Summary Statistics Research Articles

Genetically predicted gut bacteria, circulating bacteria-associated metabolites and pancreatic ductal adenocarcinoma: a Mendelian randomisation study

Pancreatic ductal adenocarcinoma (PDAC) has high mortality and rising incidence rates. Recent data indicate that the gut microbiome and associated metabolites may play a role in the development of PDAC. To complement and inform observational studies, we investigated associations of genetically predicted abundances of individual gut bacteria and genetically predicted circulating concentrations of microbiome-associated metabolites with PDAC using Mendelian randomisation (MR). Gut microbiome-associated metabolites were identified through a comprehensive search of Pubmed, Exposome Explorer and Human Metabolome Database. Single Nucleotide Polymorphisms (SNPs) associated by Genome-Wide Association Studies (GWAS) with circulating levels of 109 of these metabolites were collated from Pubmed and the GWAS catalogue. SNPs for 119 taxonomically defined gut genera were selected from a meta-analysis performed by the MiBioGen consortium. Two-sample MR was conducted using GWAS summary statistics from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4), including a total of 8,769 cases and 7,055 controls. Inverse variance-weighted MR analyses were performed along with sensitivity analyses to assess potential violations of MR assumptions. Nominally significant associations were noted for genetically predicted circulating concentrations of mannitol (odds ratio per standard deviation [ORSD] = 0.97; 95% confidence interval [CI]: 0.95–0.99, p = 0.006), methionine (ORSD= 0.97; 95%CI: 0.94-1.00, p = 0.031), stearic acid (ORSD= 0.93; 95%CI: 0.87–0.99, p = 0.027), carnitine = (ORSD=1.01; 95% CI: 1.00-1.03, p = 0.027), hippuric acid (ORSD= 1.02; 95%CI: 1.00-1.04, p = 0.038) and 3-methylhistidine (ORSD= 1.05; 95%CI: 1.01–1.10, p = 0.02). Two gut microbiome genera were associated with reduced PDAC risk; Clostridium sensu stricto 1 (OR: 0.88; 95%CI: 0.78–0.99, p = 0.027) and Romboutsia (OR: 0.87; 95%CI: 0.80–0.96, p = 0.004). These results, though based only on genetically predicted gut microbiome characteristics and circulating bacteria-related metabolite concentrations, provide evidence for causal associations with pancreatic carcinogenesis.

Inferring Cosmological Parameters on SDSS via Domain-generalized Neural Networks and Light-cone Simulations

We present a proof-of-concept simulation-based inference on Ωm and σ 8 from the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey (BOSS) LOWZ Northern Galactic Cap (NGC) catalog using neural networks and domain generalization techniques without the need of summary statistics. Using rapid light-cone simulations L-picola, mock galaxy catalogs are produced that fully incorporate the observational effects. The collection of galaxies is fed as input to a point cloud-based network, Minkowski-PointNet . We also add relatively more accurate Gadget mocks to obtain robust and generalizable neural networks. By explicitly learning the representations that reduce the discrepancies between the two different data sets via the semantic alignment loss term, we show that the latent space configuration aligns into a single plane in which the two cosmological parameters form clear axes. Consequently, during inference, the SDSS BOSS LOWZ NGC catalog maps onto the plane, demonstrating effective generalization and improving prediction accuracy compared to non-generalized models. Results from the ensemble of 25 independently trained machines find Ωm = 0.339 ± 0.056 and σ 8 = 0.801 ± 0.061, inferred only from the distribution of galaxies in the light-cone slices without relying on any indirect summary statistics. A single machine that best adapts to the Gadget mocks yields a tighter prediction of Ωm = 0.282 ± 0.014 and σ 8 = 0.786 ± 0.036. We emphasize that adaptation across multiple domains can enhance the robustness of the neural networks in observational data.

41 Neonatal outcomes with exclusive breast milk in infants with gastroschisis: A systematic review and meta-analysis

Abstract Background Infants with gastroschisis may experience prolonged stays in hospital as they transition from parenteral nutrition to enteral feeds. Some studies suggest that patients who receive exclusive breast milk experience better outcomes than those who receive supplemental or exclusive formula. These findings are inconsistent and the evidence for exclusive breast milk remains uncertain. Objectives To systematically review and meta-analyze the effectiveness of exclusive breast milk in infants with gastroschisis. Design/Methods We searched Medline, Embase, and Cochrane without language restriction from inception until June 2023. Manual searches of trial registries and reference lists of relevant articles were also performed. We included studies that assessed the effectiveness of exclusive breast milk compared to formula among infants with gastroschisis. Title, abstract, full-text screening, and data extraction were completed by two independent reviewers. The Newcastle-Ottawa Scale was used to assess the risk of bias. Our outcomes of interest were time to full enteral feeds, days of parenteral nutrition, length of stay, necrotizing enterocolitis (NEC), cholestasis, and mortality. RevMan was used to meta-analyze treatment effects using a random effects model whenever possible. Summary statistics were reported as relative risk (RR) with 95% confidence intervals (95% CI). Results We identified 130 studies of which six were selected for review. All included studies were observational and retrospective. Only 3 studies (n=3211 participants) reported outcome data that were amenable to meta-analysis. All participants received exclusive breast milk (n=1009) or supplemental or exclusive formula (n=2202). Exposure to exclusive breast milk was associated with a lower risk of mortality compared to those who received formula (unadjusted RR=0.36; 95% CI: 0.13-0.96, 2 studies, n=3157, I2=0%). Exposure to exclusive breast milk was not associated with a decrease in NEC (unadjusted RR=0.20; 95% CI: 0.03-1.14; 3 studies, n=3211, I2=0%). The primary source of bias among included studies was lack of adjustment for confounders. We identified one randomized controlled trial comparing exclusive breast milk to formula. This study was stopped due to low recruitment. Conclusion The use of exclusive breast milk among infants with gastroschisis may be associated with a reduction in mortality; however, this estimate is based on small numbers from observational studies. Estimates may change as more data become available. Further research is needed to establish best feeding practices, including the role of human donor milk.

Anorexia nervosa polygenic risk, beyond diagnoses: relationship with adolescent disordered eating and behaviors in an Australian female twin population.

It is well established that there is a substantial genetic component to eating disorders (EDs). Polygenic risk scores (PRSs) can be used to quantify cumulative genetic risk for a trait at an individual level. Recent studies suggest PRSs for anorexia nervosa (AN) may also predict risk for other disordered eating behaviors, but no study has examined if PRS for AN can predict disordered eating as a global continuous measure. This study aimed to investigate whether PRS for AN predicted overall levels of disordered eating, or specific lifetime disordered eating behaviors, in an Australian adolescent female population. PRSs were calculated based on summary statistics from the largest Psychiatric Genomics Consortium AN genome-wide association study to date. Analyses were performed using genome-wide complex trait analysis to test the associations between AN PRS and disordered eating global scores, avoidance of eating, objective bulimic episodes, self-induced vomiting, and driven exercise in a sample of Australian adolescent female twins recruited from the Australian Twin Registry (N = 383). After applying the false-discovery rate correction, the AN PRS was significantly associated with all disordered eating outcomes. Findings suggest shared genetic etiology across disordered eating presentations and provide insight into the utility of AN PRS for predicting disordered eating behaviors in the general population. In the future, PRSs for EDs may have clinical utility in early disordered eating risk identification, prevention, and intervention.

Blood-derived mitochondrial DNA copy number is associated with Alzheimer disease, Alzheimer-related biomarkers and serum metabolites

BackgroundBlood-derived mitochondrial DNA copy number (mtDNA-CN) is a proxy measurement of mitochondrial function in the peripheral and central systems. Abnormal mtDNA-CN not only indicates impaired mtDNA replication and transcription machinery but also dysregulated biological processes such as energy and lipid metabolism. However, the relationship between mtDNA-CN and Alzheimer disease (AD) is unclear.MethodsWe performed two-sample Mendelian randomization (MR) using publicly available summary statistics from GWAS for mtDNA-CN and AD to investigate the causal relationship between mtDNA-CN and AD. We estimated mtDNA-CN using whole-genome sequence data from blood and brain samples of 13,799 individuals from the Alzheimer’s Disease Sequencing Project. Linear and Cox proportional hazards models adjusting for age, sex, and study phase were used to assess the association of mtDNA-CN with AD. The association of AD biomarkers and serum metabolites with mtDNA-CN in blood was evaluated in Alzheimer’s Disease Neuroimaging Initiative using linear regression. We conducted a causal mediation analysis to test the natural indirect effects of mtDNA-CN change on AD risk through the significantly associated biomarkers and metabolites.ResultsMR analysis suggested a causal relationship between decreased blood-derived mtDNA-CN and increased risk of AD (OR = 0.68; P = 0.013). Survival analysis showed that decreased mtDNA-CN was significantly associated with higher risk of conversion from mild cognitive impairment to AD (HR = 0.80; P = 0.002). We also identified significant associations of mtDNA-CN with brain FDG-PET (β = 0.103; P = 0.022), amyloid-PET (β = 0.117; P = 0.034), CSF amyloid-β (Aβ) 42/40 (β=-0.124; P = 0.017), CSF t-Tau (β = 0.128; P = 0.015), p-Tau (β = 0.140; P = 0.008), and plasma NFL (β=-0.124; P = 0.004) in females. Several lipid species, amino acids, biogenic amines in serum were also significantly associated with mtDNA-CN. Causal mediation analyses showed that about a third of the effect of mtDNA-CN on AD risk was mediated by plasma NFL (P = 0.009), and this effect was more significant in females (P < 0.005).ConclusionsOur study indicates that mtDNA-CN measured in blood is predictive of AD and is associated with AD biomarkers including plasma NFL particularly in females. Further, we illustrate that decreased mtDNA-CN possibly increases AD risk through dysregulation of mitochondrial lipid metabolism and inflammation.

Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss Functions.

Polygenic risk scores (PRS) aim to predict a trait from genetic information, relying on common genetic variants with low to medium effect sizes. As genotype data are high-dimensional in nature, it is crucial to develop methods that can be applied to large-scale data (large and large ). Many PRS tools aggregate univariate summary statistics from genome-wide association studies into a single score. Recent advancements allow simultaneous modeling of variant effects from individual-level genotype data. In this context, we introduced snpboost, an algorithm that applies statistical boosting on individual-level genotype data to estimate PRS via multivariable regression models. By processing variants iteratively in batches, snpboost can deal with large-scale cohort data. Having solved the technical obstacles due to data dimensionality, the methodological scope can now be broadened-focusing on key objectives for the clinical application of PRS. Similar to most methods in this context, snpboost has, so far, been restricted to quantitative and binary traits. Now, we incorporate more advanced alternatives-targeted to the particular aim and outcome. Adapting the loss function extends the snpboost framework to further data situations such as time-to-event and count data. Furthermore, alternative loss functions for continuous outcomes allow us to focus not only on the mean of the conditional distribution but also on other aspects that may be more helpful in the risk stratification of individual patients and can quantify prediction uncertainty, for example, median or quantile regression. This work enhances PRS fitting across multiple model classes previously unfeasible for this data type.

Causal associations between kidney function and aortic valve stenosis: a bidirectional Mendelian randomization analysis

Background Aortic valve stenosis (AVS) is currently the most common heart valve disease. The results of observational studies on the incidence of AVS in the renal dysfunction population are contradictory due to the short follow-up period and different diagnostic criteria, etc. This study aimed to explore the causal relationship between kidney function and AVS using Mendelian randomization (MR) analysis. Methods We acquired summary statistics of estimated glomerular filtration rate (eGFR) and chronic kidney disease (CKD) from the CKDGen Consortium and a study on AVS from the FinnGen biobank. Univariate and multivariable MR analyses were conducted to evaluate the causal associations. The MR-Egger intercept and MR-PRESSO Global test were applied to assess the pleiotropic effects. The heterogeneity of MR results was tested by Cochran’s Q statistic. Moreover, the Bonferroni and FDR corrections were performed for multiple tests. Results Genetically predicted decreased eGFR may be associated with a raised risk of AVS (OR = 0.045, p = 1.317e-04 by IVW; OR = 0.002, p = 0.004 by MR-Egger, OR = 0.091, p = 0.057 by WM). The causal association still established after multiple comparisons. Quality control analyses indicated the absence of heterogeneity and pleiotropy in our MR research. In addition, the causality of eGFR and AVS remained significant in multivariable MR analysis after adjusting BMI, hypertension, T2DM, LDL-C, and smoking. Conclusion Our MR study discovered that reduced eGFR may be a causative risk factor for AVS. In addition, the evidence did not support a significant causal association of AVS on kidney function.

Integrative Multi-Omics Approach for Improving Causal Gene Identification.

Transcriptome-wide association studies (TWAS) have been widely used to identify thousands of likely causal genes for diseases and complex traits using predicted expression models. However, most existing TWAS methods rely on gene expression alone and overlook other regulatory mechanisms of gene expression, including DNA methylation and splicing, that contribute to the genetic basis of these complex traits and diseases. Here we introduce a multi-omics method that integrates gene expression, DNA methylation, and splicing data to improve the identification of associated genes with our traits of interest. Through simulations and by analyzing genome-wide association study (GWAS) summary statistics for 24 complex traits, we show that our integrated method, which leverages these complementary omics biomarkers, achieves higher statistical power, and improves the accuracy of likely causal gene identification in blood tissues over individual omics methods. Finally, we apply our integrated model to a lung cancer GWAS data set, demonstrating the integrated models improved identification of prioritized genes for lung cancer risk.

Association between antithrombotic agents use and hepatocellular carcinoma risk: a two-sample mendelian randomization analysis

BackgroundHepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide. Multiple observational studies demonstrated a negative association between the use of antithrombotic agents and the risk of HCC. However, the precise causal relationship between these factors remains uncertain. Therefore, our study used a two-sample Mendelian randomization (MR) analysis to assess the causal link between these two factors.MethodThe summary statistics of single nucleotide polymorphisms (SNPs) associated with the use of antithrombotic agents were acquired from a genome-wide association study (GWAS) performed on individuals of European descent. A two-sample MR analysis was performed using the inverse variance weighting (IVW), the weighted median estimate, the MR-Egger regression, and the weighted-mode estimate. Sensitivity analysis of the primary findings was performed using MR-PRESSO, MR-Egger regression, Cochran’s Q test, and Leave-one-out analysis.ResultsTen SNPs associated with the use of antithrombotic agents were selected as instrumental variables. The MR analysis performed using the four methods mentioned above revealed a negative causal association between the use of antithrombotic agents and HCC. Univariate MR estimates based on the inverse variance weighting (IVW) method suggested a negative causal association between the use of antithrombotic agents and HCC [odds ratio (OR) 0.444, 95% confidence interval (CI) 0.279 to 0.707, P = 0.001]. The other methods also produced similar results. No heterogeneity and horizontal pleiotropy were found.ConclusionOur findings suggested an inverse causal association of antithrombotic agents with the risk of HCC.

Genetic insight into the relationship between inflammatory bowel disease and Clostridioides difficile infection.

Patients with inflammatory bowel disease (IBD) are at increased risk of Clostridioides difficile infection (CDI). Herein, we aimed to determine if genetic risk contributes to this observed association. We carried out a genome-wide association study (GWAS) analysis in the Michigan Genomics Initiative and the United Kingdom Biobank for CDI based on ICD codes and meta-analyzed these results with similar publicly accessible GWAS summary statistics from Finngen. Conditional and joint multi-SNP analyses were used to identify independent associations. Imputation of the human leukocyte antigen (HLA) region with fine mapping was used to try to identify causal HLA allele groups. Two-sample bidirectional Mendelian randomization (MR) was implemented to determine causal relationships between IBD and CDI. A total of 3,500 cases of CDI and 674,323 controls were meta-analyzed, revealing one genome-wide significant variant for CDI, HLA-C;LINC02571-rs3134745-C (P = 4.27E-08), which annotated to the major histocompatibility complex on chromosome 6. While fine mapping did not identify a statistically significant HLA allele group, there was a suggestive signal for HLA-B*35:01 (P = 4.74e-04). Using two-sample MR, genetically predicted IBD was associated with increased risk of CDI (MR Egger [odds ratio {OR} 1.16, 95% confidence interval {CI} 1.02-1.31]). Subset analysis revealed that risk was primarily driven by genetically predicted ulcerative colitis (MR Egger [OR 1.22, 95% CI 1.05-1.41]). These results highlight the importance of the host immune response in CDI pathogenesis, help explain the observed relationship between IBD and CDI, and open new avenues for targeted treatment of CDI in IBD.IMPORTANCEData from this paper (i) provide reproducible evidence that susceptibility CDI is genetically mediated, (ii) highlight genetic risk as a mechanism for the increased risk of CDI in patients with inflammatory bowel disease, and (iii) point toward anti-interleukin-23 therapy as a common therapeutic strategy.

Assessing Neurosurgery Training: Accreditation Council for Graduate Medical Education Case Minimums Versus Surgical Autonomy.

The Accreditation Council for Graduate Medical Education (ACGME) requires neurosurgery residents to reach a set number of cases in specified procedure types (case minimums) before graduation and mandates completion of Milestones. We used the Surgical Autonomy Program, a validated method of autonomy-based resident evaluation, to determine the number of cases it took for residents to become competent and compared these with the ACGME case minimums. We collected data from neurosurgery residents at Duke University on 7 procedures (tumor craniotomy, trauma craniotomy, ventriculoperitoneal shunt, anterior cervical discectomy and fusion (ACDF), posterior cervical fusion (PCF), discectomy/laminectomy, and posterior thoracolumbar spinal fusion [PSF]). We defined competency as being graded at the highest autonomy level in the Surgical Autonomy Program by attending neurosurgeons for the first and second time and determined the case volume on the ACGME case log when these were achieved. These results were analyzed with summary statistics. The median case volume among residents (N = 4-8) for the first and second competency rating (and ACGME minimum) for each procedure type was found to be: tumor: 44.5 and 64.5 (min. 60), trauma: 21 and 30 (min. 60), ventriculoperitoneal shunt: 11.3 and 13 (min. 20), ACDF: 30 and 32.5 (min. 20), PCF: 24 and 40 (min. 30), discectomy/laminectomy: 28 and 36 (min. 30), and PSF: 51 and 54 (min. 30). We found variation in the case numbers to reach competency and that for some procedures (tumor, ACDF, PCF, discectomy/laminectomy, and PSF), most residents required more cases than the ACGME case minimums to achieve competency. The ACGME case minimums may not accurately reflect the number of cases required for neurosurgical residents to reach competency. To promote trainee-centered education, individualized, competency-based evaluation systems may be better determining readiness for graduation, including a system that builds off the established ACGME Milestones.

Controlling for polygenic genetic confounding in epidemiologic association studies

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.

Assessing the harmonization of structured electronic health record data to reference terminologies and data completeness through data provenance

AbstractIntroduction(1) Assess the harmonization of structured electronic health record data (laboratory results and medications) to reference terminologies and characterize the severity of issues. (2) Identify issues of data completeness by comparing complementary data domains, stratifying by time, care setting, and provenance.MethodsQueries were distributed to 3 Data Partners (DP). Using harmonization queries, we examined the top 200 laboratory results and medications by volume, identifying outliers and computing summary statistics. The completeness queries looked at 4 conditions of interest and related clinical concepts. Counts were generated for each condition, stratified by year, encounter type, and provenance. We analyzed trends over time within and across DPs.ResultsWe found that the median number of codes associated with a given laboratory/medication name (and vice versa) generally met expectations, though there were DP‐specific issues that resulted in outliers. In addition, there were drastic differences in the percentage of patients with a given concept depending on provenance.ConclusionsThe harmonization queries surfaced several mapping errors, as well as issues with overly specific codes and records with “null” codes. The completeness queries demonstrated having access to multiple types of data provenance provides more robust results compared with any single provenance type. Harmonization errors between source data and reference terminologies may not be widespread but do exist within CDMs, affecting tens of thousands or even millions of records. Provenance information can help identify potential completeness issues with EHR data, but only if it is represented in the CDM and then populated by DPs.

Skin Microbiota, Immune Cell, and Skin Fibrosis: A Comprehensive Mendelian Randomization Study.

Microbiota dysbiosis has been reported to lead to leaky epithelia and trigger numerous dermatological conditions. However, potential causal associations between skin microbiota and skin fibrosis and whether immune cells act as mediators remain unclear. Summary statistics of skin microbiota, immune cells, and skin fibrosis were identified from large-scale genome-wide association studies summary data. Bidirectional Mendelian randomization was performed to ascertain unidirectional causal effects between skin microbiota, immune cells, and skin fibrosis. We performed a mediation analysis to identify the role of immune cells in the pathway from skin microbiota to skin fibrosis. Three specific skin microbiotas were positively associated with skin fibrosis, while the other three were negative. A total of 15 immune cell traits were associated with increased skin fibrosis risk, while 27 were associated with a decreased risk. Moreover, two immune cell traits were identified as mediating factors. Causal associations were identified between skin microbiota, immune cells, and skin fibrosis. There is evidence that immune cells exert mediating effects on skin microbiota in skin fibrosis. In addition, some strains exhibit different effects on skin fibrosis in distinct environments.

Analyses of GWAS signal using GRIN identify additional genes contributing to suicidal behavior

Genome-wide association studies (GWAS) identify genetic variants underlying complex traits but are limited by stringent genome-wide significance thresholds. We present GRIN (Gene set Refinement through Interacting Networks), which increases confidence in the expanded gene set by retaining genes strongly connected by biological networks when GWAS thresholds are relaxed. GRIN was validated on both simulated interrelated gene sets as well as multiple GWAS traits. From multiple GWAS summary statistics of suicide attempt, a complex phenotype, GRIN identified additional genes that replicated across independent cohorts and retained biologically interrelated genes despite a relaxed significance threshold. We present a conceptual model of how these retained genes interact through neurobiological pathways that may influence suicidal behavior, and identify existing drugs associated with these pathways that would not have been identified under traditional GWAS thresholds. We demonstrate GRIN’s utility in boosting GWAS results by increasing the number of true positive genes identified from GWAS results.

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.

Causal Relationships between Circulating Immune Cell Traits and the Risk of Rheumatoid Arthritis and Osteoarthritis: A Bidirectional Two-Sample Mendelian Randomization Study.

Rheumatoid arthritis (RA) and osteoarthritis (OA) are prevalent chronic joint disorders with immunological pathogenesis. However, the causal relationships between circulating immune cells and them remain largely unknown. Therefore, we conducted a bidirectional two-sample Mendelian randomization (MR) study to determine their causal relationship. Genome-wide association study summary statistics were extracted from publicly available databases regarding immune cell phenotypes, RA, and OA. MR analysis was conducted using five MR methods, with inverse-variance-weighted (IVW) as the primary analysis method. False discovery rate correction (FDR) was used to reduce the likelihood of type 1 errors. We also conducted MR-Egger intercept tests to evaluate horizontal pleiotropy. After FDR adjustment of the P values for the IVW method, the CD27 expression on memory B cells was negatively related to the risk of RA (P < 0.001), and the human leukocyte antigen (HLA)--DR expression on CD14+ monocytes was negatively related to the risk of OA (P < 0.001). We also found that RA was negatively associated with the expression of HLA-DR on myeloid dendritic cells (P < 0.001), but significant horizontal pleiotropy was observed. Our study demonstrates a causal relationship between specific immune cell traits and RA as well as OA, providing further insight into the role of immune cells in the pathogenesis of these disorders.

Factors associated with perceived social support among adolescents in Gamo Zone, Southern Ethiopia: a community-based cross-sectional study.

Perceived social support plays a crucial role in adolescent development, health, well-being, and resilience. Understanding the factors associated with perceived social support among adolescents is essential for designing effective interventions. However, research in this area, particularly within specific contexts, remains limited. Therefore, this study aims to identify the factors associated with perceived social support among adolescents in the Gamo Zone, South Ethiopia Regional State. A community-based cross-sectional study was conducted, involving 1172 adolescents selected through a stratified multi-stage sampling method. Structured face-to-face interviews were employed for data collection. Summary statistics were utilized for data presentation. Multiple linear regressions were then performed to identify factors associated with perceived social support. The strength and direction of associations were presented using β coefficients and 95% confidence intervals (CIs). The level of statistical significance was set at alpha 5%. The total mean for perceived social support among adolescents was 57.02 ± 12.68. Adolescents living with their biological parents reported significantly higher levels of perceived social support (β = 4.17, 95% CI: 2.00 to 6.34) compared to their counterparts. Similarly, adolescents engaged in paid work within the last 12 months reported higher perceived social support (β = 3.43, 95% CI: 1.39 to 5.46). Higher levels of parental monitoring were also associated with increased perceived social support (β = 3.03, 95% CI: 1.23 to 4.82). Additionally, adolescents who perceived risks for sexual and reproductive health (SRH) problems reported higher levels of perceived social support (β = 2.76, 95% CI: 0.84 to 4.69). Moreover, adolescents with good knowledge about SRH rights exhibited increased perceived social support (β = 2.46, 95% CI: 0.89 to 4.02). Furthermore, adolescents residing in rural areas reported higher levels of perceived social support compared to those in urban areas (β = 1.56, 95% CI: 0.16 to 3.11). The findings of this study reveal that factors such as living arrangements, employment status, parental monitoring, perceived risks for SRH problems, knowledge about SRH rights, and geographical context emerged as important predictors of perceived social support. Therefore, implementing interventions and initiatives to address these identified factors holds promise for nurturing resilient adolescent social support networks.

Causal Relationship Between Sjögren's Syndrome and Gut Microbiota: A Two-Sample Mendelian Randomization Study.

Background: Gut microbiota have been previously reported to be related to a variety of immune diseases. However, the causal connection between Sjögren's syndrome (SS) and gut microbiota has yet to be clarified. Methods: We employed a two-sample Mendelian randomization (MR) analysis to evaluate the causal connection between gut microbiota and SS, utilizing summary statistics from genome-wide association studies (GWASs) obtained from the MiBioGen and FinnGen consortia. The inverse variance weighted (IVW) approach represents the primary method of Mendelian randomization (MR) analysis. Sensitivity analysis was used to eliminate instrumental variables heterogeneity and horizontal pleiotropy. In addition, we performed an analysis using independent GWAS summary statistics for SS from the European Bioinformatics Institute (EBI) dataset for further verify our results. Results: IVW results demonstrated that the phylum Lentisphaerae (OR = 0.79, 95% CI: 0.63-0.99, p = 0.037), class Deltaproteobacteria (OR = 0.67, 95% CI: 0.47-0.96, p = 0.030), family Porphyromonadaceae (OR = 0.60, 95% CI: 0.38-0.94, p = 0.026), genus Eubacterium coprostanoligenes group (OR = 0.61, 95% CI: 0.4-0.93, p = 0.021), genus Blautia (OR = 0.62, 95% CI: 0.43-0.90, p = 0.012), genus Butyricicoccus (OR = 0.61, 95% CI: 0.42-0.90, p = 0.012), genus Escherichia.Shigella (OR = 0.7, 95% CI: 0.49-0.99, p = 0.045) and genus Subdoligranulum (OR = 0.61, 95% CI: 0.44-0.86, p = 0.005) exhibited protective effects on SS. Relevant heterogeneity of horizontal pleiotropy or instrumental variables was not detected. Furthermore, repeating our results with an independent cohort provided by the EBI dataset, only the genus Eubacterium coprostanoligenes group remained significantly associated with the protective effect on SS (OR = 0.41, 95% CI: 0.18-0.91, p = 0.029). Two-step MR analysis further revealed that genus Eubacterium coprostanoligenes group exerts its protective effect by reducing CXCL6 levels in SS (OR, 0.87; 95% CI = 0.76-0.99, p = 0.033). Conclusions: Our study using two-sample MR analysis identified a causal association between multiple genera and SS. A two-step MR result calculated that genus Eubacterium coprostanoligenes group mediated its protective effect by reducing CXCL6 levels in SS. However, the datasets available from the MiBioGen and FinnGen consortia do not provide sufficient information or comprehensive demographic data for subgroup analyses. Additional validation using various omics technologies is necessary to comprehend the development of SS in the intricate interplay between genes and the environment over a period of time.

Identification of Potential Drug Targets for Sleep Apnea Through Mendelian Randomization

Background: Sleep apnea is a significant health impediment, and it presently lacks efficacious therapeutic interventions. Thus, it is imperative to discover novel therapeutic targets that could guide clinical treatment strategies. Objective: This study aims to utilize an integrative analytic approach to unearth previously unappreciated protein-encoding genes implicated in sleep apnea susceptibility. Methods: Through the Multi-Marker Analysis of Genomic Annotation (MAGMA), we aligned Single-Nucleotide Polymorphism (SNP) summary statistics from Genome-Wide Association Studies (GWAS) of gene bodies to discern potential risk genes. Following the MAGMA results, we conducted a round of Transcriptome-Wide Association Studies (TWAS) and Proteome-Wide Association Studies (PWAS) to expedite the conversion of genetic associations into probable protein targets. Mendelian Randomization (MR) and co-localization analysis were employed to ascertain the causal linkage between the candidate target genes and sleep apnea. Finally, a mediation analysis was undertaken to explore the possible intermediary role of 150 inflammatory metabolites and 1,124 proteins. Results: The MAGNA analysis revealed 2,819 genes in association with sleep apnea. TWAS and PWAS analyses indicated that cis-regulation of nine particular genes could play a role in sleep apnea onset via blood protein level alterations. MR and co-localization analyses also suggested a causal relationship with sleep apnea for three genes (ACADVL, CCDC134, UPP1). Consistent associations were found between genetically predicted biomarkers and Albumin, HCC-1, N-acetyl carnosine, and RELT, pointing towards their potential mediating roles in sleep apnea's etiological pathway. result: The MAGNA analysis revealed 2,819 genes in association with sleep apnea. TWAS and PWAS analyses indicated that cis-regulation of nine particular genes could play a role in sleep apnea onset via blood protein level alterations. MR and co-localization analyses also suggested a causal relationship with sleep apnea for three genes (ACADVL, CCDC134, UPP1). Consistent associations were found between genetically predicted biomarkers and Albumin, HCC-1, N-acetylcarnosine, and RELT, pointing towards their potential mediating roles in sleep apnea&amp;#039;s etiological pathway. Conclusion: Our findings indicate that ACADVL, CCDC134, and UPP1 genes are potentially significant targets for further functional investigation and therapeutic interventions for sleep apnea.