METHODS: We conducted a two-sample Mendelian randomization (MR) study to examine the genetically predicted effects of epigenetic age acceleration as measured by HannumAge (nine single-nucleotide polymorphisms (SNPs)), Horvath Intrinsic Age (24 SNPs), PhenoAge (11 SNPs), and GrimAge (4 SNPs) on multiple cancers (i.e. breast, prostate, colorectal, ovarian and lung cancer). We obtained genome-wide association data for biological ageing from a meta-analysis (N = 34,710), and for cancer from the UK Biobank (N cases = 2671-13,879; N controls = 173,493-372,016), FinnGen (N cases = 719-8401; N controls = 74,685-174,006) and several international cancer genetic consortia (N cases = 11,348-122,977; N controls = 15,861-105,974). Main analyses were performed using multiplicative random effects inverse variance weighted (IVW) MR. Individual study estimates were pooled using fixed effect meta-analysis. Sensitivity analyses included MR-Egger, weighted median, weighted mode and Causal Analysis using Summary Effect Estimates (CAUSE) methods, which are robust to some of the assumptions of the IVW approach.
RESULTS: Meta-analysed IVW MR findings suggested that higher GrimAge acceleration increased the risk of colorectal cancer (OR = 1.12 per year increase in GrimAge acceleration, 95% CI 1.04-1.20, p = 0.002). The direction of the genetically predicted effects was consistent across main and sensitivity MR analyses. Among subtypes, the genetically predicted effect of GrimAge acceleration was greater for colon cancer (IVW OR = 1.15, 95% CI 1.09-1.21, p = 0.006), than rectal cancer (IVW OR = 1.05, 95% CI 0.97-1.13, p = 0.24). Results were less consistent for associations between other epigenetic clocks and cancers.
CONCLUSIONS: GrimAge acceleration may increase the risk of colorectal cancer. Findings for other clocks and cancers were inconsistent. Further work is required to investigate the potential mechanisms underlying the results.
FUNDING: FMB was supported by a Wellcome Trust PhD studentship in Molecular, Genetic and Lifecourse Epidemiology (224982/Z/22/Z which is part of grant 218495/Z/19/Z). KKT was supported by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme) and by the Hellenic Republic's Operational Programme 'Competitiveness, Entrepreneurship & Innovation' (OΠΣ 5047228). PH was supported by Cancer Research UK (C18281/A29019). RMM was supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). RMM is a National Institute for Health Research Senior Investigator (NIHR202411). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. GDS and CLR were supported by the Medical Research Council (MC_UU_00011/1 and MC_UU_00011/5, respectively) and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). REM was supported by an Alzheimer's Society project grant (AS-PG-19b-010) and NIH grant (U01 AG-18-018, PI: Steve Horvath). RCR is a de Pass Vice Chancellor's Research Fellow at the University of Bristol.
METHODS: We obtained information on medication use and cancer diagnosis from National Health and Nutrition Examination Survey participants. After propensity score matching, we conducted survey-weighted multivariate logistic regression and restricted cubic spline analysis to assess the observed correlation between medication use and cancer while adjusting for multiple covariates. We also performed MR analysis to investigate causality based on summary data from genome-wide association studies on medication use and cancers. We performed sensitivity analyses, replication analysis, genetic correlation analysis, and reverse MR analysis to improve the reliability of MR findings.
RESULTS: We found that the use of agents acting on the renin-angiotensin system was associated with reduced risk of prostate cancer (odds ratio (OR) = 0.42; 95% confidence interval (CI) = 0.27-0.63, P
METHODS: We used two diffusion tensor imaging measures, fractional anisotropy (FA) and mean diffusivity (MD), in the most up-to-date UK Biobank neuroimaging data release (FA: n = 6401; MD: n = 6390).
RESULTS: We found significantly lower FA in the superior longitudinal fasciculus (β = -.035, pcorrected = .029) and significantly higher MD in a global measure of thalamic radiations (β = .029, pcorrected = .021), as well as higher MD in the superior (β = .034, pcorrected = .039) and inferior (β = .029, pcorrected = .043) longitudinal fasciculus and in the anterior (β = .025, pcorrected = .046) and superior (β = .027, pcorrected = .043) thalamic radiation associated with NETRIN1-PRS. Genomic-PRS was also associated with lower FA and higher MD in several tracts.
CONCLUSIONS: Our findings indicate that variation in the NETRIN1 signaling pathway may confer risk for major depressive disorder through effects on a number of white matter tracts.
METHODS: The genotypes were assessed on 144 histologically confirmed NAFLD patients and 198 controls using a Sequenom MassARRAY platform.
RESULTS: The GCKR rs1260326 and rs780094 allele T were associated with susceptibility to NAFLD (OR 1.49, 95 % CI 1.09-2.05, p = 0.012; and OR 1.51, 95 % CI 1.09-2.09, p = 0.013, respectively), non-alcoholic steatohepatitis (NASH) (OR 1.55, 95 % CI 1.10-2.17, p = 0.013; and OR 1.56, 95 % CI 1.10-2.20, p = 0.012, respectively) and NASH with significant fibrosis (OR 1.50, 95 % CI 1.01-2.21, p = 0.044; and OR 1.52, 95 % CI 1.03-2.26, p = 0.038, respectively). Following stratification by ethnicity, significant association was seen in Indian patients between the two SNPs and susceptibility to NAFLD (OR 2.64, 95 % CI 1.28-5.43, p = 0.009; and OR 4.35, 95 % CI 1.93-9.81, p < 0.0001, respectively). The joint effect of GCKR with adiponutrin rs738409 indicated greatly increased the risk of NAFLD (OR 4.14, 95 % CI 1.41-12.18, p = 0.010). Histological data showed significant association of GCKR rs1260326 with high steatosis grade (OR 1.76, 95 % CI 1.08-2.85, p = 0.04).
CONCLUSION: This study suggests that risk allele T of the GCKR rs780094 and rs1260326 is associated with predisposition to NAFLD and NASH with significant fibrosis. The GCKR and PNPLA3 genes interact to result in increased susceptibility to NAFLD.
METHODS: A total of 1114 subjects comprising of 536 PD patients and 578 healthy controls of Malay ancestry were recruited and genotyped using Taqman® allelic discrimination assays.
RESULTS: The G allele of rs10513789 (OR = 0.83, p = 0.001) and A allele of rs12637471 (OR = 0.79, p = 0.007) in the MCCC1/LAMP3 locus were associated with a protective effect against developing PD in the Malay population. A recessive model of penetrance showed a protective effect of the GG genotype for rs10513789 and the AA genotype for rs12637471. No association with PD was found with the other MCCC1/LAMP3 rs12493050 variant or with the DGKQ (rs11248060) variant. No significant associations were found between the four variants with the age at PD diagnosis.
CONCLUSION: MCCC1/LAMP3 variants rs10513789 and rs12637471 protect against PD in the Malay population.