Association of circulating leptin concentration with the risk of Clostridium difficile colitis and exploration of their associated mediators: A mendelian randomisation study

Article Figures and data Abstract Editor's evaluation Introduction Methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Age-related macular degeneration (AMD) is a leading cause of blindness in the industrialised world and is projected to affect >280 million people worldwide by 2040. Aiming to identify causal factors and potential therapeutic targets for this common condition, we designed and undertook a phenome-wide Mendelian randomisation (MR) study. Methods: We evaluated the effect of 4591 exposure traits on early AMD using univariable MR. Statistically significant results were explored further using: validation in an advanced AMD cohort; MR Bayesian model averaging (MR-BMA); and multivariable MR. Results: Overall, 44 traits were found to be putatively causal for early AMD in univariable analysis. Serum proteins that were found to have significant relationships with AMD included S100-A5 (odds ratio [OR] = 1.07, p-value = 6.80E−06), cathepsin F (OR = 1.10, p-value = 7.16E−05), and serine palmitoyltransferase 2 (OR = 0.86, p-value = 1.00E−03). Univariable MR analysis also supported roles for complement and immune cell traits. Although numerous lipid traits were found to be significantly related to AMD, MR-BMA suggested a driving causal role for serum sphingomyelin (marginal inclusion probability [MIP] = 0.76; model-averaged causal estimate [MACE] = 0.29). Conclusions: The results of this MR study support several putative causal factors for AMD and highlight avenues for future translational research. Funding: This project was funded by the Wellcome Trust (224643/Z/21/Z; 200990/Z/16/Z); the University of Manchester’s Wellcome Institutional Strategic Support Fund (Wellcome ISSF) grant (204796/Z/16/Z); the UK National Institute for Health Research (NIHR) Academic Clinical Fellow and Clinical Lecturer Programmes; Retina UK and Fight for Sight (GR586); the Australian National Health and Medical Research Council (NHMRC) (1150144). Editor's evaluation The findings of this study as well as the strength of the provided evidence are important and have significance beyond a single subfield. This manuscript is of interest to readers in the fields of ophthalmology, epidemiology and public health. The identification of both known and previously unknown risk factors for age-related macular degeneration (AMD) using genetically informed approaches can be combined with traditional epidemiological approaches to develop interventions that reduce the risk of AMD. The key claims of the manuscript are well supported by the data, and the approaches used are thoughtful and rigorous. https://doi.org/10.7554/eLife.82546.sa0 Decision letter eLife's review process Introduction Age-related macular degeneration (AMD) is a common retinal condition that affects individuals who are ≥50 years old. It is caused by the complex interplay of multiple genetic and environmental risk factors, and genome-wide association studies (GWAS) have identified AMD-implicated variants in at least 69 loci. These include important risk alleles in the 1q32 and 10q26 genomic regions (corresponding to the CFH [complement factor H] and ARMS2/HTRA1 locus, respectively) (Fritsche et al., 2016; Winkler et al., 2020). Other key risk factors include age, smoking, alcohol consumption, and low dietary intake of antioxidants (carotenoids, zinc) (Chakravarthy et al., 2010). AMD can be categorised according to severity (early, intermediate, or advanced) or based on the presence of neovascularisation (neovascular or non-neovascular). Advanced AMD results in loss of central vision, often leading to severe visual impairment (Fleckenstein et al., 2021). Notably, AMD is a major cause of blindness in the elderly population and represents a substantial global burden that is expected to continue to grow into the future as an ageing population expands worldwide (Chakravarthy et al., 2010). Mendelian randomisation (MR) is a statistical approach that uses genetic variation to look for causal relationships between exposures (such as smoking) and outcomes (such as risk of a specific disease) (Julian et al., 2021). MR is increasingly being utilised as it can, to a degree, address a major limitation of observational studies: unmeasured confounding (Sanderson et al., 2022). To minimise issues with certain types of confounding and to support causal inference statements, MR uses genetic variation as an instrument (i.e. as a variable that is associated with the exposure but is independent of confounders and is not associated with the outcome, other than through the exposure). The principles of MR are based on Mendel’s laws of segregation and independent assortment, which state that offspring inherit alleles randomly from their parents and randomly with respect to other locations in the genome. A key concept is the use of genetic variants that are related to an exposure of interest to proxy the part of the exposure that is independent of possible confounding influences (e.g. from the environment or from other traits). It is noted that analogies have been drawn between MR and randomised controlled trials with these two approaches considered proximal in terms of hierarchy of evidence (Julian et al., 2021). To date, the use of MR approaches in the context of AMD has been limited although these methods have been successfully implemented to explore the relationship between AMD and a small number of traits including lipids, thyroid function, CRP, and complement factors (Cipriani et al., 2021; Han et al., 2021; Han et al., 2020b; Li et al., 2022; Zuber et al., 2020). In this study, we developed a systematic, broad (‘phenome-wide’) MR-based analytical approach and used it to investigate the relationship between early AMD and several thousand exposure variables. A set of traits that are robustly associated with genetic liability to AMD were identified. Methods Data sources Outcome data Two AMD phenotypes were used as outcome measures in this study. The first one was early AMD. The GWAS summary statistics for this phenotype were taken from a meta-analysis by Winkler et al., 2020. This meta-analysis focussed on populations of European ancestries and used data from the ARIC, AugUR, CHS, GHS, IAMDGC, KORA S4, LIFE-Adult NICOLA, UKBB, and WHI studies (14,034 early AMD cases and 91,214 controls overall). A full description of how these studies classified participants as ‘early AMD’ can be found in the relevant publication Winkler et al., 2020; briefly, a number of approaches considering drusen size/area and the presence or absence of pigmentary abnormalities were utilised including the 3 Continent Consortium (3CC) severity scale (Klein et al., 2014), the Rotterdam Eye Study classification (Korb et al., 2014), the Beckman clinical classification (Ferris et al., 2013), and the AREDS-9 step classification scheme (Davis et al., 2005). All relevant studies used colour fundus photography for grading purposes. The second phenotype that we studied was advanced AMD. For this trait, we drew on GWAS summary statistics from a multiple trait analysis of GWAS (MTAG) study by Han et al., 2020a. This meta-analysis also focussed on individuals with European ancestries and derived data from the IAMDGC 2013 (17,181 cases and 60,074 controls) (Fritsche et al., 2013) and IAMDGC 2016 (16,144 advanced AMD cases and 17,832 controls) (Fritsche et al., 2016) studies as well as the GERA study (4017 cases and 14,984 controls) (Kvale et al., 2015). The relevant summary statistics are primarily reflective of advanced AMD, but the GERA cohort included both advanced and intermediate AMD cases. Advanced AMD was broadly defined by the presence of geographic atrophy or choroidal neovascularisation, although there was a degree of variability in the criteria used in the included studies. Notably, the MTAG approach can leverage the high genetic correlation between the input phenotypes to detect genetic associations relevant only to advanced AMD. Exposure data A phenome-wide screen was performed to make causal inferences on the role of a wide range of traits in early and advanced AMD. To achieve this, both published and unpublished GWAS data from the IEU open GWAS database were used; these were accessible via the TwoSampleMR programme in R (Hemani et al., 2018). All European GWAS within this database were included with the exception of imaging phenotypes and expression quantitative trait locus related data which were removed. The restriction to European datasets limits the generalisability of the results to other populations but is necessary to produce reliable findings. In the early AMD analysis, studies from the ‘ukb’ and ‘met-d’ batches were excluded as data for these studies were entirely from the UK Biobank resource and, as a result, there was extensive population overlap with the early AMD GWAS (Sudlow et al., 2015). In the advanced AMD analysis, the ‘ukb’ and ‘met-d’ batches were included. The early AMD analysis was conducted on 30/12/2021 and a total of 10,979 traits were considered for analysis. The advanced AMD analysis was conducted on 08/01/2022. On 26/01/2022 we added the newly published ‘finn-b’ (n = 2803) traits to the analysis in place of the outdated ‘finn-a’ traits (n = 1489). It was impractical to manually inspect the degree of population overlap for all traits prior to conducting the analysis; instead, the degree of overlap for all significant traits was inspected after the analysis. Instrument selection A statistically driven approach to instrumental variable selection was used. Typically, an arbitrary p-value threshold is set for the identification of appropriate single-nucleotide variants (SNVs); these are subsequently used as instrumental variables (referred to thereafter as instruments). A conventional p-value threshold for the selection of instruments is >5E−08. This approach however can, in some cases, be problematic. For example, when the number of instruments exceeding this threshold is small, the analysis can be underpowered or, in certain cases of unbiased screens, the results can be inflated (Boddy et al., 2022). With this in mind, the p-value for instrument selection for each trait was set to the level where >5 instruments were available for each analysis. More specifically, for each trait, the analysis would first be conducted with a p-value threshold for inclusion of 5E−8 before sequentially increasing the threshold by a factor of 10 each time until >5 eligible instruments are identified. A predefined maximum p-value of 5E−05 was used and the final range of pvalues for inclusion was 5E−06 to 5E−08. Proxies Where an exposure instrument was not present in the outcome dataset, a suitable proxy was identified (Hartwig et al., 2016). In the early AMD analysis, this was achieved by using the TwoSampleMR software with a linkage disequilibrium R2 value of ≥0.9 (Purcell et al., 2007). For the advanced AMD phenotype, data that were not derived from the TwoSampleMR resource were used and therefore the Ensembl server was utilised to identify proxies (Cunningham et al., 2022; Hemani et al., 2018). Clumping SNVs were clumped using a linkage disequilibrium R2 value of 0.001 and a genetic distance cut-off of 10,000 kilo-bases. A European reference panel was used for clumping. Harmonisation The effects of instruments on outcomes and exposures were harmonised to ensure that the beta values (i.e. the regression analysis estimates of effect size) were expressed per additional copy of the same allele (Hartwig et al., 2016). Palindromic alleles (i.e. alleles that are the same on the forward as on the reverse strand) with a minor allele frequency >0.42 were omitted from the analysis in order to reduce the risk of errors. Removal of pleiotropic genetic variants and outliers Pleiotropic instruments and outliers were removed from the analysis by using a statistical approach that removes instruments which are found to be more significant for the outcome than for the exposure (Hemani et al., 2017). Radial MR, a simulation-based approach that detects outlying instruments, was also utilised (Bowden et al., 2018). Causal inference MR relies on three key assumptions with regard to the instrumental variable: (1) the instrumental SNV should be associated with the exposure; (2) the SNV should not be associated with confounders; (3) the SNV should influence the outcome only through the exposure (Julian et al., 2021). MR estimation was primarily performed using a multiplicative random effects (MRE) inverse variance weighted (IVW) method. MRE IVW was selected over a fixed effects (FE) approach as it allows inclusion of heterogeneous instruments (this was certain to occur within the breadth of this screen) (Burgess et al., 2019). A range of ‘robust measures’ were used to increase the accuracy of the results and to account for violations of the above key MR assumptions (Burgess et al., 2019); these measures included weighted median (Bowden et al., 2016a), Egger (Burgess and Thompson, 2017), weighted mode (Hartwig et al., 2017), and radial MR with modified second-order weights (Bowden et al., 2018). Further quality control The instrument strength was determined using the F-statistic (which tests the association between the instruments and the exposure) (Burgess and Thompson, 2011). F-Statistics were calculated against the final set of instruments that were included. A mean F-statistic >10 was considered sufficiently strong. The Cochran’s Q test was performed for each analysis. Cochran’s Q is a measure of heterogeneity among causal estimates and serves as an indicator of the presence of horizontal pleiotropy (which occurs when an instrument exhibits effects on the outcome through pathways other than the exposure) (Bowden and Holmes, 2019). It is noted that a heterogeneous instrument is not necessarily invalid, but rather calls for a primary assessment with an MRE IVW rather than an FE approach; this has been conducted as standard throughout our analysis. The MR-Egger intercept test was used to detect horizontal pleiotropy. When this occurs, the Egger regression is robust to horizontal pleiotropy (under the assumption that that pleiotropy is uncorrelated with the association between the SNV and the exposure) (Burgess and Thompson, 2017). Unless otherwise indicated by the Egger intercept, the assumption that no demonstrable horizontal pleiotropy is present was made and Egger regression was not utilised to determine causal effects (given the low power of this approach in the context of a small number of SNVs) (Bowden et al., 2015). The I2 statistic was calculated as a measure of heterogeneity between variant-specific causal estimates. An I2 < 0.9 indicates that Egger is more likely to be biased towards the null through violation of the ‘NO Measurement Error’ (NOME) assumption (Bowden et al., 2016b). Leave-one-out cross-validation was performed for every analysis to determine if any particular SNV was driving the significance of the causal estimates. Management of duplicate traits As the GWAS database that was used contained multiple different GWAS for certain traits, some exposures were analysed on multiple occasions. Where this occurred, the largest sample size study was considered to be the primary analysis. Where there were duplicate studies in the same population, the study with the largest F-statistic was used. Identification of significant results Before considering an MR result to be significant, the results of a range of causal inference and quality control tests should be taken into account. Notably, it is not necessary for a study to find significance in all measures to determine a true causal relationship. MR is a low power study type and, as such, an overly conservative approach to multiple testing can be excessive (Burgess et al., 2019). However, in the context of the present study the results of the early AMD phenome-wide screen were considered significant only if they remained: significant after false discovery rate (FDR) correction in the MRE IVW; nominally significant in weighted mode and weighted median; and nominally significant throughout the leave-one-out analysis (MRE IVW) (Benjamini and Hochberg, 1995). This conservative approach was selected as a large number of phenotypes was studied and because we wanted to focus on high confidence signals. Where causal traits for early AMD were identified, the relationship between these traits and advanced AMD was studied. These two AMD classifications are phenotypically distinct but are generally part of the same disease spectrum. When traits failed to replicate as causal factors in the advanced AMD dataset, it could not be inferred that these traits are not truly causal for early AMD. However, significance in both AMD phenotypes provided support for the detected causal links and evidence that a factor plays a role across the disease spectrum. Multivariable MR Multivariable MR was performed in circumstances where it was important to estimate the effect of >1 closely related (and/or potentially confounding) exposure trait (Sanderson et al., 2019). P-values for the inclusion of instruments for the exposures of interest were optimised to obtain sufficiently high (>10) conditional F-statistics for reliable analysis (Sanderson et al., 2021). With this in mind, selection for exposures began at a p-value threshold of >5E−08. Where trait’s instruments had a conditional F-statistic <10, the p-value for selection was reduced in an automated manner by factor of 10 until an F-statistic >10 was obtained. The same clumping procedure as in the univariable MR analysis was used. Adjusted Cochran’s Q-statistics were calculated, with a p-value of <0.05 indicating significant heterogeneity. Where the Cochran’s Q-statistic indicated heterogeneity, a Q-statistic minimisation procedure was used to evaluate the causal relationship; testing assumed both high (0.9) and low (0.1) levels of phenotypic correlation (Sanderson et al., 2021). Two-sample multivariable Mendelian randomisation approach based on Bayesian model averaging (MR-BMA) Multivariable MR can be used to obtain effect estimates for a few (potentially related) traits. However, it cannot be directly applied when many traits need to be considered. In contrast, Mendelian randomisation Bayesian model averaging (MR-BMA), a Bayesian approach first described by Zuber et al., 2020, can search over large sets of potential risk factors to determine which are most likely to be causal. Notably, Zuber et al., 2020 previously performed an in-depth analysis which considered the role of lipids against an older AMD GWAS. The relevant study served as proof-of-concept for the MR-BMA method and demonstrated that several lipid traits have causal roles in AMD. However, the analysis had two potential limitations. First, it downweighed fatty acid traits through limiting composite traits for SNV identification to HDL, LDL, and triglycerides. Second, numerous lipid traits with a potential role in AMD were not included in the analysis. For these reasons, we chose to conduct a more comprehensive analysis with a slightly altered approach. The following study design modifications were made compared to the study by Zuber et al., 2020: Fatty acids were included as a composite trait (utilising GWAS data for serum fatty acids derived from the Nightingale Health 2020 resource as listed in TwoSampleMR package [Hemani et al., 2018]). All lipid and fatty acid measures included in a GWAS by Kettunen et al., 2016 were considered as potential causal traits (n = 102 traits). A more recent AMD GWAS was used (Winkler et al., 2020). In general, multivariable MR (of any sort) cannot produce reliable results where the studied traits are ≥0.99 correlated with respect to the included instruments. For this reason, where two traits were highly correlated, one was removed at random rather than by manually selecting traits in a manner which risks selection bias. MR-BMA for immune cell and complement phenotypes was additionally performed. In this analysis, instruments were obtained at genome-wide significance for every included exposure in the model (given that composite traits were not For the immune cell all immune traits that were studied in a GWAS by et al., 2020 and were present in the TwoSampleMR package were used as In the complement analysis, all complement traits available in relevant studies by et al., and et al., were with the exception of complement For the MR-BMA analysis, the prior probability was set to and the prior variance was set to A search with 10,000 was and pvalues with were of effect are to of the of putative risk For the univariable MR analysis, these are as per standard of in the exposure for traits, and as beta values for exposure traits. This approach was selected because are for exposure traits (Burgess and 2018). beta values are not by all MR where an exposure variable is and it is often that these values are only Multivariable MR effect are as beta value estimates of the of the exposure variable (given that the role of multivariable MR within this study was to identify MR-BMA effect are in the of a model-averaged causal estimate The is a conservative estimate of the causal effect of an exposure on an outcome across It is noted that the primary of MR-BMA is to highlight the causal trait among a number of causal risk Although the MR-BMA findings can be used to the of they should not be necessarily as an to et al., 2020). R TwoSampleMR MR-BMA was from 2021; Zuber et al., 2020). Results on early AMD, univariable MR analysis was applied to a broad range of traits. quality significant results were in an advanced AMD and further were conducted using multivariable MR and MR-BMA et al., 2020). Overall, 4591 traits were eligible for analysis. 44 were found to be putatively causal for early AMD data of these causal traits were serum and measures (n = Other significant traits identified included immune cell phenotypes (n = serum proteins (n = and disease phenotypes (n = results detected in a phenome-wide univariable Mendelian randomisation (MR) analysis of early age-related macular degeneration traits the conservative quality control criteria described in the methods are IVW IVW cell cell cell on on in in lipids in of in in in large in large in large lipids in large of large in large in large acid in in in lipids in of in in small lipids in small in small total in large lipids in small of small in small palmitoyltransferase factor false discovery inverse variance multiplicative random as a not exposure traits of not produce beta values can be used to of effect but not necessarily These traits in data had sample overlap with the early AMD dataset, a minor degree of overlap which is to the causal traits had no sample overlap with early AMD. is to AMD risk Univariable MR demonstrated significant causal relationships for serum measures and serum fatty acid in early AMD These relationships were also supported by the results of our advanced AMD analysis data Serum are highly correlated traits, and the instruments for serum and fatty acid measures in the present study were correlated MR-BMA was used in order to which traits were driving the causal In our analysis, two genetic variants and were found to be outliers in terms of Q-statistic and across These SNVs were therefore omitted and the analysis was In no SNVs were identified. The 10 in terms of probability are in the data the 10 risk factors in terms of inclusion probability defined as the of the over all the where the risk factor is are in The of all the traits included this analysis are in The traits with respect to their were serum = = in = = = = and in small = = It is noted that MR-BMA is designed to the likely causal risk factor among a set of causal traits, it is often not possible to achieve this with As such, a of for lipid traits cannot be obtained within the utilised MR causal inference the between the beta values for the considered in our Mendelian randomisation Bayesian model averaging (MR-BMA) analysis for early age-related macular degeneration This represents the correlation between the genetic associations of the exposure variables with respect to their instruments. The traits are according to their further information can be found in the data 2 the with respect to their probability in the first of Mendelian randomisation Bayesian model averaging (MR-BMA) of traits in early age-related macular degeneration and present distance and present Cochran’s instruments are The Cochran’s Q is a measure which serves to identify variants with respect to the of the The Q-statistic is used to identify heterogeneity in a and to specific variants as The of variants to the Q-statistic is as the weighted between the and association with the in order to identify distance on the other is utilised to identify (i.e. variants which have a association with the variants are removed from the analysis because they have an influence over variable leading to which that well but 2 causal traits identified by Mendelian randomisation Bayesian model averaging (MR-BMA) of phenotypes in early age-related macular degeneration (AMD) according to their inclusion probability factor

A recent study has reported that anti-reflux surgery reduced the risk of lung cancer. However, the exact causal association between gastro-esophageal reflux disease (GORD) and lung cancer remains obscure. Therefore, we conducted a multivariable and network Mendelian randomization (MR) study to explore this potential association and mediation effect. Independent single nucleotide polymorphisms (SNPs) strongly associated with GORD were selected as instrumental variables (IVs) from the corresponding genome-wide association studies (GWAS). The summary statistics were obtained from the largest GORD GWAS meta-analysis of 367 441 (78 707 cases) European individuals, and the summary statistics of lung cancer and pathological subtypes came from International Lung Cancer Consortium (ILCCO) and FinnGen databases. Univariable and multivariable MR analyses were performed to investigate and verify the causal relationship between genetically predicted GORD and lung cancer. Network MR analysis was conducted to reveal the mediating role of GORD between smoking initiation and lung cancer. The univariable MR analysis demonstrated that GORD was associated with an increased risk of total lung cancer in both ILCCO [inverse variance weighted (IVW): odds ratio (OR) = 1.37, 95% confidence interval (CI) 1.16-1.62, P = 1.70E-04] and FinnGen database (IVW: OR = 1.25, 95% confidence interval CI 1.03-1.52, P = 2.27E-02). The consistent results were observed after adjusting the potential confounders [smoking traits, body mass index (BMI) and type 2 diabetes] in multivariable MR analyses. In subtype analyses, GORD was associated with lung adenocarcinoma (IVW: OR = 1.27, 95% CI 1.02-1.59, P = 3.48E-02) and lung squamous cell carcinomas (IVW: OR = 1.50, 95% CI 1.22-1.86, P = 1.52E-04). Moreover, GORD mediated 32.43% (95% CI 14.18-49.82%) and 25.00% (95% CI 3.13-50.00%) of the smoking initiation effects on lung cancer risk in the ILCCO and FinnGen databases, respectively. This study provides credible evidence that genetically predicted GORD was significantly associated with an increased risk of total lung cancer, lung adenocarcinoma and lung squamous cell carcinomas. Furthermore, our results suggest GORD is involved in the mechanism of smoking initiation-induced lung cancer.

Childhood-onset asthma is associated with an increased risk of severe mental illnesses later in life. However, the causal relationship between childhood-onset asthma and major mental disorders remains unclear. A two-sample Mendelian randomization (MR) analysis was conducted to investigate the causal effects of childhood-onset asthma (n = 327,670) on six major mental illnesses, including major depressive disorder (n = 143,265), bipolar disorder (n = 353,899), schizophrenia (n = 130,644), anxiety (n = 10,240), autism (n = 46,350), and ADHD (n = 225,534), using summary statistics of genome-wide association studies (GWAS). The inverse variance weighted (IVW) method, along with the weighted median and the MR-Egger method, was employed to obtain causal estimates. Multiple sensitivity analyses were conducted to examine the robustness of the estimates. Additionally, the direct effects of childhood-onset asthma on mental disorders after accounting for the effects of adult-onset asthma were evaluated through the multivariable MR (MVMR) analysis. To eliminate potential reverse causality, a reverse MR analysis was conducted, treating mental disorders as the exposure and childhood-onset asthma as the outcome. Genetically determined, childhood-onset asthma was significantly associated with an increased risk of depression (IVW OR = 1.059, 95% CI: 1.025-1.095, p = 5.72e-04) and bipolar disorder (IVW OR = 1.065, 95% CI: 1.027- 0.105, p = 6.75e-04). However, it was not associated with other mental disorders. Further MVMR analysis indicated that the causal relationships remained significant after accounting for adult-onset asthma. Interestingly, childhood- and adult-onset asthma exerted distinct causal effects on depression and bipolar disorder. Reverse MR analysis revealed no causal relationships between the six assessed mental disorders and either childhood-onset asthma or the age of asthma onset. The MR analysis revealed a significant causal relationship between genetically determined, childhood-onset asthma and an elevated risk of depression and bipolar disorder later in life. The causal effects of childhood-onset asthma were distinct from those of adult-onset asthma. Further studies are warranted to investigate the underlying mechanisms of these causal relationships.

BackgroundPrevious observational studies have indicated a potential association between liver function markers and prostate cancer (PCa), but the causal relationship of this association remains unclear. Additionally, genetic variations across populations may influence the direction and strength of this association. This study employed Mendelian Randomization (MR) to investigate the genetic causal relationship between liver function markers and PCa in European and East Asian populations. The aim was to uncover potential gene-disease associations across ancestries and provide novel insights for the prevention and treatment of PCa.MethodsSingle nucleotide polymorphisms (SNPs) significantly associated with liver function markers and PCa were selected from large-scale genome-wide association studies (GWAS) as instrumental variables (IVs). Univariate, multivariable, and bidirectional MR analyses were conducted to evaluate the causal relationships between liver function markers and PCa. The inverse variance weighting (IVW) method was used as the primary MR approach, complemented by sensitivity analyses to ensure the robustness and reliability of the findings.ResultsIn European populations, univariate MR analysis suggested that ALT (OR 0.85, 95% CI 0.75–0.95, P = 0.005) and AST (OR 0.90, 95% CI 0.81–1.00, P = 0.045) were associated with a reduced risk of PCa. However, multivariable MR analysis, after adjusting for confounders, showed that these associations were no longer statistically significant. Reverse MR analysis provided no evidence supporting a causal effect of PCa on liver function markers in European populations. Sensitivity analyses revealed heterogeneity in the IVs but did not detect evidence of horizontal pleiotropy. In East Asian populations, total bilirubin (OR 0.94, 95% CI 0.88–1.00, P = 0.049) and direct bilirubin (OR 0.91, 95% CI 0.84–0.99, P = 0.022) were causally associated with a reduced risk of PCa. After adjusting for confounders in multivariable MR, the association between total bilirubin and PCa remained significant (OR 0.74, 95% CI 0.55–0.99, P = 0.044). Reverse MR analysis suggested a causal effect of PCa on reduced ALT levels (OR 0.93, 95% CI 0.88–0.98, P = 0.007). Sensitivity analyses did not reveal heterogeneity or horizontal pleiotropy.ConclusionThe relationship between liver function markers and PCa seems to be influenced by genetic background. In East Asian populations, total bilirubin was identified as an independent protective factor against PCa, while reverse MR suggested a causal effect of PCa on reduced ALT levels. In European populations, there was insufficient evidence for a causal relationship between liver function markers and the risk of PCa. These findings may inform strategies for the clinical prevention, monitoring, and treatment of PCa, and further research is warranted to elucidate the underlying mechanisms driving these associations.

Article Figures and data Abstract Editor's evaluation eLife digest Introduction Materials and methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Whether the positive associations of smoking and alcohol consumption with gastrointestinal diseases are causal is uncertain. We conducted this Mendelian randomization (MR) to comprehensively examine associations of smoking and alcohol consumption with common gastrointestinal diseases. Methods: Genetic variants associated with smoking initiation and alcohol consumption at the genome-wide significance level were selected as instrumental variables. Genetic associations with 24 gastrointestinal diseases were obtained from the UK Biobank, FinnGen study, and other large consortia. Univariable and multivariable MR analyses were conducted to estimate the overall and independent MR associations after mutual adjustment for genetic liability to smoking and alcohol consumption. Results: Genetic predisposition to smoking initiation was associated with increased risk of 20 of 24 gastrointestinal diseases, including 7 upper gastrointestinal diseases (gastroesophageal reflux, esophageal cancer, gastric ulcer, duodenal ulcer, acute gastritis, chronic gastritis, and gastric cancer), 4 lower gastrointestinal diseases (irritable bowel syndrome, diverticular disease, Crohn’s disease, and ulcerative colitis), 8 hepatobiliary and pancreatic diseases (non-alcoholic fatty liver disease, alcoholic liver disease, cirrhosis, liver cancer, cholecystitis, cholelithiasis, and acute and chronic pancreatitis), and acute appendicitis. Fifteen out of 20 associations persisted after adjusting for genetically predicted alcohol consumption. Genetically predicted higher alcohol consumption was associated with increased risk of duodenal ulcer, alcoholic liver disease, cirrhosis, and chronic pancreatitis; however, the association for duodenal ulcer did not remain statistically significant after adjustment for genetic predisposition to smoking initiation. Conclusions: This study provides MR evidence supporting causal associations of smoking with a broad range of gastrointestinal diseases, whereas alcohol consumption was associated with only a few gastrointestinal diseases. Funding: The Natural Science Fund for Distinguished Young Scholars of Zhejiang Province; National Natural Science Foundation of China; Key Project of Research and Development Plan of Hunan Province; the Swedish Heart Lung Foundation; the Swedish Research Council; the Swedish Cancer Society. Editor's evaluation This is a valuable article that is methodologically convincing and provides evidence, through Mendelian Randomisation, that genetic predisposition to smoking and alcohol consumption influences the risk to develop different gastrointestinal diseases. The findings largely corroborate the findings from observational studies, especially for the effects of smoking. The major strength of the paper is the use of the largest possible genetic datasets for both the exposures and outcomes, which makes the findings more robust. https://doi.org/10.7554/eLife.84051.sa0 Decision letter eLife's review process eLife digest People who smoke cigarettes or drink large amounts of alcohol are more likely to develop disorders with their digestive system. But it is difficult to prove that heavy drinking or smoking is the primary cause of these gastrointestinal diseases. For example, it is possible that having a digestive disorder makes people more likely to take up these habits to reduce pain or discomfort caused by the illness (an effect known as reverse causation). The association may also be the result of confounding factors, such as age or diet, which contribute to digestive problems as well as the health outcomes of smoking and drinking. Additionally, many people who smoke also drink alcohol and vice versa, making it challenging to determine if one or both behaviors contribute to the disease. One solution is to employ Mendelian randomization which uses genetics to determine if two variables are linked. Using this statistical approach, Yuan, Chen, Ruan et al. investigated if people who display genetic variants that predispose someone to becoming a smoker or drinker are at greater risk of developing certain digestive disorders. This reduces the possibility of confounding and reverse causation, as any association between genetic variants will have been present since birth, and will have not been impacted by external factors. Yuan, Chen, Ruan et al. used data from two studies that had collected the genetic and health information of thousands of people living in the United Kingdom or Finland. The analyses revealed that genetic variants associated with cigarette smoking increase the risk of 20 of the 24 gastrointestinal diseases investigated. This risk persisted for most of the disorders, even after adjusting for genes linked with alcohol consumption. Further analysis showed that genetic variants linked to heavy drinking increase the risk of duodenal ulcer, alcoholic liver disease, cirrhosis, and chronic pancreatitis. However, accounting for smoking-linked genes eliminated the relationship with duodenal ulcer. These findings suggest that smoking has detrimental effects on gastrointestinal health. Reducing the number of people who start smoking or encouraging smokers to quit may help prevent digestive diseases. Even though there were fewer associations between heavy alcohol consumption and gastrointestinal illness, further studies are needed to investigate this relationship in more depth. Introduction Tobacco smoking and alcohol consumption are leading causes of the global burden of disease and are major contributors to premature mortality (GBD 2016 Alcohol Collaborators, 2018; GBD 2016 Alcohol Collaborators, 2020). Gastrointestinal diseases account for considerable health care use and expenditures, and a holistic approach to lifestyle interventions may result in more health gains and less economic burdens (Peery et al., 2022). Population-based studies have identified tobacco smoking as a risk factor for several gastrointestinal diseases, including gastroesophageal reflux disease (Eusebi et al., 2018), esophageal cancer (Fund WCR and Research AIfC, 2007), Crohn’s disease (Piovani et al., 2019), liver cancer (McGee et al., 2019), and pancreatitis (Yadav and Whitcomb, 2010). Evidence on the association between tobacco smoking and risk of other gastrointestinal diseases is limited and inconsistent. Like smoking, heavy alcohol consumption has been associated with increased risk of several gastrointestinal outcomes, including gastritis (Bujanda, 2000), gastric cancer (Laszkowska et al., 2021), colorectal cancer (McNabb et al., 2020), cirrhosis (Simpson et al., 2019), liver cancer (McGee et al., 2019), and pancreatitis (Yadav and Whitcomb, 2010). However, whether these associations are all causal remains unestablished, since most of the evidence was obtained from observational studies where the results may be biased by reverse causality and confounding. Of note, even though reverse causality may not be an issue in the studies for any of studied gastroenterological outcomes, it might exist for certain gastroenterological diseases causing pain, which smoker patients may try to increase smoking dose to mitigate via an intake of higher levels of nicotine. In addition, as smoking and alcohol consumption are phenotypically and genetically correlated (Roberts et al., 2020; Liu et al., 2019), the independent impacts of smoking and alcohol consumption on gastrointestinal diseases are unclear. Establishing the causal association of tobacco smoking and alcohol consumption with gastrointestinal diseases is crucial, as this provides further evidence for subsequent recommending public policies and clinical interventions. Mendelian randomization (MR) is an epidemiological approach that utilizes genetic variants as an instrument to strengthen the causal inference in an exposure-outcome association (Davey Smith and Hemani, 2014). MR is by nature not prone to confounding since genetic variants are randomly assorted at conception and thus unrelated to environmental and self-adopted factors that usually act as confounders. Additionally, this method can minimize reverse causality since fixed alleles are unaffected by the onset and progression of disease. Previous MR studies have examined the associations of smoking and alcohol consumption with several gastrointestinal diseases (Yuan and Larsson, 2022a; Larsson et al., 2020; Yuan and Larsson, 2022b; Yuan et al., 2022c; Chen et al., 2022; Yuan et al., 2021). Nevertheless, whether smoking and alcohol consumption exert influence on a wide range of gastrointestinal outcomes has not been investigated in a comprehensive way. A thorough investigation on the gastrointestinal consequences of smoking and alcohol drinking is of great importance to develop non-pharmacological interventions on gastrointestinal diseases. Here, we conducted an MR investigation of the associations of smoking and alcohol consumption with the risk of common gastrointestinal diseases to fill in above knowledge gaps. Materials and methods Figure 1 shows the study design overview. The study was based on publicly available genome-wide association studies (GWAS), and the detailed information on used studies was presented in Supplementary file 1A. The genetic associations were estimated using data from the UK Biobank study (Sudlow et al., 2015), the FinnGen study (Kurki et al., 2022; https://www.finngen.fi/en), and several large consortia. The summary effect estimates were combined using meta-analysis for each gastrointestinal disease from different data resources. Included studies had been approved by corresponding institutional review boards and ethical committees, and consent forms had been signed by all participants. Figure 1 Download asset Open asset Overview of the present study design. GERA, Genetic Epidemiology Research on Aging; IIBDGC, the International Inflammatory Bowel Disease Genetics Consortium; MR, Mendelian randomization; MR-PRESSO, Mendelian randomization pleiotropy residual sum and outlier; SNP, single nucleotide polymorphism. Instrumental variable selection A total of 378 and 99 single nucleotide polymorphisms (SNPs) associated with smoking initiation (a binary phenotype indicating whether an individual had ever being a regular smoker, 1,232,091 individuals of European descent) and alcohol consumption (log-transformed drinks per week, 941,280 individuals of European descent) at the genome-wide significance threshold (p<5 × 10–8) were identified by the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) study (Liu et al., 2019). These SNPs explained approximately 2.3 and 0.3% of the variation in smoking initiation and alcohol consumption, respectively (Liu et al., 2019). SNPs in linkage disequilibrium (defined as r2 >0.01 or clump distance <10,000 kb) and with the weaker associations with the exposure were removed, leaving 314 independent SNPs as instrumental variables for smoking initiation and 84 for alcohol consumption. Smoking initiation and alcohol consumption shared two index genetic variants, which were rs1713676 and rs11692435. Considering partial sample overlap (around 30%) between the GSCAN study with full data and the UK Biobank study (Liu et al., 2019), we performed sensitivity analyses for smoking initiation and alcohol consumption using summary statistics data from the analysis excluding the UK Biobank and 23andMe. For a supplementary analysis of smoking behavior, we used 126 SNPs associated with a lifetime smoking index that considered smoking duration, heaviness, and cessation (Wootton et al., 2020). The set of genetic instruments captured around 0.36% of the variance in lifetime smoking (Wootton et al., 2020). We also conducted a sensitivity analysis using rs1229984 in ADH1B gene that encodes alcohol dehydrogenase 1B enzyme as the genetic instrument for alcohol consumption to minimize pleiotropy. Detailed information on used SNPs is presented in Supplementary file 1B. Gastrointestinal disease data sources Genetic associations with 24 gastrointestinal diseases were obtained from the UK Biobank study (Sudlow et al., 2015), the FinnGen study (Kurki et al., 2022), and two large consortia, including the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC) (Liu et al., 2015) and Genetic Epidemiology Research on Aging (GERA) (Guindo-Martínez et al., 2021). Included outcomes were classified into four major categories according to the disease onset site: (1) upper gastrointestinal diseases (gastroesophageal reflux disease, esophageal cancer, gastric ulcer, acute gastritis, chronic gastritis, and gastric cancer); (2) lower gastrointestinal diseases (irritable bowel disease, celiac disease, diverticular disease, Crohn’s disease, ulcerative colitis, and colorectal cancer); (3) hepatobiliary and pancreatic diseases (non-alcoholic fatty liver disease, alcoholic liver disease, cirrhosis, liver cancer, cholangitis, cholecystitis, cholelithiasis, acute pancreatitis, chronic pancreatitis, and pancreatic cancer); and (4) other (acute appendicitis). The UK Biobank study is a large multicenter cohort study of 500,000 participants recruited in the United Kingdom between 2006 and 2010 (Sudlow et al., 2015). We used the summary statistics of European ancestry from GWAS conducted by Lee lab, where the gastrointestinal outcomes were defined by codes of the International Classification of Diseases 9th Revision (ICD-9) and ICD-10 (Zhou et al., 2020). Genetic associations were estimated by logistic regression with adjustment for sex, birth year, and the first four genetic principal components. For the FinnGen study, we used summary-level data on the genetic associations with gastrointestinal diseases from the last publicly available R7 data release (Kurki et al., 2022). The FinnGen study is a nationwide genetic study where genetic and electronic health record data were collected. The gastrointestinal diseases were ascertained by the codes of the ICD-8, ICD-9, and ICD-10. Genome-wide association analyses were adjusted for sex, age, genetic components, and genotyping batch. Summary-level genetic data on Crohn’s disease (5956 cases and 14,927 controls) and ulcerative colitis (6968 cases and 20,464 controls) were additionally obtained from the IIBDGC (Liu et al., 2015), and data on irritable bowel syndrome (3117 cases and 53,520 controls) were obtained from the GERA (Guindo-Martínez et al., 2021). Detailed diagnostic codes are listed in Supplementary file 1C. Statistical analysis Data were harmonized to omit ambiguous SNPs with non-concordant alleles and palindromic SNPs with ambiguous minor allele frequency (>0.42 and <0.58) were removed from the analysis. The primary MR analyses were performed by the multiplicative random-effects inverse-variance weighted (IVW) method, which provides the most precise estimates though assuming that all SNPs are valid instruments. The analysis of rs1229984 for alcohol consumption was conducted by the Wald method. Estimates for each association from different sources were combined using fixed-effects meta-analysis, and the heterogeneity of the associations from different data sources was evaluated by the I2 statistic. Heterogeneity among SNPs’ estimates in each association was assessed by Cochran’s Q value. Multivariable MR analyses were conducted to mutually adjust for smoking initiation and alcohol consumption. To detect potential unbalanced pleiotropy (horizontal pleiotropy) and examine the consistency of the associations, three sensitivity analyses including the weighted median (Yavorska and Burgess, 2017), MR-Egger (Burgess and Thompson, 2017), and MR pleiotropy residual sum and outlier (MR-PRESSO) (Verbanck et al., 2018) analyses were performed. The weighted median method can provide consistent estimates when more than 50% of the weight comes from valid instrument variants (Yavorska and Burgess, 2017). The MR-Egger intercept test can detect unmeasured pleiotropy, and MR-Egger regression can generate estimates after accounting for horizontal pleiotropy albeit with less precision (Burgess and Thompson, 2017). The MR-PRESSO method can identify SNP outliers and provide results identical to that from IVW after removal of outliers (Verbanck et al., 2018). The F-statistic was estimated to quantify instrument strength, and an F-statistic >10 suggested a sufficiently strong instrument. Power analysis was performed using an online tool (Brion et al., 2013). The Benjamini-Hochberg correction that controls the false discovery rate was applied to correct for multiple testing. The association with a nominal p-value <0.05 but Benjamini-Hochberg adjusted p-value >0.05 was regarded suggestive, and the association with a Benjamini-Hochberg adjusted p-value <0.05 was deemed significant. All analyses were two-sided and performed using the TwoSampleMR (Hemani et al., 2018), MendelianRandomization (Yavorska and Burgess, 2017), and MRPRESSO R packages (Verbanck et al., 2018) in R software 4.1.2. Results The F-statistic for each genetic variant was above 10, suggesting a good strength of used genetic instruments (Supplementary file 1B). Most associations were well powered (Supplementary file 1D). For smoking initiation, there was 80% power to detect the smallest odds ratio (OR) ranging from 1.08 to 1.40 for included outcomes. Although power was lower for alcohol consumption, it was adequate to detect a moderate effect size for most common gastrointestinal diseases. Smoking and gastrointestinal diseases Genetic predisposition to smoking initiation was associated with 20 of the 24 studied gastrointestinal diseases, and all these associations remained after multiple comparison correction (Table 1 and Supplementary file 1E). In detail, genetic liability to smoking initiation was positively associated with seven upper gastrointestinal diseases: gastroesophageal reflux (OR, 1.28; 95% confidence interval [CI], 1.20–1.37; p=4.09 × 10−14), esophageal cancer (OR, 1.67; 95% CI, 1.24–2.25; p=6.84 × 10−4), gastric ulcer (OR, 1.54; 95% CI, 1.37–1.72; p=3.83 × 10−14), duodenal ulcer (OR, 1.53; 95% CI, 1.34–1.75; p=8.47 × 10−10), acute gastritis (OR, 1.29; 95% CI, 1.09–1.53; p=0.003), chronic gastritis (OR, 1.33; 95% CI, 1.18–1.49; p=1.55 × 10–6), and gastric cancer (OR, 1.42; 95% CI, 1.13–1.79; p=0.003); genetic liability to smoking initiation was positively associated with four lower gastrointestinal diseases: irritable bowel syndrome (OR, 1.22; 95% CI, 1.12–1.32; p=3.50 × 10−6), diverticular disease (OR, 1.25; 95% CI, 1.18–1.33; p=5.23 × 10−14), Crohn’s disease (OR, 1.25; 95% CI, 1.11–1.40; p=3.03 × 10−4), and ulcerative colitis (OR, 1.15; 95% CI, 1.04–1.26; p=0.004); genetic liability to smoking initiation was positively associated with eight hepatobiliary and pancreatic diseases: non-alcoholic fatty liver disease (OR, 1.49; 95% CI, 1.26–1.76; p=3.82 × 10−6), alcoholic liver disease (OR, 1.99; 95% CI, 1.65–2.41; p=1.49 × 10−12), cirrhosis (OR, 1.68; 95% CI, 1.40–2.02; p=3.39 × 10−8), liver cancer (OR, 1.57; 95% CI, 1.13–2.17; p=0.007), cholecystitis (OR, 1.47; 95% CI, 1.29–1.68; p=4.71 × 10−9), cholelithiasis (OR, 1.20; 95% CI, 1.13–1.27; p=5.75 × 10−9), acute pancreatitis (OR, 1.39; 95% CI, 1.23–1.56; p=6.71 × 10−8), and chronic pancreatitis (OR, 1.38; 95% CI, 1.17–1.64; p=1.79 × 10−4); genetic liability to smoking initiation was positively associated with acute appendicitis (OR, 1.15; 95% CI, 1.08–1.23; p=1.27 × 10−5). Results were consistent in sensitivity analyses. An indication of horizontal pleiotropy was observed in the analysis of esophageal cancer in the FinnGen study (p for MR-Egger intercept <0.05, Supplementary file 1F). Although MR-PRESSO detected one to three outliers, the associations persisted and remained significant after removal of these out-lying SNPs (Supplementary file 1F). When using the genetic variants for smoking initiation based on data without the UK Biobank and 23andMe studies, the associations attenuated slightly albeit remained significant after multiple comparisons (Supplementary file 1L and Supplementary file 1G). All associations were replicated in the supplementary analysis of the lifetime smoking index (Supplementary file 1G). After correcting for multiple testing, genetically predicted lifetime smoking index was significantly associated with 17 of 24 gastrointestinal diseases, where the patterns of associations were generally similar to the analysis for smoking initiation (Supplementary file 1M and Supplementary file 1G). In distinction to the analysis of smoking initiation, genetically predicted lifetime smoking index was not significantly associated with acute gastritis, gastric cancer, Crohn’s disease, and ulcerative colitis, whereas genetically predicted lifetime smoking index was significantly associated with pancreatic cancer (OR, 2.09; 95% CI, 1.30–3.36). Table 1 Associations of genetic predisposition to smoking initiation with 24 gastrointestinal diseases in univariable and multivariable Mendelian randomization analyses. DiseaseTotal casesTotal controlsUVMRMVMR adjusted for alcohol consumptionOR (95% CI)p ValueI2 (95% CI)OR (95% CI)p ValueUpper gastrointestinal diseasesGastroesophageal reflux34,135634,6291.28 (1.20, 1.37)4.09 × 10-14*46.241.65 (1.35, 2.02)1.38 × 10-6*Esophageal cancer1130702,1161.67 (1.24, 2.25)6.84 × 10-4*22.684.78 (2.10, 10.90)1.97 × 10-4*Gastric ulcer8651666,8791.54 (1.37, 1.72)3.83 × 10-14*44.961.95 (1.40, 2.71)7.31 × 10-5*Duodenal ulcer5713666,8791.53 (1.34, 1.75)8.47 × 10-10*0.001.64 (1.07, 2.52)0.024Acute gastritis3048643,4781.29 (1.09, 1.53)0.003*0.001.54 (0.91, 2.62)0.106Chronic gastritis7975643,4781.33 (1.18, 1.49)1.55 × 10-6*77.041.33 (0.96, 1.86)0.091Gastric cancer1608701,4721.42 (1.13, 1.79)0.003*0.002.29 (1.14, 4.59)0.020Lower gastrointestinal diseasesIrritable bowel disease15,718641,4891.22 (1.12, 1.32)3.50 × 10-6*11.841.43 (1.10, 1.85)0.008*Celiac disease4808631,7000.82 (0.66, 1.02)0.0710.000.87 (0.53, 1.43)0.590Diverticular disease50,065587,9691.25 (1.18, 1.33)5.23 × 10-14*67.291.56 (1.30, 1.87)1.41 × 10-6*Crohn’s disease10,846645,7181.25 (1.11, 1.40)3.03 × 10-4*0.001.48 (1.01, 2.16)0.042Ulcerative colitis16,770651,2551.15 (1.04, 1.26)0.004*0.000.94 (0.71, 1.25)0.677Colorectal cancer9519686,9531.03 (0.92, 1.14)0.63229.941.03 (0.76, 1.39)0.841Hepatobiliary and pancreatic diseasesNon-alcoholic fatty liver disease3242707,6311.49 (1.26, 1.76)3.82 × 10-6*0.002.11 (1.15, 3.88)0.016*Alcoholic liver disease2955680,3691.99 (1.65, 2.41)1.49 × 10-12*92.682.26 (1.26, 4.03)0.006Cirrhosis5904706,2001.68 (1.40, 2.02)3.39 × 10-8*0.001.92 (1.06, 3.47)0.032Liver cancer714702,0081.57 (1.13, 2.17)0.007*0.001.96 (0.73, 5.25)0.183Cholangitis1708664,7491.02 (0.80, 1.29)0.8920.001.31 (0.61, 2.84)0.489Cholecystitis5893664,7491.47 (1.29, 1.68)4.71 × 10-9*84.722.38 (1.57, 3.60)4.14 × 10-5*Cholelithiasis42,510664,7491.20 (1.13, 1.27)5.75 × 10-9*0.001.33 (1.02, 1.73)0.035Acute pancreatitis6634679,7131.39 (1.23, 1.56)6.71 × (1.04, × (1.06, × (0.92, association after multiple testing. univariable Mendelian randomization; multivariable Mendelian randomization; odds CI, confidence association after multiple testing. In multivariable MR analysis adjusted for genetically predicted alcohol consumption, the associations between genetically predicted smoking initiation and gastrointestinal diseases were consistent with that from univariable MR analysis (Table 1 and Supplementary file However, the associations with in the associations for gastrointestinal reflux, esophageal cancer, gastric ulcer, irritable bowel syndrome, diverticular disease, non-alcoholic fatty liver disease, alcoholic liver disease, and cholecystitis (Table In addition, the association for pancreatic cancer significant from Alcohol consumption and gastrointestinal diseases Genetically predicted alcohol consumption was positively associated with esophageal cancer (OR, 95% CI, duodenal ulcer (OR, 95% CI, alcoholic liver disease (OR, 95% CI, × cirrhosis (OR, 95% CI, and chronic pancreatitis (OR, 95% CI, × and associated with irritable bowel disease (OR, 95% (Table After Benjamini-Hochberg the associations for duodenal ulcer, alcoholic liver disease, cirrhosis, and chronic pancreatitis remained (Supplementary file 1E). Results were consistent in sensitivity and horizontal pleiotropy was detected (Supplementary file One outlier was detected in the analysis of duodenal ulcer in the FinnGen study, and the association slightly after removal of this outlier (Supplementary file Results were consistent in the sensitivity where the genetic associations with alcohol consumption were obtained from the genome-wide association analysis excluding the UK Biobank and 23andMe studies (Supplementary file and Supplementary file 1G). The associations were consistent albeit with in the where alcohol consumption was by rs1229984 (Supplementary file The associations for alcoholic liver disease, cirrhosis, and chronic pancreatitis persisted after adjustment for genetic liability to smoking initiation and multiple correction (Table and Supplementary file Table Associations of genetically predicted alcohol consumption with 24 gastrointestinal diseases in univariable and multivariable Mendelian randomization analyses. DiseaseTotal casesTotal controlsUVMRMVMR adjusted for smoking (95% CI)p ValueI2 (95% CI)OR (95% CI)p ValueUpper gastrointestinal diseasesGastroesophageal (1.18, (1.23, (1.01, gastritis7975643,4781.33 gastrointestinal diseasesIrritable bowel (0.53, (0.76, and pancreatic diseasesNon-alcoholic fatty liver liver × × (1.29, (0.91, (0.91, × × (0.61, association after multiple testing. univariable Mendelian randomization; multivariable Mendelian randomization; odds CI, confidence Discussion We conducted a comprehensive MR investigation to examine the causal of smoking and alcohol consumption in 24 gastrointestinal diseases, and the result summary of this comprehensive analysis is in Figure and Supplementary file We associations between genetic predisposition to smoking and increased risk of gastrointestinal outcomes independent of alcohol consumption, an on gastrointestinal health. In genetically predicted alcohol consumption was and associated with increased risk of liver and pancreatic diseases, including alcoholic liver disease, cirrhosis, and chronic pancreatitis after adjustment for smoking. Figure Download asset Open asset of associations of genetically predicted smoking initiation, lifetime smoking, and alcohol consumption with 24 gastrointestinal diseases. univariable Mendelian randomization; multivariable Mendelian The in the are the odds for associations of exposure for gastrointestinal diseases. The association with a p-value <0.05 but Benjamini-Hochberg adjusted p-value >0.05 was regarded suggestive, and the association with a Benjamini-Hochberg adjusted p-value <0.05 was deemed

People who smoke cigarettes or drink large amounts of alcohol are more likely to develop disorders with their digestive system. But it is difficult to prove that heavy drinking or smoking is the primary cause of these gastrointestinal diseases. For example, it is possible that having a digestive disorder makes people more likely to take up these habits to reduce pain or discomfort caused by the illness (an effect known as reverse causation). The association may also be the result of confounding factors, such as age or diet, which contribute to digestive problems as well as the health outcomes of smoking and drinking. Additionally, many people who smoke also drink alcohol and vice versa, making it challenging to determine if one or both behaviors contribute to the disease. One solution is to employ Mendelian randomization which uses genetics to determine if two variables are linked. Using this statistical approach, Yuan, Chen, Ruan et al. investigated if people who display genetic variants that predispose someone to becoming a smoker or drinker are at greater risk of developing certain digestive disorders. This reduces the possibility of confounding and reverse causation, as any association between genetic variants will have been present since birth, and will have not been impacted by external factors. Yuan, Chen, Ruan et al. used data from two studies that had collected the genetic and health information of thousands of people living in the United Kingdom or Finland. The analyses revealed that genetic variants associated with cigarette smoking increase the risk of 20 of the 24 gastrointestinal diseases investigated. This risk persisted for most of the disorders, even after adjusting for genes linked with alcohol consumption. Further analysis showed that genetic variants linked to heavy drinking increase the risk of duodenal ulcer, alcoholic liver disease, cirrhosis, and chronic pancreatitis. However, accounting for smoking-linked genes eliminated the relationship with duodenal ulcer. These findings suggest that smoking has detrimental effects on gastrointestinal health. Reducing the number of people who start smoking or encouraging smokers to quit may help prevent digestive diseases. Even though there were fewer associations between heavy alcohol consumption and gastrointestinal illness, further studies are needed to investigate this relationship in more depth.

People who smoke cigarettes or drink large amounts of alcohol are more likely to develop disorders with their digestive system. But it is difficult to prove that heavy drinking or smoking is the primary cause of these gastrointestinal diseases. For example, it is possible that having a digestive disorder makes people more likely to take up these habits to reduce pain or discomfort caused by the illness (an effect known as reverse causation). The association may also be the result of confounding factors, such as age or diet, which contribute to digestive problems as well as the health outcomes of smoking and drinking. Additionally, many people who smoke also drink alcohol and vice versa, making it challenging to determine if one or both behaviors contribute to the disease. One solution is to employ Mendelian randomization which uses genetics to determine if two variables are linked. Using this statistical approach, Yuan, Chen, Ruan et al. investigated if people who display genetic variants that predispose someone to becoming a smoker or drinker are at greater risk of developing certain digestive disorders. This reduces the possibility of confounding and reverse causation, as any association between genetic variants will have been present since birth, and will have not been impacted by external factors. Yuan, Chen, Ruan et al. used data from two studies that had collected the genetic and health information of thousands of people living in the United Kingdom or Finland. The analyses revealed that genetic variants associated with cigarette smoking increase the risk of 20 of the 24 gastrointestinal diseases investigated. This risk persisted for most of the disorders, even after adjusting for genes linked with alcohol consumption. Further analysis showed that genetic variants linked to heavy drinking increase the risk of duodenal ulcer, alcoholic liver disease, cirrhosis, and chronic pancreatitis. However, accounting for smoking-linked genes eliminated the relationship with duodenal ulcer. These findings suggest that smoking has detrimental effects on gastrointestinal health. Reducing the number of people who start smoking or encouraging smokers to quit may help prevent digestive diseases. Even though there were fewer associations between heavy alcohol consumption and gastrointestinal illness, further studies are needed to investigate this relationship in more depth.

Chronic rhinosinusitis (CRS) is associated with gastroesophageal reflux (GERD). However, the causal relationship is controversial. We conducted a two-sample Mendelian Randomization (MR) analysis to explore this potential association. Based on genome-wide association studies (GWAS), a univariable MR was performed to explore the causal relationship of GERD with CRS. Instrumental variables (IVs) pertinent to anti-GERD treatment were employed as a means of validation. The primary MR outcome was established using an inverse variance weighted (IVW) method, supplemented by multiple sensitivity analyses. Subsequently, a multivariable MR was conducted to account for potential confounding variables, thereby ascertaining a direct effect of GERD on CRS. Finally, a network MR analysis was carried out to elucidate the mediating role of asthma in the relationship between GERD and CRS. The univariable MR demonstrated an association between GERD and an elevated risk of CRS (IVW OR = 1.30, 95% CI = 1.18-1.45, p = 4.19 × 10-7). Omeprazole usage was associated with a reduction in CRS risk (IVW OR = 0.64, 95% CI = 0.42-0.98, p = 0.039). The causal relationship between GERD and CRS remained after adjusting for potential confounders, such as smoking characteristics, body mass index, asthma, allergic rhinitis, in the multivariable MR analysis. Besides, the proportion of the causal effect of GERD on CRS mediated by asthma was 19.65% (95% CI = 2.69%-36.62%). GERD was independently associated with an increased risk of CRS. The mediating role of asthma between GERD and CRS also reveals that GERD is one of the mechanisms underlying unified airway disease. 3 Laryngoscope, 134:3086-3092, 2024.

A novel Mendelian randomisation framework unravels one gene expression component, correlated with proliferation and genome stability-related features, associated with telomere length in lung adenocarcinoma tumours, which provides insights into how telomere length influences the genetic basis of lung cancer aetiology.

A novel Mendelian randomisation framework unravels one gene expression component, correlated with proliferation and genome stability-related features, associated with telomere length in lung adenocarcinoma tumours, which provides insights into how telomere length influences the genetic basis of lung cancer aetiology.

ABSTRACTBackgroundIn several observational studies, vitamins B6, B9, B12, C and 25‐hydroxyvitamin D[25(OH)D] concentrations were associated with type 2 diabetes mellitus (T2DM). Although vitamins play a role in the development of type 2 diabetes mellitus (T2DM), their associations remain unclear.ObjectiveThis study employed Mendelian randomisation (MR) to explore the causal relationships between circulating concentrations of vitamins B6, B9, B12, C, 25‐hydroxyvitamin D and T2DM.MethodsSingle‐nucleotide polymorphisms (SNPs) linked to vitamin B6, vitamin B9, vitamin B12, vitamin C and 25(OH)D levels were used as instrumental variables (IVs) in this study. We have two outcomes related to T2DM derived from two genome‐wide association studies (GWAS). The first study, referenced by PMID: 3417140, encompasses a cohort of 406,831 individuals of European descent. The second study, identified by PMID: 29892013, includes a sample size of 468,298 Europeans.ResultsBoth univariable Mendelian randomization (UVMR) and multivariable Mendelian randomization (MVMR) analyses demonstrate that genetically predicted elevated levels of serum 25(OH)D are consistently associated with a reduced risk of T2DM. In the UVMR analyses, A 1‐SD increase in genetically predicted serum 25(OH)D levels, the inverse‐variance weighted (IVW) p = 3.8 × 10−7, pfdr = 7.6 × 10−7, the odds ratio(OR) of T2DM (GCST90013942) was 0.67, 95% confidence interval (CI): 0.57–0.78. Furthermore, a 1‐SD increase in genetically predicted serum 25(OH)D levels was associated with an OR of 0.987 for T2DM (GCST90029024), the IVW p = 1.1 × 10−4, pfdr = 1.1 × 10−4 with a 95% CI of 0.981–0.994. In the MVMR analyses, genetically predicted higher serum 25(OH)D levels were associated with a decreased risk of T2DM by the IVW p = 1.2 × 10−5, pfdr = 5.9 × 10−5 in GCST90013942 and IVW p = 4.9 × 10−4, pfdr = 2.5 × 10−3 in GCST90029024. In contrast, levels of vitamins B6, B9, B12, and C did not domenstrate a significant association with T2DM.ConclusionOur research reveals that higher circulating serum 25(OH)D levels reduce the possibility of T2DM.

NSAID medication mediates the causal effect of genetically predicted major depressive disorder on falls: Evidence from a Mendelian randomization study

Observational studies suggest an association between telomere length (TL) and blood lipid (BL) levels. Nevertheless, the causal connections between these two traits remain unclear. We aimed to elucidate whether genetically predicted TL is associated with BL levels via Mendelian randomization (MR) and vice versa. We obtained genetic instruments associated with TL, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A-1 (ApoA-1) and apolipoprotein B (ApoB) from large-scale genome-wide association studies (GWASs). The causal relationships between TL and BL were investigated via bidirectional MR, multivariable MR and mediation analysis methods. The inverse variance weighted (IVW) method was employed as the principal methodology, complemented by several other estimators to enhance the robustness of the analysis. In the forward MR analyses, we identified significant positive correlation between genetically predicted TL and the levels of TG (β=0.04, 95% confidence interval [CI]: 0.01 to 0.06, p = 0.003). In the reverse MR analysis, TG (β=0.02, 95% CI: 0.01 to 0.03, p = 0.004), LDL-C (β=0.03, 95% CI: 0.01 to 0.04, p = 0.001) and ApoB (β=0.03, 95% CI: 0.01 to 0.04, p = 9.71×10-5) were significantly positively associated with TL, although this relationship was not observed in the multivariate MR analysis. The mediation analysis via two-step MR showed no significant mediation effects acting through obesity-related phenotypes in analysis of TL with TG, while the effect of LDL-C on TL was partially mediated by body mass index (BMI) in the reverse direction, with mediated proportion of 12.83% (95% CI: 0.62% to 25.04%). Our study indicated that longer TL were associated with higher TG levels, while conversely, higher TG, LDL-C, and ApoB levels predicted longer TL, with BMI partially mediating these effects. Our findings present valuable insights into the development of preventive strategies and interventions that specifically target TL-related aging and age-related diseases.

This Mendelian randomization (MR) study investigates the causal relationships between circulating inflammatory proteins, blood metabolites, and Clostridium difficile colitis (CDC), with a focus on potential metabolic mediation. Bidirectional and mediation MR analyses were performed using genome-wide association study (GWAS) summary statistics from 3384 CDC cases and 406,048 controls. The primary MR methods included inverse variance weighting (IVW) and MR-Egger regression, with MR-PRESSO applied to detect and correct horizontal pleiotropy. Sensitivity analyses, including leave-one-out tests, were conducted to ensure the robustness of the findings. Mediation analysis was performed using a two-step MR framework to estimate the causal effects of inflammatory proteins on metabolites and, subsequently, the impact of these metabolites on CDC risk. Bidirectional MR identified 2 inflammatory proteins associated with CDC risk. Higher IL-2 receptor subunit beta (IL-2RB) levels were protective against CDC (OR = 0.827, 95% CI = 0.718–0.953, P < .01), whereas increased IL-22 receptor subunit alpha-1 (IL-22RA1) levels elevated CDC risk (OR = 1.300, 95% CI = 1.078–1.570, P < .01). Unidirectional MR identified 11 plasma metabolites associated with CDC, including a positive correlation with spermidine-to-choline ratio. Mediation MR analysis suggested that 17.4% (−6.21%, 41%) of IL-2RB’s protective effect on CDC was mediated through this metabolite. Sensitivity analyses confirmed the robustness of these associations. Our findings suggest that higher IL-2RB levels were protective against CDC, while increased IL-22RA1 levels were associated with higher risk. The Spermidine-to-choline ratio partially mediated the protective effect of IL-2RB, suggesting a metabolic link between inflammation and CDC.

Uric acid is the final metabolic product of purines in the human body and has been implicated in the pathogenesis of various diseases. Nevertheless, the relationship between serum uric acid levels and male infertility remains inconclusive. This Mendelian randomization (MR) study aims to elucidate the potential impact of serum uric acid levels on the risk of male infertility. We conducted the bidirectional MR analysis utilizing summary data from genome-wide association studies (GWAS) on serum uric acid levels and male infertility. The inverse variance weighted (IVW) method was employed to evaluate the primary outcomes, and multivariate MR analyses were conducted to combine estimates of the causal effects of multiple risk factors. Additionally, sensitivity analyses were performed to confirm the robustness of the results. In the univariable MR analysis, serum uric acid levels did not exhibit a significant association with the risk of male infertility [IVW odds ratio (OR) 0.92, 95% confidence interval (CI): 0.614-1.390, P=0.70]. Similar conclusions were drawn from the reverse MR analysis (IVW OR 1.000, 95% CI: 0.997-1.003, P=0.96). In the multivariable MR (MVMR) analysis, after adjusting for confounding factors such as body mass index (BMI), type 2 diabetes, alcohol consumption, and smoking, serum uric acid levels remained unassociated with male infertility (IVW OR 0.839, 95% CI: 0.613-1.148, P=0.27). Consistent results were observed in the reverse analysis (IVW OR 1.003, 95% CI: 0.994-1.012, P=0.49). Our study provides genetic evidence indicating no significant causal relationship between serum uric acid levels and male infertility in the general population, suggesting that serum uric acid is not a potential risk factor for male infertility.