Commentary: Does mortality from smoking have implications for future Mendelian randomization studies?
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ABSTRACT:
SUBMITTER: Taylor AE
PROVIDER: S-EPMC4190520 | biostudies-literature |
REPOSITORIES: biostudies-literature
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| S-EPMC5841830 | biostudies-literature
Project description:Evidence from clinical and epidemiological studies indicates that asthma is associated with allergic diseases including hay fever, allergic rhinitis, and eczema. Genetic analysis demonstrated that asthma had a positive genetic correlation with allergic diseases. A Mendelian randomization (MR) analysis using the rs16969968 single-nucleotide variant as the instrumental variable indicated that smoking was associated with increased risk of asthma. However, in a different MR analysis, smoking was significantly associated with reduced hay fever and reduced allergic sensitization risk. These findings revealed inconsistencies in the association of smoking with asthma and allergic diseases. Hence, we conducted an updated MR analysis to investigate the causal association between lifetime smoking and asthma risk by using 124 genetic variants as the instrumental variables. No significant pleiotropy was detected using the MR-Egger intercept test. We found that increased lifetime smoking was significantly associated with decreased asthma risk by using the inverse variance weighted (IVW) method (OR = 0.97, 95% CI 0.956-0.986, and P = 1.77E-04), the weighted median regression method (OR = 0.976, 95% CI 0.96-0.994, and P = 8.00E-03), and the MR-Egger method (OR = 0.919, 95% CI 0.847-0.998, and P = 4.5E-02). Importantly, MR pleiotropy residual sum and outlier (MR-PRESSO) MR analysis also indicated a significant association between increased lifetime smoking and decreased asthma risk with OR = 0.971, 95% CI 0.956-0.986, and P = 2.69E-04. After the outlier was removed, MR-PRESSO outlier test further supported the significant association with OR = 0.971, 95% CI 0.959-0.984, P = 1.57E-05.
| S-EPMC7438748 | biostudies-literature
Project description:BackgroundStriking changes in the demographic pattern of multiple sclerosis (MS) strongly indicate an influence of modifiable exposures, which lend themselves well to intervention. It is important to pinpoint which of the many environmental, lifestyle, and sociodemographic changes that have occurred over the past decades, such as higher smoking and obesity rates, are responsible. Mendelian randomization (MR) is an elegant tool to overcome limitations inherent to observational studies and leverage human genetics to inform prevention strategies in MS.MethodsWe use genetic variants from the largest genome-wide association study for smoking phenotypes (initiation: N = 378, heaviness: N = 55, lifetime smoking: N = 126) and body mass index (BMI, N = 656) and apply these as instrumental variables in a two-sample MR analysis to the most recent meta-analysis for MS. We adjust for the genetic correlation between smoking and BMI in a multivariable MR.ResultsIn univariable and multivariable MR, smoking does not have an effect on MS risk nor explains part of the association between BMI and MS risk. In contrast, in both analyses each standard deviation increase in BMI, corresponding to roughly 5 kg/m2 units, confers a 30% increase in MS risk.ConclusionDespite observational studies repeatedly reporting an association between smoking and increased risk for MS, MR analyses on smoking phenotypes and MS risk could not confirm a causal relationship. This is in contrast with BMI, where observational studies and MR agree on a causal contribution. The reasons for the discrepancy between observational studies and our MR study concerning smoking and MS require further investigation.
| S-EPMC7501136 | biostudies-literature
Project description:BackgroundSmoking was strongly associated with breast cancer in previous studies. Whether smoking promotes breast cancer through DNA methylation remains unknown.MethodsTwo-sample Mendelian randomization (MR) analyses were conducted to assess the causal effect of smoking-related DNA methylation on breast cancer risk. We used 436 smoking-related CpG sites extracted from 846 middle-aged women in the ARIES project as exposure data. We collected summary data of breast cancer from one of the largest meta-analyses, including 69,501 cases for ER+ breast cancer and 21,468 cases for ER- breast cancer. A total of 485 single-nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) for smoking-related DNA methylation. We further performed an MR Steiger test to estimate the likely direction of causal estimate between DNA methylation and breast cancer. We also conducted colocalization analysis to evaluate whether smoking-related CpG sites shared a common genetic causal SNP with breast cancer in a given region.ResultsWe established four significant associations after multiple testing correction: the CpG sites of cg2583948 [OR = 0.94, 95% CI (0.91-0.97)], cg0760265 [OR = 1.07, 95% CI (1.03-1.11)], cg0420946 [OR = 0.95, 95% CI (0.93-0.98)], and cg2037583 [OR =1.09, 95% CI (1.04-1.15)] were associated with the risk of ER+ breast cancer. All the four smoking-related CpG sites had a larger variance than that in ER+ breast cancer (all p < 1.83 × 10-11) in the MR Steiger test. Further colocalization analysis showed that there was strong evidence (based on PPH4 > 0.8) supporting a common genetic causal SNP between the CpG site of cg2583948 [with IMP3 expression (PPH4 = 0.958)] and ER+ breast cancer. There were no causal associations between smoking-related DNA methylation and ER- breast cancer.ConclusionsThese findings highlight potential targets for the prevention of ER+ breast cancer. Tissue-specific epigenetic data are required to confirm these results.
| S-EPMC8507148 | biostudies-literature
Project description:Habitual coffee and caffeine consumption has been reported to be associated with numerous health outcomes. This perspective focuses on Mendelian Randomization (MR) approaches for determining whether such associations are causal. Genetic instruments for coffee and caffeine consumption are described, along with key concepts of MR and particular challenges when applying this approach to studies of coffee and caffeine. To date, at least fifteen MR studies have investigated the causal role of coffee or caffeine use on risk of type 2 diabetes, cardiovascular disease, Alzheimer's disease, Parkinson's disease, gout, osteoarthritis, cancers, sleep disturbances and other substance use. Most studies provide no consistent support for a causal role of coffee or caffeine on these health outcomes. Common study limitations include low statistical power, potential pleiotropy, and risk of collider bias. As a result, in many cases a causal role cannot confidently be ruled out. Conceptual challenges also arise from the different aspects of coffee and caffeine use captured by current genetic instruments. Nevertheless, with continued genome-wide searches for coffee and caffeine related loci along with advanced statistical methods and MR designs, MR promises to be a valuable approach to understanding the causal impact that coffee and caffeine have in human health.
| S-EPMC6213346 | biostudies-literature
Project description:Ovarian cancer (OC) is one of the deadliest gynecological cancers worldwide. Previous observational epidemiological studies have revealed associations between modifiable environmental risk factors and OC risk. However, these studies are prone to confounding, measurement error, and reverse causation, undermining robust causal inference. Mendelian randomization (MR) analysis has been established as a reliable method to investigate the causal relationship between risk factors and diseases using genetic variants to proxy modifiable exposures. Over recent years, MR analysis in OC research has received extensive attention, providing valuable insights into the etiology of OC as well as holding promise for identifying potential therapeutic interventions. This review provides a comprehensive overview of the key principles and assumptions of MR analysis. Published MR studies focusing on the causality between different risk factors and OC risk are summarized, along with comprehensive analysis of the method and its future applications. The results of MR studies on OC showed that higher BMI and height, earlier age at menarche, endometriosis, schizophrenia, and higher circulating β-carotene and circulating zinc levels are associated with an increased risk of OC. In contrast, polycystic ovary syndrome; vitiligo; higher circulating vitamin D, magnesium, and testosterone levels; and HMG-CoA reductase inhibition are associated with a reduced risk of OC. MR analysis presents a2 valuable approach to understanding the causality between different risk factors and OC after full consideration of its inherent assumptions and limitations.
| S-EPMC8385140 | biostudies-literature
Project description:The causal effects of alcohol-in-moderation on cardiometabolic health are continuously debated. Mendelian randomization (MR) is an established method to address causal questions in observational studies. We performed a systematic review of the current evidence from MR studies on the association between alcohol consumption and cardiometabolic diseases, all-cause mortality and cardiovascular risk factors. We performed a systematic search of the literature, including search terms on type of design and exposure. We assessed methodological quality based on key elements of the MR design: use of a full instrumental variable analysis and validation of the three key MR assumptions. We additionally looked at exploration of non-linearity. We reported the direction of the studied associations. Our search yielded 24 studies that were eligible for inclusion. A full instrumental variable analysis was performed in 17 studies (71%) and 13 out of 24 studies (54%) validated all three key assumptions. Five studies (21%) assessed potential non-linearity. In general, null associations were reported for genetically predicted alcohol consumption with the primary outcomes cardiovascular disease (67%) and diabetes (75%), while the only study on all-cause mortality reported a detrimental association. Considering the heterogeneity in methodological quality of the included MR studies, it is not yet possible to draw conclusions on the causal role of moderate alcohol consumption on cardiometabolic health. As MR is a rapidly evolving field, we expect that future MR studies, especially with recent developments regarding instrument selection and non-linearity methodology, will further substantiate this discussion.
| S-EPMC9329419 | biostudies-literature
Project description:There is evidence for a positive relationship between cigarette and coffee consumption in smokers. Cigarette smoke increases metabolism of caffeine, so this may represent a causal effect of smoking on caffeine intake.We performed Mendelian randomization analyses in the UK Biobank (N = 114 029), the Norwegian HUNT study (N = 56 664) and the Copenhagen General Population Study (CGPS) (N = 78 650). We used the rs16969968 genetic variant as a proxy for smoking heaviness in all studies and rs4410790 and rs2472297 as proxies for coffee consumption in UK Biobank and CGPS. Analyses were conducted using linear regression and meta-analysed across studies.Each additional cigarette per day consumed by current smokers was associated with higher coffee consumption (0.10 cups per day, 95% CI: 0.03, 0.17). There was weak evidence for an increase in tea consumption per additional cigarette smoked per day (0.04 cups per day, 95% CI: -0.002, 0.07). There was strong evidence that each additional copy of the minor allele of rs16969968 (which increases daily cigarette consumption) in current smokers was associated with higher coffee consumption (0.16 cups per day, 95% CI: 0.11, 0.20), but only weak evidence for an association with tea consumption (0.04 cups per day, 95% CI: -0.01, 0.09). There was no clear evidence that rs16969968 was associated with coffee or tea consumption in never or former smokers or that the coffee-related variants were associated with cigarette consumption.Higher cigarette consumption causally increases coffee intake. This is consistent with faster metabolism of caffeine by smokers, but could also reflect a behavioural effect of smoking on coffee drinking.
| S-EPMC5837196 | biostudies-literature
Project description:Purpose of review:In this paper, we summarize prior studies that have used Mendelian Randomization (MR) methods to study the effects of exposures, lifestyle factors, physical traits, and/or biomarkers on cancer risk in humans. Many such risk factors have been associated with cancer risk in observational studies, and the MR approach can be used to provide evidence as to whether these associations represent causal relationships. MR methods require a risk factor of interest to have known genetic determinants that can be used as proxies for the risk factor (i.e., "instrumental variables" or IVs), and these can be used to obtain an effect estimate that, under certain assumptions, is not prone to bias caused by unobserved confounding or reverse causality. This review seeks to describe how MR studies have contributed to our understanding of cancer causation. Recent findings:We searched the published literature and identified 76 MR studies of cancer risk published prior to October 31, 2017. Risk factors commonly studied included alcohol consumption, Vitamin D, anthropometric traits, telomere length, lipid traits, glycemic traits, and markers of inflammation. Risk factors showing compelling evidence of a causal association with risk for at least one cancer type include alcohol consumption (for head/neck and colorectal), adult body mass index (increases risk for multiple cancers, but decreases risk for breast), height (increases risk for breast, colorectal, and lung; decreases risk for esophageal), telomere length (increases risk for lung adenocarcinoma, melanoma, renal cell carcinoma, glioma, B-cell lymphoma subtypes, chronic lymphocytic leukemia, and neuroblastoma), and hormonal factors (affects risk for sex-steroid sensitive cancers). Summary:This review highlights alcohol consumption, body mass index, height, telomere length, and the hormonal exposures as factors likely to contribute to cancer causation. This review also highlights the need to study specific cancer types, ideally subtypes, as the effects of risk factors can be heterogeneous across cancer types. As consortia-based genome-wide association studies increase in sample size and analytical methods for MR continue to become more sophisticated, MR will become an increasingly powerful tool for understanding cancer causation.
| S-EPMC6053056 | biostudies-literature
Project description:Many biomarkers are associated with type 2 diabetes (T2D) risk in epidemiological observations. The aim of this study was to identify and summarize current evidence for causal effects of biomarkers on T2D. A systematic literature search in PubMed and EMBASE (until April 2015) was done to identify Mendelian randomization studies that examined potential causal effects of biomarkers on T2D. To replicate the findings of identified studies, data from two large-scale, genome-wide association studies (GWAS) were used: DIAbetes Genetics Replication And Meta-analysis (DIAGRAMv3) for T2D and the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for glycaemic traits. GWAS summary statistics were extracted for the same genetic variants (or proxy variants), which were used in the original Mendelian randomization studies. Of the 21 biomarkers (from 28 studies), ten have been reported to be causally associated with T2D in Mendelian randomization. Most biomarkers were investigated in a single cohort study or population. Of the ten biomarkers that were identified, nominally significant associations with T2D or glycaemic traits were reached for those genetic variants related to bilirubin, pro-B-type natriuretic peptide, delta-6 desaturase and dimethylglycine based on the summary data from DIAGRAMv3 or MAGIC. Several Mendelian randomization studies investigated the nature of associations of biomarkers with T2D. However, there were only a few biomarkers that may have causal effects on T2D. Further research is needed to broadly evaluate the causal effects of multiple biomarkers on T2D and glycaemic traits using data from large-scale cohorts or GWAS including many different genetic variants.
| S-EPMC4654400 | biostudies-literature