Causal relationships of inflammatory cytokines and serum metabolites with colorectal carcinoma: A Mendelian randomization study.

Background and purposeThe aim was to investigate the causal relationships of inflammatory cytokines and serum metabolites in cerebral small vessel disease (CSVD).MethodsBidirectional Mendelian randomization was first conducted to screen inflammatory cytokines and serum metabolites that were associated with imaging features of CSVD, including white matter hyperintensities, recent small subcortical infarcts, cortical cerebral microinfarcts, cerebral microbleeds, lacunes and enlarged perivascular spaces. Sensitivity analyses were performed to evaluate the robustness and pleiotropy of these results. Subsequently, inflammatory cytokines and serum metabolites that were associated with CSVD were subjected to functional enrichment. Finally, mediation analysis was employed to investigate whether inflammatory cytokines or serum metabolites acted as an intermediary for the other in their causal relationship with CSVD.ResultsOf the inflammatory cytokines, five were risk factors (e.g., tumour‐necrosis‐factor‐related apoptosis‐inducing ligand) and five (e.g., fibroblast growth factor 19) were protective factors for CSVD. Eleven serum metabolites that increased CSVD risk and 13 metabolites that decreased CSVD risk were also identified. The majority of these markers of CSVD susceptibility were lipid metabolites. Natural killer cell receptor sub‐type 2B4 was determined to act as a mediating factor of an unidentified metabolite for the enlargement of perivascular spaces.ConclusionSeveral inflammatory cytokines and serum metabolites had causal relationships with imaging features of CSVD. A natural killer cell receptor mediated in part the promotional effect of a metabolite on perivascular space enlargement.

The relationship between the gut microbiota, metabolites and body fat percentage (BFP) remains unexplored. We systematically assessed the causal relationships between gut microbiota, metabolites and BFP using Mendelian randomization analysis. Single nucleotide polymorphisms associated with gut microbiota, blood metabolites and BFP were screened via a genome-wide association study enrolling individuals of European descent. Summary data from genome-wide association studies were extracted from the MiBioGen consortium and the UK Biobank. The inverse variance-weighted model was the primary method used to estimate these causal relationships. Sensitivity analyses were performed using pleiotropy, Mendelian randomization-Egger regression, heterogeneity tests and leave-one-out tests. In the aspect of phyla, classes, orders, families and genera, we observed that o_Bifidobacteriales [β = -0.05; 95% confidence interval (CI): -0.07 to -0.03; false discovery rate (FDR) = 2.76 × 10-3], f_Bifidobacteriaceae (β = -0.05; 95% CI: -0.07 to -0.07; FDR = 2.76 × 10-3), p_Actinobacteria (β = -0.06; 95% CI: -0.09 to -0.03; FDR = 6.36 × 10-3), c_Actinobacteria (β = -0.05; 95% CI: -0.08 to -0.02; FDR = 1.06 × 10-2), g_Bifidobacterium (β = -0.05; 95% CI: -0.07 to -0.02; FDR = 1.85 × 10-2), g_Ruminiclostridium9 (β = -0.03; 95% CI: -0.06 to -0.01; FDR = 4.81 × 10-2) were negatively associated with BFP. G_Olsenella (β = 0.02; 95% CI: 0.01-0.03; FDR = 2.16 × 10-2) was positively associated with BFP. Among the gut microbiotas, f_Bifidobacteriales, o_Bifidobacteriales, c_Actinobacteria and p_Actinobacteria were shown to be significantly associated with BFP in the validated dataset. In the aspect of metabolites, we only observed that valine (β = 0.77; 95% CI: 0.5-1.04; FDR = 8.65 × 10-6) was associated with BFP. Multiple gut microbiota and metabolites were strongly associated with an increased BFP. Further studies are required to elucidate the mechanisms underlying this putative causality. In addition, BFP, a key indicator of obesity, suggests that obesity-related interventions can be developed from gut microbiota and metabolite perspectives.

Background Patients with type 2 diabetes (T2D) are at substantially higher risk for developing colorectal carcinoma (CC) than the general population. Clinical studies have investigated the effects of sodium-glucose co-transporter-2 inhibitor (SGLT-2i) use on the development of incident CC in patients with T2D, but the findings are inconsistent. Purpose This study aimed to examine the association between SGLT-2i use and incident CC risk in patients with T2D. Methods We conducted a nationwide retrospective cohort study using the National Health Insurance Research Database (2015–2021). The primary outcome was the risk of incident CC by estimating hazard ratios (HRs) and 95% confidence intervals (CIs). Therefore, multiple Cox regression modeling was conducted to analyze the association between SGLT-2i use and incident CC risk in patients with T2D. Results 268,495 propensity score-matched pairs of patients with T2D using SGLT-2i and non-using SGLT-2i, 1557 and 1264 incident CCs were recorded, respectively. There was a decreased risk of incident CC for SGLT-2i users after adjusting for the index year, sex, age, comorbidities, and concurrent medication (adjusted HR 0.79, 95% CI 0.74–0.84) compared with non-SGLT-2i users. The sensitivity test for the propensity score 1:2-matched analyses showed similar result (adjusted HR 0.79, 95% CI 0.74–0.85). Conclusions This population-based cohort study found that SLGT2i user was associated with a lower risk of CC by 21% compared to non-SGLT-2i users in T2D patients. More studies are needed to credibly evaluate the effects of SGLT-2i therapy on CC prevention in patients with T2D.

Background The role of sodium‐glucose co‐transporter‐2 inhibitor (SGLT2I) in colorectal carcinoma (CRC) risk in patients with type 2 diabetes mellitus (T2D) remains controversial. This study aimed to examine the association between SGLT2I use and the risk of incident CRC in patients with T2D. Methods This nationwide retrospective cohort study was conducted using the National Health Insurance Research Database (2015–2021). The primary outcome was the risk of incident CRC, estimated using hazard ratios (HRs) and 95% confidence intervals (CIs). Multiple Cox regression modeling was conducted to analyze the association between SGLT2I use and incident CRC risk in patients with T2D. Results A total of 304 698 SGLT2I users and 609 396 nonusers were matched in a 1:2 ratio by age, sex, and index year from 2 617 996 patients with T2D. Among patients with T2D, 1436 and 3555 incident CRCs were recorded in SGLT2I users and nonusers, respectively. After adjusting for the index year, sex, age, and comorbidities, a significantly decreased risk of CRC was observed among SGLT2I users compared to nonusers (adjusted HR 0.80, 95% CI 0.74–0.85). The sensitivity test for the propensity score 1:1‐matched analyses also showed similar results (adjusted HR 0.79, 95% CI 0.74–0.85). Conclusions This population‐based cohort study found that SGLT2I use was associated with a lower risk of CRC than nonuse of SGLT2I in patients with T2D. More studies are needed to evaluate the effects of SGLT2I therapy on CRC prevention in patients with T2D.

Considerable epidemiological studies have examined the correlation between polymorphic single-nucleotide variants (SNPs) in miRNA genes and colorectal carcinoma (CRC) risk, yielding inconsistent results. Herein, we sought to systematically investigate the association between miRNA-SNPs and CRC susceptibility by combined evaluation using pairwise and network meta-analysis, the FPRP analysis (false positive report probability), and the Thakkinstian's algorithm. The MEDLINE, EMBASE, WOS, and Cochrane Library databases were searched through May 2024 to find relevant association literatures. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were computed by the pairwise meta-analysis. Network meta-analysis and the Thakkinstian's method were applied for determining the potentially optimal genetic models; additionally, the FPRP was used to identify noteworthy associations. Totally, 39 case-control trials involving 18,028 CRC cases, and 21,816 normal participants were included in the study. Eleven SNPs within nine genes were examined for their predisposition to CRC. miR-27a (rs895819) was found to significantly increase CRC risk among overall population (OR 1.58, 95% CI: 1.32-1.89) and Asians (OR 1.62, 95% CI: 1.31-2.01), with the recessive models identified as the optimal models. Furthermore, miR-196a2 (rs11614913), miR-143/145 (rs41291957), and miR-34b/c (rs4938723) were significantly related to reduced CRC risk among Asian descendants under the optimal dominant (OR 0.75, 95% CI: 0.65-0.86), recessive (OR 0.72, 95% CI: 0.60-0.85), and recessive models (OR 0.69, 95% CI: 0.56-0.85), respectively. The results were also proposed by the network meta-analysis or the Thakkinstian's method and confirmed by the FPRP criterion. The miR-27a (rs895819) is correlated with elevated CRC risk among overall population and Asians, and the recessive model is found to be optimal for predicting CRC risk. Additionally, the miR-196a2 (rs11614913), miR-143/145 (rs41291957), and miR-34b/c (rs4938723), with the dominant, recessive, and recessive models identified as the optimal, might confer protective effects against CRC among Asians.

Oral microbiota, cytokines, and schizophrenia and bipolar disorder: a mediation mendelian randomization study.

Recent research linked changes in the gut microbiota and serum metabolite concentrations to intracerebral hemorrhage (ICH). However, the potential causal relationship remained unclear. Therefore, the current study aims to estimate the effects of genetically predicted causality between gut microbiota, serum metabolites, and ICH. Summary data from genome-wide association studies (GWAS) of gut microbiota, serum metabolites, and ICH were obtained separately. Gut microbiota GWAS (N = 18,340) were acquired from the MiBioGen study, serum metabolites GWAS (N = 7,824) from the TwinsUK and KORA studies, and GWAS summary-level data for ICH from the FinnGen R9 (ICH, 3,749 cases; 339,914 controls). A two-sample Mendelian randomization (MR) study was conducted to explore the causal effects between gut microbiota, serum metabolites, and ICH. The random-effects inverse variance-weighted (IVW) MR analyses were performed as the primary results, together with a series of sensitivity analyses to assess the robustness of the results. Besides, a reverse MR was conducted to evaluate the possibility of reverse causation. To validate the relevant findings, we further selected data from the UK Biobank for analysis. MR analysis results revealed a nominal association (p < 0.05) between 17 gut microbial taxa, 31 serum metabolites, and ICH. Among gut microbiota, the higher level of genus Eubacterium xylanophilum (odds ratio (OR): 1.327, 95% confidence interval (CI):1.154-1.526; Bonferroni-corrected p = 7.28 × 10-5) retained a strong causal relationship with a higher risk of ICH after the Bonferroni corrected test. Concurrently, the genus Senegalimassilia (OR: 0.843, 95% CI: 0.778-0.915; Bonferroni-corrected p = 4.10 × 10-5) was associated with lower ICH risk. Moreover, after Bonferroni correction, only two serum metabolites remained out of the initial 31 serum metabolites. One of the serum metabolites, Isovalerate (OR: 7.130, 95% CI: 2.648-19.199; Bonferroni-corrected p = 1.01 × 10-4) showed a very strong causal relationship with a higher risk of ICH, whereas the other metabolite was unidentified and excluded from further analysis. Various sensitivity analyses yielded similar results, with no heterogeneity or directional pleiotropy observed. This two-sample MR study revealed the significant influence of gut microbiota and serum metabolites on the risk of ICH. The specific bacterial taxa and metabolites engaged in ICH development were identified. Further research is required in the future to delve deeper into the mechanisms behind these findings.

BackgroundThe risk of sporadic colorectal cancer is mainly associated with lifestyle factors and may be modulated by several genetic factors of low penetrance. Genetic variants represented by single nucleotide polymorphisms in genes encoding key players in the adenoma carcinoma sequence may contribute to variation in susceptibility to colorectal cancer. In this study, we aimed to evaluate whether the recently identified haplotype encompassing genes of DNA repair and apoptosis, is associated with increased risk of colorectal adenomas and carcinomas.MethodsWe used a case-control study design (156 carcinomas, 981 adenomas and 399 controls) to test the association between polymorphisms in the chromosomal region 19q13.2-3, encompassing the genes ERCC1, ASE-1 and RAI, and risk of colorectal adenomas and carcinomas in a Norwegian cohort. Odds ratio (OR) and 95% confidence interval (CI) were estimated by binary logistic regression model adjusting for age and gender.ResultsThe ASE-1 polymorphism was associated with an increased risk of adenomas, OR of 1.39 (95% CI 1.06–1.81), which upon stratification was apparent among women only, OR of 1.66 (95% CI 1.15–2.39). The RAI polymorphism showed a trend towards risk reduction for both adenomas (OR of 0.70, 95% CI 0.49–1.01) and carcinomas (OR of 0.49, 95% CI 0.21–1.13) among women, although not significant. Women who were homozygous carriers of the high risk haplotype had an increased risk of colorectal cancer, OR of 2.19 (95% CI 0.95–5.04) compared to all non-carriers although the estimate was not statistically significant.ConclusionWe found no evidence that the studied polymorphisms were associated with risk of adenomas or colorectal cancer among men, but we found weak indications that the chromosomal region may influence risk of colorectal cancer and adenoma development in women.

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

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

Vertigo is a prevalent clinical symptom, frequently associated with benign paroxysmal positional vertigo (BPPV), Ménière disease (MD), and vestibular neuritis (VN), which are three common peripheral vestibular disorders. However, there is a relative lack of research in epidemiology and etiology, with some existing studies presenting discrepancies in their conclusions. We conducted a two-sample Mendelian randomization (MR) analysis to explore potential risk and protective factors for these three peripheral vestibular disorders. Based on genome-wide association studies, we executed a univariable MR to investigate the potential associations between 38 phenotypes and MD, BPPV, and VN. We used the inverse variance weighted method as the primary MR result and conducted multiple sensitivity analyses. We used false discovery rate (FDR) correction to control for type I errors. For findings that were significant in the univariable MR, a multivariable MR analysis was implemented to ascertain direct effects. In addition, we replicated analyses of significant results from the univariable MR to enhance the robustness of our analyses. For BPPV, both alcohol consumption (odds ratio [OR] = 0.57, 95% confidence interval [CI] = 0.43 to 0.76, FDR Q = 0.004) and educational attainment (OR = 0.77, 95% CI = 0.68 to 0.88, FDR Q = 0.003) were found to decrease the risk. The genetic prediction analysis identified major depression (OR = 1.75, 95% CI = 1.28 to 2.39, FDR Q = 0.008) and anxiety (OR = 5.25, 95% CI = 1.79 to 15.42, FDR Q = 0.036) increased the risk of MD. However, the impact of major depression on MD could be influenced by potential horizontal pleiotropy. Systolic blood pressures (OR = 1.03, 95% CI = 1.02 to 1.04, FDR Q = 4.00 × 10 -7 ) and diastolic blood pressures (OR = 1.05, 95% CI = 1.03 to 1.07, FDR Q = 2.83 × 10 -6 ) were associated with an increased risk of VN, whereas high-density lipoprotein (OR = 0.77, 95% CI = 0.67 to 0.89, FDR Q = 0.009) and urate (OR = 0.75, 95% CI = 0.63 to 0.91, FDR Q = 0.041) reduces the risk of VN. Only the relationship between urate and VN was not replicated in the replication analysis. Multivariable MR showed that the protective effect of education on BPPV was independent of Townsend deprivation index. The protective effect of high-density lipoprotein against VN was independent of triglycerides and apolipoprotein A1. The risk impacts of systolic and diastolic blood pressures on VN exhibited collinearity, but both are independent of chronic kidney disease and estimated glomerular filtration rate. The impacts of anxiety and severe depression on MD demonstrated collinearity. Our study identified the risk association between systolic and diastolic blood pressure with VN and the protective influence of high-density lipoprotein on VN, which may support the vascular hypothesis underlying VN. Furthermore, we observed an elevated risk of MD associated with anxiety. The potential protective effects of education and alcohol consumption on BPPV need further exploration in subsequent studies to elucidate specific mechanistic pathways. In summary, our MR study offers novel insights into the etiology of three peripheral vestibular diseases from a genetic epidemiological standpoint.

The causal relationship between cathepsins and neurological diseases remains uncertain. To address this, we utilized a two-sample Mendelian randomization (MR) approach to assess the potential causal effect of cathepsins on the development of neurological diseases. This study conducted a two-sample two-way MR study using pooled data from published genome-wide association studies to evaluate the relationship between 10 cathepsins (B, D, E, F, G, H, L2, O, S, and Z) and 7 neurological diseases, which included ischemic stroke, cerebral hemorrhage, Alzheimer's disease, Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, and epilepsy. The analysis employed various methods such as inverse variance weighting (IVW), weighted median, MR Egger regression, MR pleiotropy residual sum and outlier, Cochran Q statistic, and leave-one-out analysis. We found a causal relationship between cathepsins and neurological diseases, including Cathepsin B and Parkinson's disease (IVW odds ratio (OR): 0.89, 95% confidence interval (CI): 0.83, 0.95, p = 0.001); Cathepsin D and Parkinson's disease (OR: 0.80, 95%CI: 0.68, 0.95, p = 0.012); Cathepsin E and ischemic stroke (OR: 1.05, 95%CI: 1.01, 1.09, p = 0.015); Cathepsin O and ischemic stroke (OR: 1.05, 95%CI: 1.01, 1.10, p = 0.021). Reverse MR analyses revealed that multiple sclerosis and Cathepsin E (OR: 1.05, 95%CI: 1.01, 1.10, p = 0.030). There is currently no significant relationship has been found between other cathepsins and neurological diseases. Our study reveals a causal relationship between Cathepsins B, D, E, and O and neurological diseases, offering valuable insights for research aimed at improving the diagnosis and treatment of such conditions.

Breast cancer is a common cancer type that leads to cancer-related deaths among women. HER2-positive breast cancer, in particular, is associated with poor prognosis due to its high aggressiveness, increased risk of recurrence, and metastasis potential. Previous observational studies have explored potential associations between inflammatory cytokines and the risk of two breast cancer subtypes (HER2-positive and HER2-negative), but the results have been inconsistent. To further elucidate the causal relationship between inflammatory cytokines and the two breast cancer subtypes, we conducted a two-sample Mendelian randomization (MR) study. We employed a two-sample bidirectional MR analysis using publicly available genome-wide association study (GWAS) statistics. After obtaining instrumental variables, we conducted MR analyses using five different methods to ensure the reliability of our results. Additionally, we performed tests for heterogeneity and horizontal pleiotropy. Subsequently, we conducted a reverse MR study by reversing exposure and outcome variables. Evidence from our IVW analysis revealed that genetically predicted levels of IL-5 [odds ratio (OR): 1.18, 95% confidence interval (CI): 1.04-1.35, P = 0.012], IL-7 (OR: 1.11, 95% CI: 1.01-1.22, P = 0.037), and IL-16 (OR: 1.13, 95% CI: 1.02-1.25, P = 0.025) were associated with an increased risk of HER2-positive breast cancer. Conversely, IL-10 (OR: 1.14, 95% CI: 1.03-1.26, P = 0.012) was associated with an increased risk of HER2-negative breast cancer. These results showed no evidence of heterogeneity or horizontal pleiotropy (P > 0.05). Results from the reverse MR analysis indicated no potential causal association between breast cancer and inflammatory cytokines (P > 0.05). Our findings demonstrate that IL-5, IL-7, and IL-16 are risk factors for HER2-positive breast cancer, with varying degrees of increased probability of HER2-positive breast cancer associated with elevated levels of these inflammatory cytokines. Conversely, IL-10 is a risk factor for HER2-negative breast cancer. Reverse studies have confirmed that breast cancer is not a risk factor for elevated levels of inflammatory cytokines. This series of results clarifies the causal relationship between different types of inflammatory cytokines and different subtypes of breast cancer. Based on this research, potential directions for the mechanism research of different inflammatory cytokines and different subtypes of breast cancer have been provided, and potential genetic basis for identifying and treating different subtypes of breast cancer have been suggested.

The objective is to investigate the causal link between inflammatory cytokines and lung cancer through the application of a two-sample bidirectional Mendelian randomization (MR) approach. This study gathered data from genome-wide association studies (GWAS), focusing on 91 inflammatory cytokines associated with lung cancer. The investigation into the causal link between inflammatory cytokines and lung cancer was performed utilizing several methods, including the inverse variance weighted (IVW) approach, weighted median (WM) method, MR Egger regression, weighted mode, and simple mode. The IVW method served as the primary evaluative metric, while various sensitivity analyses were carried out to assess the heterogeneity and horizontal pleiotropy of the MR outcomes. A total of 3 Inflammatory cytokines were identified as having a positive causal association with lung cancer. Among these, CCL25 (OR = 1.060, 95% CI 1.020-1.102) is a risk factor for lung cancer, increasing the risk of developing the disease; while GDNF (OR = 0.872, 95% CI 0.762-1.000) and IL-18 (OR = 0.852, 95% CI 0.781-0.930) are protective factors for lung cancer, reducing the risk of developing the disease. This research indicates a possible causal link between inflammatory cytokines and lung cancer, highlighting the involvement of several inflammatory cytokines in the disease's development. It offers fresh perspectives on how inflammatory cytokines mediate the pathogenesis of lung cancer, holding great importance for prevention, treatment, and symptom enhancement of the disease.

Observational studies demonstrated controversial effect of polyunsaturated fatty acids (PUFAs) on Parkinson's disease (PD) with limited causality evidence. Randomized control trials showed possible improvement in PD symptoms with PUFA supplement but had small study population and limited intervention time. A two-sample Mendelian randomization was designed to evaluate the causal relevance between PUFAs and PD, using genetic variants of PUFAs as instrumental variables and PD data from the largest genome-wide association study as outcome. Inverse variance weighted (IVW) method was applied to obtain the primary outcome. Mendelian randomization Egger regression, weighted median and weighted mode methods were exploited to assist result analyses. Strict Mendelian randomization and multivariable Mendelian randomization (MVMR) were used to estimate direct effects of PUFAs on PD, eliminating pleiotropic effect. Debiased inverse variance weighted estimator was implemented when weak instrument bias was introduced into the analysis. A variety of sensitivity analyses were utilized to assess validity of the results. Our study included 33,674 PD cases and 449,056 controls. Higher plasma level of arachidonic acid (AA) was associated with a 3% increase of PD risk per 1-standard deviation (SD) increase of AA (IVW; Odds ratio (OR)=1.03 [95% confidence interval (CI) 1.01-1.04], P = 2.24E-04). After MVMR (IVW; OR=1.03 [95% CI 1.02-1.04], P =6.15E-08) and deletion of pleiotropic single-nucleotide polymorphisms overlapping with other lipids (IVW; OR=1.03 [95% CI 1.01-1.05], P =5.88E-04), result was still significant. Increased level of eicosapentaenoic acid (EPA) showed possible relevance with increased PD risk after adjustment of pleiotropy (MVMR; OR=1.05 [95% CI 1.01-1.08], P =5.40E-03). Linoleic acid (LA), docosahexaenoic acid (DHA), docosapentaenoic acid (DPA) and alpha-linolenic acid (ALA) were found not causally relevant to PD risk. Various sensitivity analyses verified the validity of our results. In conclusion, our findings from Mendelian randomization suggested that elevated levels of AA and possibly EPA might be linked to a higher risk of PD. No association between PD risk and LA, DHA, DPA, or ALA was found. The odds ratio for plasma AA and PD risk was weak. It is important to approach our results with caution in clinical practice and to conduct additional studies on the relationship between PUFAs and PD risk.