doi:10.1093/brain/awl028
Brain (2006), 129, 1050–1058
Age-related differences in levodopa dynamics in
Parkinson’s: implications for motor complications
Vesna Sossi,1 Raúl de la Fuente-Fernández,3 Michael Schulzer,1 John Adams2 and Jon Stoessl1,2
1
3
University of British Columbia, 2Pacific Parkinson’s Research Centre, Vancouver, Canada and
Division of Neurology, Hospital Arquitecto Marcide, 15405 Ferrol (A Coruña), Spain
Correspondence to: Vesna Sossi, PhD, Pacific Parkinson’s Research Centre, Room M37, Purdy Pavilion,
2221 Wesbrook Mall, Vancouver, Canada BC V6T 2B5
E-mail: [email protected]
Treatment-related motor complications in Parkinson’s disease have been previously linked to disease-induced
pre-synaptic alterations: dopaminergic denervation and changes in dopamine (DA) release patterns. The
occurrence of such complications is also known to be partly dependent on the age of disease onset, occurring
more frequently in patients with disease onset at a younger age. Using positron emission tomography (PET)
and 4-h-long 18F-fluorodopa (FD) scans we have investigated in vivo an age dependence of disease-induced
changes in DA turnover as a possible contributing factor to the age-related differences in treatment-related
motor complications. We evaluated the relative changes in DA turnover (measured by its direct inverse,
effective DA distribution volume—EDV) and DA synthesis and vesicular storage capacity (quantified by the
plasma input uptake rate constant Ki) in Parkinson’s disease patients as a function of age (n = 27, age range
38–79 years). After correcting for disease severity, a significant negative correlation was found between age
and magnitude of disease-induced decrease in EDV and in Ki in the putamen (P < 0.001, P = 0.02, respectively).
However, the difference between the disease-induced decrease in EDV and that in Ki also exhibited an age
dependence (P < 0.001), indicating a relatively higher disease-induced increase in DA turnover (inverse of EDV)
compared with the decrease in DA synthesis and storage rate in patients of younger age compared with older
patients. This finding implies that DA turnover in younger-onset patients undergoes a relatively greater
alteration and thus likely contributes to a greater imbalance between DA synthesis, storage and release,
which could lead to larger swings in synaptic DA levels. It has indeed been suggested on theoretical grounds
that such imbalance may contribute to the greater propensity for motor fluctuations. These results provide
one possible explanation for the age-dependent occurrence of complications and support the existence of a
pre-synaptic contribution to the occurrence of motor complications.
Keywords: dopaminergic system; DA turnover; motor complications; age dependence
Abbreviations: DA = dopamine; EDV = effective DA distribution volume; FD = 18F-fluorodopa; 18F-DA =
F-fluorodopamine; Ki = FD uptake rate constant; 3OMFD = 3-O-methylfluorodopa; PET = positron emission
tomography; ROIs = regions of interest
18
Received July 28, 2005. Revised January 6, 2006. Accepted January 12, 2006. Advance Access publication February 13, 2006.
Introduction
Parkinson’s disease is characterized by a progressive degeneration of the dopaminergic system leading to an increasingly
more severe dopamine (DA) depletion state. It is known that
clinical symptoms, tremor, rigidity and bradykinesia, generally occur when at least 50% of the dopaminergic neurons
have died thus implying the existence of a relatively long
pre-clinical period during which several disease-induced
regulatory and compensatory changes have taken place. At
#
present, all forms of treatment are of symptomatic nature,
with the most common being the administration of
levodopa. Most patients respond well to levodopa; however,
a substantial proportion (50%) develop motor complications (motor fluctuations and dyskinesia) within 5 years of
chronic levodopa treatment (Marsden and Parkes, 1976;
Marsden et al., 1982). Although present in patients of any
age at disease onset, the occurrence of treatment-related
The Author (2006). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: [email protected]
Motor complications in Parkinson’s disease
motor complications has been observed to be more
frequent in patients with disease onset at a younger age
(Kostic et al., 1991; de la Fuente-Fernandez et al., 2000;
Obeso et al., 2000).
The mechanisms underlying such motor complications are
as yet not completely understood: the currently favoured
hypothesis is the concept that motor complications are related to abnormal pulsatile stimulation of denervated DA receptors by relatively short acting dopaminergic agents such as
levodopa. In support of this hypothesis recent studies suggest
that motor complications can be reduced with a continuous
infusion of levodopa (Stocchi et al., 2005). In this light, motor
fluctuations would thus arise from a combined contribution
from pre-synaptic and post-synaptic components: altered
DA release pattern resulting from nigrostriatal damage
and consequent post-synaptic changes due to the resulting
pulsatile stimulation of DA receptors.
Recently, a theoretical model describing a possible mechanism for the pre-synaptic involvement in the occurrence
of motor complications was developed (de la FuenteFernandez et al., 2004a). In this model a probabilistic approach
was used to estimate the amount of DA released by exocytosis from the vesicles. The model was able to reproduce the
time course of the clinical benefits of treatment for stable
responders, wearing-off fluctuators and on–off fluctuators
(patients characterized by an increasingly shortened duration
of clinical benefits of treatment). The proportion of vesicles
that undergo exocytosis per unit time (therefore a measure of
DA turnover) was identified as one of the two most important
parameters in modelling the time course of levodopa response
(together with the re-uptake capacity of surviving terminals).
Furthermore the model showed that differences in nigrostriatal DA damage are not strictly necessary to explain the
occurrence of motor fluctuations: that is, for equal disease
severity a relatively higher turnover is sufficient to induce
motor complications. The model also demonstrated that the
relative increase in DA release rate necessary to cause
fluctuations was lower for a higher level of DA depletion
(and thus disease severity).
Further experimental evidence of pre-synaptic involvement
in the occurrence of motor complications comes from recent
positron emission tomography (PET) studies that showed an
altered pattern of exogenous levodopa-derived DA release
(an indirect measure of DA turnover) in Parkinson’s disease
patients who were stable at the time of the PET scans, but who
went on to develop motor fluctuations (de la FuenteFernandez et al., 2001). These data have shown that such
alterations, resulting in a more rapid DA release, precede
the occurrence of motor fluctuations thus potentially pointing to an intrinsic ‘predisposition’ towards the onset of
motor complications induced by altered DA kinetics, in keeping with the theoretical model. Likewise, it was found that
changes in synaptic DA levels after levodopa administration
are greater in patients suffering from dyskinesia compared
with patients without motor complications (de la FuenteFernandez et al., 2004b).
Brain (2006), 129, 1050–1058
1051
An increased DA turnover associated with Parkinson’s
disease has been previously found with post-mortem and
in vivo studies (Hornykiewicz, 1982; Kuwabara et al.,
1993). Recently however it was also shown that a large fraction of this change occurs very early in Parkinson’s disease;
such increase was interpreted as an early disease-induced
compensatory mechanism (Sossi et al., 2002, 2004). Likewise,
a relative increase in DA synthesis (and vesicular storage) was
also identified as a compensatory mechanism (Lee et al.,
2000). It is thus conceivable that a different time course of
these regulatory changes as a function of disease progression
might lead to a situation where the relationship between
the level of DA depletion and DA release rate favours
the occurrence of motor complications (de la FuenteFernandez et al., 2004a). This hypothesis taken together
with the observation that younger-onset patients are more
likely to develop treatment-related motor complications
prompted the investigation on how disease-related changes
in DA turnover are affected by age. We predicted that subjects
with disease onset at a younger age might have a relatively
greater change in DA turnover compared with the change in
DA synthesis and storage rate when compared with subjects
with disease onset at an older age.
Methods
Patient selection, scanning protocol and
regions of interest (ROI) placement
A group of 10 healthy volunteers [age 58.5 6 11.2 years (mean 6
SD), range 43–76; 5 males (M) and 5 females (F)] was used to investigate the age dependence of the mechanisms under investigation.
The Parkinson’s disease subject group comprised 27 patients
[age 63.4 6 9.5 years (mean 6 SD), range 38–79; 22 M and 5 F].
The Unified Parkinson’s Disease Rating Scale (UPDRS) score,
assessed after a minimum of 12 h of all antiparkinson medication,
was 27.9 6 10.8 (mean 6 SD), range 11–51. Disease duration from
symptom onset was 7.9 6 5.4 years (range 1–22). Six patients were
still untreated at the time of the scan. The other 21 patients were on
chronic levodopa treatment, and 11 of them were also taking direct
DA agonists [standard levodopa equivalents 913 6 425 mg/day
(mean 6 SD)]. There was no correlation between patient age and
amount of medication. All medications were stopped the evening
before the scan.
All subjects underwent a 4-h-long 18F-fluorodopa (FD) scan on a
Siemens/CTI ECAT 953B PET tomograph (Spinks et al., 1992)
operating in the 3D mode (Sossi et al., 1998) after injection of 7
mCi of FD (effective dose equivalent 4.2 mSv). All the subjects were
administered 150 mg of the peripheral decarboxylase inhibitor carbidopa 1 h prior to FD injection. Data were corrected for attenuation
using an external 68Ge source. The scanning sequence consisted of 9 ·
10 min scans, after which the patients could leave the bed for 60 min,
followed by a final sequence of 9 · 10 min scans. The repositioning
was aided by use of a thermoplastic mask, which also helped to
minimize head motion during the scans. Images from the first
and second scanning sequences were realigned using the AIR algorithm (Woods et al., 1993). Four 61 mm2 circular ROIs were placed
on each striatum, one on the caudate nucleus and three on the
putamen [anterior (P1), middle (P2) and posterior putamen
1052
Brain (2006), 129, 1050–1058
(P3)], and six 270 mm2 circular ROIs were placed on the occipital
cortex on five adjacent planes containing the striatal image on each
frame of the dynamic sequence.
For each study, 37 arterial blood samples were taken to obtain the
plasma radioactivity time course and 12 were analysed for metabolites to identify the authentic FD plasma fraction. The metabolite
separation was performed as previously described with an alumina
extraction method with anion/cation exchange columns (Doudet
et al., 1998; McLellan et al., 1991). With this method, the cation
column captures the positively charged metabolites [18F-fluorodopamine (18F-DA) and 6–fluoro-3-methoxytyramine] and the anion
column the negatively charged metabolites [L-3,4-dihydroxy-6-
[18F]fluorophenylacetic acid (FDOPAC), [18F]6-fluorohomovanillic acid (FHVA) and sulphated conjugates]. FD
and 3-O-methylfluorodopa (3OMFD), not retained by the
columns, are then separated by alumina extraction. All
subjects gave written informed consent. The study received
the approval of the University of British Columbia Ethics
Committee.
Modelling
After tracer injection, FD is converted into (18F-DA) and stored into
synaptic vesicles. The DA synthesis and vesicular storage rate constant (FD uptake rate constant) Ki was determined using the plasma
slope-intercept graphical approach (Gjedde, 1981, 1982, Patlak
et al., 1983) from the first 90 min of data [Ki = K1k3/(k2 + k3)],
where K1 and k2 are the clearance rate constants from plasma into
tissue and from tissue into plasma, respectively, and k3 is the rate
constant describing the trapping of brain FD. In order to eliminate
the contribution of the FD metabolite 3OMFD, the radioactivity
present in an ROI placed on the occipital cortex was subtracted
from the radioactivity determined in an ROI placed on the striatal
regions (Martin et al., 1989). While during the first 90 min after
injection the data defined by the graphical analysis generally follow a
straight line (except for very advanced patients), at times later than
90–120 min after tracer injection the data start to deviate from a
straight line, indicating some degree of reversibility. This reversibility
is considered primarily due to DA release and subsequent metabolism, either synaptic or cytoplasmic following DA re-uptake in the
pre-synaptic terminal. It is likely that some intracytoplasmic metabolism occurs before the initial storage of DA (i.e. when DA is
synthesized ). However, this process should not affect the turnover
measurement, since this fraction of DA is never trapped. A reversible
tracer approach model was thus applied to the data acquired between
150 and 240 min post-injection to determine effective DA distribution volume (EDV). We have previously shown that the EDV
estimated in this way is mathematically and conceptually equal to
Ki/kloss, where the rate constant kloss quantifies the degree of tracer
reversibility (Sossi et al., 2001).
EDV is the direct inverse of the effective DA turnover (Doudet
et al., 1998) and the two terms are thus used interchangeably.
Statistical analysis however was performed on EDV, since EDV is
the quantity directly measured. Thus, while Ki is primarily a measure
of DA synthesis rate (thus dopa decarboxylase activity) and initial
vesicular storage (thus terminal density), DA turnover provides
additional information on the neuronal DA release rate.
Data analysis
Radiotracer concentrations in the caudate nucleus and putamen were
estimated by averaging the values in the two caudate ROIs and in
V. Sossi et al.
the six putaminal ROIs, and Ki and EDV were calculated from the
resulting time courses.
Age dependence of EDV and Ki in control subjects
Regression analysis was used to derive the age relation in normal
controls for Ki and EDV for the caudate and putamen separately.
Age dependence of disease-induced changes in Ki and EDV
(Kiputdiff and EDVputdiff) in Parkinson’s disease patients
Once the age relation was established in the normal controls
(significance was reached only for the putamen—see Results) the
difference between the observed putaminal Ki and EDV values for
each patient and the corresponding age-matched values in the
normal controls (Kiputdiff and EDVputdiff) were calculated. Such
differences, intrinsically negative since representing diseaseinduced changes, were then further regressed on subject age. We
also tested for non-linear effects in age. Multiple regression analysis
was performed on age and disease duration (defined as the time
elapsed since onset of clinical symptoms) and age and UPDRS,
where UPDRS was considered to be a clinical measure of disease
severity. EDVputdiff was also regressed on age and Ki putamen (Kiput),
where Kiput was taken to represent an alternative measure of disease
severity.
Relative age dependence of EDVputdiff versus Kiputdiff
In order to test whether the relative magnitude (and thus presumably
the time course) of these changes varied as a function of subject
age, it was first necessary to express EDVputdiff and Kiputdiff in
commensurate units.
This was achieved by standardizing these measurements, by
dividing them by their corresponding standard errors (SEs),
EDV*putdiff = EDVputdiff/SE(EDVputdiff) and K*iputdiff = Kiputdiff/se
(Kiputdiff). However, as the directly estimated SEs reflected also the
effects of inter-subject variation in age and in severity, age- and
UPDRS-adjusted SEs were calculated by means of multiple regressions. The resulting SEs of the regressions represented the pure
inter-subject variation in the measurements controlled for age and
severity. To relate these standardized values of EDV*putdiff and K*iputdiff
to age and UPDRS, bivariate multiple regression was carried out, as
these two sets of standardized variables were highly correlated within
patients. The slopes on subject age were compared. For an even more
direct comparison, the variable DIFF, defined as EDV*putdiff K*iputdiff,
was regressed on age and UPDRS.
Results
Age dependence of EDV and Ki in
control subjects
Linear regression was found to best describe the data in
all cases. A significant age dependence (negative correlation)
was found for EDV and Ki for the putamen (P = 0.03 and
P < 0.0001, respectively; Fig. 1). No significant age
dependence was found for the caudate nucleus for either
variable (P = 0.32 and 0.09, respectively). Further
analysis was thus performed for the putamen only (Kiput
and EDVput).
Motor complications in Parkinson’s disease
Brain (2006), 129, 1050–1058
Age dependence - EDVput
0.025
30
0.02
25
EDVput values
Kiput values
Age dependence - K iput
1053
0.015
0.01
0.005
20
15
10
5
0
0
30
40
50
60
70
80
30
40
50
Age
60
70
80
Age
Fig. 1 Age dependence for Kiput (left) and EDVput (right) in healthy controls.
Table 1 Results from the statistical analysis for Kiputdiff and EDVputdiff
Variable
Independent variable
Individual P-value
Regression coefficients
Slope
Kiputdiff
EDVputdiff
Intercept
Age
UPDRS
Age
0.10
0.24
0.95
0.104 · 10
0.067 · 10
0.415 · 10
3
Age2
Dur
0.81
0.53
0.119 · 10
0.732 · 10
5
Age
UPDRS
0.09
0.04
0.118 · 10
0.120 · 10
3
Age
Age
UPDRS
Age
0.02
<0.0001
0.84
0.95
0.157 · 10
0.2213
0.0118
0.031
3
Age2
Dur
0.60
0.46
Overall P-value
3
4
4
3
0.0138
0.0053
0.0095
0.10
0.24
0.26
0.0141
0.223
0.0138
0.03
23.41
9.04
15.90
<0.0001
0.84
<0.0001
0.0021
0.0694
23.71
<0.0001
0.2346
0.1024
23.43
<0.0001
34.41
<0.0001
Age
UPDRS
<0.0001
0.02
Age
Kiput
<0.0001
<0.0001
0.266
741.63
Age
<0.0001
0.295
Significant P-values are given in boldface.
Age dependence of disease-induced
changes in Kiput and EDVput (Kiputdiff and
EDVputdiff) in Parkinson’s disease patients
The significance levels for each regression and the regression
coefficients are shown in Table 1, while the scatter-plots
associated with the regression analysis are shown in
Figures 2 and 3. Regression of Kiputdiff on age or UPDRS
alone did not reach statistical significance (P = 0.10 and
0.24, respectively); however, when both age and UPDRS
were included in the regression, statistical significance was
reached for both variables (P = 0.02 and 0.04, respectively).
On the other hand, the regression of EDVputdiff on age was
highly significant (P < 0.0001), unlike the regression on
UPDRS alone (P = 0.84). When a multiple regression on
age and UPDRS was performed, the contribution from
both variables became significant (P < 0.0001 and P =
0.02). Multiple regression on age and on Kiput taken as an
index of disease severity showed the contribution from both
variables to be highly significant (P < 0.0001 for both variables). Dependence on disease duration was found to be not
significant for either variable.
1054
Brain (2006), 129, 1050–1058
V. Sossi et al.
Kiputdif f
A
Kiputdif
B
0.004
0.004
0
0
30
40
50
60
70
0
80
-0.004
-0.004
-0.008
-0.008
-0.012
10
20
30
40
50
60
-0.012
Age
C
UPDRS
D
Kiputdiff - UPDRS adjusted
Kiputdiff - Age adjusted
0.004
0
0
-0.005
0
20
40
60
80
100
0
20
40
60
-0.01
-0.004
-0.015
-0.008
-0.02
-0.012
-0.025
UPDRS
Ag e
Fig. 2 Kiputdiff age and UPDRS dependence for the Parkinson’s disease subjects—raw and adjusted values. Adjusted values were
plotted when the Kiputdiff dependence on the particular variable was found to be significant. Open circles = untreated subjects.
EDVputdiff - UPDRS adjus ted
EDVputdiff
EDVputdiff - Kiput adjusted
0
0
0
20
40
60
80
100
-2 0
-5
-4
-10
-6
-8
-15
-10
0
20
40
60
80
100
0
20
40
60
80
100
-10
-15
-20
-12
-20
-5
-14
-25
Age
Age
Age
EDVputdiff - Age adjusted
EDVputdiff
0
0
0
20
40
60
-5 0
-5
-10
-10
-15
-20
-15
-25
20
40
60
-30
-20
-35
UPDRS
UPDRS
Fig. 3 EDVputdiff age and UPDRS dependence for the Parkinson’s disease subjects—raw and adjusted values. Adjusted values were
plotted when the Kiputdiff dependence on the particular variable was found to be significant. Open circles = untreated subjects.
Relative age dependence of
EDVputdiff versus Kiputdiff
The multiple regression analysis provided the following regression coefficients: K*i putdiff = 5.006 + 0.0569 age 0.0436
UPDRS (age coefficient P < 0.0001, and UPDRS coefficient P
= 0.0107) and EDV*putdiff = 10.821 + 0.1231 age 0.0473
UPDRS (age coefficient P = 0.0071 and UPDRS coefficient P =
0.0187) . The age-related regression coefficients are significantly different (P = 0.001), indicating an approximately
double age effect for EDVputdiff compared with Kiputdiff.
The multiple regression for DIFF yielded DIFF = 5.814 +
0.0662 age 0.0037 UPDRS, where the age coefficient had P <
0.0001 and the UPDRS coefficient had P = 0.65 (Fig. 4).
Motor complications in Parkinson’s disease
Brain (2006), 129, 1050–1058
DIFF - Age adjusted
DIFF
0
-0.5 0
20
40
1055
60
80
100
0
-1 0
10
20
30
40
50
60
-2
-3
-1
-1.5
-4
-5
-6
-2
-2.5
-3
-7
-8
-3.5
Age
UPDRS
Fig. 4 DIFF (EDV*putdiff K*iputdiff) dependence on function of age (left) and age adjusted DIFF dependence on UPDRS (right).
Adjusted values were plotted when the DIFF dependence on the particular variable was found to be significant. Open circles =
untreated subjects.
Discussion
Age dependence of EDV and Ki in
control subjects
This study shows in vivo a significant decrease of the EDV
(and consequent increase in DA turnover) as a function of age
using PET and a direct analysis method to determine EDV.
These results are in agreement with post-mortem studies
(Haycock et al., 2003) and with recent PET imaging results
where an EDV was calculated from a separate determination
of a modified Ki and kloss (Kumakura et al., 2005). Such
increase could be compensatory in nature, where DA turnover (DA release) increases to compensate for declining DA
levels (Kish et al., 1992; Haycock et al., 2003) arising as a
consequence of the age-related loss of the dopaminergic
neurons (McGeer et al., 1977; Fearnley and Lees, 1991). In
addition, an age-related increase of the catecholamineoxidizing enzyme monoamine oxidase-B (MAO-B) may
also contribute to a declining capacity to retain 18F-DA within
the brain for extended periods (Fowler et al., 1997; Kumar and
Andersen, 2004).
The effect of normal ageing on Ki is more controversial.
While there seems to be a general consensus about agerelated loss of dopaminergic neurons in human postmortem studies (McGeer et al., 1977; Fearnley and Lees,
1991), PET studies involving measures of DA synthesis and
storage have given inconsistent conclusions, with some
reports failing to reveal a significant decline (Sawle et al.,
1990; Eidelberg et al., 1993) and others showing a decline
(Martin et al., 1989; Cordes et al., 1994). Part of the reason
could be the use of different analysis methods that have different sensitivity to issues such as presence of the FD metabolite 3OMFD or to kloss, (Sossi et al., 2003; Kumakura et al.,
2005), possible age-dependent behaviour of the selected reference region (Sossi et al., 2005; Kops et al., 2002) or age range
of the subjects included in the study (Haycock et al., 2003).
However the age dependence of Kiput found in this study was
extremely robust (P < 0.0001) and the age range of the normal
volunteers included in this study spanned almost the entire
age range of the Parkinson’s disease patients considered.
Results from the regression analysis suggest a loss of 0.7%
per year of extrapolated Kiput at birth. This value is in good
agreement with human post-mortem studies on the loss of
dopaminergic neurons from the substantia part compacta,
indicating 0.5–0.7% per year reduction (McGeer et al.,
1977; Fearnley and Lees, 1991). In addition, Frey et al.
(1996) have found a very similar (0.7%) age-related loss
rate for specific binding of the vesicular monoamine
transporter marker [11C](+)-dihydrotetrabenazine using
PET. Although FD Ki and DTBZ specific binding trace two
different (although related) aspects of the dopaminergic
system, it is not surprising that, in the absence of agerelated regulatory changes, the values of both parameters
remain proportional to the number of existing neurons
and thus change at a similar rate as a function of age.
Results from a similar estimate derived from the regression
analysis show a loss of 0.9% per year of extrapolated EDVput at
birth. This value is slightly higher compared with that
obtained for Kiput, likely reflecting not only the effect of
age-related neuronal loss but also the age-related increase
in MAO-B.
Age dependence of disease-induced
changes in Ki and EDV: (Kiputdiff and
EDVputdiff) in Parkinson’s disease patients
It is interesting to notice that for Parkinson’s disease
patients the Kiputdiff achieved a significant age dependence
only when its values were adjusted for the UPDRS
scores, implying that both disease severity and age contribute significantly to the decrease of Kiput values. In particular, a comparison of Fig. 2B and D emphasizes the much
more consistent dependence of Kiputdiff on UPDRS, once the
data are adjusted for the age effect. For Parkinson’s disease
patients there was no age dependence in the Kiput values
themselves once adjusted for UPDRS (P = 0.47) (while
there is a significant dependence on UPDRS alone, P =
0.04), indicating that age of onset does not influence the
absolute severity of nigrostriatal damage, in agreement with
previous reports (de la Fuente-Fernandez et al., 2003). Taken
together, these two findings indicate that the change from an
age-matched baseline is greater in younger-onset patients
compared with those with disease onset at an older age
1056
Brain (2006), 129, 1050–1058
evaluated at equal disease severity as estimated by clinical
measures. If one assumes similar disease mechanisms in
younger- and older-onset patients, younger-onset patients
might thus be experiencing a longer pre-clinical period. Invoking the further assumption of a non-linear time dependence
of disease progression (Lee et al., 2004) one might speculate
that the observed relative slower progression of disease severity in younger patients could reflect the fact that they are
indeed at a later stage of disease relative to its onset.
In contrast to Kiputdiff, EDVputdiff shows a significant age
dependence even when disease severity, evaluated either
clinically by UPDRS scores or by Kiput, is not taken into
account (P < 0.0001). Multiple regression analysis however
demonstrates an additional significant dependence on either
UPDRS or Kiput. As observed for Kiput, there is a lack of
age dependence for the EDVput values themselves in Parkinson’s disease (P = 0.71), which are more highly related to
UPDRS (P = 0.02). Again, these findings emphasize the
fact that the absolute disease severity is independent of the
age of onset, while, in this case, the change from age-matched
baseline is very highly dependent on age.
The fact that neither Kiputdiff nor EDVputdiff depends significantly on duration of clinical symptoms can be interpreted
as further evidence that clinical symptoms might appear
at different time-stages of actual neuronal degeneration for
different ages of onset.
Relative age dependence of
EDVputdiff versus Kiputdiff
The analysis of EDV*putdiff and K*iputdiff showed the effect of
age to be approximately twice as great for EDV*putdiff
compared with K*iputdiff, while interestingly the regression
coefficients for UPDRS were very similar. This is consistent
with previous results (Sossi et al., 2004) showing that as
disease progresses the relative magnitude of changes in Ki
and EDV become comparable. The results of the DIFF analysis confirm this finding (Fig. 4).
Since both Kiputdiff and EDVputdiff represent disease-specific
changes (i.e. their expected value in the absence of disease is
zero), these results would suggest that the differential effect
of age on Kiputdiff and EDVputdiff indeed uncovers age-related
differences in the way patients handle Parkinson’s disease
pathology. The finding that the change in EDVputdiff
compared with Kiputdiff (i.e. DIFF) is greater in Parkinson’s
disease patients with disease onset at a younger age indicates
that the increase in DA turnover compared with age-matched
normal conditions is relatively greater in younger people relative to the decrease in their ability to synthesize and package
DA into synaptic vesicles. While in the very early stages of the
disease such changes might act as compensatory mechanisms
to maintain quasi-normal synaptic DA levels and thus prove
beneficial, as disease progresses the younger-onset subjects are
more likely to experience large swings in synaptic DA levels.
According to the theoretical model described above (de la
Fuente-Fernandez et al., 2004) such conditions would be
V. Sossi et al.
exactly those required to increase the probability of motor
fluctuations. Interestingly, in a study that showed alteration in
DA release rate to precede the occurrence of motor fluctuations (de la Fuente-Fernandez et al., 2001a) the four patients,
who at the time of PET scanning showed a faster DA release
and consequently developed motor fluctuations, were indeed
younger.
While this study provides experimental evidence for a presynaptic basis for the occurrence of motor complications
by indirectly examining changes in DA release rate, it is
not in disagreement with the hypothesis of a post-synaptic
contribution to these phenomena. A greater disease-induced
increase in DA turnover is necessarily associated with more
severely altered DA release patterns. Thus, when administered
exogenous therapeutic levodopa such change in DA release
pattern might lead to a more pulsatile-like behaviour of the
synaptic DA concentration, which, as previously argued,
might lead to post-synaptic receptor changes associated
with the occurrence of motor complications. However, as
pointed out previously (de la Fuente-Fernandez et al.,
2004a), these results would also suggest that levodopa therapy
by itself is likely not the cause of motor complication. In fact, a
closer look at the plots shown in Figs 2–4 reveals that all the
variable-specific values encountered in the untreated group
are completely consistent with those observed in treated
patients. Rather, age dependency of regulatory changes
associated with Parkinson’s disease pathology may contribute
to the different time-response to levodopa treatment often
observed as a function of age of onset. It is also possible to
speculate that part of the reason why younger-onset patients
initially respond to levodopa therapy better (Granérus et al.,
1979) might indeed be the relatively larger increase in turnover and thus more rapid and efficient DA utilization.
A final note of caution is in order. The findings reported in
this study depend crucially on the existence of an age dependence of DA turnover in the normal population. However, in
addition to our own results, data in the literature seem to
support such dependence in a rather robust fashion both in
human and animal studies (Barrio et al., 1990; Haycock
et al., 2003; Kumakura et al., 2005), thus lending support
to our conclusions. While there is more controversy surrounding the age dependence of Ki, our fundamental conclusions
would only be strengthened by lack of such age dependency,
by increasing the disparity of the magnitude of EDVputdiff
and Kiputdiff as a function of age.
Conclusions
This study demonstrated in vivo that the degree of abnormality in DA turnover and DA synthesis and storage rate
depend upon the age of disease onset. These results thus
suggest an age-related difference in the way patients respond
to the pathology of Parkinson’s disease. In particular, in
younger-onset patients the relative difference between the
abnormality in DA turnover and that in DA synthesis and
storage rate change is greater compared with older-onset
Motor complications in Parkinson’s disease
patients. Such conditions, which have been previously theoretically hypothesized to increase the probability of motor
complications, might explain the greater propensity towards
such complications clinically observed in patients with disease
onset at an younger age.
Acknowledgements
The authors wish to thank the Director of the PET centre
Dr Thomas Ruth, Dr J. Holden for the helpful discussion,
Dr Ajit Kumar for patient assessment, Mr Edwin Mak for
help with the statistical analysis, Mss Linda Grantier and
Sharon Yardley for patient information and the UBC/
TRIUMF PET group for their technical support. This work
was supported by the National Science and Research Council
(V.S.), the Michael Smith Foundation for Health Research
(V.S.), a Canada Research Chair (A.J.S.), the Canadian
Institutes for Health Research and a Triumf Life Science grant.
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