Parkinson's disease (PD) is characterized by progressive degeneration of dopaminergic neurons in the substantia nigra, causing depletion in striatal dopamine level, variable clinical expressions, and a slowly deteriorating course (Dauer and Przedborski, 2003; Obeso et al., 2010). In general, there is an association between disease severity and disease duration, but the rate of disease progression is variable among patients. Thus far, there are no biological markers that can predict rate of disease progression in an individual patient (Kuriakose and Stoessl, 2010).

The inter-individual difference in severity of motor symptoms during early and mid duration of PD may be attributed to existence or lack of compensatory mechanisms for dopamine loss (Obeso et al., 2004). Thus far, research of compensatory mechanisms has mostly concentrated on the transition between pre-motor stage and the emergence of the motor symptoms (Bezard et al., 2003). However, it is also plausible that compensatory mechanisms play a role in the motor symptomatic period, thus manipulating disease severity and progression (Brotchie and Fitzer-Attas, 2009). These functional mechanisms may counterbalance dopamine loss in early PD, attenuating the severity and progression of motor symptoms (Biju and de la Fuente-Fernández, 2009). Identification of such mechanisms is of major scientific and clinical importance.

Herein we introduce an original approach to discover genetic variants contribution to endogenous compensatory mechanisms in early PD, achieved by combining clinical, genetic and neuroimaging data. Our hypothesis is that a mismatch between mild symptomatic disease, manifested by low motor score on the Unified PD Rating Scale (UPDRS), and extensive Nigro-Striatal (NS) degeneration, manifested by reduced uptake of [¹²³I]FP-CIT, is indicative of compensatory processes. Genetic variations probably predispose some individuals to better compensatory potential for the striatal dopamine loss. We believe that this type of systematic research will offer initial tools required to identify these variants, and to trace genes that may be involved in compensatory mechanisms.

This perspective paper briefly reviews several of the currently known compensatory mechanisms in PD. Then, we present our algorithm and report initial findings of a small “proof of concept” study for identification of genetic compensatory mechanisms in early PD. Naturally, the small sample size used in this report is underpowered, but the goal in this context is to present the new method and its possible applicability, and not to reach definitive conclusions.

It is well established that parkinsonian signs appear when 70–80% of the dopaminergic neurons are already lost (Bernheimer et al., 1973; Lee et al., 2000; Perez et al., 2008; de la Fuente-Fernández et al., 2011). The postmortem analysis of parkinsonian brains indicates that extensive loss of dopamine in the putamen and in the caudate nucleus can still be accompanied by only minor clinical symptoms (Bernheimer et al., 1973; Agid, 1991). According to Braak's pathological staging of PD, the process begins in the dorsal nucleus of the vagus and the olfactory bulb, reaching the substantia nigra only in the third pathological stage (Braak et al., 2003). This explains the appearance of autonomic and other non-motor symptoms before the appearance of the motor ones. Another explanation might be that intra- and extrastriatal compensatory mechanisms are responsible for the late manifestation of motor symptoms (Brotchie and Fitzer-Attas, 2009). After the appearance of the first motor manifestations, degeneration of dopaminergic neurons continues, causing further decline in motor performance. However, the variability of disease progression among patients is high, indicating that compensatory mechanisms play a role also in this stage. These mechanisms may be related to genetic predisposition, but also to environmental ones [e.g., consumption of nicotine and caffeine—which have a neuroprotective effect (Sugita et al., 2001; Quik et al., 2007; Prediger, 2010)]. Demographic factors, like age at disease onset, also influence compensatory potential, in favor of younger age (de la Fuente-Fernández et al., 2011).

Our hypothesis focuses on the possible influence of genetic variants on severity of symptoms in early PD. We became aware to patients with marked decrease of [¹²³I]FP-CIT uptake in the striatum at early stage of the disease, while their clinical phenotype was compatible with a mild disease. We assume that a mismatch between dopamine binding and symptom severity may be indicative of compensatory mechanisms that act in the early motor stage of the disease.

The basic hypothesis for our model is that if genetic variants are associated with the severity of motor symptoms, while the level of striatal terminals degeneration measured by striatal [¹²³I]FP-CIT uptake is taken into account and controlled in the analysis, then these variants may be involved in functional compensatory mechanisms for the dopamine deficit in the striatum. Our algorithm is based on a statistical analysis which combines three parameters: clinical, genetic and neuroimaging data.

Following selection of Single Nucleotide Polymorphisms (SNPs) of interest and genotyping them in a suitable sample of PD patients, multivariate linear regression model is implemented to predict UPDRS score as a function of selected SNPs together with relevant covariates (gender, age, disease duration). The UPDRS score (indicator of motor severity evaluated in unmedicated patients or if already treated with dopaminergic agents, after a “wash-out” period) is the dependent variable in this association study, and the SNP genotype is the independent variable (linear regression). We first force enter [¹²³I]FP-CIT uptake values as a covariate that controls for degree of striatal terminals degeneration. Then, a second block is entered into the model using stepwise method, contains a single SNP. Thus, this SNP potential unique contribution to the UPDRS variance could be examined. This step is performed separately for each of the studied SNPs of interests.

To demonstrate the feasibility of this algorithm, we have performed a small “proof of concept” candidate gene study. The study group consisted of 28 Israeli Jewish patients with early idiopathic PD who had not yet been treated with any anti-PD drugs. The study was approved by the Ethics Committee of Rabin Medical Center, and all patients signed an informed consent form. All patients underwent [¹²³I]-FP-CIT SPECT within few weeks from assessment of severity of the motor symptoms by the UPDRS motor sub-score. For SPECT imaging and analysis, see Appendix Methods.

Tagging SNPs were selected in 4 candidate genes participating in dopamine metabolism pathway: tyrosine hydroxylase (TH), catechol-O-methyltransferase (COMT), and monoamine oxidase (MAO) A and B. The selection was performed on the basis of the international HapMap project CEU group data, using the tagger option of the Haploview program, version 4.1 (Barrett et al., 2005). Criteria for inclusion were minor allele frequency of >5% and minimum r² threshold of 0.8. In total, twenty-six SNPs were successfully genotyped.

The first stage was to evaluate univariate associations between these SNPs and the UPDRS score. Table A1 shows that only the rs6356 SNP in the TH gene (encoding for amino acid Val to Met substitution at position 81) showed a nominal significant association with the UPDRS score (p = 0.007).

Table 1 shows the clinical characteristics and FP-CIT uptake data of the whole sample and of the carriers/non-carriers of the rs6356 minor allele. Twenty patients were carriers of the rs6356 AA/AG genotypes, and 8 were carriers of the major allele “G” homozygous genotype (GG). There was a trend for shorter disease duration among the carriers of the “A” allele compared to homozygotes for the “G” allele (p = 0.06). The mean UPDRS score was significantly lower in the AA/AG genotype carriers than the GG homozygotes (p = 0.007) despite the lack of significant differences in [¹²³]FP-CIT uptake in the contralateral (to the more affected side of the body) putamen, caudate, or mean putaminal uptake between these groups (Figure A1).

The second stage examined the effect of rs6356 on disease severity using stepwise linear regression. The SPECT data and disease duration (block 1, control variables) were force-entered first, and then the gene variant was added using the stepwise method (block 2). The two control variables, by themselves, explained 31% of the inter-individual variance in the UPDRS scores (R² = 0.31, p = 0.009) (Table 2). After the second block (containing only the rs6356 SNP) was entered, the model yielded a total R² of 0.45. This means that when the duration of PD and the level of ligand uptake were held constant, the contribution of the rs6356 “A” allele (AA and AG genotypes) was statistically significant (p = 0.01), with rs6356 explaining about one-third (R² = 0.14) of the total variance in UPDRS scores. Adding the genetic variable to a model that included non-genetic predictors of PD severity may increase the percentage of variance explained by the model (Table 2). Similar and consistent results were achieved with values of the contralateral caudate and mean putamen uptake (Table A2).

In this example, the Val81Met polymorphism (rs6356) in the TH gene influences disease severity in our small sample of patients with early PD. The contribution of this genetic variant to the prediction of UPDRS score is adjunctive to the combined effect of variables of FP-CIT uptake and disease duration, as demonstrated by linear regression analysis. In other words, the genetic variant has an effect on motor symptoms severity in PD when level of NS degeneration level and disease duration are held constant, and therefore may be involved in compensatory mechanisms of the disease.

The results of this “proof of concept” study should be interpreted with caution, and may actually represent false-positive findings, due to the small sample size and the burden of multiple testing. TH association does not withstand correction for multiple testing (required p-value of 0.002, according to Benofferoni correction). In general, the best strategy to confirm results of genetic association studies and to differentiate true positive from false negative results, is to perform a replication study in an independent sample. Unfortunately, we do not have access to a validation sample, which contains the required data for this analysis. It should be recognized that our sample is unique, as all participants were of Jewish ancestry (reduced genetic heterogeneity), and naïve to anti-Parkinson medications at the time of clinical assessment and SPECT scanning. If drug-treated patients are included in future samples, then motor assessment should be performed following an acceptable “wash-out” period.

Nevertheless, our goal here was to present the novel method in a “hypothesis generating” manner. Larger and well powered samples are warranted to confirm (or deny) the current preliminary results, and to discover additional potential genetic variants associated with compensatory potential in PD.

In contrast to the candidate gene approach, implementing the hypothesis free GWAS, will enable to identify other genes, not restricted to the “usual suspects” of dopamine homeostasis in the striatum but also unexpected ones, advancing the field beyond current knowledge. These may include, for example, genes and pathways involved in novel mechanisms of attenuating the indirect pathway activity, or affecting regions and network in the cortex and cerebellum. It should be acknowledged, however, that the current analytic approach is in line with the “common disease-common variant” hypothesis, resuming that large number of common genetic variants account for the genetic contribution to the phenotype, and each SNP has a small size effect. However, low frequency and even rare variants (with large effect size and variable penetrance) may also play role in genetic contribution to compensatory mechanisms in PD. To systematically evaluate the compensatory potential of low frequency/rare variants such as these, very large samples are required, incorporating methods such as whole exome/genome sequencing.

Beside valuable insight to development of PD modifying drugs that slow rate of disease progression, identification of genetic variants involved in endogenous compensatory mechanisms can lead to “individualized medicine.” A priori identification of patients carrying variants that increase the compensatory potential may have impact on therapeutic decisions. This can also bear implications for patients' stratification in clinical trials, evaluating efficacy of novel drugs.

Our genomic imaging model, with appropriate adaptation, might be applicable not only for prediction of motor severity in PD but also to other non-motor symptoms such as dementia. Also, it can be used in other neurodegenerative diseases. For example, improvement of imaging strategies in Alzheimer disease will enable to find genes and molecular pathways involved in disease progression, in a similar way.

As a final point, one should keep in mind that compensatory mechanisms in PD probably represent complex and different processes in various disease stages, not related only to genetics but also to environmental, demographic and stochastic factors. Careful planning of future studies, taking these important factors into account is required.