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Results for “mendelian randomization study”

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PubMed2026

Genetically predicted lower FLT3L levels increase the risk of hypertrophic cardiomyopathy partly mediated by phosphate: Evidence from a 2-step Mendelian randomization analysis.

We performed a 2-step Mendelian randomization (MR) study to investigate the associations of Fms-related tyrosine kinase 3 ligand (FLT3L) and phosphate levels with the risk of hypertrophic cardiomyopathy (HCM). Genetic instruments for 75 circulating inflammatory factors were obtained from the NHGRI-EBI GWAS Catalog, while summary statistics for circulating phosphate and HCM were derived from the UK Biobank and FinnGen, respectively. Univariable MR analysis using the inverse-variance weighted method indicated that genetically predicted higher phosphate levels were associated with an increased risk of HCM (OR = 1.36, P = 4.82 × 10-2). Among the inflammatory markers, FLT3L emerged as a significant candidate and showed inverse associations with phosphate levels (β = -0.05, P = 1.70 × 10-9) and HCM (OR = 0.79, P = 4.10 × 10-2). Bidirectional MR analyses did not support a causal effect of phosphate on FLT3L. Mediation analysis suggested that phosphate levels accounted for an estimated 12.05% of the total effect of FLT3L on HCM. Genetic liability to lower FLT3L levels is associated with a higher risk of HCM, and this relationship may be partially mediated through circulating phosphate levels.

Humans↗

PubMed2026

The tissue-specific effects of glucose-lowering drug targets on aging mediated through DNA methylation: a multi-omics genetic study.

BACKGROUND: DNA methylation plays a key role in mediating the anti-aging effects of glucose-lowering drugs. This study aims to systematically explore the potential anti-aging effects of target genes of FDA-approved glucose-lowering drugs and the underlying epigenetic mediators. METHODS: We conducted a two-sample Mendelian randomization (MR) study to investigate the putative causal relationships between the gene expression levels of glucose-lowering drug targets and 10 aging-related phenotypes, followed by a two-step MR to estimate the mediation effect of DNA methylation. Drug candidates were selected according to the latest review of clinical drug use for type 2 diabetes, and their target genes were obtained from the DGIdb. Tissue-specific cis-expression quantitative trait loci (eQTLs) from GTEx Consortium were selected as genetic instruments to proxy the expression level of drug-target genes. Glycemic phenotypes were used as positive controls to validate the instruments. The cis- and trans-methylation QTLs of Cytosine-phosphate-Guanine sites near the drug target genes were obtained from GoDMC Consortium. Additionally, we performed enrichment analyses focused on tissue specificity and aging pathways to further corroborate our findings. RESULTS: We obtained 194 target genes interacting with 36 FDA-approved anti-diabetic drugs, of which the tissue-specific eQTLs were used to proxy the drug target effects. MR showed strong evidence that nine interacting genes of six glucose-lowering drugs showed anti-aging potential on one or more aging-related phenotypes mediated by DNA methylation: EHMT2, HSPA4, IGF2BP2, IRS1, LPL, NDUFAF1, NDUFS3, SLC22A3, and TCF7L2. These genes were distributed in 17 tissues, especially in the central nervous system, suggesting a potential neural component in their anti-aging effects. For instance, expression of EHMT2 in several brain basal ganglia regions, where the gene interacted with Tolazamide, showed a protective effect on frailty (odds ratio (OR) in caudate = 1.02, 95%CI = 1.01-1.04, FDR adjusted P = 1.69 × 10-2; OR in putamen = 1.02, 95% CI = 1.01-1.03, PFDR = 3.37 × 10-2, OR in nucleus accumbens = 1.02, 95% CI = 1.01-1.04, PFDR = 3.37 × 10-2). These associations were externally validated by searching literature evidence in existing EWAS and TWAS studies, as well as evidence from enrichment analyses. CONCLUSIONS: This study prioritizes nine glucose-lowering genes as anti-aging drug targets in specific tissues and prioritizes their epigenetic regulation through DNA methylation for future drug development.

DNA Methylation↗

PubMed2006

Stillbirth and slow metabolizers of caffeine: comparison by genotypes.

BACKGROUND: Cytochrome P4501A2 (CYP1A2) and N-acetyltransferase 2 (NAT2) are key enzymes in the metabolism of caffeine. The polymorphism of these genes facilitates the detection of fast and slow metabolizers, and if caffeine is causally related to stillbirth, we expect slow metabolizers to have a higher risk of stillbirth at any given intake of caffeine. Gluthatione S-transferase alpha1 (GSTA1) may also be active in the metabolism of caffeine as it conjugates glutathione to aromatic amines. Our study, therefore, included analyses of the association between GSTA1 and stillbirth. METHODS: A nested case non-case study among women who participated in the Danish National Birth Cohort: 142 cases of singleton stillbirths and 157 controls of singleton live births. RESULTS: Slow oxidizer status (CYP1A2), slow acetylator status (NAT2), and low activity of GSTA1 were not individually associated with the risk of stillbirth [odds ratio (OR) = 1.06, 95% confidence interval (95% CI) 0.67-1.67, OR = 0.95, 95% CI 0.60-1.51, and OR = 1.42, 95% CI 0.88-2.28, respectively]. We did, however, observe that subjects with a combination of slow CYP1A2, slow NAT2, and low GSTA1 genes had almost a 2-fold risk of stillbirth compared with subjects with other combinations of genotypes. CONCLUSIONS: We found no link between any single genotype and the risk of stillbirth. An association between a combination of genotypes and stillbirth was discovered. Caffeine may be causally related to stillbirth, but larger studies using Mendelian randomization are needed to verify this.

Acetyltransferases↗

PubMed2026

Identification of putative causal associations between MicroRNAs and breast cancer via Mendelian randomization and bioinformatic analysis.

MicroRNAs (miRNAs) are implicated in breast cancer progression and prognosis. This study employed a Mendelian randomization (MR) framework to investigate causal relationships between plasma circulating miRNAs and breast cancer. miRNA expression quantitative trait loci were extracted from 2 independent cohorts. High-confidence miRNAs and their associated single-nucleotide polymorphisms were selected for 2-sample MR analyses using inverse-variance weighted and MR-Egger methods. Differential expression analysis and univariate Cox regression identified survival-associated genes in breast cancer, while enrichment analyses revealed pathways and biological processes linked to candidate targets. Pan-cancer analyses of miRNAs and targets were conducted via the ENCORI platform. Initial MR analyses in the discovery phase identified hsa-miR-100-5p, hsa-miR-125b-5p, and hsa-miR-339-5p as significantly associated with reduced breast cancer risk (P < .05), suggesting potential protective roles. A total of 1291 survival-associated differentially expressed genes were identified, with 39 overlapping targets implicated in miRNA-mediated breast cancer intervention. Enrichment analyses highlighted their involvement in cell cycle regulation and p53 signaling pathway. In the validation cohort, only hsa-miR-339-5p confirmed a protective effect on breast cancer risk, while hsa-miR-100-5p and hsa-miR-125b-5p did not reach significance. Pan-cancer profiling demonstrated aberrant miRNA expression across malignancies, prognostic relevance in multiple cancers, and significant negative correlations between miRNAs and target genes in breast tumors. Our findings provide novel insights into the causal roles of miRNAs in breast cancer pathogenesis and underscore their potential as noninvasive biomarkers and therapeutic targets. Future studies should prioritize functional validation and clinical translation of these miRNAs.

Humans↗

PubMed2025

EP300-mediated lactylation leads to ulcerative colitis via CD86-positive plasmacytoid dendritic cells: A Mendelian randomization and mediation analysis.

This study explores the potential mechanism between lactylation and ulcerative colitis (UC) using two-sample Mendelian randomization and multi-omics analysis. This study employed expression quantitative trait loci and protein quantitative trait loci as exposures, with UC from the Finnish database as the outcome, to conduct Mendelian randomization analysis on lactylation-related target genes, aiming to investigate the causal relationships between these exposures and the outcome. Sensitivity and pleiotropy tests, combined with colocalization analysis, are performed to identify the best target genes and ensure the robustness of the results. Finally, immune cells are included for mediation analysis between lactylation and UC to explore potential mechanisms of action. Through Mendelian randomization analysis combined with sensitivity and pleiotropy tests, 2 lactylation target genes were found to have a significant causal relationship with UC. Subsequent colocalization analysis confirmed EP300 as a potential gene target. After including immune cells in the mediation analysis, it was discovered that there is a potential mechanism involving EP300, CD86+ plasmacytoid dendritic cells (pDCs), and UC. There is a significant causal relationship between lactylation and UC. Furthermore, the lactylation-modified gene EP300 may lead to UC occurrence by regulating CD86+ pDCs.

Humans↗

PubMed2026

Integrative genetic and transcriptomic analyses prioritize CDC16 as a candidate marker for gastric cancer.

BackgroundGastric cancer (GC) remains a major cause of cancer-related mortality, and biomarkers for early detection are needed.MethodsStomach and blood expression quantitative trait loci were integrated with two GC genome-wide association studies using Mendelian randomization (MR), Bayesian colocalization, and summary-data-based MR/heterogeneity in dependent instruments (SMR/HEIDI) testing. Bulk and single-cell transcriptomic analyses characterized candidate expression and lesion-associated patterns. CDC16 protein expression was evaluated by immunohistochemistry in 53 paired GC and non-neoplastic tissues, followed by paired and exploratory receiver operating characteristic analyses.ResultsMR prioritized PILRB, CDC16, and GABPB1-AS1; SMR/HEIDI provided complementary support, while colocalization for CDC16 and GABPB1-AS1 was suggestive and model-dependent. Bulk-tissue CDC16 abundance was higher in GC, but the modest TCGA-STAD tumor-normal difference (log2FC = 0.210, FDR = 0.019) was attenuated after proliferation adjustment (log2FC = -0.002, FDR = 0.987), indicating close coupling with proliferative activity. Single-cell analysis localized CDC16 predominantly to epithelial populations, and the proportion of CDC16-detectable epithelial cells increased across lesion categories (ρ = 0.735; permutation P = 0.031). CDC16 H-scores were higher in GC than in paired non-neoplastic tissues (161.15 ± 45.11 vs 102.15 ± 54.50; P < 0.001), with higher cancer-tissue scores in 41 of 53 cases. Exploratory AUC was 0.794 (95% CI, 0.704-0.874; sensitivity, 66.0%; specificity, 79.2%).ConclusionsConvergent genetic, transcriptomic, and protein-level evidence prioritizes CDC16 as a GC-associated candidate tissue marker whose expression is closely linked to proliferative activity. Prospective validation in independent cohorts, including appropriate disease controls and blood-based evaluation, is warranted.

Stomach Neoplasms↗

PubMed2026

Leveraging the genetics of psychiatric disorders to prioritize potential drug targets and compounds.

Genetics can inform biologically relevant drug development and repurposing, which may improve patient care. Here, we leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds. We used the genome-wide association studies of four psychiatric disorders [attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] and genes encoding drug targets. We conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We conducted multiple molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder. We reveal candidate drug targets associated with a single or multiple disorders that implicate glutamate signaling. Drug prioritization indicated genetic support for psychotropic medications, including several top-ranked antipsychotics for schizophrenia. We also observed genetic support for commonly used psychotropics for psychiatric treatment (e.g., clozapine, duloxetine, and lithium). Revealed opportunities for drug repurposing included cholinergic drugs for ADHD, estrogen modulators for depression, and matrix metalloproteinases for ADHD and depression. Our findings indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission. Our extensive analyses highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders, providing novel opportunities for improving patient outcomes. Depicted is the series of analyses conducted to generate a list of prioritized drug targets and compounds. First pairings of genome-wide association study (GWAS) traits with drugs are generated using enrichment analyses. Next, a series of molecular trait analyses is conducted to generate and rank a list of potential drug targets for each GWAS trait. Finally, enrichment and molecular trait results are combined to generate a ranked list of prioritized drugs for each GWAS trait based on supporting genetic evidence. ADHD = Attention deficit hyperactivity disorder, BIP = Bipolar disorder, DEP = Depression, SCZ = Schizophrenia, DBP = Diastolic blood pressure, T2D = Type 2 diabetes, RNA = ribonucleic acid, XWAS = both transcriptome and proteome-wide association studies, MR = Mendelian randomization, coloc = colocalization.

Humans↗

PubMed2025

A Multi-omics Exploration Revealing SLIT2 as a Prime Therapeutic Target for Peripheral Facial Paralysis: Integrating Single-Cell Transcriptomics and Plasma Proteome Data.

Peripheral facial paralysis (PFP) is a common neurological disorder characterized by facial-nerve dysfunction. Identifying therapeutic targets and understanding the molecular and cellular mechanisms underlying PFP are crucial for developing effective treatment strategies. This study combined Mendelian randomization (MR) analysis and single-cell RNA sequencing (scRNA-seq) to explore potential therapeutic candidates and their roles in PFP pathophysiology. The MR analysis included 1925 publicly available plasma protein cis-heritability instruments. Instrumental variables were selected for MR analysis to identify plasma proteins associated with PFP, followed by colocalization analysis to evaluate shared genetic variants between the identified proteins and PFP. After the initial identification of plasma proteins associated with Bell's palsy using MR analysis, a rat model of facial-nerve injury was established to further dissect underlying mechanisms at cellular and molecular levels. Using scRNA-seq technology, we delved deeply into cellular Heterogeneity and dynamic changes in gene expression in the facial-nerve nucleus tissues under both injured and control conditions, thereby achieving a systematic study ranging from macroscopic genetic associations to microscopic cellular functions. Finally, expression patterns were preliminarily validated by performing in vitro immunofluorescence analysis on the facial-nerve nucleus samples of SD rats. The MR analysis results identified 30 plasma proteins significantly associated with PFP, with nine target genes showing differential expression in the scRNA-seq data. Colocalization analysis demonstrated that slit guidance Ligand 2 (SLIT2), semaphorin 4D (SEMA4D), EGF containing fibulin extracellular matrix protein 1 (EFEMP1), and sprouty related EVH1 domain containing 2 (SPRED2) shared causal variants with PFP. SLIT2 was highly expressed in the microglia and inhibitory neurons in the experimental group, whereas SEMA4D showed elevated expression across multiple glial cell types in the same group. In contrast, EFEMP1 and SPRED2 showed distinct expression patterns in fibroblasts and oligodendrocytes. The role of SLIT2 has been previously well-documented in many central nervous system diseases. However, for the first time, this study detected SLIT2 alteration after facial-nerve injury. Altered intercellular signaling, particularly enhanced SLIT2-ROBO signaling between neurons and glial cells, was observed in the PFP group. Pseudotime analysis revealed dynamic SLIT2 expression during microglia and inhibitory neuron differentiation, mirroring changes in ROBO1 expression. Immunofluorescence analysis of rat facial-nerve nucleus samples verified that SLIT2 protein levels were significantly increased in the facial-nerve nuclei of injured samples. In conclusion, despite the fact that this study is primarily founded on animal models and despite notable differences existing between animals and humans in terms of the facial motor nucleus, this study successfully identified SLIT2 as potential therapeutic targets for PFP. The SLIT2-ROBO axis stands out as a particularly promising candidate. SLIT2 may play a role in modulating neuroimmune interactions and promoting nerve repair. These findings provide a foundation for future clinical studies and targeted interventions to enhance recovery from PFP. Future research should focus on human sample validation to enhance clinical translation.

Animals↗

PubMed2026

Cross-ancestry proteome-wide Mendelian randomization prioritizes 12 plasma protein candidates for breast cancer risk.

The plasma proteome provides a molecular bridge between genetic variation and disease risk, yet its contribution to breast cancer susceptibility across ancestries remains unclear. We conducted a proteome-wide Mendelian randomization (MR) study of 2,923 plasma proteins using cis-protein quantitative trait loci from 34,557 European participants in the UK Biobank Pharma Proteomics Project, integrated with genome-wide association studies of 156,901 breast cancer cases and 204,634 controls of European, East Asian, and African ancestries. Cross-ancestry meta-analysis identified 12 candidate proteins associated with breast cancer risk (P < 2.5×10-5), including six previously reported and six newly implicated in MR studies. DNPH1 showed cross-ancestry heterogeneity, with a risk-increasing association in European populations and a nominally inverse association in East Asian populations. CASP8, RALB, and USP28 displayed subtype-differentiated associations. Orthogonal validation provided variable support: six demonstrated strong evidence of statistical colocalization; four replicated in an independent European proteomic dataset (deCODE, n = 35,559); two replicated in an independent East Asian proteomic dataset (JCTF, n = 1,384); and four were supported by polygenic-score analyses in the ancestrally diverse All of Us cohort (9,250 cases, 214,857 controls). These findings prioritize a high-confidence subset of plasma proteins, including LRRC25, PARK7, and LRRC37A2, for future mechanistic and translational investigation.

Mendelian randomization↗

PubMed2024

Stroke genetics and how it Informs novel drug discovery.

INTRODUCTION: Stroke is one of the main causes of death and disability worldwide. Nevertheless, despite the global burden of this disease, our understanding is limited and there is still a lack of highly efficient etiopathology-based treatment. It is partly due to the complexity and heterogenicity of the disease. It is estimated that around one-third of ischemic stroke is heritable, emphasizing the importance of genetic factors identification and targeting for therapeutic purposes. AREAS COVERED: In this review, the authors provide an overview of the current knowledge of stroke genetics and its value in diagnostics, personalized treatment, and prognostication. EXPERT OPINION: As the scale of genetic testing increases and the cost decreases, integration of genetic data into clinical practice is inevitable, enabling assessing individual risk, providing personalized prognostic models and identifying new therapeutic targets and biomarkers. Although expanding stroke genetics data provides different diagnostics and treatment perspectives, there are some limitations and challenges to face. One of them is the threat of health disparities as non-European populations are underrepresented in genetic datasets. Finally, a deeper understanding of underlying mechanisms of potential targets is still lacking, delaying the application of novel therapies into routine clinical practice.

Humans↗

PubMed2025

The association between milk fat intake and atopic dermatitis: A study based on NHANES from 1999 to 2006 and Mendelian randomization.

Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease imposing significant global burden. While dietary factors are implicated in AD, the relationship between milk fat intake and AD risk remains unclear, particularly regarding optimal fat levels. This study aimed to investigate the association between milk fat intake and AD risk in adults. Relevant data (included a total of 9760 participants) from National Health and Nutrition Examination Survey between 1999 and 2006 were selected, and the relationship between milk fat intake and AD was assessed using weighted multifactorial logistic regression. Subsequently, a 2-sample Mendelian randomization (MR) study was conducted using the summary statistics of genome-wide association studies, and the causal relationship between the 2 was verified through inverse variance weighting, Bayesian weighted MR, and other supplementary MR methods. Weighted multifactorial logistic regression analysis adjusted for other covariates showed that, compared with the intake of full-fat milk, the intake of 1% fat milk (M3: odds ratio [OR]: 1.476, 95% confidence interval [CI]: 1.157-1.874, P = .005), nonfat milk (M3: OR: 1.578, 95% CI: 1.288-1.930, P < .001), as well as for milk abstainers (M3: OR: 1.303, 95% CI: 1.061-1.600, P = .025) increased the risk of AD. MR analysis further validated a significant inverse association between milk fat intake and AD risk, with both primary methods demonstrating statistical significance (P < .05) and no significant pleiotropy or heterogeneity detected in sensitivity analyses. Compared with the population consuming full-fat milk, the risk of AD may be higher in American adults consuming 1% fat milk, nonfat milk, and milk abstainers.

Humans↗

PubMed2005

Obstacles and opportunities in meta-analysis of genetic association studies.

Genetic association studies have the potential to advance our understanding of genotype-phenotype relationships, especially for common, complex diseases where other approaches, such as linkage, are less powerful. Unfortunately, many reported studies are not replicated or corroborated. This lack of reproducibility has many potential causes, relating to study design, sample size, and power issues, and from sources of true variability among populations. Genetic association studies can be considered as more similar to randomized trials than other types of observational epidemiological studies because of "Mendelian randomization" (Mendel's second law). The rationale and methodology for synthesizing randomized trials is highly relevant to the meta-analysis of genetic association studies. Nevertheless, there are a number of obstacles to overcome when performing such meta-analyses. In this review, the impacts of Type I error, lack of power, and publication and reporting biases are explored, and the role of multiple testing is discussed. A number of special features of association studies are especially pertinent, because they may lead to true variability among study results. These include population dynamics and structure, linkage disequilibrium, conformity to Hardy-Weinberg Equilibrium, bias, population stratification, statistical heterogeneity, epistatic and environmental interactions, and the choice of statistical models used in the analysis. Approaches to dealing with these issues are outlined. The supreme importance of complete and consistent study reporting and of making data readily available is also highlighted as a prerequisite for sound meta-analysis. We believe that systematic review and meta-analysis has an important role to play in understanding genetic association studies and should help us to separate the wheat from the chaff.

Epistasis, Genetic↗

PubMed2025

New evidence for the protective effect of gut microbiota regulation of ferroptosis-related proteins against osteoporosis.

Osteoporosis (OP), characterized by bone degradation and increased fracture susceptibility, constitutes a significant global health burden. Recent findings implicate gut microbiota and ferroptosis in the regulation of bone metabolism; however, causal evidence for the gut microbiota's influence on OP specifically via ferroptosis regulation remains to be established. This study employed two-sample Mendelian randomization (MR) using genome-wide association study (GWAS) summary statistics to investigate these causal relationships and delineate mediating pathways.We assessed causal links between gut microbiota, ferroptosis-related proteins, and OP risk. Associations for gut microbiota abundance and ferroptosis-related proteins were derived from GWAS data and Icelandic blood-derived protein quantitative trait loci, respectively. Outcome data for OP were obtained from the FinnGen Release R12. The primary analysis utilized the inverse variance weighted (IVW) method, supplemented by sensitivity analyses to evaluate heterogeneity and horizontal pleiotropy.  MR analysis identified 33 gut microbial taxa causally associated with OP risk: 13 protective and 20 detrimental. Similarly, 34 ferroptosis-related proteins were categorized as protective (18) or detrimental (16) for OP. Mediation analysis revealed that the protective effect of Terrisporobacter othiniensis on OP is partially mediated by the ferroptosis regulator MDM4 (indirect effect β = -0.020, 95% CI: -0.068 to 0.029), accounting for 6.8% of the total effect. Sensitivity analyses showed no significant evidence of heterogeneity or horizontal pleiotropy. This study provides the first genetically validated evidence supporting a causal relationship between specific gut microbiota, ferroptosis-associated proteins, and OP susceptibility. Specifically, Terrisporobacter othiniensis demonstrates a novel protective mechanism, modulating OP risk partly through the ferroptosis regulator MDM4. These findings broaden understanding of the "gut-bone axis" and highlight the gut microbiota-ferroptosis pathway, particularly the MDM4/p53 axis, as a promising target for novel OP prevention and therapeutic strategies.

Ferroptosis↗

PubMed2025

Exploring the causal association between television viewing and meniscal injuries: A two-sample Mendelian randomization analysis.

The aim of this study was to assess whether there is a potential causal relationship between sedentary behavior and meniscal injuries based on the Mendelian randomization (MR) method. This study used a two-sample MR design to integrate pooled data from a large-scale genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) that were significantly associated with sedentary behavior (represented by daily TV-viewing time) and independent of each other were selected as instrumental variables, while focusing on data from populations of European ancestry. To ensure the robustness and reliability of the analyses, 3 mainstream MR analysis methods were combined in this study: inverse variance weighted (IVW), weighted median estimation (WME) and MR-Egger regression. Heterogeneity test, horizontal multivariate analysis, and leave-one-out sensitivity test were also conducted to further validate the stability of causal estimation. The results of the IVW method showed that sedentary behavior was significantly associated with the risk of meniscus injury, with an OR (95% CI) of 2.93 (1.89-4.52), and a P-value of < .001, suggesting that sedentary behavior may be an important risk factor for meniscus injury. No significant bias was found in the heterogeneity test and the assessment of multiple validity, and the sensitivity analysis showed that the effect of individual SNPs on the overall estimation was small, and the results had good robustness. This study provides genetic epidemiological evidence of a positive causal effect of sedentary behavior on meniscal injuries based on a causal inference approach with genetic instrumental variables. The results suggest that reducing sedentary time, especially prolonged TV watching behavior, may reduce the risk of meniscus injury to some extent.

Humans↗

PubMed2024

Mendelian randomization analysis reveals higher whole body water mass may increase risk of bacterial infections.

BACKGROUND AND PURPOSE: The association of water loading with several infections remains unclear. Observational studies are hard to investigate definitively due to potential confounders. In this study, we employed Mendelian randomization (MR) analysis to assess the association between genetically predicted whole body water mass (BWM) and several infections. METHODS: BWM levels were predicted among 331,315 Europeans in UK Biobank using 418 SNPs associated with BWM. For outcomes, we used genome-wide association data from the UK Biobank and FinnGen consortium, including sepsis, pneumonia, intestinal infections, urinary tract infections (UTIs) and skin and soft tissue infections (SSTIs). Inverse-variance weighted MR analyses as well as a series of sensitivity analyses were conducted. RESULTS: Genetic prediction of BWM is associated with an increased risk of sepsis (OR 1.34; 95% CI 1.19 to 1.51; P = 1.57 × 10- 6), pneumonia (OR: 1.17; 95% CI 1.08 to 1.29; P = 3.53 × 10- 4), UTIs (OR: 1.26; 95% CI 1.16 to 1.37; P = 6.29 × 10- 8), and SSTIs (OR: 1.57; 95% CI 1.25 to 1.96; P = 7.35 × 10- 5). In the sepsis and pneumonia subgroup analyses, the relationship between BWM and infection was observed in bacterial but not in viral infections. Suggestive evidence suggests that BWM has an effect on viral intestinal infections (OR: 0.86; 95% CI 0.75 to 0.99; P = 0.03). There is limited evidence of an association between BWM levels and bacteria intestinal infections, and genitourinary tract infection (GUI) in pregnancy. In addition, MR analyses supported the risk of BWM for several edematous diseases. However, multivariable MR analysis shows that the associations of BWM with sepsis, pneumonia, UTIs and SSTIs remains unaffected when accounting for these traits. CONCLUSIONS: In this study, the causal relationship between BWM and infectious diseases was systematically investigated. Further prospective studies are necessary to validate these findings.

Humans↗

PubMed2026

Refining the link between REM sleep behavior disorder and neurodegeneration: Genetic correlation, Mendelian randomization, and colocalization evidence.

Observational studies have proposed a link between isolated rapid eye movement sleep behavior disorder (iRBD) and several neurodegenerative diseases. We employed genome-wide linkage disequilibrium score regression (LDSC), standard two-sample Mendelian randomization (MR), and colocalization analysis to assess the causal links between iRBD and these neurodegenerative conditions. iRBD demonstrated a positive causal association with Alzheimer disease (odds ratio [OR] = 1.02, 95% confidence interval [CI]: 1.00-1.03, P = 1.10E-02), Parkinson disease (OR = 1.10, 95% CI: 1.03-1.16, P = 2.96E-03), and multiple sclerosis (OR = 1.09, 95% CI: 1.02-1.17, P = 1.61E-02). A strong positive genetic correlation with dementia with Lewy bodies was observed (rg = 1.6313, P = .0002), along with a causal association (OR = 1.45, 95% CI: 1.03-2.06, P = 3.53E-02), further supported by colocalization analysis. No significant causal relationship was identified between iRBD and amyotrophic lateral sclerosis (all P > .05). Additionally, reverse Mendelian randomization analyses did not reveal any causal relationships between the neurodegenerative diseases studied and iRBD. Our findings provide robust genetic evidence supporting a causal relationship between iRBD and the risk of multiple neurodegenerative diseases, highlighting the potential for shared pathophysiological mechanisms.

Humans↗

PubMed2026

Modelling time-varying genetic effects on binary disease risk via functional Mendelian randomization.

MOTIVATION: Genome-wide association studies have identified thousands of genetic variants associated with complex traits, establishing Mendelian randomization (MR) as a powerful framework for causal inference using variants as natural experiments. However, existing MR methods treat causal effects as static, relying on cross-sectional exposure measurements and ignoring how genetic predispositions to disease operate dynamically across the life course. Recovering age-specific causal effect functions from longitudinal data requires combining functional data representations of exposure trajectories with instrumental variable estimation strategies suitable for binary disease endpoints, a methodological gap that has remained unaddressed. RESULTS: We develop a functional MR framework for binary outcomes that integrates functional principal component analysis with two-stage residual inclusion (2SRI), ensuring consistent estimation under the nonlinear logistic link function that renders standard instrumental variable estimators inconsistent. Simulations across different causal effect trajectory shapes, varying measurement densities, and varying instrument strengths demonstrate accurate recovery of time-varying genetically predicted effects with minimal bias. Applied to UK Biobank data, the framework identifies an age-specific causal effect of genetically predicted body mass index on type 2 diabetes risk concentrated in early mid-adulthood and progressively attenuating thereafter. Concordance between the proposed 2SRI estimator applied to type 2 diabetes and the established continuous-outcome functional MR estimator applied to the paired glycated haemoglobin marker in the same cohort provides indirect empirical support for the validity of the proposed approach. AVAILABILITY AND IMPLEMENTATION: The method is implemented in the R package mvfmr, with a full tutorial vignette.

Mendelian Randomization Analysis↗

PubMed2025

The relationship between major depression, attention-deficit hyperactivity disorder and coronary artery disease: A two-sample Mendelian randomization analysis.

Coronary artery disease (CAD) constitutes a principal cause of global morbidity and mortality. Studies imply a connection between mental health disorders, especially major depression (MD) and attention-deficit hyperactivity disorder (ADHD), and the risk of CAD. To investigate the causal influence of genetic susceptibility to MD and attention-deficit/hyperactivity disorder (ADHD) on the risk of CAD, summary-level data from genome-wide association studies involving individuals of European descent were utilized. This analysis identified 11 single-nucleotide polymorphisms (SNPs) associated with MD, 60 SNPs linked to ADHD, and 10 SNPs related to CAD as instrumental variables. The inverse variance weighted method was employed for causal estimation, complemented by sensitivity analyses using MR-Egger regression and the weighted median estimator. A positive causal relationship was identified between MD, attention-deficit/hyperactivity disorder (ADHD), and the risk of CAD [MD: Odds Ratio (OR): 42.66, 95% Confidence Interval (CI): 7.55-241.2; ADHD: OR: 1.055, 95% CI: 1.006-1.106]. No significant causal association was observed between obesity and ADHD. In the multivariable Mendelian randomization (MVMR) analysis, the causal effect of ADHD on CAD was found to be diminished (OR: 1.005, 95% CI: 0.998-1.020), while the impact of body mass index on CAD remained stable (OR: 1.557, 95% CI: 1.459-1.661). This Mendelian randomization study reveals the lack of a consistent association among MD, ADHD, and CAD, suggesting a causal relationship and bidirectional effects between ADHD and obesity.

Humans↗