Anne Paakinaho

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.

Real‐world data are increasingly important for regulatory and health technology assessment decision‐making, yet their effective use across Europe remains limited by technical, methodological, regulatory, and societal challenges. Drawing on the experiences of six Horizon Europe projects in the MetReal cluster, this review summarizes common barriers and emerging solutions for the use of real‐world data in regulatory decision‐making. Key challenges include complex and lengthy data access procedures, fragmented governance frameworks, variable data quality and completeness, limited interoperability, methodological difficulties in analyzing heterogeneous datasets, and infrastructure constraints within secure data environments. The article highlights opportunities to address these barriers through improved metadata, harmonization, federated analytics, synthetic data sandboxes, sustainable funding, and stronger collaboration between researchers, regulators, health technology assessment bodies, data holders, and citizens. These recommendations aim to strengthen Europe's health data ecosystem and support more robust, efficient, and trusted evidence generation for public health and patient benefit.

Background: Target trial emulations (TTEs) may be sensitive to cohort-entry definitions. We investigated how varying cohort-entry criteria (one or two dispensings and interval between dispensings) influence study population and short- and long-term comparative effectiveness estimates in TTE of heart failure-related (HF) outcomes among sodium-glucose cotransporter-2 inhibitor (SGLT2i) users.Methods: We compared three cohort-entry definitions: a first-dispensing cohort based on first dispensing and two second-dispensing cohorts requiring a second dispensing within 44 or 128 days (indexed at second dispensing) in adults aged ≥40 years with type 2 diabetes initiating SGLT2i or dipeptidyl peptidase-4 inhibitors (DPP4i) from Finnish national registers (2017–2023). We derived hazard ratios (HRs) and numbers needed to treat (NNTs) with stabilized inverse-probability of treatment-weighted Cox models under an as-treated framework.Findings: The first-dispensing, 44-day, and 128-day cohorts included 126,093, 64,677, and 109,146 initiators, respectively. The first-dispensing cohort showed a pronounced increase in incident HF risk during early follow-up (HR 3·15, 95%CI 2·43-4·09 versus 1·06 [0·71-1·58] and 1·10 [0·64-1·89] in 128-day and 44-day cohorts). HRs converged over time. At 7 years, HRs for incident HF were 0·44 (0·38-0·49), 0·43 (0·36-0·51), and 0·37 (0·33-0·41), corresponding to NNTs of 14, 14, and 13, respectively. Long-term reductions were consistent across outcomes.Interpretation: Although cohort-entry definitions substantially influenced early HF risk estimates, they had little impact on long-term estimates. Requiring at least two dispensings may mitigate biases arising from first dispensing. Careful alignment of cohort entry with treatment strategy remains essential in TTE.

Purpose: Prevalence of breast cancer (BC) increases with age, but the external validity of data obtained from pivotal trials of medicinal products remains a concern due to the underrepresentation of frail and older adults. Cyclin‐dependent kinase inhibitors (CDKi), palbociclib, abemaciclib, and ribociclib are considered an essential part of the standard‐of‐care in the management of advanced/metastatic breast cancer. We investigated age, comorbidities, and survival in a nationwide real‐world cohort of CDKi users. Methods: Data from the Finnish Cancer Registry, reimbursed dispensed prescriptions, Electronic Prescription Database, Care Register for Health Care (CRHC), and Causes of Death Register were combined and analysed. Results: Altogether 1921 women with BC initiated CDKi treatment in 2018–2022. The median age at initiation was 66.9 years, with 43.2% of the study cohort being < 65 years of age, 36.1% 65–74 years, 19.0% 75–84 years, and 1.7% 85 years or older. The prevalence of age or frailty‐related comorbidities in individuals ≥ 65 years was low, highest for cardiovascular diseases, 32.3%, and diabetes, 16.9%. The median survival was 25.3 months, with longer median survival times in younger age groups (27.7 months in < 65 year‐olds, 25.1 months in aged 65–74, 21.4 months in aged 75–84 years, and 15.4 months in 85 years or older). Conclusions: Real‐world CDKi users are older than women included in the pivotal trials, and the survival differences between age groups imply challenges in the generalisability of data from pivotal trials. The real‐world CDKi user population is characterised by a low prevalence of major comorbidities.

A French signal detection study identified an association between sulfonamide diuretics use, particularly furosemide, and lower Parkinson's disease (PD) risk. Our aim was to confirm this association in a Finnish nationwide nested case–control study (FINPARK), primarily in an indication‐restricted case–control study of persons with heart or renal failure. Altogether 19 568 PD cases diagnosed in 1999–2015 and 130 156 age, sex and region‐matched controls were included. The case–control study restricted to those with heart or renal failure included 1222 PD cases and 4766 matched controls. Furosemide use was identified from Prescription Register (1995–2015). The main analysis considered exposure at least 3 years before the matching date (3‐year lag). Additional analyses with no lag, 5‐ and 8‐year lags were conducted. Furosemide was not associated with PD risk (adjusted OR, 95% CI 1.00, 0.87–1.15 for user–non‐user comparison with 3‐year lag). A statistically significant borderline association between higher exposure levels and lower PD risk was observed in comparing risk across categories of cumulative furosemide exposure (adjusted OR, 95% CI 0.80, 0.64–1.00 with 3‐year lag). Our nationwide indication‐restricted case–control study of persons with PD found no robust evidence on the association between furosemide use and PD risk. However, there was suggestive evidence of reduced risk for participants in the highest exposure category.

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: Pain is a common symptom of Parkinson's disease (PD). It occurs also as a prodromal sign of PD. It has not yet been described how the use of analgesics changes over time in persons with PD. We investigated the incidence of opioid use from 5 years before to 5 years after PD diagnosis and compared the incidence to a matched cohort. Methods: This study included 15,763 people diagnosed with incident PD in 2001–2014 and 62,907 matched comparison persons without PD from the Finnish nationwide register‐based study on Parkinson's disease (FINPARK). Initiation rates of opioid use during the follow‐up were calculated in 6‐month time windows, and the difference between persons with and without PD was described using incidence rate ratios (IRRs). Results: Opioid initiation was more common among persons with PD (37.0%) compared to people without PD (31.2%). The difference between the groups emerged 3 years before the PD diagnosis IRR 1.33 (1.16–1.53), and remained until the end of the follow‐up. Mild opioids, such as tramadol and codeine, were the most commonly initiated opioids, and the largest difference in their use was observed 6 months prior to the diagnosis date, while differences in strong opioids emerged after the PD diagnosis. Initiation rates increased over time and remained above those of the comparison group after the PD diagnosis for all opioid classes. Conclusions: The findings demonstrate the incidence of moderate/severe pain in PD, which requires treatment with opioid‐class analgesics. Further studies are needed to understand the long‐term impact of opioid use in persons with PD. Significance Statement: Initiation rate of opioids is increased in persons with Parkinson's disease already at premotor or early motor stage, before the diagnosis is confirmed. Shift towards stronger opioids is observed over the disease progress. Further studies are needed to investigate optimized pain management strategies in Parkinson's disease.

Background: Antiepileptics are used to treat epilepsy but also, e.g., neuropathic pain, essential tremor and dystonia. It is not known whether they are more commonly used in persons with Parkinson's disease (PD). Objective: To assess the incidence of antiepileptic use in a nationwide cohort of persons with PD before and after the diagnosis and compared the findings to a matched cohort without PD. Methods: This register-based Finnish nationwide cohort included 18365 persons diagnosed with PD between 2001–2015. Incidence of antiepileptic initiations, from 10 years before until 10 years after the PD diagnosis, was compared to an age-, sex-, and region-matched cohort without PD. Results: Antiepileptics were more commonly initiated for persons with PD (29.3% of PD cohort and 15.2% of comparison cohort). Gabapentinoids were the most commonly initiated antiepileptics in both cohorts. A similar pattern in initiation rates was observed for both gabapentinoids and other antiepileptics, with increased incidence in the PD cohort approximately three years before the diagnosis and a significant peak around the time of PD diagnosis (the initiation rate at the time of PD diagnosis 3/100 and 1/100 person-years, for the PD and comparison cohorts, respectively). Clonazepam initiations were more common in the PD cohort (26.7% of initiations vs. 5.8% in the comparison cohort). Conclusions: The increase in antiepileptic initiation rates before the diagnosis of PD suggests that they might be used for prodromal motor or non-motor symptoms.

Introduction: Although β 2-adrenoceptor (β 2AR) agonists have been associated with a lower risk of Parkinson’s disease (PD), the findings are inconclusive and may reflect confounding by indication. We studied the association between inhaled β 2AR agonists and risk of PD in persons with asthma or chronic obstructive pulmonary disease (COPD). Methods: The nested case-control study was conducted within a register-based Finnish Parkinson’s disease study (FINPARK) and included 1406 clinically verified PD cases diagnosed during 1999– 2015, who also had asthma/COPD > 3 years before PD. PD cases were matched with up to seven controls by age, sex, duration of asthma/COPD, pulmonary diagnosis, and region (N = 8630). Cumulative and average annual exposure to short- and long-acting β 2AR agonists before a 3-year lag period was assessed with quartiles of defined daily doses (DDDs). Adjusted odds ratios (aORs) were calculated with 95% confidence intervals (CIs) using conditional logistic regression. Results: Cumulative exposure to either short- or long-acting β 2AR agonists was not associated with a risk of PD. With average annual exposure, a decreased risk was observed only for the highest quartile of long-acting β 2AR agonists (aOR 0.75; 95% CI 0.58– 0.97). In the stratified analysis the lowest risk estimates were observed among those with both asthma and COPD diagnoses. The suggestion of an inverse association was seen for the highest quartile of long-acting β 2AR agonists in asthma. Discussion: Higher levels of exposure to β 2AR agonists were not consistently associated with a reduced risk of PD. The inverse association in the highest category of average annual exposure to long-acting β 2AR agonists may be explained by unmeasured confounding, such as disease severity or smoking.