Dopamine Pathway and Parkinson's Risk Variants Are Associated with Levodopa-Induced Dyskinesia

Background: Levodopa-induced dyskinesia (LID) is a common adverse effect of levodopa, one of the main therapeutics used to treat the motor symptoms of Parkinson's disease (PD). Previous evidence suggests a connection between LID and a disruption of the dopaminergic system as well as genes implicated in PD, including GBA1 and LRRK2. Objectives: Our goal was to investigate the effects of genetic variants on risk and time to LID. Methods: We performed a genome-wide association study (GWAS) and analyses focused on GBA1 and LRRK2 variants. We also calculated polygenic risk scores (PRS) including risk variants for PD and variants in genes involved in the dopaminergic transmission pathway. To test the influence of genetics on LID risk we used logistic regression, and to examine its impact on time to LID we performed Cox regression including 1612 PD patients with and 3175 without LID. Results: We found that GBA1 variants were associated with LID risk (odds ratio [OR] = 1.65; 95% confidence interval [CI], 1.21-2.26; P = 0.0017) and LRRK2 variants with reduced time to LID onset (hazard ratio [HR] = 1.42; 95% CI, 1.09-1.84; P = 0.0098). The fourth quartile of the PD PRS was associated with increased LID risk (OR = 1.27; 95% CI, 1.03-1.56; P = 0.0210). The third and fourth dopamine pathway PRS quartiles were associated with a reduced time to development of LID (HR = 1.38; 95% CI, 1.07-1.79; P = 0.0128; HR = 1.38; 95% CI = 1.06-1.78; P = 0.0147). Conclusions: This study suggests that variants implicated in PD and in the dopaminergic transmission pathway play a role in the risk/time to develop LID. Further studies will be necessary to examine how these findings can inform clinical care. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Age at onset as stratifier in idiopathic Parkinson’s disease – effect of ageing and polygenic risk score on clinical phenotypes

Several phenotypic differences observed in Parkinson’s disease (PD) patients have been linked to age at onset (AAO). We endeavoured to find out whether these differences are due to the ageing process itself by using a combined dataset of idiopathic PD (n = 430) and healthy controls (HC; n = 556) excluding carriers of known PD-linked genetic mutations in both groups. We found several significant effects of AAO on motor and non-motor symptoms in PD, but when comparing the effects of age on these symptoms with HC (using age at assessment, AAA), only positive associations of AAA with burden of motor symptoms and cognitive impairment were significantly different between PD vs HC. Furthermore, we explored a potential effect of polygenic risk score (PRS) on clinical phenotype and identified a significant inverse correlation of AAO and PRS in PD. No significant association between PRS and severity of clinical symptoms was found. We conclude that the observed non-motor phenotypic differences in PD based on AAO are largely driven by the ageing process itself and not by a specific profile of neurodegeneration linked to AAO in the idiopathic PD patients

Highly scalable and standardized organ-on-chip platform with TEER for biological barrier modeling.

peer reviewedThe development of new therapies is hampered by the lack of predictive, and patient-relevant in vitro models. Organ-on-chip (OOC) technologies can potentially recreate physiological features and hold great promise for tissue and disease modeling. However, the non-standardized design of these chips and perfusion control systems has been a barrier to quantitative high-throughput screening (HTS). Here we present a scalable OOC microfluidic platform for applied kinetic in vitro assays (AKITA) that is applicable for high, medium, and low throughput. Its standard 96-well plate and 384-well plate layouts ensure compatibility with existing laboratory workflows and high-throughput data collection and analysis tools. The AKITA plate is optimized for the modeling of vascularized biological barriers, primarily the blood-brain barrier, skin, and lung, with precise flow control on a custom rocker. The integration of trans-epithelial electrical resistance (TEER) sensors allows rapid and repeated monitoring of barrier integrity over long time periods. Together with automated liquid handling and compound permeability testing analyses, we demonstrate the flexibility of the AKITA platform for establishing human-relevant models for preclinical drug and precision medicine's efficacy, toxicity, and permeability under near-physiological conditions

Author Correction: Accurate long-read sequencing identified GBA1 as major risk factor in the Luxembourgish Parkinson’s study

npj Parkinson’s Disease, published online 23 November 2023 In this article the wrong Table appeared as Table 3.; the Table should have appeared as shown below. The original article has been corrected. The following text that describes data from the previous version of Table 3 has also been removed: “Compared to severe (6.4 ± 4.7) and risk (4.4 ± 4.9) variant carriers, the mild (1.7 ± 1.4) variant carriers show a significantly shorter disease duration (Table 3).” (Table presented.) Features All pathogenic variants (n = 67) Severe (n = 21) Mild (n = 7) Risk (n = 39) Non carriers (n = 554) AAA, mean (SD) 66.5 (±10.2) [OR = 0.31; p = 0.3977] 65.1 (±10.2) [OR = 0.08; p = 0.292] 67.1 (±15.6) [OR = 0.59; p = 0.8959] 67.1 (±9.2) [OR = 0.57; p = 0.7512] 67.6 (±10.7) Sex, Male n (%) 40 (59.7%) [OR = 0.71; p = 0.1912] 13 (61.9%) [OR = 0.78; p = 0.5795] 5 (71.4%) [OR = 1.19; p = 0.8336] 22 (56.4%) [OR = 0.62; p = 0.151] 375 (67.7%) AAO, mean (SD) 61.6 (±11.5) [OR = 0.35; p = 0.484] 58.6 (±13.1) [OR = 0.02; p = 0.1158] 65.4 (±17.0) [OR = 16.16; p = 0.5308] 62.5 (±9.3) [OR = 0.9; p = 0.9548] 62.6 (±11.6) AAO &lt; 45, N (%) 8 (11.9%) [OR = 1.74; p = 0.1767] 2 (28.6%) [OR = 5.14; p = 0.0549] 1 (2.6%) [OR = 0.34; p = 0.2907] 40 (7.2%) Disease Duration, mean (SD) 4.7 (±4.8) [OR = 0.79; p = 0.7303] 6.4 (±4.7) [OR = 4.07; p = 0.2238] 1.7 (±1.4) [OR = 0.04; p =0.0981] 4.4 (±4.9) [OR = 0.57; p = 0.5103] 5.0 (±5.2) Family History, N (%) 8 (38.1%) [OR = 1.8; p = 0.2001] 2 (28.6% [OR = 1.17; p = 0.8508] 15 (38.5%) [OR = 1.83; p = 0.0782] 141 (25.5%).</p

Gut microbiome is not associated with mild cognitive impairment in Parkinson’s disease

Gut microbiome differences between people with Parkinson’s disease (PD) and control subjects without Parkinsonism are widely reported, but potential alterations related to PD with mild cognitive impairment (MCI) have yet to be comprehensively explored. We compared gut microbial features of PD with MCI (n = 58) to cognitively unimpaired PD (n = 60) and control subjects (n = 90) with normal cognition. Our results did not support a specific microbiome signature related to MCI in PD

Accurate long-read sequencing identified GBA1 as major risk factor in the Luxembourgish Parkinson’s study

Heterozygous variants in the glucocerebrosidase GBA1 gene are an increasingly recognized risk factor for Parkinson’s disease (PD). Due to the GBAP1 pseudogene, which shares 96% sequence homology with the GBA1 coding region, accurate variant calling by array-based or short-read sequencing methods remains a major challenge in understanding the genetic landscape of GBA1-associated PD. We analyzed 660 patients with PD, 100 patients with Parkinsonism and 808 healthy controls from the Luxembourg Parkinson’s study, sequenced using amplicon-based long-read DNA sequencing technology. We found that 12.1% (77/637) of PD patients carried GBA1 variants, with 10.5% (67/637) of them carrying known pathogenic variants (including severe, mild, risk variants). In comparison, 5% (34/675) of the healthy controls carried GBA1 variants, and among them, 4.3% (29/675) were identified as pathogenic variant carriers. We found four GBA1 variants in patients with atypical parkinsonism. Pathogenic GBA1 variants were 2.6-fold more frequently observed in PD patients compared to controls (OR = 2.6; CI = [1.6,4.1]). Three novel variants of unknown significance (VUS) were identified. Using a structure-based approach, we defined a potential risk prediction method for VUS. This study describes the full landscape of GBA1-related parkinsonism in Luxembourg, showing a high prevalence of GBA1 variants as the major genetic risk for PD. Although the long-read DNA sequencing technique used in our study may be limited in its effectiveness to detect potential structural variants, our approach provides an important advancement for highly accurate GBA1 variant calling, which is essential for providing access to emerging causative therapies for GBA1 carriers

Education as Risk Factor of Mild Cognitive Impairment:The Link to the Gut Microbiome

Background: With differences apparent in the gut microbiome in mild cognitive impairment (MCI) and dementia, and risk factors of dementia linked to alterations of the gut microbiome, the question remains if gut microbiome characteristics may mediate associations of education with MCI. Objectives: We sought to examine potential mediation of the association of education and MCI by gut microbiome diversity or composition. Design: Cross-sectional study. Setting: Luxembourg, the Greater Region (surrounding areas in Belgium, France, Germany). Participants: Control participants of the Luxembourg Parkinson’s Study. Measurements: Gut microbiome composition, ascertained with 16S rRNA gene amplicon sequencing. Differential abundance, assessed across education groups (0–10, 11–16, 16+ years of education). Alpha diversity (Chao1, Shannon and inverse Simpson indices). Mediation analysis with effect decomposition was conducted with education as exposure, MCI as outcome and gut microbiome metrics as mediators. Results: After exclusion of participants below 50, or with missing data, n=258 participants (n=58 MCI) were included (M [SD] Age=64.6 [8.3] years). Higher education (16+ years) was associated with MCI (Odds ratio natural direct effect=0.35 [95% CI 0.15–0.81]. Streptococcus and Lachnospiraceae-UCG-001 genera were more abundant in higher education. Conclusions: Education is associated with gut microbiome composition and MCI risk without clear evidence for mediation. However, our results suggest signatures of the gut microbiome that have been identified previously in AD and MCI to be reflected in lower education and suggest education as important covariate in microbiome studies

Education as Risk Factor of Mild Cognitive Impairment: The Link to the Gut Microbiome

peer reviewedBackground: With differences apparent in the gut microbiome in mild cognitive impairment (MCI) and dementia, and risk factors of dementia linked to alterations of the gut microbiome, the question remains if gut microbiome characteristics may mediate associations of education with MCI. Objectives: We sought to examine potential mediation of the association of education and MCI by gut microbiome diversity or composition. Design: Cross-sectional study. Setting: Luxembourg, the Greater Region (surrounding areas in Belgium, France, Germany). Participants: Control participants of the Luxembourg Parkinson’s Study. Measurements: Gut microbiome composition, ascertained with 16S rRNA gene amplicon sequencing. Differential abundance, assessed across education groups (0–10, 11–16, 16+ years of education). Alpha diversity (Chao1, Shannon and inverse Simpson indices). Mediation analysis with effect decomposition was conducted with education as exposure, MCI as outcome and gut microbiome metrics as mediators. Results: After exclusion of participants below 50, or with missing data, n=258 participants (n=58 MCI) were included (M [SD] Age=64.6 [8.3] years). Higher education (16+ years) was associated with MCI (Odds ratio natural direct effect=0.35 [95% CI 0.15–0.81]. Streptococcus and Lachnospiraceae-UCG-001 genera were more abundant in higher education. Conclusions: Education is associated with gut microbiome composition and MCI risk without clear evidence for mediation. However, our results suggest signatures of the gut microbiome that have been identified previously in AD and MCI to be reflected in lower education and suggest education as important covariate in microbiome studies.MCI-BIOME_20193. Good health and well-bein

Host factors are associated with vaginal microbiome structure in pregnancy in the ECHO Cohort Consortium

Using pooled vaginal microbiota data from pregnancy cohorts (N = 683 participants) in the Environmental influences on Child Health Outcomes (ECHO) Program, we analyzed 16S rRNA gene amplicon sequences to identify clinical and demographic host factors that associate with vaginal microbiota structure in pregnancy both within and across diverse cohorts. Using PERMANOVA models, we assessed factors associated with vaginal community structure in pregnancy, examined whether host factors were conserved across populations, and tested the independent and combined effects of host factors on vaginal community state types (CSTs) using multinomial logistic regression models. Demographic and social factors explained a larger amount of variation in the vaginal microbiome in pregnancy than clinical factors. After adjustment, lower education, rather than self-identified race, remained a robust predictor of L. iners dominant (CST III) and diverse (CST IV) (OR = 8.44, 95% CI = 4.06–17.6 and OR = 4.18, 95% CI = 1.88–9.26, respectively). In random forest models, we identified specific taxonomic features of host factors, particularly urogenital pathogens associated with pregnancy complications (Aerococcus christensenii and Gardnerella spp.) among other facultative anaerobes and key markers of community instability (L. iners). Sociodemographic factors were robustly associated with vaginal microbiota structure in pregnancy and should be considered as sources of variation in human microbiome studies