Searching for causal relationships of glioma: a phenome-wide Mendelian randomisation study

Glioma raises periodontitis risk via CD8 upregulation on NKT cells: a Mendelian randomization study

OBJECTIVES

Gliomas, primary tumors of the central nervous system, and periodontitis, a chronic inflammatory disease impacting oral health, have both been subjects of extensive research due to their significant impact on patients' well-being. This study delves into the question of whether there is a causal relationship between glioma and periodontitis, mediated by systemic immunological changes.

METHODS

This research draws from a wealth of publicly available genetic data, including genome-wide association studies for glioma, periodontitis, and immune cell traits. A comprehensive Mendelian randomization (MR) analysis is conducted, incorporating multiple MR methods and statistical tests to assess causality and account for possible biases.

RESULTS

The findings indicate that individuals genetically predisposed to glioma face an increased risk of developing periodontitis. Furthermore, CD8 upregulation on NKT cells was identified as a mediator in this causal pathway, providing a partial explanation for the observed connection. This discovery aligns with clinical observations of glioma patients exhibiting a higher prevalence of poor periodontal health.

CONCLUSIONS

This study advances our understanding of the complex interplay between glioma and systemic diseases like periodontitis. It underscores feasible implications for patient care and opens avenues for future research to explore the mechanistic underpinnings of this relationship.

Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with poor prognosis and limited treatment options. Despite advances in understanding its molecular mechanisms, effective therapeutic strategies remain elusive due to the tumor's genetic complexity and heterogeneity.

METHODS

This study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM. Five publicly available gene expression datasets were analyzed to identify differentially expressed genes (DEGs) associated with GBM. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify GBM-related gene modules. Further, gene set enrichment and variation analyses were conducted to explore the biological pathways involved. The machine learning models were evaluated using Receiver Operating Characteristic (ROC) curves and confusion matrices to assess their predictive accuracy, with the best-performing model validated across external datasets. MR analysis was performed to establish causal relationships between genetically predicted gene expression levels and GBM outcomes.

RESULTS

The study identified 286 DEGs between GBM and adjacent normal tissues across five datasets. WGCNA highlighted the yellow module as the most relevant to GBM, containing key genes such as KLHL3, FOXO4, and MAP1A. Of the 113 machine learning models tested, Ridge regression achieved the highest area under the curve (AUC) of 0.92, demonstrating robust predictive accuracy. Validation using external datasets confirmed the model's reliability, with a classification accuracy of 89.5% in the training set and 85.3% in the validation sets. MR analysis provided strong evidence of a causal relationship between the expression levels of the identified genes and GBM risk.

CONCLUSIONS

This study demonstrates the power of combining machine learning and Mendelian Randomization to uncover novel genetic markers for GBM. The identified genes offer promising potential as biomarkers for GBM diagnosis and therapy, providing new avenues for personalized treatment strategies.

Sjögren's syndrome and Parkinson's disease: a bidirectional Mendelian randomization study

Background

Previous epidemiological studies have reported an association between Sjögren's syndrome (SS) and Parkinson's disease (PD); however, the causality and direction of this relationship remain unclear. In this study, we aimed to investigate the causal relationship between genetically determined SS and the risk of PD using bidirectional Mendelian randomization (MR).

Methods

Summary statistics for Sjögren's syndrome used as exposure were obtained from the FinnGen database, comprising 1,290 cases and 213,145 controls. The outcome dataset for PD was derived from the United Kingdom Biobank database, including 6,998 cases and 415,466 controls. Various MR methods, such as inverse variance weighted (IVW), Mendelian randomization Egger regression (MR-Egger), weighted median (WM), simple mode, weighted mode, MR-pleiotropy residual sum and outlier (MR-PRESSO), and robust adjusted profile score (RAPS), were employed to investigate the causal effects of SS on PD. Instrumental variable strength evaluation and sensitivity analyses were conducted to ensure the reliability of the results. In addition, reverse MR analysis was performed to examine the causal effects of PD on SS.

Results

The WM, IVW, RAPS and MR-PRESSO methods demonstrated a significant association between genetically predicted SS and reduced risk of PD (odds ratio ORWM = 0.9988, ORIVW = 0.9987, ORRAPS = 0.9987, ORMR-PRESSO = 0.9987, respectively, P < 0.05). None of the MR analyses showed evidence of horizontal pleiotropy (P > 0.05) based on the MR-Egger and MR-PRESSO tests, and there was no statistical heterogeneity in the test results of the MR-Egger and IVW methods. The leave-one-out sensitivity analysis confirmed the robustness of the causal relationship between SS and PD. Furthermore, reverse MR analysis did not support any causal effects of PD on SS.

Conclusion

Our MR study supports a potential causal association between SS and a reduced risk of PD. Further extensive clinical investigations and comprehensive fundamental research are warranted to elucidate the underlying mechanisms linking SS and PD.

Deciphering the causal relationship between plasma and cerebrospinal fluid metabolites and glioblastoma multiforme: a Mendelian Randomization study

BACKGROUND

Glioblastoma Multiforme (GBM) is one of the most aggressive and fatal brain cancers. The study of metabolites could be crucial for understanding GBM's biology and reveal new treatment strategies.

METHODS

The GWAS data for GBM were sourced from the FinnGen database. A total of 1400 plasma metabolites were collected from the GWAS Catalog dataset. The cerebrospinal fluid (CSF) metabolites data were collected from subsets of participants in the WADRC and WRAP studies. We utilized the inverse variance weighting (IVW) method as the primary tool to explore the causal relationship between metabolites in plasma and CSF and glioblastoma, ensuring the exclusion of instances with horizontal pleiotropy. Additionally, four supplementary analytical methods were applied to reinforce our findings. Aberrant results were identified and omitted based on the outcomes of the leave-one-out sensitivity analysis. Conclusively, a reverse Mendelian Randomization analysis was also conducted to further substantiate our results.

RESULTS

The study identified 69 plasma metabolites associated with GBM. Of these, 40 metabolites demonstrated a significant positive causal relationship with GBM, while 29 exhibited a significant negative causal association. Notably, Trimethylamine N-oxide (TMAO) levels in plasma, not CSF, were found to be a significant exposure factor for GBM (OR = 3.1627, 95% CI = (1.6347, 6.1189), P = 0.0006). The study did not find a reverse causal relationship between GBM and plasma TMAO levels.

CONCLUSIONS

This research has identified 69 plasma metabolites potentially associated with the incidence of GBM, among which TMAO stands out as a promising candidate for an early detectable biomarker for GBM.

Design and quality control of large-scale two-sample Mendelian randomization studies

Background

Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors.

Methods

We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set.

Results

We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls.

Conclusions

In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).

A Guide for Selection of Genetic Instruments in Mendelian Randomization Studies of Type 2 Diabetes and HbA1c: Toward an Integrated Approach

In this study we examine the instrument selection strategies currently used throughout the type 2 diabetes and HbA1c Mendelian randomization (MR) literature. We then argue for a more integrated and thorough approach, providing a framework to do this in the context of HbA1c and diabetes. We conducted a literature search for MR studies that have instrumented diabetes and/or HbA1c. We also used data from the UK Biobank (UKB) (N = 349,326) to calculate instrument strength metrics that are key in MR studies (the F statistic for average strength and R2 for total strength) with two different methods ("individual-level data regression" and Cragg-Donald formula). We used a 157-single nucleotide polymorphism (SNP) instrument for diabetes and a 51-SNP instrument (with partition into glycemic and erythrocytic as well) for HbA1c. Our literature search yielded 48 studies for diabetes and 22 for HbA1c. Our UKB empirical examples showed that irrespective of the method used to calculate metrics of strength and whether the instrument was the main one or included partition by function, the HbA1c genetic instrument is strong in terms of both average and total strength. For diabetes, a 157-SNP instrument was shown to have good average strength and total strength, but these were both substantially lesser than those of the HbA1c instrument. We provide a careful set of five recommendations to researchers who wish to genetically instrument type 2 diabetes and/or HbA1c. In MR studies of glycemia, investigators should take a more integrated approach when selecting genetic instruments, and we give specific guidance on how to do this.

Predictive models and survival analysis of postoperative mental health disturbances in adult glioma patients

Background and Objectives

Patients with primary malignant brain tumors may experience mental health disturbances that can significantly affect their daily life. This study aims to identify risk factors and generate predictive models for postoperative mental health disturbances (PMHDs) in adult glioma patients in accordance with different clinical periods; additionally, survival analyses will be performed.

Methods

This longitudinal cohort study included 2,243 adult patients (age at diagnosis ≥ 18 years) with nonrecurrent glioma who were pathologically diagnosed and had undergone initial surgical resection. Six indicators of distress, sadness, fear, irritability, mood and enjoyment of life, ranging from 0-10, were selected to assess PMHDs in glioma patients in the third month after surgery, mainly referring to the M.D. Anderson Symptom Inventory Brain Tumor Module (MDASI-BT). Factor analysis (FA) was applied on these indicators to divide participants into PMHD and control groups based on composite factor scores. Survival analyses were performed, and separate logistic regression models were formulated for preoperative and postoperative factors predicting PMHDs.

Results

A total of 2,243 adult glioma patients were included in this study. Based on factor analysis results, 300 glioma patients had PMHDs in the third postoperative month, and the remaining 1,943 were controls. Candidate predictors for PMHDs in the preoperative model were associated with age, clinical symptoms (intracranial space-occupying lesion, muscle weakness and memory deterioration), and tumor location (corpus callosum, basal ganglia and brainstem), whereas age, clinical symptoms (nausea and memory deterioration), tumor location (basal ganglia and brainstem), hospitalization days, WHO grade 4, postoperative chemotherapy or radiotherapy and postoperative Karnofsky Performance Scale (KPS) served as important factors in the postoperative model. In addition, the median overall survival (OS) time for glioma patients with PMHDs was 19 months, compared to 13 months for glioblastoma, IDH-wild type (GBM) patients with PMHDs.

Conclusion

The risk factors for PMHDs were identified. These findings may provide new insights into predicting the probability of PMHD occurrence in glioma patients in addition to aiding effective early intervention and improving prognosis based on different clinical stages.

Treatments against Polymorphosal discrepancies in Glioblastoma Multiforme

Glioblastoma (GB) are aggressive tumors that obstruct normal brain function. While the skull cannot expand in response to cancer growth, the growing pressure in the brain is generally the first sign. It can produce more frequent headaches, unexplained nausea or vomiting, blurred peripheral vision, double vision, a loss of feeling or movement in an arm or leg, and difficulty speaking and concentrating; all depend on the tumor's location. GB can also cause vascular thrombi, damaging endothelial cells and leading to red blood cell leakage. Latest studies have revealed the role of single nucleotide polymorphisms (SNPs) in developing and spreading cancers such as GB and breast cancer. Many discovered SNPs are associated with GB, particularly in great abundance in the promoter region, creating polygenetic vulnerability to glioma. This study aims to compile a list of some of the most frequent and significant SNPs implicated with GB formation and proliferation.

Relationship between genetically determined telomere length and glioma risk

Background

Telomere maintenance is increasingly recognized as being fundamental to glioma oncogenesis with longer leukocyte telomere length (LTL) reported to increase risk of glioma. To gain further insight into the relationship between telomere genetics and risk of glioma, we conducted several complementary analyses, using genome-wide association studies data on LTL (78 592 individuals) and glioma (12 488 cases and 18 169 controls).

Methods

We performed both classical and summary Mendelian randomization (SMR), coupled with heterogeneity in dependent instruments tests, at genome-wide significant LTL loci to examine if an association was mediated by the same causal variant in glioma. To prioritize genes underscoring glioma-LTL associations, we analyzed gene expression and DNA methylation data.

Results

Genetically increased LTL was significantly associated with increased glioma risk, random-effects inverse variance weighted ORs per 1 SD unit increase in the putative risk factor (odds ratio [OR]_SD) 4.79 (95% confidence interval: 2.11-10.85; P = 1.76 × 10⁻⁴). SMR confirmed the previously reported LTL associations at 3q26.2 (TERC; P_SMR = 1.33 × 10⁻⁵), 5p15.33 (TERT; P_SMR = 9.80 × 10⁻²⁷), 10q24.33 (STN1 alias OBFC1; P_SMR = 4.31 × 10⁻⁵), and 20q13.3 (STMN3/RTEL1; P_SMR = 2.47 × 10⁻⁴) glioma risk loci. Our analysis implicates variation at 1q42.12 (P_SMR = 1.55 × 10⁻²), 6p21.3 (P_SMR = 9.76 × 10⁻³), 6p22.2 (P_SMR = 5.45 × 10⁻³), 7q31.33 (P_SMR = 6.52 × 10⁻³), and 11q22.3 (P_SMR = 8.89 × 10⁻⁴) as risk factors for glioma risk. While complicated by patterns of linkage disequilibrium, genetic variation involving PARP1, PRRC2A, CARMIL1, POT1, and ATM-NPAT1 was implicated in the etiology of glioma.

Conclusions

These observations extend the role of telomere-related genes in the development of glioma.

Methodology in phenome-wide association studies: a systematic review

Phenome-wide association study (PheWAS) has been increasingly used to identify novel genetic associations across a wide spectrum of phenotypes. This systematic review aims to summarise the PheWAS methodology, discuss the advantages and challenges of PheWAS, and provide potential implications for future PheWAS studies. Medical Literature Analysis and Retrieval System Online (MEDLINE) and Excerpta Medica Database (EMBASE) databases were searched to identify all published PheWAS studies up until 24 April 2021. The PheWAS methodology incorporating how to perform PheWAS analysis and which software/tool could be used, were summarised based on the extracted information. A total of 1035 studies were identified and 195 eligible articles were finally included. Among them, 137 (77.0%) contained 10 000 or more study participants, 164 (92.1%) defined the phenome based on electronic medical records data, 140 (78.7%) used genetic variants as predictors, and 73 (41.0%) conducted replication analysis to validate PheWAS findings and almost all of them (94.5%) received consistent results. The methodology applied in these PheWAS studies was dissected into several critical steps, including quality control of the phenome, selecting predictors, phenotyping, statistical analysis, interpretation and visualisation of PheWAS results, and the workflow for performing a PheWAS was established with detailed instructions on each step. This study provides a comprehensive overview of PheWAS methodology to help practitioners achieve a better understanding of the PheWAS design, to detect understudied or overstudied outcomes, and to direct their research by applying the most appropriate software and online tools for their study data structure.

Search for AL amyloidosis risk factors using Mendelian randomization

In amyloid light chain (AL) amyloidosis, amyloid fibrils derived from immunoglobulin light chain are deposited in many organs, interfering with their function. The etiology of AL amyloidosis is poorly understood. Summary data from genome-wide association studies (GWASs) of multiple phenotypes can be exploited by Mendelian randomization (MR) methodology to search for factors influencing AL amyloidosis risk. We performed a 2-sample MR analyzing 72 phenotypes, proxied by 3461 genetic variants, and summary genetic data from a GWAS of 1129 AL amyloidosis cases and 7589 controls. Associations with a Bonferroni-defined significance level were observed for genetically predicted increased monocyte counts (P = 3.8 × 10-4) and the tumor necrosis factor receptor superfamily member 17 (TNFRSF17) gene (P = 3.4 × 10-5). Two other associations with the TNFRSF (members 6 and 19L) reached a nominal significance level. The association between genetically predicted decreased fibrinogen levels may be related to roles of fibrinogen other than blood clotting. be related to its nonhemostatic role. It is plausible that a causal relationship with monocyte concentration could be explained by selection of a light chain-producing clone during progression of monoclonal gammopathy of unknown significance toward AL amyloidosis. Because TNFRSF proteins have key functions in lymphocyte biology, it is entirely plausible that they offer a potential link to AL amyloidosis pathophysiology. Our study provides insight into AL amyloidosis etiology, suggesting high circulating levels of monocytes and TNFRSF proteins as risk factors.

Realising the full potential of MR-PHeWAS in cancer

MR-PHeWAS is a powerful new design for discovering causal mechanisms between a disease and its many candidate risk factors in a hypothesis-free manner. This technique has great potential in the field of cancer research, provided that both powerful and principled statistical approaches are used.