Progression subtypes in Parkinson's disease identified by a data-driven multi cohort analysis.

The progression of Parkinson's disease (PD) is heterogeneous across patients, affecting counseling and inflating the number of patients needed to test potential neuroprotective treatments. Moreover, disease subtypes might require different therapies. This work uses a data-driven approach to investigate how observed heterogeneity in PD can be explained by the existence of distinct PD progression subtypes. To derive stable PD progression subtypes in an unbiased manner, we analyzed multimodal longitudinal data from three large PD cohorts and performed extensive cross-cohort validation. A latent time joint mixed-effects model (LTJMM) was used to align patients on a common disease timescale. Progression subtypes were identified by variational deep embedding with recurrence (VaDER). In each cohort, we identified a fast-progressing and a slow-progressing subtype, reflected by different patterns of motor and non-motor symptoms progression, survival rates, treatment response, features extracted from DaTSCAN imaging and digital gait assessments, education, and Alzheimer's disease pathology. Progression subtypes could be predicted with ROC-AUC up to 0.79 for individual patients when a one-year observation period was used for model training. Simulations demonstrated that enriching clinical trials with fast-progressing patients based on these predictions can reduce the required cohort size by 43%. Our results show that heterogeneity in PD can be explained by two distinct subtypes of PD progression that are stable across cohorts. These subtypes align with the brain-first vs. body-first concept, which potentially provides a biological explanation for subtype differences. Our predictive models will enable clinical trials with significantly lower sample sizes by enriching fast-progressing patients.

[How the implementation of a school for people with Parkinson's disease can succeed-Results of a consensus study and a formative evaluation]. / Wie die Umsetzung einer Schule für Menschen mit Parkinson-Krankheit gelingen kann ­ Ergebnisse eines Konsensusverfahrens und einer formativen Evaluation.

BACKGROUND: As the most rapidly increasing neurodegenerative disease worldwide, Parkinson's disease is highly relevant to society. Successful treatment requires active patient participation. Patient education has been successfully implemented for many chronic diseases, such as diabetes and could also provide people with Parkinson's disease with skills to manage the disease better and to participate in shared decision making. MATERIAL AND METHODS: To prepare the implementation of a concept for patient education for people with Parkinson's disease, a structured consensus study was conducted and a pilot project formatively evaluated. The structured consensus study included experts from all over Germany. It consisted of two online surveys and an online consensus conference. The formative evaluation was conducted as three focus groups. Transcripts were evaluated using content-structuring qualitative content analysis. RESULTS: From the consensus procedure 59 consented statements emerged, mainly regarding the contents of a patient school and a group size of 6-8 persons. Only two statements could not be consented. The formative evaluation detected a tendency towards a positive attitude for a digital training format and a very positive evaluation of the contents. DISCUSSION: Overall, important recommendations for a patient school can be drawn from this study. The following subjects require further investigation: format, inclusion criteria, group composition and inclusion of caregivers.

Development and Validation of a Fall Questionnaire for Patients with Parkinson's Disease.

Background: In Parkinson's disease, postural instability and falls are of particular socioeconomic relevance. Although effective fall prevention and the prophylaxis of fall-related injuries depend on low-threshold symptom monitoring, validated instruments are lacking. Objectives: To develop a self-report questionnaire for the assessment of falls, near falls, fear of falling, fall-related injuries, and causes of falls for patients with Parkinson's disease (PwPD). Methods: A pool of potential items was generated from a literature review and by discussion in an expert panel. The first version of the Dresden Fall Questionnaire (DREFAQ) was tested in a group of German-speaking movement disorder specialists as well as PwPD. The resulting 5-item questionnaire was assessed in a validation cohort of 36 PwPD who documented fall events and near-fall events in a calendar for 3 months and completed the DREFAQ at the end of the study. The questionnaire was subsequently used in a separate cohort of 46 PwPD to determine test-retest reliability and confirm the factor structure. Results: The DREFAQ showed good internal consistency (Cronbach's α = 0.84) and good test-retest reliability (intraclass correlation coefficient, 0.76; 95% confidence interval, 0.60-0.86). The total DREFAQ score showed good concurrent validity with fall events (Spearman's ρ = 0.82) and near-fall events (Spearman's ρ = 0.78) as determined by fall and near-fall diaries. Factor analysis revealed a 2-factor structure composed of near falls with fear of falling and severe falls with injuries. Conclusions: The DREFAQ is a reliable and valid 5-item questionnaire for determining the incidence of falls, near falls, fear of falling, fall-related injuries, and causes of falls in PwPD.

Model-Based Inference and Classification of Immunologic Control Mechanisms from TKI Cessation and Dose Reduction in Patients with CML.

Recent clinical findings in patients with chronic myeloid leukemia (CML) suggest that the risk of molecular recurrence after stopping tyrosine kinase inhibitor (TKI) treatment substantially depends on an individual's leukemia-specific immune response. However, it is still not possible to prospectively identify patients that will remain in treatment-free remission (TFR). Here, we used an ordinary differential equation model for CML, which explicitly includes an antileukemic immunologic effect, and applied it to 21 patients with CML for whom BCR-ABL1/ABL1 time courses had been quantified before and after TKI cessation. Immunologic control was conceptually necessary to explain TFR as observed in about half of the patients. Fitting the model simulations to data, we identified patient-specific parameters and classified patients into three different groups according to their predicted immune system configuration ("immunologic landscapes"). While one class of patients required complete CML eradication to achieve TFR, other patients were able to control residual leukemia levels after treatment cessation. Among them were a third class of patients that maintained TFR only if an optimal balance between leukemia abundance and immunologic activation was achieved before treatment cessation. Model simulations further suggested that changes in the BCR-ABL1 dynamics resulting from TKI dose reduction convey information about the patient-specific immune system and allow prediction of outcome after treatment cessation. This inference of individual immunologic configurations based on treatment alterations can also be applied to other cancer types in which the endogenous immune system supports maintenance therapy, long-term disease control, or even cure. SIGNIFICANCE: This mathematical modeling approach provides strong evidence that different immunologic configurations in patients with CML determine their response to therapy cessation and that dose reductions can help to prospectively infer different risk groups.See related commentary by Triche Jr, p. 2083.