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Andrew Singleton and colleagues report a large-scale meta-analysis of genome-wide association data in Parkinson's disease using over 13,000 cases and 95,000 controls plus additional samples for replication. They identify 6 new risk loci and replicate 28 independent risk variants for Parkinson's disease across 24 loci. We conducted a meta-analysis of Parkinson's disease genome-wide association studies using a common set of 7,893,274 variants across 13,708 cases and 95,282 controls. Twenty-six loci were identified as having genome-wide significant association; these and 6 additional previously reported loci were then tested in an independent set of 5,353 cases and 5,551 controls. Of the 32 tested SNPs, 24 replicated, including 6 newly identified loci. Conditional analyses within loci showed that four loci, including GBA , GAK - DGKQ , SNCA and the HLA region, contain a secondary independent risk variant. In total, we identified and replicated 28 independent risk variants for Parkinson's disease across 24 loci. Although the effect of each individual locus was small, risk profile analysis showed substantial cumulative risk in a comparison of the highest and lowest quintiles of genetic risk (odds ratio (OR) = 3.31, 95% confidence interval (CI) = 2.55–4.30; P = 2 × 10 −16 ). We also show six risk loci associated with proximal gene expression or DNA methylation.

Long-term health sequelae of the Coronavirus Disease 2019 (COVID-19) are a major public health concern. However, evidence on post-acute COVID-19 syndrome (post-COVID-19) is still limited, particularly for children and adolescents. Utilizing comprehensive healthcare data on approximately 46% of the German population, we investigated post-COVID-19-associated morbidity in children/adolescents and adults. We used routine data from German statutory health insurance organizations covering the period between January 1, 2019 and December 31, 2020. The base population included all individuals insured for at least 1 day in 2020. Based on documented diagnoses, we identified individuals with polymerase chain reaction (PCR)-confirmed COVID-19 through June 30, 2020. A control cohort was assigned using 1:5 exact matching on age and sex, and propensity score matching on preexisting medical conditions. The date of COVID-19 diagnosis was used as index date for both cohorts, which were followed for incident morbidity outcomes documented in the second quarter after index date or later.Overall, 96 prespecified outcomes were aggregated into 13 diagnosis/symptom complexes and 3 domains (physical health, mental health, and physical/mental overlap domain). We used Poisson regression to estimate incidence rate ratios (IRRs) with 95% confidence intervals (95% CIs). The study population included 11,950 children/adolescents (48.1% female, 67.2% aged between 0 and 11 years) and 145,184 adults (60.2% female, 51.1% aged between 18 and 49 years). The mean follow-up time was 236 days (standard deviation (SD) = 44 days, range = 121 to 339 days) in children/adolescents and 254 days (SD = 36 days, range = 93 to 340 days) in adults. COVID-19 and control cohort were well balanced regarding covariates. The specific outcomes with the highest IRR and an incidence rate (IR) of at least 1/100 person-years in the COVID-19 cohort in children and adolescents were malaise/fatigue/exhaustion (IRR: 2.28, 95% CI: 1.71 to 3.06, p < 0.01, IR COVID-19: 12.58, IR Control: 5.51), cough (IRR: 1.74, 95% CI: 1.48 to 2.04, p < 0.01, IR COVID-19: 36.56, IR Control: 21.06), and throat/chest pain (IRR: 1.72, 95% CI: 1.39 to 2.12, p < 0.01, IR COVID-19: 20.01, IR Control: 11.66). In adults, these included disturbances of smell and taste (IRR: 6.69, 95% CI: 5.88 to 7.60, p < 0.01, IR COVID-19: 12.42, IR Control: 1.86), fever (IRR: 3.33, 95% CI: 3.01 to 3.68, p < 0.01, IR COVID-19: 11.53, IR Control: 3.46), and dyspnea (IRR: 2.88, 95% CI: 2.74 to 3.02, p < 0.01, IR COVID-19: 43.91, IR Control: 15.27). For all health outcomes combined, IRs per 1,000 person-years in the COVID-19 cohort were significantly higher than those in the control cohort in both children/adolescents (IRR: 1.30, 95% CI: 1.25 to 1.35, p < 0.01, IR COVID-19: 436.91, IR Control: 335.98) and adults (IRR: 1.33, 95% CI: 1.31 to 1.34, p < 0.01, IR COVID-19: 615.82, IR Control: 464.15). The relative magnitude of increased documented morbidity was similar for the physical, mental, and physical/mental overlap domain. In the COVID-19 cohort, IRs were significantly higher in all 13 diagnosis/symptom complexes in adults and in 10 diagnosis/symptom complexes in children/adolescents. IRR estimates were similar for age groups 0 to 11 and 12 to 17. IRs in children/adolescents were consistently lower than those in adults. Limitations of our study include potentially unmeasured confounding and detection bias. In this retrospective matched cohort study, we observed significant new onset morbidity in children, adolescents, and adults across 13 prespecified diagnosis/symptom complexes, following COVID-19 infection. These findings expand the existing available evidence on post-COVID-19 conditions in younger age groups and confirm previous findings in adults. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT05074953.

Mutations in leucine-rich repeat kinase 2 (LRRK2) cause inherited Parkinson disease (PD), and common variants around LRRK2 are a risk factor for sporadic PD. Using protein–protein interaction arrays, we identified BCL2-associated athanogene 5, Rab7L1 (RAB7, member RAS oncogene family-like 1), and Cyclin-G–associated kinase as binding partners of LRRK2. The latter two genes are candidate genes for risk for sporadic PD identified by genome-wide association studies. These proteins form a complex that promotes clearance of Golgi-derived vesicles through the autophagy–lysosome system both in vitro and in vivo. We propose that three different genes for PD have a common biological function. More generally, data integration from multiple unbiased screens can provide insight into human disease mechanisms.

Parkinson's disease has been reported in a small number of patients with chromosome 22q11.2 deletion syndrome. In this study, we screened a series of large, independent Parkinson's disease case-control studies for deletions at 22q11.2. We used data on deletions spanning the 22q11.2 locus from four independent case-control Parkinson's disease studies (UK Wellcome Trust Case Control Consortium 2, Dutch Parkinson's Disease Genetics Consortium, US National Institute on Aging, and International Parkinson's Disease Genomics Consortium studies), which were independent of the original reports of chromosome 22q11.2 deletion syndrome. We did case-control association analysis to compare the proportion of 22q11.2 deletions found, using the Fisher's exact test for the independent case-control studies and the Mantel-Haenszel test for the meta-analyses. We retrieved clinical details of patients with Parkinson's disease who had 22q11.2 deletions from the medical records of these patients. We included array-based copy number variation data from 9387 patients with Parkinson's disease and 13 863 controls. Eight patients with Parkinson's disease and none of the controls had 22q11.2 deletions (p=0·00082). In the 8451 patients for whom age at onset data were available, deletions at 22q11.2 were associated with Parkinson's disease age at onset (Mann-Whitney U test p=0·001). Age at onset of Parkinson's disease was lower in patients carrying a 22q11.2 deletion (median 37 years, 95% CI 32·0–55·5; mean 42·1 years [SD 11·9]) than in those who did not carry a deletion (median 61 years, 95% CI 60·5–61·0; mean 60·3 years [SD 12·8]). A 22q11.2 deletion was present in more patients with early-onset (p<0·0001) and late-onset Parkinson's disease (p=0·016) than in controls, and in more patients with early-onset than late-onset Parkinson's disease (p=0·005). Clinicians should be alert to the possibility of 22q11.2 deletions in patients with Parkinson's disease who have early presentation or features associated with the chromosome 22q11.2 deletion syndrome, or both. UK Medical Research Council, UK Wellcome Trust, Parkinson's UK, Patrick Berthoud Trust, National Institutes of Health, “Investissements d'Avenir” ANR-10-IAIHU-06, Dutch Parkinson Foundation (Parkinson Vereniging), Neuroscience Campus Amsterdam, National Institute for Health Research, National Institute on Aging, National Institutes of Health.

Parkinson's disease can be caused by rare familial genetic mutations, but in most cases it is likely to result from an interaction between multiple genetic and environmental risk factors. Over recent years, many variants in a growing number of genes involved in the pathogenesis of Parkinson's disease have been identified. Mutations in several genes have been shown to cause familial parkinsonism. In this review, we discuss 12 of them (SNCA, LRRK2, Parkin, PINK1, DJ1, ATP13A2, PLA2G6, FBXO7, UCHL1, GIGYF2, HTRA2, and EIF4G1). Additionally, six genes have been shown conclusively to be risk factors for sporadic Parkinson's disease, and are also discussed (GBA, MAPT, BST1, PARK16, GAK, and HLA). Many more genes and genetic loci have been suggested, but need confirmation. There is evidence that pathways involved in the rare familial forms also play a role in the sporadic form, and that the respective genes might also be risk factors for sporadic Parkinson's disease. The identification of genes involved in the development of Parkinson's disease will improve our understanding of the underlying molecular mechanisms, and will hopefully lead to new drug targets and treatment strategies.

The clinicopathological heterogeneity in Lewy-body diseases (LBD) highlights the need for pathology-driven biomarkers  in-vivo . Misfolded alpha-synuclein (α-Syn) is a lead candidate based on its crucial role in disease pathophysiology. Real-time quaking-induced conversion (RT-QuIC) analysis of CSF has recently shown high sensitivity and specificity for the detection of misfolded α-Syn in patients with Parkinson's disease (PD) and dementia with Lewy bodies (DLB). In this study we performed the CSF RT-QuIC assay in 236 PD and 49 DLB patients enriched for different genetic forms with mutations in  GBA ,  parkin ,  PINK1 ,  DJ1, and  LRRK2 . A subgroup of 100 PD patients was also analysed longitudinally. We correlated kinetic seeding parameters of RT-QuIC with genetic status and CSF protein levels of molecular pathways linked to α-Syn proteostasis. Overall, 85% of PD and 86% of DLB patients showed positive RT-QuIC α-Syn seeding activity. Seeding profiles were significantly associated with mutation status across the spectrum of genetic LBD. In PD patients, we detected positive α-Syn seeding in 93% of patients carrying severe  GBA  mutations, in 78% with LRRK2 mutations, in 59% carrying heterozygous mutations in recessive genes, and in none of those with bi-allelic mutations in recessive genes. Among PD patients, those with severe  GBA  mutations showed the highest seeding activity based on RT-QuIC kinetic parameters and the highest proportion of samples with 4 out of 4 positive replicates. In DLB patients, 100% with  GBA  mutations showed positive α-Syn seeding compared to 79% of wildtype DLB. Moreover, we found an association between α-Syn seeding activity and reduced CSF levels of proteins linked to α-Syn proteostasis, specifically lysosome-associated membrane glycoprotein 2 and neurosecretory protein VGF. These findings highlight the value of α-Syn seeding activity as an  in-vivo  marker of Lewy-body pathology and support its use for patient stratification in clinical trials targeting α-Syn.

Alzheimer’s disease (AD) is the most common neurodegenerative disorder causing huge emotional and economic burden to our societies. An effective therapy has not been implicated yet, which is in part also due to the fact that pathological changes occur years before clinical symptoms manifest. Thus, there is a great need for the development of a translatable biomarker. Recent evidence highlights microRNAs as candidate biomarkers. In this study, we use next-generation sequencing to study the small noncoding RNAome (sncRNAome) in exosomes derived from human cerebrospinal fluid (CSF). We show that the sncRNAome from CSF-derived exosomes is dominated not only by microRNAs (miRNAs) but also by PIWI-interacting RNAs (piRNAs). We define a combined signature consisting of three miRNAs and three piRNAs that are suitable to detect AD with an AUC of 0.83 in a replication cohort and furthermore predict the conversion of mild–cognitive impaired (MCI) patients to AD dementia with an AUC of 0.86 for the piRNA signature. When combining the smallRNA signature with pTau and Aβ 42/40 ratio the AUC reaches 0.98. Our study reports a novel exosomal small noncoding RNA signature to detect AD pathology and provides the first evidence that in addition to miRNAs, piRNAs should also be considered as a candidate biomarker for AD.

Seed amplification assays have been implemented in Parkinson’s disease to reveal disease-specific misfolded alpha-synuclein aggregates in biospecimens. While the assays’ qualitative dichotomous seeding response is valuable to stratify and enrich cohorts for alpha-synuclein pathology in general, more quantitative parameters that are associated with clinical dynamics of disease progression and that might potentially serve as exploratory outcome measures in clinical trials targeting alpha-synuclein would add important information. To evaluate whether the seeding kinetic parameters time required to reach the seeding threshold (LAG phase), the peak of fluorescence response (Imax), and the area under the curve (AUC) are associated with clinical trajectories, we analyzed LAG, Imax, and AUC in relation to the development of cognitive decline in a longitudinal cohort of 199 people with Parkinson’s disease with positive CSF alpha-synuclein seeding status. Patients were stratified into tertiles based on their individual CSF alpha-synuclein seeding kinetic properties. The effect of the kinetic parameters on longitudinal development of cognitive impairment defined by MoCA ≤25 was analyzed by Cox-Regression. Patients with a higher number of positive seeding replicates and tertile groups of shorter LAG, higher Imax, and higher AUC showed a higher prevalence of and a shorter duration until cognitive impairment longitudinally (3, 6, and 3 years earlier with p  ≤ 0.001, respectively). Results remained similar in separate subgroup analyses of patients with and without GBA mutation. We conclude that a more prominent alpha-synuclein seeding kinetic profile translates into a more rapid development of cognitive decline.

Parkinson's disease (PD) shows high heterogeneity with regard to the underlying molecular pathogenesis involving multiple pathways and mechanisms. Diagnosis is still challenging and rests entirely on clinical features. Thus, there is an urgent need for robust diagnostic biofluid markers. Untargeted metabolomics allows establishing low-molecular compound biomarkers in a wide range of complex diseases by the measurement of various molecular classes in biofluids such as blood plasma, serum, and cerebrospinal fluid (CSF). Here, we applied untargeted high-resolution mass spectrometry to determine plasma and CSF metabolite profiles. We semiquantitatively determined small-molecule levels (≤1.5 kDa) in the plasma and CSF from early PD patients (disease duration 0-4 years; = 80 and 40, respectively), and sex- and age-matched controls ( = 76 and 38, respectively). We performed statistical analyses utilizing partial least square and random forest analysis with a 70/30 training and testing split approach, leading to the identification of 20 promising plasma and 14 CSF metabolites. These metabolites differentiated the test set with an AUC of 0.8 (plasma) and 0.9 (CSF). Characteristics of the metabolites indicate perturbations in the glycerophospholipid, sphingolipid, and amino acid metabolism in PD, which underscores the high power of metabolomic approaches. Further studies will enable to develop a potential metabolite-based biomarker panel specific for PD.

Cerebrospinal fluid (CSF) has often been used as the source of choice for biomarker discovery with the goal to support the diagnosis of neurodegenerative diseases. For this study, we selected 15 CSF protein markers which were identified in previously published clinical investigations and proposed as potential biomarkers for PD diagnosis. We aimed at investigating and confirming their suitability for early stage diagnosis of the disease. The current study was performed in a two-fold confirmatory approach. Firstly, the CSF protein markers were analysed in confirmatory cohort I comprising 80 controls and 80 early clinical PD patients. Through univariate analysis we found significant changes of six potential biomarkers (α-syn, DJ-1, Aβ42, S100β, p-Tau and t-Tau). In order to increase robustness of the observations for potential patient differentiation, we developed-based on a machine learning approach-an algorithm which enabled identifying a panel of markers which would improve clinical diagnosis. Based on that model, a panel comprised of α-syn, S100β and UCHL1 were suggested as promising candidates. Secondly, we aimed at replicating our observations in an independent cohort (confirmatory cohort II) comprising 30 controls and 30 PD patients. The univariate analysis demonstrated Aβ42 as the only reproducible potential biomarker. Taking into account both technical and clinical aspects, these observations suggest that the large majority of the investigated CSF proteins currently proposed as potential biomarkers lack robustness and reproducibility in supporting diagnosis in the early clinical stages of PD.