Rodriguez Salgado, A.M.; Pinilla-Monsalve, G.D.; Tanner, C.; Llibre-Guerra, J.J.; Prina, M. Prevalence and Incidence of Parkinson’s Disease in Latin America: A Meta-Analysis. Mov. Disord. 2024, 10, 105–118. [Google Scholar] [CrossRef] [PubMed]
  97. Collier, T.J.; Kanaan, N.M.; Kordower, J.H. Ageing as a primary risk factor for Parkinson’s disease: Evidence from studies of non-human primates. Nat. Rev. Neurosci. 2011, 12, 359–366. [Google Scholar] [CrossRef]
  98. Pintado, C.; Gavilán, M.P.; Gavilán, E.; García-Cuervo, L.; Gutiérrez, A.; Vitorica, J.; Castaño, A.; Ríos, R.M.; Ruano, D. Lipopolysaccharide-induced neuroinflammation leads to the accumulation of ubiquitinated proteins and increases susceptibility to neurodegeneration induced by proteasome inhibition in rat hippocampus. J. Neuroinflamm. 2012, 9, 87. [Google Scholar] [CrossRef] [PubMed]
  99. Moreno-García, A.; Kun, A.; Calero, M.; Calero, O. The neuromelanin paradox and its dual role in oxidative stress and neurodegeneration. Antioxidants 2021, 10, 124. [Google Scholar] [CrossRef] [PubMed]
  100. Parvand, M.; Rankin, C.H. Is There a Shared Etiology of Olfactory Impairments in Normal Aging and Neurodegenerative Disease? J. Alzheimer’s Dis. 2020, 73, 1–21. [Google Scholar] [CrossRef] [PubMed]
  101. Piccini, A.; Russo, C.; Gliozzi, A.; Relini, A.; Vitali, A.; Borghi, R.; Giliberto, L.; Armirotti, A.; D’Arrigo, C.; Bachi, A.; et al. β-amyloid is different in normal aging and in Alzheimer disease. J. Biol. Chem. 2005, 280, 34186–34192. [Google Scholar] [CrossRef] [PubMed]
  102. Kujawska, M.; Chmielarz, P.; Singh, Y. Impact of Aging on Animal Models of Parkinson’s Disease. 2022. Available online: https://www.frontiersin.org/articles/10.3389/fnagi.2022.909273/full (accessed on 13 December 2023).
  103. Twelves, D.; Perkins, K.S.M.; Counsell, C. Systematic Review of Incidence Studies of Parkinson’s Disease. Mov. Disord. 2003, 18, 19–31. [Google Scholar] [CrossRef] [PubMed]
  104. Wooten, G.F.; Currie, L.J.; Bovbjerg, V.E.; Lee, J.K.; Patrie, J. Are men at greater risk for Parkinson’s disease than women? J. Neurol. Neurosurg. Psychiatry 2004, 75, 637–639. [Google Scholar] [CrossRef]
  105. Taylor, K.S.M.; Cook, J.A.; Counsell, C.E. Heterogeneity in male to female risk for Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 2007, 78, 905–906. [Google Scholar] [CrossRef] [PubMed]
  106. Haaxma, C.A.; Bloem, B.R.; Borm, G.F.; Oyen, W.J.G.; Leenders, K.L.; Eshuis, S.; Booij, J.; Dluzen, D.E.; Horstink, M.W. Gender differences in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 2007, 78, 819–824. [Google Scholar] [CrossRef] [PubMed]
  107. Murphy, M.P.; Wu, P.H.; Milgram, N.W.; Ivy, G.O. Monoamine Oxidase Inhibition by L-Deprenyl Depends on Both Sex and Route of Administration in the Rat. Neurochem. Res. 1993, 18, 1299–1304. [Google Scholar] [CrossRef] [PubMed]
  108. Georgiev, D.; Hamberg, K.; Hariz, M.; Forsgren, L.; Hariz, G.M. Gender differences in Parkinson’s disease: A clinical perspective. Acta Neurol. Scand. 2017, 136, 570–584. [Google Scholar] [CrossRef]
  109. Picillo, M.; Amboni, M.; Erro, R.; Longo, K.; Vitale, C.; Moccia, M.; Pierro, A.; Santangelo, G.; De Rosa, A.; De Michele, G.; et al. Gender differences in non-motor symptoms in early, drug naïve Parkinson’s disease. J. Neurol. 2013, 260, 2849–2855. [Google Scholar] [CrossRef]
  110. Ray Dorsey, E.; Elbaz, A.; Nichols, E.; Abd-Allah, F.; Abdelalim, A.; Adsuar, J.C.; Ansha, M.G.; Brayne, C.; Choi, J.-Y.J.; Collado-Mateo, D.; et al. Global, regional, and national burden of Parkinson’s disease, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2018, 17, 939–953. [Google Scholar] [CrossRef]
  111. Hirsch, L.; Jette, N.; Frolkis, A.; Steeves, T.; Pringsheim, T. The Incidence of Parkinson’s Disease: A Systematic Review and Meta-Analysis. Neuroepidemiology 2016, 46, 292–300. [Google Scholar] [CrossRef]
  112. Patel, R.; Kompoliti, K. Sex and Gender Differences in Parkinson’s Disease. Neurol. Clin. 2023, 41, 371–379. [Google Scholar] [CrossRef] [PubMed]
  113. Dhandapani, K.M.; Brann, D.W. Role of astrocytes in estrogen-mediated neuroprotection. Exp. Gerontol. 2007, 42, 70–75. [Google Scholar] [CrossRef] [PubMed]
  114. Cimarosti, H.; Siqueira, I.R.; Zamin, L.L.; Nassif, M.; Balk, R.; Frozza, R.; Dalmaz, C.; Netto, C.A.; Salbego, C. Neuroprotection and protein damage prevention by estradiol replacement in rat hippocampal slices exposed to oxygen-glucose deprivation. Neurochem. Res. 2005, 30, 583–589. [Google Scholar] [CrossRef] [PubMed]
  115. Shulman, L.M. Is there a connection between estrogen and Parkinson’s disease? Park. Relat. Disord. 2002, 8, 289–295. [Google Scholar] [CrossRef] [PubMed]
  116. Rugbjerg, K.; Christensen, J.; Tjønneland, A.; Olsen, J.H. Exposure to estrogen and women’s risk for Parkinson’s disease: A prospective cohort study in Denmark. Park. Relat. Disord. 2013, 19, 457–460. [Google Scholar] [CrossRef] [PubMed]
  117. Rocca, W.A.; Bower, J.H.; Maraganore, D.M.; Ahlskog, J.E.; Grossardt, B.R.; De Andrade, M.; Melton, L.J. Increased risk of parkinsonism in women who underwent oophorectomy before menopause. Neurology 2008, 70, 200–209. [Google Scholar] [CrossRef] [PubMed]
  118. Benedetti, M.D.; Maraganore, D.M.; Bower, J.H.; McDonnell, S.K.; Peterson, B.J.; Ahlskog, J.E.; Schaid, D.J.; Rocca, W.A. Hysterectomy, menopause, and estrogen use preceding Parkinson’s disease: An exploratory case-control study. Mov. Disord. 2001, 16, 830–837. [Google Scholar] [CrossRef]
  119. Ross, O.A.; Conneely, K.N.; Wang, T.; Vilarino-Guell, C.; Soto-Ortolaza, A.I.; Rajput, A.; Wszolek, Z.K.; Uitti, R.J.; Louis, E.D.; Clark, L.N.; et al. Genetic variants of α-synuclein are not associated with essential tremor. Mov. Disord. 2011, 26, 2552–2556. [Google Scholar] [CrossRef]
  120. Shen, Y.; Huang, J.Y.; Li, J.; Liu, C.F. Excessive Daytime Sleepiness in Parkinson’s Disease: Clinical Implications and Management. Chin. Med. J. 2018, 131, 974–981. [Google Scholar] [CrossRef]
  121. Yadav, S.K.; Pandey, S.; Singh, B. Role of estrogen and levodopa in 1-methyl-4-pheny-l-1,2,3,6-tetrahydropyridine (mptp)-induced cognitive deficit in Parkinsonian ovariectomized mice model: A comparative study. J. Chem. Neuroanat. 2017, 85, 50–59. [Google Scholar] [CrossRef]
  122. Pedersen, A.L.; Brownrout, J.L.; Saldanha, C.J. Neuroinflammation and neurosteroidogenesis: Reciprocal modulation during injury to the adult zebra finch brain. Physiol. Behav. 2018, 187, 51–56. [Google Scholar] [CrossRef]
  123. Nilsen, J.; Chen, S.; Irwin, R.W.; Iwamoto, S.J.; Brinton, R.D. Estrogen protects neuronal cells from amyloid beta-induced apoptosis via regulation of mitochondrial proteins and function. BMC Neurosci. 2006, 7, 74. [Google Scholar] [CrossRef] [PubMed]
  124. Grimm, A.; Schmitt, K.; Lang, U.E.; Mensah-Nyagan, A.G.; Eckert, A. Improvement of neuronal bioenergetics by neurosteroids: Implications for age-related neurodegenerative disorders. Biochim. Biophys. Acta Mol. Basis Dis. 2014, 1842, 2427–2438. [Google Scholar] [CrossRef] [PubMed]
  125. Mohajeri, M.; Martín-Jiménez, C.; Barreto, G.E.; Sahebkar, A. Effects of estrogens and androgens on mitochondria under normal and pathological conditions. Prog. Neurobiol. 2019, 176, 54–72. [Google Scholar] [CrossRef] [PubMed]
  126. Morgan, T.E.; Finch, C.E. Astrocytic estrogen receptors and impaired neurotrophic responses in a rat model of perimenopause. Front. Aging Neurosci. 2015, 7, 179. [Google Scholar] [CrossRef]
  127. Hu, Z.; Yang, Y.; Gao, K.; Rudd, J.A.; Fang, M. Ovarian hormones ameliorate memory impairment, cholinergic deficit, neuronal apoptosis and astrogliosis in a rat model of Alzheimer’s disease. Exp. Ther. Med. 2016, 11, 89–97. [Google Scholar] [CrossRef] [PubMed]
  128. Arbo, B.D.; Bennetti, F.; Ribeiro, M.F. Astrocytes as a target for neuroprotection: Modulation by progesterone and dehydroepiandrosterone. Prog. Neurobiol. 2016, 144, 27–47. [Google Scholar] [CrossRef] [PubMed]
  129. Singh, M.; Su, C. Progesterone and neuroprotection. Horm. Behav. 2013, 63, 284–290. [Google Scholar] [CrossRef]
  130. Ishrat, T.; Sayeed, I.; Atif, F.; Hua, F.; Stein, D.G. Progesterone and allopregnanolone attenuate blood-brain barrier dysfunction following permanent focal ischemia by regulating the expression of matrix metalloproteinases. Exp. Neurol. 2010, 226, 183–190. [Google Scholar] [CrossRef]
  131. Zhang, Z.; Yang, R.; Cai, W.; Bai, Y.; Sokabe, M.; Chen, L. Treatment with progesterone after focal cerebral ischemia suppresses proliferation of progenitor cells but enhances survival of newborn neurons in adult male mice. Neuropharmacology 2010, 58, 930–939. [Google Scholar] [CrossRef]
  132. De Nicola, A.F.; Garay, L.I.; Meyer, M.; Guennoun, R.; Sitruk-Ware, R.; Schumacher, M.; Gonzalez Deniselle, M.C. Neurosteroidogenesis and progesterone anti-inflammatory/neuroprotective effects. J. Neuroendocr. 2018, 30, e12502. [Google Scholar] [CrossRef] [PubMed]
  133. Shahrokhi, N.; Haddad, M.K.; Joukar, S.; Shabani, M.; Keshavarzi, Z.; Shahozehi, B. Neuroprotective antioxidant effect of sex steroid hormones in traumatic brain injury. Pak. J. Pharm. Sci. 2012, 25, 219–225. [Google Scholar] [PubMed]
  134. Guennoun, R.; Fréchou, M.; Gaignard, P.; Liere, P.; Slama, A.; Schumacher, M.; Denier, C.; Mattern, C. Intranasal administration of progesterone: A potential efficient route of delivery for cerebroprotection after acute brain injuries. Neuropharmacology 2019, 145, 283–291. [Google Scholar] [CrossRef] [PubMed]
  135. Robertson, C.L.; Puskar, A.; Hoffman, G.E.; Murphy, A.Z.; Saraswati, M.; Fiskum, G. Physiologic progesterone reduces mitochondrial dysfunction and hippocampal cell loss after traumatic brain injury in female rats. Exp. Neurol. 2006, 197, 235–243. [Google Scholar] [CrossRef] [PubMed]
  136. Liu, F.; Liao, F.; Li, W.; Han, Y.; Liao, D. Progesterone alters Nogo-A, GFAP and GAP-43 expression in a rat model of traumatic brain injury. Mol. Med. Rep. 2014, 9, 1225–1231. [Google Scholar] [CrossRef] [PubMed]
  137. Monderer, R.; Thorpy, M. Sleep Disorders and Daytime Sleepiness in Parkinson’s Disease. Curr. Neurol. Neurosci. Rep. 2009, 9, 173–180. [Google Scholar] [CrossRef] [PubMed]
  138. Videnovic, A.; Marlin, C.; Alibiglou, B.L.; Planetta, P.J.; Vaillancourt, D.E.; Mackinnon, C.D. Increased REM sleep without atonia in Parkinson disease with freezing of gait. Neurology 2013, 81, 1030–1035. [Google Scholar] [CrossRef] [PubMed]
  139. Wade, R.; Pachana, N.A.; Dissanayaka, N. Factors Related to Sleep Disturbances for Informal Carers of Individuals with PD and Dyadic Relationship: A Rural Perspective. J. Geriatr. Psychiatry Neurol. 2021, 34, 389–396. [Google Scholar] [CrossRef]
  140. Gros, P.; Videnovic, A. Overview of Sleep and Circadian Rhythm Disorders in Parkinson Disease. Clin. Geriatr. Med. 2020, 36, 119–130. [Google Scholar] [CrossRef]
  141. Hurt, C.S.; Rixon, L.; Chaudhuri, K.R.; Moss-Morris, R.; Samuel, M.; Brown, R.G. Identifying barriers to help-seeking for non-motor symptoms in people with Parkinson’s disease. J. Health Psychol. 2019, 24, 561–571. [Google Scholar] [CrossRef]
  142. Lu, J.; Sorooshyari, S.K. Machine Learning Identifies a Rat Model of Parkinson’s Disease via Sleep-Wake Electroencephalogram. Neuroscience 2023, 510, 1–8. [Google Scholar] [CrossRef] [PubMed]
  143. Barber, T.R.; Muhammed, K.; Drew, D.; Bradley, K.M.; McGowan, D.R.; Klein, J.C.; Manohar, S.G.; Hu, M.T.M.; Husain, M. Reward insensitivity is associated with dopaminergic deficit in rapid eye movement sleep behaviour disorder. Brain 2023, 146, 2502–2511. [Google Scholar] [CrossRef] [PubMed]
  144. Clarenbach, P. Parkinson’s Disease and Sleep. 2000. Available online: https://link.springer.com/article/10.1007/PL00022915 (accessed on 13 December 2023).
  145. Abbott, R.D.; Ross, G.W.; White, L.R.; Tanner, C.M.; Masaki, K.H.; Nelson, J.S.; Curb, J.D.; Petrovitch, H. Excessive daytime sleepiness and subsequent development of Parkinson disease. Neurology 2005, 65, 1442–1446. [Google Scholar] [CrossRef]
  146. Medeiros, C.A.M.; Carvalhedo De Bruin, P.F.; Lopes, L.A.; Magalhães, M.C.; De Lourdes Seabra, M.; Sales De Bruin, V.M. Effect of exogenous melatonin on sleep and motor dysfunction in Parkinson’s disease: A randomized, double blind, placebo-controlled study. J. Neurol. 2007, 254, 459–464. [Google Scholar] [CrossRef] [PubMed]
  147. Rahayel, S.; Gaubert, M.; Postuma, R.B.; Montplaisir, J.; Carrier, J.; Monchi, O.; Rémillard-Pelchat, D.; Bourgouin, P.A.; Panisset, M.; Chouinard, S.; et al. Brain atrophy in Parkinson’s disease with polysomnographyconfirmed REM sleep behavior disorder. Sleep 2019, 42, zsz062. [Google Scholar] [CrossRef] [PubMed]
  148. Vetrivelan, R.; Bandaru, S.S. Neural Control of REM Sleep and Motor Atonia: Current Perspectives. Curr. Neurol. Neurosci. Rep. 2023, 23, 907–923. [Google Scholar] [CrossRef] [PubMed]
  149. Gjerstad, M.D.; Tysnes, O.B.; Larsen, J.P. Increased risk of leg motor restlessness but not RLS in early Parkinson disease. Neurology 2011, 77, 1941–1946. [Google Scholar] [CrossRef]
  150. Verbaan, D.; van Rooden, S.M.; van Hilten, J.J.; Rijsman, R.M. Prevalence and clinical profile of restless legs syndrome in Parkinson’s disease. Mov. Disord. 2010, 25, 2142–2147. [Google Scholar] [CrossRef]
  151. De Castro Medeiros, D.; Plewnia, C.; Mendes, R.V.; Pisanò, C.A.; Boi, L.; Moraes, M.F.D.; Aguiar, C.L.; Fisone, G. A mouse model of sleep disorders in Parkinson’s disease showing distinct effects of dopamine D2-like receptor activation. Prog. Neurobiol. 2023, 231, 102536. [Google Scholar]
  152. Wishart, S.; Macphee, G.J.A. Evaluation and management of the non-motor features of Parkinson’s disease. Ther. Adv. Chronic Dis. 2011, 2, 69–85. [Google Scholar] [CrossRef]
  153. Iranzo, A.; Molinuevo, J.L.; Santamaría, J.; Serradell, M.; Martí, M.J.; Valldeoriola, F.; Tolosa, E. Rapid-eye-movement sleep behaviour disorder as an early marker for a neurodegenerative disorder: A descriptive study. Lancet Neurol. 2006, 5, 572–577. [Google Scholar] [CrossRef]
  154. Postuma, R.B.; Berg, D. Advances in markers of prodromal Parkinson disease. Nat. Rev. Neurol. 2016, 12, 622–634. [Google Scholar] [CrossRef]
  155. Onofrj, M.; Thomas, A.; D’Andreamatteo, G.; Iacono, D.; Luciano, A.L.; Di Rollo, A.; Di Mascio, R.; Ballone, E.; Di Iorio, A. Incidence of RBD and hallucination in patients affected by Parkinson’s disease: 8-year follow-up. Neurol. Sci. 2002, 23, S91–S94. [Google Scholar] [CrossRef]
  156. Jørgensen, J.T.; Schernhammer, E.; Papantoniou, K.; Hansen, J.; Westendorp, R.G.J.; Stayner, L.; Simonsen, M.K.; Andersen, Z.J. Night work and incidence of Parkinson’s disease in the Danish Nurse Cohort. Occup. Environ. Med. 2021, 78, 419–425. [Google Scholar] [CrossRef]
  157. Schernhammer, E.S.; Lassen, C.F.; Kenborg, L.; Ritz, B.; Olsen, J.H.; Hansen, J. Occupational history of night shift work and Parkinson’s disease in Denmark. Scand. J. Work. Environ. Health 2015, 41, 377–383. [Google Scholar] [CrossRef] [PubMed]
  158. Chen, H.; Schernhammer, E.; Schwarzschild, M.A.; Ascherio, A. A prospective study of night shift work, sleep duration, and risk of Parkinson’s disease. Am. J. Epidemiol. 2006, 163, 726–730. [Google Scholar] [CrossRef] [PubMed]
  159. Chesselet, M.F.; Richter, F.; Zhu, C.; Magen, I.; Watson, M.B.; Subramaniam, S.R. A Progressive Mouse Model of Parkinson’s Disease: The Thy1-aSyn (“Line 61”) Mice. Neurotherapeutics 2012, 9, 297–314. [Google Scholar] [CrossRef] [PubMed]
  160. Taguchi, T.; Ikuno, M.; Yamakado, H.; Takahashi, R. Animal model for prodromal Parkinson’s disease. Int. J. Mol. Sci. 2020, 21, 1961. [Google Scholar] [CrossRef] [PubMed]
  161. Willison, L.D.; Kudo, T.; Loh, D.H.; Kuljis, D.; Colwell, C.S. Circadian dysfunction may be a key component of the non-motor symptoms of Parkinson’s disease: Insights from a transgenic mouse model. Exp. Neurol. 2013, 243, 57–66. [Google Scholar] [CrossRef] [PubMed]
  162. De Castro Medeiros, D.; Aguiar, C.L.; Moraes, M.F.D.; Fisone, G. Sleep disorders in rodent models of Parkinson’s disease. Front. Pharmacol. 2019, 10, 1414. [Google Scholar] [CrossRef] [PubMed]
  163. Chikatimalla, R.; Dasaradhan, T.; Koneti, J.; Cherukuri, S.P.; Kalluru, R.; Gadde, S. Depression in Parkinson’s Disease: A Narrative Review. Cureus 2022, 14, e27750. [Google Scholar] [CrossRef] [PubMed]
  164. Bang, Y.; Lim, J.; Choi, H.J. Recent advances in the pathology of prodromal non-motor symptoms olfactory deficit and depression in Parkinson’s disease: Clues to early diagnosis and effective treatment. Arch. Pharmacal Res. 2021, 44, 588–604. [Google Scholar] [CrossRef] [PubMed]
  165. Débora Silberman, C.; Laks, J.; Figueiredo Capitão, C.; Soares Rodrigues, C.; Moreira, I.; Engelhardt, E. Accuracy and specificity of two depression rating scale. Arq. Neuro-Psiquiatr. 2006, 64, 407–411. [Google Scholar] [CrossRef]
  166. Custodio, N.; Alva-Diaz, C.; Morán-Mariños, C.; Mejía-Rojas, K.; Lira, D.; Montesinos, R.; Herrera-Pérez, E.; Castro-Suárez, S.; Bardales, Y. Factors associated with depression in patients with Parkinson’s disease: A multicenter study in Lima, Peru. Dement. Neuropsychol. 2018, 12, 292–298. [Google Scholar] [CrossRef] [PubMed]
  167. Agüera-Ortiz, L.; García-Ramos, R.; Grandas Pérez, F.J.; López-Álvarez, J.; Montes Rodríguez, J.M.; Olazarán Rodríguez, F.J.; Olivera Pueyo, J.; Pelegrín Valero, C.; Porta-Etessam, J. Focus on Depression in Parkinson’s Disease: A Delphi Consensus of Experts in Psychiatry, Neurology, and Geriatrics. Park. Dis. 2021, 2021, 6621991. [Google Scholar] [CrossRef] [PubMed]
  168. Lieberman, A. Managing the neuropsychiatric symptoms of Parkinson’s disease. Neurology 1998, 50, S33–S38. [Google Scholar] [CrossRef] [PubMed]
  169. Starkstein, S.E.; Mayberg, H.S.; Leiguarda, R.; Preziosi, T.J.; Robinson, R.G. A prospective longitudinal study of depression, cognitive decline, and physical impairments in patients with Parkinson’s disease. Neurosurg. Psychiatry 1992, 55, 377–382. [Google Scholar] [CrossRef] [PubMed]
  170. Kuzis, G.; Sabe, L.; Tiberti, C.; Leiguarda, R.; Starkstein, S.E. Cognitive Functions in Major Depression and Parkinson Disease. Arch. Neurol. 1997, 54, 982–986. [Google Scholar] [CrossRef]
  171. Schäbitz, W.R.; Glatz, K.; Schuhan, C.; Sommer, C.; Berger, C.; Schwaninger, M.; Hartmann, M.; Hilmar Goebel, H.; Meinck, H.M. Severe Forward Flexion of the Trunk in Parkinson’s Disease: Focal Myopathy of the Paraspinal Muscles Mimicking Camptocormia. Mov. Disord. 2003, 18, 408–414. [Google Scholar] [CrossRef]
  172. Schuurman, A.G.; Van Den Akker, M.; Ensinck, K.T.J.L.; Metsemakers, J.F.M.; Knottnerus, J.A.; Leentjens, A.F.G.; Buntinx, F. Increased risk of Parkinson’s disease after depression A retrospective cohort study. Neurology 2002, 58, 1501–1504. [Google Scholar] [CrossRef]
  173. Ishihara, L.; Brayne, C. A systematic review of depression and mental illness preceding Parkinson’s disease. Acta Neurol. Scand. 2006, 113, 211–220. [Google Scholar] [CrossRef] [PubMed]
  174. Jeong, W.; Kim, H.; Joo, J.H.; Jang, S.I.; Park, E.C. Association between depression and risk of Parkinson’s disease in South Korean adults. J. Affect. Disord. 2021, 292, 75–80. [Google Scholar] [CrossRef] [PubMed]
  175. Chakraborty, A.; Diwan, A. Depression and Parkinson’s disease: A Chicken-Egg story. AIMS Neurosci. 2022, 9, 479–490. [Google Scholar] [CrossRef] [PubMed]
  176. Colman, I.; Ataullahjan, A. Life Course Perspectives on the Epidemiology of Depression. Can. J. Psychiatry 2010, 55, 622–632. [Google Scholar] [CrossRef] [PubMed]
  177. Vink, D.; Aartsen, M.J.; Schoevers, R.A. Risk factors for anxiety and depression in the elderly: A review. J. Affect. Disord. 2008, 106, 29–44. [Google Scholar] [CrossRef] [PubMed]
  178. Van Eekelen, J.A.M.; Ellis, J.A.; Pennell, C.E.; Craig, J.; Saffery, R.; Mattes, E.; Olsson, C.A. Stress-sensitive neurosignalling in depression: An integrated network biology approach to candidate gene selection for genetic association analysis. Ment. Illn. 2012, 4, 105–114. [Google Scholar] [CrossRef] [PubMed]
  179. Knezevic, E.; Nenic, K.; Milanovic, V.; Knezevic, N.N. The Role of Cortisol in Chronic Stress, Neurodegenerative Diseases, and Psychological Disorders. Cells. Cells 2023, 12, 2726. [Google Scholar] [CrossRef]
  180. Kibel, A.; Drenjančević-Perić, I. Impact of glucocorticoids and chronic stress on progression of Parkinson’s disease. Med. Hypotheses 2008, 71, 952–956. [Google Scholar] [CrossRef]
  181. Zou, K.; Guo, W.; Tang, G.; Zheng, B.; Zheng, Z. Acase of early onset Parkinson’s disease after major stress. Neuropsychiatr. Dis. Treat. 2013, 9, 1067–1069. [Google Scholar]
  182. Chinta, S.J.; Lieu, C.A.; Demaria, M.; Laberge, R.M.; Campisi, J.; Andersen, J.K. Environmental stress, ageing and glial cell senescence: A novel mechanistic link to parkinson’s disease? J. Intern. Med. 2013, 273, 429–436. [Google Scholar] [CrossRef]
  183. Djamshidian, A.; Lees, A.J. Can stress trigger Parkinson’s disease? J. Neurol. Neurosurg. Psychiatry 2014, 85, 879–882. [Google Scholar] [CrossRef] [PubMed]
  184. Fontoura, J.L.; Baptista, C.; Pedroso, F.D.B.; Pochapski, J.A.; Miyoshi, E.; Ferro, M.M. Depression in Parkinson’s Disease: The Contribution from Animal Studies. Park. Dis. 2017, 2017, 9124160. [Google Scholar] [CrossRef] [PubMed]
  185. Faivre, F.; Joshi, A.; Bezard, E.; Barrot, M. The hidden side of Parkinson’s disease: Studying pain, anxiety and depression in animal models. Neurosci. Biobehav. Rev. 2019, 96, 335–352. [Google Scholar] [CrossRef] [PubMed]
  186. Serretti, A. Anhedonia and Depressive Disorders. Clin. Psychopharmacol. Neurosci. 2023, 21, 401–409. [Google Scholar] [CrossRef] [PubMed]
  187. Primo, M.J.; Fonseca-Rodrigues, D.; Almeida, A.; Teixeira, P.M.; Pinto-Ribeiro, F. Sucrose preference test: A systematic review of protocols for the assessment of anhedonia in rodents. Eur. Neuropsychopharmacol. 2023, 77, 80–92. [Google Scholar] [CrossRef] [PubMed]
  188. Gonzalez-Riano, C.; Saiz, J.; Barbas, C.; Bergareche, A.; Huerta, J.M.; Ardanaz, E.; Konjevod, M.; Mondragon, E.; Erro, M.E.; Chirlaque, M.D. Prognostic biomarkers of Parkinson’s disease in the Spanish EPIC cohort: A multiplatform metabolomics approach. NPJ Park. Dis. 2021, 7, 73. [Google Scholar] [CrossRef] [PubMed]
  189. Siderowf, A.; Concha-Marambio, L.; Lafontant, D.E.; Farris, C.M.; Ma, Y.; Urenia, P.A.; Nguyen, H.; Alcalay, R.N.; Chahine, L.M.; Foroud, T. Assessment of heterogeneity among participants in the Parkinson’s Progression Markers Initiative cohort using α-synuclein seed amplification: A cross-sectional study. Lancet Neurol. 2023, 22, 407–417. [Google Scholar] [CrossRef]
  190. Christodoulou, C.C.; Onisiforou, A.; Zanos, P.; Papanicolaou, E.Z. Unraveling the transcriptomic signatures of Parkinson’s disease and major depression using single-cell and bulk data. Front. Aging Neurosci. 2023, 15, 1273855. [Google Scholar] [CrossRef]
  191. Bao, Y.; Wang, L.; Liu, H.; Yang, J.; Yu, F.; Cui, C.; Huang, D. A Diagnostic Model for Parkinson’s Disease Based on Anoikis-Related Genes. Mol. Neurobiol. 2023. [Google Scholar] [CrossRef]
  192. Vallianatou, T.; Nilsson, A.; Bjärterot, P.; Shariatgorji, R.; Slijkhuis, N.; Aerts, J.T.; Jansson, E.T. Rapid Metabolic Profiling of 1 μL Crude Cerebrospinal Fluid by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Can Differentiate De Novo Parkinson’s Disease. Anal. Chem. 2023, 95, 18352–18360. [Google Scholar] [CrossRef]
  193. Yan, S.; Jiang, C.; Janzen, A.; Barber, T.R.; Seger, A.; Sommerauer, M.; Davis, J.J.; Marek, K.; Hu, M.T.; Oertel, W.H.; et al. Neuronally Derived Extracellular Vesicle α-Synuclein as a Serum Biomarker for Individuals at Risk of Developing Parkinson Disease. JAMA Neurol. 2024, 81, 59–68. [Google Scholar] [CrossRef]
Figure 1. The study of risk factors and/or prodromal signs related to PD in animal models: advanced age, male sex, sleep alterations, and depression.
Figure 1. The study of risk factors and/or prodromal signs related to PD in animal models: advanced age, male sex, sleep alterations, and depression.
Brainsci 14 00156 g001
Figure 2. Schematic comparison of the progression of behavioral, cellular, and neurochemical deficits in the non-motor and motor phases of human PD and in pharmacological rodent models.
Figure 2. Schematic comparison of the progression of behavioral, cellular, and neurochemical deficits in the non-motor and motor phases of human PD and in pharmacological rodent models.
Brainsci 14 00156 g002
Table 1. Quantitative summary of pre-clinical studies on Parkinson’s disease carried out in animal or cell culture models *.
Table 1. Quantitative summary of pre-clinical studies on Parkinson’s disease carried out in animal or cell culture models *.
ModelFirst Publication
(Year)
Number of
Published Studies
Percentage
(%)
Reserpine19505302.58
Haloperidol19629634.69
6-OHDA1975524225.50
Genetic1977286913.96
Cell culture1979236711.52
MPTP1983673532.77
Rotenone198718478.99
* Search and selection by the title of the articles until December 2023. Pubmed database. Terms of search: “drug name” and “rat” or “mice” (rodent models); “cell culture”; and “Parkinson”.
Table 2. Risk factors and main results obtained in cited articles of pharmacological rodent models for PD.
Table 2. Risk factors and main results obtained in cited articles of pharmacological rodent models for PD.
Investigated FactorStrainPharmacological ModelMeasuresOutcomesPublication
AgeMale mice C57BL/6MPTP injection in elderly and young animalsHistologicalReduction in fluorescence in noradrenergic neurons of the locus coerulus and dopaminergic neurons of SNpc and VTA.Gupta et al., 1986 [64]
Male mice C57BL/6MPTP injection in elderly animalsHistologicalReduction of immunostaining for tyrosine hydroxylase in the striatum, as well as Nurr1 gene expression, and increased density of dopamine transporter in SN.Tremblay et al., 2006 [65]
Male mice C57BL/6MPTP injection in elderly and young animalsThe mitochondrial content of ATP;
Histological; and
Behavioral test
Deficits in the activity of the respiratory chain in mitochondria, decreased antioxidant enzymes and cytochrome c, and a significant reduction in TH and DA uptake transporter. In addition, the older animals had impaired movement when compared to younger mice.Patki et al., 2009 [66]
Male mice C57BL/6MPTP injection in elderly and young animalsHistologicalLoss of tyrosine hydroxylase throughout the nigro-striatal circuit compared to young mice.Grimmig et al., 2018 [67]
Male Wistar ratsRepeated injections of a low dose of reserpineHistological and Behavioral testsElderly animals were more susceptible to the effects of the treatment compared to adult animals. Elderly rats developed motor deficits earlier than adult rats. Elderly rats showed a reduction in tyrosine hydroxylase immunoreactivity in SNpc, striatum, and VTA.Melo et al., 2022 [68]
SexFemale and Male Long-Evans rats6-OHDA injectionHistological and Behavioral testsMale animals reduced the use of their hind limbs compared to females, despite the deficit in forelimb movements being similar between sexes. In addition, males were more likely to contact the cylinder wall with their dorsal surface to keep an erect posture. Female animals had a less severe reduction in the number of dopaminergic cells compared to males.Field et al., 2006 [69]
Female and Male Wistar ratsRepeated injections of a low dose of reserpineHistological and Behavioral testsFemales were more resistant to the deleterious effects of the treatment. Indeed, this sex did not present reduced TH immunoreactivity in the dorsal striatum and VTA.Bispo et al., 2019 [70]
Female and Male Wistar ratsRepeated injections of a low dose of reserpineHistological and Behavioral testsFemale animals did not present cognitive alterations and TH immunoreactivity reduction. In addition, females presented attenuated motor impairment compared to males.Lima et al., 2021 [71]
SleepMale Wistar rats6-OHDA injectionPolysomnographic recordingsRats with bilateral 6-OHDA lesion in the VTA show reduced REM sleep during the light period and an increase in total sleep time during the dark phase.Sakata et al., 2002 [72]
Male Sprague-Dawley rats6-OHDA injectionPolysomnographic and video recordingsRats with a unilateral 6-OHDA lesion of the medial forebrain bundle show decreased sleep time during their inactive phase (light) of the 24 h light–dark cycle.Vo et al., 2014 [73]
Male Sprague-Dawley rats6-OHDA injectionPolysomnographic recordingsRats with bilateral 6-OHDA lesion in the caudoputamen increased wake time during the 12 h dark cycle. These animals exhibited sleep–wake fragmentation and reduced diurnal variability of sleep.Qiu et al., 2016 [74]
DepressionMael Swiss miceRepeated injections of a low dose of reserpineBehavioral testNo differences were observed between animals with different depressive-like behavior profiles.Soares et al., 2021 [75]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Silva, R.H.; Lopes-Silva, L.B.; Cunha, D.G.; Becegato, M.; Ribeiro, A.M.; Santos, J.R. Animal Approaches to Studying Risk Factors for Parkinson’s Disease: A Narrative Review. Brain Sci. 2024, 14, 156. https://doi.org/10.3390/brainsci14020156

AMA Style

Silva RH, Lopes-Silva LB, Cunha DG, Becegato M, Ribeiro AM, Santos JR. Animal Approaches to Studying Risk Factors for Parkinson’s Disease: A Narrative Review. Brain Sciences. 2024; 14(2):156. https://doi.org/10.3390/brainsci14020156

Chicago/Turabian Style

Silva, R. H., L. B. Lopes-Silva, D. G. Cunha, M. Becegato, A. M. Ribeiro, and J. R. Santos. 2024. "Animal Approaches to Studying Risk Factors for Parkinson’s Disease: A Narrative Review" Brain Sciences 14, no. 2: 156. https://doi.org/10.3390/brainsci14020156

APA Style

Silva, R. H., Lopes-Silva, L. B., Cunha, D. G., Becegato, M., Ribeiro, A. M., & Santos, J. R. (2024). Animal Approaches to Studying Risk Factors for Parkinson’s Disease: A Narrative Review. Brain Sciences, 14(2), 156. https://doi.org/10.3390/brainsci14020156

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop