Aims To identify the metabolic pattern and prognostic predictors in anti‐gamma‐aminobutyric‐acid B (GABAB) receptor encephalitis using 18F‐fluorodeoxy‐glucose positron emission tomography (18F‐FDG‐PET). Methods Twenty‐one patients diagnosed anti‐GABAB receptor encephalitis who underwent 18F‐FDG‐PET at first hospitalization were retrospectively reviewed. 18F‐FDG‐PET images were analyzed in comparison with controls. Further group comparisons of 18F‐FDG‐PET data were carried out between prognostic subgroups. Results 18F‐FDG‐PET was abnormal in 81% patients with anti‐GABAB receptor encephalitis and was more sensitive than MRI (81% vs. 42.9%, p = 0.025). Alter limbic lobe glucose metabolism (mostly hypermetabolism) was observed in 14 patients (66.7%), of whom 10 (10/14, 71.4%) demonstrated hypermetabolism in the medial temporal lobe (MTL). Group analysis also confirmed MTL hypermetabolism in association with relative frontal and parietal hypometabolism was a general metabolic pattern. After a median follow‐up of 33 months, the group comparisons revealed that patients with poor outcome demonstrated increased metabolism in the MTL compared to those with good outcome. Conclusion 18F‐FDG‐PET may be more sensitive than MRI in the early diagnosis of anti‐GABAB receptor encephalitis. MTL hypermetabolism was associated with relative frontal or parietal hypometabolism and may serve as a prognostic biomarker in anti‐GABAB receptor encephalitis.
Purpose Smoking, alcohol consumption, allergic rhinitis (AR), asthma, and obesity are associated with chronic rhinosinusitis (CRS), albeit the causal relationships between them remain elusive. Therefore, we conducted a bidirectional two-sample Mendelian randomization (MR) study to investigate the bidirectional causal effects between these potential risk factors and CRS. Methods The data for daily cigarette consumption, age of smoking initiation, weekly alcohol consumption, AR, asthma, body mass index (BMI), and CRS were drawn from large sample size genome-wide association studies. Single-nucleotide polymorphisms associated with each exposure were considered instrumental variables in this study. We investigated causal effects by using the inverse-variance weighted (IVW) method with random effects, and weighted median and MR–Egger methods were used for sensitivity analyses. Pleiotropic effects were detected and corrected by the MR pleiotropy residual sum and outlier test and MR–Egger model. Results We found the causal effects of daily cigarette consumption (IVW, OR = 1.15, 95% CI 1.00−1.32, p = 0.046), AR (IVW, OR = 4.77, 95% CI 1.61−14.13, p = 0.005), asthma (IVW, OR = 1.45, 95% CI 1.31 − 1.60, p < 0.001), and BMI (IVW, OR = 1.05, 95% CI 1.00−1.09, p = 0.028) on CRS. Furthermore, we found a causal effect of CRS on asthma (IVW OR = 1.08, 95% CI 1.05−1.12, p < 0.001). Conclusions We confirmed the causal effects of daily cigarette consumption, AR, asthma, and BMI on CRS, and the causal effect of CRS on asthma, while no causal relationship between age of smoking initiation, weekly alcohol consumption, and CRS was found. These findings are expected to provide high-quality causal evidence for clinical practice and the pathogenesis of CRS and asthma. Supplementary Information The online version contains supplementary material available at 10.1007/s00405-022-07798-6.
Background This study aims to explore the relationship between psychiatric disorders and the risk of epilepsy using Mendelian randomization (MR) analysis. Methods We collected summary statistics of seven psychiatric traits from recent largest genome‐wide association study (GWAS), including major depressive disorder (MDD), anxiety disorder, autism spectrum disorder (ASD), bipolar disorder (BIP), attention deficit hyperactivity disorder (ADHD), schizophrenia (SCZ), and insomnia. Then, MR analysis estimates were performed based on International League Against Epilepsy (ILAE) consortium data ( n case = 15,212 and n control = 29,677), the results of which were subsequently validated in FinnGen consortium ( n case = 6260 and n control = 176,107). Finally, a meta‐analysis was conducted based on the ILAE and FinnGen data. Results We found significant causal effects of MDD and ADHD on epilepsy in the meta‐analysis of the ILAE and FinnGen, with corresponding odds ratios (OR) of 1.20 (95% CI 1.08–1.34, p = .001) and 1.08 (95% CI 1.01–1.16, p = .020) by the inverse‐variance weighted (IVW) method respectively. MDD increases the risk of focal epilepsy while ADHD has a risk effect on generalized epilepsy. No reliable evidence regarding causal effects of other psychiatric traits on epilepsy was identified. Conclusions This study suggests that major depressive disorder and attention deficit hyperactivity disorder may causally increase the risk of epilepsy.
Autoimmune encephalitis is an immune-mediated inflammatory disease of the central nervous system caused by abnormal immune response against surface or intracellular antigens. 1 In addition to the previously discovered classical paraneoplastic encephalitisassociated antibodies, a variety of autoimmune encephalitis-related antibodies have been reported in recent years, including antibodies targeting N-methyld-aspartate receptor (NMDAR), alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR), leucine-rich glioma inactivated 1 (LGI1), contactin-associated proteinlike 2 (CASPR2), gamma-aminobutyric acid receptor (GABAR) A/B, dipeptidyl-peptidase-like protein-6 (DPPX), glycine receptor (GlyR), glutamic acid decarboxylase 65 (GAD65), dipeptidyl-peptidase-like protein-6 (DPPX), IgLON5 et al. 2 The clinical manifestations, severity, and prognosis with specific antibodies are different, but in general, early detection and early treatment are the principles to handle this disease. However, it has to be admitted that there are still difficulties in the early diagnosis of autoimmune encephalitis, although we have made improvements and revision in the diagnostic criteria based on the clinical criteria defined by Graus et al. 3 in 2016 to avoid excessive reliance on the detection of autoimmune antibodies from serum or cerebrospinal fluid (CSF). The problems focus on the heterogeneity of clinical manifestations, low positive rate of imaging evidence, and lack of specificity of EEG. For example, autoimmune encephalitis may involve
Objectives To establish a model in order to predict the functional outcomes of patients with anti‐leucine‐rich glioma‐inactivated 1 (LGI1) encephalitis and identify significant predictive factors using a random forest algorithm. Methods Seventy‐nine patients with confirmed LGI1 antibodies were retrospectively reviewed between January 2015 and July 2020. Clinical information was obtained from medical records and functional outcomes were followed up in interviews with patients or their relatives. Neurological functional outcome was assessed using a modified Rankin Scale (mRS), the cutoff of which was 2. The prognostic model was established using the random forest algorithm, which was subsequently compared with logistic regression analysis, Naive Bayes and Support vector machine (SVM) metrics based on the area under the curve (AUC) and the accuracy. Results A total of 79 patients were included in the final analysis. After a median follow‐up of 24 months (range, 8–60 months), 20 patients (25%) experienced poor functional outcomes. A random forest model consisting of 16 variables used to predict the poor functional outcomes of anti‐LGI1 encephalitis was successfully constructed with an accuracy of 83% and an F1 score of 60%. In addition, the random forest algorithm demonstrated a more precise predictive performance for poor functional outcomes in patients with anti‐LGI1 encephalitis compared with three other models (AUC, 0.90 vs 0.80 vs 0.70 vs 0.64). Conclusions The random forest model can predict poor functional outcomes of patients with anti‐LGI1 encephalitis. This model was more accurate and reliable than the logistic regression, Naive Bayes, and SVM algorithm.
<b><i>Introduction:</i></b> Observational studies have reported that allergic rhinitis (AR) was associated with chronic lower respiratory diseases (CLRDs) and lung function; however, their causal effects remain elusive. Therefore, to investigate the causal effects of AR on CLRDs and lung function, we conducted the two-sample Mendelian randomization (MR) study. <b><i>Methods:</i></b> The data for AR, asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis, idiopathic pulmonary fibrosis (IPF), and the forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratio were obtained from genome-wide association studies, which were large sample studies on people of European ancestry. In this study, single-nucleotide polymorphisms associated with AR were considered instrumental variables. We employed the inverse-variance weighted (IVW) method with random effects to evaluate causal effects, and the weighted median and MR-Egger methods were used for sensitivity analyses. Significant causal associations were attempted for replication and meta-analysis. <b><i>Results:</i></b> In the discovery stage, we found that AR exhibited a significant causal effect on asthma (IVW, odds ratio [OR] = 16.91, 95% CI, 8.03–35.65, <i>p</i> < 0.001) and a suggestive effect on FEV1/FVC ratio (IVW, OR = 0.82, 95% CI, 0.68–0.99, <i>p</i> = 0.039). No causal effect of AR was observed on COPD, bronchiectasis, and IPF. In the replication stage, the causal effect of AR on asthma was replicated (IVW, OR = 11.57, 95% CI, 4.90–27.37, <i>p</i> < 0.001). The meta-analysis demonstrated that the combined OR of AR on asthma was 14.37 (IVW, 95% CI, 8.18–25.24, <i>p</i> < 0.001). <b><i>Conclusions:</i></b> We demonstrated and measured the causal effects of AR on asthma (OR = 14.37) and FEV1/FVC ratio (OR = 0.82), while there was no evidence to support a causal effect of AR on COPD, bronchiectasis, and IPF. These results suggest that AR tends to have a causal effect on lower airway disease of similar inflammatory types and can provide high-quality causal evidence for clinical practice as well as the pathogenesis and prevention of AR and asthma.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.