Diabetes and epilepsy in children and adolescents

Assessing the causal association between human blood metabolites and the risk of gout

BACKGROUND

The occurrence of gout is closely related to metabolism, but there is still a lack of evidence on the causal role of metabolites in promoting or preventing gout.

METHODS

We applied a two-sample Mendelian randomization (MR) analysis to assess the association between 486 serum metabolites and gout using genome-wide association study statistics. The inverse variance weighting method was used to generate the main results, while sensitivity analyses using MR-Egger, weighted median, Cochran's Q test, Egger intercept test, and leave-one-out analysis, were performed to assess the stability and reliability of the results. We also performed a metabolic pathway analysis to identify potential metabolic pathways.

RESULTS

After screening, 486 metabolites were retained for MR analysis. After screening by IVW and sensitivity analysis, 14 metabolites were identified with causal effect on gout (P < 0.05), among which hexadecanedioate was the most significant candidate metabolite associated with a lower risk of gout (IVW OR = 0.50; 95% CI = 0.38-0.67; P = 1.65 × 10-6 ). Metabolic pathway analysis identified one pathway that may be associated with the disease.

CONCLUSION

This MR study combining genomics with metabolomics provides a novel insight into the causal role of blood metabolites in the risk of gout, which implies that examination of certain blood metabolites would be a feasible strategy for screening populations with a higher risk of gout.

Type 1 diabetes and the risk of epilepsy: A meta-analysis

AIMS/INTRODUCTION

An overrepresentation of epilepsy has been suggested in patients with type 1 diabetes (T1D). This meta-analysis was conducted to evaluate if type 1 diabetes is associated with a higher incidence of epilepsy.

MATERIALS AND METHODS

Longitudinal observational studies which are relevant to the purpose of the meta-analysis were screened and obtained by searching PubMed, Embase, and Web of Science databases. Random-effects models were used when significant heterogeneity was observed; otherwise, fixed-effects models were used.

RESULTS

Six observational studies involving 10 datasets of 8,001,899 participants were included, with six datasets including children and only one dataset including older people. Among them, 100,414 (1.25%) had type 1 diabetes. During the follow-up duration of 5.4-15.2 years (mean: 9.5 years), 98,644 cases (1.23%) of epilepsy were observed. Compared with participants with normoglycemia, those with type 1 diabetes were shown to have a higher incidence of epilepsy (risk ratio [RR]: 2.41, 95% confidence interval 1.69-3.44, P < 0.001; I2  = 95%) after adjustment of potential confounding variables including age and sex. Subgroup analysis showed consistent results in nested case-control and retrospective cohort studies, and in studies of children, non-elderly adult, and older participants (P for subgroup difference = 0.42 and 0.07). In addition, a stronger association of type 1 diabetes and epilepsy was suggested in studies with follow-up duration <10 years compared with those ≥10 years (RR: 3.34 vs 1.61, P for subgroup difference < 0.001).

CONCLUSION

Patients with type 1 diabetes may have a higher risk of epilepsy, which was mainly driven by datasets including children.

Causal relationship between telomere length and epilepsy: A bidirectional Mendelian randomization study

OBJECTIVE

Observational studies have suggested a link between telomere length (TL) and epilepsy, but the direction of the effect and whether it is causal or not is still being debated. The objective of this study was to investigate the causal relationship between TL and epilepsy using Mendelian randomization (MR) analysis.

METHODS

We performed a bidirectional two-sample MR analysis using pooled statistics from genome-wide association studies (GWAS) of TL and epilepsy. Additionally, we conducted a replication analysis using data from another GWAS study on epilepsy to validate our findings. The final results were analyzed using five MR methods, with the inverse-variance weighted (IVW) method as the primary outcome. We applied methods such as radial MR, MR pleiotropy residual and outlier test and MR Steiger filters to exclude outliers. Sensitivity analyses were also conducted to assess heterogeneity and pleiotropy.

RESULTS

Our analysis found no evidence of a causal relationship between epilepsy and TL (all p-values >0.05). The sensitivity analysis confirms the robustness of these results.

SIGNIFICANCE

In summary, our study contradicts existing observational reports by not finding any evidence to support a causal relationship between epilepsy and TL. Further research is necessary to determine the underlying mechanism behind the association observed in observational studies.

Association of epilepsy, anti-epileptic drugs (AEDs), and type 2 diabetes mellitus (T2DM): a population-based cohort retrospective study, impact of AEDs on T2DM-related molecular pathway, and via peroxisome proliferator-activated receptor γ transactivation

Introduction

A potential association between epilepsy and subsequent type 2 diabetes mellitus (T2DM) has emerged in recent studies. However, the association between epilepsy, anti-epileptic drugs (AEDs), and the risk of T2DM development remains controversial. We aimed to conduct a nationwide, population-based, retrospective, cohort study to evaluate this relationship.

Methods

We extracted data from the Taiwan Longitudinal Generation Tracking Database of patients with new-onset epilepsy and compared it with that of a comparison cohort of patients without epilepsy. A Cox proportional hazards regression model was used to analyze the difference in the risk of developing T2DM between the two cohorts. Next-generation RNA sequencing was used to characterize T2DM-related molecularchanges induced by AEDs and the T2DM-associated pathways they alter. The potential of AEDs to induce peroxisome proliferator-activated receptor γ (PPARγ) transactivation was also evaluated.

Results

After adjusting for comorbidities and confounding factors, the case group (N = 14,089) had a higher risk for T2DM than the control group (N = 14,089) [adjusted hazards ratio (aHR), 1.27]. Patients with epilepsy not treated with AEDs exhibited a significantly higher risk of T2DM (aHR, 1.70) than non-epileptic controls. In those treated with AEDs, the risk of developing T2DM was significantly lower than in those not treated (all aHR ≤ 0.60). However, an increase in the defined daily dose of phenytoin (PHE), but not of valproate (VPA), increased the risk of T2DM development (aHR, 2.28). Functional enrichment analysis of differentially expressed genes showed that compared to PHE, VPA induced multiple beneficial genes associated with glucose homeostasis. Among AEDs, VPA induced the specific transactivation of PPARγ.

Discussion

Our study shows epilepsy increases the risk of T2DM development, however, some AEDs such as VPA might yield a protective effect against it. Thus, screening blood glucose levels in patients with epilepsy is required to explore the specific role and impact of AEDs in the development of T2DM. Future in depth research on the possibility to repurpose VPA for the treatment of T2DM, will offer valuable insight regarding the relationship between epilepsy and T2DM.

Glycaemic Imbalances in Seizures and Epilepsy of Paediatric Age: A Literature Review

Cerebral excitability and systemic metabolic balance are closely interconnected. Energy supply to neurons depends critically on glucose, whose fluctuations can promote immediate hyperexcitability resulting in acute symptomatic seizures. On the other hand, chronic disorders of sugar metabolism (e.g., diabetes mellitus) are often associated with long-term epilepsy. In this paper, we aim to review the existing knowledge on the association between acute and chronic glycaemic imbalances (hyper- and hypoglycaemia) with seizures and epilepsy, especially in the developing brain, focusing on clinical and instrumental features in order to optimize the care of children and adolescents and prevent the development of chronic neurological conditions in young patients.

Roles of gut microbiome in epilepsy risk: A Mendelian randomization study

Background

Recent studies have suggested an association between gut microbiomes (GMs) and epilepsy. However, the GM taxa identified in different studies are variable. In addition, observational studies cannot indicate causality. Therefore, our study aimed to explore the causal association of GMs with epilepsy and identify the most influential GM taxa.

Methods

We conducted a Mendelian randomization (MR) study using summary statistics from genome-wide association studies (GWAS) of 211 GM taxa and epilepsy. The GWAS summary statistics for 211 GM taxa (from phylum to genus level) were generated by the MiBioGen consortium, while the FinnGen consortium provided the GWAS summary statistics for epilepsy. The primary analytical method to assess causality was the inverse-variance weighted (IVW) approach. To complement the IVW method, we also applied four additional MR methods: MR-Egger, weighted median, simple mode, and weighted. In addition, we conducted sensitivity analyses using Cochrane's Q-test, MR-Egger intercept test, MR-PRESSO global test, and leave-one-out analysis.

Results

We evaluated the causal effect of 211 GM taxa (from phylum to genus level) on epilepsy, generalized epilepsy, and focal epilepsy. After using the Bonferroni method for multiple testing correction, Class Betaproteobacteria [odds ratio (OR) = 1.357, 95% confidence interval (CI): 1.126-1.635, p = 0.001] and Order Burkholderiales (OR = 1.336, 95% CI: 1.112-1.606, p = 0.002). In addition, 21 nominally significant causal relationships were also identified. Further, the MR-Egger intercept test and MR-PRESSO global test suggested that our MR analysis was unaffected by horizontal pleiotropy (p > 0.05). Finally, the leave-one-out analysis suggested the robustness of the results.

Conclusion

Through the MR study, we analyzed the causal relationship of 211 GM taxa with epilepsy and determined the specific intestinal flora associated with increased epilepsy risk. Our findings may provide helpful biomarkers for disease progression and potential candidate therapeutic targets for epilepsy. In addition, in-depth analysis of large-scale microbiome GWAS datasets based on metagenomics sequencing is necessary for future studies.

Identification of RNA N6-methyladenosine regulation in epilepsy: Significance of the cell death mode, glycometabolism, and drug reactivity

Epilepsy, a functional disease caused by abnormal discharge of neurons, has attracted the attention of neurologists due to its complex characteristics. N6-methyladenosine (m6A) is a reversible mRNA modification that plays essential role in various biological processes. Nevertheless, no previous study has systematically evaluated the role of m6A regulators in epilepsy. Here, using gene expression screening in the Gene Expression Omnibus GSE143272, we identified seven significant m6A regulator genes in epileptic and non-epileptic patients. The random forest (RF) model was applied to the screening, and seven m6A regulators (HNRNPC, WATP, RBM15, YTHDC1, YTHDC2, CBLL1, and RBMX) were selected as the candidate genes for predicting the risk of epilepsy. A nomogram model was then established based on the seven-candidate m6A regulators. Decision curve analysis preliminarily showed that patients with epilepsy could benefit from the nomogram model. The consensus clustering method was performed to divide patients with epilepsy into two m6A patterns (clusterA and clusterB) based on the selected significant m6A regulators. Principal component analysis algorithms were constructed to calculate the m6A score for each sample to quantify the m6A patterns. Patients in clusterB had higher m6A scores than those in clusterA. Furthermore, the patients in each cluster had unique immune cell components and different cell death patterns. Meanwhile, based on the M6A classification, a correlation between epilepsy and glucose metabolism was laterally verified. In conclusion, the m6A regulation pattern plays a vital role in the pathogenesis of epilepsy. The research on m6A regulatory factors will play a key role in guiding the immune-related treatment, drug selection, and identification of metabolism conditions and mechanisms of epilepsy in the future.

Assessing the causal association between human blood metabolites and the risk of epilepsy

Background

Metabolic disturbance has been reported in patients with epilepsy. Still, the evidence about the causal role of metabolites in facilitating or preventing epilepsy is lacking. Systematically investigating the causality between blood metabolites and epilepsy would help provide novel targets for epilepsy screening and prevention.

Methods

We conducted two-sample Mendelian randomization (MR) analysis. Data for 486 human blood metabolites came from a genome-wide association study (GWAS) comprising 7824 participants. GWAS data for epilepsy were obtained from the International League Against Epilepsy (ILAE) consortium for primary analysis and the FinnGen consortium for replication and meta-analysis. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy.

Results

482 out of 486 metabolites were included for MR analysis following rigorous genetic variants selection. After IVW and sensitivity analysis filtration, six metabolites with causal effects on epilepsy were identified from the ILAE consortium. Only four metabolites remained significant associations with epilepsy when combined with the FinnGen consortium [uridine: odds ratio (OR) = 2.34, 95% confidence interval (CI) = 1.48–3.71, P = 0.0003; 2-hydroxystearate: OR = 1.61, 95% CI = 1.19–2.18, P = 0.002; decanoylcarnitine: OR = 0.82, 95% CI = 0.72–0.94, P = 0.004; myo-inositol: OR = 0.77, 95% CI = 0.62–0.96, P = 0.02].

Conclusion

The evidence that the four metabolites mentioned above are associated with epilepsy in a causal way provides a novel insight into the underlying mechanisms of epilepsy by integrating genomics with metabolism, and has an implication for epilepsy screening and prevention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03648-5.

Risk Assessment of Electroconvulsive Therapy in Clinical Routine: A 3-Year Analysis of Life-Threatening Events in More Than 3,000 Treatment Sessions

Background: Extensive research has reported that electroconvulsive therapy (ECT) can be highly effective in approximately 80% of patients suffering from depression. Its clinical use is mainly limited by historical objections and the concern about unwanted adverse effects (AEs), including serious and potentially life-threatening adverse events (pLTAEs), induced either by ECT or by anesthesia. Objective risk estimation is, therefore, a decisive factor in determining an indication for ECT. Methods: This paper presents a retrospective analysis of 3-year safety protocols and patient files of 157 patients who received a total of 3,106 ECT applications in a psychiatric inpatient setting at a psychiatric community hospital. This patient group comprises 5.3% of inpatients admitted with comparable diagnoses. Adverse events were analyzed from standardized safety protocols and patient files with a focus on pLTAEs. Results: Adverse events were reported for 30 (19.1%) of the 157 participants during 39 (6.1%) of 641 hospital stays. Serious pLTAEs occurred during three electroconvulsive stimulations in three patients, who needed action through the administration of medication or mechanical respiration. No patient suffered permanent damage to health, and no patient died. The incidence of these and other AEs was independent of sex, age, and diagnosis of patients, and anesthesia medication. Minor AEs occurred more often with higher stimulus doses and an increasing number of treatments. Conclusion: The low incidence rate of 0.097% of serious pLTAEs that require medical action may allow the conclusion that ECT is a rather safe treatment when performed in a controlled setting. The beneficial risk profile of ECT performed in the standard care of psychiatric hospitals suggests a more generous indication of this treatment method. We recommend that ECT facilities collect individual safety data to allow a reliable judgment of their institutional ECT risk profile.

A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases

Neurological diseases (NDs) are progressive disorders, the progression of which can be significantly affected by a range of common diseases that present as comorbidities. Clinical studies, including epidemiological and neuropathological analyses, indicate that patients with type 2 diabetes (T2D) have worse progression of NDs, suggesting pathogenic links between NDs and T2D. However, finding causal or predisposing factors that link T2D and NDs remains challenging. To address these problems, we developed a high-throughput network-based quantitative pipeline using agnostic approaches to identify genes expressed abnormally in both T2D and NDs, to identify some of the shared molecular pathways that may underpin T2D and ND interaction. We employed gene expression transcriptomic datasets from control and disease-affected individuals and identified differentially expressed genes (DEGs) in tissues of patients with T2D and ND when compared to unaffected control individuals. One hundred and ninety seven DEGs (99 up-regulated and 98 down-regulated in affected individuals) that were common to both the T2D and the ND datasets were identified. Functional annotation of these identified DEGs revealed the involvement of significant cell signaling associated molecular pathways. The overlapping DEGs (i.e., seen in both T2D and ND datasets) were then used to extract the most significant GO terms. We performed validation of these results with gold benchmark databases and literature searching, which identified which genes and pathways had been previously linked to NDs or T2D and which are novel. Hub proteins in the pathways were identified (including DNM2, DNM1, MYH14, PACSIN2, TFRC, PDE4D, ENTPD1, PLK4, CDC20B, and CDC14A) using protein-protein interaction analysis which have not previously been described as playing a role in these diseases. To reveal the transcriptional and post-transcriptional regulators of the DEGs we used transcription factor (TF) interactions analysis and DEG-microRNAs (miRNAs) interaction analysis, respectively. We thus identified the following TFs as important in driving expression of our T2D/ND common genes: FOXC1, GATA2, FOXL1, YY1, E2F1, NFIC, NFYA, USF2, HINFP, MEF2A, SRF, NFKB1, USF2, HINFP, MEF2A, SRF, NFKB1, PDE4D, CREB1, SP1, HOXA5, SREBF1, TFAP2A, STAT3, POU2F2, TP53, PPARG, and JUN. MicroRNAs that affect expression of these genes include mir-335-5p, mir-16-5p, mir-93-5p, mir-17-5p, mir-124-3p. Thus, our transcriptomic data analysis identifies novel potential links between NDs and T2D pathologies that may underlie comorbidity interactions, links that may include potential targets for therapeutic intervention. In sum, our neighborhood-based benchmarking and multilayer network topology methods identified novel putative biomarkers that indicate how type 2 diabetes (T2D) and these neurological diseases interact and pathways that, in the future, may be targeted for treatment.

Overrepresentation of epilepsy in children with type 1 diabetes is declining in a longitudinal population study in Finland

AIM

The aim was to determine temporal changes in increased risk of epilepsy among children with type 1 diabetes.

METHODS

The incidence of epilepsy up to age 15 in children with prior type 1 diabetes was analysed regarding the general Finnish child population using data from the Finnish nationwide hospital register. Type 1 diabetes and epilepsy were identified by the International Classification of Diseases 9th and 10th revision codes. Epilepsy was defined according to ILAE guidelines. The analyses were done using negative binomial regression models.

RESULTS

Preceding type 1 diabetes was diagnosed in 6162 (0.91%) of the 679 375 general children population. Incidence rate of new-onset epilepsy among children with type 1 diabetes was higher than in controls (140 vs 82 per 100 000 person-years at risk, respectively). The excess incidence diminished with time (P = 0.033 for diabetes to birth cohort interaction), from over twofold in birth cohort 1990-1993 [incidence rate ratio 2.2 (95% CI 1.7-2.9)] to 40% in birth cohort 1998-2000 [1.4 (95% CI 1.001-1.9)].

CONCLUSION

In a population study setting, children with type 1 diabetes had an increased, but slowly declining risk of developing epilepsy. Future research may elucidate the underlying mechanisms.

Epilepsy in children with type 1 diabetes mellitus: Pathophysiological basis and clinical hallmarks

We provide an overview on the current knowledge about the association between epilepsy and type 1 diabetes mellitus (T1DM). People with T1DM have a 2-6-fold higher risk of epilepsy than the general population. The onset of T1DM anticipates the onset of epilepsy by a mean period between 1,5 and 2,8 years. These two disorders share four potential distinct pathogenic factors: a) genetic predisposition; b) factors involved in autoimmune responses (i.e. anti-glutamic acid decarboxylase antibodies-GADAbs); c) effects of hypo/hyperglycaemia; d) cerebrovascular damages resulting in ischaemic processes. Seizures semiology prominently includes focal (up to patterns of epilepsia partialis continua) or secondarily generalized seizures but also reflex seizures and various forms of generalized seizures. EEG abnormalities are more common in people with an inappropriate metabolic control with a prominent involvement of fronto-temporal regions. Epilepsy management does not differ between patients with and without diabetes and insulin, nutritional recommendations and physical activity may also produce significant benefits on seizures control. Possible therapeutic alternatives in selected cases include immunosuppressive drugs (in patients with GADAbs) and ketogenic diet.

A population-based study of epilepsy incidence in association with type 2 diabetes and severe hypoglycaemia

AIMS

This study was conducted to investigate potential link between type 2 diabetes mellitus (T2DM) and epilepsy, and the role of severe hypoglycaemia (SH) might play in the relationship.

METHODS

This was a cohort study based on Taiwan's National Health insurance claims. Totally 751,792 people with T2DM and 824,253 matched controls were identified in 2002-2003 and followed to incidence of epilepsy or end of 2011. We used Cox proportional hazard model to relate epilepsy incidence to separate and joint effects of T2DM and SH. A possible mediation effect of SH on the association between T2DM and epilepsy was analyzed.

RESULTS

Over a 10-year follow-up, patients with T2DM had a higher incidence rate of epilepsy than controls (35.0 vs 21.9 per 10,000 person-years). After controlling for potential confounders including SH, T2DM increased the hazard of epilepsy by some 50%. The stratified analysis further indicated that T2DM (hazard ratio (HR)=1.44, 95% confidence interval (CI) = 1.40-1.47), and SH (HR = 2.22, 95% CI = 1.76-2.81) were both independent risk factors for epilepsy. SH did not modify but mediated 12% of the association between T2DM and epilepsy.

CONCLUSION

Our findings supported that SH may increase the risk of epilepsy, and that T2DM may increase risk of epilepsy independent of SH.