Parkinsonism and Related Disorders 20S1 (2014) S104–S107

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Predictors of Parkinson’s disease dementia: Towards targeted therapies for a heterogeneous disease Sarah F. Moore *, Roger A. Barker John Van Geest Centre for Brain Repair, University of Cambridge, Cambridge, CB2 0PY, UK

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Keywords: Parkinson’s disease Dementia Predictors Biomarkers PD PDD

Parkinson’s disease dementia (PDD) has become an increasing area of research as treatments for the motor features of Parkinson’s disease (PD) have improved and the population of patients with PD grows and ages. If predictors could be used to identify a sub-population of patients at risk of developing an early PDD then research into its neuropathological basis and treatment could be more effectively targeted to specific individuals. At present the predictors with the most evidence have arisen from longitudinal studies tracking the development of dementia in populations of incident, newly diagnosed patients with PD. Evidence exists for predictors across multiple domains: clinical, biological, neuroimaging and genetic. Some of the most robust of these suggest that PDD may develop as the result of an age and tau dependent, posterior cortically based process driven in some cases by mutations in the gene for glucocerebrosidase (GBA). It is clear, though, that more research needs to be undertaken into finding reliable predictors of PDD. At present the best approach may be to combine a set of predictors already identified in order to provide a basis for understanding why and how it occurs. Through this, new therapeutic strategies may emerge. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction Parkinson’s disease (PD) is a heterogeneous neurodegenerative condition with a characteristic motor phenotype but also multiple non-motor manifestations. As options to ameliorate motor features improve, scrutiny has moved towards the non-motor characteristics of PD. Parkinson’s disease dementia (PDD) is a critical non-motor evolution of PD. It is a key prognostic factor for entry to a nursing home and carries with it an increase in mortality [1]. In 2007 a taskforce set up by the Movement Disorder Society (MDS) came up with a set of clinical diagnostic criteria for probable PDD which require an established diagnosis of PD and a dementia syndrome of insidious onset for diagnosis [2]. Due to their relatively recent publication many cited studies have not used these criteria but they should help increase concordance between study results in the future. The estimated prevalence of PDD varies but in a recent community based longitudinal incident cohort study the cumulative probability of developing dementia was 46 per cent by 10 years from diagnosis [3] with the number increasing to 80% at 20 years of follow up in a separate study [4]. This suggests that most patients with PD will end up with a dementia but that they reach it at different rates and if the factors underlying this could be elucidated, * Corresponding author. Tel.: +44 1223 331160; fax: +44 1223 331174. E-mail address: [email protected] (S.F. Moore). 1353-8020/$ – see front matter © 2013 Elsevier Ltd. All rights reserved.

then novel targets for treatment may emerge which could then be matched to patients with fast and slow rates of conversion to PDD. Being able to predict who will develop PD dementia, and when, needs to be robust and accurate long enough before it develops to provide an opportunity for intervention. It should also be easily measurable in a manner that is acceptable to patients and costeffective for healthcare providers. 2. Heterogeneity of cognitive impairment within Parkinson’s disease To understand the search for predictors of PDD it is important to recognise the heterogeneous nature of cognitive impairments within PD. A theory that we have put forward is that cognitive impairments in PD can be broadly split into two independent processes, with the caveat that these may overlap in individual patients who have both processes going on at the same time. One process involves a dopaminergically mediated fronto-striatal based deficit of executive function which occurs throughout the disease and is driven by a complex interaction of disease stage, L-dopa dose and COMT genotype and which does not progress to dementia. On the other hand, there is a dopamine independent cognitive decline to dementia that involves a loss of posterior cortically based cognitive tasks and is related to age, tau haplotype, GBA gene status and possibly cortical amyloid-b [3,5–7]. In this review we will focus on this type of cognitive impairment, which some

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would term PD-MCI, although we would prefer the term prodromal PD dementia [8]. Whilst we will explore this aspect of PD dementia, there is a school of thought that the dementia in PD is also predicted by frontally based deficits of executive function or indeed a combination of cognitive deficits across a range of domains. In this respect an MDS task force was set up to draw up new guidelines to standardise all this into a diagnosis of PD-MCI, but predictor findings related solely to the broad criteria of PD-MCI will only be valid if it can be proven that PD-MCI in its current broad definition progresses to PDD, which at present is not certain [9]. For new data to be robust, correlations with PD-MCI are not therefore sufficient and longitudinal cohort follow up studies correlating predictors with clinical findings of progression to dementia are needed. Finally it should be noted that PDD has historically been treated as a distinct entity from dementia with Lewy bodies (DLB) with regard to treatment and research. PDD diagnosis requires motor symptoms of PD to be present for one year before the dementia is diagnosed whilst DLB has the opposite temporal relationship but there is significant cross-over between the two diseases. Furthermore the distinction between PDD and Alzheimer’s Disease (AD) has also become blurred with increasing evidence for overlap of pathology in a substantial number of patients [6,10]. Thus whilst we talk about PDD as a discrete entity, this is something of a simplification. 3. Clinical predictors of PDD 3.1. Age Age has been shown to be an important predictor of PDD, dependent on the absolute age of a person with PD rather than the age of onset of the disease [3,4,11]. The influence of age, independent of disease duration, suggests a role for other pathology in addition to Lewy body disease in the development of PDD. In our own CamPaIGN study, the 10-year longitudinal follow up data found an association between PDD and MAPT genotype which influences tau transcription [3]. Another study found that greater cortical amyloid-b deposition and ageing may be closely related and together influence the onset of dementia in PD [6]. This all fits with the hypothesis that there is an overlap of pathologies (a-synuclein, tau and amyloid-b) in patients with PDD and perhaps with increasing age there may be more concomitant age-related pathologies present [10]. 3.2. Motor features Motor phenotype (tremor dominant [TD] versus postural instability and gait difficulties [PIGD]) appears to have a significant relationship to cognitive decline and the development of dementia in PD. In an 8-year prospective cohort study no patients with persistent TD disease developed dementia and their Mini Mental State Examination (MMSE) scores remained stable whereas those who had PIGD at baseline, or who converted to PIGD, had a significantly higher incidence of dementia and decline in MMSE score [12]. The progression from TD to PIGD phenotype is unidirectional and irreversible and symptoms show limited response to dopaminergic medication, similar to PDD, so it is postulated that this may indicate a common underlying spreading pathological process [12]. However in our CamPaIGN study, we found no significant overlap between motor phenotype and dementia for reasons that are not clear. Although at present the relationship between the neuropathology of PIGD and dementia remains uncertain, a link to amyloid-b deposition and/or cholinergic pathways may exist. A recent crosssectional study suggested a relationship between lower levels of

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cerebrospinal fluid (CSF) amyloid-b and a PIGD presentation [13], which links to a study showing that reduced CSF amyloid-b may be a predictor of dementia [14]. In addition, cholinesterase inhibitors have been shown to improve cognition in PDD [15] and reduce the risk of falls in PD patients with significant postural instability [16]. If a relationship exists between dementia and PIGD it is clearly complex but the presence of PIGD can be easily measured clinically and thus if further evidence emerges, it might be a useful predictor of PDD. 3.3. Cognitive features Limbic and posterior cortical deficits have been shown to predict subsequent dementia. Impaired semantic fluency and pentagon copying at diagnosis of PD predicted dementia and rate of cognitive decline over 10 years in the CamPaIGN study [3]. In a smaller study conducted over 18 months, similar impairments in verbal learning, semantic fluency and silhouette-perception were also linked to the subsequent development of dementia [14]. In addition to providing practical neuropsychological predictive tests that can be easily undertaken at minimal cost, these findings also have implications for our understanding of the neuroanatomical pathology of PDD. Semantic fluency impairment is thought to represent temporal lobe dysfunction and impaired pentagon copying as a marker of visuospatial and constructional ability to represent parietal lobe dysfunction. These would support the concept of PDD being a posterior cortically based process with a cholinergic and dopaminergic element to it given the effects that drugs acting on these systems have in modifying the expression of these features [3]. There are also studies that have found correlations between frontal executive function and subsequent dementia [17]. However, phonemic fluency as a measure of executive function has not been shown to correlate with subsequent dementia in other studies and is instead thought to represent independent frontostriatal, dopaminergic cognitive impairment [5,14]. This area is still controversial and further research is needed on it. 3.4. Other non-motor features Rapid eye movement (REM) sleep behaviour disorder is characterised by loss of the normal atonia that accompanies REM sleep. In a small cohort followed over 4 years, the presence of REM sleep behaviour disorder (RBD) predicted the subsequent development of a dementia [18]. Indeed in this study, only patients with RBD developed PDD. A further study with a short follow up confirmed these findings, showing a significant relationship between clinically apparent RBD and the development of dementia in PD, though this time the development of dementia was not exclusive to patients with RBD. The authors posit that the significance of development of dementia only in those with clinical, rather than sub-clinical, RBD may reflect the spread of Lewy body pathology out to the limbic system [19]. Hallucinations are common in PD and LBD and have been used to differentiate Alzheimer’s disease from them [2]. Visual hallucinations (VH) are associated with increased incidence of PDD as well as increased rate of cognitive decline (see e.g. [20]). Despite no predictive link with PDD being established, it has been shown that autonomic symptoms including constipation, urinary incontinence, orthostatic hypotension and erectile dysfunction can precede the onset of PD or DLB by up to 20 years and are both common in PDD and a possible predictor of survival [21]. The implication of the presence of these autonomic features in PD is similar to that of the presence of dementia, namely it is a clinical expression of a more malignant spread of pathology to areas outside the substantia nigra.

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4. Biological predictors of PDD

5.2. Positron emission tomography (PET)

4.1. CSF markers

PET scanning has also been investigated as a predictor of PDD. A recent study with controls, patients with PD and patients with PDD, compared three forms of PET scanning: [18 F]FDG-PET which measures cerebral glucose uptake, [11 C]PIB-PET a marker of amyloid and [11 C](R)PK11195-PET a marker of microglial activation. Results showed that cortical microglial activation and reduced glucose metabolism were detected in PD and PDD. Microglial activation was inversely correlated with MMSE score and reduced cerebral glucose metabolism correlated with MMSE. The authors suggest that this provides evidence for persistent microglial activation as a cause of neuronal damage and that therapeutic targets might include agents that suppress or reverse this. In addition they found no consistent increase in cortical amyloid in patients with PDD [27]. This correlates with findings from another recent study examining [11 C]PIB-PET in PDD where 2 out of 10 patients had significant cortical amyloid whilst the non-PDD cases only had an increase in signal in the pons and midbrain [28]. As imaging techniques continue to improve there is hope that further predictors might emerge from this avenue of research and there is certainly scope for these being included in future longitudinal trials. The practicalities of using neuroimaging as a predictor may be better than those approaches relying on lengthy follow up using complex clinical measures as patients with dementia may not be able to cooperate with such tests over time.

Decreased levels of amyloid-b (Ab) in the CSF have been shown to predict cognitive decline and the development of a subsequent dementia [14,22]. The suggested neuropathological basis for this makes intuitive sense with low levels of Ab reflecting increased sequestration of Ab within the brain parenchyma which allies with the concept of PDD as a heterogeneous disease with complex underlying pathologies of a-synuclein, tau and amyloid-b [10]. The same studies found that CSF tau was not a predictor of a later dementia, though increased levels have previously been shown in some patients with PDD [14,22]. CSF a-synuclein levels have only recently begun to be tested and despite discrepancies between trial results, there are some initial results suggesting a possible link between low levels of CSF-a-synuclein and synucleinopathies as well as significantly lower levels in those with DLB – a finding which may be applicable to PDD in future [23]. 4.2. Serum markers Plasma epidermal growth factor (EGF) levels in the lowest quartile of a PD cohort predicted an eight times higher risk of developing dementia [24]. Despite some limitations of the study these results are exciting. Firstly, they provide the first evidence for a possible serum based predictor of dementia within a population of patients with PD. Secondly, they provide new avenues for research into the pathological basis of PDD. Indeed the finding correlates with suggestions that EGF may have neuroprotective properties [24]. Higher serum uric acid levels have been shown to decrease the risk of developing all types of dementia and in this respect a recent review found that in PD, higher serum concentrations may infer reduced risk for getting PD as well as slowing down disease progression [25]. It remains to be seen whether the same will be shown in PDD, though the implication that it exerts its protective effects through dopaminergic neurons suggests it may be of more relevance to the development of the impairment associated with dopaminergic fronto-striatal based cognitive deficits than dementia per se. Further research is needed to ascertain if the above results can be replicated, ideally in a large incident cohort of patients with PD followed prospectively. 5. Neuroimaging predictors of PDD 5.1. Magnetic resonance imaging (MRI) MRI has been proposed as a tool for predicting and following the development of cognitive impairment and dementia in PD, following some success in its use within AD. A review of the available literature on the subject found that although there were MRI detectable disruptions of white tract matter and atrophy of grey matter across all stages of PD, there is not enough evidence at present to ascertain their utility as biomarkers and further longitudinal studies are required to gather this information [26]. Since then a further study has been published that supports the use of MRI as a predictor of PDD. Cortical thinning of the frontal and anterior cingulate regions were significant predictors of subsequent development of dementia in a cohort of PD patients. The authors found that it was difficult to fit frontal cortical imaging predictors with established neuropsychological deficits. However changes in the anterior cingulate, which is affected pathologically in PDD, correlated with neuropsychological deficits found to be predictors of PDD such as verbal memory and word generation [14].

6. Genetic predictors of PDD Microtubule associated protein tau (MAPT) H1/H1 genotype has been found in our CamPaIGN study to correlate with rate of cognitive decline and development of an early dementia [3]. The H1 haplotype is associated with an increase in 4-repeat tau isoforms in the brain and this is thought to play a key role in the pathogenic development of this condition, although the exact mechanism for this is currently unclear [5]. Moreover a recent in vitro study has suggested one possible mechanism for an interaction with tau cross seeding to promote a-synuclein aggregation [29]. Glucocerebrosidase (GBA) mutations have been shown to increase not only the risk of developing PD but also the rate of cognitive decline [30]. These findings have been confirmed in a prospective study of incident cases of newly diagnosed PD where the increased risk of progression to dementia was 5 times greater in those with a GBA mutation compared to those without [7]. In addition a crosssectional study found cognitive impairment to be more frequent and more severe in patients with mutations of GBA than matched controls without GBA mutations [30]. Apolipoprotein E (APOE) genotype is an established factor in the susceptibility to Alzheimer’s disease (AD). The establishment of pathological overlap between AD and PDD has stimulated research to see whether there may be a role for APOE genotype in risk of PDD. A large meta-analysis however has failed to show clear support for this [31]. Further to this other genotypes have been explored including the COMT Val158 Met genotype which has been found to correlate with the executive dysfunction in PD rather than the development of PDD [5]. Similarly the BDNF Val66 Met polymorphism, which may play a role in cognitive impairment in PD, has not been established to be important in longitudinal studies [3]. 7. Interpretation of predictors Although predictors of PDD across many domains are being actively researched there has been no single test identified that can conclusively detect those who will develop dementia.

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At present the best evidence comes from combining predictors from different domains to produce a cumulative effect of prediction. An example can be seen from a recent study where abnormalities in 3 biomarkers (CSF, neuropsychological and MRI based) improved accuracy of diagnosis to 100% [14]. Despite the limitations of that study, this illustrates the possibility of developing a small set of predictors which in combination can increase the accuracy of prognosis. In developing those markers it is important that future studies, where possible, use unified diagnostic criteria and longitudinal cohorts to provide robust evidence for inclusion of predictors so that they can be properly tested. Importantly it must be borne in mind that dementia may develop at different rates in different individuals with PD and so ultimately there may be no unifying predictor or neuropathological basis for it. 8. Conclusions With the identification of predictors of PDD there is the exciting prospect of being able to tailor treatment for patients with Parkinson’s disease based on a stratification of risk, such that those with higher risk of developing an early dementia may be preferentially enrolled into disease modifying therapy trials. It is crucial for these trials that the predictors of risk for PDD are accurate or the opportunity to prove efficacy for new medications may be lost. Acknowledgements Some of the work in this review has been supported by funding from EU FP7 TRANSEURO, Cure PD, Parkinson’s UK, Wellcome Trust and the NIHR Biomedical Research Centre to Addenbrooke’s Hospital and the University of Cambridge. Conflict of interests The authors have no conflicts of interest to declare. References [1] Levy G, Tang MX, Louis ED, Cote LJ, Alfaro B, Mejia H, et al. The association of incident dementia with mortality in PD. Neurology 2002;59(11):1708–13. [2] Emre M, Aarsland D, Brown R, Burn DJ, Duyckaerts C, Mizuno Y, et al. Clinical diagnostic criteria for dementia associated with Parkinson’s disease. Mov Disord 2007;22(12):1689–707; quiz: 837. [3] Williams-Gray CH, Mason SL, Evans JR, Foltynie T, Brayne C, Robbins TW, et al. The CamPaIGN study of Parkinson’s disease: 10-year outlook in an incident population-based cohort. J Neurol Neurosurg Psychiatry 2013;84(11):1258–64. [4] Hely MA, Reid WG, Adena MA, Halliday GM, Morris JG. The Sydney multicenter study of Parkinson’s disease: the inevitability of dementia at 20 years. Mov Disord 2008;23(6):837–44. [5] Williams-Gray CH, Evans JR, Goris A, Foltynie T, Ban M, Robbins TW, et al. The distinct cognitive syndromes of Parkinson’s disease: 5 year follow-up of the CamPaIGN cohort. Brain 2009;132(Pt 11):2958–69. [6] Compta Y, Parkkinen L, O’Sullivan SS, Vandrovcova J, Holton JL, Collins C, et al. Lewy- and Alzheimer-type pathologies in Parkinson’s disease dementia: which is more important? Brain 2011;134(Pt 5):1493–505. [7] Winder-Rhodes SE, Evans JR, Ban M, Mason SL, Williams-Gray CH, Foltynie T, et al. Glucocerebrosidase mutations influence the natural history of Parkinson’s disease in a community-based incident cohort. Brain 2013;136(Pt 2):392–9. [8] Burn DJ, Barker RA. Mild cognitive impairment in Parkinson’s disease: millstone or milestone? Pract Neurol 2013;13(2):68–9.

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