Motor Symptoms in Early- versus Late-Onset Alzheimer’s Disease

Abstract

Background:

Age at onset was suggested as one possible risk factor for motor dysfunction in Alzheimer’s disease (AD).

Objective:

We investigated the association of motor symptoms with cognition or neurodegeneration in patients with AD, and whether this association differs by the age at onset.

Methods:

We included 113 amyloid positive AD patients and divided them into early-onset AD (EOAD) and late-onset AD (LOAD), who underwent the Unified Parkinson’s Disease Rating Scale (UPDRS)-Part III (=UPDRS) scoring, Mini-Mental State Examination (MMSE)/Clinical Deterioration Rating Sum-of-Boxes (CDR-SOB), and magnetic resonance image (MRI). Multiple linear regression was used to evaluate the association of UPDRS and MMSE/CDR-SOB or MRI neurodegeneration measures, and whether the association differs according to the group.

Results:

The prevalence of motor symptoms and their severity did not differ between the groups. Lower MMSE (β= –1.1, p < 0.001) and higher CDR-SOB (β= 2.0, p < 0.001) were significantly associated with higher UPDRS. There was no interaction effect between MMSE/CDR-SOB and AD group on UPDRS. Global or all regional cortical thickness and putaminal volume were negatively associated with UPDRS score, but the interaction effect of neurodegeneration and AD group on UPDRS score was significant only in parietal lobe (p for interaction = 0.035), which showed EOAD to have a more pronounced association between parietal thinning and motor symptoms.

Conclusion:

Our study suggested that the severity of motor deterioration in AD is related to the severity of cognitive impairment itself rather than age at onset, and motor symptoms might occur through multiple mechanisms including cortical and subcortical atrophy.

Keywords

Alzheimer’s disease neuropsychological tests parkinsonian disorders

INTRODUCTION

Alzheimer’s disease (AD) is the most common cause of dementia in the older population, which is characterized by progressive neurodegeneration and cognitive impairment. Although not regarded as a typical presenting symptom, motor dysfunction including gait disturbances and extrapyramidal signs commonly occur in AD, at prevalence rates ranging from 12 to 92% [1, 2]. The underlying pathomechanism of motor dysfunction in AD remains to be elucidated, with conflicting reports on neuronal loss in substantia nigra (SN) [3 –8], presynaptic dopaminergic deficit on in vivo molecular imaging targeting dopaminergic transporters (DAT), or emerging evidence supporting neurotransmitter abnormalities [9 –13]. Irrespective of the underlying pathomechanism, many studies have reported that motor symptoms contribute to cognitive and functional decline, institutionalization, and mortality in AD [14 –17]. Therefore, understanding the prevalence and risk factors for the occurrence of motor symptoms during the clinical course might be crucial for planning future management and predicting prognosis.

Among multiple possible risk factors for motor dysfunction, age at onset has been suggested as one in AD [18, 19]. AD has been divided into two groups according to the cut-off age at onset of 65 years [20]: early-onset AD (EOAD) and late-onset AD (LOAD). Numerous studies have shown some differences in the clinical manifestations and neuroimaging characteristics between the two [21 –27]; EOAD tends to show more frequent atypical presentations such as prominent apraxia, language problems, or executive dysfunction, whereas LOAD typically presents with memory impairment. Consistent with these clinical features, neuroimaging studies also have demonstrated that EOAD showed more widespread neocortical atrophy with more rapid cortical thinning compared to LOAD, which typically shows pronounced hippocampal atrophy [21–26 , 28]. Furthermore, a previous study from our group also has shown EOAD to have a more rapid volumetric decline in the caudate, putamen, and thalamus than LOAD does [29]. Given this clinical and imaging distinction between EOAD and LOAD, we considered that the age at onset may differentially affect motor dysfunction in AD, although there is little published information on comparison of motor symptoms between EOAD and LOAD [18].

In this study, we compared the motor symptoms of EOAD and LOAD patients as per their clinical stages and investigated the association between their motor symptoms and magnetic resonance imaging (MRI) markers of neurodegeneration. We conducted amyloid positron emission tomography (PET) to confirm AD pathology in clinically diagnosed AD patients. We hypothesized that patients with EOAD had more prominent motor impairment than patients with LOAD did, even at the same clinical stage, given the frequent atypical presentations in EOAD, and that their widespread atrophy and hypometabolism of brain could contribute to their motor deterioration.

METHODS

Participants

We prospectively recruited 113 patients with dementia due to AD (79 EOAD, 34 LOAD) who underwent the amyloid PET scans, from the memory clinic at Samsung Medical Center between October 2016 and November 2017. The enrolled individuals underwent detailed history investigating their cognition and daily functioning, and also received a comprehensive neuropsychological assessment, as described previously [30]. All patients met the probable AD proposed by the National Institute on Aging-Alzheimer’s Association workgroups [31]. All patients included in this study were confirmed to have amyloid-β peptide (Aβ) positivity on amyloid PET scans.

All patients underwent laboratory tests including complete blood count, blood chemistry, vitamin B12, folate, syphilis serology, and thyroid function tests to rule out the possibility of secondary causes of cognitive deficits. All patients underwent brain MRI to exclude other structural lesions that can cause motor symptoms, such as intracranial hemorrhage, territorial infarction, brain tumor, hydrocephalus, or significant white matter hyperintensities (WMH), defined as periventricular WMH at least 10 mm and deep WMH at lease 25 mm on MRI. We also excluded patients who had been taking any antipsychotics at the time of the study, those with neurodegenerative disorders other than AD including frontotemporal dementia, dementia associated with cortical basal degeneration, progressive supranuclear palsy, idiopathic Parkinson’s disease, diffuse Lewy body disease, or those with hallucinations, rapid eye movement sleep behavior disorder at any time prior to the onset of cognitive impairment. This study was approved by the Institutional Review Board of Samsung Medical Center, and written informed consent was obtained from all patients and their study partners.

Amyloid PET acquisition and Aβ positivity

All participants underwent amyloid PET scans as follows: 89 ¹⁸F-florbetaben PET and 24 ¹⁸F-flutemetamol PET scans at Samsung Medical Center using a Discovery STe PET/CT scanner (GE Medical Systems, Milwaukee, WI) or a Biograph mCT PET/CT scanner (Siemens Medical Solutions, Malvern, PA) in three-dimensional (3D) scanning mode that examined 35 slices of 4.25 mm thickness spanning the entire brain.

For ¹⁸F-florbetaben PET and ¹⁸F-flutemetamol PET, a 20-min emission PET scan with dynamic mode (consisting of 4×5 min frames) was performed 90 min after injecting a bolus mean dose of 318 MBq ¹⁸F-florbetaben or 185 MBq ¹⁸F-flutemetamol, respectively.

Two experienced clinicians (one nuclear medicine physician and one neurologist) who were blind to the clinical information assessed the amyloid PET images and dichotomized them as either Aβ-positive (A+) or Aβ-negative (A–). We classified ¹⁸F-florbetaben PET as positive if the amyloid plaque load on the scans scored two or three based on the plaque load scoring system. In addition, ¹⁸F-flutemetamol PET scans were regarded as positive when one of the five brain regions (frontal, parietal, posterior cingulate and precuneus, striatum, and lateral temporal lobes) was positive in either hemisphere [32]. In case of disagreement, consensus between the two was reached through discussion.

Assessment of motor symptoms

Trained neurologists assessed each patient by Unified Parkinson’s Disease Rating Scale (UPDRS)-Part III, which is the most widely used scale to rate parkinsonism [33]. The cardinal motor symptoms included in the UPDRS-Part III were categorized as tremor (items 20 and 21), rigidity (items 22), bradykinesia (items 23, 24, 25, and 26), and axial features (items 18, 19, 27, 28, 29, and 30) [34]. Despite the limitation that the UPDRS-Part III technically indicates motor signs instead of symptoms, we defined cardinal motor symptoms as present if the patient scored at least one in each category.

Cognitive scales used for correlation with motor impairment

Patients went through a comprehensive battery but in this study, we used two cognitive scales to investigate the correlation between cognitive and motor impairments: a Korean version of Mini-Mental State Examination (K-MMSE) and the Clinical Deterioration Rating (CDR). The CDR consists of six domains (memory, orientation, judgement and problem solving, performance in community affairs, home and hobbies, and personal care) and the scores are summed to obtain the CDR Sum-of-Boxes scores (CDR-SOB) [35].

Brain MRI acquisition and measurement of cortical thickness and subcortical structural volume

Among 113 patients, 105 (75 EOAD and 30 LOAD) underwent the standardized 3D T1-weighted MRI. The standardized 3D T1 turbo field echo images and two-dimensional (2D) fluid attenuated inversion recovery (FLAIR) images were acquired using the same MRI scanner (Philips 3.0 T Achieva; Philips Health care). The 3D T1 parameters were the following: sagittal slice thickness, 1.0 mm, over contiguous slices with 50% overlap; no gap; repetition time (TR) = 9.9 ms; echo time (TE) = 4.6 ms; flip angle = 8°; and matrix size = 240×240 pixels reconstructed to 480×480 over a field of view of 240 mm. The 2D FLAIR images were set to the following parameters: axial slice thickness of 2 mm; no gap; TR = 11,000 ms; TE = 125 ms; flip angle = 90°; and matrix size = 512×512 pixels. The mean interval between MRI and UPDRS-Part III scoring was 1.0±0.8 year.

Images were processed using the CIVET anatomical pipeline [36]. Native T1-weighted images were registered to the standard Montreal Neurological Institute anatomical pipeline [37], and intensity-based non-uniformities were corrected using the N3 algorithm [38]. The images were then divided into white matter, gray matter, cerebrospinal fluid (CSF), and background using the Intensity-Normalized Stereotaxic Environment for Classification of Tissues algorithm [36]. We used Automated Nonlinear Imaging Matching and Anatomical Labelling parcellation on the native space, and obtained the ventricular volume (lateral, third, and fourth ventricle) and extracerebral CSF volume [37]. The surfaces of the inner and outer cortices were automatically extracted using the Constrained Laplacian-Based Automated Segmentation with Proximities algorithm [39]. Cortical thickness was defined as the Euclidean distance between the linked vertices of these areas [40] but the value was calculated in the native brain space instead of Talairach space owing to the limitations of linear stereotaxic normalization. With a linear formation matrix, the MR volume in the native space was transformed into stereotaxic space and reconstructed by the inverse transformation matrix [41]. Finally, the cortical surface of each subject was calculated into global and lobar regional thickness (frontal, temporal, parietal, occipital, and cingulate lobes) via automated processes, we obtained the mean cortical thickness of each lobe (cingulate, frontal, parietal, temporal, and occipital) and subcortical structural volume (caudate nucleus, putamen, globus pallidus, substantia nigra, thalamus, andhippocampus).

As 10 patients were excluded due to imaging preprocessing errors, 95 patients (68 EOAD and 27 LOAD) were finally included in the analysis.

Statistical analyses

The comparisons of demographics and clinical characteristics between the EOAD and LOAD groups were conducted using independent-sample t-tests and chi-square tests, as appropriate. Comparison of cognition and MRI neurodegeneration markers between EOAD and LOAD groups was performed using analysis of covariance after adjusting for age, sex, education, and intracranial volume. We used multiple linear regression analysis to evaluate the association of UPDRS-Part III scores with cognition or MRI neurodegeneration markers. The independent variables included cognitive scores (MMSE/CDR-SOB) or MRI neurodegeneration measures (cortical thickness, hippocampal volume, and subcortical gray matter volume), AD group (EOAD versus LOAD), and interaction term (AD group*MMSE/CDR-SOB or AD group*MRI neurodegeneration measures) and the dependent variable was UPDRS-Part III scores. As covariates, for the association between cognition and UPDRS-Part III scores, we included age, sex, education, and disease duration, whereas for the association between MRI markers of neurodegeneration and UPDRS-Part III, we included age, intracranial volume (ICV) [41], and disease. All statistical analyses were performed with STATA/SE version 15.0. Statistical significance was defined as a two-tailed p < 0.05.

RESULTS

Participant characteristics

The mean age of EOAD group at the time of UPDRS-Part III scoring was younger than that of LOAD group (61.5±6.0 versus 78.1±5.1, p < 0.001). Female prevalence, education years, the frequency of APOE4 carriers, disease duration, and total UPDRS-Part III scores did not differ between the two groups.

EOAD had worse MMSE and CDR-SOB at the time of UPDRS-Part III scorings than LOAD (MMSE, 12.1±7.9 versus 18.0±6.2, p < 0001; CDR-SOB, 9.3±4.5 versus 7.3±4.2, p = 0.032). There were no significant differences between the EOAD and LOAD in regional and global cortical thickness, and subcortical structural volume (caudate nucleus, putamen, substantia nigra, and thalamus) (Table 1).

Table 1

Demographic and clinical characteristics of patients

	EOAD	LOAD	p
	(N = 79)	(N = 34)
Demographics
Age at onset	56.0±5.5	72.4±5.1	<0.001
Age at UPDRS-Part III	61.5±6.0	78.1±5.7	<0.001
MMSE	12.1±7.9	18.0±6.2	<0.001
CDR-SOB	9.3±4.5	7.3±4.2	0.032
UPDRS-Part III	11.9±15.6	9.1±11.1	0.355
Education year	11.3±4.3	11.6±4.3	0.795
Disease duration	5.5±2.3	5.7±3.0	0.788
Female sex, n (%)	53 (67.1)	24 (70.6)	0.714
APOE4 carrier, n (%)	39 (51.3)	19 (61.3)	0.348
Cortical Thickness (mm)	(N = 68)	(N = 27)	p ^†
Cingulate	2.9±0.2	3.1±0.2	0.452
Frontal	3.0±0.2	3.0±0.1	0.122
Parietal	2.8±0.2	2.9±0.2	0.360
Temporal	3.0±0.2	3.1±0.2	0.088
Occipital	2.8±0.2	2.9±0.2	0.384
Global	2.9±0.2	3.0±0.1	0.154
Subcortical volume (mm³)	(N = 74)	(N = 31)	p ^†
Caudate nucleus	4,027.0±694.1	4,349.2±705.1	0.607
Putamen	3,415.8±399.5	3,384.1±347.7	0.545
Globus pallidus	874.9±98.5	842.0±120.3	0.283
Substantia nigra	42.3±6.7	40.5±6.1	0.246
Thalamus	6,252.4±481.3	6,153.0±547.0	0.938

Values are expressed as means±standard deviations or number (%). ^†adjusted for age and intracranial volume. EOAD, early-onset Alzheimer’s disease; LOAD, late-onset Alzheimer’s disease; UPDRS, Unified Parkinson’s Disease Rating Scale; MMSE, Mini-Mental State Examination; CDR-SOB, Clinical Deterioration Rating Sum-of-Boxes scores; APOE, Apolipoprotein E; ICV, intracranial volume.

The presence of cardinal motor symptoms in each group is presented in Table 2. The most common symptoms were axial features (64.6% in EOAD, 67.7% in LOAD), whereas the least common symptoms were tremors (27.9% in EOAD, 29.4% in LOAD). The prevalence and severity of each symptom in both groups did not differ (Table 2).

Table 2

Distribution of UPDRS-Part III sub-scores

	Prevalence of motor symptoms			Severity of motor symptoms
EOAD	LOAD	p	EOAD	LOAD	p
Axial	51 (64.6)	23 (67.7)	0.751	3.6±4.6	3.4±4.1	0.794
Tremor	22 (27.9)	10 (29.4)	0.866	0.7±1.5	0.9±1.8	0.535
Rigidity	33 (41.8)	13 (38.2)	0.726	2.2±4.1	1.1±1.8	0.157
Bradykinesia	48 (60.8)	20 (58.8)	0.847	5.4±7.6	3.7±5.0	0.243

Values are presented as n (%) or mean±standard deviation appropriately. Prevalence indicates the number of participants who scored at least 1 on each category, and severity indicates the total summation of UPDRS-Part III scores in each category. UPDRS, Unified Parkinson’s Disease Rating Scale; EOAD, early-onset Alzheimer’s disease; LOAD, late-onset Alzheimer’s disease.

The association between cognition and UPDRS-Part III score

Multiple regression analyses showed lower MMSE (β-coefficient (standard error) [β(SE)] –1.1(0.2), p < 0.001) or higher CDR-SOB (β(SE) 2.0(0.3), p < 0.001) to be significantly associated with higher UPDRS-Part III score upon adjusting for age, sex, education, and disease duration. This association remained distinctively same in both groups (EOAD: MMSE, β(SE) –1.3(0.2), p < 0.001; CDR-SOB, β(SE) 2.8(0.4), p < 0.001; LOAD: MMSE, β(SE) –1.0(0.3), p = 0.002; CDR-SOB, β(SE) 1.3(0.4), p = 0.009). There was no interaction effect between MMSE/CDR-SOB and AD group on UPDRS-Part III (MMSE, p for interaction = 0.773; CDR-SOB, p for interaction = 0.747) (Table 3, Fig. 1).

Table 3

Results of regression analysis to investigate the effect of cognition\\ and its interaction with age at onset on UPDRS-Part III

Effect on UPDRS-Part III	Independent variable	β (SE)	p
Model 1	MMSE	–1.1 (0.2)	<0.001
	CDR-SOB	2.0 (0.3)	<0.001
Model 2	MMSE*AD group	0.1 (0.4)	0.773
	CDR-SOB*AD group	–0.2 (0.6)	0.747

Model 1: cognition, age, sex, education, disease duration; Model 2: cognition, AD group, cognition*AD group, age, sex, education, disease duration. UPDRS, Unified Parkinson’s Disease Rating Scale; AD, Alzheimer’s disease; MMSE, Mini-Mental State Examination; CDR-SOB, Clinical Deterioration Rating Sum-of-Boxes scores.

Fig. 1

The interaction effects of AD group and cognition on UPDRS-Part III scores. Effect of (A) MMSE and (B) CDR-SOB on UPDRS-Part III scores. Each point in scatter plot represents the UPDRS-Part III scores of individual patients. The lines represent the model fits of the multiple regression analysis. (Model: cognition, AD group, cognition*AD group, age, sex, education, disease duration). EOAD, early-onset Alzheimer’s disease; LOAD, late-onset Alzheimer’s disease; UPDRS, Unified Parkinson’s Disease Rating Scale; MMSE, Mini-Mental State Examination; CDR-SOB, Clinical Deterioration Rating Sum-of-Boxes scores.

Table 4

Results of regression analysis to investigate the effect of MRI neurodegeneration measures and its interaction with age at onset on UPDRS-Part III

Model 1	β (SE)	p	Model 2	β (SE)	p
Cortical Thickness			Cortical Thickness *AD group
Cingulate	–19.3 (6.2)	0.002	Cingulate * AD group	19.9 (16.4)	0.229
Frontal	–28.7 (7.5)	<0.001	Frontal* AD group	29.3 (19.1)	0.128
Parietal	–23.9 (6.0)	<0.001	Parietal* AD group	33.2 (15.4)	0.035
Temporal	–24.0 (6.2)	<0.001	Temporal* AD group	4.1 (16.5)	0.805
Occipital	–18.1 (6.0)	0.003	Occipital* AD group	10.2 (13.6)	0.453
Global	–30.6 (7.3)	<0.001	Global* AD group	21.9 (18.8)	0.247
Subcortical volume			Subcortical volume* AD group
Caudate nucleus	–0.001 (0.002)	0.538	Caudate nucleus* AD group	–0.002 (0.004)	0.586
Putamen	–0.014 (0.004)	0.001	Putamen* AD group	0.003 (0.008)	0.717
Globus pallidus	0.020 (0.014)	0.146	Globus pallidus* AD group	–0.034 (0.026)	0.194
Substantia nigra	–0.420 (0.217)	0.055	Substantia nigra* AD group	0.456 (0.469)	0.334
Thalamus	–0.006 (0.004)	0.090	Thalamus* AD group	0.005 (0.006)	0.340
Hippocampus	–0.002 (0.002)	0.387	Hippocampus* AD group	0.006 (0.005)	0.242

Model 1: MRI neurodegeneration measures, age, intracranial volume, disease duration. Model 2: MRI neurodegeneration measures, AD group, MRI neurodegeneration measures*AD group, age, intracranial volume, disease duration. UPDRS, Unified Parkinson’s Disease Rating Scale; AD, Alzheimer’s disease.

The association between MRI neurodegeneration measures and UPDRS-Part III scores

Multiple regression analyses showed that lower global or regional cortical thickness was significantly associated with higher UPDRS-Part III score after adjusting the age, sex, disease duration, and ICV (global, β(SE) –30.6(7.3), p < 0.001; cingulate, β(SE) –19.3(6.2), p = 0.002; frontal, β(SE) –28.7(7.5), p < 0.001; parietal, β(SE) –23.9(6.0), p < 0.001; temporal, β(SE) –24.0(6.2), p < 0.001; occipital, β(SE) –18.1(6.0), p = 0.003). The interaction effect of cortical thickness and the AD groups on UPDRS-Part III score was significant in parietal lobe (p for interaction = 0.035), which showed EOAD to have a more pronounced association between parietal thinning and UPDRS-Part III score.

Furthermore, multiple regression analysis of subcortical structures showed that putaminal volume was negatively correlated with UPDRS-Part III score (β(SE) –0.014(0.004), p = 0.001). There was no interaction effect between putaminal volume and AD groups on UPDRS-Part III (p for interaction = 0.717), which indicated that the association between putaminal atrophy and motor symptoms did not differ according to the age at onset. Other subcortical structural volume did not show significant association with UPDRS-Part III scores (Table 4).

DISCUSSION

The major findings of our study were as follows: first, greater cognitive impairment measured by MMSE and CDR-SOB was correlated with worse motor symptoms represented by UPDRS-Part III scores in AD regardless of the age at onset, and this association was not different between the EOAD and LOAD groups; second, the cortical thickness in all regions was negatively correlated with UPDRS-Part III scores in all AD group; however, EOAD has more pronounced association between parietal thinning and UPDRS-Part III score. Finally, among the six subcortical structures, only the volume of putamen was negatively associated with UPDRS-Part III score. Taken together, our results suggest that motor symptoms in AD are associated with cognitive decline itself regardless of age at onset, and cortical and subcortical atrophy could contribute to the development of motor symptoms.

The first major finding of this study was that cognitive impairment was positively associated with UPDRS-Part III scores in the total AD group. Contrary to our expectations, however, the associations between cognition and UPDRS-Part III scores did not differ between EOAD and LOAD patients. Based on previous studies, we hypothesized that EOAD patients would have more severe motor symptoms than would LOAD at the same cognitive status. A previous study from our group showed that EOAD patients had lower basal ganglia metabolism than did young controls [42]; EOAD also had more rapid striatal atrophy than did LOAD [29]. In addition, EOAD showed a greater striatal and thalamic amyloid deposition than did LOAD [19]. Therefore, we expected that EOAD might have more pathology in striatal structures, possibly making motor symptoms more likely to develop along with cognitive impairment. However, no interaction effect of cognition and AD group on UPDRS-Part III suggests that the severity of motor impairment in AD is not dependent on the age at onset, but on the degree of cognitive impairment, which is the hallmark of disease severity. Thus, we suggest that more motor symptoms a reflect higher disease burden in AD regardless of age at onset. Although this study could not establish the underlying patho-mechanism for motor dysfunction in AD, several hypotheses could be revisited aside from factors such as less motivation for exercise, disuse atrophy of muscle, and osteoporosis that develop as the disease progresses over time. First, it might be due to propagation of pathology into subcortical structures, which includes Alzheimer pathologies (tau, and amyloid) and even α-synuclein pathology that often accompanies AD pathology [2, 6]. Defective dopaminergic neurotransmission might also contribute to motor symptoms [5]. Alternatively, cholinergic dysfunction in AD [43] may contribute to motor impairment by potentially affecting attention and executive function [12, 13], or cortical atrophy itself could lead to motor dysfunction that manifests as symptoms, such as frontal gait or apraxia [6, 8].

Our second major finding was that cortical thickness in all regions was negatively correlated with UPDRS-Part III scores in the total group. First, cognitive impairment along with cortical dysfunction could impair objective motor assessment. Alternatively, cortical dysfunction might present as motor disturbances: Oppositional tone and lack of motivation to move (apathy), which are symptoms of frontal lobe dysfunction, could be seen as rigidity and bradykinesia and apraxia associated with parietal lobe dysfunction could also present as bradykinesia. Motor control involves various processes, such as sensory feedback, intention, planning, programming and execution, and proper cortical function is essential for successful control of movement [44 –46]. Since AD is characterized by cortical thinning and changes in cortical excitability [46 –48], it is not surprising that motor symptoms worsen with cortical thinning. Interestingly, we found a significant interaction effect of EOAD and parietal thinning on UPDRS-Part III score; that is, lower parietal thickness was associated with worse motor symptoms, but its impact was greater in EOAD rather than in LOAD. One possible explanation for this finding is that, compared with LOAD, EOAD might be more vulnerable to the clinical effects of parietal atrophy. Hypothetically, additional pathologic burden or pathophysiologic mechanism involving parietal lobe such as network dysfunction might contribute to more remarkable parietal dysfunction in EOAD. A previous interesting study showed that even in Parkinson’s disease, young-onset and late-onset have different clinical-biochemical profiles, which indicated that distinct molecular dynamics might be linked to neuronal damage according to age at onset [49]. Therefore, the distinct molecular dynamics underlying parietal thinning might affect motor symptoms differently according to the age at onset in AD. However, further studies using network analysis or fluid biomarkers are required to ascertain this possible phenomenon.

The last major finding of this study was that the putaminal volume was associated with UPDRS-Part III score. We considered that striatal abnormalities to be one of the underlying pathophysiologies contributing to motor impairment in AD. Several studies have already suggested that extrapyramidal symptoms in AD depend on neuronal loss in the striatum [6 –8], which is consistent with our findings, although conflicting reports exist as well [3, 4]. Our previous study excluded presynaptic dopaminergic deficits as a plausible explanation for motor symptoms in EOAD by demonstrating that, compared with controls, EOAD patients with or without motor symptoms did not have any significant decrease of ¹⁸F-FP-CIT PET uptake [11]. We had also demonstrated that striatal amyloid deposition was not associated with motor symptoms in AD, which excluded the possible effect of striatal amyloid on motor symptoms. Therefore, we considered that striatal neurodegeneration, unrelated to presynaptic dopaminergic deficit or striatal amyloid, might be another possible explanation for motor symptoms in AD. As a previous study from our group demonstrated that EOAD had more rapid progression of striatal atrophy than did LOAD [23], and this could be why clinicians experience more marked motor symptoms in EOAD. However, our finding of no interaction effect between putaminal volume and AD group on UPDRS-Part III score demonstrates that the association between putaminal volume and motor symptoms does not differ according to ageat onset.

This study had several limitations. First, in the absence of pathologic confirmation, we could not exclude other pathologies coexisting with amyloid pathology to conclude the underlying pathology for motor symptoms in AD. Although we performed amyloid PET to confirm the AD diagnosis and excluded patients presenting with any of symptoms in criteria for dementia of Lewy body, it should be acknowledged that some instances of misdiagnosis could have introduced bias in the study results without pathologic confirmation. Second, the number of patients in the early- versus late-onset AD groups was not balanced. In our study, we only included patients who underwent amyloid PET, and early-onset patients tended to be more avid to receiving amyloid PET than were late-onset patients [50], which inevitably resulted in more EOAD patients. Third, we only used MMSE and CDR-SOB for cognitive measures, which failed to show detailed neuropsychological characteristics, and we had no healthy controls to differentiate aging-related mild parkinsonian symptoms. Fourth, although UPDRS-Part III is commonly used to evaluate motor symptoms in dementia with parkinsonism [51, 52], motor symptoms might be overestimated because of generalized slowing or apraxia in patients with advanced dementia. Finally, longitudinal data were unavailable. Therefore, further studies are required to compare the trajectories of motor deterioration and investigate their association with brain morphological measures.

In conclusion, our study suggests that the severity of motor symptoms in AD is related to the severity of cognitive impairment itself rather than the age at onset, and the development of motor symptoms might be due to multiple mechanisms including cortical atrophy— parietal atrophy particularly in EOAD— and putaminal atrophy. We believe that our findings could help clinicians explain the motor symptoms in AD, possibly predict motor deterioration, and provide a related guide to AD patients based on the severity of cognitive impairment and neurodegeneration.

Footnotes

ACKNOWLEDGMENTS

This research was supported by the Ministry of Health & Welfare, Republic of Korea (HI19C1132), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1A2C1009778), and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health Welfare, Republic of Korea (HR21C0885). This study was also supported by Future Medicine 2030 Project of the Samsung Medical Center [#SMX1210771] and the “National Institute of Health” research project (project No. 2021-ER1004-01).

Authors’ disclosures available online ().

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