Occurrence of yeast bloodstream infections between 1987 and 1995 in five Dutch university hospitals

The aim of this study was to identify retrospectively trends in fungal bloodstream infections in The Netherlands in the period from 1987 to 1995. Results of over 395,000 blood cultures from five Dutch university hospitals were evaluated. Overall, there were more than 12 million patient days of care during the nine-year study period. The rate of candidemia doubled in the study period, reaching an incidence of 0.71 episodes per 10,000 patient days in 1995. The general increase in candidemia was paralleled by an increase in non-Candida albicans bloodstream infections, mainly due toCandida glabrata. However, more than 60% of the infections were caused byCandida albicans. Fluconazoleresistant species such asCandida krusei did not emerge during the study period. The increasing rate of candidemia found in Dutch university hospitals is similar to the trend observed in the USA, but the rate is lower and the increase is less pronounced

Genome-wide association analysis of genetic generalized epilepsies implicates susceptibility loci at 1q43, 2p16.1, 2q22.3 and 17q21.32

Genetic generalized epilepsies (GGEs) have a lifetime prevalence of 0.3% and account for 20-30% of all epilepsies. Despite their high heritability of 80%, the genetic factors predisposing to GGEs remain elusive. To identify susceptibility variants shared across common GGE syndromes, we carried out a two-stage genome-wide association study (GWAS) including 3020 patients with GGEs and 3954 controls of European ancestry. To dissect out syndrome-related variants, we also explored two distinct GGE subgroups comprising 1434 patients with genetic absence epilepsies (GAEs) and 1134 patients with juvenile myoclonic epilepsy (JME). Joint Stage-1 and 2 analyses revealed genome-wide significant associations for GGEs at 2p16.1 (rs13026414, Pmeta = 2.5 × 10−9, OR[T] = 0.81) and 17q21.32 (rs72823592, Pmeta = 9.3 × 10−9, OR[A] = 0.77). The search for syndrome-related susceptibility alleles identified significant associations for GAEs at 2q22.3 (rs10496964, Pmeta = 9.1 × 10−9, OR[T] = 0.68) and at 1q43 for JME (rs12059546, Pmeta = 4.1 × 10−8, OR[G] = 1.42). Suggestive evidence for an association with GGEs was found in the region 2q24.3 (rs11890028, Pmeta = 4.0 × 10−6) nearby the SCN1A gene, which is currently the gene with the largest number of known epilepsy-related mutations. The associated regions harbor high-ranking candidate genes: CHRM3 at 1q43, VRK2 at 2p16.1, ZEB2 at 2q22.3, SCN1A at 2q24.3 and PNPO at 17q21.32. Further replication efforts are necessary to elucidate whether these positional candidate genes contribute to the heritability of the common GGE syndrome

Analysis of shared common genetic risk between amyotrophic lateral sclerosis and epilepsy

Because hyper-excitability has been shown to be a shared pathophysiological mechanism, we used the latest and largest genome-wide studies in amyotrophic lateral sclerosis (n = 36,052) and epilepsy (n = 38,349) to determine genetic overlap between these conditions. First, we showed no significant genetic correlation, also when binned on minor allele frequency. Second, we confirmed the absence of polygenic overlap using genomic risk score analysis. Finally, we did not identify pleiotropic variants in meta-analyses of the 2 diseases. Our findings indicate that amyotrophic lateral sclerosis and epilepsy do not share common genetic risk, showing that hyper-excitability in both disorders has distinct origins

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

A Large-Scale Genetic Analysis Reveals a Strong Contribution of the HLA Class II Region to Giant Cell Arteritis Susceptibility

We conducted a large-scale genetic analysis on giant cell arteritis (GCA), a polygenic immune-mediated vasculitis. A case-control cohort, comprising 1,651 case subjects with GCA and 15,306 unrelated control subjects from six different countries of European ancestry, was genotyped by the Immunochip array. We also imputed HLA data with a previously validated imputation method to perform a more comprehensive analysis of this genomic region. The strongest association signals were observed in the HLA region, with rs477515 representing the highest peak (p = 4.05 × 10−40, OR = 1.73). A multivariate model including class II amino acids of HLA-DRβ1 and HLA-DQα1 and one class I amino acid of HLA-B explained most of the HLA association with GCA, consistent with previously reported associations of classical HLA alleles like HLA-DRB1∗04. An omnibus test on polymorphic amino acid positions highlighted DRβ1 13 (p = 4.08 × 10−43) and HLA-DQα1 47 (p = 4.02 × 10−46), 56, and 76 (both p = 1.84 × 10−45) as relevant positions for disease susceptibility. Outside the HLA region, the most significant loci included PTPN22 (rs2476601, p = 1.73 × 10−6, OR = 1.38), LRRC32 (rs10160518, p = 4.39 × 10−6, OR = 1.20), and REL (rs115674477, p = 1.10 × 10−5, OR = 1.63). Our study provides evidence of a strong contribution of HLA class I and II molecules to susceptibility to GCA. In the non-HLA region, we confirmed a key role for the functional PTPN22 rs2476601 variant and proposed other putative risk loci for GCA involved in Th1, Th17, and Treg cell function

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment

Galactosylation and Sialylation Levels of IgG Predict Relapse in Patients With PR3-ANCA Associated Vasculitis

Objective: The objective of our study is to investigate the Fc glycosylation profiles of both antigen-specific IgG targeted against proteinase 3 (PR3-ANCA) and total IgG as prognostic markers of relapse in patients with Granulomatosis with Polyangiitis (GPA). Methods: Seventy-five patients with GPA and a PR3-ANCA rise during follow-up were included, of whom 43 patients relapsed within a median period of 8 (2–16) months. The N-glycan at Asn297 of affinity-purified and denatured total IgG and PR3-ANCA was determined by mass spectrometry of glycopeptides in samples obtained at the time of the PR3-ANCA rise and at the time of the relapse or time-matched during remission. Results: Patients with total IgG1 exhibiting low galactosylation or low sialylation were highly prone to relapse after an ANCA rise (HR 3.46 [95%-CI 1.73–6.96], p < 0.0001 and HR 3.22 [95%-CI 1.52–6.83], p = 0.002, respectively). In relapsing patients, total IgG1 galactosylation, sialylation and bisection significantly decreased and fucosylation significantly increased from the time of the PR3-ANCA rise to the relapse (p < 0.0001, p = 0.0087, p < 0.0001 and p = 0.0025), while the glycosylation profile remained similar in non-relapsing patients. PR3-ANCA IgG1 galactosylation, sialylation and fucosylation of PR3-ANCA IgG1 decreased in relapsing patients (p = 0.0073, p = 0.0049 and p = 0.0205), but also in non-relapsing patients (p = 0.0007, p = 0.0114 and p = 0.0002), while bisection increased only in non-relapsing patients (p < 0.0001). Conclusion: While Fc glycosylation profiles have been associated with clinically manifest autoimmune diseases, in the present study we show that low galactosylation and sialyation in total IgG1 but not PR3-ANCA IgG1 predicts disease reactivation in patients with GPA who experience an ANCA rise during follow-up. We postulate that glycosylation profiles may be useful in pre-emptive therapy studies using ANCA rises as guideline

Ultra-high field MTR and qR2*differentiates subpial cortical lesions from normal-appearing gray matter in multiple sclerosis

Abstract Background: Cortical gray matter (GM) demyelination is frequent and clinically relevant in multiple sclerosis (MS). Quantitative magnetic resonance imaging (qMRI) sequences such as magnetization transfer ratio (MTR) and quantitative R2* (qR2*) can capture pathological subtleties missed by conventional magnetic resonance imaging (MRI) sequences. Although differences in MTR and qR2* have been reported between lesional and non-lesional tissue, differences between lesion types or lesion types and myelin density matched normal-appearing gray matter (NAGM) have not been found or investigated. Objective: Identify quantitative differences in histopathologically verified GM lesion types and matched NAGM at ultra-high field strength. Methods: Using 7T post-mortem MRI, MRI lesions were marked on T2 images and co-registered to the calculated MTR and qR2* maps for further evaluation. In all, 15 brain slices were collected, containing a total of 74 cortical GM lesions and 45 areas of NAGM. Results: Intracortical lesions had lower MTR and qR2* values compared to NAGM. Type I lesions showed lower MTR than type III lesions. Type III lesions showed lower MTR than matched NAGM, and type I and IV lesions showed lower qR2* than matched NAGM. Conclusion: qMRI at 7T can provide additional information on extent of cortical pathology, especially concerning subpial lesions. This may be relevant for monitoring disease progression and potential treatment effects