Potential Causal Relationship Research Articles

The role of genetically predicted serum iron levels on neurodegenerative and cardiovascular traits.

Iron is an essential mineral that supports numerous biological functions. Studies have reported associations between iron dysregulation and certain cardiovascular and neurodegenerative diseases, but the direction of influence is not clear. Our goal was to use computational approaches to better understand the role of genetically predicted iron levels on disease risk. We meta-analyzed genome-wide association study summary statistics for serum iron levels from two cohorts and two previous meta-analyses. We then obtained summary statistics from 11 neurodegenerative, cerebrovascular, cardiovascular or lipid traits to assess global and regional genetic correlation between iron levels and these traits. We used two-sample Mendelian randomization (MR) to estimate causal effects. Sex-stratified analyses were also carried out to identify effects potentially differing by sex. Overall, we identified three significant global correlations between iron levels and (i) coronary heart disease, (ii) triglycerides, and (iii) high-density lipoprotein (HDL) cholesterol levels. A total of 194 genomic regions had significant (after correction for multiple testing) local correlations between iron levels and the 11 tested traits. MR analysis revealed two potential causal relationships, between genetically predicted iron levels and (i) total cholesterol or (ii) non-HDL cholesterol. Sex-stratified analyses suggested a potential protective effect of iron levels on Parkinson's disease risk in females, but not in males. Our results will contribute to a better understanding of the genetic basis underlying iron in cardiovascular and neurological health in aging, and to the eventual identification of new preventive interventions or therapeutic avenues for diseases which affect women and men worldwide.

Association Between Glaucoma and Brain Structural Connectivity Based on Diffusion Tensor Tractography: A Bidirectional Mendelian Randomization Study.

Glaucoma is a neurodegenerative ocular disease that is accompanied by cerebral damage extending beyond the visual system. Recent studies based on diffusion tensor tractography have suggested an association between glaucoma and brain structural connectivity but have not clarified causality. To explore the causal associations between glaucoma and brain structural connectivity, a bidirectional Mendelian randomization (MR) study was conducted involving glaucoma and 206 diffusion tensor tractography traits. Highly associated genetic variations were applied as instrumental variables and statistical data were sourced from the database of FinnGen and UK Biobank. The inverse-variance weighted method was applied to assess causal relationships. Additional sensitivity analyses were also performed. Glaucoma was potentially causally associated with alterations in three brain structural connectivities (from the SN to the thalamus, from the DAN to the putamen, and within the LN network) in the forward MR analysis, whereas the inverse MR results identified thirteen brain structural connectivity traits with a potential causal relationship to the risk of glaucoma. Both forward and reverse MR analyses satisfied the sensitivity test with no significant horizontal pleiotropy or heterogeneity. This study offered suggestive evidence for the potential causality between the risk of glaucoma and brain structural connectivity. Our findings also provided novel insights into the neurodegenerative mechanism of glaucoma.

Cannabis use disorder increases risk of large-artery atherosclerotic stroke and migraine with aura through mendelian randomization study

Observational studies have shown some association between cannabis use disorder (CUD) and neurological disorders, but their causal relationship is unclear. In this study, we tested the potential causal relationship between CUD and three common neurological disorders using two-sample Mendelian randomization (TSMR) and multivariate MR (MVMR) methods. Thirty-two genetic loci were extracted as exposure factors from the largest genome-wide association study (GWAS) summary statistics for CUD to date. TSMR results showed that genetic prediction of CUD with all stroke, ischemic stroke, large-artery atherosclerotic stroke, migraine with aura, and Alzheimer’s disease (AD) had a positive causal relationship (P < 0.05), which was not found in several other diseases. The association between CUD and stroke, ischemic stroke, and AD in the MVMR study may have been influenced by confounding factors (P > 0.05). Subgroup analyses highlighted a causal relationship between genetically predicted CUD and large-artery atherosclerotic stroke (OR = 1.169; 95%CI 1.030–1.328; P = 0.016) and migraine with aura (OR = 1.142; 95% 1.021–1.278; P = 0.020). Our further functional mapping and annotation enrichment analyses using FUMA suggest that the brain-gut axis may serve as another layer of explanation for the existence of an association between CUD and neurological disorders.

Association of aging related genes and immune microenvironment with major depressive disorder

ObjectiveTo study the relationship between aging related genes (ARGs) and Major Depressive Disorder (MDD). MethodsThe datasets GSE98793, GSE52790 and GSE39653 for MDD were obtained from the GEO database, and ARGs were obtained from the Human Aging Genome Resources database. Differential expression genes (DEGs) screening and GO, KEGG enrichment analysis were performed to uncover the underlying mechanisms. To identify key ARGs associated with MDD (key ARG-DEGs), we employed machine learning methods such as LASSO, SVM, and Random Forest, as well as the plug-ins CytoHubba-MCC and MCODE methods. SsGSEA was used to analyze the immune infiltration of MDD and healthy controls. Furthermore, we created risk prediction nomograms model and ROC curves to assess not only the ability of key ARG-DEGs to diagnose MDD, but also predicted miRNAs and transcription factors (TFs) that might interact. Finally, a two-sample Mendelian randomization (MR) study was performed to confirm the association of identified key ARG-DEGs with depression. ResultsDEGs of ARGs between MDD and healthy controls led to the identification of eight ARG-DEGs. GO and KEGG analysis revealed that the pathways associated with these eight ARG-DEGs were primarily concentrated in Foxo pathway, JAK-STAT pathway, Pl3K-AKT pathway, and metabolic diseases. A comprehensive analysis further narrowed down the 8 ARG-DEGs to 4 key ARG-DEGs: MMP9, IL7R, S100B, and EGF. Immune infiltration analysis indicated significant differences in CD8(+) T cells, macrophages, neutrophils, Th2 cells, and TIL cells between MDD and control groups, correlating with these four key ARG-DEGs. Based on these four key ARG-DEGs, a risk prediction model for MDD was developed. The miRNA-TF-mRNA interaction network of the key ARG-DEGs highlights the complexity of the regulatory process, providing valuable insights for future related research. The MR study suggested a potential causal relationship between MMP9 and the risk of depression. ConclusionThe process of aging, immune dysregulation, and MDD are closely interconnected. MMP9, IL7R, S100B, and EGF may be used as novel diagnostic biomarkers and potential therapeutic targets for MDD, especially MMP9.

Causal relationship between IgA nephropathy and common neuropsychiatric disorders: A two-sample Mendelian randomization analysis

ObjectiveUtilizing two-sample Mendelian randomization (TSMR), this study seeks to determine the causal relationship between IgA nephropathy and five prevalent neuropsychiatric disorders. By exploring the genetic associations between IgA nephropathy and neuropsychiatric disorders, this research aims to contribute to the prevention of neuropsychiatric disorders in patients with IgA nephropathy. MethodsPublic genome-wide association study databases were searched, with IgA nephropathy as the exposure factor, including 15,587 patients with IgA nephropathy and 462,197 healthy controls. The outcome factors were five common neuropsychiatric disorders (bipolar disorder, depressive disorder, schizophrenia, anorexia nervosa, and Alzheimer's disease), with outcome data drawn from five datasets in the PGC and GWAScatalog databases. Inverse-variance weighting served as the primary method for causal effect estimation, supplemented by MR-Egger, weighted median, weighted mode, and simple mode methods. Heterogeneity was tested using Cochran's Q test, and sensitivity analysis was conducted using MR-Egger and MR-presso. ResultsMR analysis indicated a positive causal relationship between IgA nephropathy and depressive disorder (OR = 1.010, 95%CI: 1.003–1.017, P = 3.27 × 10−3), and a potential positive causal relationship between IgA nephropathy and anorexia nervosa (OR = 1.165, 95%CI: 1.030–1.319, P = 0.015). ConclusionThere is a positive causal relationship between IgA nephropathy and depressive disorder, and a potential positive causal relationship with anorexia nervosa. No causal relationship was found with schizophrenia, bipolar disorder, or Alzheimer's disease.

Using genetic variations to reveal the complex relationships between vegetarianism and well-being, depressive symptoms and neuroticism.

The relationship between vegetarianism and mental well-being remains a debated topic in traditional observational studies. Recent studies have revealed the genetic factors in vegetarianism. We aimed to use genetic variations to explore the potential causal relationships between vegetarianism and mental well-being, offering insights from a new perspective. We conducted the inverse variance weighted approach as the primary analysis to explore the bidirectional genetic associations between vegetarianism (N = 442,589) and depressive symptoms (N = 180,866), neuroticism (N = 170,910), and subjective well-being (N = 298,420). The analysis used the summary data from the largest genome-wide association studies (GWAS). We also performed sensitivity analyses to ensure the robustness of the findings, accounting for potential heterogeneity and pleiotropy. Genetically predicted vegetarianism showed positive causal relationships with depressive symptoms (odds ratio [OR], 3.26; 95% confidence interval [CI], 1.03-10.31; p = 0.044) and neuroticism (OR, 6.72; 95% CI, 2.29-19.74; p = 5.31 × 10-4), as well as a negative causal relationship with subjective well-being (OR, 0.20; 95% CI, 0.05-0.77; p = 0.019). Additionally, depressive symptoms were found to have a causal influence on vegetarianism (OR, 1.01; 95% CI, 1.00-1.02; p = 6.87 × 10-3). No significant heterogeneity or pleiotropy was detected. Vegetarianism is causally correlated with negative mental well-being, reflected in an increased risk of depressive symptoms and neuroticism, as well as lower subjective well-being. Further research should explore the underlying mechanisms in broader populations.

Genetic and therapeutic for oral lichen planus and diabetes mellitus: a comprehensive study

BackgroundThis study employed a bidirectional Mendelian Randomization (MR) approach to explore the causal relationships between Oral Lichen Planus (OLP), diabetes mellitus (DM), and glycemic control. It also aims to identify potential pharmacological and herbal treatments that effectively address both OLP and the metabolic dysfunctions associated with DM.MethodsThis study employs a two-way MR approach to investigate the potential causal relationships between diabetes type and glycated hemoglobin (HbA1c) levels, and the risk of OLP. We analyzed differentially expressed genes from the OLP dataset in the Genomics Expression Omnibus (GEO) database, cross-referencing these with HbA1c-related genes for enrichment analysis. Additionally, the Drug-Gene Interaction Database (DGIdb) and Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) were utilized to assess the effectiveness of specific drugs, herbs, and ingredients in treating OLP while managing blood glucose levels.ResultsThe MR analysis revealed a significant association between Type 1 Diabetes mellitus (T1DM) and an increased risk of OLP, unlike Type 2 Diabetes mellitus (T2DM). This finding indicates a unique immunological interaction in T1DM that may predispose individuals to OLP. The drug prediction analysis focused on core targets linked to OLP and HbA1c, evaluating the therapeutic potential of retinoic acid, prednisone, and thalidomide for treating OLP and regulating blood glucose levels. Additionally, herbal medicines such as Ecliptae herbaand Amygdalus communis vas, along with herbal ingredients like quercetin, luteolin, and 17-beta-estradiol, were identified for their anti-inflammatory properties and potential to mitigate metabolic dysfunction in diabetes.ConclusionThe study highlighted a complex interplay between diabetes and OLP, underscoring the efficacy of integrated therapeutic strategies that target both conditions. The findings suggest that both pharmaceutical and herbal treatments can effectively manage the clinical manifestations of OLP and associated metabolic challenges. This holistic approach to treatment could significantly enhance patient outcomes by addressing the interconnected aspects of these chronic conditions.

Association of cardiovascular disease and urate levels with aortic aneurysm: a bilateral mendelian randomization study

The aim of this study is to investigate the potential causal relationships between coronary artery disease (CAD), myocardial infarction (MI), urate levels, and aortic aneurysm (AA), abdominal aortic aneurysm (AAA), thoracic aortic aneurysm(TAA), aortic dissection (AD) in individuals of European ancestry. To examine the potential causal relationships between CAD, MI, and urate levels with AA, AAA, TAA, AD, respectively, we performed a two-sample Mendelian randomization (MR) analysis. Genetic instruments that reached genome-wide significance (p < 5 × 10 − 8) for risk factors were obtained from genome-wide association studies(GWASs) conducted on individuals of European origin. On the other hand, genetic instruments of AA, AAA, TAA or AD were chosen from the FinnGen cohort. The primary analysis employed the inverse-variance weighted MR method, while sensitivity analyses were conducted using MR-Egger, weighted median MR, MR pleiotropy residual sum and outlier, and Phenoscanner searching. In addition, we performed the MR-Egger intercept analysis to identify potential pleiotropy and utilized Cochran’s Q statistics to evaluate heterogeneity. Additionally, we conducted bidirectional Mendelian randomization experiments to mitigate the potential influence of reverse causation. According to the results of our study, there were statistically significant higher risks for AA in relation to CAD/MI(odds ratio (OR) with 95% confidence interval (CI): 1.309 (1.150–1.490), and 1.255 (1.147–1.373). Similarly, there were statistically significant higher risks for AAA in relation to CAD and MI (OR with 95% CI: 1.383 (1.189–1.609), and 1.352 (1.178–1.552). The sensitivity analysis demonstrated that the causative effects of CAD/MI, and AA /AAA, were robust. A positive causal link was observed between CAD/MI, and AA/AAA. Nevertheless, no causal link was found between CAD, MI, urate levels, and TAA .

Partnership quality and maternal depressive symptoms in the transition to parenthood: a prospective cohort study

BackgroundPregnancy and childbirth are critical life events which lead to significant changes in family structures and roles, thus having a substantial impact on partner relationship and maternal wellbeing. A dysfunctional partnership during this critical time of life has been associated with maternal depressiveness. However, sub-components of partnership quality and the causal relation with maternal symptoms of depression in the perinatal period have been sparsely studied so far. The current study aims to longitudinally assess the course of relationship quality and its sub-components from pregnancy to postpartum and to test a potential causal association with maternal symptoms of depression in the perinatal period.MethodsDiffering from previous studies, partnership quality and symptoms of depression have been assessed prospectively and longitudinally from an early stage of pregnancy (second trimester) until six months postpartum. Cross-lagged panel models were applied to investigate a potential causal relationship between partnership quality and maternal depressive symptoms.ResultsRelationship quality decreased significantly during the transition to parenthood (p < .05) with the steepest decline referring to tenderness (p < .001). We also found a substantial association of relationship quality and maternal depressiveness, but no indication for a clear causal direction of this association.ConclusionsOur results suggest that relationship quality and maternal depressiveness are substantially related in the perinatal period, thus pointing to the need of early prevention and intervention programs for peripartum women and their partners to prevent adverse outcome for the couple and the family.

The role of mitochondrial DNA copy number in autoimmune disease: a bidirectional two sample mendelian randomization study.

Mitochondrial DNA (mtDNA) plays an important role in autoimmune diseases (AD), yet the relationship between mitochondria and autoimmune disease is controversial. This study employed bidirectional Mendelian randomization (MR) to explore the causal relationship between mtDNA copy number and 13 ADs (including ankylosing spondylitis [AS], Crohn's disease [CD], juvenile rheumatoid arthritis [JRA], polymyalgia rheumatica [PMR], psoriasis [PSO], rheumatoid arthritis [RA], Sjogren's syndrome [SS], systemic lupus erythematosus [SLE], thyrotoxicosis, type 1 diabetes mellitus [T1DM], ulcerative colitis [UC], and vitiligo). A two-sample MR analysis was performed to assess the causal relationship between mtDNA copy number and AD. Genome-wide association study (GWAS) for mtDNA copy number were obtained from the UK Biobank (UKBB), while those associated with AD were sourced from the FinnGen Biobank. Inverse variance weighting (IVW) was the primary analysis method, complemented by three sensitivity analyses (MR-Egger, weighted median, weighted mode) to validate the results. IVW MR analysis identified significant associations between mtDNA copy number and CD (OR=2.51, 95% CI 1.56-4.22, P<0.001), JRA (OR=1.87, 95% CI 1.17-7.65, P=0.022), RA (OR=1.71, 95%CI 1.18-2.47, P=0.004), thyrotoxicosis (OR=0.51, 95% CI0.27-0.96, P=0.038), and T1DM (OR=0.51, 95% CI 0.27-0.96, P=0.038). Sensitivity analyses indicated no horizontal pleiotropy. Our study revealed a potential causal relationship between mtDNA copy number and ADs, indicating that these markers may be relevant in exploring new therapeutic approaches.

Oral Disease and Atherosclerosis May Be Associated with Overlapping Metabolic Pathways.

Dental caries and periodontitis are among the most prevalent chronic diseases worldwide and have been associated with atherosclerotic cardiovascular diseases (ASCVD). This study aimed to determine (1) the independent associations between subclinical ASCVD markers (carotid intima media thickness [CIMT] and coronary artery calcification [CAC]) and quantitative indices of oral disease including the decayed, missing, and filled teeth (DMFT) index, gingivitis parameters, periodontal status, and number of teeth lost and (2) the extent to which metabolites altered in individuals with oral disease overlapped with those altered in individuals with ASCVD. We used data from 552 participants recruited through the Dental Strategies Concentrating on Risk Evaluation project. Oral examinations were conducted, and CIMT and CAC were measured. Multiple linear regression models were constructed with CIMT and CAC as dependent variables in the epidemiologic analysis. In the metabolomic analysis, logistic or linear regression was used to test 1,228 metabolites for association with each phenotype adjusted for age, sex, race, blood pressure, smoking, diabetes, cholesterol, high-sensitivity C-reactive protein, and interleukin-6. None of the oral disease markers were significant predictors of ASCVD markers in the fully adjusted models. However, critical lipid and lipid-signaling pathway metabolites were significantly associated with gingivitis, periodontitis, and DMFT: the lysophospholipid pathway (odds ratio [OR] = 2.29, false discovery rate [FDR]-adjusted P = 0.038) and arachidonate with gingivitis (OR = 2.35, FDR-adjusted P = 0.015), the sphingolipid metabolism pathway with periodontitis (OR = 2.09, FDR-adjusted P = 0.029), and borderline associations between plasmalogen and lysophospholipid pathways and DMFT (P = 0.055). Further, the same metabolite from the sphingolipid metabolism pathway, sphingomyelin (d17:1/14:0, d16:1/15:0), was inversely associated with both CIMT (β = -0.14, FDR-adjusted P = 0.014) and gingivitis (OR = 0.04, FDR-adjusted P = 0.033). The discovery of a common sphingomyelin metabolite in both disease processes is a novel finding suggesting that gingivitis and periodontitis may be associated with some overlapping metabolic pathways associated with ASCVD and indicating potential shared mechanisms among these diseases. The same metabolites may be altered in atherosclerosis and oral disease. Specifically, a common sphingomyelin metabolite was inversely associated with gingivitis and carotid intima media thickness, a subclinical marker of atherosclerotic cardiovascular disease. These findings can provide valuable insights for future mechanistic studies to establish potential causal relationships, with the hope of influencing disease prevention and targeted early treatment.

Lipid-lowering drugs and risk of rapid renal function decline: a mendelian randomization study.

Chronic kidney disease (CKD) patients face the risk of rapid kidney function decline leading to adverse outcomes like dialysis and mortality. Lipid metabolism might contribute to acute kidney function decline in CKD patients. Here, we utilized the Mendelian Randomization approach to investigate potential causal relationships between drug target-mediated lipid phenotypes and rapid renal function decline. In this study, we utilized two methodologies: summarized data-based Mendelian randomization (SMR) and inverse variance-weighted Mendelian randomization (IVW-MR), to approximate exposure to lipid-lowering drugs. This entailed leveraging expression quantitative trait loci (eQTL) for drug target genes and genetic variants proximal to drug target gene regions, which encode proteins associated with low-density lipoprotein (LDL) cholesterol, as identified in genome-wide association studies. The objective was to investigate causal associations with the progression of rapid kidney function decline. The SMR analysis revealed a potential association between high expression of PCSK9 and rapid kidney function decline (OR = 1.11, 95% CI= [1.001-1.23]; p = 0.044). Similarly, IVW-MR analysis demonstrated a negative association between LDL cholesterol mediated by HMGCR and kidney function decline (OR = 0.74, 95% CI = 0.60-0.90; p = 0.003). Genetically predicted inhibition of HMGCR is linked with the progression of kidney function decline, while genetically predicted PCSK9 inhibition is negatively associated with kidney function decline. Future research should incorporate clinical trials to validate the relevance of PCSK9 in preventing kidney function decline.

Association of the gut microbiome and different phenotypes of COPD and asthma: a bidirectional Mendelian randomization study.

Mounting evidence has revealed the association between gut microbiota and both chronic obstructive pulmonary disease (COPD) and asthma; however, the causal association between gut microbiota and specific disease phenotypes remains to be determined. This study employed bidirectional two-sample Mendelian randomization (MR) analyses to investigate the potential causal relationship between gut microbiota and these conditions. The research utilized genome-wide association study (GWAS) data from the MiBioGen consortium for gut microbiota and the integrative epidemiology unit (IEU) Open GWAS for these conditions. Four MR analysis methods were employed: the inverse variance weighted (IVW) test, MR-Egger, weighted median, and weighted mode methods. The IVW method results are considered the primary findings. Sensitivity analyses, including heterogeneity tests, horizontal pleiotropy analysis, and leave-one-out analysis, were used to enhance robustness. Our MR study identified eight gut microbiota taxa potentially associated with the risk of different types of COPD and asthma. These include two taxa for early-onset COPD: Streptococcaceae [odds ratio (OR) = 1.315, 95% confidence interval (CI) = 1.071-1.616, P = 0.009] and Holdemanella (OR = 1.199, 95% CI = 1.063-1.352, P = 0.003); three for later-onset COPD: Acidaminococcaceae (OR = 1.312, 95% CI = 1.098-1.567, P = 0.003), Holdemania (OR = 1.165, 95% CI = 1.039-1.305, P = 0.009), and Marvinbryantia (OR = 0.814, 95% CI = 0.697-0.951, P = 0.009); one for allergic asthma: Butyricimonas (OR = 0.794, 95% CI = 0.693-0.908, P = 0.001); and two for non-allergic asthma: Clostridia (OR = 1.255, 95% CI = 1.043-1.511, P = 0.016) and Clostridiales (OR = 1.256, 95% CI = 1.048-1.506, P = 0.014).IMPORTANCEIndividuals with diverse phenotypes of chronic obstructive pulmonary disease (COPD) and asthma exhibit different responses to the conventional "one treatment fits all" approach. Recent research has revealed the significant role of the gut-lung axis in both COPD and asthma. However, the specific impact of gut microbiota on different subtypes of these conditions remains poorly understood. Our study has identified eight gut microbiota that may be associated with the risk of different types of COPD and asthma. These findings provide evidence suggesting a potential causal relationship between gut microbiota and various phenotypes of COPD and asthma. This offers a new perspective on the origins of different disease phenotypes and points toward future exploration of phenotype-specific and personalized therapies.

Genetic Determinants of Telomere Length and Risk of Aneurysmal Subarachnoid Hemorrhage: A Bidirectional Two-Sample Mendelian Randomization Study

Background Our objective is to investigate the potential causal relationship between telomere length (TL) and aneurysmal subarachnoid hemorrhage (aSAH) and intracranial aneurysms (IAs) by conducting a bidirectional two-sample Mendelian Randomization (MR) study. Methods We utilized publicly available summary data from genome-wide association studies (GWAS) for comprehensive analysis. Telomere length-associated data were sourced from the Epidemiology Unit (IEU) GWAS database (n = 472,174), while data pertaining to intracranial aneurysms were derived from a GWAS meta-analysis conducted by Bakker et al, encompassing aneurysmal subtypes including aSAH (n = 77,074), IAs (n = 79,429), and unruptured intracranial aneurysms (uIA) (n = 74,004), all sampled from European populations. The primary method for MR analysis employed was the Inverse Variance Weighted (IVW) method. Additionally, we conducted various sensitivity analyses to assess the heterogeneity and pleiotropy of study findings. Reverse MR analysis was employed to explore potential reverse causality. Results In the forward MR analysis, the IVW method indicated a negative association between TL and aSAH (OR = 0.636, 95% CI: 0.459–0.883, p = 0.006) as well as IAs (OR = 0.670, 95% CI: 0.499–0.900, p = 0.0079). There was no evidence of heterogeneity or horizontal pleiotropy in the forward MR analysis. Reverse MR analysis did not reveal any causal relationship between aSAH, IAs, uIA and TL. Conclusions In European populations, there exists a causal relationship between longer TL and reduced risks of aSAH and IAs Further research is warranted to elucidate the underlying mechanisms and the potential of TL as an intervention target for lowering the incidence of aSAH and IAs.

Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.

To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation. Microarray and RNA-sequencing datasets (GSE47184, GSE96804, GSE104948, GSE104954, GSE142025 and GSE175759) were obtained from the Gene Expression Omnibus database. Differential expression analysis identified the differentially expressed genes (DEGs) between patients with DN and controls. Diverse machine learning algorithms, including least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, were used to enhance gene selection accuracy and predictive power. We integrated summary-level data from genome-wide association studies on DN with expression quantitative trait loci data to identify genes with potential causal relationships to DN. The predictive performance of the biomarker gene was validated using receiver operating characteristic (ROC) curves. Gene set enrichment and correlation analyses were conducted to investigate potential mechanisms. Finally, the biomarker gene was validated using quantitative real-time polymerase chain reaction in clinical samples from patients with DN and controls. Based on identified 314 DEGs, seven characteristic genes with high predictive performance were identified using three integrated machine learning algorithms. MR analysis revealed 219 genes with significant causal effects on DN, ultimately identifying one co-expressed gene, carbonic anhydrase II (CA2), as a key biomarker for DN. The ROC curves demonstrated the excellent predictive performance of CA2, with area under the curve values consistently above 0.878 across all datasets. Additionally, our analysis indicated a significant association between CA2 and infiltrating immune cells in DN, providing potential mechanistic insights. This biomarker was validated using clinical samples, confirming the reliability of our findings in clinical practice. By integrating machine learning, MR and experimental validation, we successfully identified and validated CA2 as a promising biomarker for DN with excellent predictive performance. The biomarker may play a role in the pathogenesis and progression of DN via immune-related pathways. These findings provide important insights into the molecular mechanisms underlying DN and may inform the development of personalized treatment strategies for this disease.

Potential causal and temporal relationship between plasma triglyceride levels and circulating leukocyte

BackgroundCirculating triglyceride (TG) and leukocytes, the main components of the vascular system, may impact each other and co-fuel atherosclerosis. While the causal relationship between plasma TG levels and leukocyte counts remains unclear. MethodsBidirectional mendelian randomisation (MR) analysis was conducted to investigate the potential causal relationship between TG levels and the counts of leukocytes and their subtypes. A cross-lagged panel model (CLPM) using a longitudinal healthy screening data (13,389 adults with an follow-up of 4 years) was fitted to examine the temporal relationship between them. ResultsGenetically predicted plasma TG levels were positively associated with total leukocyte counts (TLC) [β(se)=0.195(0.01)], lymphocyte counts (LC) [β(se)=0.196(0.019)], and neutrophill counts (NC) [β(se)=0.086(0.01)], which remained significant after adjusting for several confounders. Inversely, the genetically predicted TLC [β(se)=0.033(0.008)], LC [β(se)=0.053(0.008)], and NC [β(se)=0.034(0.008)] were positively associated with plasma TG levels. However, when all three of them were put into the MR model adjusted for each other, only LC was significantly associated with TG levels. There was no association between genetically predicted TG levels and monocyte counts (MC), basophil counts, and eosinophill counts. The results of CLPM showed that the temporal effect of elevated TLC, MC, LC, and NC on plasma TG levels were stronger than the inverse effect. ConclusionOur findings suggesting a causal associations of plasma TG levels with TLC, LC, and NC. In turn, LC was positively associated with plasma TG levels. Additionally, elevated circulating LC may precede high plasma TG levels.

Evidence of the association between asthma and lung cancer risk from mendelian randomization analysis

Asthma and lung cancer are both significant public health concerns worldwide. Previous observational studies have indicated a potential link between asthma and an increased risk of lung cancer, whereas the causal relationship remains uncertain. We aimed to investigate the potential causal relationship between asthma and lung cancer risk utilizing Mendelian randomization (MR) design.The present study employed a two-sample MR analysis utilizing summary statistics from genome-wide association studies (GWAS) with European descent of asthma and lung cancer. The MR analysis was performed using inverse variance weighting (IVW), supplemented with MR-Egger regression and weighted median method to investigate the potential causality between asthma and lung cancer. Furthermore, Sensitivity analyses were also conducted to ensure the reliability of the findings. The MR analysis showed that genetically predicted asthma had suggestive causal association with the elevated risk of lung cancer [odds ratio (OR), 1.05 (95%Cl,1.01–1.09), P = 0.01]. The consistent direction of effects observed in the three methods further supported this finding. In addition, sensitivity analyses demonstrated the reliability of the results. This study provided potential evidence supporting a causal association between asthma and lung cancer. These findings highlighted the importance of early detection and prevention strategies for lung cancer in individuals with asthma. Further research was needed to elucidate the underlying mechanisms linking asthma and lung cancer.

The Causal Relationship between Inflammatory Cytokines and Liver Cirrhosis in European Descent: A Bidirectional Two-Sample Mendelian Randomization Study and the First Conclusions.

Observational studies have highlighted the pivotal role of inflammatory cytokines in cirrhosis progression. However, the existence of a causal link between inflammatory cytokines and cirrhosis remains uncertain. In this study, we conducted a bidirectional Mendelian randomization (MR) analysis at a summarized level to illuminate the potential causal relationship between the two variables. This study utilized genetic variance in cirrhosis and inflammatory cytokines from a genome-wide association study (GWAS) of European descent. The MR-PRESSO outlier test, Cochran's Q test, and MR-Egger regression were applied to assess outliers, heterogeneity, and pleiotropy. The inverse variance weighted method and multiple sensitivity analyses were used to evaluate causalities. Furthermore, the validation set was used for simultaneous data validation. The inflammatory cytokine monocyte chemoattractant protein 3 (MCP-3) was supposedly associated with a greater risk of cirrhosis. And cirrhosis was significantly correlated with increased levels of hepatocyte growth factor (HGF). This study suggests that MCP-3 might be associated with the etiology of cirrhosis, while several inflammatory cytokines could potentially play a role in its downstream development. Additionally, the progression of cirrhosis was associated with elevated levels of HGF, suggesting a possible role for liver repair functions.

Investigating the Genetic Association of 40 Biochemical Indicators with Parkinson's Disease.

The mechanisms of Parkinson's disease (PD) are not fully understood, which hinders the development of effective therapies. Research indicates that lower levels of biochemical indicators like bilirubin, vitamin D, and cholesterol may elevate the risk of PD. However, clinical studies on abnormal levels of biochemical indicators in PD patients' circulation are inconsistent, leading to ongoing debate about their association with PD. Here, we investigate the genetic correlation between 40 biochemical indicators and PD using a bidirectional two-sample Mendelian randomization (MR) approach to uncover potential causal relationships. Data from genome-wide association studies (GWAS) were utilized, with genetic variations from specific lineages serving as instrumental variables (IVs). The methodology followed the STROBE-MR checklist and adhered to the three principal assumptions of MR. Statistical analyses employed methods including inverse variance weighting (IVW), MR-Egger, weighted median, and weighted mode. Biochemical indicators including albumin, C-reactive protein (CRP), and sex hormone-binding globulin (SHBG) showed significant associations with PD risk. Elevated levels of albumin (OR = 1.246, 95% CI 1.006-1.542, P = 0.043) and SHBG (OR = 1.239, 95% CI 1.065-1.439, P = 0.005) were linked to higher PD risk. Conversely, increased CRP levels (OR = 0.663, 95% CI 0.517-0.851; P = 0.001) could potentially lower PD risk. The robustness of the results was confirmed through various MR analysis techniques, including assessments of directional pleiotropy and heterogeneity using MR-Egger intercept and MR-PRESSO methods. This study systematically reveals, for the first time at the genetic level, the relationship between 40 biochemical indicators and PD risk. Our research verifies the role of inflammation in PD and provides new genetic evidence, further advancing the understanding of PD pathogenesis. The study shows a positive correlation between albumin and SHBG with PD risk and a negative correlation between CRP and PD risk. This study identifies for the first time that SHBG may be involved in the onset of PD and potentially worsen disease progression.

Immune cell traits and causal relationships with cholecystitis: a mendelian randomization analysis.

Cholecystitis, characterized by inflammation of the gallbladder, is intricately linked to immune cells and the cytokines they produce. Despite this association, the specific contributions of immune cells to the onset and progression of cholecystitis remain to be fully understood. To delineate this relationship, we utilized the Mendelian randomization (MR) method to scrutinize the causal connections between 731 immune cell phenotypes and cholecystitis. By conducting MR analysis on 731 immune cell markers from public datasets, this study seeks to understand their potential impact on the risk of cholecystitis. It aims to elucidate the interactions between immune phenotypes and the disease, aiming to lay the groundwork for advancing precision medicine and developing effective treatment strategies for cholecystitis. Taking immune cell phenotypes as the exposure factor and cholecystitis as the outcome event, this study used single nucleotide polymorphisms (SNPs) closely associated with both immune cell phenotypes and cholecystitis as genetic instrumental variables. We conducted a two-sample MR analysis on genome-wide association studies (GWAS) data. Our research thoroughly examined 731 immune cell markers, to determine potential causal relationships with susceptibility to cholecystitis. Sensitivity analyses were performed to ensure the robustness of our findings, excluding the potential impacts of heterogeneity and pleiotropy. To avoid reverse causality, we conducted reverse MR analyses with cholecystitis as the exposure factor and immune cell phenotypes as the outcome event. Among the 731 immune phenotypes, our study identified 21 phenotypes with a causal relationship to cholecystitis (P < 0.05). Of these, eight immune phenotypes exhibited a protective effect against cholecystitis (odds ratio (OR) < 1), while the other 13 immune phenotypes were associated with an increased risk of developing cholecystitis (OR > 1). Additionally, employing the false discovery rate (FDR) method at a significance level of 0.2, no significant causal relationship was found between cholecystitis and immune phenotypes. Our research has uncovered a significant causal relationship between immune cell phenotypes and cholecystitis. This discovery not only enhances our understanding of the role of immune cells in the onset and progression of cholecystitis but also establishes a foundation for developing more precise biomarkers and targeted therapeutic strategies. It provides a scientific basis for more effective and personalized treatments in the future. These findings are expected to substantially improve the quality of life for patients with cholecystitis and mitigate the impact of the disease on patients and their families.