The elevated risk of Parkinson’s disease in patients with diabetes might be mitigated depending on the type of drugs prescribed to treat diabetes. Population data for risk of Parkinson’s disease in users of the newer types of drugs used in diabetes are scarce. We compared the risk of Parkinson’s disease in patients with diabetes exposed to thiazolidinediones (glitazones), glucagon-like peptide-1 (GLP-1) receptor agonists and dipeptidyl peptidase 4 (DPP4) inhibitors, with the risk of Parkinson’s disease of users of any other oral glucose lowering drugs. A population-based, longitudinal, cohort study was conducted using historic primary care data from The Health Improvement Network. Patients with a diagnosis of diabetes and a minimum of two prescriptions for diabetes medications between January 2006 and January 2019 were included in our study. The primary outcome was the first recording of a diagnosis of Parkinson’s disease after the index date, identified from clinical records. We compared the risk of Parkinson’s disease in individuals treated with glitazones or DPP4 inhibitors and/or GLP-1 receptor agonists to individuals treated with other antidiabetic agents using a Cox regression with inverse probability of treatment weighting based on propensity scores. Results were analysed separately for insulin users. Among 100 288 patients [mean age 62.8 years (standard deviation 12.6)], 329 (0.3%) were diagnosed with Parkinson’s disease during the median follow-up of 3.33 years. The incidence of Parkinson’s disease was 8 per 10 000 person-years in 21 175 patients using glitazones, 5 per 10 000 person-years in 36 897 patients using DPP4 inhibitors and 4 per 10 000 person-years in 10 684 using GLP-1 mimetics, 6861 of whom were prescribed GTZ and/or DPP4 inhibitors prior to using GLP-1 mimetics. Compared with the incidence of Parkinson’s disease in the comparison group (10 per 10 000 person-years), adjusted results showed no evidence of any association between the use of glitazones and Parkinson’s disease [incidence rate ratio (IRR) 1.17; 95% confidence interval (CI) 0.76–1.63; P = 0.467], but there was strong evidence of an inverse association between use of DPP4 inhibitors and GLP-1 mimetics and the onset of Parkinson’s disease (IRR 0.64; 95% CI 0.43–0.88; P < 0.01 and IRR 0.38; 95% CI 0.17–0.60; P < 0.01, respectively). Results for insulin users were in the same direction, but the overall size of this group was small. The incidence of Parkinson’s disease in patients diagnosed with diabetes varies substantially depending on the treatment for diabetes received. The use of DPP4 inhibitors and/or GLP-1 mimetics is associated with a lower rate of Parkinson’s disease compared to the use of other oral antidiabetic drugs.

Remote and objective assessment of the motor symptoms of Parkinson’s disease is an area of great interest particularly since the COVID-19 crisis emerged. In this paper, we focus on a) the challenges of assessing motor severity via videos and b) the use of emerging video-based Artificial Intelligence (AI)/Machine Learning techniques to quantitate human movement and its potential utility in assessing motor severity in patients with Parkinson’s disease. While we conclude that video-based assessment may be an accessible and useful way of monitoring motor severity of Parkinson’s disease, the potential of video-based AI to diagnose and quantify disease severity in the clinical context is dependent on research with large, diverse samples, and further validation using carefully considered performance standards.

Parkinson’s disease (PD) is a common neurological disorder, with bradykinesia being one of its cardinal features. Objective quantification of bradykinesia using computer vision has the potential to standardise decision-making, for patient treatment and clinical trials, while facilitating remote assessment. We utilised a dataset of part-3 MDS-UPDRS motor assessments, collected at four independent clinical and one research sites on two continents, to build computer-vision-based models capable of inferring the correct severity rating robustly and consistently across all identifiable subgroups of patients. These results contrast with previous work limited by small sample sizes and small numbers of sites. Our bradykinesia estimation corresponded well with clinician ratings (interclass correlation 0.74). This agreement was consistent across four clinical sites. This result demonstrates how such technology can be successfully deployed into existing clinical workflows, with consumer-grade smartphone or tablet devices, adding minimal equipment cost and time.

Advances in marker--less motion capture technology now allow the accurate replication of facial motion and deformation in computer--generated imagery (CGI). A forced--choice discrimination paradigm using such CGI facial animations showed that human observers can categorise identity solely from facial motion cues. Animations were generated from motion captures acquired during natural speech, thus eliciting both rigid (head rotations and translations) and nonrigid (expressional changes) motion. To limit interferences from individual differences in facial form, all animations shared the same appearance. Observers were required to discriminate between different videos of facial motion and between the facial motions of different people. Performance was compared to the control condition of orientation--inverted facial motion. The results show that observers are able to make accurate discriminations of identity in the absence of all cues except facial motion. A clear inversion effect in both tasks provided consistency with previous studies, supporting the configural view of human face perception. The accuracy of this motion capture technology thus allowed stimuli to be generated which closely resembled real moving faces. Future studies may wish to implement such methodology when studying human face perception.