Pramipexole

In this paper, we re-analyse 392 sessions of behavioural data derived from a probabilistic reward task ([10 (link)]; adapted from [55 (link)]), which is displayed schematically in Figure 1A. Central to the task is that an asymmetrical reinforcement scheme was used to induce a response bias: Correct responses to one stimulus, designated “rich”, were more likely to be rewarded than correct responses to the other stimulus, designated “lean” (Figure 1B). No feedback was given on other trials, including incorrect trials, and no explicit information about the asymmetry was provided. Participants were explicitly encouraged to win as much money as possible, and so could benefit from reporting the rich, rather than the lean, stimulus when in doubt. One measure of the tendency to do this is the response bias [10 (link)]:
$\begin{array}{ll}\frac{1}{2}log\left(\frac{n\left(a_1\right|s_r\left)n\right(a_1\left|s_l\right)}{n\left(a_2\right|s_r\left)n\right(a_2\left|s_l\right)}\right)& \end{array}$
where s_r and s_l indicate presentation of the rich and lean stimulus, respectively, a₁ and a₂ are the two possible key presses, and n(a|s) is the number of times a particular choice was made in response to that stimulus. Each count n was augmented by $\frac{1}{2}$ to avoid numerical instabilities. Outlier trials with very short (<150 ms) or very long (>1500 ms) reaction times are excluded (see [10 (link)] for a full description of the 2-step procedure used to exclude trials with outlier responses). Figure 1F shows the fraction of correct responses for each of the 392 individual experimental sessions. In addition to the computer task, participants completed self-report questionnaires (see Table 2). The datasets and manipulations are shown in Table 1. Briefly, the studies examined the effect of i) depression (categorical diagnosis according to DSM-IV; continuous quantification based on self-report measures of depressive features and anhedonia; and past history of MDD); ii) bipolar disorder, currently euthymic (categorical diagnosis according to DSM-IV); iii) stress; and iv) low-dose D ₂ agonist pramipexole. The low dose (0.5 mg) of pramipexole was assumed to reduce phasic DA bursts to unexpected rewards due to presynaptic (autoreceptor) activation [53 ,56 (link)]. We note that the dataset ‘Stress’ differs from the others because a more difficult version of the task was used [54 (link)].

Chi-square for categorical variables and Mann–Whitney U test for continuous variables due to non-normality were used to compare the baseline characteristics between patients with RLS and RLS-free controls. Cox proportional hazards regression models were applied to explore the association between RLS and the risk of dementia after adjusting for age, sex, income, residence, CCI, and history of other comorbidities. Among the Cox regression models, we used the Fine–Gray subdistribution hazard model with mortality as a competing risk given the old age of the study population. The proportional hazard assumption was satisfied in our Cox model (Schoenfeld individual test p-value > 0.05).
Sensitivity analyses were performed using four different models. In model 1, dementia was defined as the prescription of anti-dementia medications (donepezil, rivastigmine, galantamine, and memantine) at least twice and a diagnosis of the ICD-code of dementia. Although these medications were approved for only AD (rivastigmine additionally for Parkinson’s disease dementia), they can be used for cognitive symptoms in other types of dementia based on recommendations from multiple guidelines [31 (link)–33 (link)]. The previous study revealed that the definition of all-cause dementia by ICD-10 code plus anti-dementia medications had a positive predictive value of 94.7% when reviewing the medical records of 972 patients in two hospitals [34 (link)]. In model 2, medication history was added to the ICD code to define RLS. Patients with RLS ICD-code (G25.8) who had taken dopamine agonists (ropinirole or pramipexole) twice or more were regarded as patients with RLS (n = 1458). In this sensitivity model, we excluded patients with Parkinson’s disease because they could also take dopamine agonists. In model 3, patients taking antipsychotic agents were excluded because the antidopaminergic property of antipsychotic agents could lead to a misdiagnosis of RLS (n = 2482). The following antipsychotic agents approved in South Korea were used in this study: haloperidol, sulpiride, chlorpromazine, perphenazine, pimozide, risperidone, olanzapine, quetiapine, paliperidone, amisulpride, aripiprazole, ziprasidone, clozapine, blonanserin, and zotepine. In model 4, patients with RLS only diagnosed by psychiatrists or neurologists were included (n = 1154) to preclude the possible misdiagnosis by non-expert physicians.
To evaluate the effect of dopamine agonists (pramipexole and ropinirole) on the development of dementia, the risk of dementia was compared after dividing RLS patients by dopamine agonist use. Patients with RLS who were prescribed pramipexole or ropinirole at least once were considered dopamine agonist users. All missing data were addressed using listwise deletion. Data processing and statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA). Statistical significance was set at a two-tailed p-value of < 0.05.