Abstract
Background:
Neuropsychiatric symptoms (NPS) are frequent in Alzheimer’s disease (AD) dementia, but a higher NPS burden is found in dementia with Lewy bodies (DLB). Lewy body (LB) pathology frequently co-occurs with AD pathology and may not meet neuropathological criteria for DLB (ADLB). NPS trajectories over disease course in these subgroups is not well understood.
Objective:
We investigated changes in NPS severity over time, at two time points, comparing clinicopathologically defined cohorts of AD (without LB), ADLB, DLB, and controls.
Methods:
Cases with two available Neuropsychiatric Inventory-Questionnaire (NPIQ), at the time of enrollment and within 2.5 years of death, were selected from the Arizona Study of Aging and Neurodegenerative Disorders. Differences and rate of change in NPIQ scores were compared between AD (n = 75), ADLB (n = 48) DLB (n = 65), and controls (n = 32) with covariates for age, sex, and cognition.
Results:
First NPIQ scores were highest in ADLB when compared to AD (p = 0.04) and controls (p = 0.01) but not different from DLB. A significant increase in NPS severity was observed in DLB and AD (p < 0.001) over a mean follow up time of 4.9 ± 3.0 years, and the rate of change was significantly greater in DLB when compared to other groups. Final NPIQ scores were highest in DLB when compared to AD (p = 0.03) but not ADLB, and in DLB, ADLB, and AD than controls (all p < 0.001).
Conclusions:
Early NPS burden as well as NPS severity progression rate, independently of cognitive status, might be useful clinical metrics and may help predict underlying pathological diagnoses.
Keywords: aging, alpha-synuclein, Alzheimer’s disease, apathy, cognition, Lewy body dementia, neuropsychiatric inventory-questionnaire
Introduction
Neuropsychiatric symptoms (NPS) are frequent in individuals with Alzheimer’s disease (AD) and related disorders1 and contribute to clinical diagnostic criteria.2–4 For example, visual hallucinations are a core clinical feature in dementia with Lewy bodies (DLB), while apathy and disinhibition are core features of the diagnostic criteria of behavioral variant frontotemporal dementia. Agitation is described in at least 50% of cases with AD, and depression and anxiety can be seen in 60–80% of cases with AD and related disorders.5 Presence of NPS is also associated with progression to dementia in individuals with mild cognitive impairment (MCI), and can be a harbinger of neurodegenerative disease in cognitively unimpaired individuals.6 NPS burden has been associated with higher caregiver burden, lower quality of life and poorer survival in individuals with AD dementia, highlighting the importance of better characterizing their clinical course.7,8
A higher NPS burden is seen in Lewy body diseases (LBD) compared to AD, specifically for hallucinations and delusions,9–11 although one large study noted similar frequencies in both groups.1 Recent literature suggests that neuropathological mechanisms underlying NPS may be different than those underlying cognitive decline and result from specific mechanisms underlying LBD.12 Comorbid Lewy body (LB) pathology is commonly present in more than 60% of cases with AD.13–16 While a neuropathological diagnosis of DLB has been defined on a sliding scale relative to co-morbid AD pathology, by the presence of relatively high levels of LB pathology,17 AD subjects with comorbid LB not meeting the pathological distribution and density for DLB have been classified as Alzheimer’s disease with Lewy bodies (ADLB) (See Supplemental Table 1).18 In these cases, the LB pathology is frequently not expressed clinically and diagnosed as clinical AD. Previous studies have shown that ADLB cases may have a faster cognitive decline compared to AD using Mini-Mental State Examination (MMSE) as a marker of global deterioration, whereas death or nursing home placement may occur earlier in DLB.19–21 The differences in NPS severity over disease course in these pathologic subgroups is not well defined.
We, therefore, investigated changes in NPS frequency and severity over time, using the Neuropsychiatric Inventory-Questionnaire (NPIQ), and comparing total score as well as each NPS subdomains at baseline and before death, between pathologically defined cohorts of AD (without LB), ADLB, DLB and controls (without LB or a defined clinicopathological diagnosis) with covariates for sex, age, and cognitive status as measured by the MMSE. We hypothesized that cases with Lewy body pathology would exhibit higher NPS burden early on in comparison to cases with AD pathology only.
Methods
Participants selection and clinical data
Participants included in this study were volunteers enrolled in the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) and Brain and Body Donation Program (BBDP), a longitudinal clinicopathological study at Banner Sun Health Research Institute (BSHRI).22 All subjects signed informed consents, approved by BSHRI Institutional Review Boards, for both clinical assessment and brain donation for research purposes. Participants are clinically characterized with annual standardized test batteries, consisting of general neurological, cognitive, and movement disorders components that are assessed by subspecialty cognitive/behavioral neurologists, movement disorders neurologists, and neuropsychologists.
Participants included in this study were selected from the AZSAND/BBDP database if they had a final clinicopathological consensus diagnosis of AD (n = 75), DLB (n = 39), ADLB (n = 48), or control (n = 32). AD, DLB, and ADLB participants all had a final clinical diagnosis of dementia while controls were defined as clinically lacking dementia, MCI, and parkinsonism. Frequency and severity of neuropsychiatric symptoms was evaluated through the Neuropsychiatric Inventory-Questionnaire (NPIQ).23 To be included, participants needed to have completed at least two NPIQ questionnaires. While some subject had performed more than 2 NPIQ tests, only the first NPIQ available, performed at the time of enrollment in the program, as well as the last NPIQ before death, within 2.5 years prior to death, were used for further comparisons. Follow-up time was calculated as the time in between the first and last NPIQ assessments. The NPIQ was filled out by a study partner who had frequent contact with the participant. The 12 items of the NPIQ (delusions, hallucinations, agitation or aggression, depression or dysphoria, anxiety, elation or euphoria, disinhibition, irritability or lability, motor disturbance, nighttime behaviors, appetite and eating) were recorded as present or not (0 or 1) over the last month, while severity was scored as mild, moderate, or severe (1 to 3) when an item was recorded as present. Total NPIQ score was reported as frequency multiplied by severity for a total possible score of 36. Cognitive status was assessed using the MMSE. For some cases, the MMSE test was not scheduled at the same BBDP visit; the mean duration between MMSE and NPIQ test was of 1.02 (1.6) months with a median of 0 and an interval between 0 and 11 months. Onset of cognitive symptoms and onset of dementia was also available from the first visit and used to calculate the duration of cognitive symptoms and dementia. Apolipoprotein E (APOE) allele 4 status and last Unified Parkinson’s Disease Rating Scale (UPDRS) part III were also reported.
Table 1 shows demographic and clinical characteristics of included participants and Table 2 shows the neuropathological characteristics of the different groups.
Table 1.
Demographics and clinical characteristics of cases.
| Controls (a) | AD (b) | ADLB (c) | DLB (d) | Group differences | |
|---|---|---|---|---|---|
|
| |||||
| Nb. of cases | 32 | 75 | 48 | 39 | - |
| Age at death | 87.6 (6.2) | 88.2 (7.5) | 88.0 (7.7) | 85.6 (8.2) | ns |
| Cog. sx, age onset | 82.3 (10.2) | 79.2 (9.4) | 77.7 (9.8) | 76.3 (9.3) | ns |
| Cog. sx duration (y) | 0.27 (0.9) | 9.0 (5.0) | 10.5 (4.5) | 9.3 (6.2) | a < b, c, d |
| Dementia, age onset | - | 81.9 (9.9) | 81.0 (10.7) | 78.4 (9.7) | ns |
| Duration of dementia | - | 6.3 (4.7) | 7.2 (4.0) | 7.2 (4.4) | ns |
| Sex (female/male) | 20/12 | 35/41 | 19/29 | 8/31 | d < a, b; a > c |
| Education (y) | 14.7 (2.6) | 14.5 (2.7) | 14.0 (2.5) | 15.9 (3.5) | ns |
| First MMSE | 28.7 (1.4) | 23.8 (7.3) | 20.3 (8.2) | 20.6 (8.5) | a > b, c, d |
| Last MMSE | 28.4 (1.2) | 17.5 (7.6) | 11.3 (8.8) | 14.0 (8.2) | a > b, c, d; b > c |
| First NPIQ | 4.5 (4.1) | 4.5 (4.1) | 7.3 (5.5) | 5.1 (3.8) | a > b, c, d; b < c |
| Last NPIQ | 7.9 (4.7) | 7.9 (4.7) | 8.3 (5.0) | 10.9 (6.2) | a > b, c, d; b < d |
| Follow-up time (y) | 5.9 (2.9) | 5.0 (3.0) | 4.7 (3.1) | 4.0 (2.6) | a > d |
| Last UPDRS-III | 6.5 (7.1) | 18.1 (18.1) | 18.1 (16.5) | 27.9 (22.7) | a < b, c, d; d > b |
| APOE genotype (%) | 40.6 | 45.3 | 45.8 | 35.9 | ns |
Data are presented as means and standard deviations (SD). AD: Alzheimer’s disease dementia; DLB: Dementia with Lewy bodies; ADLB: Alzheimer’s disease with Lewy bodies (not meeting criteria for DLB); Cog. sx: cognitive symptom; MMSE: Mini-Mental State Examination score; NPIQ: total score on the neuropsychiatric inventory questionnaire; Follow-up time is defined as the time between first and last NPIQ in years; UPDRS-III: last Unified Parkinson’s Disease Rating Scale, part 3 motor score; % ApoE genotype: % of apolipoprotein E allele 4. For group differences, p < 0.05 for post hoc paired comparisons with Bonferroni correction, a = controls, b = AD, c = ADLB, d = DLB.
Table 2.
Pathological characteristics of cases.
| Controls (a) | AD (b) | ADLB (c) | DLB (d) | Group differences | |
|---|---|---|---|---|---|
|
| |||||
| USSLB stage | 0 | 0 | 2.2 (0.7) | 3.8 (0.4) | |
| % Stage 0 | 100 | l00 | 0 | 0 | |
| % Stage I | 0 | 0 | 14.6 | 0 | |
| % Stage II | 0 | 0 | 54.2 | 0 | |
| % Stage III | 0 | 0 | 31.3 | 20.5 | |
| % Stage IV | 0 | 0 | 0 | 79.5 | |
| LB density | 0 | 0 | 11.1 (7.9) | 31.9 (5.4) | |
| Braak NF stage | 3.0 (0.9) | 4.9 (0.9) | 5.3 (0.6) | 4.3 (1.0) | a < b, c, d; b > d |
| Plaque density | 1.6 (1.2) | 3.0 (0.1) | 3.0 (0.1) | 2.7 (0.8) | a < b, c, d |
| Total brain NF load | 4.5 (1.9) | 11.0 (3.4) | 12.7 (2.4) | 9.0 (3.2) | a < b, c, d; b > d; c > b, d |
| Total brain plaque load | 6.2 (5.9) | 12.9 (2.6) | 13.7 (4.7) | 11.1 (4.2) | a < b, c, d; c > d |
USSLB stage: mean Unified Staging System for Lewy Body Disorders stage and percentages (%) of cases at each Lewy pathology stage; LB density: summary score of regional brain Lewy-type synucleinopathy density scores with a maximum score of 40. Total brain NF Score and plaque score: summary regional brain density scores for neurofibrillary tangles and amyloid plaques, with a maximum score of 15 for each.
Neuropathological evaluation
As part of AZSAND/BBDP, a complete neuropathological examination is performed after death, as previously described.22 Assignments for AD Braak neurofibrillary (NF) stages,24 CERAD neuritic plaque density score,25 Thal amyloid phase for Aβ plaque brain distribution26 and alpha-synuclein (aSyn) stage according to the Unified Staging System for Lewy Body Disorders (USSLB)18 are available. Neuropathological AD diagnoses were defined as having “intermediate” or “high” criteria according to the National Institute on Aging/Reagan Institute criteria for all cases as well as National Institute on Aging/Alzheimer’s Association (NIA-AA) criteria for cases coming to autopsy after 2011.27,28 AD cases were selected not to have any concomitant CNS pathological Lewy body disease at autopsy. DLB cases were defined as meeting “intermediate” or “high” clinicopathological diagnostic criteria for DLB according to the DLB Consortium’s consensus criteria4,17 which are adjusted for the levels of concomitant AD pathology. ADLB cases were AD cases also having pathologically confirmed CNS Lewy body disease but with “low” DLB Consortium criteria, not meeting pathology distribution and density thresholds for DLB diagnosis.18 While controls may have some low levels of AD pathology, control cases were selected not to have any Lewy body pathology at autopsy or any major clinicopathological diagnosis. Supplemental Table 1 presents the group classification according to neuropathology diagnostic criteria for DLB, AD and ADLB using an adapted table from the DLB Consortium consensus criteria.4,27
Statistical analysis
Statistical analyses were performed using SPSS software (IBM SPSS Statistics 23.0). ANOVAs, ANCOVAs and Chi-square tests were used as appropriate for group comparison. Total composite NPS scores were created by multiplying each NPI symptom sub-score (severity × frequency) for statistical comparisons and were compared at two time points (first and last total NPIQ). The rate of change was calculated by subtracting the first NPIQ to the last NPIQ and dividing by the follow-up time between both tests. The rate of change was compared between groups using an ANCOVA with covariates for age, sex, and baseline MMSE. Within subjects paired t-tests were used to compare the difference between the first and last NPIQ for each group. ANCOVAs with covariates for sex, age and baseline MMSE and follow-up time were performed to compare group differences in total NPIQs. For all these models, Bonferroni correction was applied on post hoc group comparisons. Multivariate non-parametric Kruskal-Wallis ANOVAs, with post hoc Bonferroni corrections for paired comparisons, or Chi-square tests were used, as appropriate, to assess mean group differences and the proportion of cases reporting NPS for each subdomain of the NPIQ. For all analyses, the significance was set at p < 0.05.
Results
Clinical and demographic features
A total of 194 participants with a final clinicopathological diagnosis of AD (n = 75), ADLB (n = 48), DLB (n = 39), and controls (n = 32) were included in this study. Most of the DLB cases, 35 cases (90%) also had AD pathology. No significant age differences were observed between groups. The age at onset of cognitive symptoms and duration of cognitive symptoms and dementia was similar between AD, ADLB, and DLB groups. The sex distribution was significantly different in DLB, with a significantly lower proportion of female than in controls (p = 0.003) and in AD (p = 0.007) as well as in ADLB when compared to controls (p = 0.04). First and last MMSE test scores were significantly higher in controls when compared to AD, ADLB, and DLB (p < 0.001). While first MMSE was not significantly different between AD, ADLB, and DLB, last MMSE was significantly lower in ADLB than in AD (p > 0.001) and no differences were observed between ADLB and DLB. Follow-up time between first and last NPIQs was significantly shorter in DLB when compared to controls (p = 0.031) while no group differences were observed between AD, ADLB, and DLB. When compared to controls, UPDRS-III was significantly higher in AD (p = 0.014), ADLB (p = 0.28), and DLB (p < 0.001) as well as in DLB when compared to AD (p = 0.032). Tables 1 and 2 describe the clinical and neuropathological characteristics of the different groups.
Rate of change and change over time between first and last NPIQ
The mean follow-up duration between first and last NPIQ score was 4.9 ± 3.0 years when including all groups; the duration was significantly shorter in DLB when compared to controls (p = 0.03), while no other group differences were observed (Table 1). The rate of change over time was significantly greater in DLB when compared to controls (p = 0.009), AD (p = 0.019), and ADLB (p = 0.010) groups; See Figure 1. When assessing within group change over time, a significant increase between the first and last NPIQ was observed in AD (t = −5.674; p < 0.001) and DLB (t = −5.771; p < 0.001) while no differences between the first and last NPIQ were observed in ADLB and controls; See Figure 2.
Figure 1.
Rate of change over time between first and last NPIQ in comparison between AD, DLB, ADLB, and controls. DLB shows a significantly greater rate of change in comparison to other groups.
Figure 2.
Scatterplots representing the differences between first and last NPIQ tests for each group. A significant increase in NPS severity between the first and last NPIQ tests was observed in the AD and DLB groups. Results are presented as mean and standard error of the mean.
Group comparison for first and last NPIQ
For the first NPIQ, univariate ANCOVA with covariates for sex, age and MMSE at first NPIQ showed a significant effect of group [F(3185) = 4.41; p = 0.005]. First NPIQ was found to be higher in ADLB when compared to controls (p = 0.008) and in ADLB when compared to DLB (p = 0.039) but without a significant difference between ADLB and AD; See Figure 1.
For the last NPIQ measured prior to death, univariate ANCOVA with covariates for sex, age, baseline MMSE, and follow up time, also showed significant group differences [F(3181) = 11.86; p < 0.001]. Mean last NPIQ was found to be higher in DLB, ADLB and AD when compared to controls (all p < 0.001) and in DLB when compared to AD (p = 0.034) but without a significant difference between ADLB and DLB; See Figure 1.
Group differences for each sub-domain of the NPIQ
When analyzing each subdomain of the NPIQ (frequency× severity) on the first NPIQ, an effect of group was observed for delusions (p = 0.03), hallucinations (p = 0.013), anxiety (p = 0.021), apathy (p = 0.002), motor disturbances (p = 0.006), nighttime behaviors (p = 0029) and appetite/eating (p = 0.010). Post hoc group comparison further revealed that delusions were significantly higher in ADLB than in controls (p = 0.003). Hallucinations were significantly higher in DLB than controls (p = 0.011). Apathy was significantly higher in AD (p = 0.026), ADLB (p = 0.002), and DLB (p = 0.010) when compared to controls. Motor disturbance was higher in ADLB than in AD (p = 0.015) and appetite/eating was higher in ADLB than in controls (p = 0.006) (Figure 3A).
Figure 3.
Severity of NPS for each subdomain in comparison between each group for the first NPIQ (A) and the last NPIQ (B). Each line represents a different group, the middle of the spider diagram represents gradings of 0 while the extremities represent a higher severity of NPS.
For the last NPIQ, an effect of group was observed for delusions (p < 0.001), hallucinations (p < 0.001), anxiety (p = 0.011), apathy (p < 0.001), disinhibition (p = 0.028), motor disturbances (p=0.006), and nighttime behaviors (p = 0.006). Post hoc tests revealed that delusions were significantly higher in DLB than in controls (p < 0.001) and AD (p = 0.005). Hallucinations were higher in DLB than in controls (p < 0.001), AD (p < 0.001) and ADLB cases (p = 0.033) and in ADLB than in controls (p = 0.043) Anxiety was higher in DLB than in controls (p = 0.017), AD (p = 0.024), and ADLB (p = 0.037) cases. Apathy was higher in DLB than in controls, AD and ADLB (all p < 0.001). Disinhibition was higher in DLB than in controls (p = 0.018), ADLB (p = 0.038) and AD (p = 0.009). Motor disturbances were both higher in DLB than in controls (p = 0.04)). Nighttime behaviors were more severe in DLB (p = 0.04) and AD (p = 0.029) compared to controls (Figure 3B).
As a large number of cases did not report any NPS for several domains, we also opted to look at group differences in the proportions of cases reporting NPS (without taking severity into consideration). These results are presented in Figure 4 and Supplemental Table 2.
Figure 4.
Percentage of cases presenting with NPS for each subdomain of the NPIQ. Only differences between pathologic groups (AD, ADLB, DLB) and not with controls are represented. Chi-square tests *p < 0.005. See Supplemental Table 2 for statistical tests results.
Discussion
This clinicopathological study investigated changes in frequency and severity of NPS over time using two time points, that is at enrollment and before death, in clinicopathologically defined cases with AD (without LB), ADLB, DLB, and controls without LB or any clinicopathologically-defined neurodegenerative disorder. Our main finding demonstrates that ADLB had a higher NPS burden at enrollment than AD, while DLB was comparable to AD, despite similar levels of global cognitive impairment. The burden of NPS significantly increased over time in AD and DLB, with a significantly higher rate of change in DLB leading to an ultimately a more severe NPS burden in DLB, in contrast to ADLB or controls which remained relatively stable in overall severity over time. These findings show that cross-sectional NPS severity may differ from progression rates, so documenting both of these may help us better understand group differences. Further, our results suggest that NPS may be driven by different neuropathological types and loads than cognitive symptoms. Specifically, NPS screening in clinical settings may improve diagnostic accuracy, raise suspicion for comorbid LB pathology, and help to differentiate pathological ADLB, DLB and AD subgroups in combination with emerging biomarkers.
We demonstrate that comorbid LB pathology in AD, even in the absence of neocortical LB, can lead to more NPS burden earlier in disease course. This finding was independent of cognitive severity at the time of NPIQ testing suggesting some specific additional underlying pathogenesis of NPS that might be relatively independent of cognitive impairment. Our findings are consistent with the literature reporting that NPS decline is independent of cognitive impairment in AD, Lewy body dementia and MCI with LB.12,29 We found higher total NPS symptoms in ADLB compared to AD, in contrast to no differences between AD and DLB, early in their respective disease courses, which is surprising and unclear. While both groups have LB and AD pathologies (90% of cases from the DLB group also meet criteria for pathological AD diagnosis27), the proportions of severity and burden may be at different stages between groups. Our group has previously reported an increased rate of cognitive decline in ADLB cases when compared to both AD and DLB,19 which contrasts with its decreased rate of NPS progression relative to DLB described in the current study. These differences are striking as DLB has greater LB pathology in terms of pathology densities and distribution, reaching the neocortical stage, while ADLB cases are restricted to limbic predominant and sometimes transitional stages.17 To explain the greater rate of cognitive decline in ADLB, we have previously suggested that ADLB might be an intrinsically more aggressive disease than AD or DLB, at least in terms of its AD pathology. The current study might suggest that the greater NPS progression rate in DLB might be due to its faster accumulation and spread of alpha-synuclein pathology to the neocortex. One may also hypothesize that this result to be driven by LB pathology in the amygdala, which is a known epicenter in the pathophysiology of both AD and LBD. Due to its involvement in emotions, alterations of the amygdala have been previously linked to NPS in AD, in addition to cognitive function. In our sample, 79% of ADLB cases had LB pathology in the amygdala. Earlier reports have demonstrated that the amygdala is one of the most frequently affected regions, also presenting higher densities than other affected regions, in AD with comorbid LB.13 Progression in DLB could be due to either greater synuclein pathology severity in amygdala or alternatively to a wider spread of synuclein pathology, i.e., throughout the neocortex, in DLB. Specifically, AD cases with amygdala predominant LB showed a lower total brain burden of α-synuclein pathology, than cases diagnosed with AD-DLB, despite similar clinical disease severity, leading authors to speculate that amygdala-predominant LB may be a neuropathologically distinct and isolated synucleinopathy.14 Future studies should further investigate changes in the amygdala in cases with ADLB. Hopefully, the ability to visualize synuclein antemortem will provide greater insights into this phenomenon in the future.
While the ADLB NPS burden at first evaluation and before death remained relatively stable over time, both AD and DLB showed significant increases, with ultimately a more severe NPS burden in DLB, over a follow up period of approximately 5 years. Other studies investigating neuropathologies in AD and related dementia have reported similar results in AD and LB dementias.1 When looking into specific NPS that might be driving this result, frequency of delusions, motor disturbances, nighttime behaviors and anxiety when compared to AD, were significantly higher in ADLB at baseline. Whether these are driven by AD or LB pathology, or a synergistic effect is still unknown. Future studies should include fluid biomarkers, collected prospectively, to determine whether one of these pathologies is a driver. Quantification of biomarkers with disease progression would further assist in identifying these associations antemortem. Motor disturbances were also more frequently reported in DLB than in AD. Delusions were more frequent in ADLB than in DLB, where visual hallucinations were more common, perhaps because hallucinations are a core diagnostic feature of DLB. With regard to the last NPIQ before death, delusions, hallucinations, disinhibition, motor disturbances, nighttime behaviors, anxiety, and apathy were all either more frequent or severe in DLB than in AD. Specific differences between DLB and ADLB were observed in the proportions of cases with hallucinations, disinhibition, nighttime behaviors, anxiety, and apathy. Generally, apathy was more frequently observed in all pathological subgroups and increased in severity over the course of the disease. This is consistent with current literature in neurocognitive disorders.1,30 Apathy, along with other affective symptoms, has been associated with greater caregiver burden,9 and there are no approved treatments for apathy despite this high prevalence.30 Neuroimaging studies across diseases have shown disruption of pathways involving the medial frontal cortex, subcortical structures, anterior cingulate cortex and ventral striatum while it is unclear whether the significance of these structures are related to pathological substrates or if these are just proxies of neurodegeneration.31
Overall, our results suggest that NPS profile could be useful for identifying patients with comorbid LB pathology early in the disease process and clinicians should consider co-morbid LB pathology in patients with high NPS burden. These results have important implications as most ADLB cases are not recognized clinically due to lack of clinical core LB features (visual hallucinations, cognitive fluctuations, parkinsonism, REM behavior disorder).29 With emerging biomarkers of alpha-synuclein in biofluids (particularly CSF), data so far has shown higher sensitivities of assays for neocortical pathology, somewhat lesser sensitivity for the combined brainstem and limbic stage (USSLB III), and relatively poor sensitivity for amygdala-predominant (USSLB IIb) and brainstem predominant (USSLB IIa) stages.32–34 The clinical identification, in combination with biomarkers and characterization of individuals with LB pathology, is critically needed as it may have implications in clinical trials for both DLB and AD. Additionally, as anti-amyloid therapies in LB disorders enters clinical trials, the different pathological subgroups may demonstrate clinical efficacy in different domains, requiring adaptive methodologies. Our study has several limitations. While we accounted for any major neuropathological diagnosis, our cases may have had co-morbid pathologies in smaller densities that can influence NPS clinically. We were also unable to account for medications used to treat NPS in our study and this would have to be accounted for in future studies. Finally, our population is primarily Caucasian and underrepresented groups may have very different expressions and experiences with NPS in the presence of AD and LB diseases.
In conclusion, we report a more severe burden of NPS in ADLB cases relative to both AD cases without LB and DLB cases, early in disease course. While both AD and DLB showed a significantly increased NPS burden over time, the rate of change over time was significantly greater in DLB when compared to other groups. These results suggest that initial NPS severity, as well as NPS severity progression rate, might be useful clinical metrics and might help predict underlying pathological diagnoses. While this is the first step of evaluating increase in NPS burden over time, future studies investigating trajectories between these groups through longitudinal changes over several timepoints will bring further insights into NPS fluctuations in a longitudinal timeframe. These findings can form the basis of future studies that aim to improve diagnostic accuracy and identify specific clinical features of LB pathology.
Supplementary Material
Acknowledgements
The authors thank the personnel who helped contribute clinical data and postmortem brains from study subjects. They also thank the donors who were recruited for this study as well as their families.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Arizona Study of Aging and Neurodegenerative Disorders and Brain and Body Donation Program has been supported by the National Institute of Neurological Disorders and Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson’s Disease and Related Disorders), the National Institute on Aging (P30 AG019610 and P30AG072980, Arizona Alzheimer’s Disease Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer’s Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05–901 and 1001 to the Arizona Parkinson’s Disease Consortium) and the Michael J. Fox Foundation for Parkinson’s Research.
Footnotes
Declaration of conflicting interests
The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: CT has been supported by fellowships from the Canadian Institutes of Health Research (CIHR) and Quebec’s Health Research Funds (FRQS). CHA received consulting fees from CND Life Sciences. HAS has received research support from Intra-cellular Therapeutics, Transposon, Parkinson Study Group/UCB, Parkinson’s Foundation, NINDS, Supernus/US World Meds, MJFF, Jazz Pharmaceuticals, Barrow Neurological Foundation, Saccadous Inc, and Cerevel Therapeutics. Dr Shill has additionally served as a consultant for the Parkinson Study Group/Nq, Biogen, AbbVie, Sage/Biogen, Praxis, KeifeRx, Fasikl and Jazz Pharmaceuticals. SM receive funding from PPMI. TGB has received consulting fees from Aprinoia Therapeutics, Biogen and Acadia Pharmaceuticals. He has received payment or honoraria from the National Institutes of Health, International Movement Disorders Association, World PD Coalition, Mayo Clinic Florida, Stanford University, and the IOS Press-Journal of Parkinson’s Disease and support for attending meetings from the Alzheimer’s Association, AD/PD/Kenes Group, Mayo Clinic Florida, and the Universitätsklinikum Hamburg-Eppendorf. He also has a leadership/fiduciary role and stock options with Vivid Genomics. AA has received honoraria or support for consulting; participating in independent data safety monitoring boards; providing educational lectures, programs, and materials; or serving on advisory boards for Acadia, Alzheimer’s Association, Alzheimer’s Disease International (ADI), AriBio, Biogen, Eisai, Life Molecular Imaging, Lundbeck, Merck, Novo Nordisk, ONO, Prothena, and Roche/Genentech. Dr Atri receives book royalties from Oxford University Press for a medical book on dementia. Dr Atri receives institutional research grant/contract funding from NIA/NIH 1P30AG072980, NIA/NIH U24AG057437, AZ DHS CTR040636, the Foundation for NIH, Washington University St Louis, and Gates Ventures. Dr Atri’s institution (Banner Health) receives/received funding for clinical trial grants, contracts and projects from government, consortia, foundations and companies for which he serves/served as contracted site-PI. PC has received research support from Lewy Body Dementia Association and Arizona Alzheimer’s Consortium.
The remaining authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Supplemental material
Supplemental material for this article is available online.
Data availability
The data supporting the findings of this study will be made available upon request to the corresponding author.
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Data Availability Statement
The data supporting the findings of this study will be made available upon request to the corresponding author.