Sulpiride

To assess the significance of group differences in unweighted network topology, we used a two-way mixed-effects ANOVA model comprising age (young, old) as a between-subject factor, drug (placebo, sulpiride) as a within-subject factor, and the age by drug interaction. This model was separately fitted to each of the following dependent variables: global and local efficiency of sparsely thresholded networks with K ∼ 0.1; global and local efficiency curves integrated over the small-world regime of costs, 0.05 ≤ K ≤ 0.34; maximum-cost efficiency; and nodal efficiency for each region in the individually estimated brain functional networks. If main effects were significant by ANOVA, post-hoc t-tests were also conducted for the following comparisons: young people after placebo (YP) versus old people after placebo (OP), young people after sulpiride (YS) versus old people after sulpiride (OS); YP versus YS; OP versus OS.
Note that the results of the multiple ANOVAs entailed for a region-by-region analysis of nodal efficiency were regarded as significant if p < 0.05, i.e., there was no correction for multiple comparisons. The data on regional localization of effects of aging and dopamine blockade on network efficiency should therefore be regarded as exploratory in nature.

The meta-analysis was conducted and reported according to recommendations of the Meta-analysis of Observational Studies in Epidemiology (MOOSE) group [36] (link). A review protocol was construed following the MOOSE guidelines. This was not published but only for internal use of this study.
A PubMed and Embase search was conducted for articles on metabolic side effect profiles of antipsychotic medication. The search term used was: ((“weight gain” OR “BMI” OR “7% weight”) AND (chlorpromazine OR haloperidol OR bromperidol OR fluphenazine OR zuclopenthixol OR pentixol OR flupentixol OR levopromazine OR perphenazine OR pimozide OR penfluridol OR sulpiride OR amisulpride OR amoxapine OR asenapine OR aripiprazole OR blonanserine OR clozapine OR iloperidone OR melperone OR olanzapine OR risperidone OR paliperidone OR quetiapine OR sertindole OR lurasidone OR ziprasidone)) NOT (addition OR additive OR adjunctive OR augmentation OR lithium OR valproate OR carbamazepine OR metformin OR topiramate OR ramelteon OR rimonabant OR modafinil OR sibutramine OR genetics OR pharmacokinetics OR vomiting OR nausea OR review OR “cognitive behavioural therapy” OR “cognitive behavioral therapy” OR delirium OR steroids OR ropinirole OR sleep OR “brain volume”)
Limits Activated: Humans, Clinical Trial, Randomized Controlled Trial, Clinical Trial, Phase IV, Controlled Clinical Trial, English, German, All Adult: 18+ years, Publication Date from 1999/01/01 to 2011/12/31.

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.

Patients with drug treatments were allocated into four categories as follows: with antidepressants, with antipsychotics, switching medication, and combined medication. In Group A (6-month follow-up group), 63 patients were treated with medications, with 42 of these patients with antidepressants, 5 patients with antipsychotics, 5 patients with switching medication, and 11 patients with combined medication. In Group B (12-month follow-up group), 25 patients were treated with medications, with 17 of these patients with antidepressants, 2 patients with antipsychotics, 4 patients with switching medication, and 2 patients with combined medication. The choice of antidepressants included four SSRI, i.e., fluoxetine, paroxetine, escitalopram, and sertraline, and one noradrenergic and specific serotonergic antidepressant (NaSSA), i.e., mirtazapine. There was a single choice of antipsychotic medication: sulpiride.

Micro-electrode arrays were purchased from Multichannel Systems (MCS, Reutlingen, Germany). MEAs consist of 60 TiN (titanium nitride) planar round electrodes (30 μm diameter; 200 μm center-to-center inter-electrode distance). MEA amplifier was kept inside an incubator with a controlled temperature (37°C) and humified atmosphere (i.e., gas flow of 5% CO₂ and 95% O₂). All measurements were performed by keeping the neurons in their culture medium. Acquired signals, after 1,200× amplification, were sampled at 10 kHz and acquired through the data acquisition hardware and MC-Rack software (MCS). For each trial, data acquisition was performed over 2 min recordings.
All experiments using drugs have been performed by adding the drugs to the culture medium under static conditions, without superfusion. For acute application, measurement started 5 min after drugs administration, in order to restore temperature and CO₂ conditions inside the incubator.
L-DOPA was purchased from Sigma-Aldrich (SIGMA, St. Louis, MO, USA) and used at 20 μM final concentration. D₁ (SCH-23390) and D₂ (sulpiride) receptors antagonist were purchased from Sigma-Aldrich (SIGMA, St. Louis, MO, USA) and used at 10 μM final concentration.
The experiments with α-synuclein (S7820, Sigma-Aldrich, Merck Darmstadt Germany) were carried out using different concentrations (0.3, 0.5, 1, 3, and 70 μM). Recordings were performed in three conditions: acute applications (see below for details), 24 and 48 h after α-synuclein addition.