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Background: Multiple studies suggest a potential connection between the gut microbiome and asthma. Our objective is to use advanced genetic and metagenomic techniques to elucidate the causal relationships and underlying mechanisms between gut microbiota and asthma. Methods: The study utilized comprehensive Linkage Disequilibrium Score Regression (LDSC) and Mendelian randomization (MR) analyses to examine the relationship between 119 gut microbiota genera and asthma, using publicly accessible genome-wide association studies (GWAS). The meta-analysis synthesized summary effect estimates obtained from LDSC, forward MR, and reverse MR. The MiBioGen collaboration, involving 18,340 individuals, identified genetic variations associated with gut bacteria. Asthma data were collected from the UK Biobank, FinnGen, and GERA, encompassing a total of 82,060 cases and 641,049 controls. Results: LDSC analysis revealed significant negative genetic correlations between asthma and RuminococcaceaeUCG004 (Rg = -0.55, p = 7.66 × 10-5) and Subdoligranulum (Rg = -0.35, p = 3.61 × 10-4). Forward MR analysis suggested associations between Butyricicoccus (OR = 0.92, p = 0.01), Turicibacter (OR = 0.95, p = 0.025), Butyrivibrio (OR = 0.98, p = 0.047), and reduced asthma risk. Conversely, Coprococcus2 (OR = 1.10, p = 0.035) and Roseburia (OR = 1.07, p = 0.039) were associated with increased risk. Reverse MR analysis indicated significant associations between genetically predicted asthma and Eubacteriumxylanophilumgroup (Beta = -0.08, p = 9.25 × 10-7), LachnospiraceaeNK4A136group (Beta = -0.05, p = 1.26 × 10-4), and Eisenbergiella (Beta = 0.06, p = 0.015, Rg_P = 0.043). Conclusion: The findings underscore significant genetic correlations and causal relationships between specific gut microbiota and asthma. These insights highlight the potential of gut microbiota as both markers and modulators of asthma risk, offering new avenues for targeted therapeutic strategies. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Glaucoma is the most common cause of irreversible blindness, and gut microbiota (GM) is associated with glaucoma. Whether this association represents a causal role remains unknown. This study aims to assess the potential association and causal link between GM and various forms of glaucoma, emphasising the need for cautious interpretation of the strength of these associations.Employing a two‐sample bidirectional Mendelian randomisation (MR) framework with false discovery rate correction and various sensitivity analyses, supplemented by genetic correlation analysis via linkage disequilibrium score regression (LDSC) and colocalisation for European summary‐level data between MiBioGen consortium and FinnGen Study, we sought to explore the relationship between GM and glaucoma.While certain microbial taxa showed potential associations with glaucoma subtypes (e.g., Erysipelotrichaceae with primary angle closure glaucoma, Senegalimassilia with exfoliation glaucoma), the overall findings suggest a complex and not definitively causal relationship between GM and glaucoma. Notably, reverse MR analysis did not establish a significant causal effect of glaucoma on GM composition, and no consistent genetic correlations were observed between GM and glaucoma.While our study provides some evidence of associations between specific GM taxa and glaucoma, the results underscore the complexity of these relationships and the need for further research to clarify the potential causal links. The findings highlight the importance of interpreting the gut‐eye axis with caution and suggest that while GM may play a role in glaucoma, it is unlikely to be a predominant causal factor. [ABSTRACT FROM AUTHOR]

Background: Numerous research studies have indicated a possible association between type 2 diabetes (T2DM) and gut microbiota. To explore specific metabolic pathways connecting gut microbiota and T2DM, we employed Mendelian randomization (MR) and linkage disequilibrium score regression (LDSC) techniques. Methods: This research utilized data from genome-wide association studies (GWAS) that are publicly accessible. We evaluated the genetic correlation between gut microbiota and T2DM using LDSC. Causality was primarily determined through the inverse variance weighted (IVW) method. To verify the robustness of our results, we conducted sensitivity analyses using several approaches, including the weighted median, MR-Egger, and MR-PRESSO. We integrated summary effect estimates from LDSC, along with forward and reverse MR, into a meta-analysis for T2DM using various data sources. Additionally, mediation analysis was performed to explore the impact of plasma metabolites on the relationship between gut microbiota and T2DM. Results: Our study indicated a significant genetic correlation between genus RuminococcaceaeUCG005 (Rg = -0.26, Rg_;P = 2.07 x 10-4) and T2DM. Moreover, the forward MR analysis identified genus RuminococcaceaeUCG010 (OR = 0.857, 95% CI 0.795, 0.924; P = 6.33 x 10-5) and order Clostridiales (OR = 0.936, 95% CI 0.878, 0.997; P = 0.039) as being significantly associated with a decreased risk of T2DM. The analysis also highlighted several plasma metabolites as significant mediators in these relationships, with metabolites like octadecadienedioate (C18:2-DC) and branched chain 14:0 dicarboxylic acid being notably involved. Conclusion: The findings demonstrate a significant impact of gut microbiota on T2DM via plasma metabolites, suggesting potential metabolic pathways for therapeutic targeting. This study enhances our understanding of the microbiota's role in T2DM pathogenesis and supports the development of microbiota-based interventions. [ABSTRACT FROM AUTHOR]

There is evidence to support a link between abnormal lipid metabolism and Alzheimer's disease (AD) risk. Similarly, observational studies suggest a comorbid relationship between AD and coronary artery disease (CAD). However, the intricate biological mechanisms of AD are poorly understood, and its relationship with lipids and CAD traits remains unresolved. Conflicting evidence further underscores the ongoing investigation into this research area. Here, we systematically assess the cross-trait genetic overlap of AD with 13 representative lipids (from eight classes) and seven CAD traits, leveraging robust analytical methods, well-powered large-scale genetic data, and rigorous replication testing. Our main analysis demonstrates a significant positive global genetic correlation of AD with triglycerides and all seven CAD traits assessed—angina pectoris, cardiac dysrhythmias, coronary arteriosclerosis, ischemic heart disease, myocardial infarction, non-specific chest pain, and coronary artery disease. Gene-level analyses largely reinforce these findings and highlight the genetic overlap between AD and three additional lipids: high-density lipoproteins (HDLs), low-density lipoproteins (LDLs), and total cholesterol. Moreover, we identify genome-wide significant genes (Fisher's combined p value [FCPgene] < 2.60 × 10−6) shared across AD, several lipids, and CAD traits, including WDR12, BAG6, HLA-DRA, PHB, ZNF652, APOE, APOC4, PVRL2, and TOMM40. Mendelian randomisation analysis found no evidence of a significant causal relationship between AD, lipids, and CAD traits. However, local genetic correlation analysis identifies several local pleiotropic hotspots contributing to the relationship of AD with lipids and CAD traits across chromosomes 6, 8, 17, and 19. Completing a three-way analysis, we confirm a strong genetic correlation between lipids and CAD traits—HDL and sphingomyelin demonstrate negative correlations, while LDL, triglycerides, and total cholesterol show positive correlations. These findings support genetic overlap between AD, specific lipids, and CAD traits, implicating shared but non-causal genetic susceptibility. The identified shared genes and pleiotropic hotspots are valuable targets for further investigation into AD and, potentially, its comorbidity with CAD traits. [ABSTRACT FROM AUTHOR]

Background The causal relationship between the level of serum 25-hydroxyvitamin D [25(OH)D] and the risk of erectile dysfunction (ED) is still unclear. Aim We tried to determine the causal relationship between the level of serum 25(OH)D and ED risk. Methods In this study, we used genome-wide association study data from the UK Biobank to analyse the relationship between serum 25(OH)D (as the exposure) and ED (as the outcome). Linkage disequilibrium score regression (LDSC) was used to assess the genetic correlation between 2 traits. The CAUSE (Causal Analysis using Summary Effect estimates) method and Mendelian randomization (MR) were employed to evaluate the bidirectional causal relationship. The MRlap method was utilized to assess the impact of sample overlap on the results. To assess potential heterogeneity and horizontal pleiotropy, we utilized methods such as MR-Egger, MR-PRESSO (Mendelian Randomization Pleiotropy Residual Sum and Outlier), weighted median, and others. Outcomes The primary outcome was defined as self or physician-reported ED, or using oral ED medication, or a history of surgery related to ED. Results The LDSC analysis did not reveal a significant genetic correlation between serum 25(OH)D and ED (rg = 0.2787, P =.3536). Additionally, the CAUSE (P value testing that the causal model is a better fit >.05) and MR analyses (odds ratio, 0.8951; 95% confidence interval, 0.7480-1.0710; P =.2260) did not support a causal relationship between 25(OH)D and ED, and our study did not detect any heterogeneity and pleiotropy. Clinical implications This study provides evidence on whether vitamin D needs to be ingested to prevent or treat ED. Strengths and limitations We used LDSC and MR to avoid bias. However, the population in this study was limited to European ancestry. Conclusion No causal relationship was found between 25(OH)D and ED. [ABSTRACT FROM AUTHOR]

Introduction: Observational studies have demonstrated strong correlations between metabolic syndrome (MetS) and its related traits. To gain insight into the genetic architecture and molecular mechanism of MetS, we investigated the shared genetic basis of MetS and its related traits and further tested their causal relationships. Methods: Using summary statistics from genome-wide association analyses of about 72,000 subjects from the Korean Genome and Epidemiological Study (KoGES), we conducted genome-wide multi-trait analyses to quantify the overall genetic correlation and Mendelian randomization analyses to infer the causal relationships between traits of interest. Results: Genetic correlation analyses revealed a significant correlation of MetS with its related traits, such as obesity traits (body mass index and waist circumference), lipid traits (triglyceride and high-density lipoprotein cholesterol), glycemic traits (fasting plasma glucose and hemoglobin A1C), and blood pressure (systolic and diastolic). Mendelian randomization analyses further demonstrated that the MetS-related traits showing significant overall genetic correlation with MetS could be genetically determined risk factors for MetS. Discussion: Our study suggests a shared genetic basis of MetS and its related traits and provides novel insights into the biological mechanisms underlying these complex traits. Our findings further inform public health interventions by supporting the important role of the management of metabolic risk factors such as obesity, unhealthy lipid profiles, diabetes, and high blood pressure in the prevention of MetS. [ABSTRACT FROM AUTHOR]

Background: Several studies have suggested a potential link between allergic rhinitis (AR) and gut microbiota. In response, we conducted a meta-analysis of Linkage Disequilibrium Score Regression (LDSC) and Mendelian randomization (MR) to detect their genetic associations. Methods: Summary statistics for 211 gut microbiota taxa were gathered from the MiBioGen study, while data for AR were sourced from the Pan-UKB, the FinnGen, and the Genetic Epidemiology Research on Aging (GERA). The genetic correlation between gut microbiota and AR was assessed using LDSC. The principal estimate of causality was determined using the Inverse-Variance Weighted (IVW) method. To assess the robustness of these findings, sensitivity analyses were conducted employing methods such as the weighted median, MR-Egger, and MR-PRESSO. The summary effect estimates of LDSC, forward MR and reverse MR were combined using meta-analysis for AR from different data resources. Results: Our study indicated a significant genetic correlation between genus Sellimonas (Rg = -0.64, p = 3.64 × 10-5, Adjust_P = 3.64 × 10-5) and AR, and a suggestive genetic correlation between seven bacterial taxa and AR. Moreover, the forward MR analysis identified genus Gordonibacter, genus Coprococcus2, genus LachnospiraceaeUCG010, genus Methanobrevibacter, and family Victivallaceae as being suggestively associated with an increased risk of AR. The reverse MR analysis indicated that AR was suggestively linked to an increased risk for genus Coprococcus2 and genus RuminococcaceaeUCG011. Conclusion: Our findings indicate a causal relationship between specific gut microbiomes and AR. This enhances our understanding of the gut microbiota's contribution to the pathophysiology of AR and lays the groundwork for innovative approaches and theoretical models for future prevention and treatment strategies in this patient population. [ABSTRACT FROM AUTHOR]

Females are twice as likely to experience PTSD as compared to males. Although sex differences in prevalence are well-established, little is known about why such sex differences occur. Biological factors that vary with sex, including sex hormone production, may contribute to these differences. Considerable evidence links sex hormones, such as testosterone, to PTSD risk though less is known about the shared genetic underpinnings. The objective of the present study was to test for genetic relationships between testosterone and PTSD. To do so, we used summary statistics from large, publicly available genetic consortia to conduct linkage disequilibrium score regression to estimate the genetic correlations between PTSD and testosterone in males and females, and two-sample, bi-directional Mendelian randomization to examine potential causal relationships of testosterone on PTSD and the reverse. Heritability estimates of testosterone were significantly higher in males (0.17, SE = 0.02) than females (0.11, SE = 0.01; z = 2.46, p = 00.01). The correlation between testosterone and PTSD was negative in males (r g = −0.11, SE = 0.02, p = 6.7 x 10-6), but not significant in females (r g = 0.002, SE = 0.03, p = 0.95). MR analyses found no evidence of a causal effect of testosterone on PTSD or the reverse. Findings are consistent with phenotypic literature suggesting a relationship between testosterone and PTSD that may be sex-specific. This work provides early evidence of a relationship between testosterone and PTSD genotypically and suggests an avenue for future research that will enable a better understanding of disparities in PTSD. [ABSTRACT FROM AUTHOR]

Background: Creatinine-cystatin C ratio (CCR) has been demonstrated as an objective marker of sarcopenia in clinical conditions but has not been evaluated as an osteoporosis marker in individuals with normal renal function. Methods: We selected 271,831 participants with normal renal function from UK Biobank cohort. Multivariable linear/logistic regression and Cox proportional hazards model were used to investigate the phenotypic relationship between CCR and osteoporosis in total subjects and gender-stratified subjects. Based on the genome-wide association study (GWAS) data, linkage disequilibrium regression (LDSC) and Mendelian randomization (MR) analysis were performed to reveal the shared genetic correlations and infer the causal effects, respectively. Results: Amongst total subjects and gender-stratified subjects, serum CCR was positively associated with eBMD after adjusting for potential risk factors (all P<0.05). The multivariable logistic regression model showed that the decrease in CCR was associated with a higher risk of osteoporosis/fracture in all models (all P<0.05). In the multivariable Cox regression analysis with adjustment for potential confounders, reduced CCR is associated with the incidence of osteoporosis and fracture in both total subjects and gender-stratified subjects (all P<0.05). A significant non-linear dose-response was observed between CCR and osteoporosis/fracture risk (Pnon-linearity < 0.05). LDSC found no significant shared genetic effects by them, but PLACO identified 42 pleiotropic SNPs shared by CCR and fracture (P<5×10-8). MR analyses indicated the causal effect from CCR to osteoporosis/fracture. Conclusions: Reduced CCR predicted increased risks of osteoporosis/fracture, and significant causal effects support their associations. These findings indicated that the muscle-origin serum CCR was a potential biomarker to assess the risks of osteoporosis and fracture. [ABSTRACT FROM AUTHOR]

Background: Rheumatoid arthritis (RA) frequently presents with oral manifestations, including gingival inflammation, loose teeth, and mouth ulcers; however, the causal connections between these conditions remain unclear. This study aims to explore the genetic correlations and causal relationships between RA and prevalent oral phenotypes. Methods: Using summary data from genome-wide association studies of European populations, a cross-trait linkage disequilibrium score regression was conducted to estimate the genetic correlations between RA and six oral phenotypes. Subsequently, a two-sample Mendelian randomization (MR) approach was employed to assess the causal relationships, corroborated by various sensitivity analyses. Heterogeneity was addressed through the RadialMR method, while potential covariates were corrected using the multivariable MR approach. Results: A significant negative genetic correlation was detected between RA and denture usage (rg = −0.192, p = 4.88 × 10−8 ). Meanwhile, a heterogenous causal relationship between RA and mouth ulcers was observed (OR = 1.027 [1.005–1.05], p = 0.016, Pheterogeneity = 4.69 × 10−8 ), which remained robust across sensitivity analyses. After excluding outlier variants, the results demonstrated robustly consistent (OR = 1.021 [1.008–1.035], p = 1.99 × 10−3, Pheterogeneity = 0.044). However, upon adjusting for covariates such as smoking, alcohol consumption, body mass index, and obesity, the significance diminished, revealing no evidence to support independent genetic associations. Conclusion: Genetically predicted RA increases the risk of mouth ulcers, and a negative genetic correlation is identified between RA and denture use. The observed heterogeneity suggests that shared immunological mechanisms and environmental factors may play significant roles. These findings highlight the importance of targeted dental management strategies for RA patients. Further clinical guidelines are required to improve oral health among vulnerable RA patients. [ABSTRACT FROM AUTHOR]

Abstract Dietary habits are essential in the mean age at menarche (AAM). However, the causal relationship between these factors remains unclear. Therefore, this study aimed to elucidate the genetic relationship between dietary habits and AAM. Genetic summary statistics for dietary habits were obtained from the UK Biobank. GWAS summary data for AAM was obtained from the ReproGen Consortium. Linkage disequilibrium score regression was used to test genetic correlations between dietary habits and AAM. The Mendelian randomization (MR) analyses used the inverse-variance weighted method. Genetic correlations with AAM were identified for 29 candi-date dietary habits, such as milk type (skimmed, semi-skimmed, full cream; coefficient = 0.2704, P ldsc = 1.13 × 10−14). MR evaluations revealed that 19 dietary habits were associated with AAM, including bread type (white vs. any other; OR 1.71, 95% CI 1.28–2.29, P mr = 3.20 × 10−4), tablespoons of cooked vegetables (OR 0.437, 95% CI 0.29–0.67; P mr = 1.30 × 10−4), and cups of coffee per day (OR 0.72, 95% CI 0.57–0.92, P mr = 8.31 × 10−3). These results were observed to be stable under the sensitivity analysis. Our study provides potential insights into the genetic mechanisms underlying AAM and evidence that dietary habits are associated with AAM.

Background: Although there is a definite correlation between the Metabolic Syndrome (MetS) and Acute Pancreatitis (AP), cause is yet unknown. The current work combined linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) approaches to fill this important information gap. Methods: In this study, we harnessed the power of publicly available gene-wide association databases (GWAS) to explore the intricate relationship between MetS and its components with AP. The cornerstone of our analysis was the Inverse-Variance Weighted (IVW) method, serving as our primary analytical tool. In addition to IVW, we complemented our investigation with several other robust MR methods, including MR-Egger, Weighted Median, Maximum Likelihood, and MR-PRESSO. By employing this diverse set of analytical approaches, we sought to ensure the comprehensiveness and robustness of our findings. Result: LDSC regression indicated a genetic correlation between MetS and AP. Univariate MR results indicated a genetic association between MetS (OR = 1.084; 95% CI, 1.005–1.170; P = 0.037), BMI (OR = 1.459; 95% CI, 1.325–1.606; P = 1.46E-14), WHR (OR = 1.189; 95% CI, 1.068–1.323; P = 1.56 E-03), TG (OR = 1.110; 95% CI, 1.001–1.231; P = 0.047), and FI (OR = 1.798; 95% CI, 1.245–2.595; P = 1.74E-03) were able to significantly increase the risk of AP. The results of multivariate MR analysis revealed that these causality associations still existed. Conclusion: Our investigation has yielded compelling evidence that substantiates the presence of both a genetic correlation and a causal relationship between MetS and AP. [ABSTRACT FROM AUTHOR]

Dietary habits are essential in the mean age at menarche (AAM). However, the causal relationship between these factors remains unclear. Therefore, this study aimed to elucidate the genetic relationship between dietary habits and AAM. Genetic summary statistics for dietary habits were obtained from the UK Biobank. GWAS summary data for AAM was obtained from the ReproGen Consortium. Linkage disequilibrium score regression was used to test genetic correlations between dietary habits and AAM. The Mendelian randomization (MR) analyses used the inverse-variance weighted method. Genetic correlations with AAM were identified for 29 candi-date dietary habits, such as milk type (skimmed, semi-skimmed, full cream; coefficient = 0.2704, Pldsc = 1.13 × 10−14). MR evaluations revealed that 19 dietary habits were associated with AAM, including bread type (white vs. any other; OR 1.71, 95% CI 1.28–2.29, Pmr = 3.20 × 10−4), tablespoons of cooked vegetables (OR 0.437, 95% CI 0.29–0.67; Pmr = 1.30 × 10−4), and cups of coffee per day (OR 0.72, 95% CI 0.57–0.92, Pmr = 8.31 × 10−3). These results were observed to be stable under the sensitivity analysis. Our study provides potential insights into the genetic mechanisms underlying AAM and evidence that dietary habits are associated with AAM. [ABSTRACT FROM AUTHOR]

BackgroundHemorrhagic stroke (HS), a leading cause of death and disability worldwide, has not been clarified in terms of the underlying biomolecular mechanisms of its development. Circulating metabolites have been closely associated with HS in recent years. Therefore, we explored the causal association between circulating metabolomes and HS using Mendelian randomization (MR) analysis and identified the molecular mechanisms of effects.MethodsWe assessed the causal relationship between circulating serum metabolites (CSMs) and HS using a bidirectional two-sample MR method supplemented with five ways: weighted median, MR Egger, simple mode, weighted mode, and MR-PRESSO. The Cochran Q-test, MR-Egger intercept test, and MR-PRESSO served for the sensitivity analyses. The Steiger test and reverse MR were used to estimate reverse causality. Metabolic pathway analyses were performed using MetaboAnalyst 5.0, and genetic effects were assessed by linkage disequilibrium score regression. Significant metabolites were further synthesized using meta-analysis, and we used multivariate MR to correct for common confounders.ResultsWe finally recognized four metabolites, biliverdin (OR 0.62, 95% CI 0.40–0.96, PMVMR = 0.030), linoleate (18. 2n6) (OR 0.20, 95% CI 0.08–0.54, PMVMR = 0.001),1-eicosadienoylglycerophosphocholine* (OR 2.21, 95% CI 1.02–4.76, PMVMR = 0.044),7-alpha-hydroxy-3 -oxo-4-cholestenoate (7-Hoca) (OR 0.27, 95% CI 0.09–0.77, PMVMR = 0.015) with significant causal relation to HS.ConclusionWe demonstrated significant causal associations between circulating serum metabolites and hemorrhagic stroke. Monitoring, diagnosis, and treatment of hemorrhagic stroke by serum metabolites might be a valuable approach.

There is evidence to support a link between abnormal lipid metabolism and Alzheimer’s disease (AD) risk. Similarly, observational studies suggest a comorbid relationship between AD and coronary artery disease (CAD). However, the intricate biological mechanisms of AD are poorly understood, and its relationship with lipids and CAD traits remains unresolved. Conflicting evidence further underscores the ongoing investigation into this research area. Here, we systematically assess the cross-trait genetic overlap of AD with 13 representative lipids (from eight classes) and seven CAD traits, leveraging robust analytical methods, well-powered large-scale genetic data, and rigorous replication testing. Our main analysis demonstrates a significant positive global genetic correlation of AD with triglycerides and all seven CAD traits assessed—angina pectoris, cardiac dysrhythmias, coronary arteriosclerosis, ischemic heart disease, myocardial infarction, non-specific chest pain, and coronary artery disease. Gene-level analyses largely reinforce these findings and highlight the genetic overlap between AD and three additional lipids: high-density lipoproteins (HDLs), low-density lipoproteins (LDLs), and total cholesterol. Moreover, we identify genome-wide significant genes (Fisher’s combined p value [FCPgene] < 2.60 × 10−6) shared across AD, several lipids, and CAD traits, including WDR12, BAG6, HLA-DRA, PHB, ZNF652, APOE, APOC4, PVRL2, and TOMM40. Mendelian randomisation analysis found no evidence of a significant causal relationship between AD, lipids, and CAD traits. However, local genetic correlation analysis identifies several local pleiotropic hotspots contributing to the relationship of AD with lipids and CAD traits across chromosomes 6, 8, 17, and 19. Completing a three-way analysis, we confirm a strong genetic correlation between lipids and CAD traits—HDL and sphingomyelin demonstrate negative correlations, while LDL, triglycerides, and total cholesterol show positive correlations. These findings support genetic overlap between AD, specific lipids, and CAD traits, implicating shared but non-causal genetic susceptibility. The identified shared genes and pleiotropic hotspots are valuable targets for further investigation into AD and, potentially, its comorbidity with CAD traits.

BackgroundPostpartum depression (PPD) is a type of depressive episode related to parents after childbirth, which causes a variety of symptoms not only for parents but also affects the development of children. The causal relationship between potential risk factors and PPD remains comprehensively elucidated.MethodsLinkage disequilibrium score regression (LDSC) analysis was conducted to screen the heritability of each instrumental variant (IV) and to calculate the genetic correlations between effective causal factors and PPD. To search for the causal effect of multiple potential risk factors on the incidence of PPD, random effects of the inverse variance weighted (IVW) method were applied. Sensitivity analyses, including weighted median, MR-Egger regression, Cochrane’s Q test, and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO), were performed to detect potential Mendelian randomization (MR) assumption violations. Multivariable MR (MVMR) was conducted to control potential multicollinearity.ResultsA total of 40 potential risk factors were investigated in this study. LDSC regression analysis reported a significant genetic correlation of potential traits with PPD. MR analysis showed that higher body mass index (BMI) (Benjamini and Hochberg (BH) corrected p = 0.05), major depression (MD) (BH corrected p = 5.04E-19), and schizophrenia (SCZ) (BH corrected p = 1.64E-05) were associated with the increased risk of PPD, whereas increased age at first birth (BH corrected p = 2.11E-04), older age at first sexual intercourse (BH corrected p = 3.02E-15), increased average total household income before tax (BH corrected p = 4.57E-02), and increased years of schooling (BH corrected p = 1.47E-11) led to a decreased probability of PPD. MVMR analysis suggested that MD (p = 3.25E-08) and older age at first birth (p = 8.18E-04) were still associated with an increased risk of PPD.ConclusionIn our MR study, we found multiple risk factors, including MD and younger age at first birth, to be deleterious causal risk factors for PPD.

Background: Observational studies have provided evidence of a close association between gut microbiota and the progression of chronic hepatitis B (CHB). However, establishing a causal relationship between gut microbiota and CHB remains a subject of investigation. Methods: Genome-wide association study (GWAS) summary data of gut microbiota came from the MiBioGen consortium, while the GWAS summary data of CHB came from the Medical Research Council Integrative Epidemiology Unit (IEU) Open GWAS project. Based on the maximum likelihood (ML), Mendelian randomization (MR)-Egger regression, inverse variance weighted (IVW), MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO), and weighted-mode and weighted-median methods, we conducted a bidirectional, two-sample, MR analysis to explore the causal relationship between the gut microbiota and CHB. Additionally, we evaluated the genetic associations between individual gut microbes and CHB using the Linkage disequilibrium score regression (LDSC) program. Results: According to the IVW method estimates, genetically predicted class Alphaproteobacteria (odds ratio [OR] = 0.57; 95% confidence interval [CI], 0.34-0.96; false discovery rate [FDR] = 0.046), genus Family XIII AD3011 group (OR = 0.60; 95% CI, 0.39-0.91; FDR = 0.026), genus Prevotella 7 (OR = 0.73; 95% CI, 0.56-0.94; FDR = 0.022) exhibited a protective effect against CHB. On the other hand, family Family XIII (OR = 1.79; 95% CI, 1.03-3.12; FDR = 0.061), genus Eggerthella group (OR = 1.34; 95% CI, 1.04-1.74; FDR = 0.043), genus Eubacterium ventriosum group (OR = 1.59; 95% CI, 1.01-2.51; FDR = 0.056), genus Holdemania (OR = 1.35; 95% CI, 1.00-1.82; FDR = 0.049), and genus Ruminococcus gauvreauii group (OR = 1.69; 95% CI, 1.10-2.61; FDR = 0.076) were associated with an increased risk of CHB. The results from LDSC also indicated a significant genetic correlation between most of the aforementioned gut microbiota and CHB. Our reverse MR analysis demonstrated no causal relationship between genetically predicted CHB and gut microbiota, and we observed no significant horizontal pleiotropy or heterogeneity of instrumental variables (IVs). Conclusion: In this study, we identified three types of gut microbiota with a protective effect on CHB and five types with an adverse impact on CHB. We postulate that this information will facilitate the clinical prevention and treatment of CHB through fecal microbiota transplantation. [ABSTRACT FROM AUTHOR]

Abstract Background Longevity is one of the most complex phenotypes, and its genetic basis remains unclear. This study aimed to explore the genetic correlation and potential causal association between gut microbiota and longevity. Results Linkage disequilibrium score (LDSC) regression analysis and a bi-directional two-sample Mendelian Randomization (MR) analysis were performed to analyze gut microbiota and longevity-related traits. LDSC analysis detected four candidate genetic correlations, including Veillonella (genetic correlation = 0.5578, P = 4.67 × 10− 2) and Roseburia (genetic correlation = 0.4491, P = 2.67 × 10− 2) for longevity, Collinsella (genetic correlation = 0.3144, P = 4.07 × 10− 2) for parental lifespan and Sporobacter (genetic correlation = 0.2092, P = 3.53 × 10− 2) for healthspan. Further MR analysis observed suggestive causation between Collinsella and parental longevity (father’s age at death) (weighted median: b = 1.79 × 10− 3, P = 3.52 × 10− 2). Reverse MR analysis also detected several causal effects of longevity-related traits on gut microbiota, such as longevity and Sporobacter (IVW: b = 7.02 × 10− 1, P = 4.21 × 10− 25). Statistical insignificance of the heterogeneity test and pleiotropy test supported the validity of the MR study. Conclusion Our study found evidence that gut microbiota is causally associated with longevity, or vice versa, providing novel clues for understanding the roles of gut microbiota in aging development.

Abstract Coronavirus disease 2019 (COVID‐19) has experienced a global pandemic, and currently, the emergence of its variants has posed challenges in terms of prevention and treatment. Nonetheless, the effect of COVID‐19 infection on female reproductive function is unclear. This study aimed to systematically evaluate for the first time the causal effect of COVID‐19 on female reproductive function. Genetic correlations were assessed using linkage disequilibrium score regression. Mendelian randomization (MR) analysis was performed using summary statistics of two variables, including COVID‐19 severity and eight female reproductive traits. The three degrees of severity had genetically significant associations with sex hormone‐binding globulin (SHBG) concentrations (rg = –0.153, p = 0.004; rg = –0.187, p 0.05). In conclusion, COVID‐19 was causally associated with lower SHBG and total testosterone concentrations, offering invaluable insights that will help guide clinical decision‐making.

Abstract Background Osteoporosis is a common metabolic bone disease that is characterized by low bone mass. However, limited efforts have been made to explore the functional relevance of the blood proteome to bone mineral density across different life stages. Methods Using genome-wide association study summary data of the blood proteome and two independent studies of bone mineral density, we conducted a genetic correlation scan of bone mineral density and the blood proteome. Linkage disequilibrium score regression analysis was conducted to assess genetic correlations between each of the 3283 plasma proteins and bone mineral density. Results Linkage disequilibrium score regression identified 18 plasma proteins showing genetic correlation signals with bone mineral density in the TB-BMD cohort, such as MYOM2 (coefficient = 0.3755, P value = 0.0328) among subjects aged 0 ~ 15, POSTN (coefficient = − 0.5694, P value = 0.0192) among subjects aged 30 ~ 45 and PARK7 (coefficient = − 0.3613, P value = 0.0052) among subjects aged over 60. Conclusions Our results identified multiple plasma proteins associated with bone mineral density and provided novel clues for revealing the functional relevance of plasma proteins to bone mineral density.

Background: Longevity is one of the most complex phenotypes, and its genetic basis remains unclear. This study aimed to explore the genetic correlation and potential causal association between gut microbiota and longevity. Results: Linkage disequilibrium score (LDSC) regression analysis and a bi-directional two-sample Mendelian Randomization (MR) analysis were performed to analyze gut microbiota and longevity-related traits. LDSC analysis detected four candidate genetic correlations, including Veillonella (genetic correlation = 0.5578, P = 4.67 × 10− 2) and Roseburia (genetic correlation = 0.4491, P = 2.67 × 10− 2) for longevity, Collinsella (genetic correlation = 0.3144, P = 4.07 × 10− 2) for parental lifespan and Sporobacter (genetic correlation = 0.2092, P = 3.53 × 10− 2) for healthspan. Further MR analysis observed suggestive causation between Collinsella and parental longevity (father's age at death) (weighted median: b = 1.79 × 10− 3, P = 3.52 × 10− 2). Reverse MR analysis also detected several causal effects of longevity-related traits on gut microbiota, such as longevity and Sporobacter (IVW: b = 7.02 × 10− 1, P = 4.21 × 10− 25). Statistical insignificance of the heterogeneity test and pleiotropy test supported the validity of the MR study. Conclusion: Our study found evidence that gut microbiota is causally associated with longevity, or vice versa, providing novel clues for understanding the roles of gut microbiota in aging development. [ABSTRACT FROM AUTHOR]

Objectives: The role of neurological proteins in the development of bipolar disorder (BD) and schizophrenia (SCZ) remains elusive now. The current study aims to explore the potential genetic correlations of plasma neurological proteins with BD and SCZ. Methods: By using the latest genome-wide association study (GWAS) summary data of BD and SCZ (including 41,917 BD cases, 11,260 SCZ cases, and 396,091 controls) derived from the Psychiatric GWAS Consortium website (PGC) and a recently released GWAS of neurological proteins (including 750 individuals), we performed a linkage disequilibrium score regression (LDSC) analysis to detect the potential genetic correlations between the two common psychiatric disorders and each of the 92 neurological proteins. Two-sample Mendelian randomisation (MR) analysis was then applied to assess the bidirectional causal relationship between the neurological proteins identified by LDSC, BD and SCZ. Results: LDSC analysis identified one neurological protein, NEP , which shows suggestive genetic correlation signals for both BD (coefficient = −0.165, p value = 0.035) and SCZ (coefficient = −0.235, p value = 0.020). However, those association did not remain significant after strict Bonferroni correction. Two sample MR analysis found that there was an association between genetically predicted level of NEP protein, BD (odd ratio [OR] = 0.87, p value = 1.61 × 10−6) and SCZ (OR = 0.90, p value = 4.04 × 10−6). However, in the opposite direction, there is no genetically predicted association between BD, SCZ, and NEP protein level. Conclusion: This study provided novel clues for understanding the genetic effects of neurological proteins on BD and SCZ. [ABSTRACT FROM AUTHOR]

Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ∼150,000 individuals give a higher accuracy than LDSC estimates based on ∼400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.

Purpose: Cognitive impairment is prevalent among individuals with epilepsy, and increasing evidence indicates that genetic factors can underlie this relationship. However, the extent to which epilepsy subtypes differ in their genetic relationship with cognitive function, and information about the specific genetic variants involved remain largely unknown., Methods: We investigated the genetic relationship between epilepsies and general cognitive ability (COG) using complementary statistical tools, including linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR). We analyzed genome-wide association study data on COG (n = 269,867) and common epilepsies (n = 27,559 cases, 42,436 controls), including the broad phenotypes 'all epilepsy', focal epilepsies and genetic generalized epilepsies (GGE), as well as specific subtypes. We functionally annotated the identified loci using several biological resources and validated the results in independent samples., Results: Using MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than 'all epilepsy', GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k - 2.9k variants). The other epilepsy phenotypes were insufficiently powered for MiXeR analysis. We quantified extensive genetic overlap between COG and epilepsy types, but with varying negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and 'all epilepsy', and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), 'all epilepsy' (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 3.62 × 10 -7 ; 'all epilepsy': p = 2.58 × 10 -3 )., Conclusion: Our study further dissects the substantial genetic basis shared between epilepsies and COG and identifies novel shared loci. An improved understanding of the genetic relationship between epilepsies and COG may lead to the development of novel comorbidity-targeted epilepsy treatments., Competing Interests: Declaration of competing interest O.A.A. has received speaker's honorarium from Lundbeck, Sunovion, Takeda, Janssen and is a consultant for CorTechs.ai and Precision Health AS. A.M.D. is a founder of and holds equity interest in CorTechs Labs and serves on its scientific advisory board. He is also a member of the Scientific Advisory Board of Healthlytix and receives research funding from General Electric Healthcare (GEHC). The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict-of-interest policies. Remaining authors have no conflicts of interest to declare., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Multiple studies suggest a potential connection between the gut microbiome and asthma. Our objective is to use advanced genetic and metagenomic techniques to elucidate the causal relationships and underlying mechanisms between gut microbiota and asthma., Methods: The study utilized comprehensive Linkage Disequilibrium Score Regression (LDSC) and Mendelian randomization (MR) analyses to examine the relationship between 119 gut microbiota genera and asthma, using publicly accessible genome-wide association studies (GWAS). The meta-analysis synthesized summary effect estimates obtained from LDSC, forward MR, and reverse MR. The MiBioGen collaboration, involving 18,340 individuals, identified genetic variations associated with gut bacteria. Asthma data were collected from the UK Biobank, FinnGen, and GERA, encompassing a total of 82,060 cases and 641,049 controls., Results: LDSC analysis revealed significant negative genetic correlations between asthma and RuminococcaceaeUCG004 (Rg = -0.55, p = 7.66 × 10 -5 ) and Subdoligranulum (Rg = -0.35, p = 3.61 × 10 -4 ). Forward MR analysis suggested associations between Butyricicoccus (OR = 0.92, p = 0.01), Turicibacter (OR = 0.95, p = 0.025), Butyrivibrio (OR = 0.98, p = 0.047), and reduced asthma risk. Conversely, Coprococcus2 (OR = 1.10, p = 0.035) and Roseburia (OR = 1.07, p = 0.039) were associated with increased risk. Reverse MR analysis indicated significant associations between genetically predicted asthma and Eubacteriumxylanophilumgroup (Beta = -0.08, p = 9.25 × 10 -7 ), LachnospiraceaeNK4A136group (Beta = -0.05, p = 1.26 × 10 -4 ), and Eisenbergiella (Beta = 0.06, p = 0.015, Rg&#95; P = 0.043)., Conclusion: The findings underscore significant genetic correlations and causal relationships between specific gut microbiota and asthma. These insights highlight the potential of gut microbiota as both markers and modulators of asthma risk, offering new avenues for targeted therapeutic strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zheng, Chen, Zhuang, Zhao, Qian and Shi.)

Modifiable factors play a key role in modulating the risk of Parkinson's disease (PD). We performed a two-stage genetic analyses to explore genetic correlations and causal relationships between modifiable factors and PD risk. The exposures consisted of GWAS summary statistics of 516 modifiable factors from UK Biobank. The outcome was summary statistics from the large meta-analyses of GWAS on PD. We first utilized linkage disequilibrium score (LDSC) regression to explore genetic correlations between a 516 modifiable factors and PD risk. Two-sample Mendelian randomization (MR) was then used to validate potentially causal relationships between genetically correlated factors and PD risk. LDSC regression showed that PD shared polygenic risk genetic factors with 3 major modifiable factors, including positive correlations with higher educational attainment, and negative correlations with increased adiposity, lower educational attainment and smoking. MR showed causal evidence that increased adiposity was associated with decreased PD risk and higher educational attainment was associated with increased PD risk. Our findings may provide insights in the etiology and pathological mechanisms of PD.

Objective: Osteoporosis (OP) is the most common bone disease. The genetic and metabolic factors play important roles in OP development. However, the genetic basis of OP is still elusive. The study aimed to explore the relationships between OP and dietary habits. Methods: This study used large-scale genome-wide association study (GWAS) summary statistics from the UK Biobank to explore potential associations between OP and 143 dietary habits. The GWAS summary data of OP included 9434 self-reported OP cases and 444,941 controls, and the GWAS summary data of the dietary habits included 455,146 participants of European ancestry. Linkage disequilibrium score regression (LDSC) was used to detect the genetic correlations between OP and each of the 143 dietary habits, followed by Mendelian randomization (MR) analysis to further assess the causal relationship between OP and candidate dietary habits identified by LDSC. Results: The LDSC analysis identified seven candidate dietary habits that showed genetic associations with OP including cereal type such as biscuit cereal (coefficient = −0.1693, p value = 0.0183), servings of raw vegetables per day (coefficient = 0.0837, p value = 0.0379), and spirits measured per month (coefficient = 0.115, p value = 0.0353). MR analysis found that OP and PC17 (butter) (odds ratio [OR] = 0.974, 95% confidence interval [CI] = (0.973, 0.976), p value = 0.000970), PC35 (decaffeinated coffee) (OR = 0.985, 95% CI = (0.983, 0.987), p value = 0.00126), PC36 (overall processed meat intake) (OR = 1.035, 95% CI = (1.033, 1.037), p value = 0.000976), PC39 (spirits measured per month) (OR = 1.014, 95% CI = (1.011, 1.015), p value = 0.00153), and servings of raw vegetables per day (OR = 0.978, 95% CI = (0.977, 0.979), p value = 0.000563) were clearly causal. Conclusions: Our findings provide new clues for understanding the genetic mechanisms of OP, which focus on the possible role of dietary habits in OP pathogenesis. [ABSTRACT FROM AUTHOR]

Background: Osteoporosis is a common metabolic bone disease that is characterized by low bone mass. However, limited efforts have been made to explore the functional relevance of the blood proteome to bone mineral density across different life stages.Methods: Using genome-wide association study summary data of the blood proteome and two independent studies of bone mineral density, we conducted a genetic correlation scan of bone mineral density and the blood proteome. Linkage disequilibrium score regression analysis was conducted to assess genetic correlations between each of the 3283 plasma proteins and bone mineral density.Results: Linkage disequilibrium score regression identified 18 plasma proteins showing genetic correlation signals with bone mineral density in the TB-BMD cohort, such as MYOM2 (coefficient = 0.3755, P value = 0.0328) among subjects aged 0 ~ 15, POSTN (coefficient = - 0.5694, P value = 0.0192) among subjects aged 30 ~ 45 and PARK7 (coefficient = - 0.3613, P value = 0.0052) among subjects aged over 60.Conclusions: Our results identified multiple plasma proteins associated with bone mineral density and provided novel clues for revealing the functional relevance of plasma proteins to bone mineral density. [ABSTRACT FROM AUTHOR]

We aim to systematically explore the potential genetic correlations between five major psychiatric disorders and gestational ages. Genome‐wide association study (GWAS) summary datasets of attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) in discovery were downloaded from the Psychiatric GWAS Consortium (PGC) website. Suggestive (Raw p < 0.05) genetic associations in the discovery phrase were further replicated in independent GWASs which downloaded from PGC, the FinnGen study or Integrative Psychiatric Research (iPSYCH) website. GWASs of gestational duration, preterm and post‐term birth were derived from previous studies of infants from the Early Growth Genetics (EGG) Consortium, the iPSYCH study, and the Genomic and Proteomic Network for Preterm Birth Research (GPN). We calculated genetic correlations using linkage disequilibrium score (LDSC) regression. Mendelian randomization (MR) analyses were performed to investigate the causal effects. We identified four suggestive genetic correlations between psychiatric disorders and gestational age factors in discovery LDSC and two replicated in a confirmation LDSC: gestational duration and ADHD (rg = −0.1405, FDR p = 0.0406), post‐term birth and SCZ (rg = −0.2003, FDR p = 0.0042). We also observed causal effect of post‐term birth on SCZ by MR (PWeighted median = 0.037, PInverse variance weighted = 0.007). Our analysis suggested no significant evidence of horizontal pleiotropy and heterogeneity. This study showed the genetic correlation evidences between gestational age phenotypes and psychiatric disorders, providing novel clues for understanding the pathogenic factors of common psychiatric disorders. Lay Summary: Whereas gestational age factors were reported to be associated with psychiatric disorders, the genetic relationship and causality remain to be revealed. The present study reported the first large‐scale genetic correlations investigation of the associations between gestational age phenotypes and psychiatric disorders. Results indicate causal relationships between post‐term birth and schizophrenia (SCZ), as well as suggestive genetic correlations between gestational duration and attention deficit/hyperactivity disorder (ADHD). This study provided novel clues for understanding the pathogenic factors of common psychiatric disorders. [ABSTRACT FROM AUTHOR]

• The Maturation stages of T cell serve as a protective factor for epilepsy and generalized epilepsy. • Myeloid cell is specifically protective for generalized epilepsy. • cDC may have the ability to promote development of generalized epilepsy with tonic-clonic seizures. While an increasing number of researchers have focused on the correlation between the immune system and epilepsy, the precise causal role of immune cells in epilepsy continues to elude scientific understanding. The aim of the study was to examine the causal relationship between peripheral immune phenotypes and epilepsy. Mendelian randomization (MR) analysis and linkage disequilibrium score regression (LDSC) were utilized to determine the causal relationship between 731 immune cell traits and various types of epilepsy in this study. LDSC revealed that 80 immunophenotypes showed genetic correlation with epilepsy, including 58 immunophenotypes associated with a single type of epilepsy (72.5 %),14 immunophenotypes associated with two types of epilepsy (17.5 %),7 immunophenotypes with 3 types of epilepsy (8.75 %) and 1 immunophenotype with 5 types of epilepsy (1.25 %). Although none of the types of epilepsy had a statistically significant effect on immunophenotypes, it is noteworthy that the MR revealed the protective effects of five immunophenotypes on epilepsy: CD45RA+CD8br AC (OR:0.86, 95 %CI:0.80–0.93), FSC-A on myeloid DC (OR:0.95, 95 %CI:0.91–0.98）, CM CD8br AC (OR:0.69, 95 %CI:0.59––0.82), CD33 on CD66b++ Myeloid cell (OR:0.88, 95 %CI:0.83–0.93) and CD127 on CD28- CD8br (OR:0.97, 95 %CI:0.95–0.98). Additionally, harmful effects were observed for two immunophenotypes on epilepsy:CD4 Treg %CD4 (OR:1.04, 95 %CI:1.02–1.06) and SSC-A on plasmacytoid DC (OR:1.01, 95 %CI:1.00–1.02). Our research has demonstrated the causal connections between immune cells and epilepsy, potentially providing valuable insights for future clinical studies. [ABSTRACT FROM AUTHOR]

Background: Rheumatoid arthritis (RA) is a complex disease with several risk factors. The effects of blood metabolites on RA remains elusive. We conducted a genetic correlation scan to explore the relevance of blood metabolism with RA. Method: The genome‐wide association study (GWAS) dataset of RA(2014) was obtained from a large scale meta‐analysis, including 29,880 RA cases and 73,758 controls. The GWAS datasets of 529 blood metabolites were derived from a recently published study. Linkage disequilibrium score regression (LDSC) analysis was performed to evaluate the genetic correlation between each of the blood metabolite and RA(2014). Then we used another GWAS data of RA(2021) and blood metabolites for LDSC analysis to verify whether the same blood metabolites were genetically correlated with RA. Mendelian randomization (MR) analysis was then applied to assess the causal relationship between the significant blood metabolites identified by LDSC and RA(2014). Result: Six suggestive blood metabolites were identified for RA(2014), including 10‐Undecenoate (correlation coefficient = −0.1686, p value = 0.0394), isovalerylcarnitine (correlation coefficient = 0.1660 p value = 0.0273), proline (correlation coefficient = 0.1647, p value = 0.0145), pantothenate (correlation coefficient = −0.3311, p value = 0.0078), tyrosine (correlation coefficient = 0.1735, p value = 0.0010), X‐14057 (correlation coefficient = 0.2695, p value = 0.0373). We identified four blood metabolites may have genetic correlations with RA(2021), including oleoylcarnitine (correlation coefficient = 0.1927, p value = 0.0432), levulinate (correlation coefficient = 0.1008, p value = 0.0413), pantothenate (correlation coefficient = −0.2311, p value = 0.0180), tyrosine (correlation coefficient = 0.1301, p value = 0.0078). There are two identical blood metabolites were found to be related with RA: pantothenate and tyrosine. It was found that there was a significant positive causal relationship between RA (exposure) and 10‐Undecenoate (outcome) (β = 0.0077, SE = 0.0033, p = 0.0192) by MR analysis. Conclusion: We investigated the genetic correlation and causal relationship between RA and blood metabolites by LDSC and MR analysis. These results may provide novel insights into the genetic mechanism of RA. [ABSTRACT FROM AUTHOR]

Background Depression is a debilitating mental disorder that often coexists with anxiety. The genetic mechanisms of depression and anxiety have considerable overlap, and studying depression in non-anxiety samples could help to discover novel gene. We assess the genetic variation of depression in non-anxiety samples, using genome-wide association studies (GWAS) and linkage disequilibrium score regression (LDSC). Methods The GWAS of depression score and self-reported depression were conducted using the UK Biobank samples, comprising 99,178 non-anxiety participants with anxiety score <5 and 86,503 non-anxiety participants without self-reported anxiety, respectively. Replication analysis was then performed using two large-scale GWAS summary data of depression from Psychiatric Genomics Consortium (PGC). LDSC was finally used to evaluate genetic correlations with 855 health-related traits based on the primary GWAS. Results Two genome-wide significant loci for non-anxiety depression were identified: rs139702470 (p = 1.54 × 10−8, OR = 0.29) locate in PIEZO2, and rs6046722 (p = 2.52 × 10−8, OR = 1.09) locate in CFAP61. These associated genes were replicated in two GWAS of depression from PGC, such as rs1040582 (preplication GWAS1 = 0.02, preplication GWAS2 = 2.71 × 10−3) in CFAP61, and rs11661122 (preplication GWAS1 = 8.16 × 10−3, preplication GWAS2 = 8.08 × 10−3) in PIEZO2. LDSC identified 19 traits genetically associated with non-anxiety depression (p < 0.001), such as marital separation/divorce (rg = 0.45, SE = 0.15). Conclusions Our findings provide novel clues for understanding of the complex genetic architecture of depression. [ABSTRACT FROM AUTHOR]

Objectives: Gout is a common inflammatory arthritis, which is caused by hyperuricemia. Limited efforts have been paid to systematically explore the relationships between gout and common psychiatric disorders. Methods: Genome-wide association study summary data of gout were obtained from the GeneATLAS, which contained 452,264 participants including 3,528 gout cases. Linkage disequilibrium score regression (LDSC) was first conducted to evaluate the genetic relationships between gout and 5 common psychiatric disorders. Transcriptome-wide association studies (TWAS) was then conducted to explore the potential biological mechanism underlying the observed genetic correlation between gout and attention-deficit hyperactivity disorder (ADHD). The Database for Annotation, Visualization and Integrated Discovery online functional annotation system was applied for pathway enrichment analysis and gene ontology enrichment analysis. Results: LDSC analysis observed significant genetic correlation between gout and ADHD (genetic correlation coefficients = 0.29, standard error = 0.09 and P value = 0.0015). Further TWAS of gout identified 105 genes with P value < 0.05 in muscle skeleton and 228 genes with P value < 0.05 in blood. TWAS of ADHD also detected 300 genes with P value < 0.05 in blood. Further comparing the TWAS results identified 9 common candidate genes shared by gout and ADHD, such as CD300C (P gout = 0.0040; P ADHD = 0.0226), KDM6B (P gout = 0.0074; P ADHD = 0.0460), and BST1 (P gout = 0.0349; P ADHD = 0.03560). Conclusion: We observed genetic correlation between gout and ADHD and identified multiple candidate genes for gout and ADHD. [ABSTRACT FROM AUTHOR]

Purpose: We used a polygenic risk score (PRS) to identify high-risk groups for primary open-angle glaucoma (POAG) within population-based cohorts., Design: Secondary analysis of 4 prospective population-based studies., Participants: We included four European-ancestry cohorts: the United States-based Nurses' Health Study, Nurses' Health Study 2, and the Health Professionals Follow-up Study and the Rotterdam Study (RS) in The Netherlands. The United States cohorts included female nurses and male health professionals ≤ 55 years of age. The RS included residents ≤ 45 years of age living in Rotterdam, The Netherlands., Methods: Polygenic risk score weights were estimated by applying the lassosum method on imputed genotype and phenotype data from the UK Biobank. This resulted in 144 020 variants, single nucleotide polymorphism and insertions or deletions, with nonzero βs that we used to calculate a PRS in the target populations. Using multivariable Cox proportional hazard models, we estimated the relationship between the standardized PRS and relative risk for POAG. Additionally, POAG prediction was tested by calculating these models' concordance (Harrell's C statistic). Finally, we assessed the association between PRS tertiles and glaucoma-related traits., Main Outcome Measures: The relative risk for POAG and Harrell's C statistic., Results: Among 1046 patients and 38 809‬ control participants, the relative risk (95% confidence interval) for POAG for participants in the highest PRS quintile was 3.99 (3.08-5.18) times higher in the United States cohorts and 4.89 (2.93-8.17) times higher in the RS, compared with participants with median genetic risk (third quintile). Combining age, sex, intraocular pressure of more than 25 mmHg, and family history resulted in a meta-analyzed concordance of 0.75 (95% CI, 0.73-0.75). Adding the PRS to this model improved the concordance to 0.82 (95% CI, 0.80-0.84). In a meta-analysis of all cohorts, patients in the highest tertile showed a larger cup-to-disc ratio at diagnosis, by 0.10 (95% CI, 0.06 0.14), and a 2.07-fold increased risk of requiring glaucoma surgery (95% CI, 1.19-3.60)., Conclusions: Incorporating a PRS into a POAG predictive model improves identification concordance from 0.75 up to 0.82, supporting its potential for guiding more cost-effective screening strategies., Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2024 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Background: Numerous research studies have indicated a possible association between type 2 diabetes (T2DM) and gut microbiota. To explore specific metabolic pathways connecting gut microbiota and T2DM, we employed Mendelian randomization (MR) and linkage disequilibrium score regression (LDSC) techniques., Methods: This research utilized data from genome-wide association studies (GWAS) that are publicly accessible. We evaluated the genetic correlation between gut microbiota and T2DM using LDSC. Causality was primarily determined through the inverse variance weighted (IVW) method. To verify the robustness of our results, we conducted sensitivity analyses using several approaches, including the weighted median, MR-Egger, and MR-PRESSO. We integrated summary effect estimates from LDSC, along with forward and reverse MR, into a meta-analysis for T2DM using various data sources. Additionally, mediation analysis was performed to explore the impact of plasma metabolites on the relationship between gut microbiota and T2DM., Results: Our study indicated a significant genetic correlation between genus RuminococcaceaeUCG005 (Rg = -0.26, Rg&#95;P = 2.07×10 -4 ) and T2DM. Moreover, the forward MR analysis identified genus RuminococcaceaeUCG010 (OR = 0.857, 95% CI 0.795, 0.924; P = 6.33×10 -5 ) and order Clostridiales (OR = 0.936, 95% CI 0.878, 0.997; P = 0.039) as being significantly associated with a decreased risk of T2DM. The analysis also highlighted several plasma metabolites as significant mediators in these relationships, with metabolites like octadecadienedioate (C18:2-DC) and branched chain 14:0 dicarboxylic acid being notably involved., Conclusion: The findings demonstrate a significant impact of gut microbiota on T2DM via plasma metabolites, suggesting potential metabolic pathways for therapeutic targeting. This study enhances our understanding of the microbiota's role in T2DM pathogenesis and supports the development of microbiota-based interventions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zheng, Chen, Zhuang, Zhao, Qian, Xu and Fan.)

Introduction: Observational studies have demonstrated strong correlations between metabolic syndrome (MetS) and its related traits. To gain insight into the genetic architecture and molecular mechanism of MetS, we investigated the shared genetic basis of MetS and its related traits and further tested their causal relationships. Methods: Using summary statistics from genome-wide association analyses of about 72,000 subjects from the Korean Genome and Epidemiological Study (KoGES), we conducted genome-wide multi-trait analyses to quantify the overall genetic correlation and Mendelian randomization analyses to infer the causal relationships between traits of interest. Results: Genetic correlation analyses revealed a significant correlation of MetS with its related traits, such as obesity traits (body mass index and waist circumference), lipid traits (triglyceride and high-density lipoprotein cholesterol), glycemic traits (fasting plasma glucose and hemoglobin A1C), and blood pressure (systolic and diastolic). Mendelian randomization analyses further demonstrated that the MetS-related traits showing significant overall genetic correlation with MetS could be genetically determined risk factors for MetS. Discussion: Our study suggests a shared genetic basis of MetS and its related traits and provides novel insights into the biological mechanisms underlying these complex traits. Our findings further inform public health interventions by supporting the important role of the management of metabolic risk factors such as obesity, unhealthy lipid profiles, diabetes, and high blood pressure in the prevention of MetS., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Kim and Cho.)

Background: Observational studies have shown possible bidirectional association between type 2 diabetes (T2D) and pulmonary function, but the causality is not well defined. The purpose of this study is to investigate genetic correlation and causal relationship of T2D and glycemic traits with pulmonary function. Methods: By leveraging summary statistics from large-scale genome-wide association studies, linkage disequilibrium score regression was first implemented to quantify genetic correlations between T2D, glycemic traits, and several spirometry indices. Then both univariable and multivariable Mendelian randomization analyses along with multiple pleiotropy-robust methods were performed in two directions to assess the causal nature of these relationships. Results: Forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) showed significant genetic correlations with T2D and fasting insulin levels and suggestive genetic correlations with fasting glucose and hemoglobin A1c. In Mendelian randomization analyses, genetically predicted higher FEV1 (OR = 0.77; 95% CI = 0.63, 0.94) and FVC (OR = 0.82; 95% CI = 0.68, 0.99) were significantly associated with lower risk of T2D. Conversely, genetic predisposition to higher risk of T2D exhibited strong association with reduced FEV1 (beta = −0.062; 95% CI = −0.100, −0.024) and FEV1 (beta = −0.088; 95% CI = −0.126, −0.050) and increased FEV1/FVC ratio (beta = 0.045; 95% CI = 0.012, 0.078). We also found a suggestive causal effect of fasting glucose on pulmonary function and of pulmonary function on fasting insulin and proinsulin. Conclusions: The present study provided supportive evidence for genetic correlation and bidirectional causal association between T2D and pulmonary function. Further studies are warranted to clarify possible mechanisms related to lung dysfunction and T2D, thus offering a new strategy for the management of the two comorbid diseases. [ABSTRACT FROM AUTHOR]

Background: We aimed to explore the genetic correlation and bidirectional causal relationships between low back pain (LBP) and three neurodegenerative diseases, Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). Methods: Summary-level statistics were obtained from genome-wide association studies of LBP (n = 177,860), AD (n = 63,926), PD (n = 482,730), and ALS (n = 80,610). We implemented linkage disequilibrium score regression to calculate heritability estimates and genetic correlations. To investigate possible causal associations between LBP and three neurodegenerative diseases, we also conducted a bidirectional two-sample Mendelian randomization (MR) study. Inverse variance-weighted MR was employed as the primary method to generate overall estimates, whereas complementary approaches and sensitivity analyses were conducted to confirm the consistency and robustness of the findings. Results: There was no evidence of genetic correlations between LBP and AD (Rg = −0.033, p = 0.766). MR analyses did not support the causal effect of LBP on AD (OR = 1.031; 95% CI, 0.924–1.150; p = 0.590) or the effect of AD on LBP (OR = 0.963; 95% CI, 0.923–1.006; p = 0.090). Likewise, this study failed to identify genetic correlations between LBP and two other neurodegenerative diseases. MR results of the associations of LBP with PD and ALS, and the reverse associations, did not reach Bonferroni-corrected significance. Conclusion: The study did not support genetic correlations or causations between LBP and three common neurodegenerative diseases, AD, PD, and ALS in the European population. [ABSTRACT FROM AUTHOR]

BackgroundObservational studies have shown possible bidirectional association between type 2 diabetes (T2D) and pulmonary function, but the causality is not well defined. The purpose of this study is to investigate genetic correlation and causal relationship of T2D and glycemic traits with pulmonary function.MethodsBy leveraging summary statistics from large-scale genome-wide association studies, linkage disequilibrium score regression was first implemented to quantify genetic correlations between T2D, glycemic traits, and several spirometry indices. Then both univariable and multivariable Mendelian randomization analyses along with multiple pleiotropy-robust methods were performed in two directions to assess the causal nature of these relationships.ResultsForced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) showed significant genetic correlations with T2D and fasting insulin levels and suggestive genetic correlations with fasting glucose and hemoglobin A1c. In Mendelian randomization analyses, genetically predicted higher FEV1 (OR = 0.77; 95% CI = 0.63, 0.94) and FVC (OR = 0.82; 95% CI = 0.68, 0.99) were significantly associated with lower risk of T2D. Conversely, genetic predisposition to higher risk of T2D exhibited strong association with reduced FEV1 (beta = −0.062; 95% CI = −0.100, −0.024) and FEV1 (beta = −0.088; 95% CI = −0.126, −0.050) and increased FEV1/FVC ratio (beta = 0.045; 95% CI = 0.012, 0.078). We also found a suggestive causal effect of fasting glucose on pulmonary function and of pulmonary function on fasting insulin and proinsulin.ConclusionsThe present study provided supportive evidence for genetic correlation and bidirectional causal association between T2D and pulmonary function. Further studies are warranted to clarify possible mechanisms related to lung dysfunction and T2D, thus offering a new strategy for the management of the two comorbid diseases.

Background: We aimed to explore the genetic correlation and bidirectional causal relationships between low back pain (LBP) and three neurodegenerative diseases, Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS).Methods: Summary-level statistics were obtained from genome-wide association studies of LBP (n = 177,860), AD (n = 63,926), PD (n = 482,730), and ALS (n = 80,610). We implemented linkage disequilibrium score regression to calculate heritability estimates and genetic correlations. To investigate possible causal associations between LBP and three neurodegenerative diseases, we also conducted a bidirectional two-sample Mendelian randomization (MR) study. Inverse variance-weighted MR was employed as the primary method to generate overall estimates, whereas complementary approaches and sensitivity analyses were conducted to confirm the consistency and robustness of the findings.Results: There was no evidence of genetic correlations between LBP and AD (Rg = −0.033, p = 0.766). MR analyses did not support the causal effect of LBP on AD (OR = 1.031; 95% CI, 0.924–1.150; p = 0.590) or the effect of AD on LBP (OR = 0.963; 95% CI, 0.923–1.006; p = 0.090). Likewise, this study failed to identify genetic correlations between LBP and two other neurodegenerative diseases. MR results of the associations of LBP with PD and ALS, and the reverse associations, did not reach Bonferroni-corrected significance.Conclusion: The study did not support genetic correlations or causations between LBP and three common neurodegenerative diseases, AD, PD, and ALS in the European population.

Background: Epidemiological studies have reported an association between Parkinson's disease (PD) and restless legs syndrome. Objectives: We aimed to use genetic data to study whether these 2 disorders are causally linked or share genetic architecture. Methods: We performed two‐sample Mendelian randomization and linkage disequilibrium score regression using summary statistics from recent genome‐wide meta‐analyses of PD and restless legs syndrome. Results: We found no evidence for a causal relationship between restless legs syndrome (as the exposure) and PD (as the outcome, inverse variance‐weighted; b = −0.003, SE = 0.031, P = 0.916; F statistic = 217.5). Reverse Mendelian randomization also did not demonstrate any causal effect of PD on restless legs syndrome (inverse variance‐weighted; b = −0.012, SE = 0.023, P = 0.592; F statistic = 191.7). Linkage disequilibrium score regression analysis demonstrated lack of genetic correlation between restless legs syndrome and PD (rg = −0.028, SE = 0.042, P = 0.507). Conclusions: There was no evidence for a causal relationship or genetic correlation between restless legs syndrome and PD. The associations observed in epidemiological studies could be attributed, in part, to confounding or nongenetic determinants. © 2021 International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Objective: Osteoporosis (OP) is the most common bone disease. The genetic and metabolic factors play important roles in OP development. However, the genetic basis of OP is still elusive. The study aimed to explore the relationships between OP and dietary habits. Methods: This study used large-scale genome-wide association study (GWAS) summary statistics from the UK Biobank to explore potential associations between OP and 143 dietary habits. The GWAS summary data of OP included 9434 self-reported OP cases and 444,941 controls, and the GWAS summary data of the dietary habits included 455,146 participants of European ancestry. Linkage disequilibrium score regression (LDSC) was used to detect the genetic correlations between OP and each of the 143 dietary habits, followed by Mendelian randomization (MR) analysis to further assess the causal relationship between OP and candidate dietary habits identified by LDSC. Results: The LDSC analysis identified seven candidate dietary habits that showed genetic associations with OP including cereal type such as biscuit cereal (coefficient = −0.1693, p value = 0.0183), servings of raw vegetables per day (coefficient = 0.0837, p value = 0.0379), and spirits measured per month (coefficient = 0.115, p value = 0.0353). MR analysis found that OP and PC17 (butter) (odds ratio [OR] = 0.974, 95% confidence interval [CI] = (0.973, 0.976), p value = 0.000970), PC35 (decaffeinated coffee) (OR = 0.985, 95% CI = (0.983, 0.987), p value = 0.00126), PC36 (overall processed meat intake) (OR = 1.035, 95% CI = (1.033, 1.037), p value = 0.000976), PC39 (spirits measured per month) (OR = 1.014, 95% CI = (1.011, 1.015), p value = 0.00153), and servings of raw vegetables per day (OR = 0.978, 95% CI = (0.977, 0.979), p value = 0.000563) were clearly causal. Conclusions: Our findings provide new clues for understanding the genetic mechanisms of OP, which focus on the possible role of dietary habits in OP pathogenesis.

Background: Several studies have suggested a potential link between allergic rhinitis (AR) and gut microbiota. In response, we conducted a meta-analysis of Linkage Disequilibrium Score Regression (LDSC) and Mendelian randomization (MR) to detect their genetic associations., Methods: Summary statistics for 211 gut microbiota taxa were gathered from the MiBioGen study, while data for AR were sourced from the Pan-UKB, the FinnGen, and the Genetic Epidemiology Research on Aging (GERA). The genetic correlation between gut microbiota and AR was assessed using LDSC. The principal estimate of causality was determined using the Inverse-Variance Weighted (IVW) method. To assess the robustness of these findings, sensitivity analyses were conducted employing methods such as the weighted median, MR-Egger, and MR-PRESSO. The summary effect estimates of LDSC, forward MR and reverse MR were combined using meta-analysis for AR from different data resources., Results: Our study indicated a significant genetic correlation between genus Sellimonas (Rg = -0.64, p  = 3.64 × 10 -5 , Adjust&#95; P  = 3.64 × 10 -5 ) and AR, and a suggestive genetic correlation between seven bacterial taxa and AR. Moreover, the forward MR analysis identified genus Gordonibacter , genus Coprococcus2 , genus LachnospiraceaeUCG010 , genus Methanobrevibacter , and family Victivallaceae as being suggestively associated with an increased risk of AR. The reverse MR analysis indicated that AR was suggestively linked to an increased risk for genus Coprococcus2 and genus RuminococcaceaeUCG011 ., Conclusion: Our findings indicate a causal relationship between specific gut microbiomes and AR. This enhances our understanding of the gut microbiota's contribution to the pathophysiology of AR and lays the groundwork for innovative approaches and theoretical models for future prevention and treatment strategies in this patient population., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zheng, Chen, Zhuang, Xu, Zhao, Qian and Shen.)

Background: Creatinine-cystatin C ratio (CCR) has been demonstrated as an objective marker of sarcopenia in clinical conditions but has not been evaluated as an osteoporosis marker in individuals with normal renal function., Methods: We selected 271,831 participants with normal renal function from UK Biobank cohort. Multivariable linear/logistic regression and Cox proportional hazards model were used to investigate the phenotypic relationship between CCR and osteoporosis in total subjects and gender-stratified subjects. Based on the genome-wide association study (GWAS) data, linkage disequilibrium regression (LDSC) and Mendelian randomization (MR) analysis were performed to reveal the shared genetic correlations and infer the causal effects, respectively., Results: Amongst total subjects and gender-stratified subjects, serum CCR was positively associated with eBMD after adjusting for potential risk factors (all P <0.05). The multivariable logistic regression model showed that the decrease in CCR was associated with a higher risk of osteoporosis/fracture in all models (all P <0.05). In the multivariable Cox regression analysis with adjustment for potential confounders, reduced CCR is associated with the incidence of osteoporosis and fracture in both total subjects and gender-stratified subjects (all P <0.05). A significant non-linear dose-response was observed between CCR and osteoporosis/fracture risk ( P non-linearity  < 0.05). LDSC found no significant shared genetic effects by them, but PLACO identified 42 pleiotropic SNPs shared by CCR and fracture (P<5×10- 8 ). MR analyses indicated the causal effect from CCR to osteoporosis/fracture., Conclusions: Reduced CCR predicted increased risks of osteoporosis/fracture, and significant causal effects support their associations. These findings indicated that the muscle-origin serum CCR was a potential biomarker to assess the risks of osteoporosis and fracture., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 He, Yang, Wang, Zhang, Gu, Hong, Bo, Deng and Lei.)

Background: Rheumatoid arthritis (RA) frequently presents with oral manifestations, including gingival inflammation, loose teeth, and mouth ulcers; however, the causal connections between these conditions remain unclear. This study aims to explore the genetic correlations and causal relationships between RA and prevalent oral phenotypes., Methods: Using summary data from genome-wide association studies of European populations, a cross-trait linkage disequilibrium score regression was conducted to estimate the genetic correlations between RA and six oral phenotypes. Subsequently, a two-sample Mendelian randomization (MR) approach was employed to assess the causal relationships, corroborated by various sensitivity analyses. Heterogeneity was addressed through the RadialMR method, while potential covariates were corrected using the multivariable MR approach., Results: A significant negative genetic correlation was detected between RA and denture usage (r g = -0.192, p = 4.88 × 10 -8 ). Meanwhile, a heterogenous causal relationship between RA and mouth ulcers was observed (OR = 1.027 [1.005-1.05], p = 0.016, P heterogeneity = 4.69 × 10 -8 ), which remained robust across sensitivity analyses. After excluding outlier variants, the results demonstrated robustly consistent (OR = 1.021 [1.008-1.035], p = 1.99 × 10 -3 , P heterogeneity = 0.044). However, upon adjusting for covariates such as smoking, alcohol consumption, body mass index, and obesity, the significance diminished, revealing no evidence to support independent genetic associations., Conclusion: Genetically predicted RA increases the risk of mouth ulcers, and a negative genetic correlation is identified between RA and denture use. The observed heterogeneity suggests that shared immunological mechanisms and environmental factors may play significant roles. These findings highlight the importance of targeted dental management strategies for RA patients. Further clinical guidelines are required to improve oral health among vulnerable RA patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Shen, Lou and Zhang.)

Background: Hemorrhagic stroke (HS), a leading cause of death and disability worldwide, has not been clarified in terms of the underlying biomolecular mechanisms of its development. Circulating metabolites have been closely associated with HS in recent years. Therefore, we explored the causal association between circulating metabolomes and HS using Mendelian randomization (MR) analysis and identified the molecular mechanisms of effects., Methods: We assessed the causal relationship between circulating serum metabolites (CSMs) and HS using a bidirectional two-sample MR method supplemented with five ways: weighted median, MR Egger, simple mode, weighted mode, and MR-PRESSO. The Cochran Q-test, MR-Egger intercept test, and MR-PRESSO served for the sensitivity analyses. The Steiger test and reverse MR were used to estimate reverse causality. Metabolic pathway analyses were performed using MetaboAnalyst 5.0, and genetic effects were assessed by linkage disequilibrium score regression. Significant metabolites were further synthesized using meta-analysis, and we used multivariate MR to correct for common confounders., Results: We finally recognized four metabolites, biliverdin (OR 0.62, 95% CI 0.40-0.96, P MVMR  = 0.030), linoleate (18. 2n6) (OR 0.20, 95% CI 0.08-0.54, P MVMR  = 0.001),1-eicosadienoylglycerophosphocholine* (OR 2.21, 95% CI 1.02-4.76, P MVMR  = 0.044),7-alpha-hydroxy-3 -oxo-4-cholestenoate (7-Hoca) (OR 0.27, 95% CI 0.09-0.77, P MVMR  = 0.015) with significant causal relation to HS., Conclusion: We demonstrated significant causal associations between circulating serum metabolites and hemorrhagic stroke. Monitoring, diagnosis, and treatment of hemorrhagic stroke by serum metabolites might be a valuable approach., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Shen, Li, Xu, Gao, Li, Rong, Gao, Liang and Zhang.)

AbstractBackground.Psychiatric disorders are a group of complex psychological syndromes with high prevalence. Recent studies observed associations between altered plasma proteins and psychiatric disorders. This study aims to systematically explore the potential genetic relationships between five major psychiatric disorders and more than 3,000 plasma proteins.Methods.The genome-wide association study (GWAS) datasets of attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) were driven from the Psychiatric GWAS Consortium. The GWAS datasets of 3,283 human plasma proteins were derived from recently published study, including 3,301 study subjects. Linkage disequilibrium score (LDSC) regression analysis were conducted to evaluate the genetic correlations between psychiatric disorders and each of the 3,283 plasma proteins.Results.LDSC observed several genetic correlations between plasma proteins and psychiatric disorders, such as ADHD and lysosomal Pro-X carboxypeptidase (p value = 0.015), ASD and extracellular superoxide dismutase (Cu-Zn; p value = 0.023), BD and alpha-N-acetylgalactosaminide alpha-2,6-sialyltransferase 6 (p value = 0.007), MDD and trefoil factor 1 (p value = 0.011), and SCZ and insulin-like growth factor-binding protein 6 (p value = 0.011). Additionally, we detected four common plasma proteins showing correlation evidence with both BD and SCZ, such as tumor necrosis factor receptor superfamily member 1B (p value = 0.012 for BD, p value = 0.011 for SCZ).Conclusions.This study provided an atlas of genetic correlations between psychiatric disorders and plasma proteome, providing novel clues for pathogenetic and biomarkers, therapeutic studies of psychiatric disorders.

Background and purpose: Considerable studies have reported inconsistent relationships between ischemic stroke and a large number of factors. These uncertainties may reflect the susceptibility to confounding in observational studies. We aimed to assess genetic correlations and causal relationships between ischemic stroke and diverse phenotypes. Methods: Summary-level data for ischemic stroke (34,217 cases and 406,111 controls) from the MEGASTROKE consortium were used as the outcome. Exposures were derived from two GWAS statistics curated databases. We explored the genetic correlations and causalities between hundreds of traits and ischemic stroke, using linkage disequilibrium score regression and Mendelian randomization (MR), respectively. Multiple sensitivity analyses were also performed. Results: Genetic correlation analyses reflected genetic overlaps between ischemic stroke and physical activity, cardiometabolic factors, smoking, and lung function. Applying MR, we found suggestive evidence that genetic predisposition to higher concentration of low-density lipoprotein particles (LDL.P) and cholesterol carried in different sizes of LDL.P (LDL.C) were associated with higher risk of ischemic stroke, particular large artery stroke. The strongest effect was observed for small LDL.P in large artery stroke (OR 1.31, 95% CI 1.09–1.56, p = 0.003). The results were overall robust for sensitivity analyses. We further observed significant positive associations of genetically predicted LDL.P and LDL.C with coronary artery disease and myocardial infarction. Conclusions: Shared genetic overlaps might exist between ischemic stroke and physical activity, cardiometabolic factors, smoking, and lung function. We provided suggestive evidence for a potential causal role of LDL.P and LDL.C in ischemic stroke, particularly in large artery stroke. Future researches are required to confirm these findings. [ABSTRACT FROM AUTHOR]