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Hartwig, Fernando Pires, Neil Martin Davies, and George Davey Smith. "Bias in Mendelian randomization due to assortative mating." Genetic Epidemiology 42, no. 7 (July 3, 2018): 608–20. http://dx.doi.org/10.1002/gepi.22138.
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Allman, Phillip H., Inmaculada Aban, Dustin M. Long, Amit Patki, Todd MacKenzie, Marguerite R. Irvin, Leslie A. Lange, Ethan Lange, Gary Cutter, and Hemant K. Tiwari. "Mendelian randomization in the multivariate general linear model framework." Genetic Epidemiology 46, no. 1 (October 21, 2021): 17–31. http://dx.doi.org/10.1002/gepi.22435.
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Burgess, Stephen, Adam Butterworth, and Simon G. Thompson. "Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data." Genetic Epidemiology 37, no. 7 (September 20, 2013): 658–65. http://dx.doi.org/10.1002/gepi.21758.
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Burgess, Stephen, Neil M. Davies, and Simon G. Thompson. "Bias due to participant overlap in two‐sample Mendelian randomization." Genetic Epidemiology 40, no. 7 (September 14, 2016): 597–608. http://dx.doi.org/10.1002/gepi.21998.
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Jiang, Lai, Karim Oualkacha, Vanessa Didelez, Antonio Ciampi, Pedro Rosa‐Neto, Andrea L. Benedet, Sulantha Mathotaarachchi, John Brent Richards, and Celia M. T. Greenwood. "Constrained instruments and their application to Mendelian randomization with pleiotropy." Genetic Epidemiology 43, no. 4 (January 12, 2019): 373–401. http://dx.doi.org/10.1002/gepi.22184.
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Deng, Lu, Han Zhang, and Kai Yu. "Power calculation for the general two‐sample Mendelian randomization analysis." Genetic Epidemiology 44, no. 3 (February 11, 2020): 290–99. http://dx.doi.org/10.1002/gepi.22284.
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Slob, Eric A. W., and Stephen Burgess. "A comparison of robust Mendelian randomization methods using summary data." Genetic Epidemiology 44, no. 4 (April 6, 2020): 313–29. http://dx.doi.org/10.1002/gepi.22295.
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Meng, Xiangrui, Xue Li, Maria N. Timofeeva, Yazhou He, Athina Spiliopoulou, Wei-Qi Wei, Aliya Gifford, et al. "Phenome-wide Mendelian-randomization study of genetically determined vitamin D on multiple health outcomes using the UK Biobank study." International Journal of Epidemiology 48, no. 5 (September 13, 2019): 1425–34. http://dx.doi.org/10.1093/ije/dyz182.
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Abstract Background Vitamin D deficiency is highly prevalent across the globe. Existing studies suggest that a low vitamin D level is associated with more than 130 outcomes. Exploring the causal role of vitamin D in health outcomes could support or question vitamin D supplementation. Methods We carried out a systematic literature review of previous Mendelian-randomization studies on vitamin D. We then implemented a Mendelian Randomization–Phenome Wide Association Study (MR-PheWAS) analysis on data from 339 256 individuals of White British origin from UK Biobank. We first ran a PheWAS analysis to test the associations between a 25(OH)D polygenic risk score and 920 disease outcomes, and then nine phenotypes (i.e. systolic blood pressure, diastolic blood pressure, risk of hypertension, T2D, ischaemic heart disease, body mass index, depression, non-vertebral fracture and all-cause mortality) that met the pre-defined inclusion criteria for further analysis were examined by multiple MR analytical approaches to explore causality. Results The PheWAS analysis did not identify any health outcome associated with the 25(OH)D polygenic risk score. Although a selection of nine outcomes were reported in previous Mendelian-randomization studies or umbrella reviews to be associated with vitamin D, our MR analysis, with substantial study power (>80% power to detect an association with an odds ratio >1.2 for per standard deviation increase of log-transformed 25[OH]D), was unable to support an interpretation of causal association. Conclusions We investigated the putative causal effects of vitamin D on multiple health outcomes in a White population. We did not support a causal effect on any of the disease outcomes tested. However, we cannot exclude small causal effects or effects on outcomes that we did not have enough power to explore due to the small number of cases.
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Chang, Y. J., L. C. Wang, C. K. Chen, C. I. Hsieh, and C. F. Kuo. "OP0193 EVALUATION OF THE CAUSAL EFFECTS BETWEEN GOUT AND HYPERTENSION: A MENDELIAN RANDOMIZATION STUDY." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 116–17. http://dx.doi.org/10.1136/annrheumdis-2021-eular.2061.
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Background:Gout is the most common inflammatory arthritis worldwide associated with comorbidities that may impair well-being and reduce longevity. Epidemiological evidence generally supports that gout patients are at high risk of hypertension. However, the causality between gout and hypertension is uncertain since confounding and other types of bias are difficult to contain in the observational study.Objectives:To test the causal link between gout and hypertension using a Mendelian Randomization (MR) analysis.Methods:A mendelian randomization analysis was conducted using individual patient data from the Taiwan Biobank featured 2452 individuals with gout and 66527 controls. We selected 12 SNPs as instrumental variables (IVs) with p-values < 5 × 10−8 and the linkage disequilibrium (LD) R2 value less than 0.8. We conducted traditional MR analysis using the inversed weighted variance (IVW) and median methods with different settings as the primary analysis. Further IV assumption-free methods, the MR-Egger methods [1], Causal Analysis Using Summary Effect Estimates (CAUSE) model [2], and structural equation modeling (SEM) [3,4] were also performed as a sensitivity analysis.Results:The prevalence of hypertension was 0.15% (n = 9549) in the cohort. Table 1 shows causal effect estimates between gout and hypertension using different methods. The average causal effect β is estimated at 0.09 and the corresponding odds ratio (OR) at 1.09 using traditional methods across different settings. Similar estimates were observed in the MR-Egger method, SEM model, and the CAUSE model, demonstrating the robustness of the causal association between gout and hypertension considering pleiotropic effects (Table 1). Furthermore, the model fit of the hypothesized SEM model is excellent with a comparative fit index of 0.978 and Tucker-Lewis index of 0.968. The SEM model explains at least 32.70% variance of hypertension and 32.6% variance of gout (Figure 1).Table 1.Estimate the causal effect of MR analysis and sensitivity analysisMethodCausal effect estimate β95% lower bound95% upper boundp-valuePrimary analysisIVW with fixed effect, first order0.09000.0656a0.1145a<10-5IVW with fixed effect, second order0.08950.0647a0.1143a<10-5IVW with random effect, first order0.09000.0656a0.1145a<10-5IVW with random effect, second order0.08950.0647a0.1143a<10-5Median method, simple0.09890.0645a0.1332a<10-5Median method, weighted0.09020.0583a0.1220a<10-5Median method, penalized0.09020.0583a0.1220a<10-5Sensitivity analysisMR-Egger0.07860.0132a0.1440a0.0183SEM0.05190.0481a0.0697a<10-5CAUSE model0.07640.0176b0.1349b<10-5* a represents the 95% confidence interval; b represents the 95% credible interval.** For the random effect model, if the estimated residual standard error is less than 1, then the MendelianRandomization package will automatically set the value of residual standard error into 1.Figure 1.Pathway analysis for SEM study assessing the relationships between gout and hypertension.Conclusion:These results strongly suggest that the association between gout and hypertension has a causal basis.References:[1]Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015 Apr; 44(2):512-525.[2]Morrison J, Knoblauch N, Marcus JH, Stephens M, He X. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet. 2020 Jul; 52(7):740-747.[3]Streiner DL. Building a better model: an introduction to structural equation modelling. Can J Psychiatry. 2006 Apr; 51(5):317-324.[4]Stephen B, Rhian MD, Adam SB, Simon GT, and the EPIC-InterAct. Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int J Epidemiol. 2015 Apr; 44(2): 484-495.Disclosure of Interests:None declared
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Evans, David M., Gunn-Helen Moen, Liang-Dar Hwang, Debbie A. Lawlor, and Nicole M. Warrington. "Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization." International Journal of Epidemiology 48, no. 3 (February 27, 2019): 861–75. http://dx.doi.org/10.1093/ije/dyz019.
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Abstract Background There is considerable interest in estimating the causal effect of a range of maternal environmental exposures on offspring health-related outcomes. Previous attempts to do this using Mendelian randomization methodologies have been hampered by the paucity of epidemiological cohorts with large numbers of genotyped mother–offspring pairs. Methods We describe a new statistical model that we have created which can be used to estimate the effect of maternal genotypes on offspring outcomes conditional on offspring genotype, using both individual-level and summary-results data, even when the extent of sample overlap is unknown. Results We describe how the estimates obtained from our method can subsequently be used in large-scale two-sample Mendelian randomization studies to investigate the causal effect of maternal environmental exposures on offspring outcomes. This includes studies that aim to assess the causal effect of in utero exposures related to fetal growth restriction on future risk of disease in offspring. We illustrate our framework using examples related to offspring birthweight and cardiometabolic disease, although the general principles we espouse are relevant for many other offspring phenotypes. Conclusions We advocate for the establishment of large-scale international genetics consortia that are focused on the identification of maternal genetic effects and committed to the public sharing of genome-wide summary-results data from such efforts. This information will facilitate the application of powerful two-sample Mendelian randomization studies of maternal exposures and offspring outcomes.
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Javidnia, Sara, Stephen Cranwell, Stefanie H. Mueller, Colin Selman, Jennifer M. A. Tullet, Karoline Kuchenbaecker, and Nazif Alic. "Mendelian randomization analyses implicate biogenesis of translation machinery in human aging." Genome Research 32, no. 2 (January 25, 2022): 258–65. http://dx.doi.org/10.1101/gr.275636.121.
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Reduced provision of protein translation machinery promotes healthy aging in a number of animal models. In humans, however, inborn impairments in translation machinery are a known cause of several developmental disorders, collectively termed ribosomopathies. Here, we use casual inference approaches in genetic epidemiology to investigate whether adult, tissue-specific biogenesis of translation machinery drives human aging. We assess naturally occurring variation in the expression of genes encoding subunits specific to the two RNA polymerases (Pols) that transcribe ribosomal and transfer RNAs, namely Pol I and III, and the variation in expression of ribosomal protein (RP) genes, using Mendelian randomization. We find each causally associated with human longevity (β = −0.15 ± 0.047, P = 9.6 × 10−4, q = 0.015; β = −0.13 ± 0.040, P = 1.4 × 10−3, q = 0.023; β = −0.048 ± 0.016, P = 3.5 × 10−3, q = 0.056, respectively), and this does not appear to be mediated by altered susceptibility to a single disease. We find that reduced expression of Pol III, RPs, or Pol I promotes longevity from different organs, namely visceral adipose, liver, and skeletal muscle, echoing the tissue specificity of ribosomopathies. Our study shows the utility of leveraging genetic variation in expression to elucidate how essential cellular processes impact human aging. The findings extend the evolutionary conservation of protein synthesis as a critical process that drives animal aging to include humans.
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Zheng, Yan, Tao Huang, Tiange Wang, Zhendong Mei, Zhonghan Sun, Tao Zhang, Christina Ellervik, et al. "Mendelian randomization analysis does not support causal associations of birth weight with hypertension risk and blood pressure in adulthood." European Journal of Epidemiology 35, no. 7 (May 7, 2020): 685–97. http://dx.doi.org/10.1007/s10654-020-00638-z.
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Wang, Xiaoliang, James Y. Dai, Demetrius Albanes, Volker Arndt, Sonja I. Berndt, Stéphane Bézieau, Hermann Brenner, et al. "Mendelian randomization analysis of C-reactive protein on colorectal cancer risk." International Journal of Epidemiology 48, no. 3 (November 21, 2018): 767–80. http://dx.doi.org/10.1093/ije/dyy244.
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Abstract Background Chronic inflammation is a risk factor for colorectal cancer (CRC). Circulating C-reactive protein (CRP) is also moderately associated with CRC risk. However, observational studies are susceptible to unmeasured confounding or reverse causality. Using genetic risk variants as instrumental variables, we investigated the causal relationship between genetically elevated CRP concentration and CRC risk, using a Mendelian randomization approach. Methods Individual-level data from 30 480 CRC cases and 22 844 controls from 33 participating studies in three international consortia were used: the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), the Colorectal Transdisciplinary Study (CORECT) and the Colon Cancer Family Registry (CCFR). As instrumental variables, we included 19 single nucleotide polymorphisms (SNPs) previously associated with CRP concentration. The SNP-CRC associations were estimated using a logistic regression model adjusted for age, sex, principal components and genotyping phases. An inverse-variance weighted method was applied to estimate the causal effect of CRP on CRC risk. Results Among the 19 CRP-associated SNPs, rs1260326 and rs6734238 were significantly associated with CRC risk (P = 7.5 × 10–4, and P = 0.003, respectively). A genetically predicted one-unit increase in the log-transformed CRP concentrations (mg/l) was not associated with increased risk of CRC [odds ratio (OR) = 1.04; 95% confidence interval (CI): 0.97, 1.12; P = 0.256). No evidence of association was observed in subgroup analyses stratified by other risk factors. Conclusions In spite of adequate statistical power to detect moderate association, we found genetically elevated CRP concentration was not associated with increased risk of CRC among individuals of European ancestry. Our findings suggested that circulating CRP is unlikely to be a causal factor in CRC development.
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Bowden, Jack, George Davey Smith, Philip C. Haycock, and Stephen Burgess. "Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator." Genetic Epidemiology 40, no. 4 (April 7, 2016): 304–14. http://dx.doi.org/10.1002/gepi.21965.
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Gleason, Kevin J., Fan Yang, and Lin S. Chen. "A robust two‐sample transcriptome‐wide Mendelian randomization method integrating GWAS with multi‐tissue eQTL summary statistics." Genetic Epidemiology 45, no. 4 (April 9, 2021): 353–71. http://dx.doi.org/10.1002/gepi.22380.
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Walton, Esther, Caroline L. Relton, and Doretta Caramaschi. "Using Openly Accessible Resources to Strengthen Causal Inference in Epigenetic Epidemiology of Neurodevelopment and Mental Health." Genes 10, no. 3 (March 1, 2019): 193. http://dx.doi.org/10.3390/genes10030193.
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The recent focus on the role of epigenetic mechanisms in mental health has led to several studies examining the association of epigenetic processes with psychiatric conditions and neurodevelopmental traits. Some studies suggest that epigenetic changes might be causal in the development of the psychiatric condition under investigation. However, other scenarios are possible, e.g., statistical confounding or reverse causation, making it particularly challenging to derive conclusions on causality. In the present review, we examine the evidence from human population studies for a possible role of epigenetic mechanisms in neurodevelopment and mental health and discuss methodological approaches on how to strengthen causal inference, including the need for replication, (quasi-)experimental approaches and Mendelian randomization. We signpost openly accessible resources (e.g., “MR-Base” “EWAS catalog” as well as tissue-specific methylation and gene expression databases) to aid the application of these approaches.
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Thanassoulis, George. "Mendelian Randomization: How Genetics Is Pushing the Boundaries of Epidemiology to Identify New Causes of Heart Disease." Canadian Journal of Cardiology 29, no. 1 (January 2013): 30–36. http://dx.doi.org/10.1016/j.cjca.2012.09.014.
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Mary, Sheon, Philipp Boder, Sandosh Padmanabhan, Martin W. McBride, Delyth Graham, Christian Delles, and Anna F. Dominiczak. "Role of Uromodulin in Salt-Sensitive Hypertension." Hypertension 79, no. 11 (November 2022): 2419–29. http://dx.doi.org/10.1161/hypertensionaha.122.19888.
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The exclusive expression of uromodulin in the kidneys has made it an intriguing protein in kidney and cardiovascular research. Genome-wide association studies discovered variants of uromodulin that are associated with chronic kidney diseases and hypertension. Urinary and circulating uromodulin levels reflect kidney and cardiovascular health as well as overall mortality. More recently, Mendelian randomization studies have shown that genetically driven levels of uromodulin have a causal and adverse effect on kidney function. On a mechanistic level, salt sensitivity is an important factor in the pathophysiology of hypertension, and uromodulin is involved in salt reabsorption via the NKCC2 (Na + -K + -2Cl − cotransporter) on epithelial cells of the ascending limb of loop of Henle. In this review, we provide an overview of the multifaceted physiology and pathophysiology of uromodulin including recent advances in its genetics; cellular trafficking; and mechanistic and clinical studies undertaken to understand the complex relationship between uromodulin, blood pressure, and kidney function. We focus on tubular sodium reabsorption as one of the best understood and pathophysiologically and clinically most important roles of uromodulin, which can lead to therapeutic interventions.
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Wang, Lu, Tao Zhang, Yafei Liu, Fang Tang, and Fuzhong Xue. "Association of Serum Uric Acid with Metabolic Syndrome and Its Components: A Mendelian Randomization Analysis." BioMed Research International 2020 (February 24, 2020): 1–7. http://dx.doi.org/10.1155/2020/6238693.
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Background. The role of uric acid on metabolic syndrome (MetS) has always been controversial. This study aims to explore associations between uric acid with MetS and its components in Chinese female health check-up population. Methods. 1381 subjects constituted the longitudinal health check-up cohort. Health examination and genotyping were performed. Unadjusted and adjusted observational analyses were implemented to evaluate observational associations between uric acid with MetS and its components. Mendelian randomization analysis was performed to estimate the causal effect using variation at rs11722228 (SLC2A9) as an instrument for uric acid. Results. An increase of 65% in risk of MetS per standard deviation increase in uric acid was found using unadjusted observational analyses. This association attenuated on adjustment for potential confounders. Similar patterns were found in the association analyses of uric acid with hyperglycemia, hypertension, and dyslipidemia. Neither by performing unadjusted nor adjusted analysis did we see evidence for association of uric acid on overweight and obesity. Mendelian randomization analyses showed no evidence of causal association between uric acid and MetS and MetS components. Conclusions. We found no causal evidence to support that increased serum uric acid is a causal risk factor for MetS or its components. Hence, there remains no strong evidence for the effeteness of undergoing urate-lowering therapy to prevent the onset of MetS or cardiovascular disease in health management.
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Bonilla, Carolina, and Lara Novaes Baccarini. "Genetic Epidemiology in Latin America: Identifying Strong Genetic Proxies for Complex Disease Risk Factors." Genes 11, no. 5 (May 4, 2020): 507. http://dx.doi.org/10.3390/genes11050507.
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Epidemiology seeks to determine the causal effects of exposures on outcomes related to the health and wellbeing of populations. Observational studies, one of the most commonly used designs in epidemiology, can be biased due to confounding and reverse causation, which makes it difficult to establish causal relationships. In recent times, genetically informed methods, like Mendelian randomization (MR), have been developed in an attempt to overcome these disadvantages. MR relies on the association of genetic variants with outcomes of interest, where the genetic variants are proxies or instruments for modifiable exposures. Because genotypes are sorted independently and at random at the time of conception, they are less prone to confounding and reverse causation. Implementation of MR depends on, among other things, a strong association of the genetic variants with the exposure, which has usually been defined via genome-wide association studies (GWAS). Because GWAS have been most often carried out in European populations, the limited identification of strong instruments in other populations poses a major problem for the application of MR in Latin America. We suggest potential solutions that can be realized with the resources at hand and others that will have to wait for increased funding and access to technology.
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Scannell Bryan, Molly, Tamar Sofer, Yasmin Mossavar-Rahmani, Bharat Thyagarajan, Donglin Zeng, Martha L. Daviglus, and Maria Argos. "Mendelian randomization of inorganic arsenic metabolism as a risk factor for hypertension- and diabetes-related traits among adults in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) cohort." International Journal of Epidemiology 48, no. 3 (March 30, 2019): 876–86. http://dx.doi.org/10.1093/ije/dyz046.
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Abstract Background Hypertension and diabetes have been associated with inefficient arsenic metabolism, primarily through studies undertaken in populations exposed through drinking water. Recently, rice has been recognized as a source of arsenic exposure, but it remains unclear whether populations with high rice consumption but no known water exposure are at risk for the health problems associated with inefficient arsenic metabolism. Methods The relationships between arsenic metabolism efficiency (% inorganic arsenic, % monomethylarsenate and % dimethylarsinate in urine) and three hypertension- and seven diabetes-related traits were estimated among 12 609 participants of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). A two-sample Mendelian randomization approach incorporated genotype-arsenic metabolism relationships from literature, and genotype-trait relationships from HCHS/SOL, with a mixed-effect linear model. Analyses were stratified by rice consumption and smoking. Results Among never smokers with high rice consumption, each percentage point increase in was associated with increases of 1.96 mmHg systolic blood pressure (P = 0.034) and 1.85 mmHg inorganic arsenic diastolic blood pressure (P = 0.003). Monomethylarsenate was associated with increased systolic (1.64 mmHg/percentage point increase; P = 0.021) and diastolic (1.33 mmHg/percentage point increase; P = 0.005) blood pressure. Dimethylarsinate, a marker of efficient metabolism, was associated with lower systolic (−0.92 mmHg/percentage point increase; P = 0.025) and diastolic (-0.79 mmHg/percentage point increase; P = 0.004) blood pressure. Among low rice consumers and ever smokers, the results were consistent with no association. Evidence for a relationship with diabetes was equivocal. Conclusions Less efficient arsenic metabolism was associated with increased blood pressure among never smokers with high rice consumption, suggesting that arsenic exposure through rice may contribute to high blood pressure in the Hispanic/Latino community.
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Sanderson, Eleanor, Tom G. Richardson, Tim T. Morris, Kate Tilling, and George Davey Smith. "Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization." PLOS Genetics 18, no. 7 (July 18, 2022): e1010290. http://dx.doi.org/10.1371/journal.pgen.1010290.
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Mendelian Randomisation (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants as instrumental variables (IVs) for the exposure. The effect estimates obtained from MR studies are often interpreted as the lifetime effect of the exposure in question. However, the causal effects of some exposures are thought to vary throughout an individual’s lifetime with periods during which an exposure has a greater effect on a particular outcome. Multivariable MR (MVMR) is an extension of MR that allows for multiple, potentially highly related, exposures to be included in an MR estimation. MVMR estimates the direct effect of each exposure on the outcome conditional on all the other exposures included in the estimation. We explore the use of MVMR to estimate the direct effect of a single exposure at different time points in an individual’s lifetime on an outcome. We use simulations to illustrate the interpretation of the results from such analyses and the key assumptions required. We show that causal effects at different time periods can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure measured at those time periods varies. However, this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome. Prior knowledge regarding the biological basis of exposure trajectories can help interpretation. We illustrate the method through estimation of the causal effects of childhood and adult BMI on C-Reactive protein and smoking behaviour.
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Hwang, Liang-Dar, Deborah A. Lawlor, Rachel M. Freathy, David M. Evans, and Nicole M. Warrington. "Using a two-sample Mendelian randomization design to investigate a possible causal effect of maternal lipid concentrations on offspring birth weight." International Journal of Epidemiology 48, no. 5 (July 23, 2019): 1457–67. http://dx.doi.org/10.1093/ije/dyz160.
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Abstract Background The intrauterine environment is critical for fetal growth and development. However, observational associations between maternal gestational lipid concentrations and offspring birth weight (BW) have been inconsistent and ascertaining causality is challenging. Methods We used a novel two-sample Mendelian randomization (MR) approach to estimate the causal effect of maternal gestational high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglyceride concentrations on offspring BW. Single nucleotide polymorphisms (SNPs) associated with serum HDL-C, LDL-C and triglyceride concentrations identified in the Global Lipids Genetics Consortium genome-wide association study meta-analysis (n = 188 577 European-ancestry individuals; sample 1) were selected as instrumental variables. The effects of these SNPs on offspring BW were estimated using a structural equation model in the UK Biobank and Early Growth Genetics consortium (n = 230 069 European-ancestry individuals; sample 2) that enabled partitioning of the genetic associations into maternal- (intrauterine) and fetal-specific effects. Results We found no evidence for a causal effect of maternal gestational HDL-C, LDL-C or triglyceride concentrations on offspring BW [standard deviation change in BW per standard deviation higher in HDL-C = −0.005 (95% confidence interval: −0.039, 0.029), LDL-C = 0.014 (−0.017, 0.045), and triglycerides = 0.014 (−0.025, 0.052)]. Conclusions Our findings suggest that maternal gestational HDL-C, LDL-C and triglyceride concentrations play a limited role in determining offspring BW. However, we cannot comment on the impact of these and other lipid fractions on fetal development more generally. Our study illustrates the power and flexibility of two-sample MR in assessing the causal effect of maternal environmental exposures on offspring outcomes.
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Kim, Min Seo, Won Jun Kim, Amit V. Khera, Jong Yeob Kim, Dong Keon Yon, Seung Won Lee, Jae Il Shin, and Hong-Hee Won. "Association between adiposity and cardiovascular outcomes: an umbrella review and meta-analysis of observational and Mendelian randomization studies." European Heart Journal 42, no. 34 (August 1, 2021): 3388–403. http://dx.doi.org/10.1093/eurheartj/ehab454.
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Abstract Aims The aim of this study was to investigate the causal relationship and evidence of an association between increased adiposity and the risk of incident cardiovascular disease (CVD) events or mortality. Methods and results Observational (informing association) and Mendelian randomization (MR) (informing causality) studies were assessed to gather mutually complementary insights and elucidate perplexing epidemiological relationships. Systematic reviews and meta-analyses of observational and MR studies that were published until January 2021 and evaluated the association between obesity-related indices and CVD risk were searched. Twelve systematic reviews with 53 meta-analyses results (including over 501 cohort studies) and 12 MR studies were included in the analysis. A body mass index (BMI) increase was associated with higher risks of coronary heart disease, heart failure, atrial fibrillation, all-cause stroke, haemorrhagic stroke, ischaemic stroke, hypertension, aortic valve stenosis, pulmonary embolism, and venous thrombo-embolism. The MR study results demonstrated a causal effect of obesity on all indices but stroke. The CVD risk increase for every 5 kg/m2 increase in BMI varied from 10% [relative risk (RR) 1.10; 95% confidence interval (CI) 1.01–1.21; certainty of evidence, low] for haemorrhagic stroke to 49% (RR 1.49; 95% CI 1.40–1.60; certainty of evidence, high) for hypertension. The all-cause and CVD-specific mortality risks increased with adiposity in cohorts, but the MR studies demonstrated no causal effect of adiposity on all-cause mortality. Conclusion High adiposity is associated with increased CVD risk despite divergent evidence gradients. Adiposity was a causal risk factor for CVD except all-cause mortality and stroke. Half (49%; 26/53) of the associations were supported by high-level evidence. The associations were consistent between sexes and across global regions. This study provides guidance on how to integrate evidence from observational (association) and genetics-driven (causation) studies accumulated to date, to enable a more reliable interpretation of epidemiological relationships.
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Ryan, David K., Ville Karhunen, Bowen Su, Matthew Traylor, Tom G. Richardson, Stephen Burgess, Ioanna Tzoulaki, and Dipender Gill. "Genetic Evidence for Protective Effects of Angiotensin-Converting Enzyme Against Alzheimer Disease But Not Other Neurodegenerative Diseases in European Populations." Neurology Genetics 8, no. 5 (August 29, 2022): e200014. http://dx.doi.org/10.1212/nxg.0000000000200014.
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Background and ObjectivesAngiotensin-converting enzyme (ACE) inhibitors are a commonly prescribed class of medication used to treat heart failure, hypertension, and chronic kidney disease. However, previous observational studies have shown conflicting directions of associations between ACE inhibitors and risk of Alzheimer disease. Genetic evidence has supported a protective effect of cerebral ACE against Alzheimer disease (AD). However, it is unclear whether this effect is mediated through blood pressure and extends to other neurodegenerative diseases.MethodsWe performed genetic colocalization investigating an effect of cortical ACE expression on AD risk in people of European ancestry. We further investigated whether any effect of ACE expression on AD risk is mediated through changes in blood pressure and whether effects extend to Parkinson disease, small-vessel disease, or cognitive function in a Mendelian randomization paradigm.ResultsThere was genetic evidence supporting a protective effect of cortical ACE expression on AD risk in people of European ancestry. Although higher cortical ACE expression was associated with higher blood pressure, there was no strong evidence to support that its association with AD was mediated through blood pressure nor that ACE expression affected risk of other neurodegenerative traits.DiscussionGenetic evidence supports protective effects of cerebral ACE expression on AD, but not other neurodegenerative outcomes in people of European ancestry. Further work is required to investigate whether therapeutic inhibition of ACE increases risk of Alzheimer disease.
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Perry, Benjamin I., Stephen Burgess, Hannah J. Jones, Stan Zammit, Rachel Upthegrove, Amy M. Mason, Felix R. Day, et al. "The potential shared role of inflammation in insulin resistance and schizophrenia: A bidirectional two-sample mendelian randomization study." PLOS Medicine 18, no. 3 (March 12, 2021): e1003455. http://dx.doi.org/10.1371/journal.pmed.1003455.
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Background Insulin resistance predisposes to cardiometabolic disorders, which are commonly comorbid with schizophrenia and are key contributors to the significant excess mortality in schizophrenia. Mechanisms for the comorbidity remain unclear, but observational studies have implicated inflammation in both schizophrenia and cardiometabolic disorders separately. We aimed to examine whether there is genetic evidence that insulin resistance and 7 related cardiometabolic traits may be causally associated with schizophrenia, and whether evidence supports inflammation as a common mechanism for cardiometabolic disorders and schizophrenia. Methods and findings We used summary data from genome-wide association studies of mostly European adults from large consortia (Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) featuring up to 108,557 participants; Diabetes Genetics Replication And Meta-analysis (DIAGRAM) featuring up to 435,387 participants; Global Lipids Genetics Consortium (GLGC) featuring up to 173,082 participants; Genetic Investigation of Anthropometric Traits (GIANT) featuring up to 339,224 participants; Psychiatric Genomics Consortium (PGC) featuring up to 105,318 participants; and Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium featuring up to 204,402 participants). We conducted two-sample uni- and multivariable mendelian randomization (MR) analysis to test whether (i) 10 cardiometabolic traits (fasting insulin, high-density lipoprotein and triglycerides representing an insulin resistance phenotype, and 7 related cardiometabolic traits: low-density lipoprotein, fasting plasma glucose, glycated haemoglobin, leptin, body mass index, glucose tolerance, and type 2 diabetes) could be causally associated with schizophrenia; and (ii) inflammation could be a shared mechanism for these phenotypes. We conducted a detailed set of sensitivity analyses to test the assumptions for a valid MR analysis. We did not find statistically significant evidence in support of a causal relationship between cardiometabolic traits and schizophrenia, or vice versa. However, we report that a genetically predicted inflammation-related insulin resistance phenotype (raised fasting insulin (raised fasting insulin (Wald ratio OR = 2.95, 95% C.I, 1.38–6.34, Holm-Bonferroni corrected p-value (p) = 0.035) and lower high-density lipoprotein (Wald ratio OR = 0.55, 95% C.I., 0.36–0.84; p = 0.035)) was associated with schizophrenia. Evidence for these associations attenuated to the null in multivariable MR analyses after adjusting for C-reactive protein, an archetypal inflammatory marker: (fasting insulin Wald ratio OR = 1.02, 95% C.I, 0.37–2.78, p = 0.975), high-density lipoprotein (Wald ratio OR = 1.00, 95% C.I., 0.85–1.16; p = 0.849), suggesting that the associations could be fully explained by inflammation. One potential limitation of the study is that the full range of gene products from the genetic variants we used as proxies for the exposures is unknown, and so we are unable to comment on potential biological mechanisms of association other than inflammation, which may also be relevant. Conclusions Our findings support a role for inflammation as a common cause for insulin resistance and schizophrenia, which may at least partly explain why the traits commonly co-occur in clinical practice. Inflammation and immune pathways may represent novel therapeutic targets for the prevention or treatment of schizophrenia and comorbid insulin resistance. Future work is needed to understand how inflammation may contribute to the risk of schizophrenia and insulin resistance.
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Wade, Kaitlin H., and Lindsay J. Hall. "Improving causality in microbiome research: can human genetic epidemiology help?" Wellcome Open Research 4 (December 10, 2019): 199. http://dx.doi.org/10.12688/wellcomeopenres.15628.1.
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Evidence supports associations between human gut microbiome variation and multiple health outcomes and diseases. Despite compelling results from in vivo and in vitro models, few findings have been translated into an understanding of modifiable causal relationships. Furthermore, epidemiological studies have been unconvincing in their ability to offer causal evidence due to their observational nature, where confounding by lifestyle and behavioural factors, reverse causation and bias are important limitations. Whilst randomized controlled trials have made steps towards understanding the causal role played by the gut microbiome in disease, they are expensive and time-consuming. This evidence that has not been translated between model systems impedes opportunities for harnessing the gut microbiome for improving population health. Therefore, there is a need for alternative approaches to interrogate causality in the context of gut microbiome research. The integration of human genetics within population health sciences have proved successful in facilitating improved causal inference (e.g., with Mendelian randomization [MR] studies) and characterising inherited disease susceptibility. MR is an established method that employs human genetic variation as natural “proxies” for clinically relevant (and ideally modifiable) traits to improve causality in observational associations between those traits and health outcomes. Here, we focus and discuss the utility of MR within the context of human gut microbiome research, review studies that have used this method and consider the strengths, limitations and challenges facing this research. Specifically, we highlight the requirements for careful examination and interpretation of derived causal estimates and host (i.e., human) genetic effects themselves, triangulation across multiple study designs and inter-disciplinary collaborations. Meeting these requirements will help support or challenge causality of the role played by the gut microbiome on human health to develop new, targeted therapies to alleviate disease symptoms to ultimately improve lives and promote good health.
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Wade, Kaitlin H., and Lindsay J. Hall. "Improving causality in microbiome research: can human genetic epidemiology help?" Wellcome Open Research 4 (April 2, 2020): 199. http://dx.doi.org/10.12688/wellcomeopenres.15628.2.
Full textAbstract:
Evidence supports associations between human gut microbiome variation and multiple health outcomes and diseases. Despite compelling results from in vivo and in vitro models, few findings have been translated into an understanding of modifiable causal relationships. Furthermore, epidemiological studies have been unconvincing in their ability to offer causal evidence due to their observational nature, where confounding by lifestyle and behavioural factors, reverse causation and bias are important limitations. Whilst randomized controlled trials have made steps towards understanding the causal role played by the gut microbiome in disease, they are expensive and time-consuming. This evidence that has not been translated between model systems impedes opportunities for harnessing the gut microbiome for improving population health. Therefore, there is a need for alternative approaches to interrogate causality in the context of gut microbiome research. The integration of human genetics within population health sciences have proved successful in facilitating improved causal inference (e.g., with Mendelian randomization [MR] studies) and characterising inherited disease susceptibility. MR is an established method that employs human genetic variation as natural “proxies” for clinically relevant (and ideally modifiable) traits to improve causality in observational associations between those traits and health outcomes. Here, we focus and discuss the utility of MR within the context of human gut microbiome research, review studies that have used this method and consider the strengths, limitations and challenges facing this research. Specifically, we highlight the requirements for careful examination and interpretation of derived causal estimates and host (i.e., human) genetic effects themselves, triangulation across multiple study designs and inter-disciplinary collaborations. Meeting these requirements will help support or challenge causality of the role played by the gut microbiome on human health to develop new, targeted therapies to alleviate disease symptoms to ultimately improve lives and promote good health.
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Wade, Kaitlin H., and Lindsay J. Hall. "Improving causality in microbiome research: can human genetic epidemiology help?" Wellcome Open Research 4 (April 24, 2020): 199. http://dx.doi.org/10.12688/wellcomeopenres.15628.3.
Full textAbstract:
Evidence supports associations between human gut microbiome variation and multiple health outcomes and diseases. Despite compelling results from in vivo and in vitro models, few findings have been translated into an understanding of modifiable causal relationships. Furthermore, epidemiological studies have been unconvincing in their ability to offer causal evidence due to their observational nature, where confounding by lifestyle and behavioural factors, reverse causation and bias are important limitations. Whilst randomized controlled trials have made steps towards understanding the causal role played by the gut microbiome in disease, they are expensive and time-consuming. This evidence that has not been translated between model systems impedes opportunities for harnessing the gut microbiome for improving population health. Therefore, there is a need for alternative approaches to interrogate causality in the context of gut microbiome research. The integration of human genetics within population health sciences have proved successful in facilitating improved causal inference (e.g., with Mendelian randomization [MR] studies) and characterising inherited disease susceptibility. MR is an established method that employs human genetic variation as natural “proxies” for clinically relevant (and ideally modifiable) traits to improve causality in observational associations between those traits and health outcomes. Here, we focus and discuss the utility of MR within the context of human gut microbiome research, review studies that have used this method and consider the strengths, limitations and challenges facing this research. Specifically, we highlight the requirements for careful examination and interpretation of derived causal estimates and host (i.e., human) genetic effects themselves, triangulation across multiple study designs and inter-disciplinary collaborations. Meeting these requirements will help support or challenge causality of the role played by the gut microbiome on human health to develop new, targeted therapies to alleviate disease symptoms to ultimately improve lives and promote good health.
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Raghavan, Sridharan, Jie Huang, Catherine Tcheandjieu, Jennifer E. Huffman, Elizabeth Litkowski, Chang Liu, Yuk-Lam A. Ho, et al. "A multi-population phenome-wide association study of genetically-predicted height in the Million Veteran Program." PLOS Genetics 18, no. 6 (June 2, 2022): e1010193. http://dx.doi.org/10.1371/journal.pgen.1010193.
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Background Height has been associated with many clinical traits but whether such associations are causal versus secondary to confounding remains unclear in many cases. To systematically examine this question, we performed a Mendelian Randomization-Phenome-wide association study (MR-PheWAS) using clinical and genetic data from a national healthcare system biobank. Methods and findings Analyses were performed using data from the US Veterans Affairs (VA) Million Veteran Program in non-Hispanic White (EA, n = 222,300) and non-Hispanic Black (AA, n = 58,151) adults in the US. We estimated height genetic risk based on 3290 height-associated variants from a recent European-ancestry genome-wide meta-analysis. We compared associations of measured and genetically-predicted height with phenome-wide traits derived from the VA electronic health record, adjusting for age, sex, and genetic principal components. We found 345 clinical traits associated with measured height in EA and an additional 17 in AA. Of these, 127 were associated with genetically-predicted height at phenome-wide significance in EA and 2 in AA. These associations were largely independent from body mass index. We confirmed several previously described MR associations between height and cardiovascular disease traits such as hypertension, hyperlipidemia, coronary heart disease (CHD), and atrial fibrillation, and further uncovered MR associations with venous circulatory disorders and peripheral neuropathy in the presence and absence of diabetes. As a number of traits associated with genetically-predicted height frequently co-occur with CHD, we evaluated effect modification by CHD status of genetically-predicted height associations with risk factors for and complications of CHD. We found modification of effects of MR associations by CHD status for atrial fibrillation/flutter but not for hypertension, hyperlipidemia, or venous circulatory disorders. Conclusions We conclude that height may be an unrecognized but biologically plausible risk factor for several common conditions in adults. However, more studies are needed to reliably exclude horizontal pleiotropy as a driving force behind at least some of the MR associations observed in this study.
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Hemminki, Kari, and Asta Försti. "Epidemiology of Amyloidosis and Genetic Pathways to Diagnosis and Typing." Hemato 2, no. 3 (July 13, 2021): 429–40. http://dx.doi.org/10.3390/hemato2030027.
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We reviewed our studies on epidemiology and germline genetics of amyloidosis. In epidemiology, we considered both hereditary and non-hereditary amyloidosis. As the source of data, we used the nationwide Swedish hospital discharge register. We estimated the incidence of hereditary ATTR amyloidosis, for which Sweden is a global endemic area, at 2/million. Surprisingly, the disease was also endemic within Sweden; the incidence in the province with the highest incidence was 100 times higher than in the rest of Sweden. Risk of non-Hodgkin lymphoma increased five-fold in the affected individuals. Among non-hereditary amyloidosis, the incidence for AL amyloidosis (abbreviated as AL) was estimated at 3.2/million, with a median survival time of 3 years. Secondary systemic amyloidosis (most likely AA amyloidosis) showed an incidence of 1.15/million for combined sexes. The female rate was two times higher than the male rate, probably relating to the higher female prevalence of rheumatoid arthritis. The median survival time was 4 years. We also identified patients who likely had familial autoinflammatory disease, characterized by early onset and immigrant background from the Eastern Mediterranean area. Young Syrian descendants had the highest incidence rate, which was over 500 times higher than that in individuals with Swedish parents. Germline genetics focused on AL on which we carried out a genome-wide association study (GWAS) in three AL cohorts (N = 1129) from Germany, UK, and Italy. Single nucleotide polymorphisms (SNPs) at 10 loci showed evidence of an association at p < 10−5; some of these were previously documented to influence multiple myeloma (MM) risk, including the SNP at the IRF4 binding site. In AL, SNP rs9344 at the splice site of cyclin D1, influencing translocation (11;14), reached the highest significance, p = 7.80 × 10−11; the SNP was only marginally significant in MM. The locus close to gene SMARCD3, involved in chromatin remodeling, was also significant. These data provide evidence for common genetic susceptibility to AL and MM. We continued by analyzing genetic associations in nine clinical profiles, characterized by organ involvement or Ig profiles. The light chain only (LCO) profile associated with the SNP at the splice site of cyclin D1 with p = 1.99 × 10−12. Even for the other profiles, distinct genetic associations were found. It was concluded that the strong association of rs9344 with LCO and t(11;14) amyloidosis offer attractive mechanistic clues to AL causation. Mendelian randomization analysis identified associations of AL with increased blood monocyte counts and the tumor necrosis factor receptor superfamily member 17 (TNFRSF17 alias BCMA) protein. Two other associations with the TNFRSF members were found. We discuss the corollaries of the findings with the recent success of treating t(11;14) AL with a novel drug venetoclax, and the application of BCMA as the common target of plasma cell immunotherapies.
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Li, Qingqin S., Chao Tian, David Hinds, and Guy R. Seabrook. "The association of clinical phenotypes to known AD/FTD genetic risk loci and their inter-relationship." PLOS ONE 15, no. 11 (November 5, 2020): e0241552. http://dx.doi.org/10.1371/journal.pone.0241552.
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To elucidate how variants in genetic risk loci previously implicated in Alzheimer’s Disease (AD) and/or frontotemporal dementia (FTD) contribute to expression of disease phenotypes, a phenome-wide association study was performed in two waves. In the first wave, we explored clinical traits associated with thirteen genetic variants previously reported to be linked to disease risk using both the 23andMe and UKB cohorts. We tested 30 additional AD variants in UKB cohort only in the second wave. APOE variants defining ε2/ε3/ε4 alleles and rs646776 were identified to be significantly associated with metabolic/cardiovascular and longevity traits. APOE variants were also significantly associated with neurological traits. ABI3 variant rs28394864 was significantly associated with cardiovascular (e.g. (hypertension, ischemic heart disease, coronary atherosclerosis, angina) and immune-related trait asthma. Both APOE variants and CLU variant were significantly associated with nearsightedness. HLA- DRB1 variant was associated with diseases with immune-related traits. Additionally, variants from 10+ AD genes (BZRAP1-AS1, ADAMTS4, ADAM10, APH1B, SCIMP, ABI3, SPPL2A, ZNF232, GRN, CD2AP, and CD33) were associated with hematological measurements such as white blood cell (leukocyte) count, monocyte count, neutrophill count, platelet count, and/or mean platelet (thrombocyte) volume (an autoimmune disease biomarker). Many of these genes are expressed specifically in microglia. The associations of ABI3 variant with cardiovascular and immune-related traits are one of the novel findings from this study. Taken together, it is evidenced that at least some AD and FTD variants are associated with multiple clinical phenotypes and not just dementia. These findings were discussed in the context of causal relationship versus pleiotropy via Mendelian randomization analysis.
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Zeng, Lingyao, Ioanna Ntalla, Thorsten Kessler, Adnan Kastrati, Jeanette Erdmann, John Danesh, Hugh Watkins, Nilesh J. Samani, Panos Deloukas, and Heribert Schunkert. "Genetically modulated educational attainment and coronary disease risk." European Heart Journal 40, no. 29 (June 6, 2019): 2413–20. http://dx.doi.org/10.1093/eurheartj/ehz328.
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Abstract Aims Genetic disposition and lifestyle factors are understood as independent components underlying the risk of multiple diseases. In this study, we aim to investigate the interplay between genetics, educational attainment—an important denominator of lifestyle—and coronary artery disease (CAD) risk. Methods and results Based on the effect sizes of 74 genetic variants associated with educational attainment, we calculated a ‘genetic education score’ in 13 080 cases and 14 471 controls and observed an inverse correlation between the score and risk of CAD [P = 1.52 × 10−8; odds ratio (OR) 0.79, 95% confidence interval (CI) 0.73–0.85 for the higher compared with the lowest score quintile]. We replicated in 146 514 individuals from UK Biobank (P = 1.85 × 10−6) and also found strong associations between the ‘genetic education score’ with ‘modifiable’ risk factors including smoking (P = 5.36 × 10−23), body mass index (BMI) (P = 1.66 × 10−30), and hypertension (P = 3.86 × 10−8). Interestingly, these associations were only modestly attenuated by adjustment for years spent in school. In contrast, a model adjusting for BMI and smoking abolished the association signal between the ‘genetic education score’ and CAD risk suggesting an intermediary role of these two risk factors. Mendelian randomization analyses performed with summary statistics from large genome-wide meta-analyses and sensitivity analysis using 1271 variants affecting educational attainment (OR 0.68 for the higher compared with the lowest score quintile; 95% CI 0.63–0.74; P = 3.99 × 10−21) further strengthened these findings. Conclusion Genetic variants known to affect educational attainment may have implications for a health-conscious lifestyle later in life and subsequently affect the risk of CAD.
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McCormick, N., C. Yokose, L. Lu, A. Joshi, and H. Choi. "OP0005 DIETARY HYPERINSULINEMIC POTENTIAL AND RISK OF INCIDENT GOUT: 3 PROSPECTIVE COHORT STUDIES OF US MEN AND WOMEN." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 2.2–2. http://dx.doi.org/10.1136/annrheumdis-2021-eular.719.
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Background:Gout and the metabolic (insulin resistance) syndrome frequently coexist. Intravenous insulin has been shown to raise serum urate (SU) levels in physiologic studies and a Mendelian Randomization study also showed a causal role of insulin on the risk of gout. However, it is unknown whether habitual hyperinsulinemic dietary intake confers gout risk.Objectives:Prospectively examine the relation between two distinct insulin-related dietary indices and risk of incident gout in three large cohorts of US women and men over 30 years.Methods:We studied 164,090 women from Nurses Health Study I (1986-2016) and II (1989-2017) and 40,598 men from Health Professionals Follow-up Study (1986-2016), who were free of gout at baseline. Dietary intake and covariates were assessed by validated questionnaires every 4 years. Insulinemic potential of diet was evaluated using 1) food-based empirical dietary index for hyperinsulinemia (EDIH) score that was pre-defined based on circulating C-peptide levels1 and reflects insulin resistance;2 and 2) dietary insulin index (DII), which reflects transient, postprandial insulin secretion.2 We assigned EDIH and DII scores for each participant, adjusted for total energy intake, and prospectively examined the association between scores and incident gout (using ACR survey criteria for gout3), adjusting for potential confounders.Results:We ascertained 2,874 incident gout cases over 5,124,490 person-years of follow-up. In pooled multivariable-adjusted analyses, those in the highest EDIH quintile had 1.76-times (95% CI: 1.56 to 1.99) higher gout risk, compared with the lowest (Table 1). This attenuated with further adjustment for BMI (a likely causal intermediate) but remained positive (RR 1.30, 1.15 to 1.48). DII scores were inversely associated with gout risk (RR 0.66, 0.58 to 0.74) (Table 1).Table 1.Risk Ratio (95% CI) of Gout According to Quintiles of Insulin-Related Dietary IndexEDIH (measure of insulin resistance)Q1:lowest circulating insulin levelsQ2Q3Q4Q5:highest circulating insulin levelsP for trendN cases430482598631733Person-years1,025,1291,025,2851,025,5741,025,3011,023,651Age-adjusted RR1.00 (Ref)1.13 (1.00-1.29)1.43 (1.26-1.61)1.53 (1.36-1.73)1.85 (1.64-2.09)<.0001MV-Adjusted*RR1.00 (Ref)1.11 (0.98-1.27)1.39 (1.22-1.57)1.47 (1.30-1.67)1.76 (1.56-1.99)<.0001MV-Adjusted**RR (+ BMI)1.00 (Ref)1.03 (0.90-1.17)1.21 (1.06-1.37)1.21 (1.07-1.37)1.30 (1.15-1.48)<.0001Dietary Insulin Index (measure of transient, post-prandial secretion and sensitivity)Q1:lowest insulin sensitivityQ2Q3Q4Q5:greatest insulin sensitivityP for trendN cases783611527498455Person-years1,024,7631,025,7301,025,0751,025,5381,023,834Age-adjusted RR1.00 (Ref)0.79 (0.71-0.88)0.69 (0.62-0.77)0.65 (0.58-0.73)0.59 (0.53-0.66)<.0001MV-Adjusted*RR1.00 (Ref)0.79 (0.71-0.88)0.69 (0.62-0.77)0.66 (0.59-0.74)0.60 (0.53-0.67)<.0001MV-Adjusted**RR (+ BMI)1.00 (Ref)0.78 (0.70-0.87)0.69 (0.62-0.77)0.67 (0.60-0.75)0.66 (0.58-0.74)<.0001*Multivariable (MV) models adjusted for age (month), White race, smoking, menopause (women only), hormone use (women only), physical activity, history of hypertension, and diuretic use **MV + BMI models further adjusted for BMI (a likely causal intermediate)Conclusion:EDIH scores, reflecting chronic hyperinsulinemia (i.e., greater insulin resistance with reduced clearance), were positively associated with the risk of incident gout, even beyond the pathway through adiposity. Conversely, higher DII scores, which reflect short-term, postprandial elevations in insulin levels (and also greater insulin clearance and sensitivity) conferred a lower risk. This corroborates human physiologic experiments and Mendelian Randomization studies showing insulin resistance can increase SU levels by decreasing renal excretion of urate, and supports lowering insulinemic potential of diet as a strategy to reduce gout risk.References:[1]Tabung et al. PMID 27821188[2]Lee et al. PMID 32618519[3]Wallace et al. PMID 856219Disclosure of Interests:Natalie McCormick: None declared, Chio Yokose: None declared, Leo Lu: None declared, Amit Joshi: None declared, Hyon Choi Consultant of: Ironwood, Selecta, Horizon, Takeda, Kowa, Vaxart, Grant/research support from: Ironwood, Horizon
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Go, Tae-Hwa, Kyeong Im Kwak, Ji-Yun Jang, Minheui Yu, Hye Sim Kim, Jang Young Kim, Sang Baek Koh, and Dae Ryong Kang. "Inference of a causal relation between low-density lipoprotein cholesterol and hypertension using mendelian randomization analysis." Clinical Hypertension 27, no. 1 (February 26, 2021). http://dx.doi.org/10.1186/s40885-021-00162-6.
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Abstract Background It is known in some studies that higher the LDL-C, the greater the risk of developing cardiovascular disease. However, studies of the causal effects between LDL-C and hypertension are limited by their observational study design, and genetic epidemiology studies of associations between LDL-C and hypertension are lacking, as are studies using data for Koreans. In this study, we confirmed the causal effect of LDL-C on hypertension using Korean chip data. Method The epidemiology and genotype data were collected from the Korean Genome and Epidemiology Study conducted by the Korea National Institute of Health and covered 20,701 subjects. Single-nucleotide polymorphisms associated with LDL-C were selected (p-value < 5 × 10− 8) from the Global Lipids Genetics Consortium database, and Mendelian randomization analysis (MRA) was performed with counted genetic risk scores and weighted genetic risk scores (WGRSs) for 24 single-nucleotide polymorphisms. Result The assumptions for MRA were statistically confirmed, and WGRSs showed a strong association with LDL-C. Interestingly, while the relationship between LDL-C and hypertension was not statistically significant in the observational study, MRA study demonstrated that the risk of hypertension increased as LDL-C increased in both men and women. Conclusions The results of this study confirmed that the relationship between LDL-C and hypertension is greatly influenced by genetic information.
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Yuan, Shuai, Lijuan Wang, Jing Sun, Lili Yu, Xuan Zhou, Jie Yang, Yimin Zhu, et al. "Genetically predicted sex hormone levels and health outcomes: phenome-wide Mendelian randomization investigation." International Journal of Epidemiology, February 26, 2022. http://dx.doi.org/10.1093/ije/dyac036.
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Abstract Background Sex hormone-binding globulin (SHBG), testosterone and oestradiol have been associated with many diseases in observational studies; however, the causality of associations remains unestablished. Methods A phenome-wide Mendelian randomization (MR) association study was performed to explore disease outcomes associated with genetically proxied circulating SHBG, testosterone and oestradiol levels by using updated genetic instruments in 339 197 unrelated White British individuals (54% female) in the UK Biobank. Two-sample MR analyses with data from large genetic studies were conducted to replicate identified associations in phenome-wide MR analyses. Multivariable MR analyses were performed to investigate mediation effects of hormone-related biomarkers in observed associations with diseases. Results Phenome-wide MR analyses examined associations of genetically predicted SHBG, testosterone and oestradiol levels with 1211 disease outcomes, and identified 28 and 13 distinct phenotypes associated with genetically predicted SHBG and testosterone, respectively; 22 out of 28 associations for SHBG and 10 out of 13 associations for testosterone were replicated in two-sample MR analyses. Higher genetically predicted SHBG levels were associated with a reduced risk of hypertension, type 2 diabetes, diabetic complications, coronary atherosclerotic outcomes, gout and benign and malignant neoplasm of uterus, but an increased risk of varicose veins and fracture (mainly in females). Higher genetically predicted testosterone levels were associated with a lower risk of type 2 diabetes, coronary atherosclerotic outcomes, gout and coeliac disease mainly in males, but an increased risk of cholelithiasis in females. Conclusions These findings suggest that sex hormones may causally affect risk of several health outcomes.
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Biddinger, Kiran J., Connor Emdin, Mary E. Haas, Minxian Wang, George Hindy, Patrick T. Ellinor, Sekar Kathiresan, Amit V. Khera, and Krishna Aragam. "Abstract 056: Alcohol Increases Risk Of Cardiovascular Disease At All Levels Of Intake." Circulation 143, Suppl_1 (May 25, 2021). http://dx.doi.org/10.1161/circ.143.suppl_1.056.
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Genetic analyses have suggested a causal association between alcohol intake and cardiovascular diseases, questioning the purported cardioprotective effects of modest intake. Though traditional approaches to genetic epidemiology are limited in ability to assess association shapes, we hypothesized that any alcohol may increase risk of cardiovascular disease. In 371,463 participants from the UK Biobank, we first examined for confounding in epidemiological associations between alcohol intake and cardiovascular diseases. Next, using traditional and non-linear genetic approaches (Mendelian randomization), we assessed for causal links of alcohol consumption with several cardiovascular diseases and evaluated the shapes of all causal associations identified. Study participants consumed 9.2 (SD, 10.6) standard drinks per week on average; 121,708 (32.8%) and 27,667 (7.5%) subjects had hypertension and CAD, respectively. Modest consumers of alcohol demonstrated healthier lifestyles - such as lower BMI and greater physical activity - than abstainers, and adjustment for lifestyle factors attenuated the observed benefits of light alcohol intake. Traditional and non-linear Mendelian randomization demonstrated consistently risk-increasing and quadratic associations between alcohol consumption and both clinical and subclinical cardiovascular disease, with exponential increases in risk across levels of drinking; relative to abstainers, consumption of 7, 14, 21, and 28 drinks per week conferred 1.2, 1.7, 3.4, and 8.9-fold odds of hypertension and 1.2, 2.3, 6.2, and 25.9-fold odds of CAD, respectively (both models p<0.001). In conclusion, coincident, favorable lifestyle factors may mediate the observational benefits of modest alcohol intake. All amounts of alcohol intake increase cardiovascular risk, but marked absolute and relative risk differences exist across levels of intake, which should inform public health recommendations around habitual alcohol consumption.
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Huang, Shu-Yi, Yu-Xiang Yang, Shi-Dong Chen, Hong-Qi Li, Xue-Qing Zhang, Kevin Kuo, Lan Tan, et al. "Investigating causal relationships between exposome and human longevity: a Mendelian randomization analysis." BMC Medicine 19, no. 1 (July 20, 2021). http://dx.doi.org/10.1186/s12916-021-02030-4.
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Abstract Background Environmental factors are associated with human longevity, but their specificity and causality remain mostly unclear. By integrating the innovative “exposome” concept developed in the field of environmental epidemiology, this study aims to determine the components of exposome causally linked to longevity using Mendelian randomization (MR) approach. Methods A total of 4587 environmental exposures extracting from 361,194 individuals from the UK biobank, in exogenous and endogenous domains of exposome were assessed. We examined the relationship between each environmental factor and two longevity outcomes (i.e., surviving to the 90th or 99th percentile age) from various cohorts of European ancestry. Significant results after false discovery rates correction underwent validation using an independent exposure dataset. Results Out of all the environmental exposures, eight age-related diseases and pathological conditions were causally associated with lower odds of longevity, including coronary atherosclerosis (odds ratio = 0.77, 95% confidence interval [0.70, 0.84], P = 4.2 × 10−8), ischemic heart disease (0.66, [0.51, 0.87], P = 0.0029), angina (0.73, [0.65, 0.83], P = 5.4 × 10−7), Alzheimer’s disease (0.80, [0.72, 0.89], P = 3.0 × 10−5), hypertension (0.70, [0.64, 0.77], P = 4.5 × 10−14), type 2 diabetes (0.88 [0.80, 0.96], P = 0.004), high cholesterol (0.81, [0.72, 0.91], P = 0.0003), and venous thromboembolism (0.92, [0.87, 0.97], P = 0.0028). After adjusting for genetic correlation between different types of blood lipids, higher levels of low-density lipoprotein cholesterol (0.72 [0.64, 0.80], P = 2.3 × 10−9) was associated with lower odds of longevity, while high-density lipoprotein cholesterol (1.36 [1.13, 1.62], P = 0.001) showed the opposite. Genetically predicted sitting/standing height was unrelated to longevity, while higher comparative height size at 10 was negatively associated with longevity. Greater body fat, especially the trunk fat mass, and never eat sugar or foods/drinks containing sugar were adversely associated with longevity, while education attainment showed the opposite. Conclusions The present study supports that some age-related diseases as well as education are causally related to longevity and highlights several new targets for achieving longevity, including management of venous thromboembolism, appropriate intake of sugar, and control of body fat. Our results warrant further studies to elucidate the underlying mechanisms of these reported causal associations.
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Kho, Minjung, Wei Zhao, Scott M. Ratliff, Farah Ammous, Thomas H. Mosley, Lulu Shang, Sharon L. R. Kardia, Xiang Zhou, and Jennifer A. Smith. "Epigenetic loci for blood pressure are associated with hypertensive target organ damage in older African Americans from the genetic epidemiology network of Arteriopathy (GENOA) study." BMC Medical Genomics 13, no. 1 (September 11, 2020). http://dx.doi.org/10.1186/s12920-020-00791-0.
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Abstract Background Hypertension is a major modifiable risk factor for arteriosclerosis that can lead to target organ damage (TOD) of heart, kidneys, and peripheral arteries. A recent epigenome-wide association study for blood pressure (BP) identified 13 CpG sites, but it is not known whether DNA methylation at these sites is also associated with TOD. Methods In 1218 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study, a cohort of hypertensive sibships, we evaluated the associations between methylation at these 13 CpG sites measured in peripheral blood leukocytes and five TOD traits assessed approximately 5 years later. Results Ten significant associations were found after adjustment for age, sex, blood cell counts, time difference between CpG and TOD measurement, and 10 genetic principal components (FDR q < 0.1): two with estimated glomerular filtration rate (eGFR, cg06690548, cg10601624), six with urinary albumin-to-creatinine ratio (UACR, cg16246545, cg14476101, cg19693031, cg06690548, cg00574958, cg22304262), and two with left ventricular mass indexed to height (LVMI, cg19693031, cg00574958). All associations with eGFR and four associations with UACR remained significant after further adjustment for body mass index (BMI), smoking status, and diabetes. We also found significant interactions between cg06690548 and BMI on UACR, and between 3 CpG sites (cg19693031, cg14476101, and cg06690548) and diabetes on UACR (FDR q < 0.1). Mediation analysis showed that 4.7% to 38.1% of the relationship between two CpG sites (cg19693031 and cg00574958) and two TOD measures (UACR and LVMI) was mediated by blood pressure (Bonferroni-corrected P < 0.05). Mendelian randomization analysis suggests that methylation at two sites (cg16246545 and cg14476101) in PHGDH may causally influence UACR. Conclusions In conclusion, we found compelling evidence for associations between arteriosclerotic traits of kidney and heart and previously identified blood pressure-associated DNA methylation sites. This study may lend insight into the role of DNA methylation in pathological mechanisms underlying target organ damage from hypertension.
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Cho, Youngjoo, Andrea Rau, Alex Reiner, and Paul L. Auer. "Mendelian randomization analysis with survival outcomes." Genetic Epidemiology, September 12, 2020. http://dx.doi.org/10.1002/gepi.22354.
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Liao, Li-Zhen, Xiu-Yun Wen, Shao-Zhao Zhang, Wei-Dong Li, and Xiao-Dong Zhuang. "Hypertension and Atrial Fibrillation: A Study on Epidemiology and Mendelian Randomization Causality." Frontiers in Cardiovascular Medicine 8 (March 23, 2021). http://dx.doi.org/10.3389/fcvm.2021.644405.
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Introduction: Hypertension (HT) and atrial fibrillation (AF) often coexist. However, the causality between these two conditions remains to be determined.Methods: We used individual participant data from the Atherosclerosis Risk in Communities (ARIC) prospective cohort with 9,474 participants. HT was ascertained at visit 1 (1987–1989), and incident AF was identified by ECGs conducted during study examinations at each visit, hospital discharge codes, and death certificates. We used the Kaplan–Meier estimate to compute the cumulative incidence of AF by the HT subgroup. Then we used Cox hazard regression model to assess the association between HT and incident AF. The causality between genetically determined HT and AF was analyzed by the two-sample Mendelian randomization (MR) based on publicly summarized genome-wide association studies (GWASs) data.Results: A total of 1,414 cases (14.9%) of AF were identified during the follow-up period (median 24.1 years). After adjusting for all covariates, the hazard ratio between the participants with HT and incident AF was 1.50 [95% confidence interval (CI) 1.29–1.73]. In the HT → AF MR analysis, we detected a causal correlation between HT and AF (OR: 1.90, 95% CI 1.18–3.04, P = 0.01) with no evidence of heterogeneity from single-nucleotide polymorphisms. Besides, the genetically determined SBP and DBP (10 mmHg) were consistently associated with a higher risk of AF.Conclusions: In the ARIC study, the incident AF increased by 50% in patients with HT. In the MR analysis, our results supported causal inference between HT and AF.
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Qiu, Shizheng, Meijie Li, Shunshan Jin, Haoyu Lu, and Yang Hu. "Rheumatoid Arthritis and Cardio-Cerebrovascular Disease: A Mendelian Randomization Study." Frontiers in Genetics 12 (October 21, 2021). http://dx.doi.org/10.3389/fgene.2021.745224.
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Significant genetic association exists between rheumatoid arthritis (RA) and cardiovascular disease. The associated mechanisms include common inflammatory mediators, changes in lipoprotein composition and function, immune responses, etc. However, the causality of RA and vascular/heart problems remains unknown. Herein, we performed Mendelian randomization (MR) analysis using a large-scale RA genome-wide association study (GWAS) dataset (462,933 cases and 457,732 controls) and six cardio-cerebrovascular disease GWAS datasets, including age angina (461,880 cases and 447,052 controls), hypertension (461,880 cases and 337,653 controls), age heart attack (10,693 cases and 451,187 controls), abnormalities of heartbeat (461,880 cases and 361,194 controls), stroke (7,055 cases and 454,825 controls), and coronary heart disease (361,194 cases and 351,037 controls) from United Kingdom biobank. We further carried out heterogeneity and sensitivity analyses. We confirmed the causality of RA with age angina (OR = 1.17, 95% CI: 1.04–1.33, p = 1.07E−02), hypertension (OR = 1.45, 95% CI: 1.20–1.75, p = 9.64E−05), age heart attack (OR = 1.15, 95% CI: 1.05–1.26, p = 3.56E−03), abnormalities of heartbeat (OR = 1.07, 95% CI: 1.01–1.12, p = 1.49E−02), stroke (OR = 1.06, 95% CI: 1.01–1.12, p = 2.79E−02), and coronary heart disease (OR = 1.19, 95% CI: 1.01–1.39, p = 3.33E−02), contributing to the understanding of the overlapping genetic mechanisms and therapeutic approaches between RA and cardiovascular disease.
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Lutz, Sharon M., Kirsten Voorhies, Ann C. Wu, John Hokanson, Stijn Vansteelandt, and Christoph Lange. "Selection bias when inferring the effect direction in Mendelian randomization." Genetic Epidemiology, May 2, 2022. http://dx.doi.org/10.1002/gepi.22452.
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Lutz, Sharon M., Ann Chen Wu, John E. Hokanson, Stijn Vansteelandt, and Christoph Lange. "Caution against examining the role of reverse causality in Mendelian Randomization." Genetic Epidemiology, May 19, 2021. http://dx.doi.org/10.1002/gepi.22385.
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Allman, Philip H., Inmaculada Aban, Dustin M. Long, S. Louis Bridges, Vinodh Srinivasasainagendra, Todd MacKenzie, Gary Cutter, and Hemant K. Tiwari. "A novel Mendelian randomization method with binary risk factor and outcome." Genetic Epidemiology, May 16, 2021. http://dx.doi.org/10.1002/gepi.22387.
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Wang, Qixia, Runchen Wang, Chao Chen, Yi Feng, Zhiming Ye, Miaorong Zhan, Hao Wen, and Kaimin Guo. "Educational attainment and endometrial cancer: A Mendelian randomization study." Frontiers in Genetics 13 (November 29, 2022). http://dx.doi.org/10.3389/fgene.2022.993731.
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Background: Low educational attainment has been reported as a risk factor for many diseases. However, conclusion on the association between educational attainment and endometrial cancer (EC) are inconsistent in previous observational studies. This study aims to explore the potential causal association between educational attainment and EC.Methods: A Mendelian Randomization analysis was performed using publicly summary-level data sets of genome-wide association studies (GWAS). A total of 306 single-nucleotide polymorphisms (SNPs) were extracted as instrumental variables for the exposure of educational attainment from the Social Science Genetic Association Consortium GWAS summary data of 1,131,881 participants of European ancestry. SNPs of EC were obtained from the Endometrial Cancer Association Consortium, the Epidemiology of Endometrial Cancer Consortium and the UK Biobank involving 121,885 people. We conducted inverse variance weighted (IVW) to estimate the causal effect as our primary outcome. And we perform several sensitivity analyses, including MR-Egger regression, weighted median method, MR-PRESSO (Mendelian Randomization Pleiotropy Residual Sum and Outlier) global test, and leave-one-out sensitivity analysis, to evaluate the effect of pleiotropism on the causal estimates.Results: Genetic predisposition towards 4.2 years of additional educational attainment was associated with 38% lower risk of EC. (odds ratio 0.72, 95% confidence interval 0.62 to 0.83; p = 1.65*10−5). The consistent results of sensitivity analyses indicated our causal estimates were reliable. Genetic predisposition towards longer educational attainment was associated with lower risk of obesity, high waist-to-hip ratio (WHR), and diabetes.Conclusion: This study indicated that low educational attainment was a causal risk factor for EC, especially for EC with endometrioid histology. Low educational attainment might lead to EC through the mediator of obesity, high WHR, and diabetes.
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Zhou, Jiawei, Jianfeng Lin, and Yuehong Zheng. "Association of cardiovascular risk factors and lifestyle behaviors with aortic aneurysm: A Mendelian randomization study." Frontiers in Genetics 13 (August 8, 2022). http://dx.doi.org/10.3389/fgene.2022.925874.
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Objective: To examine the causality between hypertension, diabetes, other cardiovascular risk factors, lifestyle behaviors, and the aortic aneurysm among patients of European ancestry.Methods: We performed two-sample Mendelian randomization (MR) analysis to investigate the causality of 12 modifiable risk factors with aortic aneurysm, including hypertension, body mass index (BMI), waist–hip ratio (WHR), diabetes, tobacco smoking, alcohol and coffee consumption, physical activity, and sleep duration. Genome-wide significant genetic instruments (p < 5 × 10–8) for risk factors were extracted from European-descent genome-wide association studies, whereas aortic aneurysm genetic instruments were selected from the UK Biobank and FinnGen cohort. The inverse-variance weighted MR was used as the main analysis, and MR-Egger (MRE), weighted median MR, MR pleiotropy residual sum and outlier, and Phenoscanner searching were performed as sensitivity analyses. Furthermore, we calculated MRE intercept to detect pleiotropy and Cochran’s Q statistics to assess heterogeneity and conducted bidirectional MR and MR Steiger tests to exclude the possibility of reverse causality.Results: We observed significantly higher risks for the aortic aneurysm in hypertension [pooled OR: 4.30 (95% CI 2.84–6.52)], BMI [OR: 1.58 (95% CI 1.37–1.81)], WHR [OR: 1.51 (95% CI 1.21–1.88)], WHR adjusted for BMI (WHRadjBMI) [OR: 1.35 (95% CI 1.12–1.63)], age of smoking initiation [OR: 1.63 (95% CI 1.18–2.26)], and tobacco use (initiation, cessation, and heaviness) [OR: 2.88 (95% CI 1.85–2.26)]. In sensitivity analysis, the causal effects of hypertension, BMI, WHRadjBMI, and tobacco use (initiation, cessation, and heaviness) remained robust.Conclusion: There was a positive causal relationship between hypertension, BMI, WHR, and WHRadjBMI and aortic aneurysm.
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Miao, Liu, Guo-Xiong Deng, Rui-Xing Yin, Rong-Jun Nie, Shuo Yang, Yong Wang, and Hui Li. "No causal effects of plasma homocysteine levels on the risk of coronary heart disease or acute myocardial infarction: A Mendelian randomization study." European Journal of Preventive Cardiology, December 16, 2019, 204748731989467. http://dx.doi.org/10.1177/2047487319894679.
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Background Although many observational studies have shown an association between plasma homocysteine levels and cardiovascular diseases, controversy remains. In this study, we estimated the role of increased plasma homocysteine levels on the etiology of coronary heart disease and acute myocardial infarction. Methods A two-sample Mendelian randomization study on disease was conducted, i.e. “coronary heart disease” ( n = 184,305) and “acute myocardial infarction” ( n = 181,875). Nine single nucleotide polymorphisms, which were genome-wide significantly associated with plasma homocysteine levels in 57,644 subjects from the Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) plus The Coronary Artery Disease (C4D) Genetics (CARDIoGRAMplusC4D) consortium genome-wide association study and were known to be associated at p < 5×10–8, were used as an instrumental variable. Results None of the nine single nucleotide polymorphisms were associated with coronary heart disease or acute myocardial infarction ( p > 0.05 for all). Mendelian randomization analysis revealed no causal effects of plasma homocysteine levels, either on coronary heart disease (inverse variance weighted; odds ratio = 1.015, 95% confidence interval = 0.923–1.106, p = 0.752) or on acute myocardial infarction (inverse variance weighted; odds ratio = 1.037, 95% confidence interval = 0.932–1.142, p = 0.499). The results were consistent in sensitivity analyses using the weighted median and Mendelian randomization-Egger methods, and no directional pleiotropy ( p = 0.213 for coronary heart disease and p = 0.343 for acute myocardial infarction) was observed. Sensitivity analyses confirmed that plasma homocysteine levels were not significantly associated with coronary heart disease or acute myocardial infarction. Conclusions The findings from this Mendelian randomization study indicate no causal relationship between plasma homocysteine levels and coronary heart disease or acute myocardial infarction. Conflicting findings from observational studies might have resulted from residual confounding or reverse causation.
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Mohammadi-Shemirani, Pedrum, Michael Chong, Marie Pigeyre, Robert W. Morton, Hertzel C. Gerstein, and Guillaume Paré. "Effects of lifelong testosterone exposure on health and disease using Mendelian randomization." eLife 9 (October 16, 2020). http://dx.doi.org/10.7554/elife.58914.
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Testosterone products are prescribed to males for a variety of possible health benefits, but causal effects are unclear. Evidence from randomized trials are difficult to obtain, particularly regarding effects on long-term or rare outcomes. Mendelian randomization analyses were performed to infer phenome-wide effects of free testosterone on 461 outcomes in 161,268 males from the UK Biobank study. Lifelong increased free testosterone had beneficial effects on increased bone mineral density, and decreased body fat; adverse effects on decreased HDL, and increased risks of prostate cancer, androgenic alopecia, spinal stenosis, and hypertension; and context-dependent effects on increased hematocrit and decreased C-reactive protein. No benefit was observed for type 2 diabetes, cardiovascular or cognitive outcomes. Mendelian randomization suggests benefits of long-term increased testosterone should be considered against adverse effects, notably increased prostate cancer and hypertension. Well-powered randomized trials are needed to conclusively address risks and benefits of testosterone treatment on these outcomes.
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Lawn, Rebecca B. "Sexual violence and hypertension risk in women: an interview with Rebecca Lawn." Future Cardiology, April 20, 2022. http://dx.doi.org/10.2217/fca-2022-0035.
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Dr Rebecca Lawn is a Postdoctoral Research Fellow at the Harvard T.H. Chan School of Public Health. She gained a first-class BSc (hons) in Psychology from Newcastle University and received her PhD from the University of Bristol, where she worked between the School of Psychological Science and the Medical Research Council (MRC) Integrative Epidemiology Unit. Dr Lawn’s PhD research applied Mendelian randomization to investigate causal relationships in evolutionary theories of development and behavior with a focus on life history theory (concentrating on age at menarche and age at first sexual intercourse) and the schizophrenia paradox. Her current research interests include the relation of trauma and post-traumatic stress disorder with health over the life course.