Intercept Term Research Articles

Mendelian Randomization Study Reveals Causal Pathways for Hypertrophic Cardiomyopathy, Cardiovascular Proteins, and Atrial Fibrillation.

Aims/Background Research evidence has demonstrated a significant association between hypertrophic cardiomyopathy (HCM) and atrial fibrillation (AF), but the causality and pattern of this link remain unexplored. Therefore, this study investigated the causal relationship between HCM and AF using a two-sample and bidirectional Mendelian randomization (MR) approach. Additionally, this assessed the role of cardiovascular proteins (CPs) associated with cardiovascular diseases between HCM and AF by applying a two-step MR analysis. Methods Data for HCM, AF, and 90 CPs were obtained from the Finn Gen and IEU Open GWAS Project databases. MR-Egger, inverse variance weighting (IVW), weighted median estimator (WME), weighted mode, and simple mode were used to estimate causal inferences. Furthermore, Cochran's Q test, MR-Egger's intercept terms, and Leave-one-out methods determined the heterogeneity, horizontal pleiotropy, and sensitivity. Additionally, mediation effects were used to assess the role of CPs in the relationship between HCM and AF. Results Two-sample and bidirectional MR analysis revealed HCM as a risk factor for AF (odds ratio (OR) = 1.008, 95% confidence interval (CI): 1.001-1.016, p = 0.029) and AF was found to increase the risk of developing HCM (OR = 1.145, 95% CI: 0.963-1.361, p = 0.126). Moreover, Two-step MR analyses indicated that 5 CPs were causally associated with HCM; 12 CPs with AF and 1 CP (Melusin) with both HCM and AF. Additionally, Melusin was observed as a protective factor for both HCM and AF and may serve as a mediator variable for these two conditions (mediation effect 0.0004, mediation ratio 5.5178%, 95% CI: 5.4624-5.5731). Conclusion HCM may increase the risk of developing AF, with Melusin serving as a mediator for this risk.

Causal Association between Arm Fat, Left Leg Fat, and Trunk Fat Masses and Risk of Polycystic Ovarian Syndrome: A Mendelian Randomization Study.

Observational studies have reported that arm fat, left leg fat, and trunk fat masses have different effects on polycystic ovarian syndrome (PCOS). However, the causal relationship between them remains unknown. A two-sample Mendelian randomization (MR) study was conducted by utilizing pooled data from the largest Genome-Wide Association Study (GWAS). Random effect inverse variance weighted (IVW) method, weighted median (WM), and MR-Egger regression analysis were the main statistical methods utilized. Finally, a sensitivity assessment was conducted. Cochran's Q test was used to analyze heterogeneity, whereas MR-Egger regression (intercept term) was used to analyze horizontal pleiotropy. The leave-one-out analysis was performed to assess if MR estimates were impacted by a single nucleotide polymorphism (SNP) exhibiting significant horizontal pleiotropy. This study discovered a significant positive correlation between left leg fat mass, arm fat mass, and trunk fat mass and genetic factors of PCOS (odds ratio (OR): 4.452, confidence interval (CI): 2.740-7.232, p < 0.001, OR: 3.321, CI: 2.248-4.907, p < 0.001, and OR: 2.518, CI: 1.722-3.682, p < 0.001, respectively). This study indicates that arm fat, left leg fat, and trunk fat masses may be genetically correlated with PCOS.

Protocol for a systematic review and individual participant data meta-analysis for risk factors for lung cancer in individuals with lung nodules identified by low-dose CT screening

BackgroundWorldwide, lung cancer (LC) is the second most frequent cancer and the leading cause of cancer related mortality. Low-dose CT (LDCT) screening reduced LC mortality by 20–24% in randomised trials...

Longitudinal Change in Proper Names from Logical Memory Tasks are Sensitive to Plasma pTau217 Levels

AbstractBackgroundPrevious studies have found connections between recall of proper names (PN) and amyloid positivity in cognitively unimpaired (CU) adults at risk for Alzheimer’s Disease (AD; Mueller et al., 2020). Given the promising prospect of employing plasma‐based biomarkers to determine amyloid burden, we looked at associations between longitudinal change in PN and total score recall from Logical Memory (LM) story recall test and plasma pTau217.MethodParticipants from the Wisconsin Registry for Alzheimer’s Prevention (WRAP) study, who were CU at baseline LM visit; had item‐level, longitudinal LM and plasma pTau217 data from a recent blood draw (n=412; EDTA plasma samples analyzed using the ALZpath pTau217 Simoa assay on a Quanterix HD‐X). Linear‐mixed effects models were utilized to examine whether the last available plasma pTau217 measure moderated retrospective age (centered at mean=63) trajectories associated with LM Total Score and PN subscores; pTau217 interactions with quadratic age were removed if they were non‐significant. Models included a person‐level random intercept term, sex, practice effects, and WRAT‐III reading standard scores as covariates.ResultsSample characteristics overall and by last observed plasma pTau217 category (Ashton et al, 2023 pre‐print) are shown in Table 1. The sample was mostly female (n=278, 67.5%), and white (n=393, 95.4%), with an average age of 58 (sd=6.44) at baseline visit. The interaction term of pTau217 was significant for all of the models for linear age (Table 2, β ranging from ‐0.50 through ‐0.08, p&lt;0.001); the interaction of pTau217 was also significant for quadratic age in the PN delayed subscore (β=‐0.00, p=0.010) and the LM delayed total score (β=‐0.01, p=0.031).ConclusionChange in LM total and PN subscores are sensitive to amyloid burden via plasma pTau217. Participants with high levels of pTau217 performed more poorly over time than those with intermediate or low levels (Figure 1). Despite being limited to only nine items on LM tasks, PN subscores continue to demonstrate their strength as a potential indicator of amyloid accumulation as measured by a quick and lower‐burden blood test.

Investigating the metabolomic pathways in female reproductive endocrine disorders: a Mendelian randomization study.

Reproductive endocrine disorders (RED), including polycystic ovary syndrome (PCOS), endometriosis (EMs), and female infertility (FI), significantly affect women's health globally, with varying prevalence across different regions. These conditions can be addressed through medication, surgical interventions, and lifestyle modifications. However, the limited understanding of RED's etiology and the substantial economic burden of its treatment highlight the importance of investigating its pathogenesis. Metabolites play a critical role in metabolic processes and are potentially linked to the development of RED. Despite existing studies suggesting correlations between metabolites and RED, conclusive evidence remains scarce, primarily due to the observational nature of these studies, which are prone to confounding factors. This study utilized Mendelian Randomization (MR) to explore the causal relationship between metabolites and RED, leveraging genetic variants associated with metabolite levels as instrumental variables to minimize confounding and reverse causality. Data were obtained from the Metabolomics GWAS Server and the IEU OpenGWAS project. Instrumental variables were selected based on their association with the human gut microbiota composition, and the GWAS summary statistics for metabolites, PCOS, EMs, and FI were analyzed. The MR-Egger regression and random-effects inverse-variance weighted (IVW) methods were employed to validate the causal relationship. Cochran's Q test was employed to evaluate heterogeneity, sensitivity analysis was performed using leave-one-out analysis, and for pleiotropy analysis, the intercept term of MR-Egger's method was investigated. The MR analysis revealed significant associations between various metabolites and RED conditions. For instance, a positive association was found between 1-palmitoylglycerophosphocholine and PCOS, while a negative association was noted between phenylacetate and FI. The study identified several metabolites associated with an increased risk and others with protective effects against PCOS, EMs, and FI. These findings highlight the complex interplay between metabolites and RED, suggesting potential pathways through which these conditions could be influenced or treated. This MR study provides valuable insights into the causal relationship between metabolites and female reproductive endocrine disorders, suggesting that metabolic alterations play a significant role in the pathogenesis of PCOS, EMs, and FI, and offering a foundation for future research and therapeutic development.

Evaluating MCMC Convergence in a Bayesian Model of ST-Elevation Myocardial Infarction Female Patients in Malaysia

The majority of research that looked at the Bayesian Markov Chain Monte Carlo (MCMC) approach for prognostic modelling of cardiovascular disease only focused on the use of the Bayesian approach in variable selection, model selection, and prior distribution selection. But very few of this research has looked at the Markov chains' convergence in the model. In this study, convergence diagnostics were carried out to evaluate the convergence of Markov chains using both visual inspection and additional diagnostics. The National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry, which included 1248 female patients with ST-Elevation Myocardial Infarction (STEMI) between 2006 and 2013, was used for this study's analysis. The multivariate Bayesian model identified six significant variables: dyslipidaemia, myocardial infarction, smoking, renal disease, Killip class, and age group. The trace plots did not reveal any distinctive patterns based on these significant variables, and the model's MCMC mixing is typically good. While for the autocorrelation plots, mild autocorrelations for age group, Killip IV, as well as the intercept term in the model. Since there were only mild autocorrelations, no thinning is needed. Also, the Geweke diagnostic showed that the chain is divided into two windows containing a set fraction of the first and last iterations which produced standard Z-scores. The Geweke diagnostic did not provide evidence of non-convergence, as none of the Z-scores fell in the extreme tails of the N (0,1). In this study, a number of plots and additional diagnostic tools showed that the Markov chains have reached convergence, which is relevant to the general use of the MCMC approach.

Venous thromboembolism and ovarian cancer risk: a Mendelian randomized study

IntroductionA potential link between venous thromboembolism and the risk of ovarian cancer has been identified in clinical practice. However, it is unclear whether there is a causal relationship between the two. In this study, we applied a univariate two-sample Mendelian randomization method to explain the possible link between venous thromboembolism and ovarian cancer pathogenesis at the genetic level, and pointed out that lipid metabolism and ovarian cancer pathogenesis have innovative basic experimental directions.ObjectiveThis study explored the causal effect between a history of venous thromboembolism and the risk of ovarian cancer.MethodsGenome-Wide Association Study (GWAS) data of venous thromboembolism patients (n = 9176) of the same ethnicity were selected as study exposures, and GWAS data of ovarian cancer patients (n = 1218) of the same ethnicity were selected as study exposures. In this study, univariate two-sample Mendelian randomization analysis (UVMR) was performed separately using inverse variance weighted (IVW), MR-Egger regression, and weighted median (WM) to assess causal effects. In this study, Cochran’s Q test, MR-Egger regression intercept term, MR-PRESSO, and leave-one-out method were used for sensitivity analysis to assess the stability and reliability of the results.ResultsThe GWAS data screened in this study were all European ethnicity data. In this study, we found that genetically predicted history of venous thromboembolism was associated with an upward trend in ovarian cancer incidence, and the results of Weighted median, Simple mode, Weighted mode, and MR Egger showed a similar trend (OR = 1.0006, 95% CI: 1.00007–1.0013, p < 0.05). There was no heterogeneity of results (p = 0.18) and no horizontal pleiotropy (p = 0.77). The instrumental variables selected for venous thromboembolism in this study were all strong instrumental variables (F = 669.7). The results of the sensitivity analysis remained consistent.ConclusionThe results of this study indicate that patients with a history of venous thromboembolism are at increased risk of developing ovarian cancer and point to possible associations between lipid metabolism genes, such as CYP4V2, and the development of ovarian cancer, which provide interesting directions for further basic research.

Circulating micronutrients levels and their association with the risk of endometriosis.

Endometriosis, a prevalent gynecological disease, has an unclear pathogenesis. Micronutrients play a crucial role in disease development, which has led to an investigation of their association with endometriosis. In this study, we analyzed the relationship between 15 micronutrients and endometriosis using both univariate and multivariate Mendelian randomization (MR) to assess the correlation. The results were validated using high-performance liquid chromatography (HPLC). The univariate MR analysis indicated that vitamin B6 (OR = 1.7060, 95% CI: 1.1796-2.4672, p = 0.0045) and calcium (OR = 1.4834, 95% CI: 1.0747-2.0475, p = 0.0165) are associated with an increased risk of endometriosis. Higher intakes of vitamin B6 and calcium are associated with a greater likelihood of developing endometriosis. The MR Egger regression's intercept term demonstrated no evidence of pleiotropy (p > 0.05) or heterogeneity (p > 0.05) in the SNPs for calcium and vitamin B6. In multivariate MR analysis, vitamin B6 (OR = 2.397, 95% CI: 1.231-4.669, p = 0.01) was linked to an increased risk of endometriosis, independently of other exposure factors. No significant heterogeneity (p = 0.831) or pleiotropy (p = 0.369) was observed in the genetic variation of endometriosis, affirming the reliability of the multivariate MR analysis. HPLC confirmed a significant increase in serum levels of vitamin B6 and calcium, aligning with the MR analysis findings. Vitamin B6 and calcium may be associated with this disease, with vitamin B6 potentially acting as an independent risk factor. Further research is essential to elucidate the role of micronutrients in disease, offering novel insights for prevention and treatment strategies.

Longitudinal changes in BMD in adults with cystic fibrosis.

Improved survival in people with cystic fibrosis (pwCF) presents new complexities of care, including CF-related bone disease, a common complication in older pwCF. The trajectory of bone loss with age in this population remains unclear. The objective of this study was to estimate the average rate of change in BMD in adults with CF. This retrospective study included adults with CF, aged 25-48yr, followed between January 2000 and December 2021. Subjects with at least one DXA scan were included. Scans obtained posttransplantation, after the initiation of bisphosphonates or cystic fibrosis transmembrane conductance regulator modulator therapy was excluded. The primary outcome was BMD (g/cm2) at the LS and FN. A linear mixed-effects model with both random intercept and random slope terms was used to estimate the average annual change in BMD. A total of 1502 DXA scans in 500 adults (average age 28.4 y) were included. There was a statistically significant annual decline in BMD of -0.008 gm/cm2/yr (95% CI, -0.009 to -0.007) at the FN and -0.006 gm/cm2/yr (95% CI, -0.007 to -0.004) at the LS. Relative to BMD at age 25, there was a 18.8% decline at the FN by age 48yr and a 11% decline at the LS. Pancreatic insufficient subjects had a faster rate of decline in BMD compared with pancreatic sufficient subjects. After adjusting for markers of disease severity, the annual rate of decline remained significant. Individuals with CF experience bone loss at an age when it is not anticipated, thereby entering early adulthood, where further bone loss is inevitable especially with the decrease in estrogen during menopause, with suboptimal BMD. As the CF population ages, it will become very important to consider interventions to maximize bone health.

[formula omitted]-optimal designs for a multidimensional second-degree polynomial model with no intercept

The paper investigates the problem of constructing D-optimal designs for the multidimensional second-degree polynomial model without an intercept term. On a hyperparallelepiped of the given dimensionality and symmetric with respect to the origin, D-optimal designs are found in explicit analytical form.

Harnessding the Power of Machine Learning to Superchange CAPM Forecasts

This paper investigates the improvement of the Capital Asset Pricing Model (CAPM) by incorporating machine learning techniques. The objective is to address the model's conventional constraints in accurately predicting stock returns. The conventional Capital Asset Pricing Model (CAPM), which heavily depends on the beta coefficient to explain returns, frequently proves inadequate in emerging markets and neglects to consider market irregularities such as size and value effects. Our methodology enhances the estimation of beta by refining the Capital Asset Pricing Model (CAPM) to exclude the intercept term and incorporating a dynamic rolling regression method. This approach captures the fluctuations of beta more effectively across different periods, resulting in a more precise estimation. This study utilises ten years of weekly closing price data from companies listed on the NSE Nifty 50 index. It employs rolling regression and two-stage regression analysis to improve the prediction of excess returns above the risk-free rate. The revised CAPM model, which omits the intercept, exhibits a superior level of accuracy, as evidenced by higher adjusted R-squared values and improved F-statistics. In addition, the paper analyses the Fama-French models within the Chinese stock market and explores the potential of advanced machine learning models, such as Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), to improve forecasting methods in unpredictable markets. Integrating LSTM-RNN models presents a promising approach to capturing intricate patterns in financial time series data, demonstrating substantial enhancement in forecasting accuracy compared to conventional methods such as OLS.

Diet affects inflammatory arthritis: a Mendelian randomization study of 30 dietary patterns causally associated with inflammatory arthritis.

The causal associations between dietary intake and the risk and severity of Inflammatory Arthritis (IA) are currently unknown. In this study, we aimed to investigate the causal relationship between nine dietary categories (30 types of diet) and IA using Mendelian randomization (MR). We analyzed data from 30 diets and IA in a genome-wide association study (GWAS). Single nucleotide polymorphisms (SNPs) that could influence the results of MR analyses were screened out through the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test. SNPs were analyzed through two-sample bidirectional MR using inverse variance weighting, MR-Egger regression, and weighted median method. The multiplicity and heterogeneity of SNPs were assessed using MR-Egger intercept term tests and Cochran's Q tests. FDR correction was used to correct the p-values. IVW results showed that Beef intake [Odds ratio (OR) = 2.862; 95% confidence interval (CI), 1.360-6.021, p = 0.006, p_fdr < 0.05] was positively associated with rheumatoid arthritis(RA); Dried fruit intake (OR = 0.522; 95% CI, 0.349-0.781, p = 0.002, p_fdr < 0.05), and Iron intake (OR = 0.864; 95%CI, 0.777-0.960, p = 0.007, p_fdr < 0.05) were negatively associated with RA, all of which were evidence of significance. Fresh fruit intake (OR = 2.528. 95% CI, 1.063-6.011, p = 0.036, p_fdr > 0.05) was positively associated with psoriatic arthritis (PsA); Cheese intake (OR = 0.579; 95% CI, 0.367-0.914, p = 0.019, p_fdr > 0.05) was negatively associated with PsA; both were suggestive evidence. Processed meat intake (OR = 0.238; 95% CI, 0.100-0.565, p = 0.001, p_fdr < 0.05) was negatively associated with reactive arthritis (ReA), a protective factor, and significant evidence. All exposure data passed the heterogeneity check (Cochrane's Q test p > 0.05) and no directional pleiotropy was detected. Leave-one-out analyses demonstrated the robustness of the causal relationship in the positive results. Our study presents genetic evidence supporting a causal relationship between diet and an increased risk of IA. It also identifies a causal relationship between various dietary modalities and different types of IA. These findings have significant implications for the prevention and management of IA through dietary modifications.

Stroke and frailty index: a two-sample Mendelian randomisation study.

Previous observational studies have found an increased risk of frailty in patients with stroke. However, evidence of a causal relationship between stroke and frailty is scarce. The aim of this study was to investigate the potential causal relationship between stroke and frailty index (FI). Pooled data on stroke and debility were obtained from genome-wide association studies (GWAS).The MEGASTROKE Consortium provided data on stroke (N = 40,585), ischemic stroke (IS,N = 34,217), large-vessel atherosclerotic stroke (LAS,N = 4373), and cardioembolic stroke (CES,N = 7 193).Summary statistics for the FI were obtained from the most recent GWAS meta-analysis of UK BioBank participants and Swedish TwinGene participants of European ancestry (N = 175,226).Two-sample Mendelian randomization (MR) analyses were performed by inverse variance weighting (IVW), weighted median, MR-Egger regression, Simple mode, and Weighted mode, and heterogeneity and horizontal multiplicity of results were assessed using Cochran's Q test and MR-Egger regression intercept term test. The results of the current MR study showed a significant correlation between stroke gene prediction and FI (odds ratio 1.104, 95% confidence interval 1.064 - 1.144, P < 0.001). In terms of stroke subtypes, IS (odds ratio 1.081, 95% confidence interval 1.044 - 1.120, P < 0.001) and LAS (odds ratio 1.037, 95% confidence interval 1.012 - 1.062, P = 0.005). There was no causal relationship between gene-predicted CES and FI. Horizontal multidimensionality was not found in the intercept test for MR Egger regression (P > 0.05), nor in the heterogeneity test (P > 0.05). This study provides evidence for a causal relationship between stroke and FI and offers new insights into the genetic study of FI.

Plateau-linear reaction norm model analysis of number born alive in purebred Landrace pigs using meteorological data in Japan.

We performed a plateau-linear reaction norm model (RNM) analysis of number born alive (NBA) in purebred Landrace pigs, where breeding value changes according to maximum temperature at mating day, using public meteorological observation data in Japan. We analysed 52,668 NBA records obtained from 10,320 Landrace sows. Pedigree data contained 99,201 animals. Off-farm daily temperature data at the nearest weather station from each of the farms were downloaded from the Japan Meteorological Agency website. A plateau-linear RNM analysis based on daily maximum temperature on mating day (threshold temperature of 16.6°C) was performed. The percentage of the records with daily maximum temperatures at mating days of ≤16.6, ≥25.0, ≥30.0 and ≥35.0°C were 34.3%, 33.6%, 14.0% and 0.8%, respectively. The value of Akaike's information criterion for the plateau-linear RNM was lower than that for a simple repeatability model (RM). With the plateau-linear RNM, estimated value of heritability ranged from 0.14 to 0.15, while that from the RM analysis was 0.15. Additive genetic correlation between intercept and slope terms was estimated to be -0.52 from the plateau-linear RNM analysis. Estimated additive genetic correlations were >0.9 between NBA at different temperatures ranging from 16.6 to 37.6°C. For the 10,320 sows, average values of prediction reliability of the intercept and slope terms for breeding values in the plateau-linear RNM were 0.47 and 0.16, respectively. Increasing weight for slope term in linear selection index could bring positive genetic gain in the slope part, but prediction accuracy would decrease. Our results imply that genetically improving heat tolerance in sows reared in Japan focusing on NBA using RNM is possible, while RNM is more complex to implement and interpret. Therefore, further study should be encouraged to make genetic improvement for heat tolerance in sows more efficient.

Assessing the causal link between liver function and acute pancreatitis: A Mendelian randomisation study.

A correlation has been reported to exist between exposure factors (e.g. liver function) and acute pancreatitis. However, the specific causal relationship remains unclear. This study aimed to infer the causal relationship between liver function and acute pancreatitis using the Mendelian randomisation method. We employed summary data from a genome-wide association study involving individuals of European ancestry from the UK Biobank and FinnGen. Single-nucleotide polymorphisms (SCNPs), closely associated with liver function, served as instrumental variables. We used five regression models for causality assessment: MR-Egger regression, the random-effect inverse variance weighting method (IVW), the weighted median method (WME), the weighted model, and the simple model. We assessed the heterogeneity of the SNPs using Cochran's Q test. Multi-effect analysis was performed using the intercept term of the MR-Egger method and leave-one-out detection. Odds ratios (ORs) were used to evaluate the causal relationship between liver function and acute pancreatitis risk. A total of 641 SNPs were incorporated as instrumental variables. The MR-IVW method indicated a causal effect of gamma-glutamyltransferase (GGT) on acute pancreatitis (OR = 1.180, 95%CI [confidence interval]: 1.021-1.365, P = 0.025), suggesting that GGT may influence the incidence of acute pancreatitis. Conversely, the results for alkaline phosphatase (ALP) (OR = 0.997, 95%CI: 0.992-1.002, P = 0.197) and aspartate aminotransferase (AST) (OR = 0.939, 95%CI: 0.794-1.111, P = 0.464) did not show a causal effect on acute pancreatitis. Additionally, neither the intercept term nor the zero difference in the MR-Egger regression attained statistical significance (P = 0.257), and there were no observable gene effects. This study suggests that GGT levels are a potential risk factor for acute pancreatitis and may increase the associated risk. In contrast, ALP and AST levels did not affect the risk of acute pancreatitis.

Associations of hyperthyroidism with epilepsy: a Mendelian randomization study

Prior studies have revealed an increased susceptibility to epilepsy in hyperthyroid individuals, but the genetic basis of the hyperthyroidism–epilepsy relationship is not fully comprehended, prompting this study to explore this potential association. We conducted a two-sample Mendelian randomization (TSMR) study to explore the relationship between hyperthyroidism and epilepsy by utilizing aggregated statistics from Genome-Wide Association Studies (GWAS). Data for hyperthyroidism were derived from a GWAS encompassing 462,933 participants, while epilepsy data were sourced from the International League Against Epilepsy (ILAE) consortium. Five distinct methods were employed for TSMR analysis, which included the inverse variance weighting method, MR Egger method, weighted median method, simple model, and weighted model. In our sensitivity analysis, we employed the MR Egger and MR PRESSO methods to assess pleiotropy, and inverse variance weighting and MR Egger in Cochran’s Q statistics to assess heterogeneity. In the IEU database, utilizing the MR-Egger method, we obtained an odds ratio (OR) of 2.631 (95% CI 0.608, 9.796) with a p-value of 0.122. Meanwhile, employing the Weighted Median method yielded an OR of 1.813 (95% CI 0.786, 4.181) with a p-value of 0.163. The IVW method exhibited an OR of 1.986 (95% CI 1.127, 3.502) with a p-value of 0.018. In the assessment of heterogeneity, the MR-Egger method produced a Q statistic of 65.205, accompanied by a p-value of 0.087, while the IVW method recorded a Q statistic of 66.668 with a p-value of 0.083. The multifactorial analysis results showed an intercept term with a standard error (SE) value of 0.009 and a p-value of 0.291. In the FinnGen database, employing the MR-Egger method for all epilepsy data, we observed an OR of 0.952 (95% CI 0.831, 1.093) with a p-value of 0.539. Simultaneously, the Weighted Median method produced an OR of 0.986 (95% CI 0.953, 1.021) with a p-value of 0.423. The IVW method indicated an OR of 0.992 (95% CI 0.965, 1.019) with a p-value of 0.541. The MR-Egger method’s assessment of heterogeneity resulted in a Q statistic of 2.671, associated with a p-value of 0.445, while the IVW method generated a Q statistic of 3.011 with a p-value of 0.556. The multifactorial analysis results displayed an intercept term with a SE-value of 0.019 and a p-value of 0.601. Sensitivity analysis found no evidence of horizontal pleiotropy or heterogeneity. Hyperthyroidism was found to be causally related to all epilepsy but had no effect on other types of epilepsy.

A Mendelian analysis of the relationships between immune cells and breast cancer.

Emerging evidence showed immune cells were associated with the development of breast cancer. Nonetheless, the causal link between them remains uncertain. Consequently, the objective of this study was to investigate the causal connection between immune traits and the likelihood of developing breast cancer. A two-sample Mendelian randomization (MR) analysis was conducted to establish the causal relationship between immune cells and breast cancer in this study. Utilizing publicly accessible genetic data, we investigated causal connections between 731 immune cells and the occurrence of breast cancer. The primary approach for exploring this relationship was the application of the inverse-variance-weighted (IVW) method. Furthermore, sensitivity analyses, encompassing the leave-one-out analysis, Cochran Q test, and Egger intercept test were performed to validate the reliability of the Mendelian randomization results. Finally, we used Bayesian Weighted Mendelian Randomization (BWMR) approach to test the results of MR study. According to the Bonferroni correction, no immune trait was identified with a decreased or increased risk of overall breast cancer risk. As for the ER+ breast cancer, 6 immune trait was identified after the Bonferroni method. the IVW method results showed that CD45RA- CD4+ %CD4+ (p-value:1.37×10-6), CD8dim %T cell (p-value:4.62×10-43), BAFF-R on IgD+ CD38- unsw mem (p-value:6.93×10-5), CD27 on PB/PC (p-value:2.72×10-18) lowered the risk of breast cancer. However, CD19 on IgD- CD38br (p-value:1.64×10-6), CD25 on IgD+ CD38dim (p-value: - ∞) were associated with a higher risk of developing breast cancer. As for the CX3CR1 on CD14+ CD16- monocyte (p-value: 1.15×10-166), the IVW method clearly demonstrated a protective effect against ER- breast cancer. For the above positive results, BAFF-R on IgD+ CD38- unsw mem was the sole association linked to reduced breast cancer risk using the BWMR method. The intercept terms' p-values in MR-Egger regression all exceeded 0.05, indicating the absence of potential horizontal pleiotropy. Through genetic approaches, our study has illustrated the distinct correlation between immune cells and breast cancer, potentially paving the way for earlier diagnosis and more efficient treatment alternatives.

Causality Between 91 Circulating Inflammatory Proteins and Various Asthma Phenotypes: A Mendelian Randomization Study.

To investigate the causal relationship between 91 circulating inflammatory proteins and Various asthma phenotypes by means of Mendelian randomization. Genome-wide association Studies (GWAS) of 91 inflammatory proteins were pooled from the Olink Target platform with 14,824 participants. Various asthma phenotypes were derived from the FinnGen Biobank. Inverse variance weighting (IVW) was used as the main method for MR Analysis, supplemented by Mr-Egger, Weighted median, Simple mode, and Weighted mode. The MR-Egger intercept term test and Cochran's Q test were used to test the polymorphism and heterogeneity of IVs, and visual analysis was carried out to draw scatter plots, funnel plots, and leave-out-one plots. The FDR correction was performed due to the possibility of a type 1 error. Genetically predicted IVW results revealed a total of 30 data sets suggesting a potential causal relationship between circulating inflammatory proteins and asthma phenotypes. Among them, 2 results were still strongly positive after FDR correction. The level of CST5 (OR=1.184; 95% CI: 1.075-1.305; P=0.0001; P-FDR=0.028) is associated with an increased risk of non-allergic asthma. LIF-R (OR=0.723; 95% CI: 0.620-0.842; P=0.000; P-FDR=0.003) is associated with a reduced risk of asthma in children. There was no pleiotropy or heterogeneity in the remaining 16 results that suggested a potential causal relationship. Increased CST5 levels are associated with an increased risk of non-allergic asthma. LIF-R is associated with a reduced risk of asthma in children.

PERSONALIZED AND TYPICAL CONCURRENT RISK: JOINT MODELS OF RECURRENT OUTCOMES AND MORTALITY BY RACE/ETHNICITY

Abstract The growing burden of multiple chronic conditions documented by a U.S. Department of Health and Human Services initiative noted the importance of studies that 1) “Develop tools to identify and target population subgroups of individuals with multiple chronic conditions who are at high risk for poor health outcomes”, and 2) “Improve knowledge about patient trajectories temporally in relation to changes in health status, functional status, and health services use”. Expanding upon our methods for jointly modeling multiple outcomes, where each person’s shared influence on concurrent outcomes can be captured by using a random intercept term, we demonstrate how to model additional heterogeneity. Based on the estimates from the joint model, the typical concurrent risk (TCR) of each outcome (i.e., probability) can be estimated at the cohort level, providing a method for identifying average longitudinal effects of risk factors useful for healthcare system policies. In contrast, a person-specific effect reflects the probability of each outcome within a defined interval of time, while currently at risk for another non-mutually exclusive outcome (i.e., personalized concurrent risk [PCR]). Chronic conditions and sociodemographic risk factors may partially account for the heterogeneity of a cohort. However, differences in the occurrences and associations among these may differ for minoritized groups. These methods identify persons above (or below) average risk. We expanded these joint models to address truncation of measurement due to death and the interdependence of outcomes at the person-level, while maintaining the Type I error rate. We have made the code publicly available.

Rheumatoid arthritis and gastroesophageal reflux disease: a bidirectional and multivariable two-sample Mendelian randomization study.

Aims/hypothesis: The association between gastroesophageal reflux disease (GERD) and rheumatoid arthritis (RA) has been reported by many observational studies in the Asian population. This study aimed to examine the bidirectional causal effects between GERD and RA by two-sample Mendelian randomization (MR) analyses using genetic evidence. Methods: Two-sample Mendelian randomization analyses were performed to determine the causal effect of GERD (129,080 cases vs. 602,604 control participants) on RA (6,236 cases vs. 147,221 control participants) and RA on GERD, respectively. The inverse-variance weighted (IVW) method was used as the primary analysis. Weighted median and MR-Egger regression were taken as supplementary analyses. Cochran's Q test evaluated the heterogeneity. Horizontal pleiotropy was detected by estimating the intercept term of MR-Egger regression. Furthermore, multivariable MR analyses were performed to exclude the influence of confounding factors, including the years of schooling, BMI, and time spent watching television, between GERD and RA. Result: Both univariate MR (UVMR) and multivariable MR (MVMR) provided valid evidence that RA was causally and positively influenced by GERD (UVMR: OR = 1.49, 95% CI = 1.25-1.76, p = 6.18*10-6; MVMR: OR = 1.69, 95% CI = 1.24-2.31, p = 8.62*10-4), whereas GERD was not influenced by RA (UVMR: OR = 1.03, 95% CI = 1.00-1.06, p = 0.042; MVMR: OR = 1.04, 95% CI = 1.00-1.07, p = 0.0271). Conclusion: Our comprehensive bidirectional MR analysis found that for the European population, GERD can induce the occurrence of RA (OR = 1.69, p < 0.00125), whereas RA only has no significant influence on GERD. In particular, patients with GERD are suffering a 69% increased risk of RA occurrence, which means GERD is a substantial risk factor for RA.