Determinants of cognitive impairment in multiple system atrophy: Clinical and genetic study

Amina Nasri; Alya Gharbi; Ikram Sghaier; Saloua Mrabet; Amira Souissi; Amina Gargouri; Mouna Ben Djebara; Imen Kacem; Riadh Gouider

doi:10.1371/journal.pone.0277798

Abstract

Introduction

Classically, cognitive impairment (CI) was not considered as a paramount feature of multiple system atrophy(MSA) in both parkinsonian(MSA-P) and cerebellar(MSA-C) motor-subtypes. Yet, growing evidence indicates currently the commonness of such deficits among MSA patients in different populations. Our aim was to evaluate the cognitive profile of MSA Tunisian patients and to analyze the underlying clinical and genetic determinants

Methods

In a retrospective cross-sectional study, clinically-diagnosed MSA patients were included. All subjects underwent clinical and neuropsychological assessments to characterize their cognitive profile. The associations with their APOE genotype status were analyzed. Determinant of CI were specified.

Results

We included 71 MSA patients. Female gender(sex-ratio = 0.65) and MSA-P subtype(73%) were predominant. Mean age of disease onset was 59.1years. CI was found in 85.7% of patients(dementia in 12.7% and Mild cognitive impairment(MCI) in 73% of patients mainly of multiple-domain amnestic type(37.3%)). Mean MMSE score was lower among MSA-P compared to MSA-C(23.52 vs. 26.47;p = 0.027). Higher postural instability gait disorder(PIGD) and MDS-UPDRS-III scores were noted in demented MSA patients(p = 0.019;p = 0.015 respectively). The main altered cognitive domain was attention(64.8%). Executive functions and mood disorders were more affected in MSA-P(p = 0.029,p = 0.035 respectively). Clinical and neurophysiological study of dysautonomia revealed no differences across cognitive subtypes. APOE genotyping was performed in 51 MSA patients with available blood samples. Those carrying APOEε4 had 1.32 fold higher risk to develop CI, with lower MMSE score(p = 0.0001). Attention and language were significantly altered by adjusting the p value to APOEɛ4 carriers(p = 0.046 and p = 0.044 respectively). Executive dysfunction was more pronounced among MSA-PAPOEε4 carriers(p = 0.010).

Conclusion

In this study, the main determinants of CI in Tunisian MSA patients were MSA-P motor-subtype, mainly of PIGD-phenotype, disease duration and APOEε4 carrying status, defining a more altered cognitive phenotype. This effect mainly concerned executive, attention and language functions, all found to be more impaired in APOEε4 carriers with variable degrees across MSA motor-subtypes.

Citation: Nasri A, Gharbi A, Sghaier I, Mrabet S, Souissi A, Gargouri A, et al. (2022) Determinants of cognitive impairment in multiple system atrophy: Clinical and genetic study. PLoS ONE 17(12): e0277798. https://doi.org/10.1371/journal.pone.0277798

Editor: Tuhin Virmani, University of Arkansas for Medical Sciences, UNITED STATES

Received: October 8, 2021; Accepted: November 3, 2022; Published: December 12, 2022

Copyright: © 2022 Nasri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Multiple system atrophy (MSA) is a rare adult-onset and fatal neurodegenerative disorder, characterized by progressive autonomic and motor dysfunction. The pathological hallmark of MSA is the emergence of glial cytoplasmic inclusions (GCIs) corresponding to insoluble α-synuclein accumulations within oligodendrocytes, found particularly in the striatonigral and olivopontocerebellar systems in the brain [1]. Nowadays, it is established that MSA belongs to the group of synucleinopathies alongside with Lewy body disorders [1, 2]. The pathological distribution of lesions in MSA brains leads to multiple phenotypes. According to the predominant clinical features, MSA may be classified into parkinsonian (MSA-P) and cerebellar (MSA-C) motor subtypes [3, 4].

Recently, non-motor and non-autonomic signs have been increasingly recognized in MSA. Dementia or major neurocognitive impairment was considered as exclusion criteria in the first consensus diagnostic criteria [3]. Previously, it has been admitted that cognition remained preserved in MSA. However, cognitive impairment (CI) was reported in up to 37% of neuropathologically proven MSA cases [5]. Lately, this CI was more frequently described in MSA. The most common presentation is mild neurocognitive impairment (MCI) with frontal and executive dysfunction [6, 7]. Nonetheless, the cognitive spectrum in MSA patients seems to be broader, with cognitive decline affecting in other domains [8–10]. The latter could lead to major neurocognitive impairment that interferes with daily living activities [11]. Cognitive deficits can also be found in early stages of the disease in up to 22% of patients [12] and in some patients as a prodromal premotor symptom [13]. Recent follow-up studies showed that cognitive functions may have different evolution over disease course in the two motor subtypes of the disease [14]. Three different MSA cognitive profiles were described upon follow-up comprising normal cognition, stable selective attention-executive deficits, and progressive attention–executive dysfunction associated with memory and visuospatial impairment evolving over time into dementia [15].

Data from previous studies remain though inconsistent about the neuropsychological and clinical heterogeneity of cognitive profile in MSA [16] and the predictive factors of CI in this disease [17]. Among these possible factors, the role of autonomic dysfunction, assessed with various tools including neurophysiological tests, has been previously suggested [5, 18]. In addition to demographic and clinical factors, the implication of genetic factors in cognition remains understudied in MSA. In fact, although the genetic architecture of MSA is still shaping, genotype-phenotype and pathological correlations are hardly depicted [6, 9, 7, 19]. The Apolipoprotein E (APOE) gene which is known to be strongly associated with cognitive decline in neurodegenerative diseases, could be a possible candidate. Indeed, APOE has three common alleles; ɛ4, ɛ3, ɛ2 [8, 10, 20]. APOE ɛ4 is the major genetic risk factor of late onset Alzheimer’s disease (AD) and has been associated with cognition in other synucleinopathies like dementia with Lewy bodies (DLB) [21, 22]. Although APOE gene has been seldom assessed in MSA [6], its link with CI in MSA patients was even less explored. The aim of this study was to evaluate the cognitive profile of MSA patients and to analyze clinical and genetic determinants of CI in MSA.

Material and methods

1. Study subjects

A cross-sectional retrospective study was carried in the Department of Neurology at Razi University Hospital, a tertiary care hospital in Tunis in North Tunisia, over a period of 18 years (from January 2003 to December 2020). Patients with clinically diagnosed probable or possible MSA according to the revised Gilman Criteria were included [3]. All patients had a neurological examination performed by a movement disorder specialist and a systematic brain imaging. We excluded all patients with parkinsonism and/or ataxia of other origin.

2. Clinical and neuropsychological assessment

Detailed neurological examination was performed in all patients to collect anamnestic data and to evaluate the severity of motor and non motor impairment. Information was obtained from the participants and their caregivers about family and personal medical history and medication. Demographic, clinical and neuropsychological data were collected using standardized case-report forms. Disease onset was defined as the age of occurrence of either motor symptoms (parkinsonism and/or cerebellar ataxia) or autonomic dysfunction. Diagnostic delay was defined as the difference between the age at first consultation and the age of disease onset. We defined the type of initial symptoms and specified the age at onset of motor, autonomic, subjective cognitive and psychiatric symptoms (memory, language, hallucinations, depression, and behavioral disorders). For cognitive symptoms, we determined their impact on daily living by the Katz Index of Independence in Activities of Daily Living (ADL) and the Lawton Instrumental Activities of Daily Living scale (IADL) scales to assess functional capacity [23]. Autonomic symptoms included; lower urinary tract symptoms (LUTS), erectile dysfunction, orthostatic hypotension, constipation, and sudomotor symptoms. Stridor and sleep disorders, mainly RBD (rapid eye movement sleep (REM) behavior disorders) were specified. MDS-Unified Parkinson’s disease Rating Scale section III (MDS-UPDRS-III) was used to rate the severity of extrapyramidal symptoms. Parkinsonian motor phenotypes were classified into three subgroups, tremor dominant (TD), postural instability gait disorder (PIGD) or indeterminate phenotypes, according to the published formulas used in Parkinson’s disease (PD) [24]. The degree of severity of parkinsonian symptoms was measured using the Hoehn &Yahr (H&Y) scale. For the assessment of levodopa responsiveness, we used an acute pharmacological test, namely the acute levodopa challenge (ALC), which is routinely performed in our department. We used a standard protocol by administering a single dose of levodopa/ carbidopa 250/25 mg. Motor response was quantified using the MDS-UPDRS-III. During the ALC, motor examination was performed immediately before and every 30 minutes after levodopa intake until the motor conditions returned to the motor baseline status. We calculated the percentage of motor response as the ratio of the difference between the baseline and the peak-of-dose motor scores by the baseline motor score. Levodopa-responsiveness was defined as an improvement rate ≥30% of MDS- UPDRS-III [25].

Other motor signs were assessed on examination including other movement disorders, cerebellar, pyramidal, bulbar and oculomotor signs. Patients were classified into two motor subtypes: MSA-P and MSA-C. They were also classified into probable or possible MSA according to the revised 2008 Gilman Criteria, specifying the dysautonomic features that allowed this probability subtyping (urogenital dysfunction and/or orthostatic hypotension) [3].

Each patient underwent a neuropsychological examination at first consultation comprising the 30-item mini-mental state examination (MMSE) to assess overall cognitive efficiency. The MMSE has been standardized and validated in Tunisia and adjusted for age and education [26]. The frontal assessment battery (FAB) was used to evaluate executive functions and a score less than 16 was considered abnormal [27]. Episodic memory was assessed with the Free and Cued Selective Reminding Test (FCSRT) of Grober & Buschke. Visuo-spatial functions were evaluated by the Clock-Drawing Test [28]. Beck’s Depression Inventory (BDI) (if the age <65 years) [29] and Geriatric Depression Scale (GDS) (if the age≥65 years) were used to evaluate mood disturbance and detect depression [30]. Behavioral disorders were identified by the Neuropsychiatric Inventory (NPI) [31]. Other domains evaluated by neuropsychological assessment included orientation, attention, apraxia, agnosia, judgment and reasoning. Language was evaluated with a speech assessment battery including verbal fluency, grammar and syntax, comprehension, repetition, articulation, semantic knowledge, reading and writing evaluation. We classified the patients cognitively in three subgroups: 1-No cognitive impairment (NCI) (in the absence of subjective or objective cognitive deficits); 2-MCI and 3-dementia or major neurocognitive disorder according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [32]. MCI patients were further subdivided in four clinical subtypes: 1-single-domain amnestic MCI; 2- multiple-domain amnestic MCI; 3- single-domain non amnestic MCI; and 4- multiple-domain non amnestic MCI [32].

3. Neurophysiological autonomic testing

Parasympathetic cardiac control was assessed by three tests: heart rate variation to deep breathing (HR-DB), to Valsalva maneuver (HR-V) and to standing (HR-S). Patients were graded as per Ewing’s criteria for parasympathetic dysautonomia based on Ewing’s heart rate tests battery into normal, early or definite autonomic dysfunction. The sympathetic autonomic system was assessed with the SSR evaluated at the four limbs. Absent SSR at either one or more extremities was regarded as abnormal independently from its latency and/or amplitude. The absence of SSR in at least one limb defined sympathetic autonomic dysfunction [33].

4. Genetic study

Genotyping of APOE was performed using the Restriction Fragment Length Polymorphism Polymerase Chain Reaction. APOE genotypes were determined by scoring for a unique combination of fragment sizes, as depicted by Hixon et al. In fact, digestion by HhaI restriction enzyme gives various combinations of fragment sizes for each genotype as pursue: ɛ2/ ɛ2, 91 and 83 bp; ɛ3/ ɛ3, 91 and 48 bp; ɛ4/ ɛ4, 72 and 48 bp and a mixed genotype: ɛ2/ ɛ3, 91, 83, and 48 bp; ɛ3/ ɛ4, 91, 72 and 48 bp; ɛ2/ ɛ4, 91, 83, 72 and 48 bp.

5. Statistical analysis

We described demographic and clinical characteristics as well as APOE genetic frequencies then assessed the relationship between the clinical variables and APOE genotype in the total cohort and compared them across subgroups. Continuous variables were expressed as mean± standard deviation, while the median and the 1^st quartile and 3^rd quartile were used to measure the central tendency in the case of skew distribution with some extreme values. Differences in the proportions were analyzed by the Chi-square test and the Fischer’s exact test. Multinomial logistic regression was used to model outcome variables according to APOE ɛ4 carrying status. A value of p < 0.05 was considered statistically significant. Spearman rank correlation test was used to evaluate correlations. Corrections for multiple comparisons were employed with a Bonferroni correction. All statistical procedures were performed with R software for Windows using the “SNPassoc”,”multinom”, “Hmisc” and “ggplot2” packages.

5. Ethics

All subject investigations conformed to the principles outlined in the Declaration of Helsinki and have been performed with permission of the Razi hospital ethic committee. All subjects were informed about the purposes of the study and gave a written consent (patients themselves or caregivers) to participate in the study.

Results

1. Clinical and paraclinical findings in MSA patients

We included 71 patients diagnosed with MSA (mainly of MSA-P motor subtype (73.23% vs. 26.76% of MSA-C). The diagnosis of MSA was probable in 73% of cases (65% with urinary incontinence and erectile dysfunction in males, 7% with both rigorously defined urogenital symptoms and orthostatic hypotension and only one patient with orthostatic hypotension) and possible in 27%of patients. The mean follow-up of the total cohort was 3.45 years [ranging from 1 year to 11 years]. Detailed characteristics of total MSA patients as well as stratified according to motor subtype (MSA-P and MSA-C) were summarized in Table 1.

Download:

Table 1. Demographic, clinical and imaging data in MSA patients and motor subtypes.

https://doi.org/10.1371/journal.pone.0277798.t001

Female predominance was noted with sex-ratio equal to 0.65. Mean age of disease onset was 59.11±9.01years with no significant differences between MSA motor subtypes (p = 0.495). The median age of parkinsonism onset among the total group was 62 years ranging from 37 to 77. Parkinsonism duration was significantly shorter in MSA-C (p = 0.0051).

Clinically, all patients reported autonomic dysfunction. Among autonomic symptoms, LUTS dysfunction was the most prevalent (85.91%) followed by orthostatic hypotension (66.2%) then constipation (43.66%). The latter was more frequent in MSA-P than MSA-C (50.0% vs. 26.31%) with marginal association p = 0.06. Neurophysiological autonomic testing revealed dysautonomia among 87.5% of all explored MSA patients mostly parasympathetic in 80.9% then sympathetic in 47.6% (S1 Table).

Upon examination, cerebellar syndrome was found in 39.44% and parkinsonism in 88.73% mostly of PIGD phenotype in 59.15% of cases, with a median of MDS-UPDRS-III score of 29 [12–48]. Patients with MSA-P had a significantly higher frequency of dystonia and myoclonus compared to MSA-C (respectively p = 0.007 and p = 0.044). Conversely, pyramidal signs, found in 45.07% of all MSA patients, were significantly more common in MSA-C forms (73.68% vs. 34.61%; p = 0.003).

Concerning imaging features, we noted significantly higher frequency of hot cross bun sign (HCB) in patients with MSA-P compared to MSA-C forms (63.46% vs. 52.63%, p<0.001).

2. APOE genotype in MSA patients

APOE genotypes had different frequencies across MSA subtypes. In fact, ɛ3ɛ3 was the most frequent (66.7%) in total MSA population as well as in both motor subtypes, followed by ɛ3ɛ4 (21.56%). The latter was more common among MSA-P compared to MSA-C (24.4% vs. 10.0%). APOE ɛ3ɛ2 was present in 9.8% of MSA cases and finally, 1.96% of cases were carriers of ɛ4ɛ4 genotype. In summary, 23.52% of total MSA patients were carriers of at least one APOE -ɛ4 allele (24.4% of MSA-P and 20% of MSA-C) (Table 2).

Download:

Table 2. Comparison of APOE variants in MSA patients with and without dementia.

https://doi.org/10.1371/journal.pone.0277798.t002

3. Cognitive profile of MSA patients

Detailed neuropsychological characteristics of the total study population and across motor subtypes are summarized in Table 3 and illustrated in Figs 1 and 2.

Download:

Fig 1. Comparison of altered cognitive domains in multiple system atrophy according to motor subtype (MSA-P vs MSA-C and total study cohort).

Radar charts comparing the percentage (%) of AMS-C and AMS-P cases with neurophysiological findings.

https://doi.org/10.1371/journal.pone.0277798.g001

Download:

Fig 2. Remodeling of cognitive profile according to the carriage of APOEε4 and APOEε2 isoforms.

Radar charts comparing the altered cognitive domains in percentage (%) between two subtypes of AMS-C and AMS-P.

https://doi.org/10.1371/journal.pone.0277798.g002

Download:

Table 3. Cognitive profile in patients with MSA and motor subtypes.

https://doi.org/10.1371/journal.pone.0277798.t003

CI was found in 85.7% (MCI in 73% of patients and dementia in 12.7%), and 14.3% had no cognitive impairment (NCI). In MCI patients, 37.3% had multiple-domain amnestic MCI, 29.4% single-domain non-amnestic MCI, 25.5% single-domain amnestic MCI and finally 7.8% multiple-domain non amnestic MCI. Mean initial MMSE score was 24.33±5.0 among total MSA patients and was significantly lower among MSA-P compared to MSA-C (23.52 vs. 26.47; p = 0.027) (S2 Table).

On neuropsychological assessment, the main altered domains were attention (64.79%) and executive functions (60.56%) which were significantly more affected in MSA-P patients compared to MSA-C (69.23% vs. 36.84%; p = 0.029). Mean initial FAB score was 12.31+4.88 with marginal differences between both MSA motor subtypes (p = 0.074), but only deficits in conflicting instructions subset were significantly more pronounced among MSA-P compared to MSA-C (51.92 vs. 26.32; p = 0.048). Similarly, mood disorders were more frequently associated to MSA-P (p = 0.035).

3. Determinants of cognitive impairment in MSA patients

3.1. Clinical and paraclinical determinants of cognitive impairment in MSA.

Disease duration was significantly different across NCI, MCI and dementia subgroups (p = 0.005), with longer disease duration among demented MSA. The prevalence of PIGD phenotype of parkinsonism was higher among patients with dementia (88.9%) compared to NCI and MCI subgroups (60% and 54.9%). Indeed, significantly higher PIGD and MDS-UPDRS-III scores were noted in demented MSA patients (p = 0.019 and p = 0.015 respectively). The clinical and neurophysiological study of the autonomic system revealed no differences across the cognitive subtypes.

The evaluation of the correlation between cognitive scores and clinical/neurophysiological data revealed that MMSE and FAB initial scores were negatively correlated with MDS-UPDRS-III score in all MSA patients and in both motor subtypes. Both scores were inversely proportional to ataxia duration in the total cohort and in MSA-P. Moreover, MMSE and FAB were also inversely proportional to disease and dysautonomia duration in only MSA-C patients. A negative correlation was also noted between MMSE score and parkinsonism duration in MSA-C patients.

3.2. ApoEɛ4 effect on cognitive profile in MSA.

MSA- APOE ɛ4 carriers had 1.32 fold higher risk of developing CI compared to non-carriers (p = 0.04) (Table 3). By combining the APOE ɛ4 allele carriage status, NCI-MSA patients had significantly younger age of onset of the disease, of parkinsonism and dysautonomia compared to other cognitive subgroups (p = 0.05, p = 0.043 and p = 0.05 respectively).

CI severity was more pronounced in APOE ɛ4 carriers who had lower initial MMSE scores (p = 0.0001). When analyzing the cognitive profile, the presence of altered attention was statistically significant by adjusting the p value to APOE ɛ4 carriers (p = 0.046), as well as language deficits (p = 0.044). Moreover, by considering the status of carriage of APOE ɛ4 allele, significant differences appeared between MSA motor subtypes for language impairment (p = 0.0157) and FAB initial score (p = 0.046) which were more altered in MSA-P. Indeed, executive dysfunction was more pronounced in MSA-P APOE ɛ4 carriers (p = 0.0104) (Fig 2).

The evaluation of the correlation between clinical/neurophysiological data and APOE ɛ4 carrying status revealed a correlation of the carriage of APOE ɛ4 allele and ataxia duration in MSA-P patients.

Discussion

In this study, we depicted the cognitive profile of Tunisian patients with MSA and provided first data on clinical and genetic determinants of CI. As reported in our recent epidemiological study on Tunisian APS [34], we noted a predominance of female gender and MSA-P motor subtype mostly of PIGD phenotype.

Interestingly, a levodopa responsiveness was found in 52.1% of total MSA patients, especially in MSA-P (63.5%) vs and MSA-C (21.1%). Although a poor levodopa response is one of the consensus criteria for the diagnosis of MSA and helps to differentiate MSA from PD, approximately one third of patients may experience some benefit, especially if they have MSA-P, as in our patients. Indeed, in pathologically-confirmed MSA series, a positive L-dopa response was reported in 28% to 65% of patients, persisting for several years in only 13% of cases. Hence, the rate of effectiveness of levodopa found in our series is still within this range [35]. However, the degree of responsiveness is difficult to standardize between the existing studies, as several methods and scales were used across them, going from the ALC [36, 37] to prolonged levodopa intake at different doses and durations [38–42]. In our study, for the assessment of levodopa responsiveness, we used the ALC that has been proven to predict levodopa chronic responsiveness [43].

The stratification of our MSA patients according to the presence and the severity of CI revealed the high prevalence of such disorders (85.7%) mostly of mild severity (73%), and only 12.7% had dementia. The percentages of MCI and dementia in our MSA patients were within the ranges of other studies (respectively 32.7% to 80% for MCI and 5 to 31% for dementia [4, 44–46]. However, the frequency of CI in our clinically diagnosed cohort was higher than that reported in 102 autopsy-confirmed MSA patients (32%) [7]. Differences in definitions, methods and study designs could explain such discrepancies.

Beyond the determination of CI prevalence in MSA, recent studies tended to further analyze the cognitive profile in this disease revealing the broad spectrum of encountered deficits.

Hong et al. have illustrated this cognitive heterogeneity by stratifying MCI-MSA patients into four categories. Along with their results, we found the same distribution of MCI patients with a predominance of multiple-domain amnestic MCI, followed by single-domain non-amnestic MCI, and single-domain amnestic MCI. In our cohort, 7.8% of MCI patients had multiple-domain non-amnestic MCI, though none of the patients in the study of Hong et al. developed such a profile [47].

In our MSA patients, the main altered cognitive domains were attention (64.79%), executive functions (60.56%) and to a lesser extent memory. These three domains were effectively predominantly altered in several previous studies [45, 48–50]. Indeed, executive dysfunction was described in 49–69% of MSA patients [35, 50], and was more marked in MSA-P as found in our cohort (p = 0.029).

Dysexecutive syndrome was reported to comprise in details a decline in semantic and phonemic verbal fluency [4, 47, 48], preservative behavior, problem solving, flexibility, response inhibition, attention and working memory [4, 14, 16, 51]. Yet, data related to the most affected subsets of executive functions are divergent. Santangelo et al. reported that spatial planning was most compromised, followed by cognitive flexibility [14]. On the contrary, Cao et al. found that inhibitory control was the most impaired (60.9%) which corroborate to the results of our cohort where inhibitor control dysfunction reached 49% of cases [50]. Some other studies reported no impairment in verbal or nonverbal fluency [14].

Memory was affected in 65.2% of our patients which is in agreement with previous studies where it varied from 17 to 66% [3, 45, 47]. Memory deficits concerned verbal learning, immediate and delayed recall and less often recognition [46, 49].

With regards to visuospatial functions, previous studies reported conflicting results, with some suggesting preserved function [51] while others found deficits in this domain [4, 48, 49]. Visuospatial dysfunction was noted in 34.5% of our patients almost within the range previously described (10–30%) [44, 45]. Language dysfunction and apraxia were less frequently found respectively in 10–20% [4, 45], and 8–10% [52]. Interestingly, we noted the presence of hallucinations in 8.45% of our MSA patients. Indeed, hallucinations not induced by drugs are considered as non-supporting features of MSA. However, according to previous reports, the frequency of MSA patients experiencing hallucinations ranged from 5.5% in the European Multiple System Atrophy registry, to 15% in studies where Neuropsychiatric Inventory was used as in our study [53].

The predictive factors of CI are underexplored. In our study, MSA patients with dementia had longer disease and parkinsonism duration compared to patients without dementia. Disease duration was only related to executive dysfunction. Moreover, decline of global cognitive function and executive function were correlated to duration of parkinsonism and ataxia as well as motor disability. These factors have been previously reported in the literature. In fact, Hatakeyama et al. demonstrated that impairment of global cognitive function (MMSE score) was related to a long disease duration [17]. Furthermore, it has been reported that among MSA patients surviving more than 8 years, almost 50% had CI and that 14% of MSA patients were demented in the last year prior to death [52]. Nevertheless, age at onset, like in our cohort, did not seem to influence cognition [46]. According to Kitayama et al., there were no significant differences in age at onset, gender, duration of disease, or severity of cerebellar dysfunction between patients with and without dementia [20]. Regarding the other demographic features, cognitive and behavioral differences have been reported to be more compromised in MSA females [54]. Motor disability is also a major factor correlated with CI and dementia in MSA [47, 46]. Effectively, the severity of motor disability was associated with MMSE [46, 44], and FAB scores [17] as it is in our study. Otherwise, data regarding the influence of MSA motor subtype are sparse. Indeed, some observed more pronounced cognitive dysfunction in MSA-P [48], and conversely other studies showed a more prominent decline of executive function and verbal memory in MSA-C [14, 55, 56], while a third group found comparable profile in both subtypes [49]. As regards autonomic functions, we did not find any correlations with CI in our series either clinically or neurophysiologically. Few studies addressed orthostatic hypotension with controversial findings [16]. However, cardiovascular dysautonomia have been reported [12], and MMSE was positively correlated to heart rate variation on neurophysiological assessment [17].

The genetic screening of MSA for APOE ɛ4 revealed its association to CI occurrence (p = 0.04) and severity with lower MMSE score (p = 0.0001). APOE is well established lipid metabolism gene and the major genetic determinant of late onset AD [21]. APOE has also been significantly correlated to reduced α-synuclein uptake in an isoforms-dependant manner through direct regulation of α-synuclein pathology independently of its established effects on Aβ and tau [21]. Furthermore, MSA is characterized by the misfolded α-synuclein leading to formation of GCIs [57]. In addition, numerous GCIs were found in the underlying white matter in MSA patients with CI [58]. Armstrong et al. found vacuolation of GCIs in the frontal cortex in MSA patients with CI [59]. Taken all together, these findings prompt the speculations to the association of APOE ɛ4 isoform with the disruption of GCIs, the pathological hallmark of MSA, via α-synuclein misfolding, leading to CI among MSA patients.

Mostly impaired cognitive domains in MSA patients were attention (p = 0.046) and language (p = 0.044) with APOE adjustment. Moreover, executive dysfunction was more pronounced among MSA-P APOE ε4 carriers (p = 0.010). Carrying APOE ε4 allele was previously reported to be correlated with the presence of executive dysfunction in PD patients [21], which may explain the more marked executive deficits in our MSA-P patients. Moreover, imaging studies on cognitively impaired MSA patients, showed a significant atrophy in the left dorsolateral prefrontal cortex [7, 44]. The potential mechanism underlying the executive impairment is the lower prefrontal volume and the decreased neuronal activity in prefrontal areas in APOE ε4 carriers [60].

We also reported in our study the significant correlation of ataxia duration with the carriage of APOE ɛ4 isoform. These findings could be explained by the regionally specific APOE mRNA transcription in neurons. Indeed, in human cerebellar cortex, very strong APOE mRNA hybridization signal was observed in Bergmann radial glial cells [61], and a strong expression of apolipoprotein E receptor 2 in Purkinje cells of the cerebellum [62]. All these cells and structures are implicated in the occurrence of ataxia possibly modulated by APOE.

There are number of flaws in the current work that should be addressed. The small size of our sample and the inequality between both MSA motor subtypes may reduce the statistical power of our study. Similarly, the lack of APOE genotyping in all included patients and the lack of neuropathological confirmation may be major limitations of this study. In fact, our cohort comprised only clinically-diagnosed MSA patients exposing to the risk of diagnostic uncertainty because of heterogeneous clinical presentations of MSA and possible presence of MSA-look-alikes. Moreover, the new MDS criteria for the diagnosis of MSA of 2022 could not be verified in our cohort, since the cases included in the study may not be rechecked for the new requirements mainly for those of orthostatic hypotension and urinary dysfunction [63].

Conclusion

This is the first Tunisian study conducted to investigate the presence of CI in MSA and the prevalence of dementia in MSA in a Tunisian cohort. In our cohort, MSA-P, mainly of PIGD phenotype, disease duration and APOE ε4 carrying status appeared as the main determinants of CI in Tunisian MSA patients, defining a more altered cognitive phenotype. This effect mainly concerned executive, attention and language functions, all found to be more impaired in APOE ε4 carriers with variable degrees across MSA motor subtypes. Dysautonomia did not seem to play a paramount role in the occurrence nor in the profile of CI in our MSA patients. Further studies on larger samples are mandatory to analyze more extensively predictive genetic factors of cognitive decline. Prospective follow-up is also needed to assess the evolving profile of such deficits and their prognostic determinants.

Supporting information

S1 Table. Comparison of SSR and Neurophysiological cardiovascular autonomic testing in MSA patients with and without dementia.

https://doi.org/10.1371/journal.pone.0277798.s001

(DOCX)

S2 Table. Comparison of demographic, clinical and cerebral imaging data in MSA patients according to cognitive impairment level (without, mild and major).

https://doi.org/10.1371/journal.pone.0277798.s002

(DOCX)

Acknowledgments

We thank all blood donor patients, who consented and participated in the present study. We would like to express our gratitude to the sequencing Platform in Faculty of Medicine of Tunis (FMT) for their contribution and their excellent assistance in experiments.

References

1. Kaji S, Maki T, Ishimoto T, Yamakado H, Takahashi R. Insights into the pathogenesis of multiple system atrophy: focus on glial cytoplasmic inclusions. Transl Neurodegener. 2020;9: 7. pmid:32095235
- View Article
- PubMed/NCBI
- Google Scholar
2. Coon EA, Singer W. Synucleinopathies. Contin Lifelong Learn Neurol. 2020;26: 72. pmid:31996623
- View Article
- PubMed/NCBI
- Google Scholar
3. Gilman S, Wenning GK, Low PA, Brooks DJ, Mathias CJ, Trojanowski JQ, et al. Second consensus statement on the diagnosis of multiple system atrophy. Neurology. 2008;71: 670–676. pmid:18725592
- View Article
- PubMed/NCBI
- Google Scholar
4. Santangelo G, Cuoco S, Pellecchia MT, Erro R, Barone P, Picillo M. Comparative cognitive and neuropsychiatric profiles between Parkinson’s disease, multiple system atrophy and progressive supranuclear palsy. J Neurol. 2018;265: 2602–2613. pmid:30178175
- View Article
- PubMed/NCBI
- Google Scholar
5. Eschlböck S, Delazer M, Krismer F, Bodner T, Fanciulli A, Heim B, et al. Cognition in multiple system atrophy: a single-center cohort study. Ann Clin Transl Neurol. 2020;7: 219–228. pmid:32031752
- View Article
- PubMed/NCBI
- Google Scholar
6. Stankovic I, Krismer F, Jesic A, Antonini A, Benke T, Brown RG, et al. Cognitive impairment in multiple system atrophy: A position statement by the neuropsychology task force of the MDS multiple system atrophy (MODIMSA) study group: COGNITIVE IMPAIRMENT IN MSA. Mov Disord. 2014;29: 857–867. pmid:24753321
- View Article
- PubMed/NCBI
- Google Scholar
7. Koga S, Parks A, Uitti RJ, van Gerpen JA, Cheshire WP, Wszolek ZK, et al. Profile of cognitive impairment and underlying pathology in multiple system atrophy. Mov Disord. 2017;32: 405–413. pmid:27859650
- View Article
- PubMed/NCBI
- Google Scholar
8. Cairns NJ, Atkinson PF, Kovács T, Lees AJ, Daniel SE, Lantos PL. Apolipoprotein E e4 allele frequency in patients with multiple system atrophy. Neurosci Lett. 1997;221: 161–164. pmid:9121689
- View Article
- PubMed/NCBI
- Google Scholar
9. Federoff M, Schottlaender LV, Houlden H, Singleton A. Multiple system atrophy: the application of genetics in understanding etiology. Clin Auton Res. 2015;25: 19–36. pmid:25687905
- View Article
- PubMed/NCBI
- Google Scholar
10. Mirza SS, Saeed U, Knight J, Ramirez J, Stuss DT, Keith J, et al. APOE ε4, white matter hyperintensities, and cognition in Alzheimer and Lewy body dementia. Neurology. 2019;93: e1807–e1819. pmid:31575706
- View Article
- PubMed/NCBI
- Google Scholar
11. Robinson JL, Lee EB, Xie SX, Rennert L, Suh E, Bredenberg C, et al. Neurodegenerative disease concomitant proteinopathies are prevalent, age-related and APOE4-associated. Brain. 2018;141: 2181–2193. pmid:29878075
- View Article
- PubMed/NCBI
- Google Scholar
12. Köllensperger M, Geser F, Ndayisaba J-P, Boesch S, Seppi K, Ostergaard K, et al. Presentation, diagnosis, and management of multiple system atrophy in Europe: Final analysis of the European multiple system atrophy registry. Mov Disord. 2010;25: 2604–2612. pmid:20922810
- View Article
- PubMed/NCBI
- Google Scholar
13. Xia C, Postuma RB. Diagnosing multiple system atrophy at the prodromal stage. Clin Auton Res. 2020;30: 197–205. pmid:32232688
- View Article
- PubMed/NCBI
- Google Scholar
14. Santangelo G, Cuoco S, Picillo M, Erro R, Squillante M, Volpe G, et al. Evolution of neuropsychological profile in motor subtypes of multiple system atrophy. Parkinsonism Relat Disord. 2020;70: 67–73. pmid:31881520
- View Article
- PubMed/NCBI
- Google Scholar
15. Sambati L, Calandra-Buonaura G, Giannini G, Cani I, Provini F, Poda R, et al. Cognitive Profile and Its Evolution in a Cohort of Multiple System Atrophy Patients. Front Neurol. 2020;11: 1138. pmid:33329297
- View Article
- PubMed/NCBI
- Google Scholar
16. Barcelos LB, Saad F, Giacominelli C, Saba RA, de Carvalho Aguiar PM, Silva SMA, et al. Neuropsychological and clinical heterogeneity of cognitive impairment in patients with multiple system atrophy. Clin Neurol Neurosurg. 2018;164: 121–126. pmid:29223069
- View Article
- PubMed/NCBI
- Google Scholar
17. Hatakeyama M, Sato T, Takahashi T, Kanazawa M, Onodera O, Nishizawa M, et al. Predictors of cognitive impairment in multiple system atrophy. J Neurol Sci. 2018;388: 128–132. pmid:29627006
- View Article
- PubMed/NCBI
- Google Scholar
18. Ueda M, Nakamura T, Suzuki M, Imai E, Harada Y, Hara K, et al. Association of orthostatic blood pressure with the symptoms of orthostatic hypotension and cognitive impairment in patients with multiple system atrophy. J Clin Neurosci. 2020;75: 40–44. pmid:32234335
- View Article
- PubMed/NCBI
- Google Scholar
19. Koga S, Li F, Zhao N, Roemer SF, Ferman TJ, Wernick AI, et al. Clinicopathologic and genetic features of multiple system atrophy with Lewy body disease. Brain Pathol. 2020;30: 766–778. pmid:32232888
- View Article
- PubMed/NCBI
- Google Scholar
20. Kitayama M, Wada-Isoe K, Irizawa Y, Nakashima K. Assessment of dementia in patients with multiple system atrophy. Eur J Neurol. 2009;16: 589–594. pmid:19236466
- View Article
- PubMed/NCBI
- Google Scholar
21. Davis AA, Inman CE, Wargel ZM, Dube U, Freeberg BM, Galluppi A, et al. APOE genotype regulates pathology and disease progression in synucleinopathy. Sci Transl Med. 2020;12: eaay3069. pmid:32024799
- View Article
- PubMed/NCBI
- Google Scholar
22. Zhao N, Attrebi ON, Ren Y, Qiao W, Sonustun B, Martens YA, et al. APOE4 exacerbates α-synuclein pathology and related toxicity independent of amyloid. Sci Transl Med. 2020;12: eaay1809. pmid:32024798
- View Article
- PubMed/NCBI
- Google Scholar
23. Vahia VN. Diagnostic and statistical manual of mental disorders 5: A quick glance. Indian J Psychiatry. 2013;55: 220–223. pmid:24082241
- View Article
- PubMed/NCBI
- Google Scholar
24. Jankovic J, McDermott M, Carter J, Gauthier S, Goetz C, Golbe L, et al. Variable expression of Parkinson’s disease: a base-line analysis of the DATATOP cohort. The Parkinson Study Group. Neurology. 1990;40: 1529–1534. pmid:2215943
- View Article
- PubMed/NCBI
- Google Scholar
25. Zhou W, Jiang J, Peng W, Zhou X, Du J, Mo L, et al. Levodopa responsiveness in Parkinson’s disease patients and white matter alterations in diffusion tensor imaging: a cross-sectional tract-based spatial statistics study. Neuroreport. 2021;32: 636–642. pmid:33850092
- View Article
- PubMed/NCBI
- Google Scholar
26. Ben Jemaa S, Attia Romdhane N, Bahri-Mrabet A, Jendli A, Le Gall D, Bellaj T. An Arabic Version of the Cognitive Subscale of the Alzheimer’s Disease Assessment Scale (ADAS-Cog): Reliability, Validity, and Normative Data. J Alzheimers Dis JAD. 2017;60: 11–21. pmid:28505978
- View Article
- PubMed/NCBI
- Google Scholar
27. Dubois B, Slachevsky A, Litvan I, Pillon B. The FAB: a Frontal Assessment Battery at bedside. Neurology. 2000;55: 1621–1626. pmid:11113214
- View Article
- PubMed/NCBI
- Google Scholar
28. Grober E, Buschke H. Genuine memory deficits in dementia. Dev Neuropsychol. 1987;3: 13–36.
- View Article
- Google Scholar
29. Upton J. Beck Depression Inventory (BDI). In: Gellman MD, Turner JR, editors. Encyclopedia of Behavioral Medicine. New York, NY: Springer; 2013. pp. 178–179.
30. Yesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey M, et al. Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res. 1982;17: 37–49. pmid:7183759
- View Article
- PubMed/NCBI
- Google Scholar
31. Cummings JL, Mega M, Gray K, Rosenberg-Thompson S, Carusi DA, Gornbein J. The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology. 1994;44: 2308–2314. pmid:7991117
- View Article
- PubMed/NCBI
- Google Scholar
32. Sachdev PS, Blacker D, Blazer DG, Ganguli M, Jeste DV, Paulsen JS, et al. Classifying neurocognitive disorders: the DSM-5 approach. Nat Rev Neurol. 2014;10: 634–642. pmid:25266297
- View Article
- PubMed/NCBI
- Google Scholar
33. Ewing DJ, Campbell IW, Clarke BF. Assessment of Cardiovascular Effects in Diabetic Autonomic Neuropathy and Prognostic Implications. Ann Intern Med. 1980;92: 308–311. pmid:7356219
- View Article
- PubMed/NCBI
- Google Scholar
34. Nasri A, Ben Djebara M, Sghaier I, Mrabet S, Zidi S, Gargouri A, et al. Atypical parkinsonian syndromes in a North African tertiary referral center. Brain Behav. 2021;11: e01924. pmid:33179436
- View Article
- PubMed/NCBI
- Google Scholar
35. Perez‐Lloret S, Flabeau O, Fernagut P, Pavy‐Le Traon A, Rey MV, Foubert‐Samier A, et al. Current Concepts in the Treatment of Multiple System Atrophy. Mov Disord Clin Pract. 2015;2: 6–16. pmid:30363880
- View Article
- PubMed/NCBI
- Google Scholar
36. Hughes AJ, Colosimo C, Kleedorfer B, Daniel SE, Lees AJ. The dopaminergic response in multiple system atrophy. J Neurol Neurosurg Psychiatry. 1992;55: 1009–1013. pmid:1469394
- View Article
- PubMed/NCBI
- Google Scholar
37. Parati EA, Fetoni V, Geminiani GC, Soliveri P, Giovannini P, Testa D, et al. Response to L-DOPA in multiple system atrophy. Clin Neuropharmacol. 1993;16: 139–144. pmid:8477409
- View Article
- PubMed/NCBI
- Google Scholar
38. Colosimo C, Albanese A, Hughes AJ, de Bruin VMS, Lees AJ. Some Specific Clinical Features Differentiate Multiple System Atrophy (Striatonigral Variety) From Parkinson’s Disease. Arch Neurol. 1995;52: 294–298. pmid:7872884
- View Article
- PubMed/NCBI
- Google Scholar
39. Jm Fearnley, Aj Lees. Striatonigral Degeneration: A Clinicopathological Study. Brain. 1990;113: 1823–1842. pmid:2276046
- View Article
- PubMed/NCBI
- Google Scholar
40. Rajput AH, Rozdilsky B, Rajput A, Ang L. Levodopa Efficacy and Pathological Basis of Parkinson Syndrome: Clin Neuropharmacol. 1990;13: 553–558. pmid:2276120
- View Article
- PubMed/NCBI
- Google Scholar
41. Wenning GK, Tison F, ben Shlomo Y, Daniel SE, Quinn NP. Multiple system atrophy: A review of 203 pathologically proven cases. Mov Disord. 1997;12: 133–147. pmid:9087971
- View Article
- PubMed/NCBI
- Google Scholar
42. Wenning GK, Shlomo YB, Magalhães M, Danie SE, Quinn NP. Clinical features and natural history of multiple system atrophy: An analysis of 100 cases. Brain. 1994;117: 835–845. pmid:7922469
- View Article
- PubMed/NCBI
- Google Scholar
43. Vasta R, Nicoletti A, Mostile G, Dibilio V, Sciacca G, Contrafatto D, et al. Side effects induced by the acute levodopa challenge in Parkinson’s Disease and atypical parkinsonisms. PLOS ONE. 2017;12: e0172145. pmid:28207803
- View Article
- PubMed/NCBI
- Google Scholar
44. Fiorenzato E, Antonini A, Wenning G, Biundo R. Cognitive impairment in multiple system atrophy. Mov Disord. 2017;32: 1338–1339. pmid:28671311
- View Article
- PubMed/NCBI
- Google Scholar
45. Caso F, Canu E, Lukic MJ, Petrovic IN, Fontana A, Nikolic I, et al. Cognitive impairment and structural brain damage in multiple system atrophy-parkinsonian variant. J Neurol. 2020;267: 87–94. pmid:31559533
- View Article
- PubMed/NCBI
- Google Scholar
46. Kim H-J, Jeon BS, Kim YE, Kim J-Y, Kim YK, Sohn C-H, et al. Clinical and imaging characteristics of dementia in multiple system atrophy. Parkinsonism Relat Disord. 2013;19: 617–621. pmid:23529023
- View Article
- PubMed/NCBI
- Google Scholar
47. Cognitive Impairments in Multiple System Atrophy of the Cerebellar Type. J Mov Disord. [cited 5 Oct 2021]. pmid:24868391
- View Article
- PubMed/NCBI
- Google Scholar
48. Kawai Y, Suenaga M, Takeda A, Ito M, Watanabe H, Tanaka F, et al. Cognitive impairments in multiple system atrophy: MSA-C vs MSA-P. Neurology. 2008;70: 1390–1396. pmid:18413566
- View Article
- PubMed/NCBI
- Google Scholar
49. Siri C, Duerr S, Canesi M, Delazer M, Esselink R, Bloem BR, et al. A cross-sectional multicenter study of cognitive and behavioural features in multiple system atrophy patients of the parkinsonian and cerebellar type. J Neural Transm. 2013;120: 613–618. pmid:23462799
- View Article
- PubMed/NCBI
- Google Scholar
50. Cao B, Zhao B, Wei Q-Q, Chen K, Yang J, Ou R, et al. The Global Cognition, Frontal Lobe Dysfunction and Behavior Changes in Chinese Patients with Multiple System Atrophy. PLOS ONE. 2015;10: e0139773. pmid:26431430
- View Article
- PubMed/NCBI
- Google Scholar
51. Bürk K, Daum I, Rüb U. Cognitive function in multiple system atrophy of the cerebellar type. Mov Disord. 2006;21: 772–776. pmid:16475154
- View Article
- PubMed/NCBI
- Google Scholar
52. Gilman S, Low PA, Quinn N, Albanese A, Ben-Shlomo Y, Fowler CJ, et al. Consensus statement on the diagnosis of multiple system atrophy. J Neurol Sci. 1999;163: 94–98. pmid:10223419
- View Article
- PubMed/NCBI
- Google Scholar
53. Watanabe H, Riku Y, Hara K, Kawabata K, Nakamura T, Ito M, et al. Clinical and Imaging Features of Multiple System Atrophy: Challenges for an Early and Clinically Definitive Diagnosis. J Mov Disord. 2018;11: 107–120. pmid:30086614
- View Article
- PubMed/NCBI
- Google Scholar
54. Cuoco S, Picillo M, Cappiello A, Carotenuto I, Erro R, Russillo MC, et al. Effects of gender on cognitive and behavioral manifestations in multiple system atrophy. J Neural Transm. 2020;127: 925–934. pmid:32172473
- View Article
- PubMed/NCBI
- Google Scholar
55. Balas M, Balash Y, Giladi N, Gurevich T. Cognition in multiple system atrophy: neuropsychological profile and interaction with mood. J Neural Transm. 2010;117: 369–375. pmid:20091064
- View Article
- PubMed/NCBI
- Google Scholar
56. Kawahara Y, Ikeda Y, Deguchi K, Kurata T, Hishikawa N, Sato K, et al. Simultaneous assessment of cognitive and affective functions in multiple system atrophy and cortical cerebellar atrophy in relation to computerized touch-panel screening tests. J Neurol Sci. 2015;351: 24–30. pmid:25727351
- View Article
- PubMed/NCBI
- Google Scholar
57. Stankovic I, Krismer F, Jesic A, Antonini A, Benke T, Brown RG, et al. Cognitive impairment in multiple system atrophy: A position statement by the neuropsychology task force of the MDS multiple system atrophy (MODIMSA) study group. Mov Disord. 2014;29: 857–867. pmid:24753321
- View Article
- PubMed/NCBI
- Google Scholar
58. Wakabayashi K, Yoshimoto M, Tsuji S, Takahashi H. α-Synuclein immunoreactivity in glial cytoplasmic inclusions in multiple system atrophy. Neurosci Lett. 1998;249: 180–182.
- View Article
- Google Scholar
59. Armstrong RA, Cairns NJ, Lantos PL. A quantitative study of the pathological changes in white matter in multiple system atrophy. Neuropathol Off J Jpn Soc Neuropathol. 2007;27: 221–227. pmid:17645235
- View Article
- PubMed/NCBI
- Google Scholar
60. Bender AR, Raz N. Age-related differences in memory and executive functions in healthy APOE ɛ4 carriers: The contribution of individual differences in prefrontal volumes and systolic blood pressure. Neuropsychologia. 2012;50: 704–714. pmid:22245009
- View Article
- PubMed/NCBI
- Google Scholar
61. Xu P-T, Gilbert JR, Qiu H-L, Ervin J, Rothrock-Christian TR, Hulette C, et al. Specific Regional Transcription of Apolipoprotein E in Human Brain Neurons. Am J Pathol. 1999;154: 601–611. pmid:10027417
- View Article
- PubMed/NCBI
- Google Scholar
62. Clatworthy AE, Stockinger W, Christie RH, Schneider WJ, Nimpf J, Hyman BT, et al. Expression and alternate splicing of apolipoprotein E receptor 2 in brain. Neuroscience. 1999;90: 903–911. pmid:10218790
- View Article
- PubMed/NCBI
- Google Scholar
63. Wenning GK, Stankovic I, Vignatelli L, Fanciulli A, Calandra-Buonaura G, Seppi K, et al. The Movement Disorder Society Criteria for the Diagnosis of Multiple System Atrophy. Mov Disord. 2022;37: 1131–1148. pmid:35445419
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Kaji S, Maki T, Ishimoto T, Yamakado H, Takahashi R. Insights into the pathogenesis of multiple system atrophy: focus on glial cytoplasmic inclusions. Transl Neurodegener. 2020;9: 7. pmid:32095235
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Coon EA, Singer W. Synucleinopathies. Contin Lifelong Learn Neurol. 2020;26: 72. pmid:31996623
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Gilman S, Wenning GK, Low PA, Brooks DJ, Mathias CJ, Trojanowski JQ, et al. Second consensus statement on the diagnosis of multiple system atrophy. Neurology. 2008;71: 670–676. pmid:18725592
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Santangelo G, Cuoco S, Pellecchia MT, Erro R, Barone P, Picillo M. Comparative cognitive and neuropsychiatric profiles between Parkinson’s disease, multiple system atrophy and progressive supranuclear palsy. J Neurol. 2018;265: 2602–2613. pmid:30178175
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Eschlböck S, Delazer M, Krismer F, Bodner T, Fanciulli A, Heim B, et al. Cognition in multiple system atrophy: a single-center cohort study. Ann Clin Transl Neurol. 2020;7: 219–228. pmid:32031752
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Stankovic I, Krismer F, Jesic A, Antonini A, Benke T, Brown RG, et al. Cognitive impairment in multiple system atrophy: A position statement by the neuropsychology task force of the MDS multiple system atrophy (MODIMSA) study group: COGNITIVE IMPAIRMENT IN MSA. Mov Disord. 2014;29: 857–867. pmid:24753321
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Koga S, Parks A, Uitti RJ, van Gerpen JA, Cheshire WP, Wszolek ZK, et al. Profile of cognitive impairment and underlying pathology in multiple system atrophy. Mov Disord. 2017;32: 405–413. pmid:27859650
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Cairns NJ, Atkinson PF, Kovács T, Lees AJ, Daniel SE, Lantos PL. Apolipoprotein E e4 allele frequency in patients with multiple system atrophy. Neurosci Lett. 1997;221: 161–164. pmid:9121689
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Federoff M, Schottlaender LV, Houlden H, Singleton A. Multiple system atrophy: the application of genetics in understanding etiology. Clin Auton Res. 2015;25: 19–36. pmid:25687905
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref10] 10. Mirza SS, Saeed U, Knight J, Ramirez J, Stuss DT, Keith J, et al. APOE ε4, white matter hyperintensities, and cognition in Alzheimer and Lewy body dementia. Neurology. 2019;93: e1807–e1819. pmid:31575706
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref11] 11. Robinson JL, Lee EB, Xie SX, Rennert L, Suh E, Bredenberg C, et al. Neurodegenerative disease concomitant proteinopathies are prevalent, age-related and APOE4-associated. Brain. 2018;141: 2181–2193. pmid:29878075
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref12] 12. Köllensperger M, Geser F, Ndayisaba J-P, Boesch S, Seppi K, Ostergaard K, et al. Presentation, diagnosis, and management of multiple system atrophy in Europe: Final analysis of the European multiple system atrophy registry. Mov Disord. 2010;25: 2604–2612. pmid:20922810
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref13] 13. Xia C, Postuma RB. Diagnosing multiple system atrophy at the prodromal stage. Clin Auton Res. 2020;30: 197–205. pmid:32232688
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref14] 14. Santangelo G, Cuoco S, Picillo M, Erro R, Squillante M, Volpe G, et al. Evolution of neuropsychological profile in motor subtypes of multiple system atrophy. Parkinsonism Relat Disord. 2020;70: 67–73. pmid:31881520
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref15] 15. Sambati L, Calandra-Buonaura G, Giannini G, Cani I, Provini F, Poda R, et al. Cognitive Profile and Its Evolution in a Cohort of Multiple System Atrophy Patients. Front Neurol. 2020;11: 1138. pmid:33329297
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref16] 16. Barcelos LB, Saad F, Giacominelli C, Saba RA, de Carvalho Aguiar PM, Silva SMA, et al. Neuropsychological and clinical heterogeneity of cognitive impairment in patients with multiple system atrophy. Clin Neurol Neurosurg. 2018;164: 121–126. pmid:29223069
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref17] 17. Hatakeyama M, Sato T, Takahashi T, Kanazawa M, Onodera O, Nishizawa M, et al. Predictors of cognitive impairment in multiple system atrophy. J Neurol Sci. 2018;388: 128–132. pmid:29627006
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref18] 18. Ueda M, Nakamura T, Suzuki M, Imai E, Harada Y, Hara K, et al. Association of orthostatic blood pressure with the symptoms of orthostatic hypotension and cognitive impairment in patients with multiple system atrophy. J Clin Neurosci. 2020;75: 40–44. pmid:32234335
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref19] 19. Koga S, Li F, Zhao N, Roemer SF, Ferman TJ, Wernick AI, et al. Clinicopathologic and genetic features of multiple system atrophy with Lewy body disease. Brain Pathol. 2020;30: 766–778. pmid:32232888
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref20] 20. Kitayama M, Wada-Isoe K, Irizawa Y, Nakashima K. Assessment of dementia in patients with multiple system atrophy. Eur J Neurol. 2009;16: 589–594. pmid:19236466
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref21] 21. Davis AA, Inman CE, Wargel ZM, Dube U, Freeberg BM, Galluppi A, et al. APOE genotype regulates pathology and disease progression in synucleinopathy. Sci Transl Med. 2020;12: eaay3069. pmid:32024799
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref22] 22. Zhao N, Attrebi ON, Ren Y, Qiao W, Sonustun B, Martens YA, et al. APOE4 exacerbates α-synuclein pathology and related toxicity independent of amyloid. Sci Transl Med. 2020;12: eaay1809. pmid:32024798
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref23] 23. Vahia VN. Diagnostic and statistical manual of mental disorders 5: A quick glance. Indian J Psychiatry. 2013;55: 220–223. pmid:24082241
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref24] 24. Jankovic J, McDermott M, Carter J, Gauthier S, Goetz C, Golbe L, et al. Variable expression of Parkinson’s disease: a base-line analysis of the DATATOP cohort. The Parkinson Study Group. Neurology. 1990;40: 1529–1534. pmid:2215943
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref25] 25. Zhou W, Jiang J, Peng W, Zhou X, Du J, Mo L, et al. Levodopa responsiveness in Parkinson’s disease patients and white matter alterations in diffusion tensor imaging: a cross-sectional tract-based spatial statistics study. Neuroreport. 2021;32: 636–642. pmid:33850092
View Article
PubMed/NCBI
Google Scholar

[98] View Article

[99] PubMed/NCBI

[100] Google Scholar

[ref26] 26. Ben Jemaa S, Attia Romdhane N, Bahri-Mrabet A, Jendli A, Le Gall D, Bellaj T. An Arabic Version of the Cognitive Subscale of the Alzheimer’s Disease Assessment Scale (ADAS-Cog): Reliability, Validity, and Normative Data. J Alzheimers Dis JAD. 2017;60: 11–21. pmid:28505978
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref27] 27. Dubois B, Slachevsky A, Litvan I, Pillon B. The FAB: a Frontal Assessment Battery at bedside. Neurology. 2000;55: 1621–1626. pmid:11113214
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref28] 28. Grober E, Buschke H. Genuine memory deficits in dementia. Dev Neuropsychol. 1987;3: 13–36.
View Article
Google Scholar

[110] View Article

[111] Google Scholar

[ref29] 29. Upton J. Beck Depression Inventory (BDI). In: Gellman MD, Turner JR, editors. Encyclopedia of Behavioral Medicine. New York, NY: Springer; 2013. pp. 178–179.

[ref30] 30. Yesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey M, et al. Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res. 1982;17: 37–49. pmid:7183759
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref31] 31. Cummings JL, Mega M, Gray K, Rosenberg-Thompson S, Carusi DA, Gornbein J. The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology. 1994;44: 2308–2314. pmid:7991117
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref32] 32. Sachdev PS, Blacker D, Blazer DG, Ganguli M, Jeste DV, Paulsen JS, et al. Classifying neurocognitive disorders: the DSM-5 approach. Nat Rev Neurol. 2014;10: 634–642. pmid:25266297
View Article
PubMed/NCBI
Google Scholar

[122] View Article

[123] PubMed/NCBI

[124] Google Scholar

[ref33] 33. Ewing DJ, Campbell IW, Clarke BF. Assessment of Cardiovascular Effects in Diabetic Autonomic Neuropathy and Prognostic Implications. Ann Intern Med. 1980;92: 308–311. pmid:7356219
View Article
PubMed/NCBI
Google Scholar

[126] View Article

[127] PubMed/NCBI

[128] Google Scholar

[ref34] 34. Nasri A, Ben Djebara M, Sghaier I, Mrabet S, Zidi S, Gargouri A, et al. Atypical parkinsonian syndromes in a North African tertiary referral center. Brain Behav. 2021;11: e01924. pmid:33179436
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref35] 35. Perez‐Lloret S, Flabeau O, Fernagut P, Pavy‐Le Traon A, Rey MV, Foubert‐Samier A, et al. Current Concepts in the Treatment of Multiple System Atrophy. Mov Disord Clin Pract. 2015;2: 6–16. pmid:30363880
View Article
PubMed/NCBI
Google Scholar

[134] View Article

[135] PubMed/NCBI

[136] Google Scholar

[ref36] 36. Hughes AJ, Colosimo C, Kleedorfer B, Daniel SE, Lees AJ. The dopaminergic response in multiple system atrophy. J Neurol Neurosurg Psychiatry. 1992;55: 1009–1013. pmid:1469394
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref37] 37. Parati EA, Fetoni V, Geminiani GC, Soliveri P, Giovannini P, Testa D, et al. Response to L-DOPA in multiple system atrophy. Clin Neuropharmacol. 1993;16: 139–144. pmid:8477409
View Article
PubMed/NCBI
Google Scholar

[142] View Article

[143] PubMed/NCBI

[144] Google Scholar

[ref38] 38. Colosimo C, Albanese A, Hughes AJ, de Bruin VMS, Lees AJ. Some Specific Clinical Features Differentiate Multiple System Atrophy (Striatonigral Variety) From Parkinson’s Disease. Arch Neurol. 1995;52: 294–298. pmid:7872884
View Article
PubMed/NCBI
Google Scholar

[146] View Article

[147] PubMed/NCBI

[148] Google Scholar

[ref39] 39. Jm Fearnley, Aj Lees. Striatonigral Degeneration: A Clinicopathological Study. Brain. 1990;113: 1823–1842. pmid:2276046
View Article
PubMed/NCBI
Google Scholar

[150] View Article

[151] PubMed/NCBI

[152] Google Scholar

[ref40] 40. Rajput AH, Rozdilsky B, Rajput A, Ang L. Levodopa Efficacy and Pathological Basis of Parkinson Syndrome: Clin Neuropharmacol. 1990;13: 553–558. pmid:2276120
View Article
PubMed/NCBI
Google Scholar

[154] View Article

[155] PubMed/NCBI

[156] Google Scholar

[ref41] 41. Wenning GK, Tison F, ben Shlomo Y, Daniel SE, Quinn NP. Multiple system atrophy: A review of 203 pathologically proven cases. Mov Disord. 1997;12: 133–147. pmid:9087971
View Article
PubMed/NCBI
Google Scholar

[158] View Article

[159] PubMed/NCBI

[160] Google Scholar

[ref42] 42. Wenning GK, Shlomo YB, Magalhães M, Danie SE, Quinn NP. Clinical features and natural history of multiple system atrophy: An analysis of 100 cases. Brain. 1994;117: 835–845. pmid:7922469
View Article
PubMed/NCBI
Google Scholar

[162] View Article

[163] PubMed/NCBI

[164] Google Scholar

[ref43] 43. Vasta R, Nicoletti A, Mostile G, Dibilio V, Sciacca G, Contrafatto D, et al. Side effects induced by the acute levodopa challenge in Parkinson’s Disease and atypical parkinsonisms. PLOS ONE. 2017;12: e0172145. pmid:28207803
View Article
PubMed/NCBI
Google Scholar

[166] View Article

[167] PubMed/NCBI

[168] Google Scholar

[ref44] 44. Fiorenzato E, Antonini A, Wenning G, Biundo R. Cognitive impairment in multiple system atrophy. Mov Disord. 2017;32: 1338–1339. pmid:28671311
View Article
PubMed/NCBI
Google Scholar

[170] View Article

[171] PubMed/NCBI

[172] Google Scholar

[ref45] 45. Caso F, Canu E, Lukic MJ, Petrovic IN, Fontana A, Nikolic I, et al. Cognitive impairment and structural brain damage in multiple system atrophy-parkinsonian variant. J Neurol. 2020;267: 87–94. pmid:31559533
View Article
PubMed/NCBI
Google Scholar

[174] View Article

[175] PubMed/NCBI

[176] Google Scholar

[ref46] 46. Kim H-J, Jeon BS, Kim YE, Kim J-Y, Kim YK, Sohn C-H, et al. Clinical and imaging characteristics of dementia in multiple system atrophy. Parkinsonism Relat Disord. 2013;19: 617–621. pmid:23529023
View Article
PubMed/NCBI
Google Scholar

[178] View Article

[179] PubMed/NCBI

[180] Google Scholar

[ref47] 47. Cognitive Impairments in Multiple System Atrophy of the Cerebellar Type. J Mov Disord. [cited 5 Oct 2021]. pmid:24868391
View Article
PubMed/NCBI
Google Scholar

[182] View Article

[183] PubMed/NCBI

[184] Google Scholar

[ref48] 48. Kawai Y, Suenaga M, Takeda A, Ito M, Watanabe H, Tanaka F, et al. Cognitive impairments in multiple system atrophy: MSA-C vs MSA-P. Neurology. 2008;70: 1390–1396. pmid:18413566
View Article
PubMed/NCBI
Google Scholar

[186] View Article

[187] PubMed/NCBI

[188] Google Scholar

[ref49] 49. Siri C, Duerr S, Canesi M, Delazer M, Esselink R, Bloem BR, et al. A cross-sectional multicenter study of cognitive and behavioural features in multiple system atrophy patients of the parkinsonian and cerebellar type. J Neural Transm. 2013;120: 613–618. pmid:23462799
View Article
PubMed/NCBI
Google Scholar

[190] View Article

[191] PubMed/NCBI

[192] Google Scholar

[ref50] 50. Cao B, Zhao B, Wei Q-Q, Chen K, Yang J, Ou R, et al. The Global Cognition, Frontal Lobe Dysfunction and Behavior Changes in Chinese Patients with Multiple System Atrophy. PLOS ONE. 2015;10: e0139773. pmid:26431430
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref51] 51. Bürk K, Daum I, Rüb U. Cognitive function in multiple system atrophy of the cerebellar type. Mov Disord. 2006;21: 772–776. pmid:16475154
View Article
PubMed/NCBI
Google Scholar

[198] View Article

[199] PubMed/NCBI

[200] Google Scholar

[ref52] 52. Gilman S, Low PA, Quinn N, Albanese A, Ben-Shlomo Y, Fowler CJ, et al. Consensus statement on the diagnosis of multiple system atrophy. J Neurol Sci. 1999;163: 94–98. pmid:10223419
View Article
PubMed/NCBI
Google Scholar

[202] View Article

[203] PubMed/NCBI

[204] Google Scholar

[ref53] 53. Watanabe H, Riku Y, Hara K, Kawabata K, Nakamura T, Ito M, et al. Clinical and Imaging Features of Multiple System Atrophy: Challenges for an Early and Clinically Definitive Diagnosis. J Mov Disord. 2018;11: 107–120. pmid:30086614
View Article
PubMed/NCBI
Google Scholar

[206] View Article

[207] PubMed/NCBI

[208] Google Scholar

[ref54] 54. Cuoco S, Picillo M, Cappiello A, Carotenuto I, Erro R, Russillo MC, et al. Effects of gender on cognitive and behavioral manifestations in multiple system atrophy. J Neural Transm. 2020;127: 925–934. pmid:32172473
View Article
PubMed/NCBI
Google Scholar

[210] View Article

[211] PubMed/NCBI

[212] Google Scholar

[ref55] 55. Balas M, Balash Y, Giladi N, Gurevich T. Cognition in multiple system atrophy: neuropsychological profile and interaction with mood. J Neural Transm. 2010;117: 369–375. pmid:20091064
View Article
PubMed/NCBI
Google Scholar

[214] View Article

[215] PubMed/NCBI

[216] Google Scholar

[ref56] 56. Kawahara Y, Ikeda Y, Deguchi K, Kurata T, Hishikawa N, Sato K, et al. Simultaneous assessment of cognitive and affective functions in multiple system atrophy and cortical cerebellar atrophy in relation to computerized touch-panel screening tests. J Neurol Sci. 2015;351: 24–30. pmid:25727351
View Article
PubMed/NCBI
Google Scholar

[218] View Article

[219] PubMed/NCBI

[220] Google Scholar

[ref57] 57. Stankovic I, Krismer F, Jesic A, Antonini A, Benke T, Brown RG, et al. Cognitive impairment in multiple system atrophy: A position statement by the neuropsychology task force of the MDS multiple system atrophy (MODIMSA) study group. Mov Disord. 2014;29: 857–867. pmid:24753321
View Article
PubMed/NCBI
Google Scholar

[222] View Article

[223] PubMed/NCBI

[224] Google Scholar

[ref58] 58. Wakabayashi K, Yoshimoto M, Tsuji S, Takahashi H. α-Synuclein immunoreactivity in glial cytoplasmic inclusions in multiple system atrophy. Neurosci Lett. 1998;249: 180–182.
View Article
Google Scholar

[226] View Article

[227] Google Scholar

[ref59] 59. Armstrong RA, Cairns NJ, Lantos PL. A quantitative study of the pathological changes in white matter in multiple system atrophy. Neuropathol Off J Jpn Soc Neuropathol. 2007;27: 221–227. pmid:17645235
View Article
PubMed/NCBI
Google Scholar

[229] View Article

[230] PubMed/NCBI

[231] Google Scholar

[ref60] 60. Bender AR, Raz N. Age-related differences in memory and executive functions in healthy APOE ɛ4 carriers: The contribution of individual differences in prefrontal volumes and systolic blood pressure. Neuropsychologia. 2012;50: 704–714. pmid:22245009
View Article
PubMed/NCBI
Google Scholar

[233] View Article

[234] PubMed/NCBI

[235] Google Scholar

[ref61] 61. Xu P-T, Gilbert JR, Qiu H-L, Ervin J, Rothrock-Christian TR, Hulette C, et al. Specific Regional Transcription of Apolipoprotein E in Human Brain Neurons. Am J Pathol. 1999;154: 601–611. pmid:10027417
View Article
PubMed/NCBI
Google Scholar

[237] View Article

[238] PubMed/NCBI

[239] Google Scholar

[ref62] 62. Clatworthy AE, Stockinger W, Christie RH, Schneider WJ, Nimpf J, Hyman BT, et al. Expression and alternate splicing of apolipoprotein E receptor 2 in brain. Neuroscience. 1999;90: 903–911. pmid:10218790
View Article
PubMed/NCBI
Google Scholar

[241] View Article

[242] PubMed/NCBI

[243] Google Scholar

[ref63] 63. Wenning GK, Stankovic I, Vignatelli L, Fanciulli A, Calandra-Buonaura G, Seppi K, et al. The Movement Disorder Society Criteria for the Diagnosis of Multiple System Atrophy. Mov Disord. 2022;37: 1131–1148. pmid:35445419
View Article
PubMed/NCBI
Google Scholar

[245] View Article

[246] PubMed/NCBI

[247] Google Scholar

Figures

Abstract

Introduction

Methods

Results

Conclusion

Introduction

Material and methods

1. Study subjects

2. Clinical and neuropsychological assessment

3. Neurophysiological autonomic testing

4. Genetic study

5. Statistical analysis

5. Ethics

Results

1. Clinical and paraclinical findings in MSA patients

2. APOE genotype in MSA patients

3. Cognitive profile of MSA patients

3. Determinants of cognitive impairment in MSA patients

3.1. Clinical and paraclinical determinants of cognitive impairment in MSA.

3.2. ApoEɛ4 effect on cognitive profile in MSA.

Discussion

Conclusion

Supporting information

S1 Table. Comparison of SSR and Neurophysiological cardiovascular autonomic testing in MSA patients with and without dementia.

S2 Table. Comparison of demographic, clinical and cerebral imaging data in MSA patients according to cognitive impairment level (without, mild and major).

Acknowledgments

References