Dairy Intake and Parkinson's Disease: A Mendelian Randomization Study.

BACKGROUND: Previous prospective studies highlighted dairy intake as a risk factor for Parkinson's disease (PD), particularly in men. It is unclear whether this association is causal or explained by reverse causation or confounding. OBJECTIVE: The aim is to examine the association between genetically predicted dairy intake and PD using two-sample Mendelian randomization (MR). METHODS: We genotyped a well-established instrumental variable for dairy intake located in the lactase gene (rs4988235) within the Courage-PD consortium (23 studies; 9823 patients and 8376 controls of European ancestry). RESULTS: Based on a dominant model, there was an association between genetic predisposition toward higher dairy intake and PD (odds ratio [OR] per one serving per day = 1.70, 95% confidence interval = 1.12-2.60, P = 0.013) that was restricted to men (OR = 2.50 [1.37-4.56], P = 0.003; P-difference with women = 0.029). CONCLUSIONS: Using MR, our findings provide further support for a causal relationship between dairy intake and higher PD risk, not biased by confounding or reverse causation. Further studies are needed to elucidate the underlying mechanisms. © 2022 International Parkinson and Movement Disorder Society.

The Interaction between HLA-DRB1 and Smoking in Parkinson's Disease Revisited.

BACKGROUND: Two studies that examined the interaction between HLA-DRB1 and smoking in Parkinson's disease (PD) yielded findings in opposite directions. OBJECTIVE: To perform a large-scale independent replication of the HLA-DRB1 × smoking interaction. METHODS: We genotyped 182 single nucleotide polymorphism (SNPs) associated with smoking initiation in 12 424 cases and 9480 controls to perform a Mendelian randomization (MR) analysis in strata defined by HLA-DRB1. RESULTS: At the amino acid level, a valine at position 11 (V11) in HLA-DRB1 displayed the strongest association with PD. MR showed an inverse association between genetically predicted smoking initiation and PD only in absence of V11 (odds ratio, 0.74, 95% confidence interval, 0.59-0.93, PInteraction  = 0.028). In silico predictions of the influence of V11 and smoking-induced modifications of α-synuclein on binding affinity showed findings consistent with this interaction pattern. CONCLUSIONS: Despite being one of the most robust findings in PD research, the mechanisms underlying the inverse association between smoking and PD remain unknown. Our findings may help better understand this association. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A "kissing lesion": In-vivo 7T evidence of meningeal inflammation in early multiple sclerosis.

BACKGROUND: The role of cortical lesions (CLs) in disease progression and clinical deficits is increasingly recognized in multiple sclerosis (MS); however the origin of CLs in MS still remains unclear. OBJECTIVE: Here, we report a para-sulcal CL detected two years after diagnosis in a relapsing-remitting MS (RRMS) patient without manifestation of clinical deficit. METHODS: Ultra-high field (7T) MR imaging using magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) sequence was performed. RESULTS: A para-sulcal CL was detected which showed hypointense rim and iso- to hyperintense core. This was detected in the proximity of the leptomeninges in the left precentral gyrus extending to the adjacent postcentral gyrus. CONCLUSION: This finding indicates that inflammatory infiltration into the cortex through the meninges underlies cortical pathology already in the early stage of disease and in mild disease course.

Increased structural white and grey matter network connectivity compensates for functional decline in early multiple sclerosis.

BACKGROUND: The pathology of multiple sclerosis (MS) consists of demyelination and neuronal injury, which occur early in the disease; yet, remission phases indicate repair. Whether and how the central nervous system (CNS) maintains homeostasis to counteract clinical impairment is not known. OBJECTIVE: We analyse the structural connectivity of white matter (WM) and grey matter (GM) networks to understand the absence of clinical decline as the disease progresses. METHODS: A total of 138 relapsing-remitting MS patients (classified into six groups by disease duration) and 32 healthy controls were investigated using 3-Tesla magnetic resonance imaging (MRI). Networks were analysed using graph theoretical approaches based on connectivity patterns derived from diffusion-tensor imaging with probabilistic tractography for WM and voxel-based morphometry and regional-volume-correlation matrix for GM. RESULTS: In the first year after disease onset, WM networks evolved to a structure of increased modularity, strengthened local connectivity and increased local clustering while no clinical decline occurred. GM networks showed a similar dynamic of increasing modularity. This modified connectivity pattern mainly involved the cerebellum, cingulum and temporo-parietal regions. Clinical impairment was associated at later disease stages with a divergence of the network patterns. CONCLUSION: Our findings suggest that network functionality in MS is maintained through structural adaptation towards increased local and modular connectivity, patterns linked to adaptability and homeostasis.

Classification of advanced stages of Parkinson's disease: translation into stratified treatments.

Advanced stages of Parkinson's disease (advPD) still impose a challenge in terms of classification and related stage-adapted treatment recommendations. Previous concepts that define advPD by certain milestones of motor disability apparently fall short in addressing the increasingly recognized complexity of motor and non-motor symptoms and do not allow to account for the clinical heterogeneity that require more personalized approaches. Therefore, deep phenotyping approaches are required to characterize the broad-scaled, continuous and multidimensional spectrum of disease-related motor and non-motor symptoms and their progression under real-life conditions. This will also facilitate the reasoning for clinical care and therapeutic decisions, as neurologists currently have to refer to clinical trials that provide guidance on a group level; however, this does not always account for the individual needs of patients. Here, we provide an overview on different classifications for advPD that translate into critical phenotypic patterns requiring the differential therapeutic adjustments. New concepts refer to precision medicine approaches also in PD and first studies on genetic stratification for therapeutic outcomes provide a potential for more objective treatment recommendations. We define novel treatment targets that align with this concept and make use of emerging device-based assessments of real-life information on PD symptoms. As these approaches require empowerment of patients and integration into treatment decisions, we present communication strategies and decision support based on new technologies to adjust treatment of advPD according to patient demands and safety.

Association of Body Mass Index and Parkinson Disease: A Bidirectional Mendelian Randomization Study.

BACKGROUND AND OBJECTIVES: The role of body mass index (BMI) in Parkinson disease (PD) is unclear. Based on the Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in PD (Courage-PD) consortium, we used 2-sample Mendelian randomization (MR) to replicate a previously reported inverse association of genetically predicted BMI with PD and investigated whether findings were robust in analyses addressing the potential for survival and incidence-prevalence biases. We also examined whether the BMI-PD relation is bidirectional by performing a reverse MR. METHODS: We used summary statistics from a genome-wide association study (GWAS) to extract the association of 501 single-nucleotide polymorphisms (SNPs) with BMI and from the Courage-PD and international Parkinson Disease Genomics Consortium (iPDGC) to estimate their association with PD. Analyses are based on participants of European ancestry. We used the inverse-weighted method to compute odds ratios (ORIVW per 4.8 kg/m2 [95% CI]) of PD and additional pleiotropy robust methods. We performed analyses stratified by age, disease duration, and sex. For reverse MR, we used SNPs associated with PD from 2 iPDGC GWAS to assess the effect of genetic liability toward PD on BMI. RESULTS: Summary statistics for BMI are based on 806,834 participants (54% women). Summary statistics for PD are based on 8,919 (40% women) cases and 7,600 (55% women) controls from Courage-PD, and 19,438 (38% women) cases and 24,388 (51% women) controls from iPDGC. In Courage-PD, we found an inverse association between genetically predicted BMI and PD (ORIVW 0.82 [0.70-0.97], p = 0.012) without evidence for pleiotropy. This association tended to be stronger in younger participants (≤67 years, ORIVW 0.71 [0.55-0.92]) and cases with shorter disease duration (≤7 years, ORIVW 0.75 [0.62-0.91]). In pooled Courage-PD + iPDGC analyses, the association was stronger in women (ORIVW 0.85 [0.74-0.99], p = 0.032) than men (ORIVW 0.92 [0.80-1.04], p = 0.18), but the interaction was not statistically significant (p-interaction = 0.48). In reverse MR, there was evidence for pleiotropy, but pleiotropy robust methods showed a significant inverse association. DISCUSSION: Using an independent data set (Courage-PD), we replicate an inverse association of genetically predicted BMI with PD, not explained by survival or incidence-prevalence biases. Moreover, reverse MR analyses support an inverse association between genetic liability toward PD and BMI, in favor of a bidirectional relation.

Luxembourg Parkinson's study -comprehensive baseline analysis of Parkinson's disease and atypical parkinsonism.

Background: Deep phenotyping of Parkinson's disease (PD) is essential to investigate this fastest-growing neurodegenerative disorder. Since 2015, over 800 individuals with PD and atypical parkinsonism along with more than 800 control subjects have been recruited in the frame of the observational, monocentric, nation-wide, longitudinal-prospective Luxembourg Parkinson's study. Objective: To profile the baseline dataset and to explore risk factors, comorbidities and clinical profiles associated with PD, atypical parkinsonism and controls. Methods: Epidemiological and clinical characteristics of all 1,648 participants divided in disease and control groups were investigated. Then, a cross-sectional group comparison was performed between the three largest groups: PD, progressive supranuclear palsy (PSP) and controls. Subsequently, multiple linear and logistic regression models were fitted adjusting for confounders. Results: The mean (SD) age at onset (AAO) of PD was 62.3 (11.8) years with 15% early onset (AAO < 50 years), mean disease duration 4.90 (5.16) years, male sex 66.5% and mean MDS-UPDRS III 35.2 (16.3). For PSP, the respective values were: 67.6 (8.2) years, all PSP with AAO > 50 years, 2.80 (2.62) years, 62.7% and 53.3 (19.5). The highest frequency of hyposmia was detected in PD followed by PSP and controls (72.9%; 53.2%; 14.7%), challenging the use of hyposmia as discriminating feature in PD vs. PSP. Alcohol abstinence was significantly higher in PD than controls (17.6 vs. 12.9%, p = 0.003). Conclusion: Luxembourg Parkinson's study constitutes a valuable resource to strengthen the understanding of complex traits in the aforementioned neurodegenerative disorders. It corroborated several previously observed clinical profiles, and provided insight on frequency of hyposmia in PSP and dietary habits, such as alcohol abstinence in PD.Clinical trial registration: clinicaltrials.gov, NCT05266872.

Mendelian Randomisation Study of Smoking, Alcohol, and Coffee Drinking in Relation to Parkinson's Disease.

BACKGROUND: Previous studies showed that lifestyle behaviors (cigarette smoking, alcohol, coffee) are inversely associated with Parkinson's disease (PD). The prodromal phase of PD raises the possibility that these associations may be explained by reverse causation. OBJECTIVE: To examine associations of lifestyle behaviors with PD using two-sample Mendelian randomisation (MR) and the potential for survival and incidence-prevalence biases. METHODS: We used summary statistics from publicly available studies to estimate the association of genetic polymorphisms with lifestyle behaviors, and from Courage-PD (7,369 cases, 7,018 controls; European ancestry) to estimate the association of these variants with PD. We used the inverse-variance weighted method to compute odds ratios (ORIVW) of PD and 95%confidence intervals (CI). Significance was determined using a Bonferroni-corrected significance threshold (p = 0.017). RESULTS: We found a significant inverse association between smoking initiation and PD (ORIVW per 1-SD increase in the prevalence of ever smoking = 0.74, 95%CI = 0.60-0.93, p = 0.009) without significant directional pleiotropy. Associations in participants ≤67 years old and cases with disease duration ≤7 years were of a similar size. No significant associations were observed for alcohol and coffee drinking. In reverse MR, genetic liability toward PD was not associated with smoking or coffee drinking but was positively associated with alcohol drinking. CONCLUSION: Our findings are in favor of an inverse association between smoking and PD that is not explained by reverse causation, confounding, and survival or incidence-prevalence biases. Genetic liability toward PD was positively associated with alcohol drinking. Conclusions on the association of alcohol and coffee drinking with PD are hampered by insufficient statistical power.

Characterizing Microstructural Tissue Properties in Multiple Sclerosis with Diffusion MRI at 7 T and 3 T: The Impact of the Experimental Design.

The recent introduction of advanced magnetic resonance (MR) imaging techniques to characterize focal and global degeneration in multiple sclerosis (MS), like the Composite Hindered and Restricted Model of Diffusion, or CHARMED, diffusional kurtosis imaging (DKI) and Neurite Orientation Dispersion and Density Imaging (NODDI) made available new tools to image axonal pathology non-invasively in vivo. These methods already showed greater sensitivity and specificity compared to conventional diffusion tensor-based metrics (e.g., fractional anisotropy), overcoming some of its limitations. While previous studies uncovered global and focal axonal degeneration in MS patients compared to healthy controls, here our aim is to investigate and compare different diffusion MRI acquisition protocols in their ability to highlight microstructural differences between MS and control tissue over several much used models. For comparison, we contrasted the ability of fractional anisotropy measurements to uncover differences between lesion, normal-appearing white matter (WM), gray matter and healthy tissue under the same imaging protocols. We show that: (1) focal and diffuse differences in several microstructural parameters are observed under clinical settings; (2) advanced models (CHARMED, DKI and NODDI) have increased specificity and sensitivity to neurodegeneration when compared to fractional anisotropy measurements; and (3) both high (3 T) and ultra-high fields (7 T) are viable options for imaging tissue change in MS lesions and normal appearing WM, while higher b-values are less beneficial under the tested short-time (10 min acquisition) conditions.

Connecting environmental exposure and neurodegeneration using cheminformatics and high resolution mass spectrometry: potential and challenges.

Connecting chemical exposures over a lifetime to complex chronic diseases with multifactorial causes such as neurodegenerative diseases is an immense challenge requiring a long-term, interdisciplinary approach. Rapid developments in analytical and data technologies, such as non-target high resolution mass spectrometry (NT-HR-MS), have opened up new possibilities to accomplish this, inconceivable 20 years ago. While NT-HR-MS is being applied to increasingly complex research questions, there are still many unidentified chemicals and uncertainties in linking exposures to human health outcomes and environmental impacts. In this perspective, we explore the possibilities and challenges involved in using cheminformatics and NT-HR-MS to answer complex questions that cross many scientific disciplines, taking the identification of potential (small molecule) neurotoxicants in environmental or biological matrices as a case study. We explore capturing literature knowledge and patient exposure information in a form amenable to high-throughput data mining, and the related cheminformatic challenges. We then briefly cover which sample matrices are available, which method(s) could potentially be used to detect these chemicals in various matrices and what remains beyond the reach of NT-HR-MS. We touch on the potential for biological validation systems to contribute to mechanistic understanding of observations and explore which sampling and data archiving strategies may be required to form an accurate, sustained picture of small molecule signatures on extensive cohorts of patients with chronic neurodegenerative disorders. Finally, we reflect on how NT-HR-MS can support unravelling the contribution of the environment to complex diseases.

The Luxembourg Parkinson's Study: A Comprehensive Approach for Stratification and Early Diagnosis.

While genetic advances have successfully defined part of the complexity in Parkinson's disease (PD), the clinical characterization of phenotypes remains challenging. Therapeutic trials and cohort studies typically include patients with earlier disease stages and exclude comorbidities, thus ignoring a substantial part of the real-world PD population. To account for these limitations, we implemented the Luxembourg PD study as a comprehensive clinical, molecular and device-based approach including patients with typical PD and atypical parkinsonism, irrespective of their disease stage, age, comorbidities, or linguistic background. To provide a large, longitudinally followed, and deeply phenotyped set of patients and controls for clinical and fundamental research on PD, we implemented an open-source digital platform that can be harmonized with international PD cohort studies. Our interests also reflect Luxembourg-specific areas of PD research, including vision, gait, and cognition. This effort is flanked by comprehensive biosampling efforts assuring high quality and sustained availability of body liquids and tissue biopsies. We provide evidence for the feasibility of such a cohort program with deep phenotyping and high quality biosampling on parkinsonism in an environment with structural specificities and alert the international research community to our willingness to collaborate with other centers. The combination of advanced clinical phenotyping approaches including device-based assessment will create a comprehensive assessment of the disease and its variants, its interaction with comorbidities and its progression. We envision the Luxembourg Parkinson's study as an important research platform for defining early diagnosis and progression markers that translate into stratified treatment approaches.

Structural Brain Network Characteristics Can Differentiate CIS from Early RRMS.

Focal demyelinated lesions, diffuse white matter (WM) damage, and gray matter (GM) atrophy influence directly the disease progression in patients with multiple sclerosis. The aim of this study was to identify specific characteristics of GM and WM structural networks in subjects with clinically isolated syndrome (CIS) in comparison to patients with early relapsing-remitting multiple sclerosis (RRMS). Twenty patients with CIS, 33 with RRMS, and 40 healthy subjects were investigated using 3 T-MRI. Diffusion tensor imaging was applied, together with probabilistic tractography and fractional anisotropy (FA) maps for WM and cortical thickness correlation analysis for GM, to determine the structural connectivity patterns. A network topology analysis with the aid of graph theoretical approaches was used to characterize the network at different community levels (modularity, clustering coefficient, global, and local efficiencies). Finally, we applied support vector machines (SVM) to automatically discriminate the two groups. In comparison to CIS subjects, patients with RRMS were found to have increased modular connectivity and higher local clustering, highlighting increased local processing in both GM and WM. Both groups presented increased modularity and clustering coefficients in comparison to healthy controls. SVM algorithms achieved 97% accuracy using the clustering coefficient as classifier derived from GM and 65% using WM from probabilistic tractography and 67% from modularity of FA maps to differentiate between CIS and RRMS patients. We demonstrate a clear increase of modular and local connectivity in patients with early RRMS in comparison to CIS and healthy subjects. Based only on a single anatomic scan and without a priori information, we developed an automated and investigator-independent paradigm that can accurately discriminate between patients with these clinically similar disease entities, and could thus complement the current dissemination-in-time criteria for clinical diagnosis.

Identification of cortical lesions using DIR and FLAIR in early stages of multiple sclerosis.

The use of non-routine MRI sequences such as DIR has highlighted the role of gray matter (GM) pathology in multiple sclerosis (MS). The aim of this study was to assess the detection and relevance of cortical lesions (CLs) using MRI in early (<5 years) MS patients. 3D DIR and 3D FLAIR images at 3T from 122 patients [93 relapsing-remitting MS (RRMS), 29 clinically isolated syndrome (CIS)] were scored for CLs by two blinded readers. Patients were divided into two groups depending on the presence or absence of CLs. For FLAIR, 51 CLs were identified, of which 13 were purely intracortical and 38 mixed CLs; for DIR, this was 60 in total and 16 and 44, respectively. In both groups, there was no difference in GM fraction. Neuropsychological testing was performed for a subgroup of 66 patients. In 22.1 % of patients CLs were identified. The number of CLs revealed an association with lower working memory scores and semantical word fluency. Overall, CLs imaged with 3D FLAIR and 3D DIR sequences are found more frequently in RRMS patients than CIS and may also be a correlate for mild neuropsychological pathology.