Causal Estimates Research Articles

Causal relationship between neuroticism and bone mineral density: A univariable and multivariable Mendelian randomization study.

Recent observational studies have indicated that psychiatric disorders were associated with risk of bone mineral density (BMD) reduction. But the causal relationship between neuroticism and BMD remained unclear. By using public genome-wide association study data, a 2-sample bidirectional Mendelian randomization (MR) study was performed to investigate the causal relationship between neuroticism and BMD (heel BMD, forearm BMD, femoral neck BMD, lumbar spine BMD, and total body BMD). Inverse-variance weighted, weighted median, and MR-Egger were used to assess the causal effects. Multiple sensitivity analyses were conducted to assess the potential bias of the causal estimates. Multivariable MR analysis was used to assess the direct causal effects of neuroticism on BMD with adjustment of common risk factors of BMD reduction. Univariable MR analysis indicated that genetically predicted higher neuroticism was significantly associated with an increased risk of heel BMD reduction (inverse-variance weighted β = -0.039; se = 0.01; P = .0001; Bonferroni-corrected P = .0005) but not with other BMD (forearm BMD, femoral neck BMD, lumbar spine BMD, and total body BMD) potentially due to limited statistical power. The causal effects remained significant after accounting for the effects of body mass index, smoking, and drinking. Genetic proxy for higher neuroticism was significantly associated with an increased risk of heel BMD reduction. Further studies were warranted to elucidate the underlying biological mechanisms and explore the potential application in disease early screening and management.

Transcriptome-Wide Root Causal Inference.

Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm discovers root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously recovers a causal ordering of the expression levels to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.

Causal relationships between dyslexia and the risk of eight dementias

Observational and genetic studies have reported the relationship between dyslexia and Alzheimer’s disease (AD). Until now, the causal effect of dyslexia on AD risk has remained unclear. We conducted a two-sample univariable Mendelian randomization (MR) analysis to determine the causal association between dyslexia and the risk of AD, vascular dementia (VD), Lewy body dementia (LBD), and frontotemporal dementia (FTD) and its four subtypes. First, we selected 42 dyslexia genetic variants from a large-scale genome-wide association studies (GWAS) dataset and extracted their corresponding GWAS summary statistics from AD, VD, LBD, and FTD. Second, we selected four MR methods, including inverse-variance weighted (IVW), weighted median, MR-Egger, and MR-PRESSO. Heterogeneity, horizontal pleiotropy, and leave-one-out sensitivity analysis were then used to evaluate the reliability of all causal estimates. We also conducted multivariable MR (MVMR) and mediation analysis to assess the potential mediating role of cognitive performance (CP) or educational achievement (EA) on the causal association between dyslexia and AD. Two MVMR methods, including MV IVW and MV-Egger, and two-step MR were used to perform the analysis. Using IVW, we found a significant causal association between increased dyslexia and increased risk of AD (OR = 1.15, 95% CI: 1.04–1.28, P = 0.006), but not VD, LBD, FTD, or its four subtypes. MR-PRESSO further supported the statistically significant association between dyslexia and AD (OR = 1.15, 95% CI: 1.05–1.27, P = 0.006). All sensitivity analyses confirmed the reliability of causal estimates. Using MV IVW and mediation analysis, we found no causal relationship between dyslexia and AD after adjusting for CP but not EA, CP mediated the total effect of dyslexia on AD with a proportion of 46.32%. We provide genetic evidence to support a causal effect of increased dyslexia on increased risk of AD, which was largely mediated by CP. Reading activity may be a potential intervention strategy for AD by improving cognitive function.

Appraising the causal effects of circulating micronutrients on liver cirrhosis: Evidence from large‐scale Mendelian randomization analysis

AbstractAimsObservational research has identified links between micronutrient levels and chronic liver disease. However, the lack of randomized controlled experiments has impeded the ability to establish a cause‐and‐effect connection regarding micronutrients and cirrhosis of the liver. This Mendelian randomization (MR) analysis aimed to examine the causal impact of micronutrients on liver cirrhosis.MethodsWe selected genetic instrumental variables related to 12 micronutrients from genome‐wide studies on individuals of European descent, covering an array of over 9,851,867 single nucleotide polymorphisms and 460,351 participants. Data from patients with cirrhosis in the FinnGen database (https://www.finngen.fi/fi) were used. A two‐sample MR approach was employed to establish genetically causal estimates. Primary analyses used random effects and inverse variance weighted (IVW) methods, with additional sensitivity analyses for validation. MR–Egger intercept analysis and Cochran's Q test assessed horizontal pleiotropy and heterogeneity. Furthermore, multivariable Mendelian randomization (MVMR) was conducted to address potential confounding variables.ResultsIVW and weighted median methods showed that most micronutrients included were not significantly associated with a genetic susceptibility to liver cirrhosis. However, MR analysis demonstrated a significant association between circulating vitamin D levels and a reduced risk of liver cirrhosis (odds ratio IVW = 0.53, p < 0.010). The MR–Egger intercept showed no evidence of horizontal pleiotropy (p = 0.178), whereas Cochran's Q test found no heterogeneity (p = 0.799). Furthermore, MVMR analysis confirmed vitamin D was identified as a factor that independently mitigates the risk of liver cirrhosis.ConclusionThis research constitutes the most comprehensive MR investigation in this field, providing evidence supporting a protective link between circulating vitamin D levels and cirrhosis incidence. It suggests that maintaining sufficient vitamin D could be a cost‐effective strategy for early intervention in liver cirrhosis.

Association between psychiatric disorders and irritable bowel syndrome: A bidirectional Mendelian randomization study

ObjectivePrevious studies have suggested that irritable bowel syndrome (IBS) is strongly associated with psychiatric disorders. However, it is unclear whether this association is causal, concomitant, or accidental. Thus, we performed Mendelian randomization (MR) analysis to evaluate the causal effects of several psychiatric disorders on IBS. MethodsSummary data of genome-wide association studies (GWASs) were obtained mainly from the Psychiatric Genomics Consortium (PGC) on individuals of European ancestry and from a recent GWAS on IBS. We used three MR methods, the inverse-variance weighting (IVW), weighted median (WM), and MR-Egger regression (MR-Egger). In addition, two other indicators, namely, the MR-IVW Cochran's Q statistic and MR-Egger intercept, were used to assess heterogeneity and detect directional horizontal pleiotropy, respectively. ResultsHeritability was high for bipolar disorder (81.18 %, 95 % CI = 73.18–148.18 %), schizophrenia (33.88 %, 95 % CI = 33.57–38.19 %), and panic disorder (30.66 %, 95 % CI = 20.74–40.58 %). For other disorders, there was a low liability-scale SNP heritability for major depressive disorder (MDD) (0.67 %, 95 % CI = 0.61–0.73 %), anxiety disorder (7.63 %, 95 % CI = 1.67–13.59 %), PTSD (0.96 %, 95 % CI = 0.12–1.8 %), and IBS (2.44 %, 95 % CI = 2.13–2.75 %). We also observed that schizophrenia had a significant causal effect on IBS according to MR-IVW. Notably, the individual causal estimates of genetic instruments for MDD and schizophrenia were heterogeneous, but no pleiotropic effects were observed. ConclusionsOur analyses revealed the causal effects of MDD and schizophrenia on IBS, a matter that has been subject to debate for decades, and also showed that IBS had causal effects on MDD.

Causal inference of the effect of plasma proteins on the incidence of oral cancer: two-sample Mendelian randomization

ObjectiveThis study is aimed to investigate the causal relationship between plasma proteins and oral cancer risk using two-sample MR (Mendelian randomization).MethodsSummary-level GWAS (genome-wide association study) data on plasma protein levels (4,907 proteins) and oral cancer (6,034 cases, 6,585 controls) of European ancestry were utilized. SNPs (single nucleotide polymorphisms) associated with proteins at genome-wide significance were selected as instrumental variables. Multiple MR methods including IVW (inverse-variance weighted), MR-Egger, weighted median, simple mode and weighted mode were applied to estimate causal effects. Sensitivity analyses were conducted.ResultsEight plasma proteins (CCDC167, MID2, NDRG4, PEAR1, PIAS4, RCAN1, SAMHD1 and TNMD) were identified to have significant causal associations with oral cancer risk. NDRG4, RCAN1, SAMHD1 and TNMD were associated with increased oral cancer risk while PEAR1 was associated with decreased risk. The causal estimates were consistent across different methods. Sensitivity analyses indicated the results were robust without significant heterogeneity or horizontal pleiotropy. Multivariable MR adjusting for smoking, alcohol intake and periodontal disease showed CCDC167, MID2, NDRG4, PEAR1, PIAS4 and SAMHD1 still had direct effects on oral cancer.ConclusionThis two-sample MR study provides evidence for potentially causal effects of several plasma proteins on oral cancer risk. The identified proteins may serve as biomarkers and shed light on biological mechanisms underlying oral carcinogenesis. Further research is warranted to validate and extend these findings.

The relationship between mental health problems and risk of infectious diseases: A Mendelian randomization analysis.

The causal effects of mental health problems on the risk of infectious diseases remain vague. Investigating them via observational study is challenging as it presents possible confounding factors. Therefore, the objective of this study was to utilize Mendelian randomization (MR) techniques to evaluate the causal relationship between mental health problems and the risk of infectious diseases. Multivariable MR analyses were performed using genome-wide association data for sleep disorders (N = 216,700), depression (N = 500,199), anxiety (N = 290,361), nervous feelings (N = 450,700), unspecified mental disorder (N = 218,792), pneumonia (N = 486,484), skin and subcutaneous tissue infection (SSTI; N = 218,792), intestinal infectious diseases (IIDs; N = 218,792), urinary tract infection (N = 463,010), and central nervous system (CNS) infections (N = 218,792) among individuals of European ancestry. Independent genetic variants significantly (P < 10-8) associated with each exposure were considered instruments. The primary analysis used an inverse variance-weighted method, followed by a series of sensitivity analyses. Genetically predicted sleep disorders were associated with an increased risk of SSTI (odds ratio [OR], 1.29 [95% confidence interval (CI), 1.05-1.59]; P = .017). Genetically predicted depression was linked with an increased risk of CNS infections (OR, 1.59 [95% CI, 1.00-2.53]; P = .049) and SSTI (1.24 [95% CI, 1.03-1.49]; P = .024). Genetically predicted anxiety was associated with IIDs (OR, 1.19 [95% CI, 1.03-1.37]; P = .017) and SSTI (OR, 1.21 [95% CI, 1.02-1.43]; P = .029). There was no significant causal evidence for genetic prediction of nervous feelings and unspecified mental disorders in IIDs, CNS infections, SSTI, pneumonia, or urinary tract infection. Sensitivity analyses showed that the above causal association estimates were robust. In this MR study, we demonstrated a causal relationship between sleep disorders, depression, anxiety, and the risk of infectious diseases. However, no evidence was found to support causality between nervous feelings, unspecified mental disorders, and the risk of infectious diseases.

Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions

BackgroundMissing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions (“missing completely at random”, “missing at random” [MAR], “missing not at random”) are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation.MethodsWe searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically.ResultsOf the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis.ConclusionEffort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.

Mendelian randomization identifies causal associations between GWAS-associated bacteria and their metabolites and rheumatoid arthritis.

Accumulating evidence suggests that an imbalance of gut microbiota is commonly observed in patients with rheumatoid arthritis (RA). However, it remains unclear whether gut microbiota dysbiosis is a cause or consequence of RA, and the mechanisms by which gut dysbiosis contributes to RA have not been fully understood. This study aimed to investigate the causal relationship between gut microbiota and metabolites with RA. A two-sample Mendelian randomization analysis was performed to estimate the causality of gut microbiota and metabolites on RA. A genome-wide association study (GWAS) of 211 gut microbiota and 217 metabolites was used as the exposure, whereas RA was treated as the outcome. Inverse variance weighted (IVW) was regarded as the primary approach for calculating causal estimates. MR Egger method, Weighted median method, Simple mode method, and weighted mode method were used for sensitive analysis. Metabolic pathway analysis was performed via the web-based Metaconflict 5.0. Additionally, an animal study was undertaken to evaluate the results inferred by Mendelian randomization. This study indicated that six gut microbiota taxa (RuminococcaceaeUCG013, Erysipelotrichia, Erysipelotrichaceae, Erysipelotrichales, Clostridia, and Veillonellaceae) were estimated to exert a positive impact on RA. Conversely, seven gut microbiota taxa (Oxalobacter, Cyanobacteria, RuminococcaceaeUCG002, LachnospiraceaeUCG010, Christensenellaceae, Oxalobacteraceae, Anaerostipes) were estimated to exert a negative impact on RA. Three metabolites, namely indole-3-propionate (IPA), glycine and sphingomyelin (SM 16:1), were found to be linked to lower RA risk, while five metabolites (argininosuccinate, CE 20_4, TAG 58_8, PC 40_6, and LPC 20_4) were linked to higher RA risk. Additionally, four metabolic pathways were identified by metabolic pathway analysis. The collagen-induced arthritis (CIA) rats exhibited a higher relative abundance of Class_Clostridia and a lower abundance of Genus_Lachnospiraceae (p < 0.05) than the healthy controls. This study identified causal associations between specific gut microbiota, metabolites, and RA. These findings support the significant role of gut microbiota and metabolites in RA pathogenesis.

The causal effects of inflammatory and autoimmune skin diseases on thyroid diseases: evidence from Mendelian randomization study.

To clarify the controversy between inflammatory or autoimmune skin diseases and thyroid diseases, we performed two-sample Mendelian randomization (MR) analyses. Genetic data on factors associated with atopic dermatitis (AD, n=40,835), seborrheic dermatitis (SD, n=339,277), acne (n=363,927), rosacea (n=299,421), urticaria (n=374,758), psoriasis (n=373,338), psoriasis vulgaris (n=369,830), systemic lupus erythematosus (SLE, n=14,267), vitiligo (n=353,348), alopecia areata (AA, n=361,822), pemphigus (n=375,929), bullous pemphigoid (BP, n=376,274), systemic sclerosis (SSc, n=376,864), localized scleroderma (LS, n=353,449), hypothyroidism (n=314,995 or n=337,159), and hyperthyroidism (n=281,683 or n=337,159) were derived from genome-wide association summary statistics of European ancestry. The inverse variance weighted method was employed to obtain the causal estimates of inflammatory or autoimmune skin diseases on the risk of thyroid diseases, complemented by MR-Egger, weighted median, and MR-pleiotropy residual sum and outlier (MR-PRESSO). AD, SLE, SD, and psoriasis vulgaris were associated with an increased risk of hypothyroidism, whereas BP was associated with a lower risk of hypothyroidism (all with p < 0.05). The multivariable MR analyses showed that AD (OR = 1.053; 95%CI: 1.015-1.092; p = 0.006), SLE (OR = 1.093; 95%CI: 1.059-1.127; p < 0.001), and SD (OR = 1.006; 95%CI: 1.002-1.010; p = 0.006) independently and predominately contributed to the genetic causal effect on hypothyroidism after adjusting for smoking. The results showed no causal effects of inflammatory or autoimmune skin diseases on hyperthyroidism. The findings showed a causal effect of AD, SLE, SD on hypothyroidism, but further investigations should be conducted to explore the pathogenic mechanisms underlying these relationships.

A Two-Step Framework for Validating Causal Effect Estimates.

Comparing causal effect estimates obtained using observational data to those obtained from the gold standard (i.e., randomized controlled trials [RCTs]) helps assess the validity of these estimates. However, comparisons are challenging due to differences between observational data and RCT generated data. The unknown treatment assignment mechanism in the observational data and varying sampling mechanisms between the RCT and the observational data can lead to confounding and sampling bias, respectively. The objective of this study is topropose a two-step framework to validate causal effect estimates obtained from observational data by adjusting for both mechanisms. An estimator of causal effects related to the two mechanisms is constructed. A two-step framework for comparing causal effect estimates is derived from the estimator. An R package RCTrep is developed to implement the framework in practice. A simulation study is conducted to show that using our framework observational data can produce causal effect estimates similar to those of an RCT. A real-world application of the framework to validate treatment effects of adjuvant chemotherapy obtained from registry data is demonstrated. This study constructs a framework for comparing causal effect estimates between observational data and RCT data, facilitating the assessment of the validity of causal effect estimates obtained from observational data.

Harmonizing sustainable Horizons: Deciphering the intricate nexus between disaggregated financial inclusion and clean energy transition in BRICS economies

This study emphasizes the acceleration of clean energy (CE) adoption through financial inclusion (FI), particularly in the context of the rapid expansion of the BRICS economic cooperation alliance and its contribution to addressing environmental challenges. Specifically, this research assesses the impact of FI on the upward mobility of clean energy transition using annual panel data from 2004 to 2022. Utilizing a comprehensive set of pre-estimation techniques, this study employs the newly established Panel non-linear Autoregressive Distributed Lag (P-NARDL) model for empirical analysis, supplemented by robust estimation methods. The findings reveal that various dimensions of FI, including financial accessibility, financial depth, financial efficiency, and financial stability, significantly influence the progression from nonrenewable energy sources to clean energy consumption. This study extends the examination to assess both the aggregate and disaggregated relationships among the variables. Using causality estimation, it identifies the presence of both bidirectional and unidirectional causality between the FI indices and CE. These findings offer practical policy implications for developing financial resources to accelerate the adoption of CE, thereby addressing the environmental challenges faced by the BRICS economies.

Causal Relationship of Skin Microbiota on Psoriasis: A Mendelian Randomization Study.

Epidemiological investigations have indicated an association between skin microbiota imbalance and psoriasis, however, the causal relationship has not been confirmed through Mendelian randomization (MR). MR employed genetic instrumental variables (IVs) to evaluate the causal relationship between skin microbiota and psoriasis, providing new insights for potential treatments. Summary statistics for psoriasis and related traits were available from FinnGen R10 and United Kingdom Biobank (UKB) consortium. The genome-wide association studies (GWAS) on skin microbiota in three skin microenvironments came from two population-based German cohorts. Several selection processes were used to determine the optimal instrumental variables. Five MR methods were performed and different sensitivity analyses approaches yield robustness evidence under different assumptions. 449 SNPs were employed as IVs for 53 bacterial genera, with F-statistics between 20.18 and 42.44, indicating no evidence of weak instrument bias. Bacteroides was associated with psoriasis from UKB in IVW (OR, 95% CI: 0.914, 0.869-0.961; P < 0.001, PB-H = 0.007). The taxon was also associated with psoriasis vulgaris (IVW: OR, 95% CI, 0.918, 0.872-0.967; P = 0.001, P B-H = 0.054) and psoriasis and related disorders (IVW: OR, 95% CI, 0.915, 0.875-0.957; P < 0.001, P B-H = 0.008). Consistent causal estimates were identified in terms of both magnitude and direction, indicating a protective effect of Bacteroides. The MR study found that Bacteroides in the antecubital fossa may protect against psoriasis, offering genetic proof that skin microbiota helps prevent the condition.

Mendelian randomization analysis on the impacts of age at menarche on adult height: A Taiwanese population study

BackgroundsAmple evidence supports potential influence of age at menarche (AM) on adult height (AH), but multiple confounders may affect causal estimates. To address this issue, the Mendelian randomization (MR) analysis was used to explore the causal impacts of AM on AH. MethodsUsing data (n = 57,349) from the publicly accessible Taiwan Biobank and randomly splitting them into 2 equal-size subsets, we identified single nucleotide polymorphisms (SNPs) significantly associated with AM in the exploration subset and used these SNPs as instrumental variables to estimate the effects of instruments on AH in the validation subset based on two stage least squares (2SLS) regression. In addition, three more summary statistics-based approaches, namely inverse variance weighted (IVW), MR-Egger, and weighted median (WM) analyses, were used to verify the findings. We also performed heterogeneity and sensitivity analyses to evaluate the robustness of the results. ResultsWe identified 4 leading SNPs associated with AM at the genome-wide significant level, whereas rs9409082 may exert some pleiotropic effects on AH. After eliminating rs9409082, the 2SLS analysis indicated that one year delay in genetically determined AM predicted 1.5 cm height gain in adulthood (β = 1.508, 95% confidence interval [0.852, 2.163]). The causal relationship was also supported by WM (β = 1.183, [0.329, 2.038]) and IVW (β = 1.493, [0.523, 2.463]) methods. ConclusionsEvidence from the present MR study supports a causal relationship between later AM and taller AH.

Genetic causality between modifiable risk factors and the risk of rheumatoid arthritis: Evidence from Mendelian randomization.

Emerging research has investigated the potential impact of several modifiable risk factors on the risks of rheumatoid arthritis (RA), but the findings did not yield consistent results. This study aimed to comprehensively explore the genetic causality between modifiable risk factors and the susceptibility of RA risk using the Mendelian randomization (MR) approach. Genetic instruments for modifiable risk factors were selected from several genome-wide association studies at the genome-wide significance level (p < 5 × 10-8), respectively. Summary-level data for RA were sourced from a comprehensive meta-analysis. The causal estimates linking modifiable risk factors to RA risk were assessed using MR analysis with inverse variance weighting (IVW), MR-Egger, weighted, and weighted median methods. After Bonferroni correction for multiple tests, we found the presence of causality between educational attainment and RA, where there were protective effects of educational attainment (college completion) (odds ratio [OR] = 0.50, 95% CI = 0.36, 0.69, p = 2.87E-05) and educational attainment (years of education) (OR = 0.93, 95% CI = 0.90, 0.96, p = 4.18E-06) on the lower RA risks. Nevertheless, smoking initiation was observed to be associated with increased RA risks (OR = 1.27, 95% CI = 1.09, 1.47, p = .002). Moreover, there was no indication of horizontal pleiotropy of genetic variants during causal inference between modifiable risk factors and RA. Our study reveals the genetic causal impacts of educational attainment and smoking on RA risks, suggesting that the early monitoring and recognition of modifiable risk factors would be beneficial for the preventive counseling/treatment strategies for RA.

Multi-omics combined to investigate potential druggable therapeutic targets for stroke: A systematic Mendelian randomization study and transcriptome verification

ObjectiveStroke is a highly prevalent and disabling disease whose disease mechanisms are not fully understood. The discovery of disease-associated proteins with genetic evidence of pathogenicity provides an opportunity to identify new therapeutic targets. MethodWe examined the observed and causal associations of thousands of plasma and inflammatory proteins that were measured using affinity-based proteomic assays. First, we pooled >3000 relevant proteins using a fixed-effects meta-analysis of 2 population-based studies involving 48,383 participants, then investigated the causal effects of stroke and its subtype-associated proteins by forward Mendelian randomization using cis-protein quantitative locus genetic tools identified from genome-wide association studies of these >48,000 individuals. To improve the accuracy of causal estimation, we implemented a systematic Mendelian randomization model that accounts for cascading imbalances between instruments and tested the robustness of causal estimation through multi-method analyses. To further validate the hypothesis that ginsenoside Rg1 monomer acts on the five protein targets screened for drug-targeted regulation, we conducted a comparative analysis of the mRNA (gene) expression levels of a limited number of genes in the brain tissues of different groups of SD rats. The druggability of the candidate proteins was investigated and the mechanism of action and potential targeting side effects were explored by Phenome-wide MR. ResultsSix circulating proteins were identified to have a significant genetic association with stroke (PFDR < 0.05). For example, in patients with cardioembolic stroke, higher genetically predicted APRT was associated with a lower risk of cardioembolic stroke (ORivw [95 % CI] = 0.641 [0.517, 0.795]; P = 5.25 × 10−5, ORSMR [95 % CI] = 0.572, [0.397, 0.825], PSMR = 0.003). Mediation analyses suggested that atrial fibrillation, angina pectoris, and heart failure may mediate the association of CD40L, LIFR, and UPA with stroke. Molecular docking revealed promising interactions between the identified proteins and glycosides. Transcriptomic sequencing in animal models indicated that ginsenoside Rg1 may act through APRT, IL15RA, and VSIR pathways, with APRT showing significant variability in mRNA sequencing expression. Phenome-wide MR of the six target proteins showed an overwhelming predominance of PFDR > 0.05, indicating less toxicity. ConclusionsThe present study provides genetic evidence to support the potential efficacy of targeting the three druggable protein targets for the treatment of stroke. This is achieved by triangulating population genomic and proteomic data. Furthermore, the study validates the pathway mechanisms by which APRT, IL15RA, and VSIR dock ginsenoside Rg1 in animal models. This will help to prioritize stroke drug development.

Causal estimators for incorporating external controls in randomized trials with longitudinal outcomes

Abstract Incorporating external data, such as external controls, holds the promise of improving the efficiency of traditional randomized controlled trials especially when treating rare diseases or diseases with unmet needs. To this end, we propose novel weighting estimators grounded in the causal inference framework. As an alternative framework, Bayesian methods are also discussed. From trial design perspective, operating characteristics including Type I error and power are particularly important and are assessed in our realistic simulation studies representing a variety of practical scenarios. Our proposed weighting estimators achieve significant power gain, while maintaining Type I error close to the nominal value of 0.05. An empirical application of the methods is demonstrated through a Phase III clinical trial in rare disease.

The heterogeneous effects of COVID-19 lockdowns on crime across the world

There is a vast literature evaluating the empirical association between stay-at-home policies and crime during the COVID-19 pandemic. However, these academic efforts have primarily focused on the effects within specific cities or regions rather than adopting a cross-national comparative approach. Moreover, this body of literature not only generally lacks causal estimates but also has overlooked possible heterogeneities across different levels of stringency in mobility restrictions. This paper exploits the spatial and temporal variation of government responses to the pandemic in 45 cities across five continents to identify the causal impact of strict lockdown policies on the number of offenses reported to local police. We find that cities that implemented strict lockdowns experienced larger declines in some crime types (robbery, burglary, vehicle theft) but not others (assault, theft, homicide). This decline in crime rates attributed to more stringent policy responses represents only a small proportion of the effects documented in the literature.

The Value of Piped Water and Sewers: Evidence from 19th Century Chicago

Abstract We estimate the impact of piped water and sewers on property values in late 19th century Chicago. The cost of sewer construction depends sensitively on imperceptible variation in elevation, and such variation delays water and sewer service to part of the city. This delay provides quasi-random variation for causal estimates. We extrapolate ate estimates from our natural experiment to the area treated with water and sewer service during 1874-1880 using a new estimator. Water and sewer access increases property values by a factor of about 2.8. This suggests that benefits are large relative to: the value of the value of averted mortality, many other infrastructure projects, and construction costs.

IL6 receptor inhibitors: exploring the therapeutic potential across multiple diseases through drug target Mendelian randomization.

High interleukin-6 levels correlate with diseases like cancer, autoimmune disorders, and infections. IL-6 receptor inhibitors (IL-6Ri), used for rheumatoid arthritis and COVID-19, may have wider uses. We apply drug-target Mendelian Randomization (MR) to study IL-6Ri's effects. To simulate the effects of genetically blocking the IL-6R, we selected single nucleotide polymorphisms (SNPs) within or near the IL6R gene that show significant genome-wide associations with C-reactive protein. Using rheumatoid arthritis and COVID-19 as positive controls, our primary research outcomes included the risk of asthma, asthmatic pneumonia, cor pulmonale, non-small cell lung cancer, small cell lung cancer, Parkinson's disease, Alzheimer's disease, ulcerative colitis, Crohn's disease, systemic lupus erythematosus, type 1 diabetes, and type 2 diabetes. The Inverse Variance Weighted (IVW) method served as our principal analytical approach, with the hypotheses of MR being evaluated through sensitivity and colocalization analyses. Additionally, we conducted Bayesian Mendelian Randomization analyses to minimize confounding and reverse causation biases to the greatest extent possible. IL-6 inhibitors significantly reduced the risk of idiopathic pulmonary fibrosis (OR= 0.278, 95% [CI], 0.138-0.558; P <0.001), Parkinson's disease (OR = 0.354, 95% CI, 0.215-0.582; P <0.001), and positively influenced the causal relationship with Type 2 diabetes (OR = 0.759, 95% CI, 0.637-0.905; P = 0.002). However, these inhibitors increased the risk for asthma (OR = 1.327, 95% CI, 1.118-1.576; P = 0.001) and asthmatic pneumonia (OR = 1.823, 95% CI, 1.246-2.666; P = 0.002). The causal effect estimates obtained via the BWMR method are consistent with those based on the IVW approach. Similarly, sIL-6R also exerts a significant influence on these diseases.Diseases such as Alzheimer's disease, Crohn's disease, pulmonary heart disease, systemic lupus erythematosus, Type 1 diabetes, Non-small cell lung cancer and ulcerative colitis showed non-significant associations (p > 0.05) and were excluded from further analysis. Similarly, Small cell lung cancer were excluded due to inconsistent results. Notably, the colocalization evidence for asthmatic pneumonia (coloc.abf-PPH4 = 0.811) robustly supports its association with CRP. The colocalization evidence for Parkinson's disease (coloc.abf-PPH4 = 0.725) moderately supports its association with CRP. IL-6Ri may represent a promising therapeutic avenue for idiopathic pulmonary fibrosis, Parkinson's disease, and Type 2 diabetes.