Bjoern H. Falkenburger

The cGAS–STING pathway is a central mediator of antiviral immunity, yet its constitutive activation causes severe systemic autoinflammatory disease and neurodegeneration. Intermittent fasting (IF) has been proposed as a non-pharmacological strategy to attenuate inflammation and neurodegeneration. We therefore performed a comprehensive study of how IF reshapes immune phenotypes and assessed whether IF mitigates pathology in mice carrying a constitutively active STING mutation ( Sting N153S⁺/⁻). Sting N153S⁺/⁻ mice and wild-type littermates were subjected to either an IF regimen (two 24-h fasting periods per week for 3 months) or ad libitum (AL) feeding and analyzed using gene expression profiling, cytokine and chemokine quantification, flow cytometry, histopathology, and machine-learning–based analyses across blood, lung, thymus, spleen, kidney, and brain. IF induced tissue-specific immunological changes, partially normalizing STING-induced alterations in immune cell composition, gene expression and cytokine levels in select peripheral organs. However, key TNF-dependent disease manifestations, especially lung pathology and dopaminergic neuron loss in the midbrain, were not ameliorated. Furthermore, exploratory supervised and unsupervised multivariate analyses revealed distinct signaling patterns associated with genotype and diet. These findings demonstrate that IF modulates specific immune signatures at the levels of gene expression, cytokine production, and immune cell composition. Nonetheless, these changes were insufficient to ameliorate TNF-associated disease features driving pulmonary pathology and neurodegeneration in Sting N153S⁺/⁻ mice. Together, these results underscore the importance of tailoring dietary interventions to the underlying disease mechanisms.

Cognitive impairment is an important constraint for PwPD with Parkinson’s disease (PwPD). Digital assessments potentially provide more accessible measurements and obtain a richer set of data than traditional paper–pencil-based instruments. Open-source generation can support the availability of comparable assessments in different cohorts. A digital, tablet-based cognitive assessment (DiCo) comprising 13 distinct tests was implemented as an open-source tool, expanding on available frameworks. Commonly used tests were selected to cover response inhibition, cognitive flexibility, attention, and working memory. After extensive usability testing, the DiCo was applied in participants without overt cognitive impairment. 97 participants (43% women) completed the entire DiCo. Clustering of DiCo feature correlations and conditional dependencies indicated a predominantly mutual organization of cognitive performance in PwPD. Exploratory factor analysis identified five interrelated latent factors. Most factors were derived from individual tests and correlated moderately with traditional neuropsychological tests and questionnaires. Machine learning identified a test of working memory (N-back) as the most predictive feature for the Montreal Cognitive Assessment. Latent profile analysis revealed four cognitive subgroups, mainly reflecting severity. The open-source digital cognitive assessment established in this study showed good usability by PwPD. The observed overlap between individual DiCo features and MoCA and FAB scores may inform future reduced task sets for research or selected clinical settings.

Background: Lysosomal dysfunction plays a crucial role in the pathogenesis of Parkinson's disease (PD), particularly among GBA1 mutation carriers. Beyond GBA1, genes such as TMEM175, SCARB2, and CTSB identified in genome-wide association studies (GWAS) are also implicated in lysosomal pathways contributing to PD risk, although their functional effects in patients remain unclear. Proteins encoded by these lysosome-related genes have been explored as potential therapeutic targets in experimental models. Biomarker profiles, including clinical measures, alpha-synuclein seeding activity, lysosomal proteins, and sphingolipids, may facilitate patient stratification and support therapeutic monitoring in future clinical trials. Aim: The aim of this study is to investigate the impact of genetic variants of three lysosomal-related genes (TMEM175, SCARB2, and CTSB) on biomarker profiles in PD with and without GBA1 variants. Cross-sectional data from two German cohorts: the Tuebingen Parkinson Cohort (TUEPAC) and the DESCRIBE PD cohort of the German Center for Neurodegenerative Diseases were used as explorative cohorts, and data from Accelerating Medicines Partnership Parkinson's Disease (AMP-PD) were used as a validation cohort. The ultimate goal is to provide new data for patient stratification based on genetics, which might serve as a readout for target engagement and treatment efficiency assessment. Methods: Three cohorts were analyzed: TUEPAC, DESCRIBE PD, and AMP-PD. TUEPAC and DESCRIBE PD were combined into a single German discovery cohort (TUEPAC-DESCRIBE-PD), while AMP-PD served as an independent validation cohort. Within each cohort, for subgroup analyses, PD patients were classified as the overall PD cohort (PDall), and further stratified by GBA1 mutation status into PD patients without GBA1 mutations (PDGBA1_wildtype), and PD patients carrying GBA1 mutations (PDGBA1). We evaluated cognitive and motor function, as well as depression using the Montreal Cognitive Assessment (MoCA), Unified Parkinson Disease Rating Scale-part III (UPDRS III), and Beck Depression Inventory-II(BDI-II) scales. Analyzed biomarkers included CSF α-syn seeding activity using seed amplification assay (SAA), CSF lysosomal protein levels of lysosomal integral membrane protein 2 (LIMP2), also known as SCARB2, cathepsin B (CTSB) and lysosome-associated membrane protein 2 (LAMP2), blood-based enzyme activity of the lysosomal glucocerebrosidase (GCase), and CSF sphingolipid profiles. PD patients carrying risk alleles in TMEM175, SCARB2, and CTSB were compared to non-carriers. Results: Genotype-phenotype correlation analysis in TUEPAC-DESCRIBE-PD and AMP-PD revealed: (1) In PDall, the TMEM175 p.M393T risk variant was nominally associated with decreased cognitive function when adjusted for GBA1 mutation status in TUEPAC-DESCRIBE-PD; this association could not be replicated, although a similar trend was observed in the slightly smaller, but multicentric AMP-PD cohort; TMEM175 p.M393T was not significantly associated with BDI-II or UPDRS-III scores in either cohort. (2) In PDGBA1_wildtype, GCase activity was significantly lower in PD patients with SCARB2 rs6812193 risk allele in TUEPAC-DESCRIBE-PD, while a similar but non-significant trend was observed in AMP-PD; (3) In PDall, CSF levels of CTSB were nominally lower in carriers of CTSB rs1293298 risk allele compared to carriers of CTSB rs1293298 protective allele in TUEPAC-DESCRIBE-PD; in PDGBA1_wildtype, LAMP2 was significantly lower in carriers of CTSB rs1293298 risk allele compared to carriers of CTSB rs1293298 protective allele in TUEPAC-DESCRIBE-PD; (4) In PDall, TMEM175 p.M393T risk allele was nominally associated with altered sphingolipid profiles across both TUEPAC-DESCRIBE-PD and AMP-PD cohorts. Conclusion: These findings demonstrate that genetic variants in lysosomal-related genes (TMEM175, SCARB2, and CTSB) have a functional impact on biomarker profiles in PD patients. Integrating genetic characterization with biochemical profiling provides a framework for patient stratification and may serve as a translational strategy to monitor target engagement and evaluate treatment efficacy in future clinical trials.

Background: Parkinson disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with PD, informal caregivers, and health care professionals. With growing interest in digital and predictive artificial intelligence (AI) tools for disease management, understanding the needs and digital readiness of these stakeholder groups is crucial. Objective: This work aims to (1) identify digital practices for PD management among persons with PD, at-risk individuals, caregivers, and health care professionals; (2) compare these practices across groups; (3) explore stakeholder desires for AI-based tools; and (4) assess alignments and gaps to inform tailored AI solutions. Methods: An anonymous cross-sectional online survey of an exploratory nature was distributed (from December 2024 to October 2025) in 5 languages and completed by 255 respondents. Descriptive statistics summarized responses to 41 questions, including stakeholder-specific items. χ ² tests were performed to examine stakeholder differences in desired AI features. Results: Interest in predictive AI was high across stakeholder groups. Symptom tracking was the most desired feature (selected by more than 76% of the respondents), and personalized treatment recommendations came second for both persons with PD and health care professionals; however, stakeholder priorities diverged in other areas. Health care professionals rated improving patient and informal caregiver engagement as significantly more important than persons with PD did, χ ² ₁ (n=205)=34.78, P <.001, and Cramer V=0.41. Despite considerable interest, the reported use of digital tools was limited, as most persons with PD did not use symptom-tracking apps or wearables, nor were they currently monitoring their condition, although many expressed intentions to begin. Conclusions: While predictive AI tools were viewed positively across groups, there were significant gaps in stakeholder preferences, highlighting the importance of tailored, context-aware design. Early diagnosis was not prioritized by persons with PD or health care professionals, likely reflecting the complexity of diagnosing PD in the absence of disease-modifying therapies. Coupled with the emphasis placed on preventive lifestyle guidance by persons with PD and those at risk, this highlights the importance of actionability in AI-based monitoring and prediction. Such actionability may also enhance perceived relevance and uptake, given that reported interest in digital health tools and self-tracking exceeded actual use. These findings offer early-stage insight to guide the development of future AI-based solutions for PD.

INTRODUCTION: The alpha‐synuclein seed amplification assay (SAA) has shown excellent performance in the detection of Lewy body pathology in cerebrospinal fluid (CSF). Lewy body pathology is prognostically relevant in patients at risk for dementia. Current assays only provide binary results, so there is a need to quantify the extent of pathology in living patients. METHODS: In addition to the “standard” SAA, we developed a quantitative SAA (qnSAA) and measured 432 CSF samples (216 baseline–follow‐up pairs). RESULTS: qnSAA results correlated with cognitive performance. Seventy‐five percent of participants with fast qnSAA kinetics converted to dementia in the observed interval. Overall, participants with fast qnSAA kinetics accounted for 27.3% of dementia converters in the entire cohort. DISCUSSION: Findings demonstrate promising properties of qnSAA measurements in a cohort of patients at risk for dementia. qnSAA results showed improved prognostic relevance and have potential to measure target engagement of therapies against Lewy body pathology.

BACKGROUND Parkinson’s disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with Parkinson’s (PwP), informal caregivers, and healthcare professionals. With growing interest in digital and predictive Artificial Intelligence (AI) tools for disease management, understanding the needs and digital readiness of these stakeholder groups is crucial. OBJECTIVE This work aims to (1) identify digital practices for PD management among PwP, at‑risk individuals, caregivers, and healthcare professionals; (2) compare these practices across groups; (3) explore stakeholder desires for AI-based tools; and (4) assess alignments and gaps to inform tailored AI solutions. METHODS An anonymous cross-sectional online survey of exploratory nature was distributed (from Dec. 2024 to Oct. 2025) in five languages. It was completed by 255 respondents. Descriptive statistics summarized responses to 41 questions, including stakeholder-specific items. Chi-square tests were performed to examine stakeholder differences in desired AI-features. RESULTS : Interest in predictive AI was high across stakeholder groups. Symptom-tracking was the most desired feature (selected by >76% of respondents); however, stakeholder priorities diverged in other areas. Healthcare professionals rated improving patient and informal caregiver engagement as significantly more important than PwP did, χ²(1, N=205)=34.78, p<.001, Cramer’s V=0.41. Despite considerable interest, the reported use of digital tools was limited, as most PwP did not use symptom-tracking apps or wearables, nor were they currently monitoring their condition, although many expressed intentions to begin. CONCLUSIONS While AI tools were viewed positively across groups, there were significant gaps in current usage. Stakeholder-specific preferences, including informal caregiver engagement and preventive lifestyle guidance, highlight the importance of tailored design. These findings offer early-stage insight to guide development of future AI-based solutions for PD.

Background: Parkinson's disease (PD) is a slowly progressing neurodegenerative disorder, so it is likely that people with PD (PwPD) face increasing disability. PwPD thus experience various degrees of fear of progression (FoP), which can become dysfunctional. Objective: This study aims to examine the prevalence of and contributing factors to dysfunctional FoP in PwPD. Methods: The Fear of Progression Questionnaire–Short Form (FoP-Q-SF) was administered along with further questionnaires for non-motor symptoms; PD motor symptoms as reported by the Unified Parkinson's Disease Rating Scale Part III (UPDRS III) were obtained from patient charts. Results: 28% of the 105 PwPD (mean age 66 years, 56% Hoehn & Yahr stage I/II) were categorized as experiencing dysfunctional levels of FoP using the established cut-off score of ≥34. Our analyses revealed that the FoP-Q-SF underestimates the prevalence of dysfunctional FoP in older and non-working PwPD. Using a more appropriate cut-off, 33% of PwPD are classified as having dysfunctional levels of FoP. We found strong correlations of FoP with measures of anxiety, depression and quality of life. Disease duration was secondary to these factors. We found no associations between FoP and motor symptoms. Conclusions: Our findings confirm that dysfunctional FoP significantly impacts the psychological well-being of PwPD, affecting one in three PwPD and contributing to heightened anxiety, depression, and reduced quality of life. Future validation studies are needed to confirm the cut-off value proposed here and to enable a better integration of the concept of FoP into routine care for PwPD.

Digital health technologies (DHTs) are on the verge of changing treatment of Parkinson’s disease (PD). This narrative overview article describes DHTs that are already used or in development, focusing on technologies available in Germany and on their clinical utility. Examples include applications that are primarily used by patients, i.e., tools for patient education and empowerment, but also stand-alone interventions improving clinical outcomes including motor and non-motor symptoms. In Germany, these patient-centered DHTs fall under the HTA framework of digital health applications (DIGA). DHT for telemonitoring, in contrast, are designed to support physicians and other healthcare providers and therefore medical devices. DHT are also used increasingly as exploratory endpoints in trials testing neuroprotective treatments.

Parkinson's disease (PD) exhibits high heterogeneity in disease progression, complicating management and increasing required sample sizes for clinical trials. This study evaluates Brain Age Gap (BAG)-the difference between brain age and chronological age-for predicting disease progression in PD. Structural MRI-derived gray matter volumes of 451 early disease stage PD patients and 172 healthy controls were analyzed. PD patients had a mean BAG of 1.1 years at baseline with fast-progressing patients exhibiting a BAG of 3.0 years, whereas slow-progressing patients resembled BAG of healthy controls. Higher BAG was associated with more severe baseline symptoms, faster cognitive decline in several domains, increased hazard of developing mild cognitive impairment, and faster progression of dopaminergic neuron loss in longitudinal DaTSCANs. BAG-based patient stratification could reduce sample sizes of randomized clinical trials by 26%-56%. These findings suggest BAG as a prognostic biomarker of disease progression, which may accelerate development of disease-modifying treatments.