Alexis Elbaz

Background Statins represent candidates for drug repurposing in Parkinson's disease (PD). Few studies examined the role of reverse causation, statin subgroups, and dose–response relations based on time‐varying exposures. Objectives We examined whether statin use is associated with PD incidence while attempting to overcome the limitations described previously, especially reverse causation. Method We used data from the E3N cohort study of French women (follow‐up, 2004–2018). Incident PD was ascertained using multiple sources and validated by experts. New statin users were identified through linked drug claims. We set up a nested case‐control study to describe trajectories of statin prescriptions and medical consultations before diagnosis. We used time‐varying multivariable Cox proportional hazards regression models to examine the statins–PD association. Exposure indexes included ever use, cumulative duration/dose, and mean daily dose and were lagged by 5 years to address reverse causation. Results The case‐control study (693 cases, 13,784 controls) showed differences in case‐control trajectories, with changes in the 5 years before diagnosis in cases. Of 73,925 women (aged 54–79 years), 524 developed PD and 11,552 started using statins in lagged analyses. Ever use of any statin was not associated with PD (hazard ratio [HR] = 0.87, 95% confidence interval [CI] = 0.67–1.11). Alternatively, ever use of lipophilic statins was significantly associated with lower PD incidence (HR = 0.70, 95% CI = 0.51–0.98), with a dose–response relation for the mean daily dose ( P ‐linear trend = 0.02). There was no association for hydrophilic statins. Conclusion Use of lipophilic statins at least 5 years earlier was associated with reduced PD incidence in women, with a dose–response relation for the mean daily dose. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Despite experimental studies suggesting a disease-modifying role of oestrogens, results from epidemiological studies on the relation of reproductive characteristics and hormonal exposures with Parkinson disease in women are conflicting. We used the data from the E3N cohort study including 98 068 women aged 40–65 years in 1990 followed until 2018. Parkinson disease was ascertained using a validation process based on drug claim databases and medical records. Reproductive characteristics and hormonal exposures were self-reported (11 questionnaires). Associations of exposures with Parkinson disease incidence were investigated using time-varying Cox proportional hazards regression with a 5-year exposure lag and age as the timescale adjusted for confounders. We identified 1165 incident Parkinson disease cases during a mean follow-up of 22.0 years (incidence rate = 54.7 per 100 000 person-years). Parkinson disease incidence was higher in women with early (<12 years, HR = 1.21, 95% CI = 1.04–1.40) or late age at menarche (≥14 years, HR = 1.18, 95% CI = 1.03–1.35) than in women with menarche at 12–13 years. Nulliparity was not associated with Parkinson disease, but Parkinson disease incidence increased with the number of children in parous women (P-trend = 0.009). Women with artificial (surgical, iatrogenic) menopause were at greater risk than women with natural menopause (HR = 1.28, 95% CI = 1.09–1.47), especially when artificial menopause occurred at an early age (≤45.0 years). Postmenopausal hormone therapy tended to mitigate greater risk associated with artificial or early menopause (≤45.0 years). While fertility treatments were not associated with Parkinson disease overall, ever users of clomiphene were at greater Parkinson disease risk than never users (HR = 1.81, 95% CI = 1.14–2.88). Other exposures (breastfeeding, oral contraceptives) were not associated with Parkinson disease. Our findings suggest that early and late age at menarche, higher parity, and artificial menopause, in particular at an early age, are associated with increased Parkinson disease incidence in women. In addition, there was some evidence that use of exogenous hormones may increase (fertility treatments) or decrease (postmenopausal hormone therapy) Parkinson disease incidence. These findings support the hypothesis that hormonal exposures play a role in the susceptibility to neurodegenerative diseases. If confirmed, they could help to identify subgroups at high risk for Parkinson disease.

Background Available treatments for Parkinson's disease (PD) are only partially or transiently effective. Identifying existing molecules that may present a therapeutic or preventive benefit for PD (drug repositioning) is thus of utmost interest. Objective We aimed at detecting potentially protective associations between marketed drugs and PD through a large‐scale automated screening strategy. Methods We implemented a machine learning (ML) algorithm combining subsampling and lasso logistic regression in a case–control study nested in the French national health data system. Our study population comprised 40,760 incident PD patients identified by a validated algorithm during 2016 to 2018 and 176,395 controls of similar age, sex, and region of residence, all followed since 2006. Drug exposure was defined at the chemical subgroup level, then at the substance level of the Anatomical Therapeutic Chemical (ATC) classification considering the frequency of prescriptions over a 2‐year period starting 10 years before the index date to limit reverse causation bias. Sensitivity analyses were conducted using a more specific definition of PD status. Results Six drug subgroups were detected by our algorithm among the 374 screened. Sulfonamide diuretics (ATC‐C03CA), in particular furosemide (C03CA01), showed the most robust signal. Other signals included adrenergics in combination with anticholinergics (R03AL) and insulins and analogues (A10AD). Conclusions We identified several signals that deserve to be confirmed in large studies with appropriate consideration of the potential for reverse causation. Our results illustrate the value of ML‐based signal detection algorithms for identifying drugs inversely associated with PD risk in health‐care databases. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society