A biomarker is defined as a biological characteristic that is objectively measured and evaluated as an indicator of normal biological or pathologic processes or of
pharmacologic responses to a therapeutic intervention. There are many biomarkers that have been proposed as possible candidates for the development of PDD; these cover different modalities and are shown in Table 1-5 (Svenningsson et al., 2012).
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Table 1-5 Studies of potential biomarkers in PDD (adapted from Svenningsson 2012) Biomarker Biomarker
subtype
Findings Reference(s)
Imaging Structural MRI Atrophy temporal, parietal & occipital cortices in PDD
Hippocampal plus parieto- temporal atrophy predicted cognitive impairment
(Burton et al., 2004; Beyer et al., 2007; Song
et al., 2011; Weintraub et al., 2011; Melzer et al.,
2012)
(Weintraub et al., 2012) 18FDG-PET Widespread cortical
metabolism in PDD; especially temporal, parietal & occipital areas
↓ perfusion occipital & posterior cingulate cortices predicted dementia
(Huang et al., 2007; Jokinen et al., 2010; Klein
et al., 2010)
(Bohnen et al., 2011)
PiB PET No difference in PiB amyloid binding in PDD vs. PD-CN PiB binding at baseline predicted ↓ cognition during longitudinal follow-up
(Foster et al., 2010; Gomperts et al., 2012) (Gomperts et al., 2013)
AChE PET Widespread ↓AChE activity in PDD: this correlated with attention/ executive function
(Bohnen et al., 2003; Bohnen et al., 2006b; Shimada et al., 2009) SPECT perfusion Hypoperfusion parietal/
occipital regions in cognitive impairment
(Firbank et al., 2003; Nobili et al., 2009)
CSF Aβ42 ↓ PDD>PD-MCI>PD-CN; Aβ
correlated with memory impairment
↓ Aβ42 predicted cognitive decline during longitudinal follow-up (Mollenhauer et al., 2006b; Compta et al., 2009; Alves et al., 2010; Montine et al., 2010) (Siderowf et al., 2010)
tau Mixed results (Mollenhauer et al.,
2006b; Compta et al., 2009; Alves et al., 2010; Montine et al., 2010) α-synuclein ↓ total α-syn in PD & DLB cf
controls/AD
(Hong et al., 2010; Mollenhauer et al., 2011b)
Plasma Epidermal growth
factor
↓ EGF levels predicted cognitive decline
(Chen-Plotkin et al., 2011)
Neuro- physiology
EEG Low background rhythm
frequency predicted cognitive decline
(Klassen et al., 2011)
SAI Abnormal SAI in PDD and PD-
MCI cf controls
(Celebi et al., 2012; Yarnall et al., 2013)
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Neuroimaging methods used to predict PDD have included both structural and
functional techniques. These are discussed in some detail in section 1.7.4, but in brief, structural imaging has demonstrated that atrophy of the parietal, temporal and
occipital cortices occur in cross-sectional studies of those with PDD or mild cognitive impairment (PD-MCI) compared with those with normal cognition (PD-CN) (Burton et al., 2004; Beyer et al., 2007; Song et al., 2011; Weintraub et al., 2011; Melzer et al., 2012). In a recent longitudinal study, an AD-type pattern of atrophy on MRI predicted long-term cognitive decline in PD subjects (Weintraub et al., 2012). Cortical
hypometabolism and hypoperfusion were noted in similar areas using positron
emission tomography (PET) and single photon emission computed tomography (SPECT), respectively, in those with PD and cognitive impairment (Firbank et al., 2003; Huang et al., 2007; Nobili et al., 2009; Jokinen et al., 2010; Klein et al., 2010); with a longitudinal study indicating that cerebral hypometabolism in the visual association and posterior cingulate cortices may predict the development of dementia (Bohnen et al., 2011). PET imaging using radiolabelled metabolites of acetylcholinesterase (AChE) as a marker of ACh activity has demonstrated widespread reductions in AChE in those with PDD compared with AD and control participants (Bohnen et al., 2003; Shimada et al., 2009). Lastly, in terms of imaging, a recent study of amyloid burden detected using Pittsburgh Compound B (PiB) PET has shown promise in predicting cognitive decline in PD, with a higher baseline PiB retention and a diagnosis of PD-MCI associated with worsening cognition at follow-up (Gomperts et al., 2013). Greater amyloid deposition at baseline also predicted executive impairment over time.
Other biomarkers that may be used to predict PDD include cerebrospinal fluid (CSF), plasma proteins and neurophysiological techniques. CSF and short latency afferent inhibition (SAI) as biomarkers are discussed in further detail in Chapter 3 and 4, respectively. Epidermal growth factor (EGF) is a plasma protein that has been postulated to support dopaminergic neurons as a neurotrophic factor, and has been associated with both baseline cognition and an eight-fold risk of progression to
dementia for those with EGF levels in the lowest quartile (Chen-Plotkin et al., 2011). In a smaller study of de novo PD subjects followed over two years, baseline EGF levels predicted poorer performance in frontal and temporal cognitive function (Pellecchia et al., 2013). Finally, quantitative EEG may be used as a predictive biomarker for the development of dementia (Klassen et al., 2011). In a prospective study of 106 non-
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demented PD participants, those with lower than median background rhythm frequency were 13-times more likely to develop dementia than those with normal rhythms over a mean duration of 3.3 years. The authors hypothesised that disruption in thedefault network (defined as the network of brain region activity that occurs during the cognitive resting state), perhaps due to amyloid deposition, could explain these findings (Klassen et al., 2011).
In conclusion, many biomarkers have been studied as possible predictors of dementia in PD. Due to the complexity and heterogeneity of the underlying pathophysiological processes it is unlikely that a single biomarker will predict PDD. But, taken together, these laboratory, imaging and clinical risk factors may allow clinicians to predict which patients are most likely to progress to this state, thus allowing better use of targeted interventions and improved prognostication.