Causal associations between inflammatory bowel disease and anxiety:A bidirectional Mendelian randomization study

BACKGROUND Anxiety is common in patients with inflammatory bowel disease(IBD),including those with ulcerative colitis(UC)and Crohn’s disease(CD);however,the causal relationship between IBD and anxiety remains unknown.AIM To investigate the causal relationship between IBD and anxiety by using bidirectional Mendelian randomization analysis.METHODS Single nucleotide polymorphisms retrieved from genome-wide association studies(GWAS)of the European population were identified as genetic instrument variants.GWAS statistics for individuals with UC(6968 patients and 20464 controls;adults)and CD(5956 patients and 14927 controls;adults)were obtained from the International IBD Genetics Consortium.GWAS statistics for individuals with anxiety were obtained from the Psychiatric Genomics Consortium(2565 patients and 14745 controls;adults)and FinnGen project(20992 patients and 197800 controls;adults),respectively.Inverse-variance weighted was applied to assess the causal relationship,and the results were strengthened by heterogeneity,pleiotropy and leave-one-out analyses.RESULTS Genetic susceptibility to UC was associated with an increased risk of anxiety[odds ratio:1.071(95%confidence interval:1.009-1.135),P=0.023],while genetic susceptibility to CD was not associated with anxiety.Genetic susceptibility to anxiety was not associated with UC or CD.No heterogeneity or pleiotropy was observed,and the leave-one-out analysis excluded the potential influence of a particular variant.CONCLUSION This study revealed that genetic susceptibility to UC was significantly associated with anxiety and highlighted the importance of early screening for anxiety in patients with UC.

Association between inflammatory bowel disease and all-cause dementia:A two-sample Mendelian randomization study

BACKGROUND Numerous observational studies have documented a correlation between inflammatory bowel disease(IBD)and an increased risk of dementia.However,the causality of their associations remains elusive.AIM To assess the causal relationship between IBD and the occurrence of all-cause dementia using the two-sample Mendelian randomization(MR)method.METHODS Genetic variants extracted from the large genome-wide association study(GWAS)for IBD(the International IBD Genetics Consortium,n=34652)were used to identify the causal link between IBD and dementia(FinnGen,n=306102).The results of the study were validated via another IBD GWAS(United Kingdom Biobank,n=463372).Moreover,MR egger intercept,MR pleiotropy residual sum and outlier,and Cochran's Q test were employed to evaluate pleiotropy and heterogeneity.Finally,multiple MR methods were performed to estimate the effects of genetically predicted IBD on dementia,with the inverse variance weighted approach adopted as the primary analysis.RESULTS The results of the pleiotropy and heterogeneity tests revealed an absence of significant pleiotropic effects or heterogeneity across all genetic variants in outcome GWAS.No evidence of a causal effect between IBD and the risk of dementia was identified in the inverse variance weighted[odds ratio(OR)=0.980,95%CI:0.942-1.020,P value=0.325],weighted median(OR=0.964,95%CI:0.914-1.017,P value=0.180),and MR-Egger(OR=0.963,95%CI:0.867-1.070,P value=0.492)approaches.Consistent results were observed in validation analyses.Reverse MR analysis also showed no effect of dementia on the development of IBD.Furthermore,MR analysis suggested that IBD and its subtypes did not causally affect allcause dementia and its four subtypes,including dementia in Alzheimer's disease,vascular dementia,dementia in other diseases classified elsewhere,and unspecified dementia.CONCLUSION Taken together,our MR study signaled that IBD and its subentities were not genetically associated with all-cause dementia or its subtypes.Further large prospective studies are warranted to elucidate the impact of intestinal inflammation on the development of dementia.

Air target intention recognition and causal effect analysis combining uncertainty information reasoning and potential outcome framework

Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.

CONDITIONS FOR UNIFORMLY NON-CONFOUNDING OF CAUSAL DISTRIBUTION EFFECTS OVER MULTIPLE COVARIATES

Causation is a distinct concept from association and more important than association inepidemiologic studies.This paper proposes the concept of uniform non-confounding for causal distributioneffects over multiple covariates,and gives the sufficient conditions for uniform non-confoundingover a covariate set C including confounders or non-confounders,and also shows the conditions forconditionally non-confounding in the subpopulations.All these conditions can be tested by observeddata.

Pharmacological Treatment of Adult Attention-Deficit/Hyperactivity Disorder(ADHD)in a Longitudinal Observational Study:Estimated Treatment Effect Strengthened by Improved Covariate Balance

An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means of this new and more sophisticated analysis. Long term effect of medication for adult ADHD patients is not resolved. A model with causal parameters to represent effect of medication was formulated, which accounts for time-varying confounding and selection-bias from loss to follow-up. The popular marginal structural model (MSM) for causal inference, of Robins et al., adjusts for time-varying confounding, but suffers from lack of robustness for misspecification in the weights. Recent work by Imai and Ratkovic?[1][2] achieves robustness in the MSM, through improved covariate balance (CBMSM). The CBMSM (freely available software) was compared with a standard fit of a MSM and a naive regression model, to give a robust estimate of the true treatment effect in 250 previously non-medicated adults, treated for one year, in a specialized ADHD outpatient clinic in Norway. Covariate balance was greatly improved, resulting in a stronger treatment effect than without this improvement. In terms of treatment effect per week, early stages seemed to have the strongest influence. An estimated average reduction of 4 units on the symptom scale assessed at 12 weeks, for hypothetical medication in the 9 - 12 weeks period compared to no medication in this period, was found. The treatment effect persisted throughout the whole year, with an estimated average reduction of 0.7 units per week on symptoms assessed at one year, for hypothetical medication in the last 13 weeks of the year, compared to no medication in this period. The present findings support a strong and causal direct and indirect effect of pharmacological treatment of adults with ADHD on improvement in symptoms, and with a stronger treatment effect than has been reported.

Bayes Estimation of Causal Effect for a Counterfactural Model

This paper presents the Bayes estimation and empirical Bayes estimation of causal effects in a counterfactual model. It also gives three kinds of prior distribution of the assumptions of replaceability. The experiment shows that empirical Bayes estimation is better than other estimations when not knowing which assumption is true.

Learning Causal Effect Using Machine Learning with Application to China's Typhoon

Matching is a routinely used technique to balance covariates and thereby alleviate confounding bias in causal inference with observational data.Most of the matching literatures involve the estimating of propensity score with parametric model,which heavily depends on the model specification.In this paper,we employ machine learning and matching techniques to learn the average causal effect.By comparing a variety of machine learning methods in terms of propensity score under extensive scenarios,we find that the ensemble methods,especially generalized random forests,perform favorably with others.We apply all the methods to the data of tropical storms that occurred on the mainland of China since 1949.

Impact of sufficient dimension reduction in nonparametric estimation of causal effect

We consider the estimation of causal treatment effect using nonparametric regression orinverse propensity weighting together with sufficient dimension reduction for searching lowdimensional covariate subsets. A special case of this problem is the estimation of a responseeffect with data having ignorable missing response values. An issue that is not well addressedin the literature is whether the estimation of the low-dimensional covariate subsets by sufficient dimension reduction has an impact on the asymptotic variance of the resulting causaleffect estimator. With some incorrect or inaccurate statements, many researchers believe thatthe estimation of the low-dimensional covariate subsets by sufficient dimension reduction doesnot affect the asymptotic variance. We rigorously establish a result showing that this is nottrue unless the low-dimensional covariate subsets include some covariates superfluous for estimation, and including such covariates loses efficiency. Our theory is supplemented by somesimulation results.

Causal relationship between 14 site-specific cancers and venous thromboembolism

Background:It has been observed that cancer and venous thromboembolism(VTE)are associated,but anticancer therapy may violate the causality.Therefore,this study aimed to elucidate the causal relationship of various cancers to VTE using Mendelian randomization(MR).Methods:Three MR methods were used to estimate causal effects:Inverse variance weighted(IVW),MR-Egger and weighted median.Sensitivity analyses included Cochran's Q-test,MR-Egger intercept test and MRPRESSO.Gene ontology enrichment analysis was performed to elucidate the underlying mechanisms of VTE development in cancer patients.Results:The primary IVW approach showed that non-Hodgkin's lymphoma(NHL)might increase the risk of VTE(odds ratio[OR]:1.20,95%confidence interval[95%CI]:1.00–1.44,p=0.045),while melanoma possibly reduced the risk of VTE(OR:0.89,95%CI:0.82–0.97,p=0.006),although there was no significance after adjustment for multiple testing.No association was observed between VTE risk and other site-specific cancers.Gene ontology enrichment analysis revealed that vitamin D played an important role in the development of VTE in cancer patients.Conclusions:Our findings suggested that genetically predicted NHL was associated with higher VTE risk,whereas melanoma had lower VTE risk compared with other site-specific cancers.Moreover,this study suggested that anticancer therapy and increased extensive examination might play a more important role in VTE development than the nature of cancer.

Water consumption and biodiversity:Responses to global emergency events

Given that it was a once-in-a-century emergency event,the confinement measures related to the coronavirus disease 2019(COVID-19)pandemic caused diverse disruptions and changes in life and work patterns.These changes significantly affected water consumption both during and after the pandemic,with direct and indirect consequences on biodiversity.However,there has been a lack of holistic evaluation of these responses.Here,we propose a novel framework to study the impacts of this unique global emergency event by embedding an environmentally extended supply-constrained global multi-regional input-output model(MRIO)into the drivers-pressure-state-impact-response(DPSIR)framework.This framework allowed us to develop scenarios related to COVID-19 confinement measures to quantify country-sector-specific changes in freshwater consumption and the associated changes in biodiversity for the period of 2020-2025.The results suggest progressively diminishing impacts due to the implementation of COVID-19 vaccines and the socio-economic system’s self-adjustment to the new normal.In 2020,the confinement measures were estimated to decrease global water consumption by about 5.7% on average across all scenarios when compared with the baseline level with no confinement measures.Further,such a decrease is estimated to lead to a reduction of around 5% in the related pressure on biodiversity.Given the interdependencies and interactions across global supply chains,even those countries and sectors that were not directly affected by the COVID-19 shocks experienced significant impacts:Our results indicate that the supply chain propagations contributed to 79% of the total estimated decrease in water consumption and 84%of the reduction in biodiversity loss on average.Our study demonstrates that the MRIO-enhanced DSPIR framework can help quantify resource pressures and the resultant environmental impacts across supply chains when facing a global emergency event.Further,we recommend the development of more locally based water conservation measures—to mitigate the effects of trade disruptions—and the explicit inclusion of water resources in post-pandemic recovery schemes.In addition,innovations that help conserve natural resources are essential for maintaining environmental gains in the post-pandemic world.

An Alternative Doubly Robust Estimation in Causal Inference Model

Doubly robust(DR)methods that employ both the propensity score and outcome models are widely used to estimate the causal effect of a treatment and generally outperform those methods only using the propensity score or the outcome model.However,without appropriately chosen the working models,DR estimators may substantially lose efficiency.In this paper,based on the augmented inverse probability weighting procedure,we derive a new estimating equation for the causal effect by the strategy of combining estimating equations.The resulting estimator by solving the new estimating equation retains doubly robust and can improve the efficiency under the misspecification of conditional mean working model.We further show the large sample properties of the proposed estimator under some regularity conditions.Through simulation experiments and a real data analysis,we illustrate that the proposed method is competitive with its competitors,which is in line with those implied by the asymptotic theory.

Exploring the differential impacts of urban transit system on the spatial distribution of local and floating population in Beijing

The floating population has become the main driver of urban population excessive growth in China＇s mega cities. Urban transit system （UTS） is a significant factor in population spatial distributions within urban areas, especially rapid and high-capacity transit systems. This paper analyzes the causal effects of the extension of expressways and subways between 2000 and 2010 in the Beijing Metropolitan Area （BMA）, focusing on the group differences between the local residents and the floating population. Due to the endogeneity of transportation improvements and population growth, Instrumental Variable （IV） regression model is applied to avoid this problem. The results show the local residents increased in the inner suburbs but decreased in the city center, while the floating population increased in the majority areas. IV regression results show that the extension of urban transit systems had statistically significant impacts on population growth across the BMA, The results also show that the extension of urban subway system had more effects on the floating population than the local residents across the BMA. It is mainly caused by the rather low fare of urban subway system. This implies that the excessive subsidy on urban subway system could result in ex- cessive floating population growth and residential differentiation, even residential segregation Hence, it is necessary to plan and design reasonable and scientific urban transit systems in order to advance reasonable population size and promote residential integration. Moreover, the regional analysis shows that the effects of urban transportation improvements on the local residents are not statistically significant in the inner suburbs. It implies that urban transportation improvements had limited effects on inducing people to move to suburban areas and controlling center city＇s population in Beijing. Therefore, it should be stressed the differenti- ated effects of urban transportation improvements on population distribution in the process of urban planning and population control.

Novel loci and potential mechanisms of major depressive disorder,bipolar disorder,and schizophrenia

Different psychiatric disorders share genetic relationships and pleiotropic loci to certain extent.We integrated and analyzed datasets related to major depressive disorder(MDD),bipolar disorder(BIP),and schizophrenia(SCZ)from the Psychiatric Genomics Consortium using multitrait analysis of genome-wide association analysis(MTAG).MTAG significantly increased the effective sample size from 99,773 to 119,754 for MDD,from 909,061 to 1,450,972 for BIP,and from 856,677 to 940,613 for SCZ.We discovered 7,32,and 43 novel lead single nucleotide polymorphisms(SNPs)and 1,6,and 3 novel causal SNPs for MDD,BIP,and SCZ,respectively,after fine-mapping.We identified rs8039305 in the FURIN gene as a novel pleiotropic locus across the three disorders.We performed marker analysis of genomic annotation(MAGMA)and Hi-C-coupled MAGMA(H-MAGMA)based gene-set analysis and identified 101 genes associated with the three disorders,which were enriched in the regulation of postsynaptic membranes,postsynaptic membrane dopaminergic synapses,and Notch signaling pathway.Next,we performed Mendelian randomization analysis using different tools and detected a causal effect of BIP on SCZ.Overall,we demonstrated the usage of combined genome-wide association studies summary statistics for exploring potential novel mechanisms of the three psychiatric disorders,providing an alternative approach to integrate publicly available summary data.

Quantifying the impacts of human mobility restriction on the spread of coronavirus disease 2019:an empirical analysis from 344 cities of China

Background:Since the outbreak of coronavirus disease 2019(COVID-19),human mobility restriction measures have raised controversies,partly because of the inconsistent findings.An empirical study is promptly needed to reliably assess the causal effects of the mobility restriction.The purpose of this study was to quantify the causal effects of human mobility restriction on the spread of COVID-19.Methods:Our study applied the difference-in-difference(DID)model to assess the declines of population mobility at the city level,and used the log-log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time after adjusting for confounders.Results:The DID model showed that a continual expansion of the relative declines over time in 2020.After 4 weeks,population mobility declined by-54.81%(interquartile range,-65.50%to-43.56%).The accrued population mobility declines were associated with the significant reduction of cumulative COVID-19 cases throughout 6 weeks(ie,1%decline of population mobility was associated with 0.72%[95%CI:0.50%-0.93%]reduction of cumulative cases for 1 week,1.42%2 weeks,1.69%3 weeks,1.72%4 weeks,1.64%5 weeks,and 1.52%6 weeks).The impact on the weekly new cases seemed greater in the first 4 weeks but faded thereafter.The effects on cumulative cases differed by cities of different population sizes,with greater effects seen in larger cities.Conclusions:Persistent population mobility restrictions are well deserved.Implementation of mobility restrictions in major cities with large population sizes may be even more important.

Abnormal Effective Connectivity in the Brain is Involved in Auditory Verbal Hallucinations in Schizophrenia

Information flow among auditory and language processing-related regions implicated in the pathophysiology of auditory verbal hallucinations（AVHs） in schizophrenia（SZ） remains unclear. In this study, we used stochastic dynamic causal modeling（s DCM） to quantify connections among the left dorsolateral prefrontal cortex（inner speech monitoring）, auditory cortex（auditory processing）, hippocampus（memory retrieval）, thalamus（information filtering）, and Broca＇s area（language production） in 17 first-episode drug-na？¨ve SZ patients with AVHs, 15 without AVHs, and 19 healthy controls using resting-state functional magnetic resonance imaging.Finally, we performed receiver operating characteristic（ROC） analysis and correlation analysis between image measures and symptoms. s DCM revealed an increasedsensitivity of auditory cortex to its thalamic afferents and a decrease in hippocampal sensitivity to auditory inputs in SZ patients with AVHs. The area under the ROC curve showed the diagnostic value of these two connections to distinguish SZ patients with AVHs from those without AVHs. Furthermore, we found a positive correlation between the strength of the connectivity from Broca＇s area to the auditory cortex and the severity of AVHs. These findings demonstrate, for the first time, augmented AVHspecific excitatory afferents from the thalamus to the auditory cortex in SZ patients, resulting in auditory perception without external auditory stimuli. Our results provide insights into the neural mechanisms underlying AVHs in SZ. This thalamic-auditory cortical-hippocampal dysconnectivity may also serve as a diagnostic biomarker of AVHs in SZ and a therapeutic target based on direct in vivo evidence.

Investigating the gut microbiota's influence on psoriasis and psoriatic arthritis risk: a Mendelian randomization analysis

Background:Numerous investigations have revealed the interplay between gut microbiota(GM)and psoriasis(Ps)and psoriatic arthri-tis(PsA).However,the causal relationship between them remains unknown.Methods:We curated a collection of genetic variants(P<1×10^(-5))associated with GM(n=18340)derived from the MiBioGen study.To explore the intricate relationship between GM and Ps as well as PsA,we harnessed the comprehensive resources of the FinnGen database,encompassing a vast cohort of individuals,including 4510 Ps cases and 212242 controls and 1637 PsA cases and 212242 controls.Mendelian randomization(MR)was used,including an inverse variance weighting method,followed by a sensitivity analysis to verify the robustness of the results.Results:For Ps,some bacterial taxa,including Lactococcus,Ruminiclostridium 5,and Eubacterium fissicatena,were identified as risk factors;but Odoribacter demonstrated a protective effect against Ps.In the case of PsA,Lactococcus,Verrucomicrobiales,Akkermansia,Coprococ-cus 1,and Verrucomicrobiaceae were identified as risk factors;Odoribacter and Rikenellaceae exhibited a protective effect against the developmentof PsA.Conclusion:Our study establishes a causal link between the GM and Ps and PsA.These findings provide insights into the underlying mechanisms and suggest potential therapeutic targets.