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. Author manuscript; available in PMC: 2018 May 22.
Published in final edited form as: J Alzheimers Dis. 2017;60(2):483–493. doi: 10.3233/JAD-160767

Risk Factors, Neuroanatomical Correlates, and Outcome of Neuropsychiatric Symptoms in Alzheimer’s Disease

Stéphane P Poulin a,b,*, David Bergeron a, Bradford C Dickerson c,d,e,f,g, for the Alzheimer’s Disease Neuroimaging Initiative
PMCID: PMC5963953  NIHMSID: NIHMS965895  PMID: 28869463

Abstract

Background

An integrative model of neuropsychiatric symptoms (NPS) in Alzheimer’s disease (AD) is lacking.

Objective

In this study, we investigated the risk factors, anatomy, biology, and outcomes of NPS in AD.

Methods

181 subjects were included from the Alzheimer’s Disease Neuroimaging Study (ADNI). NPS were assessed with the Neuropsychiatric Inventory Questionnaire at baseline and 6 months. NPI >3 was used as a threshold for NPS positivity. Three NPS courses were characterized: 1) minimal/absent (negative at 0 and 6 months, n = 77); 2) fluctuating (positive only at one time point, n = 53); 3) persistent (positive at both time points, n = 51). We examined the association between NPS course and family history of dementia, personal history of psychiatric disorders, cerebrospinal fluid biomarkers, atrophy patterns, as well as longitudinal cognitive and functional measures at 12 and 24 months (MMSE, CDR-SOB, FAQ).

Results

AD subjects with absent, fluctuating, or persistent NPS had similar CSF amyloid-β and tau levels. AD subjects with minimal/absent NPS had less personal history of psychiatric disorders (35%) than those with fluctuating (57%; p = 0.015) or persistent NPS (47%, not significant). At 24 months, AD subjects with persistent NPS had worse cognitive (MMSE; p = 0.05) and functional (CDR-SOB; p = 0.016) outcomes. Dorsolateral prefrontal atrophy was seen in persistent NPS, but not in fluctuating NPS.

Conclusions

Our results suggest that individuals with personal history of psychiatric disorders might be more vulnerable to develop NPS throughout the course of AD. The worst cognitive and functional outcomes associated with NPS in AD underscores the importance of monitoring NPS early in the disease course.

Keywords: Alzheimer’s disease, Alzheimer’s Disease Neuroimaging Initiative, apathy, depression, neuropsychiatric inventory, neuropsychiatric symptoms, psychosis

INTRODUCTION

Although the clinical hallmarks of Alzheimer’s disease (AD) are memory, executive, and other cognitive impairments, neuropsychiatric symptoms (NPS) are common and a major source of disability and distress [1]. Despite recent advances in understanding the genetic [2] and anatomic basis [3] and prognostic significance [4] of specific cognitive symptoms in AD, relatively little similar data exist for NPS, and a number of clinically important questions remain.

One critical question is, what makes some AD patients more likely to develop NPS than others? AD patients with depression have a higher prevalence of personal history of psychiatric symptoms and/or mood disorders [5, 6]. This association suggests a pre-existing (possibly complex genetic) vulnerability, whereby patients with a history early in life of a psychiatric disorder may be at greater risk for psychiatric symptoms during the course of AD. However, Ropacki and Jeste did not identify such an association with regard to psychosis [5]. Aside from these studies, very little investigation has been performed of factors that may confer increased vulnerability to NPS in AD dementia. Similarly, while the anatomical and functional changes associated with apathy and psychosis have been extensively studied [6, 7], less attention has been devoted to other behavioral disturbances seen in AD. Another highly clinically relevant question is, is outcome worse in AD patients with NPS? Several studies suggest that some specific behavioral disturbances predict worse cognitive and functional outcome in AD [5, 8–10]. Relations between long-term outcome and NPS other than agitation and psychosis have been less commonly investigated. AD patients who eventually develop psychosis have accelerated cognitive decline even before these symptoms. This suggests that the risk factors involved in behavioral disturbances of AD, which could potentially be inherited, might also be involved in cognitive decline [11, 12]; alternatively, the presence of NPS or their treatment might accelerate cognitive decline.

The main objective of the present study was to investigate the characteristics of mild AD dementia subjects who have any type of NPS to determine whether such subjects differ in risk factor profiles, neuroanatomy, molecular markers, and two-year outcome (both cognitive and functional). Our second objective was to assess whether NPS themselves—independent of their associated risk factors and demographic variables identified through the previous sets of analyses—are predictive of more rapid cognitive/functional decline.

METHODS

Alzheimer’s Disease Neuroimaging Initiative

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.ucla.edu). The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year public-private partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment and early AD. Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials.

The Principal Investigator of this initiative is Michael W. Weiner, MD, VA Medical Center and University of California–San Francisco. ADNI is the result of efforts of many co-investigators from a broad range of academic institutions and private corporations, and subjects have been recruited from over 50 sites across the U.S. and Canada. For up-to-date information, see http://www.adni-info.org.

As part of the protocol, all subjects underwent thorough clinical and cognitive assessment including the Mini-Mental State Examination (MMSE) [13] and Clinical dementia rating scale (CDR) [14]. The diagnosis of AD was made if the subject had a MMSE score between 20 and 26, CDR score of 0.5 or 1, and met National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS/ADRDA) criteria for probable AD [15]. At screening, ADNI subjects did not have significant neurological, conditions, did not have significant cerebrovascular risk factors (modified Hachinski Ischemic Score ≤4), and were not significantly depressed (Geriatric Depression Scale ≤5). Psychiatric exclusion criteria included severe psychotic symptoms within the past 3 months, any history of schizophrenia, a history of alcohol or substance abuse within the past 2 years, or active psychiatric disorder (such as major depressive disorder or bipolar disorder) within the past 1 year. Subjects could be on stable dose of antidepressants lacking significant anticholinergic side effects. Other psychoactive medication had to be washed out at least 4 weeks prior to screening. AD subjects were followed up at 6, 12, 24, and 36 months post baseline assessment.

Standard protocol approvals, registrations, and patient consents

This study was approved by institutional review boards of all participating institutions and written informed consent was obtained from all participants or authorized representatives according to Good Clinical Practice, the Declaration of Helsinki and U.S. 21 CFR Part 50-Protection of Human Subjects, and Part 56-Institutional Review Boards.

Neuropsychiatric status

Neuropsychiatric symptoms were assessed with the Neuropsychiatric Inventory Questionnaire (NPI-Q) [16]. The NPI-Q covers 12 different domains (delusions, hallucinations, agitation, depression, anxiety, euphoria, apathy, aberrant motor behavior, disinhibition, irritability, sleep, and eating). In each domain, the informant rates the presence/severity of symptoms (0 = none, 1 = mild, 2 = moderate, 3 = severe) for a maximum total score of 36. We dichotomized AD subjects as NPS-positive and NPS-negative based on a NPI cutoff score ≥4, which has been previously used and validated as the cutoff for clinically significant NPS in prior clinical trials and observational studies [17, 18]. Based on longitudinal studies on NPS in AD—which consistently report that symptom frequency at any point in time underestimate the cumulative one-year frequency of NPS [18–21], we chose to consider the NPS summary score at two time points (baseline and 6 months) to categorize AD subjects into one of three groups. The first group consisted of subjects with persistent NPS: NPS global score ≥4 at baseline and 6 months. The second consisted of subjects with minimal/absent NPS: NPS global score <4 at baseline and 6 months. The third group consisted of subjects with fluctuating NPS: NPS global score ≥4 at baseline and <4 at 6 months or NPS global score <4 at baseline and ≥4 at 6 months. This grouping was established a priori to account for heterogeneity in NPS. In other neuropsychiatric syndromes such as schizophrenia, persistent versus fluctuating NPS may have both etiological and prognostic significance [22, 23]. To our knowledge, this type of a classification has not been used previously in studies of NPS in AD.

Risk factors

Data were analyzed on the following potential risk factors and biomarkers: 1) family history of dementia and AD, 2) personal history of psychiatric disorders, 3) apolipoprotein E (APOE) genotype (presence of at least one ε4 allele), 4) cerebrospinal fluid (CSF) amyloid-β1–42, and 5) total tau levels.

Magnetic resonance imaging and morphometry

For each patient, 2 high-resolution structural T1-weighted MP-RAGE images were acquired either on a 1.5T General Electric Healthcare, a 1.5T Siemens Medical Solutions or a 1.5T Phillips Medical System scanner. Acquisition parameters were as follows: TR 2400 ms, TE minimum full time excitation, FA 8°, TI 1000 ms, voxel size 1.25 × 1.25 × 1.2 mm. These data have been made available to the scientific community (http://www.loni.ucla.edu/ADNI/).

T1 image volumes were examined quantitatively by a cortical surface-based reconstruction and analysis of cortical thickness using FreeSurfer version 4.5 (http://surfer.nmr.mgh.harvard.edu). The general procedures for this processing method have been described in detail and applied and validated in a number of publications; the technical details can be found in select manuscripts [24, 25]. FreeSurfer processing of each brain MRI was visually co inspected and manually edited if errors were detected. We focused on the following ROIs: orbitofrontal prefrontal cortex, dorsolateral prefrontal cortex, hippocampus, amygdala, precuneus, insula anterior cingulate cortex, and posterior cingulate cortex.

Outcomes

The cognitive and functional outcomes studied here were the decline in MMSE, CDR Sum-of-boxes (CDR-SOB) scores, and Functional Assessment Questionnaire (FAQ) over 12 and 24 months.

Statistical analyses

All statistical analyses were performed with SPSS 19 (IBM, Armonk, NY). Analyses of variance (ANOVA) and chi-square analyses were used to compare groups on quantitative continuous and on categorical demographic variables respectively. The association between NPS groups and family history of dementia/AD and personal history of psychiatric disorders and APOE genotype was tested with the Chi-square test. Differences in CSF amyloid-β1–42 and total tau among the three groups were studied with ANOVA.

For the neuroanatomical data, age was entered into all models as a covariate since preliminary analyses indicated a trend-level relationship with NPS groups. Volumes of white matter hyperintensities were compared with analysis of covariance (ANCOVA). Statistical surface maps were generated by computing a general linear model for the effect of NPS groups on cortical thickness at each point. A Statistical threshold of p < 0.05 uncorrected for multiple comparisons was applied.

Finally, differences between NPS groups in cognitive (MMSE) and functional (CDR-SOB and FAQ) outcomes were assessed with ANOVA. To test whether NPS themselves—independent of their associated risk factors and demographic variables identified through the previous sets of analyses (MMSE decline, family history of AD and dementia, personal history of psychiatric disorders, CSF biomarkers)—are predictive of more rapid cognitive/functional decline we used a series of linear regression analyses. Alpha for all tests of statistical significance was set at p < 0.05 (2-sided).

Hypothesis testing

Our first sets of hypotheses were that AD subjects with persistent and fluctuating NPS have a different risk factor profile than AD subjects with minimal/absent NPS. We first hypothesized that AD subjects with persistent and fluctuating NPS have more personal history of psychiatric disorders [26]. Previous studies suggested that genetic and/or neurobiological factors involved in psychiatric disorder might contribute to the risk of AD [27] and cognitive decline [11]. Therefore, we extrapolated that AD subjects with persistent or fluctuating NPS would have less history of the genetic and/or neurobiological risk factors associated with AD and hence less family history of dementia and AD and lower likelihood of an APOE ε4 allele. We hypothesized that NPS groups would not differ in their levels of CSF amyloid-β1–42 or total tau [28].

Based on a literature review of studies of primary psychiatric disorders with persistent/chronic NPS, we hypothesized that AD subjects with persistent NPS would have higher volume of white matter hyperintensities [29], frontal cortical atrophy (dorsolateral, medial, and anterior cingulate) [30, 31], and hippocampal atrophy [32] in comparison to AD subjects with fluctuating and minimal absent NPS. We also hypothesized that fluctuating NPS would have no neuroanatomical differences in comparison to AD subjects with minimal/absent NPS [33, 34].

Finally, we hypothesized that AD subjects with persistent NPS would experience more rapid cognitive and functional decline and that more rapid functional decline would be partially independent of cognitive decline (reflecting additional functional impairment due to psychiatric symptoms). We hypothesized that cognitive and functional decline would be related to NPS themselves rather than risk factors for NPS (biomarkers, family history of AD and dementia, personal history of psychiatric disorders).

RESULTS

Of the 193 AD subjects constituting the ADNI cohort at baseline, 181 completed both baseline and 6-month NPI-Q: 77 subjects were classified as minimal/absent NPS, 53 as fluctuating NPS, and 51 as persistent NPS. Irritability, apathy, anxiety, and depression were the most prevalent NPS among the total sample (39%, 34%, 34%, and 33% of the sample, respectively); hallucinations and euphoria were the less prevalent (6% and 5%, respectively; see Fig. 1), consistent with previously published population-based studies [10, 18]. Despite not crossing the NPI <4 cutoff, NPI scores of AD subjects with persistent NPS could markedly fluctuate from 0 to 6 months ([0–14] NPI points fluctuation, 3.37 in average; see Fig. 3). Subjects who completed the 6-month follow up were similar at baseline to those who did not (n = 12) for demographic variables gender, age, and education and for clinical variables CDR-SOB and NPI-Q total score. However, they had higher MMSE score (23.4 [2.0] versus 22.2 [2.5]; F1,191 = 4.221; p = 0.041). 79/181 subjects (44%) took some form of psychiatric medication (75 on selective serotonin reuptake inhibitors [SSRI], 10 on hypnotics [trazodone, zolpidem], 9 on non-SSRI antidepressants [bupropion, duloxetine, tricyclic antidepressants], 3 on benzodiazepines, and 3 on antipsychotics; some subjects took medication from more than one category). AD subjects with minimal/absent NPS took less psychiatric medication (27%) than those with fluctuating (57%) and persistent NPS (53%).

Fig. 1.

Fig. 1

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Baseline severity of NPI subdomains. Severity of the 12 Neuropsychiatric Inventory subdomains at baseline in subjects with persistent, fluctuating, and minimal/absent neuropsychiatric symptoms. Bars indicate mean and brackets indicate 95% confidence intervals.

Fig. 3.

Fig. 3

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Longitudinal evolution of cognitive and functional measures in AD subjects with minimal, persistent and fluctuating neuropsychiatric symptoms. Spaghetti plots of longitudinal NPI, MMSE, CDR, and FAQ scores in AD subjects with minimal, persistent and fluctuating neuropsychiatric symptoms, and comparison of means with 95% confidence intervals. CDR, Clinical Dementia Rating (sum of boxes); FAQ, Functional Activities Questionnaire; MMSE, Mini-Mental State Examination; NPI, Neuropsychiatric Inventory.

At baseline, NPS groups (minimal/absent-fluctuating - persistent) were similar for gender (M/F: 37/40 – 28/25 – 31/20; χ2 = 2.00; p = 0.368), age (76.5 [7.6] – 76.0 [7.9] – 74.0 [7.0]; F2,178 = 1.75; p = 0.177), and education (14.3 [3.1] – 15.5 [2.7] – 14.7 [3.4]; F2,178 = 2.179; p = 0.116). They were similar for MMSE scores (23.6 [1.9] – 23.0 [2.0] – 23.7 [2.1]; F2,178 = 1.671; p = 0.191) but differed on CDR-SOB (3.8 [1.7] – 4.5 [1.4] – 4.7 [1.7]; F2,178 = 6.091; p = 0.003) and FAQ (11.0 [7.1] – 13.6 [5.9] – 15.2 [6.6]; F2,178 = 6.596; p = 0.002). The demographic and clinical characteristics of the sample are presented in Table 1.

Table 1.

Demographic and clinical characteristics of the patient groups

Neuropsychiatric courses
Minimal/absent Fluctuating Persistent
Demographic variables
Number (%) 77 (43) 53 (29) 51 (28)
Age, y (SD) 76.5 (7.6) 76.0 (7.9) 74.0 (7.0)
Sex, M/F 37/40 28/25 31/20
Education, y (SD)† 14.3 (3.1) 15.5 (2.7) 14.7 (3.4)
Personal and family history
Family history of dementia (%)*,§§ 44 55 29
Family history of AD (%)* 30 38 17
Personal history psychiatric disorder (%)† 35 57 47
Baseline clinical characteristics
MMSE baseline (SD) 23.6 (1.9) 23.0 (2.0) 23.7 (2.1)
MMSE 6months (SD) 22.9 (1.7) 21.8 (3.6) 22.0 (3.7)
CDR-SOB baseline (SD)†,‡‡ 3.8 (1.7) 4.5 (1.4) 4.7 (1.7)
CDR-SOB 6months (SD)†,‡‡ 4.4 (1.8) 5.5 (2.6) 5.6 (2.1)
FAQ baseline (SD)†,‡‡ 11.0 (7.1) 13.6 (5.9) 15.2 (6.6)
FAQ 6months (SD)†,‡ 13.4 (7.7) 16.8 (7.5) 16.7 (6.7)
Biomarkers profile
APOE ε4 alleles, % with 0/1/2 31/49/19 32/45/23 35/49/16
Amyloid-β1–42 (SD) 141.6 (38.8) 134.6 (31.1) 152.5 (49.3)
Total tau (SD) 125.5 (62.7) 105.8 (45.6) 122.7 (54.6)
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AD, Alzheimer’s disease; CDR-SOB, Clinical Dementia Rating - Sum of Boxes; FAQ, Functional Assessment Questionnaire; MMSE, Mini-Mental State Examination; na, not available.

*

percentage excluding subjects for which information about family history was not available.

†

p < 0.05;

††

p < 0.01: minimal/absent versus fluctuating;

‡

p < 0.05;

‡‡

p < 0.01: minimal/absent versus persistent; p < 0.05; p < 0.01: fluctuating versus persistent.

Subjects with persistent NPS had less familial history of dementia (29%) than those with fluctuating (55%; χ2 = 6.819, p = 0.009) or minimal/absent NPS (44%; n.s.), despite having similar influence of APOE ε4 genotype (65% with at least one allele versus 68% and 68%, respectively). Conversely, AD subjects with minimal/absent NPS had less personal history of psychiatric disorders than those with fluctuating (57%; χ2 = 5.915, p = 0.015) or persistent NPS (47%, n.s.). NPS groups did not differ regarding their biomarker profile: number of APOE ε4 alleles (0/1/2 allele: 31/49/19 – 32/45/23 – 35/49/16; χ2 = 0.940, p = 0.919); CSF amyloid-β1–42 (141.6 [38.8] – 134.6 [31.1] – 152.5 [49.3]; F2,97 = 1.190; p = 0.309); total tau (125.5 [62.7] – 105.8 [45.6] – 122.7 [54.6]; F2,97 = 1.427; p = 0.245). Post hoc analyses showed a trend toward higher CSF amyloid-β1–42 in AD subjects with persistent NPS compared with AD subjects with fluctuating NPS.

Cortical atrophy varied between the NPS groups. In comparison with AD subjects with minimal/absent NPS, AD subjects with persistent NPS had more left and right dorsolateral prefrontal atrophy (superior frontal gyri). In comparison with AD subjects with minimal/absent NPS, AD subjects with fluctuating NPS had more atrophy of the left insula and less atrophy in the left and right medial parietal cortex (precuneus) (see Fig. 2). There was no association between NPS groups and white matter disease and hippocampal volumes.

Fig. 2.

Fig. 2

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Neuroanatomical correlates of neuropsychiatric course in Alzheimer’s disease. A) Persistent versus minimal/absent NPS. B) Fluctuating versus minimal/absent NPS. On the blue end of the spectrum, atrophy is more significant in persistent NPS and fluctuating NPS. On the red end of the spectrum atrophy is more significant in minimal/absent NPS.

163 AD subjects completed the 12-month evaluation and 138 subjects completed the 24-month evaluation. At 12 months, NPS groups did not differ on their MMSE decline (F2,160 = 0.821; p = 0.442) or progression of CDR-SOB (F2,158 = 2.301; p = 0.104) or FAQ scores (F2,159 = 0.448; p = 0.640). At 24 months, there was a significant difference between NPS groups for MMSE decline (F2,133 = 3.052; p = 0.05) and progression of CDR-SOB (F2,132 = 4.284; p = 0.016) but not for progression of FAQ scores (F2,135 = 0.280; p = 0.757) (see Fig. 3). In post hoc analyses, AD subjects with persistent NPS had worse MMSE decline in comparison with AD with minimal/absent NPS (p = 0.016) and worse functional (CDR-SOB) decline in comparison with AD with fluctuating NPS (p = 0.015) and minimal/absent NPS (p = 0.007). Controlling for MMSE decline, the association between NPS groups and CDR-SOB at 24 months was no longer significant (F2,129 = 1.328; p = 0.269). Controlling for family history of dementia and personal history of psychiatric disorders, NPS groups were still predictive of MMSE decline (F2,129 = 3.874; p = 0.023) and CDR-SOB progression (F2,128 = 4.234; p = 0.016) at 24 months.

DISCUSSION

In this study, we investigated the characteristics of participants with mild AD dementia who have any type of NPS to determine whether such patients differ in family history of dementia or personal psychiatric history, neuroanatomy and molecular markers, and two-year outcome (both cognitive and functional). We found that the baseline presence and early course of neuropsychiatric symptoms was associated with a specific risk factor profile, neuroanatomical correlates and cognitive and functional outcomes. AD subjects with persistent NPS had less frequent family history of dementia whereas AD subjects with fluctuating NPS had more frequent history of psychiatric disorders. AD subjects with persistent NPS showed more atrophy of the right and left prefrontal cortex compared to those with minimal/absent NPS whereas AD subjects with fluctuating NPS did not differ from the latter group. AD subjects with persistent NPS showed worse cognitive and functional outcome compared to AD subjects with fluctuating or minimal/absent NPS. This association between NPS course and cognitive and functional outcomes was independent of risk factors for NPS (family history of dementia, personal history of psychiatric disorder).

The study also helps untangling pathophysiological mechanisms leading to NPS in AD. First, the fact that molecular markers of AD (APOE ε4, CSF amyloid-β1–42, total tau) were not associated with NPS symptoms in the present study suggests that NPS in AD may be at least partially independent from AD pathophysiology itself. Similar to cognitive variants of AD, behavioral expression of AD may be linked to genetically and environmentally mediated by selective vulnerability of frontal-subcortical brain circuits [35]. In that regard, previous studies have shown the psychosis in AD is associated with a distinct genetic profile [36–38]. The increased personal history of psychiatric disorders in AD subjects with fluctuating NPS evokes an alternative hypothesis whereby NPS would contribute to AD pathophysiology, perhaps through glucocorticoids-related alterations of hippocampal plasticity [39–41]. It is also possible that manifest or subclinical psychiatric conditions (personality, mood, anxiety, or psychotic disorders) are exacerbated by AD neuropathology through altered cognitive compensatory mechanisms and reduced insight. Finally, we cannot exclude that NPS in AD are the result of co-morbid pathologies, including vascular risk factors [42, 43] and traumatic brain injury [44]. Recent data suggest that vascular risk factors mediate the relation between psychiatric disorders (midlife depression) and AD [45] although this has not been found in other studies [46].

With regard to their neuroanatomical correlates, AD subjects with persistent NPS had more prominent prefrontal cortical atrophy in comparison to AD subjects with fluctuating or minimal/absent NPS. This finding also supports a possible relation between NPS and executive deficits in AD [47]. The dorsolateral prefrontal cortex is a key component of the well-known dorsolateral frontal-subcortical circuit, whose dysfunction is characterized primarily by executive deficits and motor programming abnormalities [48]. Dysfunction of frontal-subcortical circuits have been linked to NPS in AD [49–51] as well as a wide variety of primary psychiatric disorders [49, 52]. Interestingly, AD subjects with fluctuating NPS had relatively preserved right and left precuneus thickness but left insular atrophy compared with patients with minimal/absent NPS. Since these results had not been hypothesized and that we used a lenient statistical threshold, they should be interpreted cautiously. The precuneus is part of the default mode network and is involved in self-awareness [53] whereas the anterior insula is part of the salience network and is involved in affective processing including representations of feeling states [54, 55]. Additional investigation will be necessary to further interpret these results.

With regard to their cognitive and functional trajectories, AD subjects with persistent NPS demonstrated worse outcomes from those with fluctuating or minimal/absent NPS. This association was independent of the risk factors associated with NPS groups (family history of dementia, personal history of psychiatric disorders). It is possible that this is an effect of treatment of NPS, which may have cognitive side effects [56–58]. It is also possible that it may reflect non-compliance with standard treatment. These possibilities deserve further study.

Our study has limitations. First, the use of the NPI-Q, a caregiver version of the NPI, limits the reliability of NPS measurement. Ascertainment and quantification of behavioral disturbances are more reliably performed by clinicians using diagnostic interviews, and other more focused questionnaire-based instruments are also available for neuropsychiatric symptoms. Likewise, to our best knowledge, data on personal history of psychiatric disorders or familial history of dementia was obtained though self-report, which is not optimally reliable. Second, ADNI strict inclusion and exclusion criteria created selection bias by excluding the most severe NPS. Indeed, subjects with significant psychiatric impairment at the inclusion visit—including significant depression (Geriatric Depression Scale ≤5), psychotic symptoms within the past 3 months, any history of schizophrenia, or active psychiatric disorder (such as bipolar disorder) within the past 1 year—were excluded from the study. Furthermore, subjects who completed the 6-month follow up had slightly higher MMSE scores, which may lead to an underestimation of cognitive deterioration rates measured in this study.

Overall, this study suggests that persistent NPS in AD may represent a specific phenotype of the disease with a specific risk factor profile, neuroanatomical correlates and worse cognitive and functional outcome. Based on this preliminary observation, we believe that additional research on persistent NPS early in the course of AD dementia is warranted. For clinicians, these findings underscore the importance of assessment of NPS early in the course of the disease to try to identify, and potentially ameliorate, a poor prognostic indicator.

Acknowledgments

This research was supported by NIH grants (P30 AG010129, K01 AG030514) and by the Dana Foundation. This research was also supported by grants R01-AG029411, R21-AG029840, P50-AG05134, P50-AG05681, P01-AG003991, U24-RR021382, R01-MH56584, R01-AG030311, and DP1OD003312. Finally, this research was supported by the McLaughlin Dean’s grant 2008–2010, Laval University, Quebec City, Canada.

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles.

Dr. Stéphane Poulin performed all statistical analyses. He had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Authors’ disclosures available online (http://j-alz.com/manuscript-disclosures/16-0767r3).

Footnotes

Trial Registration: The Alzheimer’s Disease Neuroimaging Initiative. NCT00106899. http://clinicaltrials.gov/ct2/show/NCT00106899?term=adni&rank=4

References

  • 1.Lyketsos CG, Carrillo MC, Ryan JM, Khachaturian AS, Trzepacz P, Amatniek J, Cedarbaum J, Brashear R, Miller DS. Neuropsychiatric symptoms in Alzheimer’s disease. Alzheimers Dement. 2011;7:532–539. doi: 10.1016/j.jalz.2011.05.2410. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Snowden JS, Stopford CL, Julien CL, Thompson JC, Davidson Y, Gibbons L, Pritchard A, Lendon CL, Richardson AM, Varma A, Neary D, Mann D. Cognitive phenotypes in Alzheimer’s disease and genetic risk. Cortex. 2007;43:835–845. doi: 10.1016/s0010-9452(08)70683-x. [DOI] [PubMed] [Google Scholar]
  • 3.Dickerson BC, Wolk DA. Dysexecutive versus amnesic phenotypes of very mild Alzheimer’s disease are associated with distinct clinical, genetic and cortical thinning characteristics. J Neurol Neurosurg Psychiatry. 2011;82:45–51. doi: 10.1136/jnnp.2009.199505. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Musicco M, Salamone G, Caltagirone C, Cravello L, Fadda L, Lupo F, Mosti S, Perri R, Palmer K. Neuropsychological predictors of rapidly progressing patients with Alzheimer’s disease. Dement Geriatr Cogn Disord. 2010;30:219–228. doi: 10.1159/000319533. [DOI] [PubMed] [Google Scholar]
  • 5.Ropacki SA, Jeste DV. Epidemiology of and risk factors for psychosis of Alzheimer’s disease: A review of 55 studies published from 1990 to 2003. Am J Psychiatry. 2005;162:2022–2030. doi: 10.1176/appi.ajp.162.11.2022. [DOI] [PubMed] [Google Scholar]
  • 6.Bruen PD, McGeown WJ, Shanks MF, Venneri A. Neuroanatomical correlates of neuropsychiatric symptoms in Alzheimer’s disease. Brain. 2008;131:2455–2463. doi: 10.1093/brain/awn151. [DOI] [PubMed] [Google Scholar]
  • 7.Rosenberg PB, Nowrangi MA, Lyketsos CG. Neuropsychiatric symptoms in Alzheimer’s disease: What might be associated brain circuits? Mol Aspects Med. 2015;43–44:25–37. doi: 10.1016/j.mam.2015.05.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Lopez OL, Wisniewski SR, Becker JT, Boller F, DeKosky ST. Psychiatric medication and abnormal behavior as predictors of progression in probable Alzheimer disease. Arch Neurol. 1999;56:1266–1272. doi: 10.1001/archneur.56.10.1266. [DOI] [PubMed] [Google Scholar]
  • 9.Rabins PV, Schwartz S, Black BS, Corcoran C, Fauth E, Mielke M, Christensen J, Lyketsos C, Tschanz J. Predictors of progression to severe Alzheimer’s disease in an incidence sample. Alzheimers Dement. 2013;9:204–207. doi: 10.1016/j.jalz.2012.01.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Peters ME, Schwartz S, Han D, Rabins PV, Steinberg M, Tschanz JT, Lyketsos CG. Neuropsychiatric symptoms as predictors of progression to severe Alzheimer’s dementia and death: The Cache County Dementia Progression Study. Am J Psychiatry. 2015;172:460–465. doi: 10.1176/appi.ajp.2014.14040480. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Emanuel JE, Lopez OL, Houck PR, Becker JT, Weamer EA, Demichele-Sweet MA, Kuller L, Sweet RA. Trajectory of cognitive decline as a predictor of psychosis in early Alzheimer disease in the cardiovascular health study. Am J Geriatr Psychiatry. 2011;19:160–168. doi: 10.1097/JGP.0b013e3181e446c8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Ismail Z, Smith EE, Geda Y, Sultzer D, Brodaty H, Smith G, Aguera-Ortiz L, Sweet R, Miller D, Lyketsos CG Neuropsychiatric Symptoms Professional Interest ISTAART, Area. Neuropsychiatric symptoms as early manifestations of emergent dementia: Provisional diagnostic criteria for mild behavioral impairment. Alzheimers Dement. 2016;12:195–202. doi: 10.1016/j.jalz.2015.05.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–198. doi: 10.1016/0022-3956(75)90026-6. [DOI] [PubMed] [Google Scholar]
  • 14.Morris JC. The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology. 1993;43:2412–2414. doi: 10.1212/wnl.43.11.2412-a. [DOI] [PubMed] [Google Scholar]
  • 15.McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984;34:939–944. doi: 10.1212/wnl.34.7.939. [DOI] [PubMed] [Google Scholar]
  • 16.Kaufer DI, Cummings JL, Ketchel P, Smith V, MacMillan A, Shelley T, Lopez OL, DeKosky ST. Validation of the NPI-Q, a brief clinical form of the Neuropsychiatric Inventory. J Neuropsychiatry Clin Neurosci. 2000;12:233–239. doi: 10.1176/jnp.12.2.233. [DOI] [PubMed] [Google Scholar]
  • 17.Schneider LS, Tariot PN, Lyketsos CG, Dagerman KS, Davis KL, Davis S, Hsiao JK, Jeste DV, Katz IR, Olin JT, Pollock BG, Rabins PV, Rosenheck RA, Small GW, Lebowitz B, Lieberman JA. National Institute of Mental Health Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE): Alzheimer disease trial methodology. Am J Geriatr Psychiatry. 2001;9:346–360. [PubMed] [Google Scholar]
  • 18.Steinberg M, Tschanz JT, Corcoran C, Steffens DC, Norton MC, Lyketsos CG, Breitner JC. The persistence of neuropsychiatric symptoms in dementia: The Cache County Study. Int J Geriatr Psychiatry. 2004;19:19–26. doi: 10.1002/gps.1025. [DOI] [PubMed] [Google Scholar]
  • 19.Levy ML, Cummings JL, Fairbanks LA, Bravi D, Calvani M, Carta A. Longitudinal assessment of symptoms of depression, agitation, and psychosis in 181 patients with Alzheimer’s disease. Am J Psychiatry. 1996;153:1438–1443. doi: 10.1176/ajp.153.11.1438. [DOI] [PubMed] [Google Scholar]
  • 20.Devanand DP, Jacobs DM, Tang MX, Del Castillo-Castaneda C, Sano M, Marder K, Bell K, Bylsma FW, Brandt J, Albert M, Stern Y. The course of psychopathologic features in mild to moderate Alzheimer disease. Arch Gen Psychiatry. 1997;54:257–263. doi: 10.1001/archpsyc.1997.01830150083012. [DOI] [PubMed] [Google Scholar]
  • 21.Haupt M, Kurz A, Janner M. A 2-year follow-up of behavioural and psychological symptoms in Alzheimer’s disease. Dement Geriatr Cogn Disord. 2000;11:147–152. doi: 10.1159/000017228. [DOI] [PubMed] [Google Scholar]
  • 22.Duggan C, Sham P, Minne C, Lee A, Murray R. Family history as a predictor of poor long-term outcome in depression. Br J Psychiatry. 1998;173:527–530. doi: 10.1192/bjp.173.6.527. [DOI] [PubMed] [Google Scholar]
  • 23.Norman RM, Manchanda R, Malla AK, Harricharan R, Northcott S. The significance of family history in first-episode schizophrenia spectrum disorder. J Nerv Ment Dis. 2007;195:846–852. doi: 10.1097/NMD.0b013e318156f8e2. [DOI] [PubMed] [Google Scholar]
  • 24.Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33:341–355. doi: 10.1016/s0896-6273(02)00569-x. [DOI] [PubMed] [Google Scholar]
  • 25.Fischl B. FreeSurfer. Neuroimage. 2012;62:774–781. doi: 10.1016/j.neuroimage.2012.01.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Butt ZA, Strauss ME. Relationship of family and personal history to the occurrence of depression in persons with Alzheimer’s disease. Am J Geriatr Psychiatry. 2001;9:249–254. [PubMed] [Google Scholar]
  • 27.Green RC, Cupples LA, Kurz A, Auerbach S, Go R, Sadovnick D, Duara R, Kukull WA, Chui H, Edeki T. Depression as a risk factor for Alzheimer disease: The MIRAGE Study. Arch Neurol. 2003;60:753–759. doi: 10.1001/archneur.60.5.753. [DOI] [PubMed] [Google Scholar]
  • 28.Kramberger MG, Jelic V, Kåreholt I, Enache D, Eriksdotter Jönhagen M, Winblad B, Aarsland D. Cerebrospinal fluid Alzheimer markers in depressed elderly subjects with and without Alzheimer’s disease. Dement Geriatr Cogn Dis Extra. 2012;2:48–56. doi: 10.1159/000334644. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Taylor WD, Steffens DC, MacFall JR, McQuoid DR, Payne ME, Provenzale JM, Krishnan KRR. White matter hyperintensity progression and late-life depression outcomes. Arch Gen Psychiatry. 2003;60:1090–1096. doi: 10.1001/archpsyc.60.11.1090. [DOI] [PubMed] [Google Scholar]
  • 30.Frodl TS, Koutsouleris N, Bottlender R, Born C, Jäger M, Scupin I, Reiser M, Möller H-J, Meisenzahl EM. Depression-related variation in brain morphology over 3 years: Effects of stress? Arch Gen Psychiatry. 2008;65:1156–1165. doi: 10.1001/archpsyc.65.10.1156. [DOI] [PubMed] [Google Scholar]
  • 31.Trzepacz PT, Yu P, Bhamidipati PK, Willis B, Forrester T, Tabas L, Schwarz AJ, Saykin AJ Initiative AsDN. Frontolimbic atrophy is associated with agitation and aggression in mild cognitive impairment and Alzheimer’s disease. Alzheimers Dement. 2013;9:S95–S104. e101. doi: 10.1016/j.jalz.2012.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Oudega ML, van Exel E, Wattjes MP, Comijs HC, Scheltens P, Barkhof F, Eikelenboom P, de Craen A, Beekman A, Stek ML. White matter hyperintensities, medial temporal lobe atrophy, cortical atrophy, and response to electroconvulsive therapy in severely depressed elderly patients. J Clin Psychiatry. 2011;72:104–112. doi: 10.4088/JCP.08m04989blu. [DOI] [PubMed] [Google Scholar]
  • 33.Baldwin R, Jeffries S, Jackson A, Sutcliffe C, Thacker N, Scott M, Burns A. Treatment response in late-onset depression: Relationship to neuropsychological, neuroradiological and vascular risk factors. Psychol Med. 2004;34:125–136. doi: 10.1017/s0033291703008870. [DOI] [PubMed] [Google Scholar]
  • 34.Salvadore G, Nugent AC, Lemaitre H, Luckenbaugh DA, Tinsley R, Cannon DM, Neumeister A, Zarate CA, Drevets WC. Prefrontal cortical abnormalities in currently depressed versus currently remitted patients with major depressive disorder. Neuroimage. 2011;54:2643–2651. doi: 10.1016/j.neuroimage.2010.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Mattsson N, Schott JM, Hardy J, Turner MR, Zetterberg H. Selective vulnerability in neurodegeneration: Insights from clinical variants of Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2016;87:1000–1004. doi: 10.1136/jnnp-2015-311321. [DOI] [PubMed] [Google Scholar]
  • 36.DeMichele-Sweet MA, Sweet RA. Genetics of psychosis in Alzheimer disease. Curr Genet Med Rep. 2014;2:30–38. doi: 10.1007/s40142-014-0030-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Shah C, DeMichele-Sweet MA, Sweet RA. Genetics of psychosis of Alzheimer disease. Am J Med Genet B Neuropsychiatr Genet. 2017;174:27–35. doi: 10.1002/ajmg.b.32413. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Sweet RA, Pollock BG, Sukonick DL, Mulsant BH, Rosen J, Klunk WE, Kastango KB, DeKosky ST, Ferrell RE. The 5-HTTPR polymorphism confers liability to a combined phenotype of psychotic and aggressive behavior in Alzheimer disease. Int Psychogeriatr. 2001;13:401–409. doi: 10.1017/s1041610201007827. [DOI] [PubMed] [Google Scholar]
  • 39.de Leon MJ, McRae T, Tsai JR, George AE, Marcus DL, Freedman M, Wolf AP, McEwen B. Abnormal cortisol response in Alzheimer’s disease linked to hippocampal atrophy. Lancet. 1988;2:391–392. doi: 10.1016/s0140-6736(88)92855-3. [DOI] [PubMed] [Google Scholar]
  • 40.Elgh E, Lindqvist Astot A, Fagerlund M, Eriksson S, Olsson T, Nasman B. Cognitive dysfunction, hippocampal atrophy and glucocorticoid feedback in Alzheimer’s disease. Biol Psychiatry. 2006;59:155–161. doi: 10.1016/j.biopsych.2005.06.017. [DOI] [PubMed] [Google Scholar]
  • 41.Sapolsky RM. Glucocorticoids and hippocampal atrophy in neuropsychiatric disorders. Arch Gen Psychiatry. 2000;57:925–935. doi: 10.1001/archpsyc.57.10.925. [DOI] [PubMed] [Google Scholar]
  • 42.Casey DE. Metabolic issues and cardiovascular disease in patients with psychiatric disorders. Am J Med. 2005;118:15–22. doi: 10.1016/j.amjmed.2005.01.046. [DOI] [PubMed] [Google Scholar]
  • 43.Alexopoulos GS. The vascular depression hypothesis: 10 years later. Biol Psychiatry. 2006;60:1304–1305. doi: 10.1016/j.biopsych.2006.09.006. [DOI] [PubMed] [Google Scholar]
  • 44.Fann J, Leonetti A, Jaffe K, Katon W, Cummings P, Thompson R. Psychiatric illness and subsequent traumatic brain injury: A case control study. J Neurol Neurosurg Psychiatry. 2002;72:615–620. doi: 10.1136/jnnp.72.5.615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Barnes DE, Yaffe K, Byers AL, McCormick M, Schaefer C, Whitmer RA. Midlife vs late-life depressive symptoms and risk of dementia: Differential effects for Alzheimer disease and vascular dementia. Arch Gen Psychiatry. 2012;69:493–498. doi: 10.1001/archgenpsychiatry.2011.1481. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Luchsinger JA, Honig LS, Tang MX, Devanand DP. Depressive symptoms, vascular risk factors, and Alzheimer’s disease. Int J Geriatr Psychiatry. 2008;23:922–928. doi: 10.1002/gps.2006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Chen ST, Sultzer DL, Hinkin CH, Mahler ME, Cummings JL. Executive dysfunction in Alzheimer’s disease: Association with neuropsychiatric symptoms and functional impairment. J Neuropsychiatry Clin Neurosci. 1998;10:426–432. doi: 10.1176/jnp.10.4.426. [DOI] [PubMed] [Google Scholar]
  • 48.Cummings JL. Frontal-subcortical circuits and human behavior. Arch Neurol. 1993;50:873–880. doi: 10.1001/archneur.1993.00540080076020. [DOI] [PubMed] [Google Scholar]
  • 49.Tekin S, Mega MS, Masterman DM, Chow T, Garakian J, Vinters HV, Cummings JL. Orbitofrontal and anterior cingulate cortex neurofibrillary tangle burden is associated with agitation in Alzheimer disease. Ann Neurol. 2001;49:355–361. [PubMed] [Google Scholar]
  • 50.Zubenko GS, Moossy J, Martinez AJ, Rao G, Claassen D, Rosen J, Kopp U. Neuropathologic and neurochemical correlates of psychosis in primary dementia. Arch Neurol. 1991;48:619–624. doi: 10.1001/archneur.1991.00530180075020. [DOI] [PubMed] [Google Scholar]
  • 51.Boublay N, Schott AM, Krolak-Salmon P. Neuroimaging correlates of neuropsychiatric symptoms in Alzheimer’s disease: A review of 20 years of research. Eur J Neurol. 2016;23:1500–1509. doi: 10.1111/ene.13076. [DOI] [PubMed] [Google Scholar]
  • 52.Mega MS, Cummings JL. Frontal-subcortical circuits and neuropsychiatric disorders. J Neuropsychiatry Clin Neurosci. 1994;6:358–370. doi: 10.1176/jnp.6.4.358. [DOI] [PubMed] [Google Scholar]
  • 53.Cavanna AE, Trimble MR. The precuneus: A review of its functional anatomy and behavioural correlates. Brain. 2006;129:564–583. doi: 10.1093/brain/awl004. [DOI] [PubMed] [Google Scholar]
  • 54.Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, Reiss AL, Greicius MD. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27:2349–2356. doi: 10.1523/JNEUROSCI.5587-06.2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Craig AD. How do you feel—now? The anterior insula and human awareness. Nat Rev Neurosci. 2009;10:59–70. doi: 10.1038/nrn2555. [DOI] [PubMed] [Google Scholar]
  • 56.Porsteinsson AP, Drye LT, Pollock BG, Devanand DP, Frangakis C, Ismail Z, Marano C, Meinert CL, Mintzer JE, Munro CA, Pelton G, Rabins PV, Rosenberg PB, Schneider LS, Shade DM, Weintraub D, Yesavage J, Lyketsos CG, Cit ADRG. Effect of citalopram on agitation in Alzheimer disease: The CitAD randomized clinical trial. JAMA. 2014;311:682–691. doi: 10.1001/jama.2014.93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Steinberg M, Lyketsos CG. Atypical antipsychotic use in patients with dementia: Managing safety concerns. Am J Psychiatry. 2012;169:900–906. doi: 10.1176/appi.ajp.2012.12030342. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Takada M, Fujimoto M, Hosomi K. Association between benzodiazepine use and dementia: Data mining of different medical databases. Int J Med Sci. 2016;13:825–834. doi: 10.7150/ijms.16185. [DOI] [PMC free article] [PubMed] [Google Scholar]

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