BackgroundThis research assessed the causal influence of atrial fibrillation (AF) on depression.MethodsA two-sample Mendelian randomization (MR) approach was utilized along with data from additional databases. A genome-wide association study (GWAS) involving 463,010 participants allowed for the exploration of genetic variations associated with AF. Another GWAS with 215,644 individuals offered insights into the relationship between gene variants and depression. Data on the correlation between gene variants and depression were obtained from another GWAS encompassing 449,414 individuals. Effect sizes were assessed utilizing the inverse-variance weighted technique. Sensitivity analysis was conducted by weighted median, outlier, MR pleiotropy residual sum, weighted mode, and MR-Egger. A meta-analysis of the inverse-variance weighted (IVW) results from the two datasets was conducted.ResultsA significant association was found between genetically predicted AF and increased incidence of depression using 15 single nucleotide polymorphisms (SNPs) as markers. No evidence of gene pleiotropy was detected, as indicated by MR-Egger. Sensitivity analyses employing alternative Mendelian randomization techniques consistently yielded robust results. The combined odds ratio for depression was estimated at 29.19 (95% CI = 4.43-192.13, P < 0.001).ConclusionThis study found a causal relationship between genetically predicted AF and a heightened risk of depression.

Recent studies suggest that systemic sclerosis (SSc) may be associated with cognitive impairment and dementia. However, the causal relationship and its direction remain unclear. This study employed a two-sample bidirectional Mendelian randomization (MR) approach to systematically evaluate the genetic causal relationship between five types of dementia and SSc. Based on genome-wide association study (GWAS) summary data, we investigated the relationship between five types of dementia and SSc. The inverse variance weighted (IVW) method served as the primary analytical approach, supplemented by validation using the weighted median method, MR-Egger regression, simple mode, and weighted mode methods. Sensitivity analyses (leave-one-out, funnel plot, MR-PRESSO), pleiotropy tests (Egger intercept), and heterogeneity assessments (Cochran's Q) were conducted to ensure the robustness and reliability of the results. Additionally, reverse MR analysis was performed to further confirm the directionality of the causal relationship. Forward MR analysis revealed a significant negative association between Alzheimer's disease (AD) and the risk of SSc (IVW OR=0.530, 95% CI: 0.290-0.969, P =0.039). In contrast, no significant causal relationships were found between SSc and frontotemporal dementia, vascular dementia, dementia with Lewy bodies, or Parkinson's disease dementia. Reverse MR analysis did not identify any causal effects of SSc on the aforementioned types of dementia, further supporting the directionality of the causal relationship. Sensitivity analyses, pleiotropy tests, and heterogeneity assessments did not reveal significant horizontal pleiotropy or heterogeneity. Our study provides evidence of a potential causal relationship between AD and a reduced risk of SSc, highlighting the need for further research to explore the underlying mechanisms of this complex disease relationship.

Aplastic anemia (AA) is an immune-mediated bone marrow failure syndrome marked by pancytopenia and depletion of hematopoietic stem cells. Although autoreactive T cells and inflammatory cytokines are involved in its pathogenesis, their causal relationships remain unclear. We conducted a bidirectional two-sample Mendelian randomization using large-scale genome-wide association study datasets to explore the causal roles of 731 immune cell traits and 91 inflammatory cytokines in AA. The primary analysis used inverse-variance weighting, with additional sensitivity analyses including MR-Egger regression and MR-PRESSO. Mediation analysis was performed to determine whether cytokines mediate the effects of immune cells on disease risk. We identified 12 immune cell traits conferring protection against aplastic anemia (AA). The strongest protective association was observed for TD CD4 + AC (OR = 0.890, 95% CI: 0.817-0.968, P = 0.007). Conversely, 24 immune cell traits demonstrated positive associations with increased AA risk, with the strongest association found for CD127 - CD8br %T cell (OR = 1.135, 95% CI: 1.032-1.247, P = 0.009). Elevated levels of leukemia inhibitory factor (LIF) and its receptor were associated with reduced AA risk. Mediation analysis indicated partial mediation by LIF of the associations linking dendritic cells, HLA-DR + + monocytes, and AA risk. Sensitivity analyses supported the robustness of these findings. This study established causal relationships between specific immune cell phenotypes, inflammatory cytokines, and AA. Importantly, LIF partially mediated the effects of immune cells on AA, suggesting the LIF-LIFR axis as a potential therapeutic target.

Investigating the causal link between metformin and lung cancer risk: a two-sample mendelian randomization analysis.

Systemic inflammation has been identified as a key factor in neurodegeneration but the value of circulating inflammatory proteins in dementia risk prediction and their causal role has not been elucidated. We leveraged proteomic data from 43,685 UK Biobank participants to investigate associations between 728 Olink inflammatory proteins and incident dementia using Cox proportional-hazards (Cox-PH) models. We used Cox-PH with LASSO regularisation to calculate a sparse signature of inflammatory proteins (ProSig) predicting incident dementia. Linear regressions assessed the association between ProSig and individual proteins with brain image-derived phenotypes and Brain Age in participants with available neuroimaging data (n = 4,106). Formal mediation analyses investigated whether inflammatory proteins mediated associations between genetic and modifiable risk factors and dementia outcomes. Mendelian randomisation (MR) tested the causal relationship between inflammatory proteins and dementia outcomes. 218 inflammatory proteins were individually associated with incident dementia in Cox-PH models (pFDR < 0.05). A 20-protein signature significantly improved the prediction of incident dementia beyond known risk factors. TNFRSF11B, a protein linked to vascular damage, was associated with both incident dementia and reduced hippocampal volume. Two proteins, sFRP4 and MEPE, were linked to reduced Brain Age, with sFRP4 also being protective against dementia. Mediation analyses indicated that TNFRSF11B, APOE and C7 may partially mediate associations between modifiable risk factors and dementia. MR analyses suggested protective causal effects of TNFSF13 and IL17D. By triangulating evidence, this study shows that inflammatory proteins improve dementia risk prediction and play heterogeneous roles in dementia pathophysiology.

To investigate the association and shared pathogenic mechanisms between systemic lupus erythematosus (SLE) and thyroid cancer (TC) using an integrative multi-omics framework. A meta-analysis was performed to quantify the incidence of TC in SLE patients. Bidirectional two-sample Mendelian randomization (MR) was applied to infer causality. Transcriptomic datasets were analyzed to identify an SLE-associated gene signature (SLEscore) and assess its prognostic value in TC. Immunohistochemistry (IHC) was conducted to validate protein expression of SLEscore genes in thyroid tissues from TC patients with and without SLE. A meta-analysis demonstrated a significantly increased standardized incidence ratio (SIR) of TC in SLE patients (SIR = 1.66; 95% CI: 1.35-2.04). MR analysis revealed a unidirectional causal effect of SLE on TC in European populations (OR = 1.291; 95% CI: 1.014-1.642; P = 0.037), but no significant association in East Asian cohorts. The SLEscore, comprising four downregulated genes (IFITM1, RAP1GAP, MT1A, ALAS2), effectively stratified TC patients into high- and low-risk groups with distinct survival outcomes. These subgroups showed significant differences in pathway activation, immune cell infiltration, immune gene expression, tumor mutational burden, somatic mutation profiles, and predicted drug sensitivity (all P < 0.05). IHC confirmed lower protein levels of all four genes in SLE-TC tumor tissues, consistent with transcriptomic findings. This multi-omics analysis supports a causal link between SLE and TC in European populations and identifies the SLEscore as a potential prognostic biomarker, offering new opportunities for precision risk assessment and targeted management in SLE-associated TC.

To investigate the causal effects of body mass index (BMI) at different life stages (childhood and adulthood) on migraine and its subtypes. While previous studies suggest BMI is a risk factor for migraine, it is unclear whether BMI influences migraine differently throughout the life course. The causal effect of childhood BMI may have obscured the association between adult BMI and migraine, making it difficult to determine the independent role of adult adiposity. We conducted a life-course Mendelian randomization (MR) study. Two-sample and conventional multivariable MR analyses were performed initially. Subsequently, FLOW-MR-a newly-developed three-sample multivariable MR method-was applied to estimate the direct, indirect, and total effects of BMI at childhood or adulthood on migraine. All estimates were scaled per 1-SD increase in BMI (SDs: 1.35 kg/m² at age 1; 1.78 kg/m² at age 8; 4.76 kg/m² in adulthood). The causal effect of BMI on migraine is strictly dependent on the timing of exposure. First, we found no evidence that adult BMI influences the risk of either migraine subtype. Second, higher BMI at age 8 exerted a direct protective effect against migraine with aura (OR = 0.522). Third, BMI at age 1 exhibited a dual role: its direct risk-increasing effect (OR = 1.983) was offset by an indirect protective effect mediated through 8-year-old BMI (OR = 0.511), resulting in a null total effect on migraine with aura. Migraine without aura was unaffected by BMI at any life stage. The causal effect of BMI on migraine is dependent on developmental timing, challenging the conventional view of BMI as a uniform risk factor. We emphasize that the observed protective role of childhood BMI is a model-estimated association from genetic data, which does not constitute a recommendation to increase body weight in children.

To explore the causal relationship between several possible behavioral factors and high myopia (HM) using multivariable Mendelian randomization (MVMR) approach and to find the mediators among them with mediation analysis. The causal effects of several behavioral factors, including screen time, education time, time spent outdoors, and physical activity, on the risk of HM using univariable Mendelian randomization (MR) and MVMR analyses were first assessed. Genome-wide association study summary statistics of serum metabolites were also used in mediation analysis to determine the extent to which serum metabolites mediate the effects of behavioral factors on HM. MR analyses indicated that both increased time spent outdoors and a higher frequency of moderate physical activity significantly reduced the risk of HM. Further MVMR analysis confirmed that moderate physical activity independently contributed to a lower risk of HM. Additionally, MR analyses identified 13 serum metabolites significantly associated with HM, of which 12 were lipids and one was an amino acid derivative. Mediation analysis revealed that six lipid metabolites mediated the protective effects of moderate physical activity on HM, with the highest mediation proportion observed for 1-(1-enyl-palmitoyl)-GPC (p-16:0; 30.83%). This study suggests that in addition to outdoor time, moderate physical activity habits may have an independent protective effect against HM and pointed to lipid metabolites as priority targets for the prevention due to low physical activity. These results emphasize the importance of physical activity and metabolic health in HM and underscore the need for further study of these complex associations.

Background:Immune cells are involved in rheumatoid arthritis (RA), but the link between other blood cell indices and the disease activity of RA, along with the underlying mechanisms, is unclear.Objective:This study aimed to develop an interpretable machine learning model based on blood cell parameters to assess RA disease severity and assist in personalized treatment decisions.Methods:A retrospective case–control study was conducted with blood routine and biochemical detection data from 4401 patients at the First Affiliated Hospital of Guangxi Medical University, spanning from January 1, 2018, to January 1, 2024. The primary outcome was disease severity stratification. Recursive feature elimination was applied to identify key variables, and 10 machine learning algorithms were benchmarked on 55 clinical features with internal validation. Model interpretability was assessed with SHAP, while logistic regression and restricted cubic spline models were used to examine associations between blood cell indices and disease severity. In addition, Mendelian randomization analysis was performed to explore potential causal relationships.Design:This was a retrospective case–control study.Results:Blood cell indices were identified as the primary factors associated with RA severity. In model evaluation, the Random Forest achieved the best performance, with test set AUCs of 0.870 and 0.874. Mendelian randomization supported a causal relationship between blood cell indices and RA risk.Conclusion:These results reinforce the associations between blood cell indices and RA severity. The machine learning model demonstrates good predictive capabilities for RA severity and may assist clinicians in developing personalized treatment strategies.

Causal evidence linking chronic pain genetics to late-onset asthma via the nervous system.

Empirical investigations have identified associations between elevated branched-chain amino acids (BCAAs) levels and an increased incidence of hypertensive disorders of pregnancy (HDP). The aim of this study is to rigorously explore the causal linkage between BCAAs concentrations and the risk of developing HDP. A bidirectional Mendelian randomization (MR) analysis was conducted to ascertain the causal relationship between BCAAs levels and the risk of HDP. Instrumental genetic variables derived from the genome-wide association studies of serum BCAAs levels - encompassing total BCAAs, leucine, isoleucine and valine from the UK Biobank, and HDP data (16,417 cases and 213,893 controls) from the FinnGen consortium - were utilized. We conducted inverse-variance weighted, MR-Egger, simple mode, and weighted MR estimates. To assess the heterogeneity and potential presence of horizontal pleiotropy among the instrumental variables, Cochran Q statistic and the MR-Egger intercept were employed. Simultaneously, transcriptomic data were utilized in conjunction with machine learning to identify key genes through which BCAAs influence HDP. Single-cell techniques were employed to analyze the major cell populations. The forward MR analysis indicated a significant positive correlation between the levels of total BCAAs (OR: 1.300, 95% CI: 1.117-1.462; P < .0001), leucine (OR: 1.096, 95% CI: 1.123-1.505; P < .0001), and valine (OR: 1.299, 95% CI: 1.141-1.478; P < .0001) with the risk of HDP. Isoleucine levels (OR: 1.266, 95% CI: 1.054-1.521; P = .012) demonstrated a positive association with HDP risk that did not reach statistical significance after Bonferroni correction. The reverse analysis revealed no causal effect of HDP on the levels of total BCAAs (OR: 0.992, 95% CI: 0.967-1.018; P = .543), leucine (OR: 1.005, 95% CI: 0.973-1.039; P = .750), isoleucine (OR: 1.000, 95% CI: 0.974-1.026; P = .973), and valine (OR: 0.988, 95% CI: 0.962-1.014; P = .354). Transcriptomic analysis identified MCCC2 and BCAT2 as key genes through which BCAAs affect the occurrence of HDP, and these genes are predominantly expressed in endothelial cells. We provide robust evidence Our findings suggest that HDP was associated with an increased level of BCAAs, and the effects may be related to the expression levels of MCCC2 and BCAT2 in endothelial cells.

This study represents the first investigation employing 2-sample Mendelian randomization (MR), multi-marker analysis of genomic annotation (MAGMA), Metascape, and the Kaplan-Meier (K-M) plotter database to elucidate the causal relationship between immune cells (ICs) and human epidermal growth factor receptor 2 negative breast cancer (HER2-BC). The findings provide genetic evidence supporting the association between ICs and HER2-BC risk. The 2-sample data for the Mendelian randomization study were sourced from public databases. In this study, ICs were selected as the exposure factor, and HER2-BC was taken as the outcome factor. MR analysis was conducted on the causal relationship between ICs and HER2-BC by using various regression models. Gene-based analysis was carried out through MAGMA, and the gene functions and pathway enrichments of the genes identified through Metascape analysis were explored. Finally, based on the K-M plotter database, the survival status of some ICs was analyzed. Among the 731 ICs, a total of 33 ICs were found to have a protective effect on HER2-BC, while 17 ICs had an adverse effect. After false discovery rate-bonferroni (PFDR < .05) correction, we detected 2 risk immunophenotypes of HER2-BC: human leukocyte antigen (HLA) DR on plasmacytoid DC, activated and secreting Treg %CD4+. A total of 38 genes were identified by MAGMA analysis. Metascape analysis revealed that the identified pleiotropic genes participated in negative regulation of cell migration, VEGFA-VEGFR2 signaling pathways. The survival analysis based on K-M plotter found that when CD4, HLA-DRB1, HLA-DRA, and ESR1 are highly expressed, the upper quartile survival rate of OS, RFS, and distant metastasis free survival is longer. This study showed that the immune response affects the progress of HER2-BC in a complex mode. These findings greatly improve our understanding of the interaction between immune response and HER2-BC risk, and also help to design therapeutic strategies for HER2-BC from the perspective of immunology.

We aimed to identify plasma proteins associated with incident Crohn's disease (CD) and ulcerative colitis (UC), develop and validate predictive models for CD and UC risk, and uncover novel protein-based drug targets. The study included 46 523 participants from England in the UK. Biobank as the development set and 47 105 participants for internal replication. An external validation set comprised 5807 participants from Scotland and Wales. Plasma proteomic profiling was performed on 2911 proteins. In the development set, 49 and 34 proteins were significantly associated with incident CD and UC risk, respectively (Bonferroni P < .05). These findings were replicated in the internal replication set. Two-sample Mendelian randomization (MR) analysis identified three proteins (TIMP1, TNFRSF10A, and LTBR) with causal associations for CD and four proteins (CCL20, OSM, NOS2, and CD300E) for UC. Among these, TIMP1 and CD300E represent novel, undrugged targets, while the remaining five are currently druggable. The proteomic-based model, incorporating age, sex, and candidate proteins, demonstrated strong predictive performance in the external validation set, with a C-index of 0.94 (95% CI, 0.88-1.00) for CD and 0.82 (95% CI, 0.73-0.92) for UC. Integrating candidate proteins or the top 10 proteins into clinically based models significantly enhanced risk prediction for both CD and UC. This study identifies novel plasma protein associations with CD and UC, supported by genetic evidence, and highlights their potential as therapeutic targets. Plasma proteomics significantly improves risk prediction for incident CD and UC compared to traditional clinical models, offering new avenues for drug discovery and personalized risk assessment.

Type 2 diabetes (T2D) is an important risk factor for a range of GI malignancies. Nevertheless, the causal relationship between T2D and esophageal malignancies remains to be elucidated. Furthermore, the mechanisms leading to progression from T2D to esophageal malignancy have not been clearly characterized. The purpose of this study was to conduct a Mendelian randomization (MR) study to investigate the causal effect of T2D and its complications on the development of esophageal malignancies. In addition, this study aimed to perform a multivariate MR analysis to exclude potential confounders in this association. Genetic variation was used as an instrumental variable for T2D. Pooled data on T2D, as well as on T2D with complications and esophageal malignancies, were obtained from the European Bioinformatics Institute and Finnish databases. The study was conducted using 2-sample MR, multivariate MR. The study revealed a causal relationship between T2D (N = 4,90,089), T2D with comorbidities and esophageal malignancies (N = 1,74,238; T2D: odds ratio = 1.36, 95% confidence interval: 1.07–1.72, P = .01). Even after adjusting for confounders such as body mass index and hypertension, T2D remained statistically significant (odds ratio = 1.31, 95% confidence interval: 1.02–1.69, P = .036). The present MR study supports T2D as a causal risk factor for esophageal malignancy. Further research is warranted to investigate whether other lifestyle factors (such as diet and physical activity) have a causal role in esophageal malignancy.

ABSTRACT Background Prostate cancer (PCa) is a leading cause of male cancer-related death globally. While the gut microbiota is linked to PCa, its genetic association remains unclear. Methods We screened genetic instruments related to the gut microbiota and paired them with PCa genome-wide association study data to conduct Mendelian randomization (MR) analysis. Positive MR findings were then subjected to colocalization analysis. Subsequently, we utilized the Gene Expression Omnibus (GEO) dataset to perform differential expression analysis, aiming to identify differentially expressed associated genes (DEAGs). We determined the importance scores of these DEAGs through four machine learning models and constructed a nomogram based on these findings, and then validated it in another group of the GEO dataset. Results MR analysis found 16 gut bacteria causally linked to PCa (7 risk, 9 protective), with 144 related genes. PLCL1, VSNL1, ROR2, NRXN3, and TEAD1 were identified as feature genes for constructing a nomogram that provides a quantitative prediction of the risk of PCa onset. Conclusions This study indicates that there are causal links between the gut microbiota and PCa. Feature genes may affect the occurrence of PCa by inhibiting the epithelial–mesenchymal transition, proliferation, migration, and invasion of cells.

Purpose To investigate whether iron metabolism exerts a causal influence on chronic rhinosinusitis (CRS) and to identify iron-related biomarkers and regulatory genes with diagnostic and therapeutic potential. Methods A two-sample Mendelian randomization (MR) analysis was conducted using large-scale GWAS summary statistics for four iron-related traits and three nasal inflammatory diseases. Significant SNPs were mapped to proximal genes and analyzed via Gene Ontology (GO), KEGG pathway enrichment, and protein–protein interaction (PPI) network construction. Candidate gene expression was validated using the GSE69093 transcriptomic dataset and qRT-PCR in nasal mucosal tissues from CRS patients and healthy controls. Molecular docking simulations were performed to assess ligand interactions, and clinical association and machine learning models were applied to evaluate diagnostic relevance and predictive performance. Results MR analysis identified transferrin saturation (TSAT) as a causal protective factor for CRS (OR = 0.9988, P = 0.014). Thirty-one genes were mapped from MR-associated SNPs, with SLC17A4 highlighted as a key candidate gene. Enrichment analysis indicated involvement in iron metabolism and inflammatory regulation. SLC17A4 expression was significantly downregulated in both GSE69093 and clinical qRT-PCR samples. TSAT and SLC17A4 levels showed strong inverse correlations with Lund-Mackay and SNOT-22 scores. Molecular docking identified Troglitazone as a strong-binding ligand to SLC17A4 (−10.0 kcal/mol). Machine learning models integrating iron biomarkers and SLC17A4 expression achieved high discriminative performance (AUC = 0.828–0.849) and demonstrated good calibration and net clinical benefit according to calibration and decision curve analyses, supporting their potential clinical applicability. Conclusion TSAT confers protective effects in CRS, and SLC17A4 represents a promising biomarker and therapeutic target. The integrative strategy combining causal inference, transcriptomic validation, molecular docking, and machine learning modeling links iron homeostasis to CRS pathophysiology and demonstrates translational potential through clinically applicable predictive models.

Available studies have found associations between several cytokines and calcific aortic valvular stenosis (CAVS). To better comprehend the causal link between circulating cytokines and CAVS, we performed a bidirectional Mendelian randomization (MR) study. Genetic variants associated with 41 cytokines were obtained from public genome-wide association study (GWAS), and summary statistics for GWAS of CAVS were obtained from the FinnGen consortium. Forward MR analysis was conducted to determine the impacts of 41 cytokines on CAVS risk and reverse MR analysis was used to determine whether genetic susceptibility to CAVS altered the levels of these cytokines. Inverse-variance weighting (IVW) was implemented as the primary method, and several different sensitivity analyses were used to verify the reliability of the findings. Our results found no significant association between 41 cytokines and CAVS risk. However, increased levels of interleukin-18 (IL-18) (odds ratio [OR] = 1.080, 95% CI: 1.024-1.139) and interferon-gamma (IFN-γ) (OR = 1.157, 95% CI: 1.028-1.302) had suggestive connections with an elevated risk of CAVS, and increased levels of IL-13 (OR = 0.942, 95% CI: 0.890-0.997) and IL-5 (OR = 0.892, 95% CI: 0.804-0.990) had suggestive associations with a reduced risk of CAVS. A reverse MR analysis found that CAVS had a suggestive relationship with a reduced level of platelet-derived growth factor BB (PDGF-BB) (OR = 0.920, 95% CI: 0.853-0.993) and IL-4 (OR = 0.925, 95% CI: 0.856-1.000). Our findings suggest the causal effects of IL-18, IFN-γ, IL-13, and IL-5 on CAVS risk, and genetic predisposition of CAVS may reduce the levels of PDGF-BB and IL-4.

Background Rheumatoid arthritis (RA) is a chronic autoimmune arthritis that predominantly affects adults, whereas juvenile idiopathic arthritis (JIA) comprises a heterogeneous group of childhood−onset arthritides defined by age at onset, clinical phenotype, and immunological profile. Although RA and JIA are classified as distinct diseases, they share overlapping clinical and immunological features, and the nosological relationship between RA and some JIA subtypes remains debated. The global burden of RA and JIA in young people, and the similarities and differences in their underlying immunogenetic features, remain incompletely understood. Methods We utilized GBD 2021 data to describe trends in incidence, prevalence, and disability-adjusted life years for RA in individuals aged 0–19 years, as defined within the GBD framework, from 1990 to 2021. In parallel, we performed two−sample Mendelian randomization (MR) using genome−wide association study summary statistics from European−ancestry cohorts to assess associations of genetically proxied levels of 731 immune−cell traits, 91 circulating inflammatory proteins, and 1,400 serum metabolites with RA and JIA risk. Results From 1990 to 2021, global age−standardized incidence and prevalence of RA in individuals aged 0–19 years increased, whereas disability−adjusted life years declined slightly. The aggregated burden was higher in females and in high−sociodemographic index regions. MR identified overlapping genetically proxied immune−cell and inflammatory protein traits for RA and JIA, including CD28 on CD8 + CD45RA + T cells, CD25 on memory B cells, signaling lymphocytic activation molecule, and Fms−like tyrosine kinase 3 ligand, whereas higher genetically predicted levels of activated regulatory T cells and HLA−DR on dendritic cells were associated with lower risk in both diseases. Conclusion Our study highlights a rising global burden of GBD−defined RA among children and adolescents and delineates shared and distinct patterns of genetically predicted immune−cell, inflammatory protein, and metabolite traits associated with RA and JIA. These observations suggest that immunologically informed approaches could complement existing age- and phenotype-based classifications and help refine early recognition and risk stratification of inflammatory arthritis across the life course.

Plasma proteins have been reported as predictors and potential targets for reducing colorectal cancer (CRC) risk. However, their potential roles in CRC prognosis remain unexplored. We measured plasma levels of 367 neuro-related proteins in CRC patients from the West China Hospital (WCH) cohort (N=150, median follow-up=46.72 months) via proximity extension assay. The least absolute shrinkage and selection operator (LASSO) penalized Cox regression identified five overall survival (OS) - and eleven disease-free survival (DFS) - associated proteins, and the multi-protein signature for OS prediction was then validated in the UK Biobank (UKB) cohort (N=1,133). To overcome possible effects from confounders, we then employed Mendelian Randomization analysis leveraging protein quantitative trait loci (pQTLs) to investigate associations between genetically determined protein concentration and OS and cancer-specific survival (CSS) of CRC in the UKB. We found that multi-protein signature developed in the WCH cohort (c-index=0.784, 95% CI=0.713-0.855) showed significant discriminative ability in the external UKB cohort (c-index=0.616, 95%CI=0.559-0.673). A significant association between genetically determined PD-L1 and OS (p=0.043, HR=1.53, 95%CI=1.01-2.29) was observed, although we did not find strong evidence for colocalization. Additionally, single-cell and spatial transcriptome analyses illustrated PD-L1 expression localized predominantly to epithelial cells and immune cells (especially myeloid cells) in CRC tissue. The potential interactions of identified proteins were evaluated in the STRING database. Druggability evaluation also supported PD-L1 as a potential therapeutic target for CRC. Taken together, this study established multi-protein signatures for CRC prognosis and identified plasma PD-L1 as a possible biomarker and therapeutic target.

This study aimed to examine the relationship between genetically predicted metabolite levels and gastric cancer (GC) risk using Mendelian randomization (MR), and to identify the metabolic pathways potentially involved. We selected genetic instruments for metabolites from 64 genome-wide association studies covering 362,750 participants. A two-sample MR design was applied to evaluate the associations with GC using summary-level data from a combined analysis of the UK Biobank and FinnGen. The primary analysis relied on the inverse-variance weighted method, while the median-weighted and MR-Egger methods were used to account for potential violations of instrumental variable assumptions and provide the estimate even when a subset of instruments was invalid. The MR-Egger intercept test was performed to detect directional pleiotropy. Metabolites showing significant associations with GC were further examined using pathway enrichment analysis to identify relevant metabolic and lipid processes. MR analyses identified 25 and 17 metabolites that were positively and inversely associated with GC risk, respectively. Notably, hexanoylcarnitine and cis-4-decenoylcarnitine were strongly associated with increased risk, whereas pregnanediol disulfate, acetylcarnitine, prolyl-hydroxyproline, and X-18914 were associated with reduced risk, with no evidence of heterogeneity or directional pleiotropy. Enrichment analyses highlighted key metabolic pathways, including cysteine and methionine catabolism, beta-oxidation of pristanoyl-CoA (coenzyme A), oxidation of branched-chain fatty acids, and peroxisomal lipid metabolism. This study identified a set of genetically predicted metabolites associated with GC risk, highlighting the potential utility of metabolite panels and lipid-based biomarkers for risk stratification and early detection. However, further standardization and extensive validation are necessary prior to clinical application.