Causal Estimates Research Articles

Derivation of a multivariate longitudinal causal effects model

This paper presents a causal inference estimation method for longitudinal observational studies with multiple outcomes. The method uses marginal structural models with inverse probability treatment weights (MSM-IPTWs). In developing the proposed method, we re-define the weights as a product of inverse weights at each time point, accounting for time-varying confounders and treatment exposures and possible correlation between and within (serial) the multiple outcomes. The proposed method is evaluated by simulation studies and with an application to estimate the effect of HIV positivity awareness on condom use and multiple sexual partners using the Malawi Longitudinal Study of Families and Health (MLSFH) data. The simulation study shows that the joint MSM-IPTW performs well with coverage within the expected 95% level for a large sample size (n = 1000) and moderate to strong between and within outcome correlation strength ( ρ j = 0.3 , 0.75, ρ k = 0.4 , 0.8) when the effects are similar. The joint MSM-IPTW performed relatively the same as the adjusted standard joint model when the treatment effect estimate was the same for the outcomes. In the application, HIV positivity awareness increased the usage of condoms and did not affect the number of sexual partners. We recommend using the proposed MSM-IPTWs to correctly control for time-varying treatment and confounders when estimating causal effects for longitudinal observational studies with multiple outcomes.

Causal effect estimation for competing risk data in randomized trial: adjusting covariates to gain efficiency

The double-blinded randomized trial is considered the gold standard to estimate the average causal effect (ACE). The naive estimator without adjusting any covariate is consistent. However, incorporating the covariates that are strong predictors of the outcome could reduce the issue of unbalanced covariate distribution between the treated and controlled groups and can improve efficiency. Recent work has shown that thanks to randomization, for linear regression, an estimator under risk consistency (e.g. Random Forest) for the regression coefficients could maintain the convergence rate even when a nonparametric model is assumed for the effect of covariates. Also, such an adjusted estimator will always lead to efficiency gain compared to the naive unadjusted estimator. In this paper, we extend this result to the competing risk data setting and show that under similar assumptions, the augmented inverse probability censoring weighting (AIPCW) based adjusted estimator has the same convergence rate and efficiency gain. Extensive simulations were performed to show the efficiency gain in the finite sample setting. To illustrate our proposed method, we apply it to the Women's Health Initiative (WHI) dietary modification trial studying the effect of a low-fat diet on cardiovascular disease (CVD) related mortality among those who have prior CVD.

Reliable crash analysis: Comparing biases and error rates of empirical Bayes before-after analyses to mixed-models.

Estimating reliable causal estimates of road safety interventions is challenging, with a number of these challenges addressable through analysis choices. At a minimum, developing reliable crash modification factors (CMFs) needs to address three critical confounding factors, i.e., 1) the regression-to-the-mean (RTM) phenomenon, 2) the effect of traffic volume, and 3) the time trend for the occurrence of crashes. The current preferred crash analysis method is the empirical Bayes (EB) before-after analysis but requires complex bespoke analysis and may not be the best performing method. We compare in a simulation experiment various EB methods to a more straightforward negative binomial generalized linear mixed model (NB-GLMM) with an interaction term between treatment group and time for analysing treatment effects in crash data. Data were simulated using two broad scenarios: 1) an idealized randomized controlled design, and 2) a moderately biased site-selection scenario commonly encountered in road safety crash analyses. Both scenarios varied treatment effects, overdispersion, and sample sizes. The NB-GLMM performed best, maintaining type I error rate and providing least biased estimates across most analyses. Most standard EB methods were too liberal or generally more biased, with the exception of the EB method that incorporated a varying dispersion parameter. Incorporating mixed-effects modelling into the EB procedure improved bias. Overall, we found that using a "standard" NB-GLMM with an interaction term is sufficient for crash analysis, reducing complexity compared to bespoke EB solutions. Chosen methods should also be the least biased and possess the marginal error rates under both ideal and selection-bias conditions. Mixed-effects approaches to analysis of road safety interventions satisfy these criteria outperforming standard or other empirical Bayes approaches tested here.

Circulating inflammatory cytokines and colorectal cancer: New insights from Mendelian randomization.

Colorectal cancer (CRC) is one of the most common cancers worldwide and inflammation is believed to play an important role in CRC. In this study, we comprehensively analyzed the causal association between 91 circulating inflammatory cytokines and the risk of CRC using Mendelian randomization (MR). Based on genome-wide association study summary statistics, we examined the causal effects of 91 circulating inflammatory cytokines on CRC. A series of MR methods, including bidirectional MR, replication sample MR, and multivariable MR, were employed to provide more robust causal estimates. After the validation with 3 MR methods and a series of sensitivity analyses, 2 circulating inflammatory factors were found to be significantly associated with the risk of CRC at the genetic level. Specifically, genetically predicted circulating levels of glial cell line-derived neurotrophic factor (GDNF) (OR = 1.12; 95% CI: 1.05-1.19; P = 2.72 × 10-4) and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) (OR = 0.93; 95% CI: 0.91-0.99; P = 1.00 × 10-2) exerted causal effects on CRC risk. In conclusion, this study suggests that increased circulating levels of GDNF and TRAIL are associated with a higher and lower risk of CRC, respectively. GDNF and TRAIL may be 2 potential therapeutic targets that deserve future investigation.

Causal associations between iron levels in subcortical brain regions and psychiatric disorders: a Mendelian randomization study

Despite observational studies linking brain iron levels to psychiatric disorders, the exact causal relationship remains poorly understood. This study aims to examine the relationship between iron levels in specific subcortical brain regions and the risk of psychiatric disorders. Utilizing two-sample Mendelian randomization (MR) analysis, this study investigates the causal associations between iron level changes in 16 subcortical nuclei and eight major psychiatric disorders, including schizophrenia (SCZ), major depressive disorder (MDD), autism spectrum disorders (ASD), attention-deficit/hyperactivity disorder, bipolar disorder, anxiety disorders, obsessive-compulsive disorder, and insomnia. The genetic instrumental variables linked to iron levels and psychiatric disorders were derived from the genome-wide association studies data of the UK Biobank Brain Imaging and Psychiatric Genomics Consortium. Bidirectional causal estimation was primarily obtained using the inverse variance weighting (IVW) method. Iron levels in the left substantia nigra showed a negative association with the risk of MDD (ORIVW = 0.94, 95% CI = 0.91–0.97, p < 0.001) and trends with risk of SCZ (ORIVW = 0.90, 95% CI = 0.82–0.98, p = 0.020). Conversely, iron levels in the left putamen were positively associated with the risk of ASD (ORIVW = 1.11, 95% CI = 1.04–1.19, p = 0.002). Additionally, several bidirectional trends were observed between subcortical iron levels and the risk for psychiatric disorders. Lower iron levels in the left substantia nigra may increase the risk of MDD, and potentially increase the risk of SCZ, indicating a potential shared pathogenic mechanism. Higher iron levels in the left putamen may lead to the development of ASD. The observed bidirectional trends between subcortical iron levels and psychiatric disorders, indicate the importance of the underlying biomechanical interactions between brain iron regulation and these disorders.

Subsidized loans and firms’ employment outcomes: evidence from a province in Iran

ABSTRACT This paper evaluates a subsidized loan program by comparing firms’ actual and expected employment outcomes based on the loan amount received and invested. Using causal effect estimation and small-firm data from a province in Iran, the results reveal a specific range of loan amounts where actual employment aligns with expectations. Outside this range, discrepancies exist: expected employment is overestimated for loans exceeding 300 million Iranian Rial and underestimated for loans below 150 million Iranian Rial. However, the difference between actual and expected employment is not statistically significant, suggesting the program successfully created the expected number of jobs.

Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects

Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecifications is vital for ensuring validity. We develop a Federated Adaptive Causal Estimation (FACE) framework to incorporate heterogeneous data from multiple sites to provide treatment effect estimation and inference for a flexibly specified target population of interest. FACE accounts for site-level heterogeneity in the distribution of covariates through density ratio weighting. To safely incorporate source sites and avoid negative transfer, we introduce an adaptive weighting procedure via a penalized regression, which achieves both consistency and optimal efficiency. Our strategy is communication-efficient and privacy-preserving, allowing participating sites to share summary statistics only once with other sites. We conduct both theoretical and numerical evaluations of FACE and apply it to conduct a comparative effectiveness study of BNT162b2 (Pfizer) and mRNA-1273 (Moderna) vaccines on COVID-19 outcomes in U.S. veterans using electronic health records from five VA regional sites. We show that compared to traditional methods, FACE meaningfully increases the precision of treatment effect estimates, with reductions in standard errors ranging from 26 % to 67 % .

The Causal Effects of Lifestyle Factors on Osteoarthritis: A Two-sample Mendelian Randomisation Study.

Modern sedentary lifestyles are prevalent among individuals with osteoarthritis. However, direct evidence linking such behaviours as causative factors of osteoarthritis remain limited due to the presence of confounding variables. This study aims to determine the extent to which lifestyle factors have causal effects on osteoarthritis through a two-sample Mendelian randomisation (MR) study. Exposure-outcome relationships were evaluated using inverse variance weighted twosample MR and summary statistics of genome-wide association studies of lifestyle factors and osteoarthritis. Weighted median, MR-PRESSO, and MR-Egger regression were used as sensitivity analyses. We obtained causality estimates, 95% confidence intervals (CI), and P-values from each MR method. Steiger filtering and radial filtering were used to exclude SNPs demonstrating reverse causality and significant heterogeneity, respectively. MR analyses demonstrated that certain lifestyle factors had causal effects on osteoarthritis, particularly insomnia (OR 1.09 (0.387-1.79), P = 0.0024), BMI (OR 6.45 (4.48-8.42), P = 1.38e-10) and protein intake (OR 2.94 (0.361-5.52), P = 0.026). Effects were consistent across sensitivity analyses using median-based MR methods. ZNF131 & SEMA3F, and potentially RWDD2B & USP8 are genetic loci identified to mediate these causal effects. Our results illustrate that lifetime exposure to certain lifestyle factors has causal effects on osteoarthritis. Further studies are required to determine the efficacy of lifestyle-based interventions in reducing the population-wide disease burden of osteoarthritis.

The Effects of At-Scale Career Pathway Investments on the Transition from High School to College

Abstract In this study, we examine the impact of a large-scale effort in California aimed at establishing career pathways and estimate its causal effects on community college enrollment. By leveraging a discontinuous assignment rule for awarding grant funds, we obtain credibly causal estimates of a $500 million initiative to expand career and technical education (CTE) pathways between K-12 districts and community colleges. The competitive grant application process used a standardized rubric, and those receiving a score above a predetermined threshold were awarded funding (i.e., treatment group) while those just below received no funding (i.e., control group), allowing for a regression discontinuity design. We found no overall enrollment increases at partner community colleges for successful grantees. However, there were suggestive enrollment increases of 13.5 percent to 14.8 percent in CTE health sector, which was the primary career sector targeted for expansion. Other targeted programs, such as manufacturing and information communication technology, saw no increases in postsecondary enrollment. The health sector enrollment increases were concentrated among female students, aligning with earlier findings by Bonilla (2020) on reduced high school dropout rates for females. These findings suggest that K-12 postsecondary partnerships may be a viable avenue for aligning enrollment with high-growth sectors.

The Economics of External Information and Risky Behavior: A Case Study of Avalanche Forecasting and Backcountry Incidents

ABSTRACTUnderstanding risk taking is an important part of the economic analysis of human behavior. When engaging in risky behaviors, individuals rely on heuristics and external information. It is an open question as to whether external information about fluctuating risk reduces bad outcomes for risk takers. We examine this question using data from avalanche incidents in wintertime backcountry recreation. The number of people traveling in avalanche terrain in the United States has grown exponentially in the past more than 2 decades, yet major avalanche accidents have remained relatively constant. Using data from reported avalanche incidents in Colorado and Utah, this paper shows that additional avalanche forecasting services reduce dangerous incidents. A policy change in Colorado allows for a difference‐in‐differences estimation with neighboring forecast centers, which gives causal estimates of forecasting reducing incidents by 42% on higher danger days. This reduction may be partially offset by an increase in incidents on lowest rated danger days.

Changes in Vertical Stratification of Neotropical Nymphalid Butterflies at Forest Edges Are Not Directly Caused by Light and Temperature Conditions.

Habitat fragmentation and land use changes threaten neotropical habitats and alter patterns of diversity at forest edges. Like other arthropod assemblages, neotropical fruit-feeding butterfly communities show strong vertical stratification within forests, with some recent work showing its potential role in speciation. At forest edges, species considered to be forest canopy specialists have been observed descending to the forest understory, with the similarity in light conditions between the canopy and understory strata at edges hypothesized to be responsible for this phenomenon. We conducted a study using standardized sampling to document and quantify this edge effect, characterize edge and forest strata, and estimate the relative contributions of temperature and light conditions to changes in nymphalid butterfly stratification at forest edges. We found strong evidence of an edge effect in these butterflies and confirmed strong differences in light and temperature, showing that the edge understory differs little from forest canopy conditions. Of 41 species common to both forests and edges, 28 shifted to have a lower canopy probability at the edge, and our model detected a decrease in canopy probability of 0.165. Furthermore, our analysis indicated the relative abundance of canopy taxa increased at the edge, and the tribes Haeterini and Morphini were especially sensitive to edge effects. However, the analyses here did not clearly implicate temperature or light magnitude in causing changes in neotropical nymphalid vertical stratification at forest edges. Instead, our results point to other mediator variables as being important for changes at tropical forest edges. From our data, edge-responsive species can be separated into two different categories, which likely relates to their resilience to anthropogenic disturbance. We also note that structural causal models have a potential place in future work on tropical conservation, given they can provide causal estimates with observational data.

A semi-empirical Bayes approach for calibrating weak instrumental bias in sex-specific Mendelian randomization studies.

Strong sex differences exist in sleep phenotypes and also cardiovascular diseases (CVDs). However, sex-specific causal effects of sleep phenotypes on CVD-related outcomes have not been thoroughly examined. Mendelian randomization (MR) analysis is a useful approach for estimating the causal effect of a risk factor on an outcome of interest when interventional studies are not available. We first conducted sex-specific genome-wide association studies (GWASs) for suboptimal-sleep phenotypes (insomnia, obstructive sleep apnea (OSA), short and long sleep durations, and excessive daytime sleepiness) utilizing the Million Veteran Program (MVP) dataset. We then developed a semi-empirical Bayesian framework that (i) calibrates variant-phenotype effect estimates by leveraging information across sex groups, and (ii) applies shrinkage sex-specific effect estimates in MR analysis, to alleviate weak instrumental bias when sex groups are analyzed in isolation. Simulation studies demonstrate that the causal effect estimates derived from our framework are substantially more efficient than those obtained through conventional methods. We estimated the causal effects of sleep phenotypes on CVD-related outcomes using sex-specific GWAS data from the MVP and All of Us. Significant sex differences in causal effects were observed, particularly between OSA and chronic kidney disease, as well as long sleep duration on several CVD-related outcomes. By applying shrinkage estimates for instrumental variable selection, we identified multiple sex-specific significant causal relationships between OSA and CVD-related phenotypes.

Unraveling the relationship between obstructive sleep apnea and osteoarthritis: A multivariate mendelian randomization highlighting the role of BMI as a confounding factor.

Osteoarthritis (OA) and obstructive sleep apnea (OSA) are prevalent chronic conditions with emerging evidence suggesting a potential link. However, the causality of this association remains unclear, possibly influenced by confounders like high body mass index (BMI). This study aimed to explore causal relationships between OA and OSA using Mendelian randomization (MR). MR analysis was performed to assess causality between OA and OSA. Inverse variance weighting (IVW) was the primary MR method, complemented by sensitivity analyses, including MR steiger, MR-Egger, MR-PRESSO, weighted median, heterogeneity tests, and leave-one-out approaches to evaluate pleiotropy and confirm the robustness of the causal estimates. To exclude confounding effects of BMI, we also used a multivariate MR (MVMR). After adjusting for BMI through MVMR, no significant causal relationship was identified between genetically predicted OSA and OA phenotypes, including knee (KOA) and hip osteoarthritis (HOA), suggesting that obesity largely drives the observed relationship between these conditions. Similarly, MR steiger doesn't support a causal effect from OA on OSA. Sensitivity analyses confirmed the robustness of these results, with no significant evidence of horizontal pleiotropy or heterogeneity affecting outcomes. The findings indicate that BMI acts as a critical confounder in the relationship between OSA and OA, rather than OSA directly contributing to OA development. Our findings indicate that there is no significant causal relationship between genetically predicted OSA and OA after adjusting for BMI. These findings underscore obesity as the primary shared risk factor, highlighting the importance of weight management as a key strategy for mitigating the risks of both conditions. Future research should aim to validate these findings in diverse populations and explore other metabolic pathways that may contribute to these complex associations.

Dying or Lying? For-Profit Hospices and End-of-Life Care

The Medicare hospice program is intended to provide palliative care to terminal patients, but patients with long stays in hospice are highly profitable, motivating concerns about overuse among the Alzheimer's and Dementia (ADRD) population in the rapidly growing for-profit sector. We provide the first causal estimates of the effect of for-profit hospice on patient spending using the entry of for-profit hospices over 20 years. We find hospice has saved money for Medicare by offsetting other expensive care among ADRD patients. As a result, policies limiting hospice use including revenue caps and antifraud lawsuits are distortionary and deter potentially cost-saving admissions. (JEL H51, I11, I12, I18, J14, L84)

The Role of Sex Hormones and Sex Hormone-binding Globulin in Functional Gatrointestinal Disorders: A Bidirectional Two-sample Mendelian Randomization Study.

Sex hormones and sex hormone-binding globulin (SHBG) have been confirmed to involve in the pathophysiology of functional gastrointestinal disorders (FGIDs), including irritable bowel syndrome (IBS) and functional dyspepsia (FD). However, causal associations have not yet been investigated. Utilizing data from Genome-wide association studies, we conducted bidirectional two-sample mendelian randomization (MR) analyses to assess the causal relationships between sex hormones, SHBG and FGIDs. Data for sex hormones including testosterone and estradiol, and SHBG were collected from the UK Biobank and FinnGen study. Relevant single nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs). Inverse variance weighted (IVW) analysis were performed to assess the causal relationships., supplemented with MR-Egger, weighted median, and weighted mode approaches. Additionally, we used Cochran's Q test to evaluate the heterogeneity of genetic variants and implemented leave-one-out analysis to assess the impact of individual SNPs on the causal estimates. Several sensitivity analyses were conducted to assess the robustness of the results. Significant negative causal relationship was found between genetically predicted testosterone and the risk of IBS (OR=0.90, 95%CI: 0.83-0.97; p=0.007). SHBG demonstrated an inverse correlation with the risk of IBS (OR=0.82, 95%CI: 0.68-0.98; p=0.035) and FD (OR=0.83, 95%CI: 0.69-0.99; p=0.048). However, no statistically significant association was found between testosterone and FD, while estradiol also showed no causal association with FGIDs. Our study revealed a negative causal relationship between testosterone and IBS risk, and SHBG appears to be inversely associated with FD. This provided new ideas for the prevention and control of IBS, and future research is warranted to elucidate the underlying mechanisms driving these associations and their potential clinical implications.

The causal association between asthma and the risk of frailty: A two-sample Mendelian randomization study

BackgroundThe correlation between asthma and frailty is increasingly garnering attention. The association between asthma and frailty remains inconclusive in observational studies, and the causality of this relationship still needs to be established.AimsTherefore, we employed two-sample Mendelian randomization analyses using genetic instruments to determine the causal association of asthma on frailty.MethodsTwo-sample Mendelian randomization (MR) analyses were performed to assess the causal effect of asthma on frailty. The genetic variants strongly associated with asthma (P < 5E-08) during the discovery and replication stages were derived from a recent meta-analysis of genome-wide association studies (GWAS) (N = 408,442) in the UK Biobank and a GWAS in the FinnGen Consortium (N = 217,421), respectively. Summary statistics of the frailty index (N = 175,226) are derived from the latest released GWAS dataset on frailty index. The inverse variance weighted (IVW) method was employed as the primary approach for calculating estimated values, with additional sensitivity analyses and heterogeneity analyses utilized to further validate the results.ResultsUsing the IVW method, genetic susceptibility to asthma was associated with an increased risk of frailty in the discovery stage (odds ratio [OR] = 1.092, 95% confidence interval [CI] = 1.075–1.109, P = 5.00E-28), which was also validated in the replication stage (OR = 1.073, 95% CI = 1.052–1.096, P = 1.41E-11). Sensitivity analyses yielded consistent causal estimate, and no significant pleiotropy was found throughout the MR study.ConclusionThe present study demonstrated that asthma is causally associated with an elevated risk of frailty. Further studies are needed to elucidate the potential pathophysiological mechanisms between asthma and frailty.

The necessity of validity diagnostics when drawing causal inferences from observational data: lessons from a multi-database evaluation of the risk of non-infectious uveitis among patients exposed to Remicade®

BackgroundAutoimmune disorders have primary manifestations such as joint pain and bowel inflammation but can also have secondary manifestations such as non-infectious uveitis (NIU). A regulatory health authority raised concerns after receiving spontaneous reports for NIU following exposure to Remicade®, a biologic therapy with multiple indications for which alternative therapies are available. In assessment of this clinical question, we applied validity diagnostics to support observational data causal inferences.MethodsWe assessed the risk of NIU among patients exposed to Remicade® compared to alternative biologics. Five databases, four study populations, and four analysis methodologies were used to estimate 80 potential treatment effects, with 20 pre-specified as primary. The study populations included inflammatory bowel conditions Crohn’s disease or ulcerative colitis (IBD), ankylosing spondylitis (AS), psoriatic conditions plaque psoriasis or psoriatic arthritis (PsO/PsA), and rheumatoid arthritis (RA). We conducted four analysis strategies intended to address limitations of causal estimation using observational data and applied four diagnostics with pre-specified quantitative rules to evaluate threats to validity from observed and unobserved confounding. We also qualitatively assessed post-propensity score matching representativeness, and bias susceptibility from outcome misclassification. We fit Cox proportional-hazards models, conditioned on propensity score-matched sets, to estimate the on-treatment risk of NIU among Remicade® initiators versus alternatives. Estimates from analyses that passed four validity tests were assessed.ResultsOf the 80 total analyses and the 20 analyses pre-specified as primary, 24% and 20% passed diagnostics, respectively. Among patients with IBD, we observed no evidence of increased risk for NIU relative to other similarly indicated biologics (pooled hazard ratio [HR] 0.75, 95% confidence interval [CI] 0.38–1.40). For patients with RA, we observed no increased risk relative to similarly indicated biologics, although results were imprecise (HR: 1.23, 95% CI 0.14–10.47).ConclusionsWe applied validity diagnostics on a heterogenous, observational setting to answer a specific research question. The results indicated that safety effect estimates from many analyses would be inappropriate to interpret as causal, given the data available and methods employed. Validity diagnostics should always be used to determine if the design and analysis are of sufficient quality to support causal inferences. The clinical implications of our findings on IBD suggests that, if an increased risk exists, it is unlikely to be greater than 40% given the 1.40 upper bound of the pooled HR confidence interval.

Causal association between metabolites and age-related macular degeneration: a bidirectional two-sample mendelian randomization study

BackgroundAge-related macular degeneration (AMD) is the leading cause of visual impairment in the elderly population. Accumulating evidence has revealed the possible association between metabolites and AMD. This study aimed to assess the effect of plasma metabolites on AMD and its two subtypes using a bidirectional two-sample Mendelian randomization approach.MethodsThe causality between plasma metabolites and AMD was assessed by a bidirectional two-sample Mendelian randomization (MR) analysis using the genome-wide association studies (GWAS) summary statistics of 1400 genetically determined metabolites (GDMs) and AMD. For this MR analysis, inverse variance weighted (IVW) was used as the primary method, with weighted median, MR-Egger, weighted mode, and simple mode as supplementary methods to examine the causality. MR-Egger intercept, Cochran’s Q, and MR-PRESSO test were employed to evaluate possible pleiotropy and heterogeneity.ResultsThe results of IVW showed significant causal associations between 13 GDMs and AMD. 1-stearoyl-GPE (18:0), androstenediol (3β,17β) monosulfate, stearoyl sphingomyelin (d18:1/18:0), xylose, and X-11,850 exhibited a protective effect on AMD, while gulonate and mannonate increased the risk of AMD. 1-stearoyl-GPE (18:0) and X-11,850 exhibited protective effects on dry AMD. DHEAS, 1-stearoyl-GPE (18:0), 5α-androstan-3β,17β-diol disulfate, xylose, androstenediol (3β,17β) monosulfate, and N2-acetyl, N6, N6-dimethyllysine exhibited a protective effect on wet AMD, while succinimide, 16a-hydroxy DHEA 3-sulfate, and X-13,553 increased the risk of wet AMD. Horizontal pleiotropy and heterogeneity did not distort the causal estimates. In the reverse MR analysis, AMD reduced the androstenediol (3β,17β) monosulfate level, and increased the stearoyl sphingomyelin(d18:1/18:0) level.ConclusionThis study supported the effect of plasma metabolites on AMD, providing novel insights for clinical diagnosis and prevention strategy.

Comprehensive Mendelian Randomization Analysis of Smoking and Its Effects on Venous Thromboembolism.

An increasing number of Mendelian randomization (MR) studies have evaluated the causal link between smoking and venous thromboembolism (VTE). However, previous studies often rely on single genetic variants related to smoking quantity and exhibit various other shortcomings, making them prone to pleiotropy and potentially leading to imprecise causal estimates. Thus, the deeper causal mechanisms remain largely unexplored. This MR study reassessed the causal relationship between smoking and VTE, including its subtypes-deep vein thrombosis (DVT) and pulmonary embolism (PE). Data on VTE were sourced from the FinnGen consortium with nonoverlapping sample sizes. The smoking phenotypes analyzed included smoking initiation, lifetime smoking, the number of cigarettes smoked per day by both current and former smokers (CigDay), and total pack-years of smoking in adulthood. The primary analytical method was inverse-variance-weighted (IVW), supplemented by multiple verification methods to ensure robust results. Statistical rigor was ensured through LDtrait pruning and Steiger filtering for reverse causation, with comprehensive sensitivity analyses including RadialMR confirming the findings' robustness. After Bonferroni correction, this study demonstrates significant causal evidence linking lifetime smoking with the incidence of VTE (odds ratio [OR]IVW = 1.50, 95% confidence interval [CI] 1.21-1.85, p = 1.75 × 10-4) and PE (ORIVW = 1.69, 95% CI 1.25-2.28, p = 6.55 × 10-4), and suggestive evidence with DVT, consistent in direction with previous studies but showing considerable differences in effect sizes and significance. Additionally, CigDay (past and current) increases the risks of VTE and DVT, while no causal link was found between smoking initiation and VTE or its subtypes (p < 0.05), both directly contradicting previous conclusions. Furthermore, our study is the first to suggest a causal link between pack-years and an increased risk of VTE. This MR study employed rigorous statistical pruning of its instrumental variables, using the most comprehensive smoking phenotype to date. It successfully mitigated biases such as winner's curse, yielding causal effect results distinct from previous studies.

Reassessing the winner-loser gap in satisfaction with democracy.

Citizens who support a party which enters government are systematically more satisfied with democracy compared to voters who supported a party which ends up in the opposition. This relationship is labelled as the "winner-loser gap," but we lack firm causal evidence of this gap. We provide a causal estimate of the effects of voting for a winning or losing party by leveraging data from surveys fielded before and after new government formations in three well established democracies (Netherlands, Norway and Iceland) were announced in contexts of very high uncertainty. Using a regression discontinuity design comparing citizens' levels of satisfaction with democracy just before and just after their electoral status (winner or loser) was revealed, we find that the impact of winning or losing is undistinguishable from zero. We conclude by discussing the implications of our findings.