AHRR (cg05575921) hypomethylation marks smoking behavior, morbidity and mortality

Rationale and objectives Self-reported smoking underestimates disease risk. Smoking affects DNA methylation, in particular the cg05575921 site in the AHRR gene. We tested the hypothesis that AHRR cg05575921 hypomethylation is associated with risk of smoking related morbidity and mortality. Methods From the Copenhagen City Heart Study representing the Danish general population, we studied 9234 individuals. Using bisulphite treated leukocyte DNA, AHRR (cg05575921) methylation was measured. Rs1051730 (CHRN3A) genotype was used to evaluate smoking heaviness. Participants were followed for up to 22 years for exacerbations of chronic obstructive pulmonary disease (COPD), event of lung cancer, and all-cause mortality. Six-year lung cancer risk was calculated according to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCOM2012). Measurements and main results AHRR (cg05575921) hypomethylation was associated with former and current smoking status, high daily and cumulative smoking, short time since smoking cessation (all p-values<7*10-31), and the smoking-related CHRN3A genotype (-0.48% per T-allele, p=0.002). The multifactorially adjusted hazard ratios for the lowest versus highest methylation quintiles were 4.58(95% confidence interval, 2.83-7.42) for COPD exacerbations, 4.87(2.31-10.3) for lung cancer, and 1.67(1.48-1.88) for allcause mortality. Finally, among 2576 high-risk smokers eligible for lung cancer screening by CT, observed cumulative incidences of lung cancer after 6 years for individuals in the lowest and highest methylation quintiles were 3.7% and 0.0% (p=2*10-7 ), whereas predicted PLCOM2012 6-year risks were similar (4.3% and 4.4%, p=0.77). Conclusions AHRR (cg05575921) hypomethylation, a marker of smoking behaviour, provides potentially clinical relevant predictions of future smoking related morbidity and mortality

Rosuvastatin to Prevent Vascular Events in Men and Women with Elevated C-Reactive Protein

Background: Increased levels of the inflammatory biomarker high-sensitivity C-reactive protein predict cardiovascular events. Since statins lower levels of high-sensitivity C-reactive protein as well as cholesterol, we hypothesized that people with elevated high-sensitivity C-reactive protein levels but without hyperlipidemia might benefit from statin treatment.Methods: We randomly assigned 17,802 apparently healthy men and women with low-density lipoprotein (LDL) cholesterol levels of less than 130 mg per deciliter (3.4 mmol per liter) and high-sensitivity C-reactive protein levels of 2.0 mg per liter or higher to rosuvastatin, 20 mg daily, or placebo and followed them for the occurrence of the combined primary end point of myocardial infarction, stroke, arterial revascularization, hospitalization for unstable angina, or death from cardiovascular causes.Results: the trial was stopped after a median follow-up of 1.9 years (maximum, 5.0). Rosuvastatin reduced LDL cholesterol levels by 50% and high-sensitivity C-reactive protein levels by 37%. the rates of the primary end point were 0.77 and 1.36 per 100 person-years of follow-up in the rosuvastatin and placebo groups, respectively (hazard ratio for rosuvastatin, 0.56; 95% confidence interval [CI], 0.46 to 0.69; P<0.00001), with corresponding rates of 0.17 and 0.37 for myocardial infarction (hazard ratio, 0.46; 95% CI, 0.30 to 0.70; P=0.0002), 0.18 and 0.34 for stroke (hazard ratio, 0.52; 95% CI, 0.34 to 0.79; P=0.002), 0.41 and 0.77 for revascularization or unstable angina (hazard ratio, 0.53; 95% CI, 0.40 to 0.70; P<0.00001), 0.45 and 0.85 for the combined end point of myocardial infarction, stroke, or death from cardiovascular causes (hazard ratio, 0.53; 95% CI, 0.40 to 0.69; P<0.00001), and 1.00 and 1.25 for death from any cause (hazard ratio, 0.80; 95% CI, 0.67 to 0.97; P=0.02). Consistent effects were observed in all subgroups evaluated. the rosuvastatin group did not have a significant increase in myopathy or cancer but did have a higher incidence of physician-reported diabetes.Conclusions: in this trial of apparently healthy persons without hyperlipidemia but with elevated high-sensitivity C-reactive protein levels, rosuvastatin significantly reduced the incidence of major cardiovascular events. (ClinicalTrials.gov number, NCT00239681.).AstraZenecaNovartisMerckAbbottRocheSanofi-AventisMerck-Schering-PloughIsisDade BehringVascular BiogenicsPfizerMerck FrosstResverlogixDupontAegerionArisaphKowaGenentechMartekReliantGenzymeGlaxoSmithKlineBoehringer IngelheimDiaDexusMedlogixAntheraBristol-Myers SquibbVIA PharmaceuticalsInterleukin GeneticsKowa Research InstituteTakedaBG MedicineOxford BiosciencesHarvard Univ, Sch Med, Brigham & Womens Hosp, Ctr Cardiovasc Dis Prevent, Boston, MA 02215 USAHarvard Univ, Sch Med, Brigham & Womens Hosp, Div Cardiovasc Med, Boston, MA 02215 USAUniversidade Federal de São Paulo, São Paulo, BrazilMcGill Univ, Ctr Hlth, Montreal, PQ, CanadaCornell Univ, Weill Cornell Med Coll, New York, NY 10021 USAUniv Amsterdam, Acad Med Ctr, Dept Vasc Med, NL-1105 AZ Amsterdam, NetherlandsUniv Ulm, Med Ctr, Ulm, GermanyHosp Cordoba, Cordoba, ArgentinaCopenhagen Univ Hosp, Herlev Hosp, Herlev, DenmarkUniv Glasgow, Glasgow, Lanark, ScotlandSt Lukes Episcopal Hosp, Texas Heart Inst, Houston, TX 77030 USAUniversidade Federal de São Paulo, São Paulo, BrazilWeb of Scienc

Prediction of individual genetic risk to prostate cancer using a polygenic score

BACKGROUND Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. METHODS We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. RESULTS The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P-=-0.0012) and the net reclassification index with 0.21 (P-=-8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. CONCLUSIONS Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction

Gene and pathway level analyses of germline DNA-repair gene variants and prostate cancer susceptibility using the iCOGS-genotyping array.

BACKGROUND: Germline mutations within DNA-repair genes are implicated in susceptibility to multiple forms of cancer. For prostate cancer (PrCa), rare mutations in BRCA2 and BRCA1 give rise to moderately elevated risk, whereas two of B100 common, low-penetrance PrCa susceptibility variants identified so far by genome-wide association studies implicate RAD51B and RAD23B. METHODS: Genotype data from the iCOGS array were imputed to the 1000 genomes phase 3 reference panel for 21 780 PrCa cases and 21 727 controls from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. We subsequently performed single variant, gene and pathway-level analyses using 81 303 SNPs within 20 Kb of a panel of 179 DNA-repair genes. RESULTS: Single SNP analyses identified only the previously reported association with RAD51B. Gene-level analyses using the SKAT-C test from the SNP-set (Sequence) Kernel Association Test (SKAT) identified a significant association with PrCa for MSH5. Pathway-level analyses suggested a possible role for the translesion synthesis pathway in PrCa risk and Homologous recombination/Fanconi Anaemia pathway for PrCa aggressiveness, even though after adjustment for multiple testing these did not remain significant. CONCLUSIONS: MSH5 is a novel candidate gene warranting additional follow-up as a prospective PrCa-risk locus. MSH5 has previously been reported as a pleiotropic susceptibility locus for lung, colorectal and serous ovarian cancers.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/bjc.2016.5

Development and validation of a model to predict incident chronic liver disease in the general population : The CLivD score

Background & Aims: Current screening strategies for chronic liver disease focus on detection of subclinical advanced liver fibrosis but cannot identify those at high future risk of severe liver disease. Our aim was to develop and validate a risk pre-diction model for incident chronic liver disease in the general population based on widely available factors. Methods: Multivariable Cox regression analyses were used to develop prediction models for liver-related outcomes with and without laboratory measures (Modellab and Modelnon-lab) in 25,760 individuals aged 40-70 years. Their data were sourced from the Finnish population-based health examination surveys FINRISK 1992-2012 and Health 2000 (derivation cohort). The models were externally validated in the Whitehall II (n = 5,058) and Copenhagen City Heart Study (CCHS) (n = 3,049) cohorts. Results: The absolute rate of incident liver outcomes per 100,000 person-years ranged from 53 to 144. The final prediction model included age, sex, alcohol use (drinks/week), waist-hip ratio, diabetes, and smoking, and Modellab also included gamma-glutamyltransferase values. Internally validated Wolbers' C -sta-tistics were 0.77 for Modellab and 0.75 for Modelnon-lab, while apparent 15-year AUCs were 0.84 (95% CI 0.75-0.93) and 0.82 (95% CI 0.74-0.91). The models identified a small proportion ( 10% absolute 15-year risk for liver events. Of all liver events, only 10% occurred in participants in the lowest risk category. In the validation cohorts, 15-year AUCs were 0.78 (Modellab) and 0.65 (Modelnon-lab) in the CCHS cohort, and 0.78 (Modelnon-lab) in the Whitehall II cohort. Conclusions: Based on widely available risk factors, the Chronic Liver Disease (CLivD) score can be used to predict risk of future advanced liver disease in the general population. Lay summary: Liver disease often progresses silently without symptoms and thus the diagnosis is often delayed until severe complications occur and prognosis becomes poor. In order to identify individuals in the general population who have a high risk of developing severe liver disease in the future, we developed and validated a Chronic Liver Disease (CLivD) risk prediction score, based on age, sex, alcohol use, waist-hip ratio, diabetes, and smoking, with or without measurement of the liver enzyme gamma-glutamyltransferase. The CLivD score can be used as part of health counseling, and for planning further liver investigations and follow-up. (C) 2022 The Author(s). Published by Elsevier B.V. on behalf of European Association for the Study of the Liver.Peer reviewe

Analysis and implementation of fractional-order chaotic system with standard components

This paper is devoted to the problem of uncertainty in fractional-order Chaotic systems implemented by means of standard electronic components. The fractional order element (FOE) is typically substituted by one complex impedance network containing a huge number of discrete resistors and capacitors. In order to balance the complexity and accuracy of the circuit, a sparse optimization based parameter selection method is proposed. The random error and the uncertainty of system implementation are analyzed through numerical simulations. The effectiveness of the method is verified by numerical and circuit simulations, tested experimentally with electronic circuit implementations. The simulations and experiments show that the proposed method reduces the order of circuit systems and finds a minimum number for the combination of commercially available standard components.This work was supported in part by the National Natural Science Foundation of China under Grant 61501385, in part by the National Nuclear Energy Development Project of State Administration for Science, Technology and Industry for National Defense, PRC under Grant 18zg6103, and in part by Sichuan Science and Technology Program under Grant 2018JY0522. We would like to thank Xinghua Feng for meaningful discussion.info:eu-repo/semantics/publishedVersio

Triglyceride-rich lipoproteins and their remnants : metabolic insights, role in atherosclerotic cardiovascular disease, and emerging therapeutic strategies-a consensus statement from the European Atherosclerosis Society

Recent advances in human genetics, together with a large body of epidemiologic, preclinical, and clinical trial results, provide strong support for a causal association between triglycerides (TG), TG-rich lipoproteins (TRL), and TRL remnants, and increased risk of myocardial infarction, ischaemic stroke, and aortic valve stenosis. These data also indicate that TRL and their remnants may contribute significantly to residual cardiovascular risk in patients on optimized low-density lipoprotein (LDL)-lowering therapy. This statement critically appraises current understanding of the structure, function, and metabolism of TRL, and their pathophysiological role in atherosclerotic cardiovascular disease (ASCVD). Key points are (i) a working definition of normo- and hypertriglyceridaemic states and their relation to risk of ASCVD, (ii) a conceptual framework for the generation of remnants due to dysregulation of TRL production, lipolysis, and remodelling, as well as clearance of remnant lipoproteins from the circulation, (iii) the pleiotropic proatherogenic actions of TRL and remnants at the arterial wall, (iv) challenges in defining, quantitating, and assessing the atherogenic properties of remnant particles, and (v) exploration of the relative atherogenicity of TRL and remnants compared to LDL. Assessment of these issues provides a foundation for evaluating approaches to effectively reduce levels of TRL and remnants by targeting either production, lipolysis, or hepatic clearance, or a combination of these mechanisms. This consensus statement updates current understanding in an integrated manner, thereby providing a platform for new therapeutic paradigms targeting TRL and their remnants, with the aim of reducing the risk of ASCVD. [GRAPHICS] .Peer reviewe

Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel

Aims To appraise the clinical and genetic evidence that low-density lipoproteins (LDLs) cause atherosclerotic cardiovascular disease (ASCVD). Methods and results We assessed whether the association between LDL and ASCVD fulfils the criteria for causality by evaluating the totality of evidence from genetic studies, prospective epidemiologic cohort studies, Mendelian randomization studies, and randomized trials of LDL-lowering therapies. In clinical studies, plasma LDL burden is usually estimated by determination of plasma LDL cholesterol level (LDL-C). Rare genetic mutations that cause reduced LDL receptor function lead to markedly higher LDL-C and a dose-dependent increase in the risk of ASCVD, whereas rare variants leading to lower LDL-C are associated with a correspondingly lower risk of ASCVD. Separate meta-analyses of over 200 prospective cohort studies, Mendelian randomization studies, and randomized trials including more than 2 million participants with over 20 million person-years of follow-up and over 150 000 cardiovascular events demonstrate a remarkably consistent dose-dependent log-linear association between the absolute magnitude of exposure of the vasculature to LDL-C and the risk of ASCVD; and this effect appears to increase with increasing duration of exposure to LDL-C. Both the naturally randomized genetic studies and the randomized intervention trials consistently demonstrate that any mechanism of lowering plasma LDL particle concentration should reduce the risk of ASCVD events proportional to the absolute reduction in LDL-C and the cumulative duration of exposure to lower LDL-C, provided that the achieved reduction in LDL-C is concordant with the reduction in LDL particle number and that there are no competing deleterious off-target effects. Conclusion Consistent evidence from numerous and multiple different types of clinical and genetic studies unequivocally establishes that LDL causes ASCVD.Peer reviewe

Smoking, blood cells, and myeloproliferative neoplasms: meta-analysis and mendelian randomization of 2.3 million people

Meta‐analyses and Mendelian randomization (MR) may clarify the associations of smoking, blood cells and myeloproliferative neoplasms (MPN). We investigated the association of smoking with blood cells in the Danish General Suburban Population Study (GESUS, n = 11 083), by meta‐analyses (including GESUS) of 92 studies (n = 531 741) and MR of smoking variant CHRNA3 (rs1051730[A]) in UK Biobank, and with MPN in a meta‐analysis of six studies (n (total/cases):1 425 529/2187), totalling 2 307 745 participants. In the meta‐analysis the random‐effects standardized mean difference (SMD) in current smokers versus non‐smokers was 0·82 (0·75–0·89, P = 2·0 * 10−108) for leukocytes, 0·09 (−0·02 to 0·21, P = 0·12) for erythrocytes, 0·53 (0·42–0·64, P = 8·0 * 10−22) for haematocrit, 0·42 (0·34–0·51, P = 7·1 * 10−21) for haemoglobin, 0·19 (0·08–0·31, P = 1·2 * 10−3) for mean corpuscular haemoglobin (MCH), 0·29 (0·19–0·39, P = 1·6 * 10−8) for mean corpuscular volume (MCV), and 0·04 (−0·04 to 0·13, P = 0·34) for platelets with trends for ever/ex‐/current smokers, light/heavy smokers and female/male smokers. Analyses presented high heterogeneity but low publication bias. Per allele in CHRNA3, cigarettes per day in current smokers was associated with increased blood cell counts (leukocytes, neutrophils), MCH, red cell distribution width (RDW) and MCV. The pooled fixed‐effects odds ratio for MPN was 1·44 [95% confidence interval (CI): 1·33–1·56; P = 1·8 * 10−19; I2 = 0%] in current smokers, 1·29 (1·15–1·44; P = 8·0 * 10−6; I2 = 0%) in ex‐smokers, 1·49 (1·26–1·77; P = 4·4 * 10−6; I2 = 0%) in light smokers and 2·04 (1·74–2·39, P = 2·3 * 10−18; I2 = 51%) in heavy smokers compared with non‐smokers. Smoking is observationally and genetically associated with increased leukocyte counts and red blood cell indices (MCH, MCV, RDW) and observationally with risk of MPN in current and ex‐smokers versus non/never‐smokers

Cluster effect for SNP–SNP interaction pairs for predicting complex traits

Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP–SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP–SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP–SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a hub SNP with a significant main effect and a large minor allele frequency (MAF) tended to have a higher false-positive rate. In addition, peripheral null SNPs in a cluster with a small MAF tended to enhance false positivity. We also demonstrated that using the modified significance criterion based on the 3 p-value rules and the bootstrap approach (3pRule + bootstrap) can reduce false positivity and maintain high true positivity. In addition, our results also showed that a pair without a significant main effect tends to have weak or no interaction. This study identified the cluster effect and suggested using the 3pRule + bootstrap approach to enhance SNP–SNP interaction detection accuracy