Mendelian Randomization Research Articles

The association between secondhand smoke exposure and accelerated biological aging: A population-based study and Mendelian randomization analysis.

Aging is an irreversible biological process significantly influenced by oxidative stress, which smoking exacerbates. While the impact of direct smoking on aging is well-documented, the association between secondhand smoke (SHS) exposure and biological aging remains less explored. This study examines the connection between SHS exposure in populations and biological aging, highlighting diabetes as a potential mediator due to its established links to both SHS exposure and accelerated aging through mechanisms such as oxidative stress and chronic inflammation. It further employs genetic tools to establish a causal relationship between SHS exposure and biological aging. This study combines secondary dataset analyses and Mendelian randomization analyses. Data from the NHANES 1999-2010 cycles were used, with serum cotinine levels indicating SHS exposure and phenotypic age, derived from age and clinical biomarkers reflecting inflammation, metabolism, and hematologic function, as the measure of biological aging. Multifactorial linear regression assessed associations, with restricted cubic splines used to explore nonlinear trends. Subgroup and mediation analyses were conducted to explore population-specific effects and the mediating role of diabetes. Two-sample Mendelian randomization (MR) using GWAS summary statistics on workplace SHS exposure (N=90168) and phenotypic age acceleration (N=6148) assessed causality. In the NHANES analysis, low SHS exposure was associated with a 0.37-year increase in biological aging (β=0.37; 95% CI: 0.04-0.70), while high exposure showed a 0.76-year increase (β=0.76; 95% CI: 0.23-1.29). A U-shaped association was found between log-transformed serum cotinine and biological aging (p<0.001), with a threshold at -1.53. Diabetes mediated 31.25% of this association. In the MR analysis, workplace SHS exposure was causally linked to a 3.05-year acceleration in aging (β=3.05; 95% CI: 0.24-5.85). SHS exposure accelerates biological aging, partly via diabetes. Genetic evidence supports a causal effect, emphasizing the need to minimize SHS exposure.

Maternal smoking around birth is associated with an increased risk of offspring constipation: Evidence from a Mendelian randomization study.

This study aimed to investigate the association between maternal smoking around birth and the incidence of offspring constipation. Genome-wide association study (GWAS) data for maternal smoking around birth and offspring constipation were obtained from the Mendelian randomization (MR) Base platform. Single nucleotide polymorphisms (SNPs) significantly associated with maternal smoking around birth were utilized as instrumental variables in two-sample MR analyses to explore the relationship between maternal smoking and offspring constipation. The analytical methods employed included the inverse-variance weighted (IVW) method, weighted median estimator, and MR-Egger regression. Twenty SNPs significantly associated with maternal smoking around birth (p<5×10-8; linkage disequilibrium r2<0.001) were identified. Across the different methods, a consistent positive association was observed between maternal smoking around birth and an increased risk of constipation in offspring (IVW: OR=4.35; 95% CI: 1.81-10.45; weighted median estimator: OR=4.23; 95% CI: 1.22-14.75; MR-Egger: OR=0.92; 95% CI: 0.01-122.07), suggesting that higher frequency of maternal smoking is associated with an elevated risk of constipation in offspring. However, we did not detect any potential effect of genetic liability to constipation risk on maternal smoking. This study provides evidence suggesting that increased maternal smoking around the time of birth may be linked to a higher risk of constipation in offspring.

Mediating Role of the ANGPTL3/TFPI Protein Ratio in Regulating T-Cell Surface Glycoprotein CD5 Levels on Knee Osteoarthritis (KOA): A Mendelian Randomization Study

This study utilized Mendelian randomization (MR) to investigate the impact of inflammatory proteins on knee osteoarthritis (KOA), measured using the ratio of protein levels (rQTLs). The primary objective was to identify potential intervention targets to mitigate KOA progression. Data from 2821 rQTLs, 91 inflammatory proteins, and KOA-related genetic variations were obtained through genome-wide association studies (GWAS). Bidirectional MR identified rQTLs with unidirectional causal relationships with KOA. Further analyses included false discovery rate (FDR) correction, colocalization, and mediation analysis. Two inflammatory proteins were found to be associated with KOA: T-cell surface glycoprotein CD5 [OR (95% CI) = 0.867 (0.760–0.990), PIVW = 0.035] and C-X-C motif chemokine 9 [OR (95% CI) = 1.150 (1.001–1.320), PIVW = 0.048]. Variations in their levels influenced rQTLs, producing differential effects on KOA. Specifically, rQTL-ANGPTL3/TFPI (human recombinant angiopoietin-like protein 3/Tissue factor pathway inhibitor) was identified as a mediator in the effect of T-cell surface glycoprotein CD5 levels on KOA. T-cell surface glycoprotein CD5 levels were negatively correlated with rQTL-ANGPTL3/TFPI (β1 = −0.084), while rQTL-ANGPTL3/TFPI was positively correlated with KOA (β2 = 0.159). These findings align with the total effect, where T-cell surface glycoprotein CD5 levels were negatively associated with KOA (β = −0.143). Thus, rQTL-ANGPTL3/TFPI may serve as a reliable mediator in the pathway through which T-cell surface glycoprotein CD5 levels affect KOA. This mediator may not only represent a potential therapeutic target but also serve as a biomarker for assessing KOA treatment efficacy, offering a novel direction for KOA diagnosis and management.

Identification of common hub genes and construction of immune regulatory networks in aplastic anemia, myelodysplastic syndromes, and acute myeloid leukemia

BackgroundAplastic anemia (AA), myelodysplastic syndromes (MDS), and acute myeloid leukemia (AML) exhibit complex pathogenic mechanisms and interrelated characteristics. We aimed to identify the common hub genes, establishing a foundation for preventing disease progression.MethodsWe selected relevant datasets from the Gene Expression Omnibus(GEO) database for differential gene expression, gene set enrichment, and weighted gene co-expression network analyses to identify hub genes, and then validated them. Subsequent analyses included immune infiltration analysis, single-cell sequencing, and cell communication analysis. We performed Mendelian randomization to screen inflammatory factors and immune cells. We used RT-qPCR, Enzyme - Linked Immunosorbent Assay(ELISA), and cell proliferation assays to validate the identified hub genes, their relationship with cellular communication mediators and inflammatory factors, and their impact on cellular function.ResultsPOLG and MAP2K7 were identified as common hub genes, with low expression observed across AA, MDS, and AML. There were distinct immune differentials among these diseases, with an enhanced correlation between immune cells and hub genes as the disease progressed. Macrophage Migration Inhibitory Factor(MIF) emerged as a key mediator of cellular communication. We identified 20 regulatory pathways of immune cells and inflammatory factors across different disease stages. In vitro validation confirmed low expression of the hub genes, which were inversely correlated with MIF and inflammatory factors, though they showed no significant impact on cell proliferation or migration.ConclusionsPOLG and MAP2K7 demonstrate crucial roles in the progression from AA to MDS and, ultimately, to AML. These genes regulate more than 20 immune regulatory pathways through MIF-mediated communication, thereby influencing disease progression.

Celiac Disease and Nephrotic Syndrome: A Two-Sample Mendelian Randomization Study

Celiac Disease and Nephrotic Syndrome: A Two-Sample Mendelian Randomization Study

Investigating the shared genetic architecture between obesity and depression: a large-scale genomewide cross-trait analysis

IntroductionIncreasing evidence suggests that individuals with obesity are at a higher risk of developing depression, and conversely, depression can contribute to the onset of obesity, creating a detrimental cycle. This study aims to investigate the potential shared biological pathways between obesity and depression by examining genetic correlations, identifying common polymorphisms, and conducting cross-trait genetic analyses.MethodsWe assessed the genetic correlation between obesity and depression using linkage disequilibrium score regression and high-density lipoprotein levels. We combined two different sources of obesity data using METAL and employed bidirectional Mendelian randomization to determine the causal relationship between obesity and depression. Additionally, we conducted multivariate trait analysis using the MTAG method to improve statistical robustness and identify novel genetic associations. Furthermore, we performed a thorough investigation of independent risk loci using GCTA-COJO, PLACO, MAGMA, POPS, and SMR, integrating different QTL information and methods to further identify risk genes and proteins.ResultsOur analysis revealed genetic correlations and bidirectional positive causal relationships between obesity and depression, highlighting shared risk SNP (rs10789340). We identified RPL31P12, NEGR1, and DCC as common risk genes for obesity and depression. Using the BLISS method, we identified SCG3 and FLRT2 as potential drug targets.LimitationMost of our data sources are from Europe, which may limit the generalization of our findings to other ethnic populations.ConclusionThis study demonstrates the genetic causal relationship and common risk SNPs, genes, proteins, and pathways between obesity and depression. These findings contribute to a deeper understanding of their pathogenesis and the identification of potential therapeutic targets.

Gut Microbiota as Targets for Preventing Ovalbumin-Induced Food Allergy

Background: Ovalbumin (OVA) is a major allergen in egg whites. Introduction: Given the crucial role of gut microbiota in OVA-induced allergy, it remains unclear whether gut microbiota could serve as a therapeutic target for OVA allergy prevention. Method: To investigate the relationship between gut microbiota and food allergy, a two-sample bidirectional Mendelian randomization approach was combined combined with gut microbiota diversity analysis in vivo. Statistical analysis was performed, with p &lt; 0.05 considered statistically significant and p &lt; 0.01 highly significant. Results and discussion: Notably, Lachnospiraceae represents a potential therapeutic target for food allergy intervention, but the discrepancy between the MR and experimental findings highlights the limitations of the current research. When targeting the genus Lachnospiraceae, we observed that narirutin administration increased the abundance of the family Lachnospiraceae and the genus Lachnospiraceae_NK4A136_group. Conclusions: Narirutin may exert protective effects by increasing Lachnospiraceae abundance, but its precise mechanism—particularly whether it depends on SCFAs—requires further investigation.

Elucidating Genetic and Immunological Pathways Mediated by Sodium-Glucose Transporter 2 Inhibitors in Reducing Gout Risk: A Two-Step Mendelian Randomization Study.

While sodium-glucose transporter 2 inhibitors (SGLT2i) demonstrate urate-lowering effects, their causal role in Gout prevention remains controversial. This study employs advanced Mendelian randomization (MR) techniques to dissect immune-mediated mechanisms underlying this relationship. Using bidirectional two-sample MR and mediation analysis, we analyzed genetic instrument variables for SGLT2i (10 single-nucleotide polymorphisms, F-statistic >20), Gout risk (6,810 cases/477,788 controls), and 731 immune cell phenotypes. Pleiotropy and heterogeneity were also assessed to ensure robustness. The study confirmed a significant indirect effect of SGLT2i, which exhibited a 2.6% reduced Gout risk (Odds Ratio [OR]: 0.9738, 95% confidence interval [CI] = 0.9623, 0.9854, P = 1.12e-05). Thirty-five immune cell phenotypes were identified as significantly affecting Gout development, with key phenotypes such as CD86 on myeloid Dendritic cell (DC) (OR: 0.9966; 95% CI = 0.9930, 0.9995), contributing to 12.8% of the overall mediation effect. No evidence of heterogeneity or pleiotropy was detected and reverse-direction MR corroborated these findings. Our study first established SGLT2i as Gout-protective agents through DC-mediated immunomodulation, offering mechanistic insights for targeted prevention strategies in clinical practice.

Associations between psoriasis and risk of 33 cancers: a Mendelian randomization study

BackgroundSeveral observational studies have reported epidemiologic associations between psoriasis and risk of some cancers, but systematic evidence is lacking. Our aim was to comprehensively estimate the association between psoriasis and the risk of 33 common cancers using systematical Mendelian randomization based on genetic data.MethodForty-nine independent single-nucleotide polymorphisms (SNPs) significantly associated with psoriasis were extracted as instrumental variables from a large-scale meta-analysis study of genome-wide association study (GWAS) for psoriasis. Outcome GWAS data were obtained from the FinnGen consortium (n = 500,348), UK Biobank (n = 420,531), and other large-scale cancer datasets. The inverse-variance weighted (IVW) was used as the primary method to infer the association between psoriasis and risk of cancer, and finally the results from multiple databases were pooled by meta-analysis.ResultsIn the UK Biobank, genetically predicted psoriasis had a suggestive association with colon (OR = 1.055, 95%CI: 1.001–1.113, P = 0.046) and uterine corpus cancer (OR = 0.922, 95%CI: 0.852–0.997, P = 0.042). In the FinnGen consortium, psoriasis had a suggestive association with vulvar cancer (OR = 1.182, 95%CI: 1.023–1.366, P = 0.024), uterine corpus cancer (OR = 0.937, 95%CI: 0.883–0.993, P = 0.028), and prostate cancer (OR = 0.973, 95%CI: 0.948–0.999, P = 0.045). In an additional large-scale cancer dataset, psoriasis also showed a suggestive association with prostate cancer (OR = 0.968, 95%CI:0.942–0.995, P = 0.020). The meta-analysis confirmed the suggestive association of psoriasis with uterine corpus (OR = 0.931, 95% CI: 0.889–0.976, P = 0.003) and prostate cancer (OR = 0.976, 95% CI: 0.955–0.997, P = 0.023). Whereas the effect of psoriasis on colon and vulvar cancer was not in the same direction across different populations. Furthermore, no association between genetically predicted psoriasis and other cancers were observed.ConclusionsThis comprehensive MR study suggests that psoriasis may be a potential protective factor for uterine corpus cancer in women and prostate cancer in men.

Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction

Blood cell phenotypes are routinely tested in healthcare to inform clinical decisions. Genetic variants influencing mean blood cell phenotypes have been used to understand disease aetiology and improve prediction; however, additional information may be captured by genetic effects on observed variance. Here, we mapped variance quantitative trait loci (vQTL), i.e. genetic loci associated with trait variance, for 29 blood cell phenotypes from the UK Biobank (N ~ 408,111). We discovered 176 independent blood cell vQTLs, of which 147 were not found by additive QTL mapping. vQTLs displayed on average 1.8-fold stronger negative selection than additive QTL, highlighting that selection acts to reduce extreme blood cell phenotypes. Variance polygenic scores (vPGSs) were constructed to stratify individuals in the INTERVAL cohort (N ~ 40,466), where the genetically most variable individuals had increased conventional PGS accuracy (by ~19%) relative to the genetically least variable individuals. Genetic prediction of blood cell traits improved by ~10% on average combining PGS with vPGS. Using Mendelian randomisation and vPGS association analyses, we found that alcohol consumption significantly increased blood cell trait variances highlighting the utility of blood cell vQTLs and vPGSs to provide novel insight into phenotype aetiology as well as improve prediction.

Exploring the role of CD13 and inflammatory factors in radiation enteritis: insights from high-throughput proteomics and Mendelian randomization analysis

BackgroundRadiation enteritis (RE) is an unavoidable complication during radiotherapy for pelvic malignancies, characterized by chronic inflammation, fibrosis, and vascular injury in the intestinal tissue. Currently, there is a lack of research that delves into the relationship between inflammatory factors and key proteins in RE. MethodsThis study employed high-throughput proteomics to analyze intestinal tissues from RE rats and healthy controls, identifying differentially expressed key proteins. The degree of intestinal damage was validated through HE staining. Furthermore, five Mendelian randomization methods were used to analyze the causal relationship between 70 serum circulating inflammatory factors and CD13 levels. Sensitivity analyses, including heterogeneity tests, leave-one-out tests, and horizontal pleiotropy tests, were performed to ensure the robustness and reliability of the results.ResultsCD13 was identified as a key differentially expressed protein, with its expression significantly upregulated in RE rats and positively correlated with disease severity. Bidirectional Mendelian randomization analysis revealed causal relationships between CD13 and four inflammatory factors: increased levels of CCL28 and EN-RAGE may promote the rise in CD13, while increased levels of TAM-binding protein may be associated with decreased CD13 levels. Additionally, higher CD13 levels were found to be associated with increased levels of interleukin-12. Sensitivity analyses indicated good consistency and reliability in terms of heterogeneity and pleiotropy for these exposure variables.ConclusionThis study reveals the potential mechanistic role of CD13 in RE. Moreover, the identified CD13-associated inflammatory factors offer potential targets for the development of new prevention and treatment strategies, with significant clinical implications.

Genetic liability to gut microbiota and inflammatory cytokines in relation to systemic lupus erythematosus risk: a multi-omics study.

Systemic lupus erythematosus (SLE) has been associated with gut microbiota in some studies. There is no clear evidence that cytokines act as mediators. We first assessed the differences in gut microbiota between SLE patients and healthy controls using 16S rDNA sequencing. Subsequently, we used the summary statistics of gut microbiota, cytokines, and SLE from large genome-wide association studies. To explore the causal relationships between gut microbiota and SLE and identify potential mediating cytokines, we performed bidirectional Mendelian randomization analyses. Finally, the levels of potentially mediating cytokines were determined by ELISA. Fecal 16S rDNA sequencing showed that there was gut microbiota disorder in SLE patients. Based on two-sample analysis, seven gut microbiota taxa were causally associated with SLE. SLE influenced the relative abundance of two gut microbiota taxa in our large-scale MR study. Mediation analyses revealed that the causal relationship between genus Lachnospiraceae UCG001 and SLE was exclusively mediated by fibroblast growth factor 19 (FGF19) levels and the causal relationship between order Lactobacillales and SLE was exclusively mediated by tumor necrosis factor receptor superfamily member 9 (TNFRSF9) levels. Elevated levels of FGF19 affected the association between the reduced relative abundance of the genus Coprobacter and SLE, mediating by a proportion of 10.64% (P = 0.030). Furthermore, ELISA showed that circulating TNFRSF9 and FGF19 levels were higher in SLE patients than healthy controls. Our study demonstrated that there is a causal link between some gut microbiota taxa and SLE. In addition, we revealed possible mediating effects in this relationship. Key Points • We first demonstrate a causal association between gut microbiota, cytokines, and SLE comprehensively. • Our experiments also confirmed that TNFRSF9 and FGF19 may play a role in SLE. These results provide new ideas for microbiome-based investigation of new mechanisms and therapies for SLE.

Relationship Between Problematic Alcohol Use and Various Psychiatric Disorders: A Genetically Informed Study.

Problematic alcohol use (PAU) adversely affects the clinical course of psychiatric disorders. Genetic studies have suggested that genetic factors underlie the co-occurrence of PAU with psychiatric disorders. This study aimed to elucidate shared genetic architectures, prioritizing genes that disorders may have in common. Using genome-wide association data of PAU including 435,563 samples from people of European ancestry, this study investigated the genetic relationship between PAU and 11 psychiatric disorders using a bivariate causal mixture model (MiXeR). Local genetic correlation and colocalization analyses were conducted to identify the genomic regions significantly associated with PAU and each psychiatric disorder. Postanalysis included the false discovery rate (FDR) and transcriptome-wide association studies (TWASs), as well as summary-data-based Mendelian randomization to prioritize shared genes by integrating brain transcriptome data. MiXeR analysis revealed a substantial polygenic overlap (39%-73%) between PAU and psychiatric disorders. Four bivariate genomic regions with high correlations suggest shared causal variants of PAU with major depression and schizophrenia. Within these regions, four and six genes for the PAU-major depression and PAU-schizophrenia pairs, respectively, were mapped by conjunctional FDR analysis. Furthermore, TTC12 and ANKK1 were identified as potential causal genes for PAU and these disorders. The findings were replicated in multi-ancestry analyses of colocalization and TWASs. Despite the varying degrees of genetic overlap and directions of shared genetic effect correlations, these results imply the presence of shared genetic factors influencing the comorbidity of PAU and psychiatric disorders. Additionally, TTC12 and ANKK1, located near DRD2, may be causally associated with comorbid conditions.

Immune cells mediate the effect of plasma lipidomes on IgA nephropathy: a Mendelian randomization study

Background IgA nephropathy (IgAN) is a leading cause of chronic kidney disease, often associated with dyslipidemia and immune dysfunction. This study employs Mendelian randomization (MR) to investigate the causal relationship between plasma lipidomes and IgAN, with a focus on the potential mediating role of immune cells. Methods We analyzed the 179 genetically predicted plasma lipidomes and the IgAN gene using two-sample Mendelian randomization (TSMR) and multivariable MR based on summary-level data from a genome-wide association study, and the results were validated by liquid chromatography-mass spectrometry. Furthermore, we quantified the proportional effect of immune cell-mediated lipidomes on IgAN using TSMR. Results This study identified significant causal relationships of 3 lipidomes on IgAN risk by examining 179 lipidome traits as exposures. To investigate whether the impact of the 3 lipid groups on IgAN is specific, we performed TSMR analyses using 3 lipidomes as exposure factors and 4 nephritides as outcomes. Specifically, only phosphatidylinositol (18:1_20:4) was found to have a significant negative relationship with IgAN incidence (IVW method, p = 0.01, OR = 0.71, 95% CI = 0.55 - 0.92). Our further analysis focused on 8 immune cells associated with IgAN. We identified 2 immune cell phenotypes that may contribute to phosphatidylinositol (18:1_20:4)-mediated IgAN by careful screening. Conclusions Our findings provide robust genetic evidence supporting a causal link between plasma lipidomes and IgAN, with immune cells acting as potential mediators. Phosphatidylinositol (18:1_20:4) emerges as a promising biomarker for IgAN risk stratification, early detection, and therapeutic intervention. Modulating its plasma levels may offer novel avenues for IgAN management.

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 geneticcontribution 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.

Genetic association of social participation with cognitive function: A bidirectional Mendelian randomization study.

BackgroundCognitive decline poses a significant challenge in aging societies. While some studies suggest that active social participation mitigates cognitive decline, others present conflicting findings.ObjectiveThis bidirectional two-sample Mendelian randomization (MR) study aimed to elucidate the causal relationship between social participation and cognitive function.MethodsThe cognitive performance dataset (n = 257,841) was used as the discovery sample, while cognitive function (n = 22,593) and Alzheimer's disease (AD) datasets (n = 394,705) served as replication samples and proxies for severe cognitive decline. Inverse variance weighting was the primary analytical method, supplemented by weighted median, MR-Egger, MR.RAPS, MR-PRESSO, and maximum likelihood methods for sensitivity analyses.ResultsSocial participation in sports club or gym (β = 0.09, 95% CI: 0.05 to 0.14, p < 0.001), religious group (β = 0.11, 95% CI: 0.08 to 0.14, p < 0.001) and other group activity (β = 0.06, 95% CI: 0.03 to 0.09, p < 0.001) reduced the risk of cognitive decline, while pub or social club (β = -0.06, 95% CI: -0.1 to -0.02, p = 0.005) and social inactivity (β = -0.05, 95% CI: -0.09 to -0.01, p = 0.017) accelerated cognitive decline. Improved cognitive performance promoted participation in beneficial activities and reduced pub or social club participation. Additionally, AD motivated visits to pub or social club (OR = 1.01, 95% CI: 1.00 to 1.03, p = 0.011).ConclusionsSpecific types of social participation may protect against cognitive decline, offering evidence for targeted interventions to prompt cognitive health in aging populations.

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.

Mendelian randomization in nutrition: the challenge of population diversity

Mendelian randomization in nutrition: the challenge of population diversity

The genetic overlap and causal relationship between attention deficit hyperactivity disorder and obstructive sleep apnea: a large-scale genomewide cross-trait analysis

BackgroundAttention deficit hyperactivity disorder (ADHD) and Obstructive sleep apnea (OSA) are highly clinically co-occurring, but the mechanisms behind this remain unclear, so this article analyzes the reasons for the co-morbidities from a genetic perspective.MethodsWe examined the genetic architecture of ADHD and OSA based on the large genome-wide association studies (GWAS). The global genetic relationship between OSA and ADHD was explored. Cross-trait analysis from single nucleotide polymorphism (SNP) and gene level was performed subsequently to detect the crucial genomic regions. Finally, we revealed the anatomical change on which genetic overlap relies and further explored whether genetic factors exert a causal effect.ResultsAfter using both linkage disequilibrium score regression (LDSC) and High-definition likelihood inference (HDL) methods, we identified a significant genetic correlation between OSA and ADHD (PLDSC = 2.45E-28, PHDL = 1.09E-25), demonstrating a consistent direction. Furthermore, through the application of various cross-trait methods, we pinpointed 5 loci and 57 genes involved in regulating the co-occurrence of these disorders. These genetic regions were thought to be associated with the prefrontal lobes (P = 3.07E-06) and the nucleus accumbens basal ganglia (P = 2.85E-06). Lastly, utilizing Mendelian randomization (MR), we established a link indicating that individuals with ADHD were at an elevated risk of developing OSA (PIVM = 0.02, OR (95%CI):1.09 (1.01–1.17)).ConclusionsThis study reveals a strong genetic correlation between ADHD and OSA. It offers insights for future drug target development and sleep management in ADHD.

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

BackgroundMigraine 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.MethodsWe 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.ResultsMR 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.ConclusionsIntegrating 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.