Valtteri Kaasinen

Background: Several Alzheimer's disease (AD) clinical trials currently focus on drug repurposing. Machine learning (ML)-based pharmacoepidemiological approaches may help identify candidate drugs for trials. Objective: We applied an ML-based signal detection method to identify drugs associated with AD risk. Methods: Using Finnish nationwide register-based data, we defined drug exposure at both subgroup and drug substance levels according to the Anatomical Therapeutic Chemical classification. Three binary embedded variables were calculated for each drug based on the frequency of dispensations over three years, starting 10 years before the index date. An algorithm combining subsampling and lasso-logistic regression was applied, with 500 replications for each drug exposure variable. Drug variables with a selection percentage (SP) ≥ 70% across replications were considered as signals. The average beta describes the direction and strength of the association. Results: The study population comprised 43,926 AD cases and 43,926 matched controls. The mean age was 80.3 years in both groups, and 65% of participants were women. Four drug subgroups and three individual drugs were associated with a lower risk of AD, whereas 14 subgroups and 11 individual drugs were associated with a higher risk. Drugs associated with decreased AD risk belonged to cardiovascular drugs: antihypertensives affecting the renin-angiotensin and diuretics. Conclusions: The identified associations are consistent with established risk factors of AD and support the importance of effective hypertension management in AD prevention. Further studies are needed to evaluate whether antihypertensive drugs may exert neuroprotective effects and represent viable candidates for drug repurposing in AD.

Cancer and neurodegenerative diseases are devastating aging-related disorders. Epidemiological data suggest a general inverse risk association between them, the mechanisms of which might provide new avenues for developing treatments and prevention strategies. In addition to this observation, meta-analyses have provided some conflicting results, apparently related to disease characterization, data validity, survival bias and unhandled exposure to risk factors. Taking these into account it seems that the association might be specific to certain types of cancers and particular types of neurodegenerative diseases. It should also be noted that, in some cases, the association is positive. Future studies investigating epidemiological association between cancer and neurodegeneration need to take into account several methodological aspects to provide data that can inform basic and translational studies on pathomechanisms. Most importantly, diagnoses must be accurate and validated, disease subtypes should be precisely defined and stratified, sources of possible bias must be explicitly addressed, time-varying trends should be analyzed as potential confounders and null association must always be published.

The dopaminergic system has long been a central focus of functional neuroimaging. Positron emission tomography (PET) with the D2/D3 receptor radioligand [ ¹¹ C]raclopride remains the most widely used method for indirectly quantifying striatal dopamine release in vivo. However, no previous meta-analysis has studied the relative magnitude and regional distribution of dopamine release across different interventions or cognitive interventions overall. To address this gap, in this meta-analysis of 92 [ ¹¹ C]raclopride PET studies ( n = 1640), we compared the magnitude and regional distribution of dopamine release induced by amphetamine, methylphenidate, ketamine, alcohol, and cognitive challenges with and without reward. Amphetamine induced approximately four-fold greater dopamine release than cognitive challenges (10.9 vs. 2.7%, p < 0.001), and approximately twice that of alcohol (4.8%, p < 0.001), with effects comparable to methylphenidate (11.5%) and slightly greater than ketamine (9.8%). Psychostimulant-induced increase in synaptic dopamine was greater in putamen and ventral striatum than in caudate, whereas alcohol preferentially engaged ventral striatum. Dopamine release did not differ between rewarded and non-rewarded cognitive tasks in the ventral striatum (p > 0.14) or overall striatum (p > 0.10). Methylphenidate-induced increases in synaptic dopamine appeared to attenuate with advancing age, whereas cognitive challenges were associated with greater dopamine release in older individuals. These findings demonstrate that individual pharmacological and cognitive interventions differ markedly in both magnitude and regional pattern of dopamine release. They also suggest that [¹¹C]raclopride PET may have limited sensitivity for distinguishing reward-related from non-reward-related dopamine release. These findings have implications for the design and interpretation of future neuroimaging studies of dopaminergic function in health and disease.

Comparison of several MRI markers revealed that a simple, generalizable midbrain linear measure and midbrain area accurately support routine differential diagnosis between progressive supranuclear palsy (PSP) and non-PSP parkinsonisms.

Purpose: To evaluate the diagnostic validity of PD identification in a national health registry requiring neurologist-confirmed diagnoses. Patients and Methods: We analyzed the Turku PD Cohort including 1626 patients diagnosed with PD between 2006 and 2020 in specialist care. Patients were identified based on ≥ 2 entries of ICD-10 code G20. Diagnoses were re-evaluated through detailed chart review after a median follow-up of approximately 10 years. Two movement disorder specialists independently classified final diagnoses based on clinical records, imaging, and treatment data. Linkage to the Finnish Register of the Entitlements to Reimbursement of Pharmaceutical Expenses identified patients granted reimbursement for antiparkinsonian medications under ICD-10 code G20. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using the final clinical diagnosis as the reference. Results: Of the 1626 patients, 1550 (95.3%) had received reimbursement for PD medications. After long-term follow-up, 1314 were confirmed as PD and 236 reclassified to alternative diagnoses. Registry identification showed high sensitivity (98.2%) and PPV (84.8%). Only 24 confirmed PD cases (1.8%) were not captured. Diagnostic revisions most commonly reflected atypical or secondary parkinsonian syndromes. Specificity was lower (18.1%), reflecting diagnostic evolution during follow-up rather than systematic miscoding. The median delay between diagnosis and registry entry was 24 days. Conclusion: A national health registry requiring neurologist-confirmed diagnoses enables highly sensitive PD case identification with good PPV for epidemiological research. However, diagnostic evolution in early parkinsonism limits specificity in cross-sectional register data. Registry studies should therefore apply follow-up exclusion algorithms for alternative parkinsonian diagnoses.

The mechanisms underlying tremor generation in Parkinson disease (PD) remain unclear. Previously, we demonstrated a connection between rest tremor amplitude and higher dopamine transporter (DAT) binding in the ipsilateral striatum among the Parkinson Progression Markers Initiative cohort. Here, we investigated the association of parkinsonian motor symptoms with striatal DAT binding in a sizable and clinically representative sample of patients with parkinsonian signs to validate the previously observed ipsilateral relationship in PD. This observational cross-sectional study included right-handed patients referred for [¹²³I]FP-CIT SPECT because of clinically uncertain parkinsonism or tremor at Turku University Hospital and the Helsinki and Uusimaa Hospital District, Finland. Each patient underwent a comprehensive clinical evaluation and follow-up (median 3.0 years [interquartile range (IQR) 2.5]). Associations between striatal tracer binding and symptoms were investigated using voxel-wise linear models, adjusting for age, sex, motor symptom severity, and medication. The primary outcome measure was the association between rest tremor amplitude and striatal DAT binding. At the end of the follow-up period, of the 414 patients included (median age 68 years [IQR 14], 49.4% female), 148 were evaluated to have PD and 79 other forms of parkinsonism with striatal DAT deficit. In total, 187 patients had normal binding. Among the patients with PD, left and right rest tremor amplitudes were positively associated with ipsilateral striatal DAT binding (β = +0.12 [95% CI +0.05, +0.19] and +0.10 [+0.05, +0.15] specific binding ratio [SBR] per point; p_{FWE + Bonf.} < 0.05, respectively). Left and right bradykinesia (β = -0.16 [-0.22, -0.09] and -0.18 [-0.25, -0.10] SBR per 5 points, p_{FWE + Bonf.} < 0.05, respectively) and rigidity (β = -0.07 [-0.08, -0.04] and -0.08 [-0.11, -0.05] SBR per point, p_{FWE + Bonf.} < 0.05, respectively) mainly showed a negative association with contralateral striatal DAT binding. No consistent associations were observed in non-PD groups. These findings confirm the positive association between rest tremor amplitude and ipsilateral striatal DAT binding in a clinical sample of PD patients. However, the non-PD groups were diagnostically heterogeneous, limiting conclusions about disease specificity.

Background: Parkinson's disease (PD) is associated with several behavioral and cognitive symptoms, the neurobiological background of which is not yet fully understood. Objectives: The aim was to investigate the association between monoamine function and four specific nonmotor symptoms in early PD using the Parkinson's Progression Markers Initiative data. Methods: [ ¹²³ I]FP‐CIT SPECT imaging data of healthy controls ( n = 166) and patients with PD at baseline ( n = 349), 2‐year ( n = 240), and 4‐year follow‐up ( n = 140) were included. Depression, anxiety, rapid eye movement (REM) sleep behavior disorder (RBD) and cognition were evaluated using validated questionnaires. The associations between symptoms and subcortical monoamine transporter binding were analyzed voxel by voxel. Whole brain networks of symptom‐specific monoaminergic abnormalities were characterized using a functional connectome ( n = 1000) and normative neurotransmitter receptor maps. Results: Cross‐sectionally, depression was associated with reduced ventral striatum (VS) binding at 2‐ and 4‐year and dorsal raphe (DRN) at 4‐year follow‐up ( P _FWE < 0.05); trait anxiety was associated with reduced DRN binding at 4‐year follow‐up ( P _FWE < 0.05). Longitudinally, lower baseline binding in these regions at baseline predicted more severe future depression and anxiety ( P _FWE < 0.05). RBD was most strongly linked to reduced VS binding at 2‐ and 4‐year follow‐up (nonsignificant after correction, P _FWE < 0.1). Each of the symptom‐specific clusters was connected to distinct brain networks, corresponding to specific monoamine receptors: serotonin (5HT _1F , 5HT _2A , 5HT _5A ) and histamine (H ₃ ) for depression; histamine (H ₁ ), serotonin (5HT _1E , 5HT _1F , 5HT _2A , 5HT _3B ), and adrenergic (α _1D ) for anxiety; and adrenergic (β ₂ ) and serotonin (5HT _1F , 5HT _2C , 5HT _5A ) receptors for RBD. Conclusions: The findings provide novel information about the monoaminergic networks underlying depression, anxiety, and RBD in PD. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Machine learning (ML)–based methods have been proposed as a potential approach for identifying candidate drugs to be repurposed as disease‐modifying treatments for Parkinson's disease (PD). We applied an ML‐based signal detection method to identify drugs associated with PD and evaluated the method's generalizability. An algorithm combining subsampling and lasso logistic regression was implemented in a case–control study of 12 257 PD cases and 81 103 matched controls identified in Finnish registers. Drug exposure was defined at the subgroup and drug substance levels of the Anatomical Therapeutic Chemical (ATC) classification, considering the frequency of dispensation over 2 years, starting 10 years before the index date (8‐year lag). Three subgroups and two individual drugs were associated with reduced PD risk. Inhalant anticholinergics, in particular tiotropium bromide, showed the most robust signal. Other signals included antimalarial drugs (aminoquinolines) and the antibiotic subgroup lincosamides. Several drugs were associated with increased PD risk, as expected. In addition to direct pharmacological effects, observed associations could be due to treatment of prodromal symptoms of PD, increased comorbidity in individuals later diagnosed with PD or a combination of these factors. These results support the feasibility of the approach. Associations of decreased PD risk observed should be further investigated in view of drug repurposing.

Background: Dopamine dysregulation syndrome (DDS) is an uncommon but debilitating complication of Parkinson’s disease (PD), characterised by a compulsive overuse of dopaminergic therapy. Most reported cases are male and involve daily oral levodopa (L-DOPA) intake between 2000 and 4000 mg. Methods: We describe a female with young-onset PD who progressively escalated oral L-DOPA intake to a peak of 10 000 mg/day prior to subthalamic nucleus deep brain stimulation (DBS). A structured psychiatric assessment was performed after DBS. Whole-exome sequencing was conducted to evaluate possible genetic susceptibility. Results: The patient developed compulsive medication use, impulse control disorders and gingival black pigmentation with near-total tooth loss. Classical hedonistic DDS features were absent. Following DBS, the L-DOPA dose stabilised at 1800 mg/day, but psychosis emerged, requiring hospitalisation. Genetic testing did not identify a pathogenic cause for early-onset PD; a rare missense variant of uncertain significance was detected without established clinical relevance. Discussion: This case represents the highest sustained oral L-DOPA dose reported in PD. Despite lacking several core DDS features, the pattern of compulsive use suggests dopaminergic dysregulation. This case highlights limitations in current DDS criteria and suggests that contextual features, such as motor disability, psychological reinforcement and individual vulnerability, should be integrated into future refinements.