Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

Causal relationship between chronic kidney disease, renal function, and venous thromboembolism: a bidirectional Mendelian randomization study

BACKGROUND

Chronic kidney disease (CKD) and impaired renal function have been implicated in venous thromboembolism (VTE), but their causal relationships remain uncertain. This study employs Mendelian randomization (MR) to elucidate the potential bidirectional causal effects between CKD, renal function biomarkers, and VTE.

METHODS

We collated datasets from genome-wide association studies conducted among European individuals to perform MR analyses. The primary method utilized was the random-effect inverse variance-weighted (IVW) approach, with MR-Egger and the weighted median approaches employed as supplemental techniques. Several sensitivity studies were performed to assess the findings' robustness.

RESULTS

We identified a link between elevated serum creatinine levels and both VTE (OR: 1.14, 95% CI: 1.05-1.24, p = 0.001) and PE (OR: 1.20, 95% CI: 1.08-1.33, p = 0.001). After outlier removal and Bonferroni correction, the Cr-VTE association lost significance (p = 0.005). A suggestive causal relationship was found between eGFR and VTE (OR: 0.38, 95% CI: 0.20-0.73, p = 0.004), DVT (OR: 0.37, 95% CI: 0.16-0.87, p = 0.022), and PE (OR: 0.29, 95% CI: 0.12-0.66, p = 0.004). No causal effects of CKD or BUN on VTE or its subtypes were observed. Reverse causality inferences did not reveal any meaningful results.

CONCLUSIONS

This MR analysis provides evidence that elevated serum creatinine is associated with a higher risk of VTE and PE, while reduced eGFR may be a potential risk factor for VTE and its subtypes. These findings highlight the need for proactive monitoring and preventive strategies in individuals with impaired renal function. Further studies are warranted to confirm these associations and explore underlying mechanisms.

Integration of transcriptome and Mendelian randomization analyses in exploring the extracellular vesicle-related biomarkers of diabetic kidney disease

BACKGROUND

Diabetic Kidney Disease (DKD) is a common complication in patients with diabetes, and its pathogenesis remains incompletely understood. Recent studies have suggested that extracellular vesicles (EVs) may play a significant role in the initiation and progression of DKD. This study aimed to identify biomarkers associated with EVs in DKD through bioinformatics and Mendelian randomization (MR) analysis.

METHODS

This study utilized two DKD-related datasets, GSE96804 and GSE30528, alongside 121 exosome-related genes (ERGs) and 200 inflammation-related genes (IRGs). Differential analysis, co-expression network construction, and MR analysis were conducted to identify candidate genes. Machine learning techniques and expression validation were then employed to determine biomarkers. Finally, the potential mechanisms of action of these biomarkers were explored through Immunohistochemistry (IHC) staining, enrichment analysis, immune infiltration analysis, and regulatory network construction.

RESULTS

A total of 22 candidate genes were identified as causally linked to DKD. CMAS and RGS10 were identified as biomarkers, with both showing reduced expression in DKD. IHC confirmed low RGS10 expression, providing new insights into DKD management. CMAS was involved primarily in mitochondria-related pathways, while RGS10 was enriched in the extracellular matrix and associated pathways. Significant differences were observed in neutrophils and M2 macrophages between DKD and normal groups, correlating strongly with the biomarkers.

CONCLUSION

This study identified two EV-associated biomarkers, CMAS and RGS10, linked to DKD and elucidated their potential roles in disease progression. These results offer valuable insights for further exploration of DKD pathogenesis and the development of new therapeutic targets.

Metformin and risk factors for chronic kidney disease in a European population based on Mendelian randomization

BACKGROUND

Metformin, widely used for type 2 diabetes, raises concerns about its use in chronic kidney disease (CKD) due to risks like lactic acidosis and renal function impact. This study uses Mendelian randomization (MR) and summary data-based MR (SMR) to explore metformin's potential causal relationship with CKD and associated genes.

METHODS

We employed MR methods (MR-Egger, weighted median, IVW) and sensitivity analyses to explore the causal relationship between metformin and CKD. SMR was used to analyze eQTL and CKD data from the UK Biobank and FinnGen, intersecting these with metformin drug targets to identify genes associated with CKD.

RESULTS

MR analysis indicated that metformin may increase CKD risk (IVW model: OR = 144.67, p < 0.01). However, given the high OR value, additional studies are warranted to validate this finding. SMR identified genes ANPEP, STK11, ACACB, and RPS6KB as significantly associated with CKD risk.

CONCLUSION

The study suggests metformin could elevate CKD risk and identifies relevant genes. Clinicians should exercise caution when prescribing metformin, particularly for patients with renal issues. Further research is needed to confirm these findings and guide clinical practices.

Mendelian randomization reveals causal effect of Hashimoto's thyroiditis on immune thrombocytopenic purpura

INTRODUCTION

Patients with immune thrombocytopenic purpura (ITP) usually express thyroid antigen-specific antibodies. The purpose of this study was to explore the causal relationship between Hashimoto's thyroiditis (HT) and ITP.

METHODS

A two-sample Mendelian randomization (TSMR) analysis was applied to investigate the potential causal relationship between HT and ITP in European population. Five complementary methods including inverse variance weighted (IVW), Mendelian Randomization-Egger (MR-Egger), weighted median, and weighted mode were performed in our study. Risk genes of HT and ITP were selected through Mendelian randomization (MR), and the common risk genes were further analysed by bioinformatics methods to explore the common pathogenesis of the two diseases.

RESULTS

The MR analysis revealed a potential causal relationship between HT and risk of ITP [odds ratio (OR) = 1.22; 95% confidence interval (CI) 1.01, 1.49; P = 0.046]. Gene eQTL data were obtained from the IEU database. HT and ITP were respectively treated as outcome variables for MR analysis, and a total of 32 common risk genes were selected, including 12 high-risk genes and 20 low-risk genes. Functional analysis including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analysis revealed that risk genes were closely related to antigen processing and presentation, and played a crucial role in the process of various viral and bacterial infections.

CONCLUSION

Our study demonstrated that HT may increase the risk of ITP, and revealed the role of their common risk genes in the development of the two diseases.

Investigating causal relationships between gene expression and major depressive disorder via brain bulk-tissue and cell type-specific eQTL: A Mendelian randomization and Bayesian colocalization study

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder with complex genetic underpinnings. While genome-wide association studies (GWAS) have identified multiple risk loci, pinpointing causal genes within the human brain remains challenging, particularly given the regulatory complexity across different cell types.

METHODS

We performed summary data-based MR (SMR) and Bayesian colocalization analyses by integrating bulk-tissue eQTL data from 888 individuals with single-cell eQTL datasets from 192 donors representing major brain cell types (excitatory and inhibitory neurons, astrocytes, microglia, oligodendrocytes, OPCs/COPs, endothelial cells, and pericytes). GWAS summary statistics for MDD (170,756 cases and 329,443 controls) were used to assess the causal impact of gene expression. Sensitivity analyses, including the heterogeneity in dependent instruments (HEIDI) test and Steiger filtering, ensured robust inference.

RESULTS

In bulk tissue analyses, five genes (BTN3A2, SLC12A5, AREL1, GMPPB, and ZNF660) emerged as having robust causal evidence for MDD, displaying consistent MR signals and strong colocalization. Cell type-specific analyses revealed additional candidate genes in excitatory neurons (FLOT1, AL450423.1), astrocytes (AL121821.1), and oligodendrocytes (YLPM1, COP1).

CONCLUSION

Our integrative approach reveals that causal gene expression profiles differ markedly between bulk-tissue and specific brain cell types, emphasizing cellular heterogeneity in MDD pathogenesis and informing precision therapeutic strategies. These findings underscore the necessity of considering cell type-specific gene regulation when developing therapeutic interventions for MDD.

Comprehensive genetic analysis based on multi - omics reveals novel therapeutic targets for mitral valve prolapse and drug molecular dynamics simulation

OBJECTIVE

Mitral valve prolapse (MVP), the most prevalent primary valvular disease, serves as a direct risk factor for multiple cardiovascular disorders and exhibits a high prevalence in the general population. As no specific pharmacological therapies currently exist for MVP, the identification of precise therapeutic targets is imperative.

METHOD

We conducted comprehensive causal genetic inference by integrating genetic data from expression quantitative trait loci (eQTL) and genome-wide association studies (GWAS). Analytical approaches included Mendelian Randomization (MR), colocalization analysis, Summary-data-based Mendelian Randomization (SMR), Linkage Disequilibrium Score Regression (LDSC), and High-Definition Likelihood (HDL) analysis. Protein quantitative trait loci (pQTL) were utilized to validate gene expression. Replication analyses were performed using additional exposure datasets. Methylation quantitative trait loci (mQTL) were employed to elucidate regulatory roles of methylation sites on genes and disease pathogenesis. Phenome-Wide Association Study (PheWAS) was conducted to predict potential adverse effects of gene-targeted therapies. Drug candidates targeting identified genes were predicted via the Drug Signature Database (DSigDB) and validated through molecular docking. Core targets were identified using the STRING database, followed by molecular dynamics simulations.

RESULT

Two-sample MR analysis showed that genetically predicted 266 genes had positive or negative causal relationships with MVP. Colocalization analysis indicated that 9 genes had a posterior probability greater than 0.75. Subsequent SMR analysis excluded the gene GAPVD1. HDL analysis showed that except for the gene PTPN1, the remaining 7 genes were all significantly genetically associated with MVP, and LDSC analysis further showed that only NMB was associated with MVP. Validation using pQTL data confirmed that increased NMB protein expression reduced the risk of MVP. Replication analysis further verified this conclusion. In addition, SMR analysis of methylation sites for 8 genes indicated that multiple methylation sites played a key role in gene regulation of mitral valve prolapse. PheWAS results showed that targeted therapy for 8 genes did not detect other causal associations at the genome-wide significance level. Molecular docking showed that quercetin had good binding ability with 8 target genes. The STRING database identified 3 core target proteins, and molecular dynamics simulations further verified the binding ability of quercetin with core target proteins.

CONCLUSION

This study successfully predicted the potential of multiple druggable genes as effective therapeutic targets for MVP through genetic methods, validated the potential of quercetin as a drug, and provided new ideas for drug treatment strategies for MVP.

Common genetic basis and causality between central nervous system disease and cancer

The epidemiological associations between central nervous system diseases and cancers have been widely studied, but the shared genetic basis and etiology between these joint phenotypes remain unclear. To explore this issue, we utilized genome-wide association study summary data to investigate the shared genetic architecture and causality between 10 central nervous system diseases and 14 cancers. We employed multiple statistical genetic approaches, including global and local genetic correlation, Mendelian randomization, shared loci and genes, and shared tissues and cell-types to systematically and robustly explore the common genetic basis and causal relationships between central nervous system diseases and cancers. Our results revealed genetic correlations between schizophrenia and both lung cancer and breast cancer, including estrogen receptor-positive breast cancer, as well as between neuroticism and both lung cancer and ovarian cancer, including serous ovarian cancer. We found causal relationships between schizophrenia and lung cancer (OR = 1.14, P = 0.009) and breast cancer (OR = 1.05, P = 3.00 × 10-5). When the whole genome was partitioned, significant local correlations of schizophrenia with breast cancer and lung cancer were further discovered within 14 specific genomic regions. Using cross-trait meta-analysis, we identified 24 pleiotropic loci associated with the two joint phenotypes. Using summary-data-based Mendelian randomization, we further identified eight functional genes shared between schizophrenia and both breast cancer and lung cancer, neuroticism and ovarian cancer. Additionally, we observed consistent patterns of single-nucleotide polymorphism heritability enrichment for schizophrenia and lung cancer in T lymphocytes. Our study provides insights into the genetic underpinnings and causal relationships of comorbidities between central nervous system diseases and cancers.

Micronutrients, Vitamin D, and Inflammatory Biomarkers in COVID-19: A Systematic Review and Meta-analysis of Causal Inference Studies

CONTEXT

Experimental and observational studies suggest that circulating micronutrients, including vitamin D (VD), may increase COVID-19 risk and its associated outcomes. Mendelian randomization (MR) studies provide valuable insight into the causal relationship between an exposure and disease outcomes.

OBJECTIVES

The aim was to conduct a systematic review and meta-analysis of causal inference studies that apply MR approaches to assess the role of these micronutrients, particularly VD, in COVID-19 risk, infection severity, and related inflammatory markers.

DATA SOURCES

Searches (up to July 2023) were conducted in 4 databases.

DATA EXTRACTION AND ANALYSIS

The quality of the studies was evaluated based on the MR-STROBE guidelines. Random-effects meta-analyses were conducted where possible.

RESULTS

There were 28 studies (2 overlapped) including 12 on micronutrients (8 on VD) and COVID-19, 4 on micronutrients (all on VD) and inflammation, and 12 on inflammatory markers and COVID-19. Some of these studies reported significant causal associations between VD or other micronutrients (vitamin C, vitamin B6, iron, zinc, copper, selenium, and magnesium) and COVID-19 outcomes. Associations in terms of causality were also nonsignificant with regard to inflammation-related markers, except for VD levels below 25 nmol/L and C-reactive protein (CRP). Some studies reported causal associations between cytokines, angiotensin-converting enzyme 2 (ACE2), and other inflammatory markers and COVID-19. Pooled MR estimates showed that VD was not significantly associated with COVID-19 outcomes, whereas ACE2 increased COVID-19 risk (MR odds ratio = 1.10; 95% CI: 1.01-1.19) but did not affect hospitalization or severity of the disease. The methodological quality of the studies was high in 13 studies, despite the majority (n = 24) utilizing 2-sample MR and evaluated pleiotropy.

CONCLUSION

MR studies exhibited diversity in their approaches but do not support a causal link between VD/micronutrients and COVID-19 outcomes. Whether inflammation mediates the VD-COVID-19 relationship remains uncertain, and highlights the need to address this aspect in future MR studies exploring micronutrient associations with COVID-19 outcomes.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration no. CRD42022328224.

Bidirectional two-sample Mendelian randomization reveals causal link between genetic blood metabolites and tuberculosis

Tuberculosis (TB), caused by infectious agent Mycobacterium tuberculosis (Mtb) seriously poses a great threat to health. An array of metabolites generated by metabolic pathways are essential for Mtb pathophysiology. However, a specific causal relationship between TB and human metabolites remains indistinct. This study aimed to investigate the relationship between 1400 metabolites and TB by Mendelian randomization (MR) analysis. In this study, a total of 1400 metabolites were utilized as exposure factors, while TB-related data served as the outcomes. And TwoSampleMR package and R software were adopted to perform this MR analysis. Various regression fitting methods were employed to conduct MR analysis, including inverse variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode. In addition, potential biases arising from linkage disequilibrium and weak instrumental variables were considered. Metabolites that failed to meet the criteria in both the heterogeneity and pleiotropy tests were considered to have no substantial causal influence on the results, ensuring the robustness and reliability of our analysis. IVW analysis showed that six human metabolites exhibited a significant causal influence (P < 0.05) on TB. Among them, dodecanedioate, myristoleate (14:1n5), and 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPE(p-16:0/20:4) demonstrated a strong causally positive effect on TB, indicating that with the increase of these metabolites, TB progressed robustly. Glycerol 3-phosphate, sphingomyelin (d18:1/20:2, d18:2/20:1, and d16:1/22:2), and 2-methylserine were significantly negatively associated with TB, an increase in these metabolites inhibited TB progression. This is the first time to reveal the causal effects of human metabolites on TB through MR, and the metabolites may be potential biomarkers candidate for TB diagnosis, and monitoring these metabolites might have great clinic significance for TB diagnosis and treatment in the future.

Genetically predicted immune cells mediates the association between hepatocellular carcinoma and inflammatory proteins: a Mendelian randomization study

BACKGROUND

Hepatocellular carcinoma (HCC) currently poses a formidable threat to human life and health, and an observable increase in the number of deaths is evident year by year. Currently, surgical resection stands as the foremost treatment modality; however, recurrence remains a persistent challenge, posing a significant barrier to the long-term prognosis for individuals diagnosed with HCC. Studies indicated that the risk of HCC may be influenced by inflammatory proteins and immune cells, but the associations between inflammatory proteins, immune cells, and HCC remained unclear.

METHODS

The investigation integrated data from 731 types of circulating immune cells and 91 inflammatory proteins, alongside a cohort involving 456,348 participants (comprising 456,220 controls and 128 cases) sourced from genome-wide association studies (GWAS). The principal objective of our research was to assess the potential causal association between inflammatory proteins and HCC by bidirectional univariate MR (UVMR) analysis. Furthermore, the total genetic prediction effect of immune cells-mediating inflammatory proteins on the likelihood of developing HCC was investigated by a two-step multivariable MR (MVMR).

RESULTS

Our results indicated that 2-positive inflammatory proteins (IL-17A and TNF-β) suggest a potential causal relationship on HCC, and HCC could affect FGF-21 by bidirectional UVMR analysis. Additionally, four immune cell types (CD25 on IgD+ CD38dim B cells, CD4 on CD39+ secreting CD4 regulatory T cells, CD25 on B cells, and CD25 on IgD+ B cells) exhibited an inverse relationship with the risk of HCC. Moreover, two inflammatory proteins demonstrated dual effects on HCC risk through modulation-either decreasing or increasing-the aforementioned four immune cell types, each with varying proportions of mediation effects as analyzed through two-step mediation MR analysis.

CONCLUSION

This study revealed the potential causality between inflammatory proteins, immune cells, and HCC risk by MR analyses, which may potentially offer a deeper comprehension for the risk of HCC and the interaction between inflammatory proteins, immune cells and HCC and may help to seek new biomarkers for predicting the likelihood of developing HCC.

Uncovering the gut microbiota's role in temporomandibular joint disorders: A bidirectional Mendelian randomization study

Previous studies have revealed the association between gut microbiota (GM) and temporomandibular joint inflammation, which leads to temporomandibular joint disorders (TMD). However, the causality of the associations remains unknown. This Mendelian randomization (MR) study aims to clarify the causal relationships between GM and TMD. We employed a two-sample MR approach to analyze GM data from the MiBioGen consortium, including 18,340 participants and 211 taxa, and TMD data from the FinnGen consortium R10 release, including 6314 cases and 222,498 controls. Various MR methods, including inverse-variance weighted (IVW), MR-Egger, and weighted median analyses, alongside comprehensive sensitivity analyses, were used to assess causality. After comprehensive sensitivity analyses, the study found causal links between 6 certain GM at the genus level and TMD, including Eubacterium fissicatena group (IVW's odds ratio [OR] = 1.18; 95% confidence interval [CI]: 1.02-1.37; P = .027), Catenibacterium (IVW's OR = 1.33; 95% CI: 1.11-1.59; P = .002), Oxalobacter (IVW's OR = 0.85; 95% CI: 0.75-0.97; P = .013), Ruminococcaceae NK4A214 (IVW's OR = 0.80; 95% CI: 0.66-0.99; P = .036), and Senegalimassilia (IVW's OR = 0.75; 95% CI: 0.59-0.96; P = .024). Following replication verification analysis, Catenibacterium (FDR = .04) at the genus level were positively correlated to the risk of TMD. The reverse MR analysis revealed that TMD had no significant effect on the 6 GMs. The findings of this MR study support a strong and negative causal association between genus Catenibacterium and TMD, highlighting potential targets for the prevention and treatment of TMD.

Potential of CETP inhibition in treating dyslipidemia in systemic lupus erythematosus: Novel and comprehensive evidence from clinical studies and Mendelian randomization

OBJECTIVE

Cardiovascular diseases from abnormal lipid metabolism significantly increase mortality in systemic lupus erythematosus (SLE). The causal link between dyslipidemia and SLE is unclear.

METHODS

Lipid metabolism in patients with SLE was evaluated based on clinical data from 511 patients with SLE and 706 healthy individuals. Bidirectional Mendelian randomization (MR) was employed to assess causal links between 179 plasma lipid metabolites, lipid-lowering drug targets, and SLE risk. Genetic instruments from GWAS and eQTL data were used to evaluate CETP and APOA4 effects. Peripheral blood CETP and apolipoprotein levels in SLE patients were validated via ELISA.

RESULTS

SLE patients exhibited reduced HDL-C (P < 0.0001), APOA1 (P < 0.0001), and APOA4 (P < 0.0001), alongside elevated triglycerides (TG, P < 0.0001), APOC3, APOD, and APOF. MR identified three lipid metabolites-PC(18:2_20:4), TG(56:6), and TG(58:7)-as causal factors for SLE (P < 2.79E-5). CETP inhibition significantly reduced SLE risk via HDL-C modulation (OR = 0.72, P = 3.38E-08) and influenced LDL-C, TG, and apolipoproteins. Clinical validation confirmed elevated CETP and reduced APOA4 in SLE, correlating with disease activity. APOA4 activation showed protective effects, while PCSK9 inhibition lacked relevance.

CONCLUSION

Bidirectional Mendelian randomization analyses confirmed dyslipidemia as a causal antecedent to SLE, with no evidence of reverse causation. A variety of MR analyses and clinical validation indicated that targeting HDL-C regulation offers significant advantages for managing dyslipidemia in patients with SLE, with CETP identified as the optimal pharmacological target.

Mendelian randomization combined with single-cell sequencing data analysis of chemokines and chemokine receptors and key genes and molecular mechanisms associated with epilepsy

OBJECTIVE

To explore the functions and potential regulatory mechanisms of chemokine and chemokine receptor (CCR)-related genes in epilepsy.

METHODS

CCRs were identified as candidate genes and their causal relationship with epilepsy was rigorously evaluated via Mendelian randomization analysis. Subsequently, single-cell RNA sequencing (scRNA-seq) data were analyzed to identify and classify cell clusters into distinct types based on cellular annotation. Differential expression analysis was conducted to pinpoint key genes by overlapping the candidate gene set with differentially expressed genes (DEGs). Furthermore, potential therapeutic drugs for epilepsy were predicted, offering novel avenues for disease management and treatment.

RESULTS

In total, 6395 DEGs were identified across the six cell clusters. After their intersection, CCRL2, XCL2, CXCR5, CXCL1, and CX3CR1 were pinpointed as key genes. Microglia, T cells, B cells, and macrophages have been emerged as critical cells. Furthermore, CXCL1 was regulated by hsa-miR-570-3p and hsa-miR-532-5p. Notably, CXCR5, CXCL1, and CX3CR1 were associated with 27 drug compounds. This comprehensive study leveraged scRNA-seq and transcriptomic data to elucidate the roles of CCR-related genes in epilepsy. Notably, CCRL2, XCL2, CXCR5, CXCL1,and CX3CR1 were identified as key genes implicated in epilepsy, whereas microglia, T cells, B cells, and macrophages were recognized as critical contributors to the development of epilepsy.

CONCLUSIONS

Regulating the expression of CCRL2, XCL2, CXCR5, CXCL1, and CX3CR1, along with the activity of these immune cells may offer therapeutic potential for the alleviation of epilepsy.

A multi-omics target study for glioblastoma multiforme (GBM) based on Mendelian randomization analysis

Background

Glioblastoma multiforme (GBM) is the most frequent type of primary malignant brain tumor. This study utilized Mendelian randomization (MR) analysis to explore the causal link between proteins in plasma and cerebrospinal fluid and GBM.

Aims

This study aimed to identify proteins in both plasma and cerebrospinal fluid (CSF) that could serve as potential therapeutic targets for GBM.

Methods

We employed previously published protein quantitative trait loci (pQTL) data from CSF and plasma as the exposure data, alongside aggregated Genome-Wide Association Study (GWAS) data on GBM for our MR analysis. Furthermore, we conducted Bayesian co-localization analysis and examined the protein-protein interaction (PPI) networks of CSF and plasma proteins related to GBM risk.

Results

MR identified three key proteins linked to GBM risk: ribophorin I (RPN1) in plasma, von Willebrand factor (vWF) and macrophage-stimulating protein (MSP). in CSF. Elevated RPN1 and MSP were associated with decreased GBM risk, while increased vWF was linked to higher risk. External validation confirmed that RPN1 served as a key protein in GBM development. Bayesian co-localization showed a 10.35 % probability of a shared causal variant between RPN1 and GBM. Protein-protein interaction analysis further highlighted related proteins for RPN1.

Conclusions

In summary, the plasma protein RPN1 and the CSF proteins vWF and MSP are causally associated with the risk of GBM. Further research is needed to clarify the roles of these candidate proteins in GBM. Notably, RPN1 may serve as a potential therapeutic target for GBM. Future clinical studies on GBM treatment could explore drugs targeting RPN1.

From Diet to Scar: Novel Mendelian Randomization and Mediation Analyses Linking Dietary Habits, Gut Microbiota, and Hypertrophic Scarring

Hypertrophic scarring (HTS) is a pathological skin condition characterized by excessive collagen deposition during wound healing. Emerging evidence suggests that dietary habits and gut microbiota composition may influence HTS risk via systemic inflammatory and metabolic pathways. However, the causal relationships between these factors remain poorly understood. Mendelian randomization (MR) analysis was conducted to investigate the causal relationships between dietary habits, gut microbiota composition, and HTS risk. Additional analyses included mediation analysis to explore potential intermediary effects of gut microbiota and co-localization analysis to assess shared genetic loci between exposures and HTS. MR analysis identified significant associations between HTS and six dietary preferences, with caffeinated/sweet liking and jam liking increasing HTS risk, while crisps, curry, oranges, and strong flavor liking were protective. For gut microbiota, Eubacterium coprostanoligenes, Collinsella, and Coprococcus1 showed protective effects, whereas Adlercreutzia was positively associated with HTS risk. Mediation analysis did not support gut microbiota as a significant mediator between dietary habits and HTS, and co-localization analysis indicated distinct genetic determinants for these traits. The study highlights the independent roles of dietary habits and gut microbiota in influencing HTS risk, suggesting potential dietary and microbial-targeted interventions for scar prevention. Further research in diverse populations is needed to validate these findings and explore their clinical applications.

Exploring the pathways linking fasting insulin to coronary artery disease: a proteome-wide Mendelian randomization study

BACKGROUND

Insulin is known to be associated with a higher risk of coronary artery disease (CAD), but molecular mechanisms remain unclear. This study aimed to explore protein-mediated pathways linking fasting insulin to CAD using Mendelian randomization (MR).

METHODS

This MR study examined the association between fasting insulin and CAD using genome-wide association study (GWAS) data from MAGIC and CARDIoGRAMplusC4D. To investigate underlying mechanisms, a two-step proteome-wide MR analysis was conducted. First, associations of fasting insulin with 2940 circulating proteins were assessed using GWAS of proteomics from UKB-PPP. Proteins affected by insulin were then analyzed for their association with CAD risk. Proteins selected in both steps were considered as potential mediators. Sensitivity analyses to test whether associations are robust to pleiotropy and replication using other GWAS data, including GWAS of proteomics from deCODE and GWAS of CAD from FinnGen Biobank, were performed.

RESULTS

Genetically predicted insulin was associated with a higher risk of CAD (odds ratio 1.79, 95% confidence interval 1.34 to 2.40). At a false discovery rate of 0.05, insulin affected 355 proteins, ten of which were both increased by insulin and linked to a higher risk of CAD. After sensitivity and replication analyses, PLA2G7, GZMA, LDLR, AGRP, and HHEX were identified as reliable mediators. Mediation analyses using non-pleiotropic instruments showed that PLA2G7, GZMA, LDLR, and AGRP explained 19.50%, 6.91%, 19.31%, and 29.66% of insulin's total effect on CAD, respectively.

CONCLUSIONS

This study identified five protein mediators linking insulin to CAD. These proteins could be considered as potential targets to mitigate insulin-related cardiovascular risk, providing novel insights for drug repurposing.

Disentangling the divergent causal pathways underlying the association between body mass index and bone mineral density: a comprehensive Mendelian randomization study

BACKGROUND

While the protective role of body mass index (BMI) in bone mass has been well-documented, the divergent associations between BMI and estimated bone mineral density (eBMD), attributed to its highly heterogeneous nature, remain insufficiently understood.

METHODS

Leveraging the hitherto largest genome-wide summary statistics, we conducted a two-sample Mendelian randomization (MR) to re-evaluate the effect of genetically predicted BMI on eBMD. Then, MR-Clust was applied to examine the potential presence of distinct causal pathways underlying the BMI-eBMD link. Utilizing tissue-partitioned MR, we estimated the distinct effects of separated tissue-specific subcomponents of BMI on eBMD, further supplemented by multivariable MR of body composition phenotypes on eBMD.

RESULTS

We reconfirmed the significant positive association between genetically predicted BMI and eBMD (βIVW = 0.13, P value = 1.28 × 10-34). Potential distinct causal pathways contributing to the observed total effect were identified by MR-Clust, with some exerting a protective effect while others leading to its deterioration. Tissue-partitioned MR suggested a marginally independent protective association between skeletal muscle-tissue instrumented BMI and eBMD (βIVW = 0.14, P value = 4.98 × 10-2) after accounting for adipose-tissue instrumented BMI, which was supported by the independent association between genetically predicted lean mass and eBMD after accounting for other body composition phenotypes.

CONCLUSIONS

Our results shed preliminary insights into the intricate relationship between obesity and bone mass, highlighting divergent causal pathways underlying the association between BMI and eBMD. Our findings emphasize the potential importance of precision obesity management over merely a general indicator as BMI in future public health strategies for osteoporosis prevention.

UDP-Glucose Ceramide Glucosyltransferase Inhibition, Immune Cell Mediation, and Endometriosis Risk: A Mendelian Randomization Study

Purpose

Previous studies have reported a link between UDP-glucose ceramide glucosyltransferase (UGCG), immune regulation, and endometriosis (EM). We hypothesized that UGCG inhibitors might exert therapeutic effects on EM. Therefore, in this study, we performed a two-sample, two-step Mendelian randomization (MR) analysis modeling genetic variation of UGCG inhibitors to investigate the causal relationship between genetically determined UGCG inhibition, EM, and mediation of immune cells.

Methods

Two-sample MR was conducted using genome-wide association study data on UGCG inhibition and EM. Furthermore, we adopted strict instrumental variable selection criteria to ensure the robustness of our results and primarily employed the inverse-variance weighted method, along with the weighted median, MR-Egger, and MR-PRESSO methods for sensitivity analyses. Additionally, a two-step MR analysis was performed to investigate the potential role of immune cells in mediating the relationship between UGCG inhibition and EM.

Results

UGCG inhibition was associated with a decreased EM risk, with an odds ratio of 0.915, 95% confidence interval of 0.859-0.975, and P = 0.006, demonstrating the robustness of our results and potential clinical significance of our study. Among 731 types of immune cells analyzed, 12 were significantly associated with UGCG inhibition and EM, 8 of which acted as mediators. Terminally differentiated CD4+ T cells (from the maturation stages of the T-cell panel) accounted for the highest proportion of mediating cells (26.727%), whereas immunoglobulin D (IgD) expression on IgD+ CD38dim B cells (from the B-cell panel) accounted for the lowest proportion (7.816%).

Conclusion

Inhibiting UGCG activity may reduce the risk of EM, and immune cells may mediate this effect. Our findings provide novel insights for the development of potential therapeutic strategies for EM treatment.

Diabetes affects AD through plasma Aβ40: A Mendelian randomization study

Amyloid and tau proteins are important proteins in the pathological changes of Alzheimer's disease (AD), while Aβ pathology and tau pathology are the most critical factors contributing to the development of AD. Some studies have shown that there is a causal relationship between AD and diabetes mellitus, but there are no studies showing a causal relationship between diabetic traits and AD biomarkers, so further exploration is needed. We first summarized and analyzed the currently published literature on the link between diabetes and AD through a systematic review. Forest plots were used to observe whether there is an association between diabetes and AD. Then a two-sample Mendelian randomization (MR) analysis based on GWAS summary statistics was performed to verify the causal relationship between diabetic traits and AD biomarkers. Based on summary statistics from the GWAS, potential causal relationships between diabetic traits and AD biomarkers were explored separately. The results of the meta-analysis part showed that diabetes can increase the risk of AD. Meanwhile, our two-sample MR results showed a significant causal relationship between diabetes and plasma Aβ40. In addition, our two-sample MR results also showed a causal relationship between increased HbA1c and plasma APLP2. Other diabetic traits may have potential effects on different AD plasma markers.

The health impacts and genetic architecture of food liking in cardio-metabolic diseases

We evaluated temporal and genetic relationships between 176 food-liking-traits and cardio-metabolic diseases using data from the UK Biobank (N = 182,087) for observational analyses and summary-level GWAS data from FinnGen and other consortia (N = 406,565-977,323) for genetic analyses. Integrating observational and genetic results, we identified two detrimental food-liking-traits (bacon and diet-fizzy-drinks) and three protective food-liking-traits (broccoli, pizza, and lentils/beans). These food-liking-traits are associated with habitual food intake and influence cardio-metabolic proteins and biological processes. Notably, we found three genetic links: diet-fizzy-drinks with heart-failure, bacon with type-2-diabetes, and lentils/beans with type-2-diabetes, identifying 54 pleiotropic single-nucleotide-variants, impacting both phenotypes. Our data show the diet-fizzy-drinks and heart-failure link maybe not direct, as diet-fizzy-drinks liking correlates with sweet food consumption and shares variants linked to BMI, adiposity, platelet count and cardio-metabolic traits. The pleiotropic single-nucleotide-variants map to 251 tissue-specific genes, with four showing high druggability potential, highlighting personalized dietary strategies for cardio-metabolic diseases.

Immunological mediators in the causal relationship between latent autoimmune diabetes in adults and breast cancer: A Mendelian randomization analysis

To investigate the link between Latent Autoimmune Diabetes in Adults (LADA) and breast cancer (BC) subtypes using Mendelian randomization (MR) and identify potential immunological mediators. We used MR with genetic variants as instruments, leveraging genome-wide association studies data from Europeans to assess the relationship between LADA and BC subtypes. The study evaluated the mediating role of immune cells and employed methods like inverse variance weighted, MR Egger, and MR-PRESSO to ensure robust results. LADA showed a negative association with overall BC, human epidermal growth factor receptor 2-positive BC, and estrogen receptor-positive BC. The FinnGen Biobank replication and meta-analysis confirmed these inverse relationships. CD11c on CD62L+ myeloid dendritic cells mediated the relationship between LADA and BC risk. Our study indicated a negative association between LADA and BC development, especially for human epidermal growth factor receptor 2-positive and estrogen receptor-positive subtypes, with CD11c on CD62L+ myeloid dendritic cells as a key mediator. These findings suggest potential mechanisms for LADA's influence on BC risk and highlight opportunities for targeted prevention and treatment.

NMRAL1 as a Causal Factor in Postherpetic Neuralgia: A Proteome-Wide Mendelian Randomization Study

Background

Postherpetic neuralgia (PHN) is a chronic pain condition following herpes zoster infection, disproportionately affecting elderly and immunocompromised individuals. Despite its significant clinical impact, the underlying mechanisms of PHN remain exclusive, and effective treatments are limited. Circulating plasma proteins offer insights into PHN pathogenesis and serve as potential biomarkers or therapeutic targets.

Methods

We analyzed FinnGen R12 GWAS data (490 PHN cases and 435,371 controls) and protein quantitative trait loci (pQTL) data for 4907 plasma proteins from 35,559 Icelanders. Mendelian randomization (MR) was conducted to investigate causal associations between plasma proteins and PHN risk. Causal effects were assessed using inverse variance weighting (IVW) and MR-Egger methods.

Results

MR analysis identified NMRAL1 as the only plasma protein causally associated with PHN. Genetically predicted higher levels of NMRAL1 were linked to a reduced risk of PHN (IVW odds ratio = 0.553, 95% confidence interval: 0.405-0.755, p = 0.000193). No evidence of heterogeneity or pleiotropy was observed, and sensitivity analyses, including leave-one-out analysis, confirmed the robustness of the findings. No other plasma proteins showed significant associations with PHN.

Conclusion

This study identifies NMRAL1 as a protective factor for PHN and underscores its potential as a biomarker and therapeutic target. The findings highlight the utility of integrating proteomic and genetic data to advance understanding of complex neurological disorders like PHN.

Role of the "inflammation-immunity-metabolism" network in non-small cell lung cancer: a multi-omics analysis

Lung cancer remains one of the leading causes of cancer-related mortality, with non-small cell lung cancer (NSCLC) accounting for 85% of cases worldwide. NSCLC pathogenesis and progression are intricately linked to inflammatory stimuli, immune evasion, and metabolic reprogramming. In this study, the impact of inflammation, immunity, and metabolism on NSCLC was investigated by a Mendelian randomization analysis taking 91 inflammatory factors, 731 immune cells, and 1400 metabolites as exposures, and the FinnGen database NSCLC cohort (ncases = 5315, ncontrol = 314,193) was the outcome. A number of metabolites, inflammatory proteins, and immune cells were identified as potentially associated with NSCLC based on mendelian randomization analysis. Validation in the UK Biobank database lung cancer cohort (ncases = 2671, ncontrols = 372,016) further confirmed the inhibitory role of the metabolite N-acetyl-aspartyl-glutamate (NAAG) on lung cancer. Subsequently, single-cell and protein-protein interaction analyses identified inflammatory protein expression patterns in NSCLC, distribution ratios of immune cells in NSCLC. Subsequent multi-omics network analysis showed key interaction nodes between NAAG and inflammatory proteins. These findings enhance the understanding of the roles of inflammation, immunity, and metabolism in NSCLC occurrence and progression, offering potential targets and strategies for further research on its treatment and management.

The Role of HbA1c in Parkinson's Disease: An Integrative Analysis by Single-Cell, Bulk Transcriptome and Mendelian Randomization

Decreased glucose tolerance is recognized as a factor associated with Parkinson's disease (PD) progression, yet the relationship between HbA1c and PD prognosis remains insufficiently explored. Using data from the Integrated Epidemiological Unit (IEU) open Genome-Wide Association Study (GWAS), PD's IEU-b-7 and HbA1c's IEU-b-104 were extracted. RNA-seq data from GSE20292 and single-cell RNA-seq data from GSE157783 were retrieved from Gene Expression Omnibus (GEO). Mendelian Randomization (MR) analysis, with HbA1c as the exposure and PD as the outcome, was performed using the inverse variance weighted (IVW) method. Differentially expressed genes (DEGs) between PD and controls in GSE20292 were identified, and overlapping instrumental variables (IVs) and DEGs pinpointed a set of candidate genes. Machine learning refinement selected biomarkers, leading to the development of a PD biomarker-based nomogram. Key cell lineages in GSE140231 were characterized, and communication and pseudotime analyses explored cell crosstalk and evolution. Using 223 independent single nucleotide polymorphisms (SNPs)as IVs, HbA1c was found causally [IVW: Odds Ratio (OR) = 1.438, P = 0.026, 95% Confidence Interval (CI) = 1.043-1.981].. Among 625 genes associated with these SNPs, 842 DEGs were identified by comparing PD vs. controls, intersecting with 27 candidate genes. Notably, five biomarkers-FASN, MICAL3, TCIRG1, CDK10, and MFSD1-emerged as potential diagnostic targets for PD. The receiver operating characteristic (ROC) curve demonstrated the high diagnostic accuracy of these biomarkers. Analysis of key cell lineages revealed strong interactions between excitatory and inhibitory cells and oligodendrocyte precursor cells and Astrocytes cells. In conclusion, HbA1c is identified as a risk factor for PD, with FASN, MICAL3, TCIRG1, CDK10, and MFSD1 representing promising targets for PD diagnosis and treatment.

Causal association between polyunsaturated fatty acids and acne: a two-sample Mendelian randomization study

BACKGROUND

Observational studies have demonstrated a close association between polyunsaturated fatty acids (PUFAs) and acne. However, the findings of clinical trials have been inconsistent, leaving the causal relationship between PUFAs and acne unclear.

OBJECTIVES

To investigate the causal association between genetically proxied PUFAs and acne risk.

METHODS

Mendelian randomization (MR) was performed using single nucleotide polymorphisms associated with PUFAs as instrumental variables. The causal associations between PUFAs and acne were estimated among 115 006 UK Biobank participants and 363 927 participants of Finnish descent.

RESULTS

Genetically predicted docosahexaenoic acid (DHA) levels [β = -0.303, 95% confidence interval (CI) -0.480 to -0.126; P = 7.74 × 10-4] and its percentage to total fatty acids (β = -0.402, 95% CI -0.651 to -0.258; P = 5.91 × 10-6) showed a significant causal association with a decreased risk of acne. Conversely, genetically predicted percentages of linoleic acid (LA) in total fatty acids (β = 0.768, 95% CI 0.411-0.126; P = 2.87 × 10-4) and omega-6 : omega-3 ratio (β = 0.373, 95% CI 0.142-0.604; P = 4.48 × 10-3) were robustly associated with an increased risk of acne. These effects were attenuated after excluding a genetic variant of rs174528 located upstream of FADS1, highlighting the biologic link between FADS1 and delta-5 desaturase activity. Multivariable MR analysis indicated that PUFAs were causally associated with acne, independent of body mass index.

CONCLUSIONS

Our study indicates that high DHA levels and their ratios to total fatty acids have causal protective effects against acne, while high LA levels and omega-6 : omega-3 ratio are associated with increased acne risk. This association was largely attributable to the influence of genetic variants related to FADS1.

The causal association between psoriasis and 32 types of cancer: a mendelian randomization study

BACKGROUND

Psoriasis is a systemic immune disease associated with the development of various cancers. However, the causal nature of this association remains unclear. This study aims to systematically investigate the potential causal relationship between psoriasis and 32 types of cancer.

METHODS

We utilized data from two large genomic databases, the UK Biobank and FinnGen, to extract GWAS summary statistics for 32 cancer types as outcomes and psoriasis-related data as exposures. Mendelian randomization (MR) analysis was performed to assess the causal effects of psoriasis on cancer risk. Sensitivity analyses, including heterogeneity and horizontal pleiotropy tests, were conducted to ensure robustness. Additionally, meta-analysis and FDR correction were applied to enhance the reliability of the results.

RESULTS

Our findings revealed significant causal relationships between psoriasis and four cancer types: Psoriasis was associated with an increased risk of laryngeal cancer (OR = 1.15, 95% CI: 1.05-1.26). Psoriasis exhibited a protective effect against oral cavity and pharyngeal cancer (OR: 0.91; 95% CI: 0.86-0.97), prostate cancer (OR: 0.97; 95% CI: 0.95-0.99), and malignant non-melanoma cancer (OR: 0.89; 95% CI: 0.82-0.96).

CONCLUSION

Psoriasis may exert bidirectional effects on the development of specific cancers through distinct mechanisms. Specifically, psoriasis may increase the risk of laryngeal cancer while reducing the risk of oral cavity and pharyngeal cancer, prostate cancer, and malignant non-melanoma cancer. These findings provide new insights into the causal relationship between psoriasis and cancer and could inform prevention and treatment strategies for these diseases.

Investigating the link between genetic predictive factors of brain functional networks and two specific sleep disorders: Sleep apnoea and snoring

BACKGROUND

Sleep disorders are a widespread public health issue globally. Investigating the causal relationship between resting-state brain functional abnormalities and sleep disorders can provide scientific evidence for precision medicine interventions.

METHODS

We screened single nucleotide polymorphisms (SNPs) associated with rs-fMRI phenotype as instrumental variables Using bidirectional two-sample Mendelian randomization (MR), mediation MR, and multivariate MR based on Bayesian methods, the study tested the causal relationship between genetically predicted rs-fMRI and nine common sleep disorders.

RESULTS

The main inverse variance weighted (IVW) analysis identified four resting state functional magnetic resonance imaging (rs-fMRI) phenotypes that are causally associated with the risk of sleep disorders. For example, increased amplitude in nodes of the parietal, precuneus, occipital, temporal, and cerebellum regions, as well as the default mode network (DMN), central executive network (CEN) and attention network (AN) was associated with an increased risk of sleep apnoea. Enhanced neural activity in the calcarine or lingual and cerebellum regions and increased functional connectivity with the visual and subcortical-cerebellum networks was associated with a reduced risk of snoring. The mediation MR analysis shows that, body mass index (BMI) plays a significant mediating role in the risk of sleep apnoea by modulating the amplitude of nodes in the parietal, temporal, and cerebellum regions, as well as the connectivity changes in the DMN, CEN, and AN.

CONCLUSIONS

This study identified three rs-fMRI phenotypes linked to increased sleep apnoea risk and one associated with decreased snoring risk, providing an important target for the treatment of sleep disorders at the level of brain functional networks.

Investigating the Causality and Pathogenesis of Primary Sclerosing Cholangitis in Colorectal Cancer Through Mendelian Randomization and Bioinformatics

Introduction: The relationship between autoimmune diseases and cancer risk has been increasingly studied. Colorectal cancer, a common malignancy with high morbidity and mortality, has been closely linked to inflammatory bowel disease (IBD) in previous research. However, the association and pathogenesis between primary sclerosing cholangitis (PSC) in autoimmune diseases and colorectal cancer remain incompletely understood. Our study directly investigated the relationship between PSC and colorectal cancer, excluding the influence of IBD, and provided new insights into this association. Methods: Mendelian randomization (MR) analysis was first used to investigate the potential causal relationship between PSC and colorectal cancer. Sensitivity analyses were performed to verify the reliability of the MR results. Transcriptomic data were then analyzed based on the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, combined with clinical prognostic data for the final identification of core differential genes. Results: MR analysis demonstrated that genetic susceptibility to PSC was associated with an increased risk of colorectal cancer in a European population cohort (ratio: 1.038, 95% confidence interval: 1.016-1.060, and p < 0.001). Furthermore, sensitivity analyses confirmed the robustness of the MR results. Univariate and multivariate Cox analyses identified five core genes: NEDD4L, PPP1R1A, NRG1, KCNJ16, and NECAB2. Patients grouped according to high or low expression of NRG1 showed significant differences in their prognosis (p < 0.001). Conclusion: Our MR study provides evidence that genetic susceptibility to PSC is significantly associated with an increased risk of colorectal cancer in European populations. Analysis of transcriptomic data suggests that NRG1 can be used as a novel biomarker to predict patient prognosis when colorectal cancer and PSC coexist.

Exploring causal associations between plasma metabolites and attention-deficit/hyperactivity disorder

BACKGROUND

Observational studies reported altered levels of plasma metabolites in attention-deficit/hyperactivity disorder (ADHD). We aim to explore the causal link between plasma metabolites and ADHD.

METHODS

We utilized Mendelian randomization (MR) analysis to assess the causal relationship between plasma metabolites and ADHD and the Genome-wide association study (GWAS) summary datasets were sourced from public databases. GWAS summary datasets were used in the study, including ADHD (n = 292,548) and 871 plasma metabolites (n = 8,299). Moreover, we used DrugBank and ChEMBL to evaluate whether the identified metabolites are potential therapeutic targets, and in addition, Bayesian colocalization analyses were conducted to assess the shared genetic signals between these metabolites and ADHD.

RESULTS

Our MR analysis identified 20 plasma metabolites that conferred protective effects against the risk of ADHD, including dimethylglycine, 3-methoxytyramine sulfate, and adenosine 3',5'-cyclic monophosphate (OR: 0.97-0.98). Additionally, 22 metabolites were associated with an increased risk of ADHD, including N-acetylneuraminate and 3-indoleglyoxylic acid (OR:1.01-1.03). Druggability evaluation showed that 12 of the ADHD-related metabolites have been targeted by pharmacological interventions. For example, doconexent has been used to increase the levels of docosahexaenoic acid. Our reverse MR analysis showed that genetic liability to ADHD may affect the abundance of 91 metabolites. Notably, several plasma metabolites had bidirectional causal associations with ADHD, including docosahexaenoate (DHA; 22:6n3), docosatrienoate (22:3n3), N1-methyladenosine, S-adenosylhomocysteine, and 4-allylcatechol sulfate.

CONCLUSIONS

Our study supported a causal role of plasma metabolites in the susceptibility to ADHD, and the identified metabolites may provide a new avenue for the prevention and treatment of ADHD.

CLINICAL TRIAL NUMBER

Not applicable.

Evaluating the Causal Role of Genetically Inferred Immune Cells and Inflammatory Cytokines on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Background/Objectives: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a multifaceted and diverse disorder with an ambiguous etiology. Recent evidence indicates that immune system impairment and inflammatory mechanisms are pivotal to the initiation and advancement of ME/CFS. Nonetheless, the causal relationships among these factors remain inadequately comprehended. Methods: This study investigated the causative contributions of immunological dysfunction and inflammatory variables in ME/CFS utilizing genome-wide association study (GWAS) data. We employed Mendelian randomization (MR) to investigate associations between 91 inflammatory cytokines, 731 immune cell characteristics, and the risk of ME/CFS. Summary statistics for immune cell traits and inflammatory cytokines were sourced from European GWAS cohorts (n = 3757 and n = 14,824, respectively), while ME/CFS data were obtained from the UK Biobank (n = 462,933, including 2076 cases). We predominantly employed the inverse variance weighted (IVW) approach, complemented by MR-Egger, weighted median, BWMR, and MR-RAPS tests to guarantee robust and precise outcomes. Results: The study revealed significant causal links between various inflammatory factors, immune cell characteristics, and the risk of ME/CFS. Increased CXCL5 and CCL20 levels were significantly linked to a higher risk of ME/CFS, while elevated TNF levels were inversely related to ME/CFS risk. Furthermore, 13 immune cell characteristics were identified as having substantial causal associations with the likelihood of ME/CFS. These data are supportive of the causality that immune system dysfunction and inflammatory variables play a pivotal role in the development of ME/CFS. Conclusions: This study provides new insights into the causal role of immune system dysfunction in the development of ME/CFS, contributing to a deeper understanding of its underlying mechanisms. These results offer a foundation for identifying diagnostic biomarkers and developing targeted therapeutic strategies. Future research should validate these findings using multi-center cohort studies and further investigate the mechanisms behind key factors to enable the development of personalized treatment approaches.

Causal links between plasma lipidome and ovarian cancer risk: evidence from Mendelian randomization

S. Plasma lipids in circulation are integral to the physiopathological processes of the ovary and may impact the development of various ovarian conditions, including ovarian cancer (OC). This study utilized a two-sample Mendelian randomization method to examine the causal link between changes in 179 plasma lipid groups and ovarian cancer (OC) to gain deeper insights into this association. We used the inverse variance weighted (IVW) method as the main tool for analysis. We utilized statistical data from plasma lipidomics involving 7,174 Finnish individuals and OC data from the FinnGen consortium, including 2,339 European OC patients and 222,078 European healthy controls. Our analysis revealed that elevated levels of four plasma lipids-Phosphatidylcholine (14:0_16:0, O-18:2_18:2, 16:0_20:4)-are linked to an increased risk of OC, while Sphingomyelin (d34:2) seems to act as a protective factor(all P < 0.05). We also conducted tests for heterogeneity and pleiotropy in the MR results. Additionally, reverse MR analysis indicated that OC does not affect plasma levels of these lipids. To determine whether the observed significant plasma lipids influence OC through common risk factors, we selected BMI as a confounder for multivariable Mendelian randomization (MVMR) analysis. The results showed that Sphingomyelin (d34:2) levels remained significantly associated with OC even after including BMI as an exposure factor. Furthermore, we investigated whether these four lipids mediated the effect of BMI on OC but found no evidence supporting their mediating role. In summary, our findings confirm a causal link between certain plasma lipid species and OC, providing fresh perspectives for risk evaluation and potential therapeutic strategies.

Body Mass Index as an Example of a Negative Confounder: Evidence and Solutions

Background: Adequate control for confounding is key to many observational study designs. Confounders are often identified based on subject matter knowledge from empirical investigations. Negative confounders, which typically generate type 2 error, i.e., false nulls, can be elusive. Such confounders can be identified comprehensively by using Mendelian randomization (MR) to search the wealth of publicly available data systematically. Here, to demonstrate the concept, we examined whether a common positive confounder, body mass index (BMI), is also a negative confounder of any common physiological exposures on health outcomes, overall and specifically by sex. Methods: We used an MR study, based on the largest overall and sex-specific genome-wide association studies of BMI (i.e., from the Genetic Investigation of ANthropometric Traits and the UK Biobank) and of relevant exposures likely affected by BMI, to assess, overall and sex-specifically, whether BMI is a negative confounder potentially obscuring effects of harmful physiological exposures. Inverse variance weighting was the main method. We assessed sex differences using a z-test. Results: BMI was a potential negative confounder for apolipoprotein B and total testosterone in men, and for both sexes regarding low-density lipoprotein cholesterol, choline, linoleic acid, polyunsaturated fatty acids, and cholesterol. Conclusions: Using BMI as an illustrative example, we demonstrate that negative confounding is an easily overlooked bias. Given negative confounding is not always obvious or known, using MR systematically to identify potential negative confounders in relevant studies may be helpful.

Causal relationship between immunophenotypes and rheumatoid arthritis: A 2-sample Mendelian randomization study

As previous studies have demonstrated an association between immune inflammation and rheumatoid arthritis (RA), our study aimed to lend novel insight by exploring the potential causal association between RA and different immunophenotypes. Data were obtained from the genome-wide association study (GWAS) from Finn Gen. The dataset of GWAS contains a cohort of 6236 RA cases and 147,221 controls in European population. Data on immune cell traits are publicly available from the GWAS catalog. A total of 731 immunophenotypes were included in this study including absolute cell counts (ACs), median fluorescence intensity, morphological parameters, and relative cell counts. Mendelian randomization analysis was performed by several methods, and sensitivity analysis and visualization of the results were also carried out. After being adjusted by false discovery rate (FDR), 6 immune phenotypes were significantly and causally associated with the development of RA: CD16 on CD14+ CD16+ monocytes (adjusted odds ratio [OR]: 0.950, 95% confidence interval [CI]: 0.924-0.977, P = 4.04 × 10-4), CD62L-CD86+ myeloid DC %DC (adjusted OR: 1.048, 95% CI: 1.021-1.076, P = 4.29 × 10-4), CD62L-CD86+ myeloid DC AC (adjusted OR: 1.050, 95% CI: 1.024-1.076, P = 1.11 × 10-4), CD62L- myeloid DC AC (adjusted OR: 1.067, 95% CI: 1.033-1.101, P = 8.35 × 10-5), DC AC (adjusted OR: 1.105, 95% CI: 1.062-1.149, P = 7.73 × 10-7), myeloid DC AC (adjusted OR: 1.060, 95% CI: 1.029-1.091, P = 9.96 × 10-5). In addition, we found that CD62L- Dendritic cell % increases with the onset of RA (OR: 1.136, 95% CI: 1.064-1.213, P = 1.36 × 10-4, PFDR = 0.099). This study explored the association between different immunophenotypes and RA, which may lend some novel insights into RA pathogenesis and facilitate the development of new treatments.

Causal relationship between cheese intake and periodontal diseases: A two-sample Mendelian randomization study

Dietary habits have been confirmed to affect periodontal disease, but whether cheese intake is associated with periodontal disease remains unclear. This study aims to explore the causal relationship between cheese intake and periodontal disease by Mendelian randomization (MR) analysis. genome-wide association study data from the UK Biobank was utilized. In order to reveal the causal relationship between exposure (cheese intake) and outcome (periodontal disease), and to ensure the reliability of the conclusions, single nucleotide polymorphisms were rigorously selected as instrumental variables to replace the exposure. The following methods were applied for MR analysis: the inverse-variance weighted (IVW), MR-Egger, weighted median, simple mode and weighted mode methods. Sensitivity analyses included heterogeneity test, horizontal pleiotropy test and leave-one-out method. The IVW method indicated that the risk of periodontal diseases decreased as cheese intake increases (OR = 0.545, 95% CI = 0.364-0.816, P = .0032). Sensitivity analyses revealed no statistical evidence of heterogeneity or horizontal pleiotropy, confirming the robustness of the results. Increased cheese intake may reduce the risk of periodontal disease. This study provides genetic evidence supporting the inclusion of cheese in the diet for periodontal disease. Further research is needed to confirm these findings in different populations and to understand the underlying mechanisms.

Metabolic pathways mediating insulin resistance and gestational diabetes mellitus discovered by high-dimensional systematic Mendelian randomization

BACKGROUND

Gestational diabetes mellitus (GDM), characterized by insulin resistance (IR) and β-cell dysfunction, is one of the most common complications of pregnancy with unmet needs of prevention methods.

OBJECTIVE

To investigate the causal role of insulin resistance and metabolic pathways in the pathogenesis of GDM with our proposed high-dimensional systematic Mendelian randomization (hdsMR) framework.

METHODS

Cases with GDM and controls with normal glucose tolerance were recruited at the University of Hong Kong-Shenzhen Hospital from 2015 to 2018. A total of 566 participants (aged > 18 years), including 274 with GDM, were enrolled after excluding subjects with major chronic diseases or long-term use of medications affecting glycolipid metabolism. Clinical characteristics and serum samples were collected during the GDM screening stage, and the genome and metabolome were tested. A novel hdsMR framework was proposed to estimate the causal role of IR index (Homeostasis Model Assessment of Insulin Resistance, HOMA-IR) and metabolic pathways in the pathogenesis of GDM.

RESULTS

Our hdsMR method confirmed that HOMA-IR was causal to GDM (odds ratio, 1.17; 95% confidence interval, 1.04-1.32) and revealed that two metabolic pathways (glyoxylate and dicarboxylate metabolism pathway and lysine degradation pathway) mediated 14.6% and 8.4%, respectively, between HOMA-IR and GDM. In an independent validation cohort comprising 255 pre-diabetic individuals, we showed that both pathways could be intervened through diet (P < 0.05). Furthermore, glyoxylate and dicarboxylate metabolism pathway was significantly associated with adverse pregnancy outcomes in GDM.

CONCLUSIONS

These results indicated that targeting specific metabolic pathways through dietary intervention is worth exploring as a possible GDM prevention approach, and hdsMR is more efficient in finding causal mediating metabolic pathways than traditional MR methods.

Uncovering Potential Susceptibility Genes for Multiple Sclerosis-Induced Neuropathic Bladder: A Mendelian Randomization Analysis

Despite lacking a genetic explanation for the causal link between multiple sclerosis (MS) and neuropathic bladder (NPB), our study aims to explore this causality and identify novel susceptibility genes for both phenotypes. We performed linkage disequilibrium score regression to assess SNP heritability for both phenotypes. Two-sample bidirectional Mendelian randomization (MR) analyses were conducted to evaluate causal relationships between MS and NPB. We performed pathway enrichment analysis on instrumental SNPs and applied summary-data-based MR (SMR) with protein and expression quantitative trait loci. Candidate susceptibility genes were further examined through colocalization analysis and differential expression studies. Our analyses indicate a substantial genetic contribution to both MS and NPB phenotypes. MR analysis revealed that MS progression increased NPB risk (OR = 1.126; 95% CI. 1.052-1.205; p < 0.001), with no evidence of reverse causality. Pathway analysis highlighted NIK/NF-kappaB signaling and autophagosome maturation as potentially shared mechanisms. SMR (p_FDR < 0.05) and colocalization analyses (PP.H4 > 0.75) identified NFKB1 and STAT3 as candidate susceptibility genes. Transcriptomic analyses confirmed significant differential expression of these genes (p < 0.05) between MS patients and healthy controls. Our findings established a causal relationship between MS progression and NPB risk, with NFKB1 and STAT3 emerging as promising therapeutic targets for MS-induced NPB.

Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors

BACKGROUND

Migraine ranks as the second-leading cause of global neurological disability, affecting approximately 1.1 billion individuals worldwide with severe quality-of-life impairments. Although adjustable risk factors-including environmental exposures, sleep disturbances, and dietary patterns-are increasingly implicated in pathogenesis of migraine, their causal roles remain insufficiently characterized, and the integration of multimodal evidence lags behind epidemiological needs.

METHODS

We developed a three-step analytical framework combining causal inference, predictive modeling, and burden projection to systematically evaluate modifiable factors associated with migraine. First, two-sample mendelian randomization (MR) assessed causality between five domains (metabolic profiles, body composition, cardiovascular markers, behavioral traits, and psychological states) and the risk of migraine. Second, we trained ensemble machine learning (ML) algorithms that incorporated these factors, with Shapley Additive exPlanations (SHAP) value analysis quantifying predictor importance. Finally, spatiotemporal burden mapping synthesized global incidence, prevalence, and disability-adjusted life years (DALYs) data to project region-specific risk and burden trajectories through 2050.

RESULTS

MR analyses identified significant causal associations between multiple adjustable factors (including overweight, obesity class 2, type 2 diabetes [T2DM], hip circumference [HC], body mass index [BMI], myocardial infarction, and feeling miserable) and the risk of migraine (P < 0.05, FDR-q < 0.05). The Random Forest (RF)-based model achieved excellent discrimination (Area under receiver operating characteristic curve [AUROC] = 0.927), identifying gender, age, HC, waist circumference [WC], BMI, and systolic blood pressure [SBP] as the predictors. Burden mapping projected a global decline in migraine incidence by 2050, yet persistently high prevalence and DALYs burdens underscored the urgency of timely interventions to maximize health gains.

CONCLUSIONS

Integrating causal inference, predictive modeling, and burden projection, this study establishes hierarchical evidence for adjustable migraine determinants and translates findings into scalable prevention frameworks. These findings bridge the gap between biological mechanisms, clinical practice, and public health policy, providing a tripartite framework that harmonizes causal inference, individualized risk prediction, and global burden mapping for migraine prevention.

Integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosis

From 2020 to 2022, colorectal cancer (CRC) cases increased, making it the third most common cancer and the second leading cause of cancer-related deaths worldwide. Early detection remains a significant challenge due to the lack of reliable diagnostic biomarkers. This study aimed to develop a robust gene diagnostic model for CRC using publicly available databases, such as GEO and GEPIA2. The approach integrated differential expression analysis, weighted gene co-expression network analysis (WGCNA), and the application of 113 machine learning combinations derived from 12 algorithms. The most effective model was then validated using independent datasets, which included analyses such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) networks, and receiver operating characteristic (ROC) curves, along with assessments of immune infiltration and tumor-node-metastasis (TNM) staging. Notably, the glmBoost + RF algorithm identified an eight-gene diagnostic model with high precision, pinpointing key genes such as CLDN1, IFITM1, and FOXQ1, which exhibited strong diagnostic performance (AUC > 0.9). Furthermore, Mendelian randomization (MR) analysis suggested that IFITM1 may be a potential causal gene for CRC, with significant associations to immune cell profiles and established roles in immune regulation and tumor progression. Collectively, these findings highlight IFITM1, SCGN, and FOXQ1 as promising early diagnostic biomarkers and therapeutic targets for CRC, laying a foundation for future research focused on enhancing early detection and intervention strategies in colorectal cancer management.

Assessing the Causal Relationship between Chronic Obstructive Pulmonary Disease and Tuberculosis: A Mendelian Randomization Study

Background

Chronic obstructive pulmonary disease (COPD) and tuberculosis are both significant global public health challenges. The co-occurrence of these two diseases is frequently observed in clinical settings. However, their causal relationship remains unclear.

Methods

We utilized genome-wide association study (GWAS) datasets to conduct bidirectional two-sample Mendelian randomization and multivariable Mendelian randomization analyses. We first analyzed COPD data from the FinnGen consortium (n = 193,638) and tuberculosis data from a genetic association study (n = 484,598). In the second phase, we stratified COPD patients by age into the EARLY COPD group (Event_Age < 65) and the LATER COPD group (Event_Age ≥ 65) to explore their causal relationships with tuberculosis separately. We then validated these results using tuberculosis data from MRC-IEU (n = 462,933). Finally, smoking and COPD-related SNPs as instrumental variables were analyzed by multivariable Mendelian randomization to further investigate the association between COPD and tuberculosis. Multiple methods were used in the Mendelian analyses to ensure a comprehensive and rigorous investigation.

Results

In the initial analysis phase utilizing the inverse variance weighting (IVW) method, tuberculosis showed no significant contribution to the incidence of COPD (IVW odds ratio (OR) = 0.9961; 95% confidence interval (CI) = 0.9828-1.0095; P = 0.564). Conversely, COPD appeared to significantly increase the risk of developing tuberculosis (IVW OR = 1.0008; 95% CI = 1.0001-1.0014; P = 0.015), particularly in patients under 65 (IVW OR = 1.0008; P = 0.011).

Conclusion

This Mendelian randomization analysis found that COPD may increase the risk of tuberculosis, while tuberculosis does not increase the risk of COPD, suggesting the necessity of enhancing prevention and screening efforts for tuberculosis among COPD patients, especially younger individuals.

Association of Genetic Liability to Allergic Diseases with Overall and Early-Onset Colorectal Cancer Risk: A Mendelian Randomization Study

BACKGROUND

The tumor immunosurveillance theory supports that allergic conditions could decrease cancer risk. However, observational evidence yielded inconsistent results for the association between allergic diseases and colorectal cancer risk. We used Mendelian randomization (MR) to examine potential causal associations of allergies with the risk of overall and early-onset colorectal cancer.

METHODS

Genome-wide association study summary statistical data were used to identify genetic variants associated with allergic diseases (Nvariants = 65) and individual allergic conditions (asthma, hay fever/allergic rhinitis, and eczema). Using two-sample MR, we examined these variants in relation to incident overall (Ncases = 52,775 cases) and early-onset colorectal cancer (Ncases = 6,176). The mediating role of white blood cells was examined using multivariable MR.

RESULTS

In inverse-variance-weighted models, genetic liability to allergic diseases was inversely associated with overall {OR per log (odds) = 0.90 [95% confidence interval (CI), 0.85-0.96]; P < 0.01} and early-onset colorectal cancer [OR = 0.83 (95% CI, 0.73-0.95); P = 0.01]. Similar inverse associations were found for hay fever/allergic rhinitis or eczema, whereas no evidence of association was found between liability to asthma-related phenotypes and colorectal cancer risk. Multivariable MR adjustment for eosinophils weakened the inverse associations for liability to allergic diseases for overall [OR = 0.96 (95% CI, 0.89-1.03); P = 0.26] and early-onset colorectal cancer [OR = 0.86 (95% CI, 0.73-1.01); P = 0.06].

CONCLUSIONS

Our study supports a potential causal association between liability to allergic diseases, specifically hay fever/allergic rhinitis or eczema, and colorectal cancer, possibly at least in part mediated via eosinophil counts.

IMPACT

Our results provide evidence that allergic responses may also have a role in immunosurveillance against colorectal cancer.

Assessing the causal relationship between psoriasis vulgaris and urolithiasis: A two-sample Mendelian randomization study

Psoriasis has been suggested to be associated with urolithiasis. However, the existing literature is based on observational studies, which provide limited evidence for the causal relationship between these two conditions. This research aims to evaluate the causal association between psoriasis vulgaris and urolithiasis using 2-sample Mendelian Randomization (MR) analysis. Exposures and outcomes were sourced from genome-wide association study data. The psoriasis vulgaris dataset included 5072 patients and 4,78,102 controls. The urolithiasis dataset included 5347 patients and 2,13,445 controls. We used the inverse-variance weighted (IVW) method as our primary analytical strategy, augmented by MR-Egger regression and the weighted median method. Cochran Q test, MR-Egger regression, leave-one-out analysis and Steiger filtering were also conducted to evaluated the stability and credibility of the results. The IVW analysis showed a significant association between psoriasis vulgaris and urolithiasis (odds ratio [OR] = 1.073, 95% confidence interval [CI] = 1.017-1.131, P = .010). The results of weighted median analysis (OR = 1.071, 95% CI = 1.013-1.133, P = .017) and MR-Egger regression (OR = 1.072, 95% CI = 0.992-1.158, P = .12) indicated a consistent directional causality with the IVW analysis. There was no significant horizontal pleiotropy or heterogeneity in the analysis. Steiger filtering further confirmed the accuracy of the directional causality. In conclusion, this MR study supports a causal association between psoriasis vulgaris and urolithiasis.

Association of FGF21 with Metabolic and Cardiovascular Diseases: A Mendelian Randomization Analysis

Studies have covered a possible relevance between fibroblast growth factor 21 (FGF21) and obesity-related metabolic complications and cardiovascular disease (CVD). Nevertheless, whether FGF21 is a causative factor in these diseases is not known. Using a bidirectional, two-sample Mendelian randomization (MR) approach, this study sought to establish a causal relationship between FGF21 and seven metabolic diseases and six CVDs. A large-scale meta-analysis dataset of genome-wide association studies (GWAS) was analyzed to generate summary-level statistics for FGF21. The diseases we studied included non-alcoholic fatty liver disease (NAFLD), obesity, type 2 diabetes (T2DM), hypertension, gestational diabetes (GDM), gestational hypertension (GHTN), pre-eclampsia or eclampsia (PE), atherosclerosis, cardiomyopathy (CMP), coronary heart disease (CHD), coronary atherosclerosis, heart failure (HF), myocardial infarction (MI) and the corresponding summary GAWS data were retrieved from the FinnGen Biobank and IEU Open GWAS Project database. The inverse variance-weighted (IVW) algorithm was the primary approach utilized for the MR analysis. The MR-Egger regression and MR-PRESSO tests were implemented to evaluate horizontal pleiotropy. The heterogeneity of instrumental variables was subsequently assessed utilizing Cochran's Q statistics.When diseases are used as exposures, MR analysis results of the IVW method indicated that NAFLD (Beta=- 0.047, 95% CI=- 0.08 to - 0.014; p=0.006), obesity (Beta=0.087, 95% CI=0.021-0.153; p=0.009), T2DM (Beta=0.071, 95% CI=0.037-0.106; p<0.001) correlated causally with FGF21. Nevertheless, FGF21 was not causally related to the remaining metabolic diseases and CVDs, according to the results of the MR analysis (p>0.05). It was demonstrated that the aforementioned results were robust and devoid of pleiotropy.Our study supports a causal association between NAFLD, obesity, and T2DM with FGF21. No substantial evidence exists to establish a causal relationship between FGF21 and other diseases. This study provides opportunities for the early prevention and innovative therapy of NAFLD, obesity, and T2DM.

Blood biochemical landscape and new insights into clinical decision-making for polycystic ovary syndrome in Chinese women: a prospective cohort study

Introduction

The Polycystic ovary syndrome (PCOS), a prevalent endocrine disorder affecting women's reproductive and metabolic health, faces diagnostic challenges due to heterogeneous clinical presentations and the absence of reliable biomarkers. This study investigates the role of Glucosaminyl (N-acetyl) transferase 2 (GCNT2) in modulating sex hormone-binding globulin (SHBG) and its potential as a therapeutic target in PCOS pathophysiology.

Methods

A prospective cohort of 103 PCOS patients treated with oral contraceptives (2021-2024) was established. Bidirectional Mendelian randomization (MR) was employed to assess genetic associations and causal relationships between PCOS and SHBG. Molecular docking studies evaluated cryptotanshinone's binding affinity to key proteins (COL1A1, COL4A2, COL6A2) in the PI3K/Akt pathway. GCNT2's regulatory effects on collagen synthesis and extracellular matrix pathways. Pharmacokinetic profiling validated therapeutic viability.

Results

Bidirectional MR revealed significant genetic associations (P < 0.001) and causal links between PCOS and SHBG, implicating GCNT2 as a key modulator. Cryptotanshinone exhibited strong binding affinity to PI3K/Akt signaling pathway proteins and favorable pharmacokinetic properties. Enrichment analyses highlighted GCNT2's role in collagen biosynthesis (FDR < 0.05) and extracellular matrix regulation.

Discussion

This study identifies GCNT2 as a critical mediator of PCOS pathophysiology through SHBG modulation and collagen remodeling. Cryptotanshinone emerges as a promising therapeutic candidate, targeting PI3K/Akt signaling pathway with high specificity. These findings advance the understanding of PCOS mechanisms and provide a foundation for biomarker-driven diagnostics and precision therapeutics. Further validation in clinical trials is warranted to translate these insights into practice.

Roles of triglyceride-glucose index in aortic valve calcification progression: a prospective and Mendelian randomization analysis

AIM

The triglyceride-glucose (TyG) index, recognized as a surrogate marker for insulin resistance, is an established cardiovascular risk factor. We aimed to prospectively investigate the association between the TyG index and aortic valve calcific (AVC) progression, as well as its relationship with incident calcific aortic valve stenosis (CAVS).

MATERIALS AND METHODS

A post hoc analysis was conducted on 5589 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) database. The TyG index was calculated using ln (fasting triglycerides [mg/dL] × fasting glucose [mg/dL]/2). Multivariate Cox regression assessed the association between baseline TyG index and AVC progression. Two-sample Mendelian randomization (MR) analysis was employed to evaluate the potential causality between the TyG index and CAVS.

RESULTS

Over a median 2.4 years follow up, 567 cases of AVC progression were idenrified. After adjusting for traditional cardiovascular risk factors, each 1-SD increase in the TyG index was associated with a 20.8% increased risk of AVC progression. Robustness was confirmed in sensitivity analyses and nearly all subgroups. Two sample MR analysis supported a causal relationship between a higher TyG index and increased risk of CAVS.

CONCLUSION

A higher TyG index independently predicts AVC progression and causally influences CAVS incidence in the general population.

Biological pathways underlying the relationship between childhood maltreatment and Multimorbidity: A two-step, multivariable Mendelian randomisation study

Childhood maltreatment has been associated with multimorbidity of depression, coronary artery disease and type 2 diabetes. However, the biological mechanisms underlying this association remain unclear. We employed two-step and multivariable Mendelian randomisation (MR) to understand the role of three potential biological mediating mechanisms - inflammation (92 proteins), metabolic processes (54 markers), and cortisol - in the link between childhood maltreatment liability and multimorbidity. Using summary statistics from large-scale genome-wide association studies of European ancestry for childhood maltreatment (N = 185,414) and multimorbidity (Neffective = 156,717), we tested for the presence of an indirect effect via each mediator individually. We found a potential role of metabolic pathways. Up to 11% of the effect of childhood maltreatment on multimorbidity was mediated by triglycerides (indirect effect [95% CI]: 0.018 [0.009-0.027]), 8% by glycated haemoglobin (indirect effect: 0.013 [0.003-0.023]), and up to 7% by high-density lipoprotein cholesterol (indirect effect: 0.011 [0.005-0.017]). We did not find evidence for mediation via any inflammatory protein or cortisol. Our findings shed light on the biological mechanisms linking childhood maltreatment liability to multimorbidity, highlighting the role of metabolic pathways. Future studies may explore underlying pathways via non-biological mediators (e.g., lifestyle factors) or via multiple mediators simultaneously.

Potential causal association between serum vitamin D levels and intervertebral disc degeneration: A mendelian randomization study

OBJECTIVES

Intervertebral disc degeneration (IDD) is a prevalent musculoskeletal disorder with substantial implications for disability and healthcare expenditures. The role of serum vitamin D (25-Hydroxyvitamin D, 25(OH)D) levels in the pathogenesis of various musculoskeletal conditions has been explored in prior observational studies, suggesting a potential association. While previous observational studies have suggested an association between the two conditions, it might confound the effect of 25(OH)D on IDD. This Mendelian randomization (MR) study seeks to elucidate the causal relationship between 25(OH)D and IDD.

METHODS

We performed a MR analysis using summary-level data from genome-wide association studies (GWAS) of 25(OH)D (sample size = 441,291 European) and IDD (sample size = 336,439 (cases = 41,669, controls = 294,770) European). Single nucleotide polymorphisms (SNPs) significantly associated with 25(OH)D (p < 5 × 10-8) were selected as instrumental variables. The associations between genetically predicted 25(OH)D and IDD were estimated using the inverse-variance weighted (IVW) method, with sensitivity analyses employing the weighted median, MR-Egger, and MR-PRESSO approaches to assess the robustness of the findings.

RESULTS

In the primary IVW analysis, genetically predicted 25(OH)D was unrelated associated with IDD (odds ratio (OR) = 0.9671, 95% confidence interval (CI): 0.8956-1.0444, p = 0.39). The results remained consistent across the sensitivity analyses, and no significant directional pleiotropy was detected (MR-Egger intercept: p = 0.64).

CONCLUSIONS

This study found no obvious evidence that 25(OH)D is causally associated with IDD risks. We call for larger sample size studies to further unravel the potential causal relationship and the exact mechanism.

Causal Association Between Inflammatory Proteins, Inflammatory Cells, and Cauda Equina Syndrome: A Two-Sample Mendelian Randomization

BACKGROUND

Recent studies have shown that inflammation plays a crucial role in the progression of cauda equina syndrome (CES). However, the exact cause-and-effect relationship between them is still unclear.

METHODS

We used CES data from the FinnGen genome-wide association study (GWAS), containing 329 cases and 408,351 control patients. Inflammatory proteins data were obtained from a large scale GWAS of 14,828 European ancestry participants, and inflammatory cells data were obtained from a GWAS summary of 3757 Sardinians. We chose inverse variance weighted as the main method and the Cochrane Q test to assess heterogeneity in the results. The MR-Egger intercept test and MR pleiotropy residual sum and outliers test were used to evaluate the horizontal pleiotropy, and sensitivity analysis was performed by leave-one-out analysis.

RESULTS

We examined robust associations between inflammatory proteins, inflammatory cells, and CES using Mendelian randomization. Two inflammatory proteins and 12 inflammatory cells were found as risk factors for CES: IL-8 and PD-L1; and basophil plasmacytoid dendritic cell, CD86+plasmacytoid dendritic cell, CD62L-plasmacytoid dendritic cell, CD39+secreting Treg, IgD+CD38-B cell, switched memory B cell, IgD+CD24+B cell, CD62L+dendritic cell, CD4+T cell, γδ T cell, and CD33dim HLA DR-myeloid cell. Two inflammatory proteins and 7 inflammatory cells were found as protective factors for CES: IL-10RA and CCL25; and transitional B cell, terminal differentiation double negative T cell, CD28-CD127-CD25++CD8br T cell, IgD+CD38br B cell, CD28+CD45RA-CD8br Treg, IgD+CD38-naive B cell, and granulocyte. Heterogeneity and pleiotropy analysis confirmed the reliability of the results. Our study reveals the causal relationship between inflammatory proteins, inflammatory cells, and CES, offering new insights for the development of future therapeutic drugs and early warning indicators.

CONCLUSIONS

Our findings extend genetic research to causal analysis between inflammatory proteins, cells, and CES. We found 2 proteins and 12 cells as risk factors and 2 proteins and 7 cells as protective factors. Further investigations are needed to verify whether these inflammation markers can be used to prevent or treat CES.

Uncovering Causal Links Between Dietary Habits and Cardiovascular Diseases

Dietary modification plays a crucial role in preventing cardiovascular diseases (CVDs), but evidence linking specific diets to stroke and acute myocardial infarction (AMI) is limited. This study investigates causal relationships between defined dietary exposures (e.g., fruit/vegetable intake, muesil consumption) and CVD outcomes, while evaluating the potential mediating role of high-density lipoprotein cholesterol (HDL-C). We employed two-sample Mendelian randomization (MR) using genetic data from genome-wide association studies (GWAS) in the UK Biobank and IEU database, validated with FinnGen data, to examine causal relationships between 83 dietary habits and CVD. Additionally, a meta-analysis was conducted using studies from PubMed and Web of Science to assess diet-stroke associations. Random-effects models were applied to estimate pooled relative risks (RR), with sensitivity analyses for robustness. MR identified eight significant diet-AMI and eighteen diet-stroke associations, but HDL-C did not mediate the diet-stroke relationship. The meta-analysis of 50 studies confirmed a link between specific diets and stroke risk. This study confirms associations between specific dietary factors and stroke/AMI, though HDL-C's role in AMI is unclear. These results reinforce the importance of targeted dietary modifications in primary prevention, and further research is needed to clarify underlying mechanisms.

Integrative single-cell RNA sequencing and mendelian randomization analysis reveal the potential role of synaptic vesicle cycling-related genes in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) involves alterations in synaptic vesicle cycling (SVC), which significantly affect neuronal communication and function. Therefore, a thorough investigation into the potential roles of SVC-related genes (SVCRGs) in AD can enhance the identification of critical biomarkers that may influence disease progression and treatment responses.

METHODS

The datasets used in this study were sourced exclusively from public databases. By integrating differential expression analysis with Mendelian randomization (MR), we identified SVCRGs as biomarkers for AD. Functional characterization of these biomarkers was performed, followed by integration into a nomogram. Further investigation of immune infiltration in AD patients and healthy individuals was carried out. Ultimately, the potential cellular mechanisms of AD were explored through single-cell RNA sequencing (scRNA-seq) analysis.

RESULTS

ATP6V1D, ATP6V1G2, CLTB, and NSF were identified as biomarkers, exhibiting a positive correlation with each other and a downregulated expression in AD. These markers were pinpointed as protective factors for AD [odds ratio (OR) < 1, P < 0.05], with potential to reduce the risk of the disease. Integrated into a nomogram, they demonstrated satisfactory diagnostic performance and clinical utility, surpassing the use of single gene. They were collectively enriched in pathways related to "interferon gamma response", "inflammatory response", and "TNFα signaling via NFκB". Additionally, an increase in infiltration of 17 immune cell types in AD was noted, particularly cells associated with neuroinflammation such as activated CD8 T cells and various dendritic cells (DCs), suggesting an inflammatory milieu in AD while also displaying a negative correlation with the biomarkers. The cell types were further annotated, revealing specific expressions of biomarkers and uncovering the heterogeneity of excitatory neurons. A significant reduction in the overall number of excitatory neurons under AD conditions was observed, alongside consistent expression of biomarkers during the developmental stages of excitatory neurons.

CONCLUSION

By using MR, we firstly identified four SVCRGs as protective factors for AD, functioning through pathways associated with mitochondrial dysfunction, chronic inflammation, immune dysregulation, and neuronal damage. These genes had the potential to modulate immune cell infiltration activated in AD patients and exhibited cell-type-specific expression profiles within AD-related cellular contexts. Their findings provide novel insights and valuable references for future research on AD pathogenesis and therapeutic strategies.

Sleep-Associated Traits and Hearing Difficulties in Noise: A Bidirectional Mendelian Randomization Study

OBJECTIVES

The aim of this study was to investigate the causal relationships between sleep-associated traits and hearing difficulties in noise (HDinN) by Mendelian randomization (MR) analysis.

DESIGN

Single nucleotide polymorphisms associated with chronotype, insomnia, sleep duration, daytime dozing or sleeping, and ease of getting up in the morning were extracted from European population genome-wide association study pooled data for bidirectional MR analysis. The MR-Egger regression, the inverse variance weighted technique, and the weighted median method were used for data analysis. The study was then expanded to include South Asian, East Asian, African, and Greater Middle Eastern populations.

RESULTS

MR analysis indicated that in European populations, ease of getting up in the morning is a protective factor for HDinN (odds ratio [OR] = 0.932, p = 4.22 × 10 -5 , pFDR = 5.62 × 10 -4 ), while shorter sleep duration was a risk factor (undersleepers: OR = 1.164, p = 0.002, pFDR = 0.014). In addition, there was an indicative causal association between daytime dozing and HDinN (OR = 1.089, p = 0.046, pFDR = 0.123). The conclusions were consistent in African populations (ease of getting up: OR = 0.696, p = 0.012, pFDR = 0.041, sleep duration: OR = 0.677, p = 0.032 pFDR = 0.091, daytime dozing: OR = 1.164, p = 0.002, pFDR = 0.014). In the reverse direction, there was a significant causal association between HDinN and both chronotype (OR = 1.413, p = 0.011, pFDR = 0.042) and ease of getting up in the morning (OR = 0.668, p = 1.75 × 10 -5 , pFDR = 3.49 × 10 -4 ) in European populations, with similar conclusions respectively reached in East Asian (OR = 1.085, p = 0.010, pFDR = 0.045) and African populations (OR = 0.936, p = 0.002, pFDR = 0.012). Furthermore, although not observed in European populations, exploratory studies in non-European populations suggested a potential association between insomnia and HDinN (East Asian: OR = 1.920, p = 0.011, pFDR = 0.043, African: OR = 2.080, p = 0.004, pFDR = 0.019, South Asian: OR = 1.981, p = 1.59 × 10 -4 , PFDR = 0.002, Greater Middle Eastern: OR = 2.394, p = 0.002, pFDR = 0.012), and vice versa (Greater Middle Eastern: OR = 1.056, p = 0.014, pFDR = 0.044).

CONCLUSIONS

This study identified a potential bidirectional causal relationship between sleep-associated traits and HDinN. However, the underlying mechanisms of the causal relationships reported here have yet to be elucidated.