Biologically informed instrument selection for dietary Mendelian randomization using chemosensory receptor variants

Background: Clinical diabetic traits have been reported to be associated with increased colorectal cancer risk (CRC) in observational studies. Using the Mendelian randomization (MR) analysis method, we examined the causal association between glycemic traits, such as fasting glucose (FG), fasting insulin (FI), and glycosylated hemoglobin A1c (HbA1c), and survival in a cohort of CRC patients. Methods: We conducted a two-sample MR analysis among a cohort of patients with locally advanced CRC at Seoul National University Hospital. Single-nucleotide polymorphisms robustly associated (P<5 × 10−8) with the three glycemic traits were obtained from the Meta-Analyses of Glucose and Insulin-related traits Consortium, Asian Genetic Epidemiology Network, and Korea Biobank Array. Three-year and five-year overall survival (OS) and progression-free survival (PFS) were used as outcomes. Survival analysis was conducted using subgroup analysis by cancer stage and subsite in a multivariate Cox proportional hazards model adjusted for age and sex to examine whether glycemic traits affected survival. Results: A total of 509 patients were included in our final analysis. MR analysis showed that HbA1c levels were associated with poor three-year OS (β=4.20, P=0.02). Sensitivity analyses did not show evidence of any violations of the MR assumptions. In the cancer subgroup analysis of the Cox proportional hazards model, pooled hazard ratios for FG were significantly associated with poor three-year OS and PFS regardless of cancer stage. FI was not significantly associated with any three-year survival endpoints. Among stage III patients, three glycemic traits were significantly associated with both five-year OS and PFS. Location-specific subgroup analysis showed a significant association between three glycemic traits and five-year PFS in patients with left-sided colon cancer. FG was associated with poor three-year survival for colon cancer but not rectal cancer. Conclusions: Our results suggest that FG and HbA1c could be used to predict prognosis in CRC patients. Lifestyle and/or pharmacological interventions targeting glycemic traits could help improve survival for CRC patients. Citation Format: So Yon Jun, Sooyoung Cho, Min Jung Kim, Ji Won Park, Seung Yong Jeong, Aesun Shin. Glycemic traits and colorectal cancer survival in a cohort of South Korean patients: A Mendelian randomization analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7319.

Observational studies have suggested potential associations between myocardial infarction (MI) and cancer risk, but the causal nature of these relationships remains unclear due to confounding factors and reverse causation. We aimed to investigate the bidirectional causal relationships between MI and urinary system cancers using genetic instruments. We conducted a two-sample Mendelian randomization (MR) analysis using summary statistics from large-scale genome-wide association studies. Genetic variants associated with MI were used as instrumental variables (n = 19 SNPs for prostate cancer [PCa] and malignant neoplasm of kidney [MRN], n = 6 SNPs for bladder cancer, n = 21 SNPs for bladder cancer [BCa] validation). We examined the causal effects of MI on PCa, BCa, and MRN risk, as well as reverse causation. Multiple MR methods were employed, including inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode approaches. Both discovery and validation datasets were analyzed to ensure robustness. Forward MR analysis revealed no significant causal effect of MI on urinary system cancer risk across all examined malignancies. For PCa, the odds ratios (ORs) ranged from 0.964 to 1.007 across different methods and datasets (all p > 0.05). Similarly, MI showed no causal association with BCa risk (OR = 1.000, 95% CI: 0.999-1.002 in discovery cohort; OR = 1.000, 95% CI: 1.000-1.001 in validation cohort) or MRN risk (OR = 0.989-1.060 across methods in discovery cohort). Reverse MR analysis demonstrated no significant causal effects of PCa or kidney malignancy on MI risk, with ORs ranging from 0.250 to 1.200 (all p > 0.05). Sensitivity analyses confirmed the absence of pleiotropy and heterogeneity. Our genetic evidence does not support causal relationships between MI and urinary system cancers in either direction. The observed associations in epidemiological studies may be attributed to shared risk factors, treatment effects, or residual confounding rather than direct causal mechanisms. These findings have important implications for cancer surveillance strategies in MI patients and understanding cardio-oncology interactions.

Non-small cell lung cancer (NSCLC) represents a major global health burden with complex genetic etiology. While observational studies have identified numerous potential risk factors, establishing causal relationships remains challenging due to confounding and reverse causation. We conducted a comprehensive Mendelian randomization (MR) analysis to investigate causal relationships between circulating biomarkers, lung tissue gene expression levels, and NSCLC risk. The analysis employed three complementary MR methods: inverse variance weighted (IVW), MR Egger, and weighted median approaches. Genetic instruments were selected from 10 to 26 single nucleotide polymorphisms (SNPs) significantly associated with exposure variables. We validated key findings through quantitative real-time PCR (qRT-PCR) analysis in A549 and H1299 NSCLC cell lines. We examined multiple biomarkers including inflammatory mediators, immune-related proteins, and particularly focused on the HLA region within the major histocompatibility complex. Among circulating biomarkers, thymic stromal lymphopoietin demonstrated a statistically significant positive association with increased NSCLC risk (OR: 1.240, 95% CI: 1.073–1.433, p = 0.003). Gene expression analysis identified 18 genes with significant causal associations (FDR < 0.05), revealing a complex pattern of protective and risk-associated effects. qRT-PCR validation in A549 and H1299 cell lines confirmed differential expression patterns, with protective genes (HLA-DQB2, GMPPA) showing lower expression and risk-associated genes (VDR, P4HTM) showing higher expression in cancer cells compared to normal bronchial epithelial cells. Notable protective genes included HLA-DQB2 (OR: 0.917), HLA-DRB9 (OR: 0.875), and GMPPA (OR: 0.697), while risk-associated genes included VDR (OR: 1.825), P4HTM (OR: 1.520), and multiple HLA variants. Regional association analysis of the HLA-DQB2 locus revealed extremely high significance levels (p-values > 10^-135) within the MHC region on chromosome 6. This MR analysis provides robust genetic evidence for causal relationships between immune system regulation, inflammatory pathways, and NSCLC development, with experimental validation supporting the functional relevance of identified genetic associations.

BackgroundInsomnia is common and associated with adverse pregnancy and perinatal outcomes in observational studies. However, those associations could be vulnerable to residual confounding or reverse causality. Our aim was to estimate the association of insomnia with stillbirth, miscarriage, gestational diabetes (GD), hypertensive disorders of pregnancy (HDP), perinatal depression, preterm birth (PTB), and low/high offspring birthweight (LBW/HBW).Methods and findingsWe used 2-sample mendelian randomization (MR) with 81 single-nucleotide polymorphisms (SNPs) instrumenting for a lifelong predisposition to insomnia. Our outcomes included ever experiencing stillbirth, ever experiencing miscarriage, GD, HDP, perinatal depression, PTB (gestational age <37 completed weeks), LBW (<2,500 grams), and HBW (>4,500 grams). We used data from women of European descent (N = 356,069, mean ages at delivery 25.5 to 30.0 years) from UK Biobank (UKB), FinnGen, Avon Longitudinal Study of Parents and Children (ALSPAC), Born in Bradford (BiB), and the Norwegian Mother, Father and Child Cohort (MoBa). Main MR analyses used inverse variance weighting (IVW), with weighted median and MR-Egger as sensitivity analyses. We compared MR estimates with multivariable regression of insomnia in pregnancy on outcomes in ALSPAC (N = 11,745). IVW showed evidence of an association of genetic susceptibility to insomnia with miscarriage (odds ratio (OR): 1.60, 95% confidence interval (CI): 1.18, 2.17, p = 0.002), perinatal depression (OR 3.56, 95% CI: 1.49, 8.54, p = 0.004), and LBW (OR 3.17, 95% CI: 1.69, 5.96, p < 0.001). IVW results did not support associations of insomnia with stillbirth, GD, HDP, PTB, and HBW, with wide CIs including the null. Associations of genetic susceptibility to insomnia with miscarriage, perinatal depression, and LBW were not observed in weighted median or MR-Egger analyses. Results from these sensitivity analyses were directionally consistent with IVW results for all outcomes, with the exception of GD, perinatal depression, and PTB in MR-Egger. Multivariable regression showed associations of insomnia at 18 weeks of gestation with perinatal depression (OR 2.96, 95% CI: 2.42, 3.63, p < 0.001), but not with LBW (OR 0.92, 95% CI: 0.69, 1.24, p = 0.60). Multivariable regression with miscarriage and stillbirth was not possible due to small numbers in index pregnancies. Key limitations are potential horizontal pleiotropy (particularly for perinatal depression) and low statistical power in MR, and residual confounding in multivariable regression.ConclusionsIn this study, we observed some evidence in support of a possible causal relationship between genetically predicted insomnia and miscarriage, perinatal depression, and LBW. Our study also found observational evidence in support of an association between insomnia in pregnancy and perinatal depression, with no clear multivariable evidence of an association with LBW. Our findings highlight the importance of healthy sleep in women of reproductive age, though replication in larger studies, including with genetic instruments specific to insomnia in pregnancy are important.

BackgroundInflammation is associated with cognitive functioning and dementia in older adults, but whether inflammation is related to cognitive functioning in youth and whether these associations are causal remains unclear. MethodsIn a population-based cohort (Avon Longitudinal Study of Parents and Children; ALSPAC), we investigated cross-sectional associations of inflammatory markers (C-reactive protein [CRP], Interleukin-6 [IL-6] and Glycoprotein acetyls [GlycA]) with measures of cold (working memory, response inhibition) and hot (emotion recognition) cognition at age 24 (N = 3,305 in multiple imputation models). Furthermore, we conducted one-sample and two-sample bidirectional Mendelian randomization (MR) analyses to examine potential causal effects of genetically-proxied inflammatory markers (CRP, GlycA, IL-6, IL-6 receptor, soluble IL-6 receptor) on cognitive measures (above) and on general cognitive ability. ResultsIn the ALSPAC cohort, there was limited evidence of an association between standardised inflammatory markers and standardised cognitive measures at age 24 after adjusting for potential confounders (N = 3,305; beta range, −0.02 [95 % confidence interval (CI) −0.06 to 0.02, p = 0.27] to 0.02 [95 % CI −0.02 to 0.05, p = 0.33]). Similarly, we found limited evidence of potential effects of 1-unit increase in genetically-proxied inflammatory markers on standardised working memory, emotion recognition or response inhibition in one-sample MR using ALSPAC data (beta range, −0.73 [95 % CI −2.47 to 1.01, p = 0.41] to 0.21 [95 % CI −1.42 to 1.84, p = 0.80]; or on standardised general cognitive ability in two-sample MR using the latest Genome-Wide Association Study (GWAS) datasets (inverse-variance weighted beta range, −0.02 [95 % CI −0.05 to 0.01, p = 0.12] to 0.03 [95 % CI −0.01 to 0.07, p = 0.19]). ConclusionsOur MR findings do not provide strong evidence of a potential causal effect of inflammatory markers (CRP, IL-6, IL-6 receptor, GlycA) on the cognitive functions examined here. Given the large confidence intervals in the one-sample MR, larger GWAS of specific cognitive measures are needed to enable well-powered MR analyses to investigate whether inflammation causally influences specific cognitive domains.

In The Lancet Diabetes & Endocrinology, Jon White and colleagues1White J Sofat R Hemani G et al.Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.Lancet Diabetes Endocrinol. 2016; (published online Jan 15.)http://dx.doi.org/10.1016/S2213-8587(15)00386-1Google Scholar consider the causal role of plasma urate in coronary heart disease, using the technique known as mendelian randomisation. They assess the association between genetic predictors of the risk factor (plasma urate) and the disease outcome (coronary heart disease), noting a statistically robust association between a genetic score based on 31 variants associated with plasma urate and coronary heart disease risk, with a straightforward application of Mendelian randomisation estimating an 18% (95% CI 8-29) relative increase in coronary heart disease risk per 1 SD increase in plasma urate. This positive finding from the mendelian randomisation investigation was in line with the observational epidemiological analysis also reported by the investigators. However, the association of the genetic score with the outcome seems to be at least partly explained by pleiotropic (off-target) associations of the genetic score, and the pattern of associations of the individual genetic variants does not show the characteristic dose–response association that would be expected if the genetic associations were solely driven by the effect of the risk factor on the outcome. Mendelian randomisation is a relatively new, but well established epidemiological technique to assess whether a modifiable risk factor is a worthwhile target for clinical or pharmacological intervention.2Davey Smith G Ebrahim S ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?.Int J Epidemiol. 2003; 32: 1-22Crossref PubMed Scopus (2488) Google Scholar, 3Burgess S Thompson SG Mendelian randomization: methods for using genetic variants in causal estimation. Chapman & Hall, London2015Crossref Scopus (195) Google Scholar An association between a genetic predictor of the risk factor and the outcome is more likely to reflect that the risk factor has a causal relation with the outcome, compared with an association of the risk factor itself from a traditional epidemiological analysis, for several reasons. First, risk factors tend to be mutually correlated. It is difficult to distinguish whether the risk factor itself is the cause, or if a correlated risk factor is driving the identified association with an outcome. By contrast, most genetic variants tend to be uncorrelated with conventional epidemiological risk factors.4Davey Smith G Lawlor D Harbord R Timpson N Day I Ebrahim S Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology.PLoS Med. 2007; 4: e352Crossref PubMed Scopus (329) Google Scholar Second, genetic variants are fixed at conception. Hence, a genetic association cannot be affected by external factors that lead to confounded associations. Finally, the associations of genetic variants are not subject to reverse causation. The gene can only ever be the cause, and never the effect. In an ideal mendelian randomisation analysis, the genetic variants have a clear biological link with the risk factor (for example, genetic variants in the CRP gene region for the analysis of C-reactive protein5CRP CHD Genetics CollaborationAssociation between C reactive protein and coronary heart disease: Mendelian randomisation analysis based on individual participant data.BMJ. 2011; 342: d548Crossref PubMed Scopus (210) Google Scholar), and are associated with the risk factor, but not with alternative risk factors. The analysis proceeds analogously to a randomised trial,6Hingorani A Humphries S Nature's randomised trials.Lancet. 2005; 366: 1906-1908Summary Full Text Full Text PDF PubMed Scopus (141) Google Scholar and compares groups of individuals with (on average) genetically raised levels of the risk factor versus those with genetically lowered levels to assess whether the risk factor is a cause of the outcome, and therefore a promising target for prioritisation in drug development. White and colleagues' analysis1White J Sofat R Hemani G et al.Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.Lancet Diabetes Endocrinol. 2016; (published online Jan 15.)http://dx.doi.org/10.1016/S2213-8587(15)00386-1Google Scholar is far from an ideal mendelian randomisation investigation. This is not a criticism, but a consequence of their investigation of a risk factor that is not a protein biomarker, and so does not have a coding gene region, as in the case of C-reactive protein. However, the consideration of multiple gene regions gives some robustness in the analysis, in that the consistency of any causal finding can be assessed. If multiple independent genetic variants associated with the risk factor are all concordantly associated with the outcome (as in the case of LDL cholesterol with coronary heart disease7Ference BA Yoo W Alesh I et al.Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a Mendelian randomization analysis.J Am Coll Cardiol. 2012; 60: 2631-2639Summary Full Text Full Text PDF PubMed Scopus (544) Google Scholar), the separate genetic associations each provide independent evidence for a causal effect of the risk factor on the outcome, strengthening the evidence for a causal finding. Another deficiency is that the composite genetic instrument considered by White and colleagues1White J Sofat R Hemani G et al.Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.Lancet Diabetes Endocrinol. 2016; (published online Jan 15.)http://dx.doi.org/10.1016/S2213-8587(15)00386-1Google Scholar is not solely associated with urate concentrations, but also shows associations with other risk factors, including blood pressure and lipid fractions. This finding is not necessarily a problem provided that the genetic associations with these factors are mediated by urate concentrations, and so one single causal pathway is represented. However, these associations might reflect pleiotropy of the genetic variants, meaning that there are multiple causal pathways from gene to disease, thereby violating the assumption of mendelian randomisation that an association between gene and disease reflects a causal effect of the risk factor. White and colleagues1White J Sofat R Hemani G et al.Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.Lancet Diabetes Endocrinol. 2016; (published online Jan 15.)http://dx.doi.org/10.1016/S2213-8587(15)00386-1Google Scholar show a statistically robust association between their 31 variant composite instrument and disease risk. However, the authors are to be commended in not stopping here, as others have done previously, but instead seeking to assess whether potential violations of the instrumental variable assumptions invalidate a causal interpretation for their findings. They use two main statistical approaches: multivariable mendelian randomisation8Burgess S Thompson S Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects.Am J Epidemiol. 2015; 181: 251-260Crossref PubMed Scopus (434) Google Scholar and Egger regression.9Bowden J Davey Smith G Burgess S Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.Int J Epidemiol. 2015; 44: 512-525Crossref PubMed Scopus (1958) Google Scholar In multivariable mendelian randomisation, alternative risk factors are accounted for by adjusting for the genetic associations with these risk factors in the analysis model. In Egger regression, the assessment is not simply of whether genetic variants that are associated with the risk factor are also associated with the outcome, but whether there is a dose–response relation in these associations—that is, are genetic variants that are more strongly associated with the risk factor also more strongly associated with the outcome? Egger regression also encompasses a test for directional pleiotropy—whether this dose–response relation suggests that a variant having zero association with the risk factor also has zero association with the outcome (as expected if there is no pleiotropy, or balanced pleiotropy). Although multivariable mendelian randomisation does not suggest that measured pleiotropy completely accounts for the causal finding, Egger regression detects pleiotropy among the genetic variants, and provides a weaker causal estimate (a 5% [95% CI −8 to 20] relative increase coronary heart disease risk per 1 SD increase in plasma urate) that is compatible with the null once this pleiotropy is accounted for. Additionally, the genetic associations with the outcome are more variable than would be expected by chance alone; this heterogeneity suggests that the genetic associations with coronary heart disease risk are not solely mediated via plasma urate concentrations. The overall finding is not unequivocal. There is an undeniable association between genetic predictors of plasma urate concentrations and risk of coronary heart disease. However, there are suggestions that the association might represent pleiotropic effects of genetic variants included in the model, rather than a causal effect of urate. In summary, the evidence presented honestly by White and colleagues1White J Sofat R Hemani G et al.Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.Lancet Diabetes Endocrinol. 2016; (published online Jan 15.)http://dx.doi.org/10.1016/S2213-8587(15)00386-1Google Scholar suggests that urate lowering should be prioritised as a potential mechanism to improve cardiovascular outcomes, but without providing a smoking gun that puts the question of causality beyond doubt. I declare no competing interests. Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysisConventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. Full-Text PDF Open Access

EpigenomicsVol. 7, No. 8 CommentaryMendelian randomization: applications and limitations in epigenetic studiesCaroline L Relton & George Davey SmithCaroline L Relton*Author for correspondence: E-mail Address: caroline.relton@bristol.ac.uk MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK & George Davey Smith MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UKPublished Online:7 Dec 2015https://doi.org/10.2217/epi.15.88AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit View articleKeywords: causal inferenceDNA methylationepidemiologyepigeneticsmediationSNPPapers of special note have been highlighted as: • of interest; •• of considerable interestReferences1 Mathers JC, Strathdee G, Relton CL. Induction of epigenetic alterations by dietary and other environmental factors. Adv. Genet. 71, 3–39 (2010).Crossref, Medline, Google Scholar2 Cortessis VK1, Thomas DC, Levine AJ et al. Environmental epigenetics: prospects for studying epigenetic mediation of exposure–response relationships. Hum. Genet. 131, 1565–1589 (2012).Crossref, Medline, CAS, Google Scholar3 Barrow TM, Michels KB. Epigenetic epidemiology of cancer. Biochem. Biophys. Res. Commun. 455, 70–83 (2014).Crossref, Medline, CAS, Google Scholar4 Urdinguio RG, Sanchez-Mut JV, Esteller M. Epigenetic mechanisms in neurological diseases: genes, syndromes, and therapies. Lancet Neurol. 8, 1056–1072 (2009).Crossref, Medline, CAS, Google Scholar5 Dick KJ, Nelson CP, Tsaprouni L et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet 383, 1990–1998 (2014).Crossref, Medline, CAS, Google Scholar6 Liang L, Willis-Owen SA, Laprise C et al. An epigenome-wide association study of total serum immunoglobulin E concentration. Nature 520(7549), 670–674 (2015).• The first major epigenome-wide association study to implement formal Mendelian randomization.Crossref, Medline, CAS, Google Scholar7 Davey Smith G, Ebrahim S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003).•• Details the application of Mendelian randomization.Crossref, Medline, Google Scholar8 Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23(R1), R89–R98 (2014).•• Recent developments and refinements in the application of Mendelian randomization.Crossref, Medline, CAS, Google Scholar9 Greenland S. An introduction to instrumental variables for epidemiologists. Int. J. Epidemiol. 29, 722–729 (2000).Crossref, Medline, CAS, Google Scholar10 Sheehan NA, Didelez V, Burton PR, Tobin MD. Mendelian randomisation and causal inference in observational epidemiology. PLoS Med. 5, e177 (2008).Crossref, Medline, Google Scholar11 Jansen RC, Nap JP. Genetical genomics: the added value from segregation. Trends Genet. 17, 388–391 (2001).Crossref, Medline, CAS, Google Scholar12 Schadt EE, Lamb J, Yang X et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37, 710–717 (2005).Crossref, Medline, CAS, Google Scholar13 Liu Y, Aryee MJ, Padyukov L et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat. Biotechnol. 31, 142–147 (2013).Crossref, Medline, CAS, Google Scholar14 Blakely T, McKenzie S, Carter K. Misclassification of the mediator matters when estimating indirect effects. J. Epidemiol. Community Health 67, 458–466 (2013).Crossref, Medline, Google Scholar15 Relton CL, Davey Smith G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int. J. Epidemiol. 41, 161–176 (2012).•• Outlines the application of Mendelian randomization to epigenetics to strengthen causal inference.Crossref, Medline, Google Scholar16 Zhang D, Cheng L, Badner JA et al. Genetic control of individual differences in gene-specific methylation in human brain. Am. J. Hum. Genet. 86, 411–419 (2010).Crossref, Medline, CAS, Google Scholar17 Bell JT, Pai AA, Pickrell JK et al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 12, R10 (2011).Crossref, Medline, CAS, Google Scholar18 Timofeeva MN, McKay JD, Davey Smith G et al. Genetic Polymorphisms in 15q25 and 19q13 loci, cotinine levels, and risk of lung cancer in EPIC. Cancer Epidemiol. Biomarkers Prev. 20, 2250–2261 (2011).Crossref, Medline, CAS, Google Scholar19 Zuccolo L, Fitz-Simon N, Gray R et al. A non-synonymous variant in ADH1B is strongly associated with prenatal alcohol use in a European sample of pregnant women. Hum. Mol. Genet. 18, 4457–4466 (2009).Crossref, Medline, CAS, Google Scholar20 Timpson NJ, Harbord R, Davey Smith G, Zacho J, Tybjaerg-Hansen A, Nordestgaard BG. Does greater adiposity increase blood pressure and hypertension risk? Mendelian randomization using the FTO/MC4R genotype. Hypertension 54, 84–90 (2009).Crossref, Medline, CAS, Google Scholar21 Benn M, Tybjaerg-Hansen A, Stender S, Frikke-Schmidt R, Nordestgaard BG. Low-density lipoprotein cholesterol and the risk of cancer: a Mendelian randomization study. J. Natl Cancer Inst. 103, 508–519 (2011).Crossref, Medline, CAS, Google Scholar22 Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 12, 529–541 (2011).Crossref, Medline, CAS, Google Scholar23 Shi J, Marconett CN, Duan J et al. Characterizing the genetic basis of methylome diversity in histologically normal human lung tissue. Nat. Commun. 5, 3365 (2014).Crossref, Medline, Google Scholar24 Cole DA, Preacher KJ. Manifest variable path analysis: potentially serious and misleading consequences due to uncorrected measurement error. Psychol. Methods 19, 300–315 (2014).Crossref, Medline, Google Scholar25 Groom A, Potter C, Swan DC et al. Postnatal growth and DNA methylation are associated with differential gene expression of the TACSTD2 gene and childhood fat mass. Diabetes 61, 391–400 (2012).Crossref, Medline, CAS, Google Scholar26 Allard C, Desgagne V, Patenaude J et al. Mendelian randomization supports causality between maternal hyperglycaemia and epigenetic regulation of leptin gene in newborns. Epigenetics 10(4), 342–351 (2015).Crossref, Medline, CAS, Google Scholar27 Kirkbride J, Susser E, Kundakovic M, Kresovich JK, Davey Smith G, Relton CL. Prenatal nutrition, epigenetics and schizophrenia risk: can we test causal effects? Epigenomics 4(3), 303–315 (2012).Link, CAS, Google Scholar28 Terry MB, Delgado-Cruzata L, Vin-Raviv N, Wu HC, Santella RM. DNA methylation in white blood cells: association with risk factors in epidemiologic studies. Epigenetics 6, 828–837 (2011).Crossref, Medline, CAS, Google Scholar29 Zeilinger S, Kühnel B, Klopp N et al. Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLoS ONE 8, e63812 (2013).Crossref, Medline, CAS, Google Scholar30 Maher B. ENCODE: the human encyclopaedia. Nature 489, 46–48 (2012).Crossref, Medline, Google Scholar31 Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44(2), 512–525 (2015).Crossref, Medline, Google ScholarFiguresReferencesRelatedDetailsCited ByEpigenome-wide epidemiologic studies of human immunodeficiency virus infection, treatment, and disease progression11 January 2022 | Clinical Epigenetics, Vol. 14, No. 1A comparison of the genes and genesets identified by GWAS and EWAS of fifteen complex traits19 December 2022 | Nature Communications, Vol. 13, No. 1Prenatal medication exposure and epigenetic outcomes: a systematic literature review and recommendations for prenatal pharmacoepigenetic studies29 April 2021 | Epigenetics, Vol. 17, No. 4Shared Genetic Basis and Causal Relationship Between Television Watching, Breakfast Skipping and Type 2 Diabetes: Evidence From a Comprehensive Genetic Analysis24 March 2022 | Frontiers in Endocrinology, Vol. 13Epigenetic Clocks14 April 2022EpigeneticsImpact of In Utero Folate Exposure on DNA Methylation and Its Potential Relevance for Later‐Life Health—Evidence from Mouse Models Translated to Human Cohorts13 December 2021 | Molecular Nutrition & Food Research, Vol. 261A meta-analysis of epigenome-wide association studies in Alzheimer's disease highlights novel differentially methylated loci across cortex10 June 2021 | Nature Communications, Vol. 12, No. 1Genetic impacts on DNA methylation: research findings and future perspectives30 April 2021 | Genome Biology, Vol. 22, No. 1Causal Inference Methods to Integrate Omics and Complex Traits17 August 2020 | Cold Spring Harbor Perspectives in Medicine, Vol. 11, No. 5Tobacco Smoking and the Association With First Incidence of Mood, Anxiety, and Substance Use Disorders: A 3-Year Prospective Population-Based Study15 March 2021 | Clinical Psychological Science, Vol. 9, No. 3Time trends in epigenetic signatures and population health risksInvited Review – A 5‐year update on epigenome‐wide association studies of DNA modifications in Alzheimer's disease: progress, practicalities and promise30 August 2020 | Neuropathology and Applied Neurobiology, Vol. 46, No. 7Understanding the Interplay Between Health Disparities and Epigenomics20 August 2020 | Frontiers in Genetics, Vol. 11Lifetime Smoking and Asthma: A Mendelian Randomization Study4 August 2020 | Frontiers in Genetics, Vol. 11Genetically, Dietary Sodium Intake Is Causally Associated with Salt-Sensitive Hypertension Risk in a Community-Based Cohort Study: a Mendelian Randomization Approach26 June 2020 | Current Hypertension Reports, Vol. 22, No. 7Antipsychotic Medications and DNA Methylation in Schizophrenia and Bipolar Disorder: A Systematic ReviewPharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, Vol. 40, No. 4Epigenetics and gestational diabetes: a review of epigenetic epidemiology studies and their use to explore epigenetic mediation and improve prediction17 October 2019 | Diabetologia, Vol. 62, No. 12DNA methylation aging clocks: challenges and recommendations25 November 2019 | Genome Biology, Vol. 20, No. 1Mendelian randomization provides support for obesity as a risk factor for meningioma22 January 2019 | Scientific Reports, Vol. 9, No. 1Appraising the causal relevance of DNA methylation for risk of lung cancer24 September 2019 | International Journal of Epidemiology, Vol. 48, No. 5Mendelian randomization studies on atherosclerotic cardiovascular disease: evidence and limitations17 May 2019 | Science China Life Sciences, Vol. 62, No. 6A brief tour of epidemiologic epigenetics and mental healthCurrent Opinion in Psychology, Vol. 27An integrative approach to detect epigenetic mechanisms that putatively mediate the influence of lifestyle exposures on disease susceptibility29 June 2019 | International Journal of Epidemiology, Vol. 48, No. 3Causal Modeling in Environmental HealthAnnual Review of Public Health, Vol. 40, No. 1Mendelian Randomization and the Environmental Epigenetics of Health: a Systematic Review18 February 2019 | Current Environmental Health Reports, Vol. 6, No. 1Postnatal Social Factors: The Epigenome and the Skeleton29 June 2019Epigenomics in tobacco risk assessment: Opportunities for integrated new approachesCurrent Opinion in Toxicology, Vol. 11-12Unravelling the Roles of Susceptibility Loci for Autoimmune Diseases in the Post-GWAS Era27 July 2018 | Genes, Vol. 9, No. 8DNA methylation in human lipid metabolism and related diseasesCurrent Opinion in Lipidology, Vol. 29, No. 2Protocol for the EMPHASIS study; 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The REGICOR study15 September 2016 | Human Molecular Genetics, Vol. 25, No. 20How to make DNA methylome wide association studies more powerfulXinyi Lin, Sheila Barton & Joanna D Holbrook7 April 2016 | Epigenomics, Vol. 8, No. 8 Vol. 7, No. 8 Follow us on social media for the latest updates Metrics Downloaded 297 times History Published online 7 December 2015 Published in print December 2015 Information© Future Medicine LtdKeywordscausal inferenceDNA methylationepidemiologyepigeneticsmediationSNPAcknowledgementsThe authors would like to thank members of the MRC Integrative Epidemiology Unit for their contribution in developing this area of research.Financial & competing interests disclosuresThe authors' work related to the application of Mendelian randomization to epigenetics is supported by the Medical Research Council Integrative Epidemiology Unit at the University of Bristol (MC_UU_12013/1 and MC_UU_12013/2). This work was also supported by CRUK (grant number C18281/A19169). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.PDF download

Establishing new approaches for the prevention and treatment of stroke relies on identifying modifiable risk factors that contribute to the development of this complex disease. Mendelian randomization (MR) studies, analogous to naturally occurring randomized trials, can assess causality of potentially modifiable biomarkers and offer new insights into biological pathways. Stroke is the second leading cause of death worldwide and the chief determinant of long-term disability. Stroke is a heterogeneous disease arising from several distinct underlying pathologies and is typically classified as ischemic or hemorrhagic, and further subclassified using imaging data. Ischemic stroke (IS), including its 3 main subtypes: small vessel disease, large vessel disease, and cardioembolic stroke, accounts for ≈80% of stroke and is the result of an interrupted blood supply, leading to localized areas of ischemia in the brain. Small vessel disease may be a consequence of nonatherosclerotic, as well as atherosclerotic, mechanisms that result in an occlusion of the small perforating arteries, whereas large vessel disease results from occlusions or emboli from plaque rupture in larger vessels, such as a carotid artery. Cardioembolic stroke arises typically from emboli from the heart. By contrast, hemorrhagic stroke is a consequence of intracerebral hemorrhage (bleeding into the brain) or subarachnoid hemorrhage (bleeding into the subarachnoid space). These diverse stroke subtypes have distinct underlying pathologies reflecting different risk factor distributions. MR studies, using genetic variants as instrumental variables, afford a powerful approach to assessing causality of risk factors and avoid biases inherent in observational studies, including confounding and reverse causation. This review considers the contribution of MR studies to stroke epidemiology and their relevance to understanding risk factors and new therapeutic targets for stroke. Meta-analyses of large prospective studies have enhanced our knowledge of classical and emerging risk factors for stroke.1–4 Classical risk factors for stroke include nonmodifiable characteristics, …

IntroductionMigraine, as a complex neurological disease, brings heavy burden to patients and society. Despite the availability of established therapies, existing medications have limited efficacy. Thus, we aimed to find the drug targets that improve the prognosis of migraine.MethodWe used Mendelian Randomization (MR) and Summary-data-based MR (SMR) analyses to study possible drug targets of migraine by summary statistics from FinnGen cohorts (nCase = 44,616, nControl = 367,565), with further replication in UK Biobank (nCase = 26,052, nControl = 487,214). Genetic instruments were obtained from eQTLGen and UKB-PPP to verify the drug targets at the gene expression and protein levels. The additional analyses including Bayesian co-localization, the heterogeneity in dependent instruments(HEIDI), Linkage Disequilibrium Score(LDSC), bidirectional MR, multivariate MR(MVMR), heterogeneity test, horizontal pleiotropy test, and Steiger filtering were implemented to consolidate the findings further. Lastly, drug prediction analysis and phenome-wide association study(PheWAS) were employed to imply the possibility of drug targets for future clinical applications.ResultThe MR analysis of eQTL data showed that four drug targets (PROCR, GSTM4, SLC4A1, and TNFRSF10A) were significantly associated with migraine risk in both the FinnGen and UK Biobank cohorts. However, only GSTM4 exhibited consistent effect directions across the two outcomes(Discovery cohort: OR(95%CI) = 0.94(0.93–0.96); p = 2.70e − 10; Replication cohort: OR(95%CI) = 0.93(0.91–0.94); p = 4.21e − 17). Furthermore, GSTM4 passed the SMR at p < 0.05 and HEIDI test at p > 0.05 at both the gene expression and protein levels. The protein-level MR analysis revealed a strong correlation between genetically predicted GSTM4 with a lower incidence of migraine and its subtypes(Overall migraine: OR(95%CI) = 0.91(0.87–0.95); p = 6.98e-05; Migraine with aura(MA): OR(95%CI) = 0.90(0.85–0.96); p = 2.54e-03; Migraine without aura(MO): OR(95%CI) = 0.90(0.83–0.96); p = 2.87e-03), indicating a strong co-localization relationship (PPH4 = 0.86). Further analyses provided additional validation for the possibility of GSTM4 as a migraine treatment target.ConclusionThis study identifies GSTM4 as a potential druggable gene and promising therapeutic target for migraine.

Preterm birth has been associated with an increased risk of myopia, but the causal relationship between these two factors remains unclear. Traditional epidemiological studies are limited by confounding factors and reverse causality. Mendelian randomization (MR) analysis, utilizing genetic variants as instrumental variables, provides a robust approach to investigate causal relationships. In this study, we aimed to explore the potential causal link between preterm birth and myopia risk using a two-sample MR analysis strategy. We conducted a Mendelian randomization study to investigate the causal relationship between preterm birth and myopia risk. Genetic variants (single nucleotide polymorphisms, SNPs) were used as instrumental variables, and summary data from genome-wide association studies (GWAS) were utilized. Four regression models, including MR-Egger regression, weighted median regression, inverse variance weighted regression, and Weighted mode regression, were employed to validate the causal relationship. Sensitivity analysis was performed using the leave-one-out method. At the same time, the funnel diagram and MR-Egger test were used to judge the stability of the research results. The MR analysis revealed a significant causal effect of preterm birth on myopia risk. Both the inverse variance weighted regression and weighted median regression models showed a p-value less than 0.05, indicating a robust association. The risk of myopia increased by approximately 30% for everyone standard deviation increase in preterm birth. Sensitivity analysis, funnel plot and MR-Egger test all confirm the stability of the research results. Our findings provide evidence supporting a causal relationship between preterm birth and myopia risk. Preterm infants are at a higher risk of developing myopia, and this association is not likely to be influenced by confounding factors or reverse causality. The SNP loci rs6699397, rs10871582, and rs2570497 should be closely monitored as they may lead to abnormal concentrations of intraocular cytokines, particularly vascular endothelial growth factor, potentially elucidating one of the pathogenic mechanisms contributing to the higher incidence of myopia in preterm infants. However the complex interconnections involved extend beyond these factors alone.

ObjectiveThe observational association between cathepsin and Parkinson’s disease (PD) has been partially explored in previous research. However, the causal relationship remains unclear. In this study, our objective is to investigate the causal link between cathepsin and PD using Mendelian randomization (MR) analysis and elucidate the underlying mechanisms governing their interaction.MethodsUtilizing bidirectional two-sample MR and multivariable MR, we systematically investigates the causal relationship between nine cathepsins and PD. The data pertaining to cathepsins were obtained from the Integrative Epidemiology Unit (IEU) Open GWAS Project, while data related to PD were sourced from versions R9 and R10 of the FinnGen database. The primary analytical method utilized was the inverse variance weighted (IVW), with MR analysis initially conducted using PD data from R9, complemented by a series of sensitivity analyses. Subsequently, replication analysis was performed on the R10 dataset, and meta-analysis were employed to merge the findings from both datasets. To explore potential mechanisms by which Cathepsins may impact PD, MR analyses were performed on significant Cathepsins with alpha-synuclein. MR analysis and colocalization analysis were conducted on expression quantitative trait loci (eQTL) data of gene related to alpha-synuclein with PD data.ResultForward MR analyses revealed more cathepsin B (CTSB) associated with less PD risk (OR = 0.898, 95%CI: 0.834–0.966, p = 0.004), while more cathepsin H (CTSH) (OR = 1.076, 95%CI: 1.007–1.149, p = 0.029) and more cathepsin S (CTSS) (OR = 1.076, 95%CI: 1.007–1.150, p = 0.030) associated with increasing PD risk. Meta-analyses validated these associations. Multivariate MR Results were consistent with those before adjustment. No significant results were observed in bidirectional MR analysis. In the investigation of the underlying mechanism, our findings demonstrate that CTSB significantly reduces the levels of alpha-synuclein (OR = 0.909, 95%CI: 0.841–0.983, p = 0.017). Concurrently, a genetically determined positive correlation between alpha-synuclein and PD is illuminated by both eQTL MR and colocalization analysis.ConclusionIn conclusion, this MR study yields robust evidence suggesting an association between elevated levels of CTSB and reduced PD risk, mediated by the downregulation of alpha-synuclein levels. Conversely, higher levels of CTSH and CTSS are associated with an increased risk of PD. These findings offer novel insights into the pathophysiological mechanisms of PD and identify potential drug targets for disease prevention and treatment warranting further clinical investigations.

Prior observational research has indicated a potential link between pediatric asthma and united airways disease (UAD). However, these findings could be subject to confounding factors and reverse causation. Therefore, our study utilizes Mendelian randomization (MR) method to further investigate the causal relationship between pediatric asthma and UAD. We conducted a comprehensive two-sample Mendelian randomization (MR) analysis to investigate the association between pediatric asthma and seven groups of UAD, including chronic sinusitis, chronic rhinitis, nasopharyngitis and pharyngitis, chronic diseases of tonsils and adenoids, chronic laryngitis and laryngotracheitis, chronic bronchitis, bronchiectasis, chronic obstructive pulmonary disease (COPD). The present study employed a range of methods for two-sample MR analysis, including inverse variance weighted (IVW), MR-Egger regression, Simple mode, weighted median, and weighted models. The conclusion of the MR analysis primarily relies on the IVW results, while other analytical methods are utilized as supplementary evidence to ensure result robustness in this MR analysis. And sensitivity analyses were conducted, including heterogeneity test, horizontal pleiotropy test, MR-PRESSO test, and leave-one-out analysis to validate the results. The results of the MR analysis indicate significant causal effects of pediatric asthma on chronic rhinitis, nasopharyngitis and pharyngitis (IVW: OR = 1.15, 95%CI: 1.05-1.26, p-value = 0.003), chronic diseases of tonsils and adenoids (IVW: OR = 1.07, 95%CI: 1.00-1.15, p-value = 0.038), chronic bronchitis (IVW: OR = 1.51, 95%CI: 1.42-1.62, p-value <0.001), bronchiectasis (IVW: OR = 1.51, 95%CI: (1.30-1.75), p-value <0.001), and COPD (IVW: OR = 1.43, 95%CI: 1.34-1.51, p-value <0.001). However, no significant causal association was observed between pediatric asthma and chronic sinusitis (IVW: OR = 1.00, 95%CI: 1.00-1.00, p-value = 0.085), chronic laryngitis and laryngotracheitis (IVW: OR = 1.05, 95%CI: 0.90-1.21, p-value = 0.558). Our findings support a potential causal relationship between pediatric asthma and UAD, suggesting that pediatric asthma may be a potential risk factor for various UAD.

Osteoarthritis (OA) is a prevalent and debilitating disorder that affects the joints and has a complex array of causes. While sedentary behavior (SB) and physical activity (PA) have been implicated in OA risk, the relationship between these factors and OA development remains unclear. This study investigates the correlation and potential causality between SB, PA, and OA using both cross-sectional and Mendelian randomization (MR) analysis. We conducted a two-phase study that included a cross-sectional analysis using data from the National Health and Nutrition Examination Survey (NHANES) and a MR analysis. A weighted analysis was performed on data from the NHANES to explore the relationship between SB, PA, and the risk of OA. Logistic regression was used to assess the association between SB, PA, and OA, adjusting for potential confounders. Non-parametric curve fitting was applied to examine the dose-response relationship between PA levels and OA onset. Additionally, MR was utilized to infer the genetic causality between SB, PA, and OA risk, using genetic instruments as proxies for SB and PA. The final analysis, which included 24,738 participants, revealed that OA prevalence was 13.47%, with individuals diagnosed with OA exhibiting significantly higher levels of SB and lower levels of PA. A U-shaped relationship was observed between PA and OA risk, with a decrease in OA incidence as PA levels increased, although the protective effect of PA was less pronounced in individuals with severe SB. MR analysis indicated that genetically inferred SB was associated with a higher likelihood of OA (IVW OR 1.20, 95% CI 1.13-1.28), while increased PA was inversely associated with OA risk (IVW OR 0.85, 95% CI 0.73-0.98). This research emphasizes the significance of SB and PA as modifiable factors influencing the risk of OA. It is recommended that individuals at risk of OA should aim to participate in regular physical activity and minimize sedentary behavior to lower their risk of developing the disease. The MR analysis results support the potential causal impact of SB and PA on OA, providing valuable information for the development of therapeutic and rehabilitative strategies.

This study aimed to investigate the causal relationship between metabolites and periodontitis using Mendelian randomization (MR), and validate findings through gingival crevicular fluid metabolomic profiling. A two-sample MR analysis used genetic data from 486 metabolite Genome-Wide Association Study (GWAS) and periodontitis statistics, with inverse-variance weighting as the primary method, supported by MR-Egger, weighted median, and weighted mode. Sensitivity analyses included Cochran Q, MR-Egger, and MR-PRESSO tests. Gingival crevicular fluid metabolomics compared five periodontitis patients and five controls, identifying differential metabolites via t tests and partial least squares-discriminant analysis (PLS-DA), with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Mendelian randomization analysis identified 17 metabolites causally linked to periodontitis, spanning amino acids, lipids, energy metabolism, and cofactors/vitamins. Protective metabolites included betaine (OR 0.478, 95% CI 0.235-0.975), laurate (0.51, 0.267-0.974), and glycerol 3-phosphate (0.312, 0.105-0.926), while phenylalanine (39.651, 2.173-723.565), pelargonate (2.527, 1.059-6.030), and 3-methylhistidine (1.481, 1.074-2.042) increased risk. Sensitivity analyses confirmed minimal heterogeneity, no pleiotropy (except 4-acetamidobutanoate), and no reverse causation. Gingival crevicular fluid metabolomics revealed 75 upregulated and 245 downregulated metabolites, with pathway enrichment in lipid, amino acid, and vitamin metabolism. Notably, betaine-protective in Mendelian randomization analysis-was significantly reduced in periodontitis, aligning with its anti-inflammatory role. This study indicates that some circulating metabolites (eg, betaine) may protect against periodontitis. Integrating Mendelian randomization and gingival crevicular fluid analyses, key metabolic risk factors were identified. Clinically, metabolites like betaine and glycerol 3-phosphate could serve as noninvasive early biomarkers, providing new avenues for personalized periodontitis prevention and treatment.

In recent years, the incidence rates of rheumatoid arthritis (RA) and heart disease (HD) have noticeably increased worldwide. Previous studies have found that patients with RA are more likely to develop HD, while the cause and effect have still remained elusive. In this study, Mendelian randomization (MR) analysis was used to indicate whether there was a potential association between RA and HD. Data of RA, ischemic heart disease (IHD), myocardial infarction (MI), atrial fibrillation (AF), and arrhythmia were based on the genome-wide association study (GWAS) dataset. No disease group was intersected. Inverse-variance weighted (IVW) method was used to calculate MR estimates, and sensitivity analysis was performed. The primary MR analysis showed that genetic susceptibility to RA was significantly associated with the risk of IHD and MI, rather than with AF and arrhythmia. Besides, there was no heterogeneity and horizontal pleiotropy between the primary and replicated analyses. There was a significant correlation between RA and the risk of IHD (odds ratio (OR), 1.0006; 95% confidence interval (CI), 1.000244-1.00104; P = 0.001552), meanwhile, there was a significant correlation between RA and the risk of MI (OR, 1.0458; 95% CI, 1.07061-1.05379; P = 0.001636). The results were similar to those of sensitivity analysis, and the sensitivity analysis also verified the conclusion. Furthermore, sensitivity and reverse MR analyses suggested that no heterogeneity, horizontal pleiotropy or reverse causality was found between RA and cardiovascular comorbidity. RA was noted to be causally associated with IHD and MI, rather than with AF and arrhythmia. This MR study might provide a new genetic basis for the causal relationship between RA and the risk of CVD. The findings suggested that the control of RA activity might reduce the risk of cardiovascular disease.